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Accessdata Password Recovery Toolkit Crack: What You Need to Know
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If you need to gain access to password-protected files, then you might have heard of Accessdata Password Recovery Toolkit (PRTK), a software that can recover passwords from encrypted files and containers. But what if you don't have a license for PRTK? Can you use a crack to bypass the activation process and use the software for free?
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In this article, we will explain what Accessdata Password Recovery Toolkit is, how it works, how to use it, and why you should avoid using a crack for it. We will also provide some alternatives to using a crack that are safer and more reliable.
Accessdata Password Recovery Toolkit is a part of Accessdata's Forensic Toolkit (FTK), a suite of tools for digital forensics and incident response. PRTK can recover passwords from various types of encrypted files and containers, such as MS Word, PDF, TrueCrypt, BitLocker, ZIP, RAR, and many more.
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PRTK uses different methods to recover passwords, such as brute-force, dictionary, rainbow tables, known-plaintext, and hybrid attacks. It can also create custom dictionaries and profiles based on the characteristics of the target file or container. PRTK can run multiple password recovery attacks simultaneously on different files or containers, using multiple processors and GPUs.
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PRTK can also integrate with other tools in FTK, such as FTK Imager, Registry Viewer, FTK Lab, and AD Enterprise. This allows you to perform comprehensive analysis and investigation on encrypted data.
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How to Use Accessdata Password Recovery Toolkit
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How to Install and Initialize the Software
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To use Accessdata Password Recovery Toolkit, you need to have a valid license for FTK. You can purchase a license from Exterro, the company that acquired Accessdata in 2020. You can also request a free trial or a demo from their website.
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Once you have a license, you can download the software from Exterro's website. You will need to register an account and provide your license information. You will also need to download FTK Imager, which is required for PRTK.
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After downloading the software, you need to install it on your computer. You will need administrator privileges to do so. You will also need to activate the software with your license key. You can do this online or offline.
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Once the software is installed and activated, you need to initialize it for first use. You will need to configure some settings, such as the location of your dictionaries, rainbow tables, profiles, logs, etc. You will also need to update your software regularly to get the latest features and fixes.
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How to Identify Encrypted Files with FTK
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Before you can recover passwords from encrypted files or containers, you need to identify them first. You can use FTK Imager to scan your hard drive or an image file for encrypted files or containers. FTK Imager can detect various types of encryption algorithms and formats.
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To use FTK Imager, you need to launch it from the Start menu or the desktop shortcut. You will see a window with four tabs: Evidence Tree, File List, Gallery View, and Hex View. You can use these tabs to view different aspects of your data.
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To scan for encrypted files or containers, you need to add an evidence item. You can do this by clicking on the File menu and selecting Add Evidence Item. You can choose from different types of evidence items, such as Physical Drive, Logical Drive, Image File, Contents of Folder, etc.
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After adding an evidence item, you will see it in the Evidence Tree tab. You can expand it by clicking on the plus sign next to it. You will see different partitions or folders under it. You can select any partition or folder and right-click on it. You will see an option called Scan For Encrypted Files/Containers. Click on it.
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A new window will pop up showing the progress of the scan. The scan may take some time depending on the size of your data. When the scan is complete, you will see a list of encrypted files or containers in the File List tab. You can sort them by name, size, type, encryption algorithm, etc.
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You can select any encrypted file or container and right-click on it. You will see an option called Export Selected Files/Containers To PRTK Queue File (.pqf). Click on it. This will create a file that contains information about the encrypted file or container that you want to decrypt with PRTK.
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How to Use the Dictionary Tool in PRTK
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A dictionary attack is one of the methods that PRTK uses to recover passwords from encrypted files or containers. A dictionary attack tries different words or phrases from a list until it finds the correct password.
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PRTK comes with some built-in dictionaries that contain common words or phrases that are used as passwords. However, you can also create your own custom dictionaries based on your knowledge of the target file or container.
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To create a custom dictionary, you need to use the Dictionary Tool in PRTK. You can launch it from the Tools menu or by clicking on the icon that looks like a book in the toolbar.
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The Dictionary Tool window has two tabs: Create Dictionary and Edit Dictionary. In the Create Dictionary tab, you can create a new dictionary by entering words or phrases in the text box at the bottom. You can also import words or phrases from a text file by clicking on the Import button.
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You can also modify an existing dictionary by using the Edit Dictionary tab. In this tab, you can open an existing dictionary by clicking on the Open button. You can then add or delete words or phrases from it.
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After creating or editing a dictionary, you need to save it by clicking on the Save button. You can give it any name you want but make sure it has a .dic extension.
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How to Use Rules and Profiles in PRTK
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Rules and profiles are another way that PRTK uses to recover passwords from encrypted files or containers. Rules are sets of instructions that tell PRTK how to modify words or phrases from dictionaries before trying them as passwords. Profiles are combinations of rules that apply different modifications at once.
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PRTK comes with some built-in rules and profiles that cover common scenarios such as adding numbers or symbols at the end of words or phrases; changing case; replacing letters with numbers; etc.
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However, you can also create your own custom rules and profiles based on your knowledge of the target file or container.
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To create a custom rule, you need to use the Rule Editor in PRTK. You can launch it from the Tools menu or by clicking on the icon that looks like a wrench in the toolbar.
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The Rule Editor window has two tabs: Create Rule and Edit Rule. In the Create Rule tab, you can create a new rule by entering commands in the text box at the bottom. Each command consists of an operator followed by one or more arguments separated by commas.
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For example:
- - $1,2,3 adds the numbers 1, 2, and 3 at the end of the word or phrase - C changes the case of the first letter of the word or phrase - R1,2 replaces the first letter of the word or phrase with the second letter You can also use variables to represent different types of characters, such as: - %l for lowercase letters - %u for uppercase letters - %d for digits - %s for symbols You can also use modifiers to apply different conditions or operations to the commands, such as: - ! to negate a command - ? to make a command optional - * to repeat a command a random number of times - + to repeat a command one or more times - n to repeat a command n times - n,m to repeat a command between n and m times For example: - C?%l+ changes the case of the first letter of the word or phrase and adds one or more lowercase letters at the end - R%l,%d2 replaces every lowercase letter in the word or phrase with two digits You can also use parentheses to group commands together and use logical operators to combine them, such as: - & for AND - | for OR - ^ for XOR For example: - (C|R%l,%u)&$%d2 applies either changing the case of the first letter or replacing every lowercase letter with an uppercase letter and adds two digits at the end After creating a rule, you need to save it by clicking on the Save button. You can give it any name you want but make sure it has a .rul extension. You can also modify an existing rule by using the Edit Rule tab. In this tab, you can open an existing rule by clicking on the Open button. You can then add or delete commands from it. To create a custom profile, you need to use the Profile Editor in PRTK. You can launch it from the Tools menu or by clicking on the icon that looks like a folder in the toolbar. The Profile Editor window has two tabs: Create Profile and Edit Profile. In the Create Profile tab, you can create a new profile by selecting rules from the list on the left and adding them to the list on the right. You can also change the order of the rules by dragging and dropping them. You can also import rules from a text file by clicking on the Import button. The text file should contain one rule per line with its name and extension. After creating a profile, you need to save it by clicking on the Save button. You can give it any name you want but make sure it has a .pro extension. You can also modify an existing profile by using the Edit Profile tab. In this tab, you can open an existing profile by clicking on the Open button. You can then add or delete rules from it.
How to Decrypt Files and Containers with PRTK
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After creating or selecting your dictionaries, rules, and profiles, you are ready to use PRTK to decrypt files and containers. To do this, you need to launch PRTK from the Start menu or the desktop shortcut.
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You will see a window with four tabs: Queue Manager, Attack Manager, Results Manager, and Log Viewer. You can use these tabs to manage your password recovery tasks.
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To decrypt files and containers with PRTK, you need to add them to the Queue Manager tab. You can do this by clicking on the Add button and selecting one of these options:
- - Add Files/Containers: This allows you to browse your computer and select individual files or containers that you want to decrypt. - Add PQF File: This allows you to select a PQF file that contains information about encrypted files or containers that you want to decrypt. You can create a PQF file using FTK Imager as explained earlier. - Add Folder: This allows you to select a folder that contains encrypted files or containers that you want to decrypt. After adding files or containers to the Queue Manager tab, you will see them in a list with some information such as name, size, type, encryption algorithm, etc. You can select any file or container and right-click on it. You will see some options such as: - Attack: This allows you to start a password recovery attack on the selected file or container. - Properties: This allows you to view more details about the selected file or container. - Remove: This allows you to remove the selected file or container from the list. - Remove All: This allows you to remove all files or containers from the list. To start a password recovery attack on a file or container, you need to select it and click on the Attack button. A new window will pop up showing different options for your attack. You can choose from different types of attacks such as: - Brute Force: This tries all possible combinations of characters until it finds the correct password. - Dictionary: This tries different words or phrases from a list until it finds the correct password. - Rainbow Tables: This uses precomputed tables of hashes and passwords to find matches. - Known Plaintext: This uses known parts of plaintext and ciphertext to find patterns. - Hybrid: This combines different types of attacks together. You can also choose different dictionaries, rules, and profiles for your attack. You can select from built-in ones or custom ones that you created earlier. You can also adjust some settings for your attack such as: - Timeout: This sets how long PRTK will try each password before moving on to the next one. - Threads: This sets how many processors PRTK will use for your attack. - GPUs: This sets how many graphics cards PRTK will use for your attack. - Priority: This sets how much CPU power PRTK will use for your attack. After choosing your options for your attack, you need to click on the Start button. PRTK will start trying different passwords for your file or container. You can monitor your attack in the Attack Manager tab. You will see some information such as status, progress, speed, elapsed time, estimated time left, etc. You can also pause or stop your attack at any time by clicking on the Pause or Stop buttons. If PRTK finds a password for your file or container, it will show it in green in the Results Manager tab. You will also see some information such as name, size, type, encryption algorithm, password length, etc. You can select any file or container and right-click on it. You will see some options such as: - Decrypt: This allows you to decrypt your file or container using PRTK. - Copy Password: This allows you to copy your password to clipboard. - Export Results: This allows you to export your results to a text file. - Remove: This allows you to remove your file or container from the list. - Remove All: This allows you to remove all files or containers from the list. the Decrypt button. A new window will pop up asking you to select a destination folder for your decrypted file or container. You can also choose to overwrite the original file or container or keep both. After selecting your destination folder, you need to click on the Decrypt button. PRTK will decrypt your file or container and save it in the destination folder. You can also decrypt your file or container using other tools such as FTK Imager or FTK Lab. You just need to copy the password from PRTK and paste it in the other tool.
The Risks and Challenges of Using a Crack for Accessdata Password Recovery Toolkit
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As you can see, Accessdata Password Recovery Toolkit is a powerful and useful software that can help you recover passwords from encrypted files and containers. However, it is not a cheap software. A license for FTK can cost thousands of dollars per year.
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That's why some people might be tempted to use a crack for PRTK. A crack is a program that modifies the software to bypass the activation process and use it for free. You can find many cracks for PRTK on the internet, especially on torrent sites.
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However, using a crack for PRTK is not a good idea. There are many risks and challenges that come with using a crack. Here are some of them:
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Legal and Ethical Issues
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Using a crack for PRTK is illegal and unethical. It violates the terms of service and the license agreement of the software. It also infringes on the intellectual property rights of Exterro, the company that owns Accessdata.
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If you use a crack for PRTK, you could face legal consequences such as fines, lawsuits, or even criminal charges. You could also damage your reputation and credibility as a professional or a student.
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Moreover, using a crack for PRTK could raise ethical questions about your motives and intentions. Why do you need to recover passwords from encrypted files or containers? Are you authorized to do so? Are you respecting the privacy and security of the owners of those files or containers?
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Using a crack for PRTK could make you look suspicious and untrustworthy. You could lose the trust and respect of your clients, colleagues, teachers, or peers.
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Security and Quality Issues
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Using a crack for PRTK is risky and unreliable. It could expose you to malware and compromise your results.
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Many cracks for PRTK are infected with viruses, trojans, worms, spyware, ransomware, or other malicious programs. These programs could harm your computer, steal your data, encrypt your files, or demand money from you.
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Even if the crack for PRTK is not infected with malware, it could still cause problems with your software. It could make it unstable, slow, buggy, or incompatible with other tools. It could also prevent you from updating your software or getting technical support from Exterro.
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Furthermore, using a crack for PRTK could affect the quality and accuracy of your password recovery results. It could make your software miss some passwords, generate false positives, or corrupt your files or containers.
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Using a crack for PRTK could jeopardize your work and waste your time and resources.
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Alternatives to Using a Crack
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Using a crack for PRTK is not worth it. There are better alternatives that are safer and more reliable.
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One alternative is to get a legitimate license for FTK. You can purchase a license from Exterro's website or contact them for more information. You can also request a free trial or a demo to test the software before buying it.
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A legitimate license for FTK will give you access to all the features and benefits of PRTK without any risks or challenges. You will be able to use the software legally and ethically, update it regularly, get technical support from Exterro, and ensure the quality and accuracy of your password recovery results.
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Another alternative is to use other tools for password recovery that are free or cheaper than FTK. There are many tools available on the internet that can recover passwords from encrypted files or containers. Some examples are:
- - John the Ripper: A free and open source password cracker that supports many encryption algorithms and formats. - Hashcat: A free and open source password recovery tool that uses GPUs to accelerate password cracking. - Elcomsoft Password Recovery Bundle: A commercial password recovery suite that supports various file types and encryption methods. - Passware Kit Forensic: A commercial password recovery software that integrates with FTK Imager and supports many file types and encryption methods. These tools may not have all the features and capabilities of PRTK, but they can still help you recover passwords from encrypted files or containers in some cases.
Conclusion
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In conclusion, Accessdata Password Recovery Toolkit is a powerful and useful software that can recover passwords from encrypted files and containers. However, it is not a cheap software. That's why some people might be tempted to use a crack for it.
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However, using a crack for PRTK is not a good idea. There are many risks and challenges that come with using a crack. It is illegal and unethical; it exposes you to malware and compromises your results; it makes you look suspicious and untrustworthy.
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Instead of using a crack for PRTK, you should consider getting a legitimate license for FTK or using other tools for password recovery that are free or cheaper than FTK. These alternatives are safer and more reliable than using a crack.
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If you need to gain access to password-protected files, then don't use a crack for PRTK. Use a legitimate license or another tool instead.
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FAQs
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What is Accessdata Password Recovery Toolkit?
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Accessdata Password Recovery Toolkit (PRTK) is a software that can recover passwords from encrypted files and containers.
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What is a crack for Accessdata Password Recovery Toolkit?
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A crack for Accessdata Password Recovery Toolkit (PRTK) is a program that modifies the software to bypass the activation process and use it for free.
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Why should I avoid using a crack for Accessdata Password Recovery Toolkit?
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You should avoid using a crack for Accessdata Password Recovery Toolkit (PRTK) because it is illegal and unethical; it exposes you to malware and compromises your results; it makes you look suspicious and untrustworthy.
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What are some alternatives to using a crack for Accessdata Password Recovery Toolkit?
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Some alternatives to using a crack for Accessdata Password Recovery Toolkit (PRTK) are getting a legitimate license for FTK or using other tools for password recovery that are free or cheaper than FTK.
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Where can I get more information about Accessdata Password Recovery Toolkit?
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You can get more information about Accessdata Password Recovery Toolkit (PRTK) from Exterro's website: https://www.exterro.com/ftk-product-downloads/password-recovery-toolkit-prtk-version-8-2-1
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How to Download and Use AnyDesk Client for Remote Desktop Access
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AnyDesk is a fast and secure remote desktop software that allows you to access, control and administrate all your devices when working remotely. Whether you need to work from home, provide technical support, collaborate with your team, or access your personal computer, AnyDesk can help you do it easily and efficiently.
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How to Download AnyDesk Client for Windows
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Downloading AnyDesk client for Windows is very simple and fast. Just follow these steps:
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How to Use AnyDesk Client for Remote Desktop Access
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Using AnyDesk client for remote desktop access is very easy and intuitive. Here are some basic steps to get you started:
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When a connection request is received, you need to accept it by clicking on "Accept" or reject it by clicking on "Reject". You can also set up unattended access or whitelist trusted devices for automatic acceptance.
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AnyDesk client is one of the best remote desktop software available in the market. Here are some of the reasons why you should choose AnyDesk for your remote desktop needs:
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AnyDesk offers dynamic performance and smooth remote desktop connections with low latency and high frame rates.
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AnyDesk ensures end-to-end security and privacy with TLS 1.2 encryption technology and verification of connections.
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AnyDesk provides flexibility and customization options with your own brand and logo, group policies, user management, account feature, permission management, and more.
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AnyDesk supports cross-compatibility and platform independence with various operating systems and devices.
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AnyDesk offers free updates and affordable pricing plans for different use cases and scenarios.
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Conclusion
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AnyDesk client is a powerful and reliable remote desktop software that can help you work remotely with ease and efficiency. Whether you need to access your personal computer, provide technical support, collaborate with your team, or manage your devices, AnyDesk can do it all for you.
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To download and use AnyDesk client for Windows, just follow the simple steps we have shown you in this article. You can also download AnyDesk for other platforms from https://anydesk.com/.
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If you have any questions or feedback about AnyDesk client, feel free to contact
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Diner De ConsLe Movie Download __LINK__ 720p Movie.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Diner De ConsLe Movie Download __LINK__ 720p Movie.md
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How to Download Diner De Cons,Le Movie in 720p Quality
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fizika masalalar yechish usullari pdf Oquv qollanma va namunalar.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fizika masalalar yechish usullari pdf Oquv qollanma va namunalar.md
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Fizika Masalalar Yechish Usullari PDF
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Introduction
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Fizika masalalar yechish usullari (physics problem solving methods) are a set of strategies and techniques that can help you solve various types of physics problems. Physics problems are often challenging and complex, requiring you to apply your knowledge, skills, and creativity to find the correct solutions. Learning and practicing fizika masalalar yechish usullari can help you improve your understanding of physics concepts, develop your logical thinking and analytical skills, and enhance your confidence and motivation in learning physics.
Whether you are a student or a teacher of physics, you can benefit from using fizika masalalar yechish usullari. As a student, you can use these methods to tackle homework assignments, prepare for exams, and participate in competitions. As a teacher, you can use these methods to design effective learning activities, assess student performance, and provide feedback and guidance. In this article, you will learn about the types of physics problems, the general steps of problem solving in physics, some specific methods and techniques that can help you solve different types of problems, and some skills that you need to develop to become a better problem solver in physics. You will also find a link to download a PDF file that contains these methods and examples for your reference.
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Types of Physics Problems
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Physics problems can be classified into different types based on their level of difficulty, content, and format. Some common types of physics problems are:
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Qualitative problems: These problems require you to explain or describe physical phenomena or concepts using words or diagrams. You do not need to perform any calculations or use any formulas. For example: Explain why objects fall when they are dropped.
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Quantitative problems: These problems require you to calculate numerical values or quantities using formulas or equations. You need to perform algebraic manipulations, unit conversions, or other mathematical operations. For example: Calculate the speed of a car that travels 100 km in 2 hours.
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Conceptual problems: These problems require you to apply your understanding of physical principles or laws to analyze or predict physical situations or outcomes. You may need to use qualitative reasoning or quantitative calculations or both. For example: Predict what will happen to the motion of a pendulum if its length is doubled.
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Application problems: These problems require you to apply your knowledge and skills of physics to solve real-world problems or scenarios. You may need to use multiple concepts or formulas or both. For example: Determine how much force is needed to lift a 50 kg box using a pulley system.
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Multiple-choice problems: These problems require you to choose the correct answer from a list of options. You may need to use any of the above types of problem solving methods or a combination of them. For example: Which of the following statements is true about gravity? (a) Gravity is a force that attracts all objects with mass. (b) Gravity is a force that depends on the distance between two objects. (c) Gravity is a force that depends on the mass of two objects. (d) All of the above.
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Each type of problem has its own features and challenges. For example, qualitative problems may require you to use your intuition or common sense, but they may also involve misconceptions or vague terms. Quantitative problems may require you to memorize or recall formulas or equations, but they may also involve errors or uncertainties in measurements or calculations. Conceptual problems may require you to synthesize or integrate multiple concepts or principles, but they may also involve assumptions or simplifications that may not be valid in reality. Application problems may require you to model or simulate real-world situations or systems, but they may also involve complex or unknown variables or parameters. Multiple-choice problems may require you to eliminate incorrect options or compare different options, but they may also involve distractors or tricks that may confuse you.
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Problem Solving Methods
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The general steps of problem solving in physics are:
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Read and understand the problem: In this step, you need to identify what is given and what is asked in the problem. You need to pay attention to the keywords, units, symbols, diagrams, graphs, tables, or other information that are provided in the problem statement. You also need to check if there are any missing or extra information that may affect the solution.
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Plan a strategy: In this step, you need to decide how to approach the problem. You need to choose an appropriate type of problem solving method based on the type of problem. You also need to select relevant concepts, formulas, equations, principles, laws, rules, or relationships that are applicable to the problem.
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Execute the solution: In this step, you need to implement your strategy by performing calculations, manipulations, operations, or other actions that are required by your chosen method. You need to show your work clearly and systematically by writing down each step with proper notation, units, and explanations. You also need to check your work for errors, consistency, and reasonableness.
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Evaluate the result: In this step, you need to verify your result by comparing it with the given information, the expected outcome, or other sources. You need to check if your result makes sense physically, logically, and mathematically. You also need to report your result with appropriate units, significance, and accuracy.
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In addition to these general steps, there are some specific methods and techniques that can help you solve different types of problems. Some examples of these methods and techniques are:
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Drawing diagrams: This method involves sketching pictures or figures that represent physical situations or systems. You can use diagrams to visualize or illustrate physical phenomena, concepts, or relationships. You can also use diagrams to label or identify given quantities, unknown variables, or other relevant information. Diagrams can help you simplify complex problems, organize your thoughts, and communicate your ideas.
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Making tables: This method involves arranging data or information into rows and columns. You can use tables to display numerical values, quantities, or units. You can also use tables to compare different options, cases, or scenarios. Tables can help you organize data, identify patterns, and perform calculations.
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Solving equations: This method involves finding unknown values or quantities by using algebraic expressions, formulas, or equations. You can use equations to model physical situations or systems mathematically. You can also use equations to manipulate variables, solve for unknowns, or substitute values. Equations can help you express relationships, apply rules, and calculate results.
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Using dimensional analysis: This method involves checking or converting units by using dimensional quantities. You can use dimensional analysis to ensure consistency and compatibility among units. You can also use dimensional analysis to convert units, simplify expressions, or derive formulas Dimensional analysis. You can use dimensional analysis to check the consistency and compatibility of units in an equation or expression. You can also use dimensional analysis to convert units, simplify expressions, or derive formulas. Dimensional analysis can help you avoid errors, ensure accuracy, and verify results.
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Using proportions: This method involves setting up ratios or fractions that are equal to each other. You can use proportions to compare or relate different quantities or variables. You can also use proportions to solve for unknowns, scale up or down values, or find percentages. Proportions can help you express relationships, apply rules, and calculate results.
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These are just some examples of the many methods and techniques that you can use to solve physics problems. You may need to use one or more of these methods or techniques depending on the type and complexity of the problem. You may also need to combine these methods or techniques with other skills or tools such as calculators, graphs, charts, or software.
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Problem Solving Skills
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To become a better problem solver in physics, you need to develop some skills that are essential for effective and efficient problem solving. Some of these skills are:
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Reading comprehension: This skill involves understanding the meaning and context of the problem statement. You need to read the problem carefully and critically, paying attention to the details and nuances of the language and information. You also need to identify the main idea and the purpose of the problem, as well as any assumptions or conditions that may affect the solution.
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Analysis: This skill involves breaking down the problem into smaller and simpler parts or components. You need to analyze the given information and the unknown variables, as well as their relationships and dependencies. You also need to analyze the type and level of difficulty of the problem, as well as the appropriate method or technique to use.
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Modeling: This skill involves representing the problem using mathematical symbols, expressions, formulas, equations, diagrams, tables, graphs, or other tools. You need to model the problem accurately and realistically, using relevant concepts and principles of physics. You also need to model the problem clearly and systematically, using proper notation, units, and explanations.
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Calculation: This skill involves performing mathematical operations or manipulations to find the solution of the problem. You need to calculate correctly and precisely, using appropriate formulas, equations, rules, or methods. You also need to calculate efficiently and logically, using shortcuts, tricks, or estimations when possible.
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Checking: This skill involves verifying the validity and accuracy of your solution. You need to check your solution against the given information, the expected outcome, or other sources. You also need to check your solution for errors, consistency, and reasonableness.
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To improve these skills, you need to practice regularly and systematically. You need to solve different types and levels of physics problems that challenge your knowledge, skills, and creativity. You also need to use feedback, reflection, and self-assessment to evaluate your performance and identify your strengths and weaknesses.
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Conclusion
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In this article, you have learned about fizika masalalar yechish usullari (physics problem solving methods), a set of strategies and techniques that can help you solve various types of physics problems. You have learned about the types of physics problems, the general steps of problem solving in physics, some specific methods and techniques that can help you solve different types of problems, and some skills that you need to develop to become a better problem solver in physics. You have also found a link to download a PDF file that contains these methods and examples for your reference.
-
Learning and practicing fizika masalalar yechish usullari can help you improve your understanding of physics concepts, develop your logical thinking and analytical skills, and enhance your confidence and motivation in learning physics. Whether you are a student or a teacher of physics, you can benefit from using fizika masalalar yechish usullari.
We hope you enjoyed this article and found it useful. We encourage you to try out these methods and share your feedback with us. Happy problem solving!
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FAQs
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Q: What is dimensional analysis?
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A: Dimensional analysis is a method that involves checking or converting units by using dimensional quantities. It can help you avoid errors, ensure accuracy, and verify results.
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Q: What is a proportion?
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A: A proportion is a ratio or fraction that is equal to another ratio or fraction. It can help you compare or relate different quantities or variables.
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Q: What is a qualitative problem?
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A: A qualitative problem is a problem that requires you to explain or describe physical phenomena or concepts using words or diagrams.
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Q: What is a multiple-choice problem?
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A: A multiple-choice problem is a problem that requires you to choose the correct answer from a list of options.
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Q: What are some skills that you need to develop to become a better problem solver in physics?
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A: Some skills that you need to develop are reading comprehension, analysis, modeling, calculation, and checking.
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FoneLab 9.1.58 Crack With Activation Number Free Download 2019
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/APK Unlock Tool The Best Way to Play PUBG Mobile at 90 FPS.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/APK Unlock Tool The Best Way to Play PUBG Mobile at 90 FPS.md
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How to Unlock 90 FPS in PUBG Mobile and Why You Should Do It
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PUBG Mobile is one of the most popular and competitive mobile games in the world. Millions of players enjoy the thrilling battle royale experience every day. However, not everyone gets to play the game at its full potential. If you want to take your PUBG Mobile gameplay to the next level, you should consider unlocking 90 FPS mode.
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In this article, we will explain what FPS is and why it matters in PUBG Mobile, how to check your FPS in the game, how to enable 90 FPS mode, and what are the advantages and disadvantages of playing at 90 FPS. We will also share some tips and tricks to optimize your PUBG Mobile performance at 90 FPS. Finally, we will answer some frequently asked questions about 90 FPS mode in PUBG Mobile. Let's get started!
What is FPS and Why Does It Matter in PUBG Mobile?
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FPS stands for frames per second and it determines how smooth the game looks and feels on your screen. The higher the FPS, the more frames are displayed per second, resulting in a smoother and more realistic motion. The lower the FPS, the fewer frames are displayed per second, resulting in a choppier and more laggy motion.
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Why does FPS matter in PUBG Mobile? Well, because it can affect your gameplay experience and performance in various ways. Higher FPS can give you an advantage in PUBG Mobile by improving your aim, reaction time, and visibility. Here are some of the benefits of playing PUBG Mobile at higher FPS:
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Better aim: Higher FPS can make your crosshair movement more fluid and accurate, allowing you to track and hit your enemies more easily.
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Faster response: Higher FPS can reduce the input lag between your touch and the game action, allowing you to react faster to the situation.
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Better visibility: Higher FPS can make the game graphics more clear and crisp, allowing you to spot your enemies and items more easily.
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Of course, playing PUBG Mobile at higher FPS also has some drawbacks, such as higher battery consumption, more heat generation, and potential compatibility issues. We will discuss these in more detail later in this article.
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How to Check Your FPS in PUBG Mobile
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Before you enable 90 FPS mode in PUBG Mobile, you may want to check your current FPS in the game. This way, you can see how much improvement you can get from 90 FPS mode. There are two ways to check your FPS in PUBG Mobile:
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Using a third-party app: You can use a third-party app like GameBench or FPS Meter to monitor your FPS in PUBG Mobile. These apps can show you a real-time overlay of your FPS on your screen while you play the game. However, you may need to grant some permissions or root access to these apps for them to work properly.
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Using the in-game settings: You can also enable the FPS counter in the game settings under Basic > Display FPS. This will show you a small number on the top left corner of your screen indicating your current FPS while you play the game. However, this method may not be very accurate or reliable as it may not update frequently or reflect the actual frame rate.
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Once you have checked your FPS in PUBG Mobile, you can proceed to enable 90 FPS mode if you want to enjoy a smoother gameplay experience.
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How to Enable 90 FPS in PUBG Mobile
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Enabling 90 FPS mode in PUBG Mobile is not very difficult, but it may not be available for everyone. Not all devices support 90 FPS mode in PUBG Mobile, only a few models from OnePlus, Samsung, Xiaomi, Google, and Apple. You can check the list of supported devices on various websites or forums.
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If you have a supported device, you can enable 90 FPS mode in PUBG Mobile by following these steps:
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Go to Settings > Graphics > Frame Rate
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Select 90 FPS from the options
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Enjoy the game at 90 FPS!
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You may need to set your graphics quality to Smooth to unlock 90 FPS option. This will lower the resolution and texture quality of the game, but it will also improve the performance and stability of the game. You can also adjust other graphics settings according to your preference and device capability.
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Advantages and Disadvantages of Playing PUBG Mobile at 90 FPS
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As we mentioned earlier, playing PUBG Mobile at 90 FPS has its pros and cons. Here are some of the advantages and disadvantages of playing PUBG Mobile at 90 FPS:
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Advantages
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Smoother gameplay: Playing PUBG Mobile at 90 FPS can make your gameplay experience more smooth and fluid. You can enjoy a more realistic motion and animation of the game characters and objects.
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Faster response: Playing PUBG Mobile at 90 FPS can make your response time faster and more accurate. You can react quicker to the enemy movements and actions, and execute your commands more precisely.
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Better accuracy: Playing PUBG Mobile at 90 FPS can make your aiming and shooting more accurate and consistent. You can track and hit your targets more easily, especially when they are moving fast or far away.
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More immersive experience: Playing PUBG Mobile at 90 FPS can make your gaming experience more immersive and enjoyable. You can feel more connected to the game world and the action, and have more fun playing the game.
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Disadvantages
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Higher battery consumption: Playing PUBG Mobile at 90 FPS can drain your battery faster than playing at lower FPS. This is because your device has to work harder to render more frames per second, which consumes more power. You may need to charge your device more often or use a power bank if you play PUBG Mobile at 90 FPS for a long time.
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More heat generation: Playing PUBG Mobile at 90 FPS can also generate more heat on your device than playing at lower FPS. This is because your device has to process more data and graphics, which generates more heat. You may feel your device getting hot or warm after playing PUBG Mobile at 90 FPS for a while. This may affect your device performance and lifespan in the long run.
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Potential compatibility issues: Playing PUBG Mobile at 90 FPS may not be compatible with some devices or features. For example, some devices may not support 90 FPS mode at all, or may have some glitches or bugs when playing at 90 FPS. Some features like screen recording or streaming may not work well with 90 FPS mode, or may cause some lag or stuttering. You may need to disable 90 FPS mode if you encounter any compatibility issues.
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Tips and Tricks to Optimize Your PUBG Mobile Performance at 90 FPS
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If you want to play PUBG Mobile at 90 FPS and get the best performance possible, you should follow some tips and tricks to optimize your device and game settings. Here are some of the tips and tricks that you can try:
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Use a device with a high refresh rate display (90 Hz or above): To enjoy the full benefits of 90 FPS mode, you should use a device that has a high refresh rate display (90 Hz or above). This means that your screen can refresh 90 times or more per second, matching the frame rate of the game. This will make the game look smoother and more responsive on your screen. If you use a device with a low refresh rate display (60 Hz or below), you will not be able to see the difference between 60 FPS and 90 FPS, as your screen can only refresh 60 times or less per second.
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Close any background apps and disable any notifications that may interfere with your game: To play PUBG Mobile at 90 FPS without any lag or interruption, you should close any background apps that may consume your memory or CPU resources. You should also disable any notifications that may pop up on your screen while you play the game. These can distract you from the game and affect your performance.
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Adjust your sensitivity settings and controls according to your preference and device size: To play PUBG Mobile at 90 FPS with better accuracy and comfort, you should adjust your sensitivity settings and controls according to your preference and device size. You can customize your sensitivity settings for different scopes, gyroscope, camera, etc., under Settings > Sensitivity. You can also customize your controls layout, size, opacity, etc., under Settings > Controls. You should experiment with different settings and controls until you find the ones that suit you best.
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Use headphones or earphones to hear the sound cues better and communicate with your teammates: To play PUBG Mobile at 90 FPS with better awareness and coordination, you should use headphones or earphones to hear the sound cues better and communicate with your teammates. Sound cues are very important in PUBG Mobile, as they can help you locate your enemies, items, vehicles, etc., by their footsteps, gunshots, explosions, etc. You should also use voice chat or text chat to communicate with your teammates, as they can provide you with valuable information, support, and strategy.
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Practice your skills in training mode or arcade mode before jumping into a classic or ranked match: To play PUBG Mobile at 90 FPS with better confidence and competence, you should practice your skills in training mode or arcade mode before jumping into a classic or ranked match. Training mode allows you to test different weapons, attachments , and vehicles in a safe and controlled environment. Arcade mode allows you to play short and fast-paced matches with different modes, maps, and rules. These modes can help you improve your shooting, aiming, driving, looting, and survival skills in PUBG Mobile.
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Conclusion
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PUBG Mobile is a fun and exciting game that can be enjoyed by anyone. However, if you want to have a more smooth and competitive gameplay experience, you should try playing the game at 90 FPS mode. This mode can make the game look and feel more realistic and responsive, giving you an edge over your opponents. However, you should also be aware of the drawbacks of playing at 90 FPS mode, such as higher battery consumption, more heat generation, and potential compatibility issues. You should also follow some tips and tricks to optimize your PUBG Mobile performance at 90 FPS mode, such as using a high refresh rate device, closing background apps, adjusting sensitivity settings and controls, using headphones or earphones, and practicing your skills in training mode or arcade mode.
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We hope this article has helped you understand how to unlock 90 FPS mode in PUBG Mobile and why you should do it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!
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FAQs
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Q1. How can I play PUBG Mobile at 90 FPS on unsupported devices?
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A1. You may need to use a third-party tool like GFX Tool or FlashDog to modify the game files and enable 90 FPS option. However, this is not recommended as it may violate the game's terms of service and result in a ban.
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Q2. How can I check if my device supports 90 FPS in PUBG Mobile?
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A2. You can check the list of supported devices on various websites or forums. Alternatively, you can go to Settings > Graphics > Frame Rate and see if the 90 FPS option is available for you.
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Q3. What is the difference between 60 FPS and 90 FPS in PUBG Mobile?
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A3. The difference between 60 FPS and 90 FPS is that the latter displays more frames per second, making the game look smoother and more responsive. However, the difference may not be noticeable for some people or on some devices.
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Q4. Does playing PUBG Mobile at 90 FPS affect my ping or network latency?
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A4. No, playing PUBG Mobile at 90 FPS does not affect your ping or network latency. Ping is determined by your internet connection speed and quality, not by your frame rate.
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Q5. What are some other ways to improve my PUBG Mobile performance besides enabling 90 FPS?
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A5. Some other ways to improve your PUBG Mobile performance are updating your game and device software, clearing your cache and storage space, using a stable Wi-Fi connection, and avoiding playing in hot or humid environments.
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Monster in My Pocket Game Download for Android
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Do you love monsters and platform games? Do you want to relive your childhood memories of collecting and playing with tiny monster figures? If you answered yes to any of these questions, then you might be interested in Monster in My Pocket, a classic NES game that is now available for Android devices. In this article, we will tell you everything you need to know about this game, including what it is, how to download and play it on your Android phone or tablet, and why you should give it a try.
Monster in My Pocket is a media franchise that was developed by American company Morrison Entertainment Group in the late 1980s and early 1990s. The franchise focused on monsters and fantastical creatures from various sources, such as religion, mythology, folklore, fairy tales, literature, science fiction, and cryptozoology. The franchise produced trading cards, comic books, books, toys, a board game, a video game, and an animated special, along with music, clothing, kites, stickers, and other items.
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A brief history of the franchise
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The most popular product of the franchise was the toy line, which was released by Matchbox in 1990. It consisted of small, soft plastic figures representing different monsters, each with a point value assigned to them. There were over 200 monsters in the collection, ranging from well-known ones like Dracula, Frankenstein's Monster, and Werewolf, to obscure ones like Catoblepas, Haniver, and Tengu. The toys were initially solid-colored, but later series added more painted colors and details.
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The toy line also inspired a comic book series that was published by Harvey Comics from 1991 to 1992. The comic book series followed the story of Vampire and Monster (the two main protagonists of the franchise) as they battled against Warlock (the main antagonist) and his army of evil monsters. The comic book series also introduced new characters and monsters that were not part of the toy line.
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The video game adaptation of the franchise was produced by Konami in 1992 for the NES platform. It was a platformer game that followed the storyline of the comic book series moderately close. It featured Vampire and Monster as playable characters who had to stop Warlock's plan to take over the world. The game had six stages that took place in different locations, such as a house, a kitchen, a sewer, a city, an oriental temple, and a mountain. The game also had various enemies and bosses that were based on the toy figures.
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The main features of the game
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The game had several features that made it stand out from other platformer games at the time. Some of these features were:
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The game had a co-op mode that allowed two players to play together as Vampire and Monster.
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The game had a double jump mechanic that enabled the characters to jump higher and farther.
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The game had a unique attack system that allowed the characters to shoot energy blasts from their hands or throw objects at their enemies.
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The game had a variety of items that could be collected or used throughout the stages, such as keys, screws, health potions, extra lives, and power-ups.
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The game had a colorful and detailed graphics style that captured the look and feel of the toy line and the comic book series.
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The game had a catchy and memorable soundtrack that matched the mood and theme of each stage.
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How
How to download and play Monster in My Pocket on Android?
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Now that you know what Monster in My Pocket is and why it is such a great game, you might be wondering how you can download and play it on your Android device. Well, the good news is that it is not very difficult to do so, as long as you follow these simple steps:
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The steps to download the APK file
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The first thing you need to do is to download the APK file of the game, which is a file format that allows you to install and run applications on Android devices. There are many websites that offer APK files of various games, but not all of them are safe and reliable. Therefore, we recommend that you use a trusted and reputable source, such as [APKPure] or [APKMirror]. Here are the steps to download the APK file of Monster in My Pocket from APKPure:
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Go to the [APKPure website] and search for "Monster in My Pocket" in the search bar.
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Select the game from the list of results and click on the "Download APK" button.
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Wait for the download to finish and locate the file in your device's storage.
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The steps to install and run the game
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The next thing you need to do is to install and run the game on your device. However, before you do that, you need to make sure that your device allows the installation of apps from unknown sources, which are sources other than the Google Play Store. To do that, you need to follow these steps:
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Go to your device's settings and look for the option "Security" or "Privacy".
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Tap on it and find the option "Unknown sources" or "Install unknown apps".
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Enable it by toggling the switch or checking the box.
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Once you have done that, you can proceed to install and run the game by following these steps:
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Go to your device's file manager and locate the APK file of Monster in My Pocket that you downloaded earlier.
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Tap on it and follow the instructions on the screen to install the game.
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Wait for the installation to finish and look for the game's icon on your device's home screen or app drawer.
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Tap on it and enjoy playing Monster in My Pocket on your Android device.
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The tips and tricks to enjoy the game
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To make the most out of your gaming experience, here are some tips and tricks that you can use while playing Monster in My Pocket:
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Choose your character wisely. Vampire and Monster have different abilities and weaknesses. Vampire can fly for a short time and shoot energy blasts, but he is weak against fire. Monster can throw objects and has more health, but he is slow and cannot fly.
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Use your double jump wisely. You can use it to reach higher places, avoid obstacles, or dodge enemies. However, you cannot use it again until you land on a solid surface.
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Collect as many items as you can. They can help you in various ways, such as restoring your health, increasing your score, or giving you extra lives or power-ups.
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Be careful with your power-ups. They can give you an edge over your enemies, but they also have drawbacks. For example, the fireball power-up lets you shoot fireballs, but it also makes you vulnerable to water. The ice power-up lets you freeze enemies, but it also makes you vulnerable to fire.
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Explore every stage thoroughly. You might find hidden areas, secret passages, or bonus rooms that contain more items or enemies.
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Watch out for environmental hazards. There are many things that can harm you in each stage, such as spikes, flames, water, electricity, or falling objects. Be alert and avoid them as much as possible.
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Why should you play Monster in My Pocket on Android?
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You might be wondering why you should play Monster in My Pocket on Android when there are so many other games available for this platform. Well, there are many reasons why this game is worth playing on Android, such as:
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The benefits of playing on a mobile device
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Playing Monster in My Pocket on Android has several advantages over playing it on other platforms, such as:
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You can play it anytime and anywhere. You don't need a TV, a console, or a controller. All you need is your Android device and an internet connection.
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You can play it with You can play it with your friends or family. You can use the co-op mode to team up with another player on the same device or on different devices. You can also compete with other players online or offline.
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You can customize your gaming experience. You can adjust the settings, such as the sound, the graphics, the controls, or the difficulty. You can also use cheats or mods to enhance your gameplay.
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The challenges and fun of the game
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Playing Monster in My Pocket on Android is also challenging and fun, because:
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The game has a high replay value. You can play it multiple times and discover new things, such as different paths, secrets, or endings. You can also try to beat your own records or achievements.
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The game has a retro appeal. You can enjoy the nostalgic and charming graphics, music, and gameplay of the original NES game. You can also appreciate the creativity and diversity of the monster designs and personalities.
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The game has a humorous tone. You can laugh at the funny and witty dialogues, situations, and references that the game offers. You can also have fun with the absurd and exaggerated scenarios that the game presents.
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The nostalgia and charm of the game
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Playing Monster in My Pocket on Android is also nostalgic and charming, because:
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The game reminds you of your childhood memories. You can relive the joy and excitement of collecting and playing with the toy figures. You can also recall the stories and adventures that you imagined or read about them.
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The game connects you with other fans of the franchise. You can share your opinions and experiences with other people who love Monster in My Pocket. You can also learn more about the franchise and its history and legacy.
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The game inspires you to explore more monsters and myths. You can learn more about the origins and backgrounds of the monsters that appear in the game. You can also discover new monsters and myths that you never knew before.
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Conclusion
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In conclusion, Monster in My Pocket is a classic NES game that is now available for Android devices. It is a platformer game that features monsters and fantastical creatures from various sources. It has several features that make it stand out from other platformer games, such as co-op mode, double jump mechanic, unique attack system, variety of items, colorful graphics, and catchy soundtrack. It also has several benefits, challenges, fun, nostalgia, and charm that make it worth playing on Android devices. If you are a fan of monsters and platform games, you should definitely download and play Monster in My Pocket on your Android device today.
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A call to action for the readers
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If you are interested in playing Monster in My Pocket on your Android device, you can download it from [APKPure] or [APKMirror] by following the steps that we explained above. You can also check out other sources that offer APK files of this game, but make sure that they are safe and reliable. Once you have downloaded and installed the game, you can start playing it right away and enjoy its features, benefits, challenges, fun, nostalgia, and charm.
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FAQs
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Here are some frequently asked questions about Monster in My Pocket:
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Q: Is Monster in My Pocket free to play on Android? A: Yes, Monster in My Pocket is free to play on Android devices. However, some websites may require you to register or complete surveys before downloading the APK file of the game.
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Q: Is Monster in My Pocket compatible with all Android devices? A: No, Monster in My Pocket may not be compatible with some Android devices due to different hardware specifications or software versions. If you encounter any problems while playing the game on your device, you may need to update your device's system or use an emulator.
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Q: Is Monster in My Pocket safe to play on Android? A: Yes, Monster in My Pocket is safe to play on Android devices as long as you download it from a trusted and reputable source, such as [APKPure] or [APKMirror]. However, you should always be careful when downloading any APK file from unknown sources, as they may contain viruses or malware that could harm your device or steal your data.
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Q: Is Monster in My Pocket still popular today? A: Yes, Monster in My Pocket still has a loyal fan base today who appreciate its retro appeal and nostalgic charm. The franchise also has a cult following among collectors who seek rare or exclusive items related to it.
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Q: Are there any other games like Monster in My Pocket? A: Yes, there are many other games like Monster in My Pocket that feature monsters and platform games, such as: - Castlevania: A series of games that feature vampire hunters and other supernatural creatures in a Gothic setting. - Ghosts 'n Goblins: A series of games that feature a knight who has to rescue his princess from demons and undead. - Little Nemo: The Dream Master: A game that features a boy who can enter the dream world and transform into different animals. - Kid Dracula: A game that features a young vampire who has to defeat his father's enemies and reclaim his throne. - Monster Party: A game that features a boy who teams up with a monster to fight against bizarre and grotesque enemies.
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I hope you enjoyed reading this article and learned something new about Monster in My Pocket. If you have any questions or comments, feel free to leave them below. Thank you for your time and attention.
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Spider-Man (2000 Video Game) Download APK: How to Play the Classic Game on Your Android Device
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If you are a fan of Spider-Man, you might remember the classic video game that was released in 2000 for PlayStation, Nintendo 64, Dreamcast, PC, and Game Boy Color. The game was based on the comic book series and featured an original story that involved Spider-Man fighting against various villains such as Venom, Carnage, Doctor Octopus, Mysterio, Rhino, Scorpion, and more. The game was praised for its graphics, gameplay, voice acting, and faithful adaptation of the Spider-Man universe.
But what if you want to play this game in 2023 on your Android device? Is it possible to download and install an apk file that will let you enjoy this classic game on your smartphone or tablet? The answer is yes, but there are some things you need to know before you do so. In this article, we will show you how to download and install Spider-Man (2000 video game) apk on your Android device, what are the features and gameplay of this game, what are the pros and cons of playing it on your mobile device, and whether it is worth playing in 2023.
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How to download and install Spider-Man (2000 video game) apk on your Android device?
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The first thing you need to do is to find a reliable source that offers the apk file for Spider-Man (2000 video game). There are many websites that claim to provide this file, but not all of them are trustworthy or safe. Some of them might contain malware or viruses that can harm your device or steal your personal information. Therefore, you need to be careful and do some research before you download anything from an unknown source.
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One of the websites that we recommend is APKCombo, which is a reputable platform that offers free apk files for various Android games and apps. You can visit their website and search for "Spider-Man 2", which is the name of the apk file for Spider-Man (2000 video game). You will see a page that shows you some information about the file, such as its size, version, developer, rating, and screenshots. You can also read some reviews from other users who have downloaded and played the game. If you are satisfied with the file, you can click on the "Download APK" button and save the file to your device.
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The next thing you need to do is to install the apk file on your device. Before you do that, you need to enable the "Unknown sources" option in your device's settings. This will allow you to install apps that are not from the official Google Play Store. To do this, go to your device's settings, then security, then unknown sources, and toggle it on. You might see a warning message that tells you about the risks of installing apps from unknown sources, but you can ignore it if you trust the source of the apk file.
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Once you have enabled the unknown sources option, you can go to your device's file manager and locate the apk file that you have downloaded. Tap on it and follow the instructions on the screen to install it. You might see some permissions requests that ask you to allow the app to access your device's storage, camera, microphone, etc. You can grant these permissions if you want to enjoy the full features of the game, or deny them if you are concerned about your privacy. After the installation is complete, you will see an icon for Spider-Man 2 on your device's home screen or app drawer. Tap on it and start playing!
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What are the features and gameplay of Spider-Man (2000 video game)?
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Spider-Man (2000 video game) is an action-adventure game that lets you control Spider-Man as he swings, crawls, fights, and explores New York City. The game has a third-person perspective and uses a combination of 3D graphics and 2D sprites. The game also features voice acting from some of the actors who played Spider-Man and his allies and enemies in various animated series, such as Rino Romano, Jennifer Hale, Dee Bradley Baker, Daran Norris, and Mark Hamill.
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The game has four modes: training, story, what if?, and gallery. The training mode teaches you the basic controls and moves of Spider-Man, such as web swinging, wall crawling, web shooting, punching, kicking, etc. The story mode follows the main plot of the game, which involves Spider-Man being framed for a bank robbery by a mysterious imposter and having to clear his name while facing various threats from his enemies. The what if? mode is a variation of the story mode that changes some of the events and outcomes of the game based on different choices and actions. The gallery mode lets you view various artworks, comic book covers, character bios, and cheats that you can unlock by playing the game.
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The gameplay of Spider-Man (2000 video game) is divided into levels that have different objectives and challenges. Some of the levels require you to reach a certain destination, while others require you to defeat a certain number of enemies or a boss. Some of the levels also have optional objectives that can reward you with bonus points or items. The game also has some stealth elements that require you to avoid detection by enemies or cameras. The game also has some puzzle elements that require you to use your webbing or other items to solve problems or access hidden areas.
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The game also has a variety of villains that Spider-Man has to face throughout the game. Some of them are classic foes from the comic books, such as Venom, Carnage, Doctor Octopus, Mysterio, Rhino, Scorpion, Lizard, Electro, Sandman, and Vulture. Some of them are original creations for the game, such as Monster Ock, a fusion of Doctor Octopus and Carnage, and the Spider-Slayers, robotic enemies that hunt Spider-Man. The game also has some allies that help Spider-Man along the way, such as Black Cat, Daredevil, Captain America, Human Torch, and Punisher.
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The game also has some easter eggs that reference other Marvel characters and events, such as the Fantastic Four, Iron Man, Thor, Hulk, X-Men, Blade, Ghost Rider, and more. Some of these easter eggs can be found by exploring the levels or using certain cheats. For example, if you enter the cheat code "GBHSRSPM", you can play as Spider-Man wearing a Fantastic Four costume with a paper bag over his head, which is a reference to an issue of the comic book where Spider-Man did the same thing.
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What are the pros and cons of playing Spider-Man (2000 video game) on your Android device?
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Playing Spider-Man (2000 video game) on your Android device can have some advantages and disadvantages compared to playing it on a console or a PC. Here are some of them:
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Pros
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You can play the game anytime and anywhere you want, as long as you have your device with you.
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You can enjoy the game on a larger screen if you have a tablet or a device that supports screen mirroring or casting.
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You can customize the game's settings and controls to suit your preferences and device's specifications.
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You can save your progress and resume it later without losing any data.
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You can access some cheats and mods that can enhance your gaming experience or make it more fun.
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Cons
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You might experience some lagging, crashing, or compatibility issues depending on your device's model, operating system, or memory.
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You might drain your device's battery faster if you play the game for a long time or at a high brightness level.
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You might use up your device's storage space if you download a large apk file or additional data for the game.
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You might expose your device to malware or viruses if you download an apk file from an untrusted source or grant unnecessary permissions to the app.
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You might violate some intellectual property rights or terms of service if you download an apk file from an unofficial source or modify the game's content without permission.
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Conclusion: Is Spider-Man (2000 video game) worth playing in 2023?
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Spider-Man (2000 video game) is a classic game that many Spider-Man fans and gamers still love and enjoy today. The game has a captivating story, engaging gameplay, impressive graphics, and memorable voice acting. The game also has a lot of features and easter eggs that make it fun and rewarding to play. The game is also compatible with Android devices, which means you can play it on your smartphone or tablet with ease.
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However, playing Spider-Man (2000 video game) on your Android device also has some drawbacks that you need to consider. The game might not run smoothly or properly on some devices due to technical issues or limitations. The game might also consume a lot of your device's battery, storage, or data. The game might also pose some risks to your device's security or privacy if you download an apk file from an unreliable source or grant unnecessary permissions to the app. The game might also infringe some legal or ethical rules if you download an apk file from an unofficial source or alter the game's content without authorization.
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Therefore, whether Spider-Man (2000 video game) is worth playing in 2023 depends on your personal preference and judgment. If you are a fan of Spider-Man or retro games, and you have a compatible and secure device, you might enjoy playing this game and reliving its nostalgia. However, if you are not interested in Spider-Man or old games, or you have an incompatible or unsafe device, you might not like playing this game and find it outdated or boring.
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The choice is yours. If you want to try out Spider-Man (2000 video game) on your Android device, you can follow the steps we have provided in this article to download and install the apk file from APKCombo. If you have any questions or feedback about this game or this article, feel free to leave a comment below. We would love to hear from you!
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FAQs
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Q: What is the difference between Spider-Man (2000 video game) and Spider-Man 2: Enter Electro?
A: Spider-Man 2: Enter Electro is the sequel to Spider-Man (2000 video game) that was released in 2001 for PlayStation and PC. The game follows a new story that involves Spider-Man trying to stop Electro from obtaining a powerful device that can amplify his powers. The game has some improvements and additions over the first game, such as new moves, costumes, levels, enemies, and bosses. However, the game also has some drawbacks, such as lower graphics quality, shorter gameplay, and less voice acting.
Q: How can I play Spider-Man (2000 video game) on other devices besides Android?
- A: If you want to play Spider-Man (2000 video game) on other devices, you have a few options. You can play the original version of the game on PlayStation, Nintendo 64, Dreamcast, PC, or Game Boy Color if you have these consoles or emulators. You can also play a remastered version of the game on PlayStation 3 or Xbox 360 if you have these consoles or emulators. You can also play a ported version of the game on iOS or Windows Phone if you have these devices or emulators.
Q: What are some of the cheats and mods that I can use for Spider-Man (2000 video game)?
- A: There are many cheats and mods that you can use for Spider-Man (2000 video game) to make it more fun or challenging. Some of the cheats are codes that you can enter in the main menu or during the game to unlock various features, such as costumes, levels, characters, abilities, etc. Some of the mods are files that you can download and install on your device to change some aspects of the game, such as graphics, sound, gameplay, etc. You can find some of the cheats and mods online from various sources, such as YouTube, Reddit, APKPure, etc.
Q: What are some of the best Spider-Man games that I can play in 2023?
- A: There are many Spider-Man games that you can play in 2023 that are based on different versions of Spider-Man from different media, such as comics, movies, cartoons, etc. Some of the best Spider-Man games that we recommend are: - Spider-Man: Miles Morales (2020): A spin-off of Spider-Man (2018) that follows Miles Morales as he becomes the new Spider-Man and faces new threats in New York City. - Marvel's Spider-Man (2018): A critically acclaimed game that features an original story and gameplay that lets you explore a realistic and dynamic open-world New York City as Spider-Man. - Spider-Man: Shattered Dimensions (2010): A unique game that lets you play as four different versions of Spider-Man from different dimensions and timelines, each with their own abilities and styles. - Ultimate Spider-Man (2005): A comic book-inspired game that lets you play as both Spider-Man and Venom in a cel-shaded world that follows the Ultimate Marvel storyline. - Spider-Man 2 (2004): A movie-based game that lets you swing freely around a large and detailed New York City as Spider-Man and face various villains and challenges.
Q: How can I learn more about Spider-Man and his universe?
- A: If you are interested in learning more about Spider-Man and his universe, there are many sources that you can check out. You can read some of the comic books that feature Spider-Man and his allies and enemies from different eras and genres. You can watch some of the movies or shows that adapt or expand on Spider-Man's stories and characters from different perspectives and styles. You can also visit some of the websites or forums that discuss or analyze Spider-Man's lore and trivia from various angles and viewpoints. 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/FIFA Mobile 22 Hack How to Unlock All Players Kits and Stadiums for Free.md b/spaces/1phancelerku/anime-remove-background/FIFA Mobile 22 Hack How to Unlock All Players Kits and Stadiums for Free.md
deleted file mode 100644
index 4b0bec610d5299e57d4a59642044c2df957d4856..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/FIFA Mobile 22 Hack How to Unlock All Players Kits and Stadiums for Free.md
+++ /dev/null
@@ -1,101 +0,0 @@
-
-
Download FIFA Mobile 22 Hack: How to Get Unlimited Coins and Gems
-
FIFA Mobile 22 is one of the most popular and exciting soccer games on mobile devices. It allows you to build your ultimate team with over 15,000 authentic players from more than 600 clubs, compete in various modes such as World Cup, Champions League, Manager Mode, or Head-to-Head, and enjoy realistic graphics and gameplay. However, FIFA Mobile 22 also has some challenges and limitations that might frustrate some players. For example, you need coins and gems to buy players, upgrade your team, unlock features, or access premium content. Coins and gems are not easy to come by in the game, especially if you are a free-to-play user. You might have to spend a lot of time, effort, or even real money to get enough coins and gems for your needs.
That's why some players might want to hack FIFA Mobile 22 to get unlimited coins and gems. By hacking FIFA Mobile 22, you can bypass the restrictions and enjoy the game without any hassle. You can buy any player you want, upgrade your team to the max level, unlock all the features and events, or access any premium content you desire. Sounds tempting, right? But before you rush to download FIFA Mobile 22 hack, you should be aware of the risks and consequences of doing so. In this article, we will explain what are the dangers of hacking FIFA Mobile 22, how to download FIFA Mobile 22 hack safely and easily, and what are some alternative ways to get coins and gems without hacking.
-
Disclaimer: The risks and consequences of hacking FIFA Mobile 22
-
Before we proceed, we have to warn you about the potential dangers of hacking FIFA Mobile 22. Hacking is an illegal and unethical activity that violates the terms of service of EA Sports, the developer of FIFA Mobile 22. If you hack FIFA Mobile 22, you might face some serious consequences, such as:
-
-
Getting banned from the game permanently or temporarily
-
Losing your progress, data, or account
-
Exposing your personal information or device to malware or hackers
-
Getting sued by EA Sports for damages or losses
-
-
Therefore, we advise you to proceed at your own risk and responsibility if you decide to hack FIFA Mobile 22. We also suggest you to use a reliable and trusted source for downloading FIFA Mobile 22 hack, such as the one we will provide in the next section. Do not download FIFA Mobile 22 hack from unknown or suspicious websites, as they might contain viruses or malware that can harm your device or steal your information.
-
How to download FIFA Mobile 22 hack: A step-by-step guide
-
If you are still interested in hacking FIFA Mobile 22, here is a step-by-step guide on how to download FIFA Mobile 22 hack from a reputable website. Follow these instructions carefully and you will be able to get unlimited coins and gems in no time.
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Go to the website [text], which is one of the best and safest sources for downloading FIFA Mobile 22 hack. You can access the website from any browser or device.
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On the homepage, you will see a button that says "Download FIFA Mobile 22 Hack". Click on it and you will be redirected to a verification page.
-
On the verification page, you will have to complete a short and simple survey or offer to prove that you are a human and not a bot. This is a necessary step to prevent abuse and ensure the quality of the service. The survey or offer will only take a few minutes and will not cost you anything.
-
After completing the verification, you will be able to download FIFA Mobile 22 hack as an APK file. Save the file on your device and locate it using a file manager.
-
Before installing the APK file, make sure that you have enabled the "Unknown Sources" option in your device settings. This will allow you to install apps from sources other than the Google Play Store.
-
Tap on the APK file and follow the instructions on the screen to install FIFA Mobile 22 hack on your device. You might have to grant some permissions to the app for it to work properly.
-
Once the installation is done, you can launch FIFA Mobile 22 hack from your app drawer or home screen. You will see a user-friendly interface that will let you customize your preferences and settings.
-
Enter the amount of coins and gems that you want to generate and click on the "Start Hack" button. The hack will start working and inject the resources into your game account.
-
Wait for a few seconds or minutes until the hack is finished. You will see a confirmation message on the screen when it is done.
-
Open FIFA Mobile 22 and enjoy your unlimited coins and gems. You can use them to buy players, upgrade your team, unlock features, or access premium content as much as you want.
-
-
Congratulations! You have successfully hacked FIFA Mobile 22 and got unlimited coins and gems. You can now enjoy the game without any limitations or restrictions. However, if you are not comfortable with hacking FIFA Mobile 22 or want to try some alternative ways to get coins and gems without hacking, keep reading.
-
Alternative ways to get coins and gems without hacking
-
Hacking FIFA Mobile 22 is not the only way to get coins and gems in the game. There are some legitimate and safe methods that you can use to earn coins and gems without breaking any rules or risking any consequences. Here are some of them:
-
-
Method
Description
Benefits
Drawbacks
-
Completing tasks
FIFA Mobile 22 offers various tasks that you can complete to earn coins and gems. These tasks include daily, weekly, monthly, seasonal, or special tasks that require you to perform certain actions or achieve certain goals in the game.
- Easy and simple - Rewarding and satisfying - Diverse and varied
- Time-consuming - Repetitive - Limited
-
Participating in events
FIFA Mobile 22 also features various events that you can participate in to earn coins and gems. These events include World Cup, Champions League, Manager Mode, Head-to-Head, or other themed events that offer different challenges and rewards.
- Fun and exciting - Competitive and challenging - Generous and lucrative
- Difficult - Demanding - Seasonal
-
Achieving achievements
FIFA Mobile 22 has a list of achievements that you can achieve to earn coins and gems. These achievements include milestones, records, feats, or accomplishments that reflect your progress and performance in the game.
- Motivating and inspiring - Reflective and rewarding - Incremental and cumulative
- Hard and rare - Fixed and finite - Hidden and obscure
-
Watching ads
FIFA Mobile 22 allows you to watch ads to earn coins and gems. These ads are usually short and relevant to the game or your interests. You can watch ads from the store, the rewards center, or the events page.
- Quick and easy - Free and unlimited - Optional and voluntary
- Boring and annoying - Low and variable - Intrusive and distracting
-
-
As you can see, there are some pros and cons of each method. You can choose the one that suits your preferences, goals, and playstyle. You can also combine different methods to maximize your coin and gem income. Here are some tips and tricks on how to optimize your coin and gem income in FIFA Mobile 22:
-
-
Play the game regularly and complete the daily tasks every day. They are the easiest and most consistent way to earn coins and gems.
-
Participate in the events that match your skill level and team strength. They are the most fun and rewarding way to earn coins and gems.
-
Achieve the achievements that are within your reach and match your playstyle. They are the most motivating and reflective way to earn coins and gems.
-
Watch ads when you have some spare time or need some extra coins or gems. They are the quickest and easiest way to earn coins and gems.
-
Save your coins and gems for the players, features, or content that you really want or need. Do not waste them on unnecessary or impulsive purchases.
-
-
Conclusion: Summarize the main points and give a final verdict
-
In conclusion, FIFA Mobile 22 is a great soccer game that offers a lot of fun and excitement for mobile gamers. However, it also has some challenges and limitations that might make some players want to hack it to get unlimited coins and gems. In this article, we have explained what are the risks and consequences of hacking FIFA Mobile 22, how to download FIFA Mobile 22 hack safely and easily, and what are some alternative ways to get coins and gems without hacking.
-
Our final verdict is that hacking FIFA Mobile 22 is not worth it. It is illegal, unethical, risky, and unnecessary. You might end up losing more than you gain by hacking FIFA Mobile 22. You might lose your account, your progress, your data, or even your device. You might also lose the fun, challenge, satisfaction, and integrity of playing FIFA Mobile 22.
-
We recommend you to play FIFA Mobile 22 without hacking it. You can still enjoy the game without unlimited coins and gems. You can still earn enough coins and gems by playing the game legitimately and safely. You can still build your ultimate team, compete in various modes, and enjoy realistic graphics and gameplay.
-
We hope you found this article helpful and informative. If you have any questions, comments, or feedback, please feel free to share them in the comments section below. We would love to hear from you. Thank you for reading!
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FAQs
-
-
Q: Is FIFA Mobile 22 hack safe to use? A: No, FIFA Mobile 22 hack is not safe to use. It might contain viruses or malware that can harm your device or steal your information. It might also get you banned from the game or sued by EA Sports.
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Q: Is FIFA Mobile 22 hack free to download? A: Yes, FIFA Mobile 22 hack is free to download from some websites. However, you might have to complete a verification process before downloading it. You might also have to pay for some features or updates of the hack.
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Q: Is FIFA Mobile 22 hack compatible with all devices? A: No, FIFA Mobile 22 hack is not compatible with all devices. It might only work on certain devices or operating systems. It might also require root or jailbreak access for some devices.
-
Q: Is FIFA Mobile 22 hack legal to use? A: No, FIFA Mobile 22 hack is not legal to use. It violates the terms of service of EA Sports, the developer of FIFA Mobile 22. It also infringes on their intellectual property rights.
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Q: Is FIFA Mobile 22 hack ethical to use? A: No, FIFA Mobile 22 hack is not ethical to use. It gives you an unfair advantage over other players who play the game without hacking it. It also ruins the fun, challenge, satisfaction, and integrity of playing FIFA Mobile 22.
- 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/2ndelement/voicevox/voicevox_engine/utility/connect_base64_waves.py b/spaces/2ndelement/voicevox/voicevox_engine/utility/connect_base64_waves.py
deleted file mode 100644
index 37f95240966f9bfed1cfe6e9090f871cea331ef7..0000000000000000000000000000000000000000
--- a/spaces/2ndelement/voicevox/voicevox_engine/utility/connect_base64_waves.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import base64
-import io
-from typing import List, Tuple
-
-import numpy as np
-import soundfile
-from scipy.signal import resample
-
-
-class ConnectBase64WavesException(Exception):
- def __init__(self, message: str):
- self.message = message
-
-
-def decode_base64_waves(waves: List[str]) -> List[Tuple[np.ndarray, int]]:
- """
- base64エンコードされた複数のwavデータをデコードする
- Parameters
- ----------
- waves: list[str]
- base64エンコードされたwavデータのリスト
- Returns
- -------
- waves_nparray_sr: List[Tuple[np.ndarray, int]]
- (NumPy配列の音声波形データ, サンプリングレート) 形式のタプルのリスト
- """
- if len(waves) == 0:
- raise ConnectBase64WavesException("wavファイルが含まれていません")
-
- waves_nparray_sr = []
- for wave in waves:
- try:
- wav_bin = base64.standard_b64decode(wave)
- except ValueError:
- raise ConnectBase64WavesException("base64デコードに失敗しました")
- try:
- _data = soundfile.read(io.BytesIO(wav_bin))
- except Exception:
- raise ConnectBase64WavesException("wavファイルを読み込めませんでした")
- waves_nparray_sr.append(_data)
-
- return waves_nparray_sr
-
-
-def connect_base64_waves(waves: List[str]) -> Tuple[np.ndarray, int]:
- waves_nparray_sr = decode_base64_waves(waves)
-
- max_sampling_rate = max([sr for _, sr in waves_nparray_sr])
- max_channels = max([x.ndim for x, _ in waves_nparray_sr])
- assert 0 < max_channels <= 2
-
- waves_nparray_list = []
- for nparray, sr in waves_nparray_sr:
- if sr != max_sampling_rate:
- nparray = resample(nparray, max_sampling_rate * len(nparray) // sr)
- if nparray.ndim < max_channels:
- nparray = np.array([nparray, nparray]).T
- waves_nparray_list.append(nparray)
-
- return np.concatenate(waves_nparray_list), max_sampling_rate
diff --git a/spaces/AIFILMS/StyleGANEX/models/mtcnn/__init__.py b/spaces/AIFILMS/StyleGANEX/models/mtcnn/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/box_utils.py b/spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/box_utils.py
deleted file mode 100644
index 1e8081b73639a7d70e4391b3d45417569550ddc6..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/box_utils.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import numpy as np
-from PIL import Image
-
-
-def nms(boxes, overlap_threshold=0.5, mode='union'):
- """Non-maximum suppression.
-
- Arguments:
- boxes: a float numpy array of shape [n, 5],
- where each row is (xmin, ymin, xmax, ymax, score).
- overlap_threshold: a float number.
- mode: 'union' or 'min'.
-
- Returns:
- list with indices of the selected boxes
- """
-
- # if there are no boxes, return the empty list
- if len(boxes) == 0:
- return []
-
- # list of picked indices
- pick = []
-
- # grab the coordinates of the bounding boxes
- x1, y1, x2, y2, score = [boxes[:, i] for i in range(5)]
-
- area = (x2 - x1 + 1.0) * (y2 - y1 + 1.0)
- ids = np.argsort(score) # in increasing order
-
- while len(ids) > 0:
-
- # grab index of the largest value
- last = len(ids) - 1
- i = ids[last]
- pick.append(i)
-
- # compute intersections
- # of the box with the largest score
- # with the rest of boxes
-
- # left top corner of intersection boxes
- ix1 = np.maximum(x1[i], x1[ids[:last]])
- iy1 = np.maximum(y1[i], y1[ids[:last]])
-
- # right bottom corner of intersection boxes
- ix2 = np.minimum(x2[i], x2[ids[:last]])
- iy2 = np.minimum(y2[i], y2[ids[:last]])
-
- # width and height of intersection boxes
- w = np.maximum(0.0, ix2 - ix1 + 1.0)
- h = np.maximum(0.0, iy2 - iy1 + 1.0)
-
- # intersections' areas
- inter = w * h
- if mode == 'min':
- overlap = inter / np.minimum(area[i], area[ids[:last]])
- elif mode == 'union':
- # intersection over union (IoU)
- overlap = inter / (area[i] + area[ids[:last]] - inter)
-
- # delete all boxes where overlap is too big
- ids = np.delete(
- ids,
- np.concatenate([[last], np.where(overlap > overlap_threshold)[0]])
- )
-
- return pick
-
-
-def convert_to_square(bboxes):
- """Convert bounding boxes to a square form.
-
- Arguments:
- bboxes: a float numpy array of shape [n, 5].
-
- Returns:
- a float numpy array of shape [n, 5],
- squared bounding boxes.
- """
-
- square_bboxes = np.zeros_like(bboxes)
- x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
- h = y2 - y1 + 1.0
- w = x2 - x1 + 1.0
- max_side = np.maximum(h, w)
- square_bboxes[:, 0] = x1 + w * 0.5 - max_side * 0.5
- square_bboxes[:, 1] = y1 + h * 0.5 - max_side * 0.5
- square_bboxes[:, 2] = square_bboxes[:, 0] + max_side - 1.0
- square_bboxes[:, 3] = square_bboxes[:, 1] + max_side - 1.0
- return square_bboxes
-
-
-def calibrate_box(bboxes, offsets):
- """Transform bounding boxes to be more like true bounding boxes.
- 'offsets' is one of the outputs of the nets.
-
- Arguments:
- bboxes: a float numpy array of shape [n, 5].
- offsets: a float numpy array of shape [n, 4].
-
- Returns:
- a float numpy array of shape [n, 5].
- """
- x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
- w = x2 - x1 + 1.0
- h = y2 - y1 + 1.0
- w = np.expand_dims(w, 1)
- h = np.expand_dims(h, 1)
-
- # this is what happening here:
- # tx1, ty1, tx2, ty2 = [offsets[:, i] for i in range(4)]
- # x1_true = x1 + tx1*w
- # y1_true = y1 + ty1*h
- # x2_true = x2 + tx2*w
- # y2_true = y2 + ty2*h
- # below is just more compact form of this
-
- # are offsets always such that
- # x1 < x2 and y1 < y2 ?
-
- translation = np.hstack([w, h, w, h]) * offsets
- bboxes[:, 0:4] = bboxes[:, 0:4] + translation
- return bboxes
-
-
-def get_image_boxes(bounding_boxes, img, size=24):
- """Cut out boxes from the image.
-
- Arguments:
- bounding_boxes: a float numpy array of shape [n, 5].
- img: an instance of PIL.Image.
- size: an integer, size of cutouts.
-
- Returns:
- a float numpy array of shape [n, 3, size, size].
- """
-
- num_boxes = len(bounding_boxes)
- width, height = img.size
-
- [dy, edy, dx, edx, y, ey, x, ex, w, h] = correct_bboxes(bounding_boxes, width, height)
- img_boxes = np.zeros((num_boxes, 3, size, size), 'float32')
-
- for i in range(num_boxes):
- img_box = np.zeros((h[i], w[i], 3), 'uint8')
-
- img_array = np.asarray(img, 'uint8')
- img_box[dy[i]:(edy[i] + 1), dx[i]:(edx[i] + 1), :] = \
- img_array[y[i]:(ey[i] + 1), x[i]:(ex[i] + 1), :]
-
- # resize
- img_box = Image.fromarray(img_box)
- img_box = img_box.resize((size, size), Image.BILINEAR)
- img_box = np.asarray(img_box, 'float32')
-
- img_boxes[i, :, :, :] = _preprocess(img_box)
-
- return img_boxes
-
-
-def correct_bboxes(bboxes, width, height):
- """Crop boxes that are too big and get coordinates
- with respect to cutouts.
-
- Arguments:
- bboxes: a float numpy array of shape [n, 5],
- where each row is (xmin, ymin, xmax, ymax, score).
- width: a float number.
- height: a float number.
-
- Returns:
- dy, dx, edy, edx: a int numpy arrays of shape [n],
- coordinates of the boxes with respect to the cutouts.
- y, x, ey, ex: a int numpy arrays of shape [n],
- corrected ymin, xmin, ymax, xmax.
- h, w: a int numpy arrays of shape [n],
- just heights and widths of boxes.
-
- in the following order:
- [dy, edy, dx, edx, y, ey, x, ex, w, h].
- """
-
- x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
- w, h = x2 - x1 + 1.0, y2 - y1 + 1.0
- num_boxes = bboxes.shape[0]
-
- # 'e' stands for end
- # (x, y) -> (ex, ey)
- x, y, ex, ey = x1, y1, x2, y2
-
- # we need to cut out a box from the image.
- # (x, y, ex, ey) are corrected coordinates of the box
- # in the image.
- # (dx, dy, edx, edy) are coordinates of the box in the cutout
- # from the image.
- dx, dy = np.zeros((num_boxes,)), np.zeros((num_boxes,))
- edx, edy = w.copy() - 1.0, h.copy() - 1.0
-
- # if box's bottom right corner is too far right
- ind = np.where(ex > width - 1.0)[0]
- edx[ind] = w[ind] + width - 2.0 - ex[ind]
- ex[ind] = width - 1.0
-
- # if box's bottom right corner is too low
- ind = np.where(ey > height - 1.0)[0]
- edy[ind] = h[ind] + height - 2.0 - ey[ind]
- ey[ind] = height - 1.0
-
- # if box's top left corner is too far left
- ind = np.where(x < 0.0)[0]
- dx[ind] = 0.0 - x[ind]
- x[ind] = 0.0
-
- # if box's top left corner is too high
- ind = np.where(y < 0.0)[0]
- dy[ind] = 0.0 - y[ind]
- y[ind] = 0.0
-
- return_list = [dy, edy, dx, edx, y, ey, x, ex, w, h]
- return_list = [i.astype('int32') for i in return_list]
-
- return return_list
-
-
-def _preprocess(img):
- """Preprocessing step before feeding the network.
-
- Arguments:
- img: a float numpy array of shape [h, w, c].
-
- Returns:
- a float numpy array of shape [1, c, h, w].
- """
- img = img.transpose((2, 0, 1))
- img = np.expand_dims(img, 0)
- img = (img - 127.5) * 0.0078125
- return img
diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/variational_autoencoder/__init__.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/variational_autoencoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/portaspeech/portaspeech.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/portaspeech/portaspeech.py
deleted file mode 100644
index e313add6037ae87f41361633fcf6d92f39e8fd92..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/portaspeech/portaspeech.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import Linear
-
-from modules.commons.conv import ConvBlocks, ConditionalConvBlocks
-from modules.commons.common_layers import Embedding
-from modules.commons.rel_transformer import RelTransformerEncoder
-from modules.commons.transformer import MultiheadAttention, FFTBlocks
-from modules.commons.align_ops import clip_mel2token_to_multiple, build_word_mask, expand_states, mel2ph_to_mel2word
-from modules.portaspeech.fs import FS_DECODERS, FastSpeech
-from modules.portaspeech.fvae import FVAE
-from utils.tts_utils import group_hidden_by_segs
-from utils.hparams import hparams
-
-class SinusoidalPosEmb(nn.Module):
- def __init__(self, dim):
- super().__init__()
- self.dim = dim
-
- def forward(self, x):
- """
-
- :param x: [B, T]
- :return: [B, T, H]
- """
- device = x.device
- half_dim = self.dim // 2
- emb = math.log(10000) / (half_dim - 1)
- emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
- emb = x[:, :, None] * emb[None, :]
- emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
- return emb
-
-
-class PortaSpeech(FastSpeech):
- def __init__(self, ph_dictionary, word_dictionary, out_dims=None):
- super().__init__(ph_dictionary, out_dims)
- # build linguistic encoder
- if hparams['use_word_encoder']:
- # default False, use independent word embedding instead of phoneme encoding to represent word
- self.word_encoder = RelTransformerEncoder(
- len(word_dictionary), self.hidden_size, self.hidden_size, self.hidden_size, 2,
- hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
- if hparams['dur_level'] == 'word':
- if hparams['word_encoder_type'] == 'rel_fft':
- self.ph2word_encoder = RelTransformerEncoder(
- 0, self.hidden_size, self.hidden_size, self.hidden_size, 2,
- hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
- if hparams['word_encoder_type'] == 'fft':
- self.ph2word_encoder = FFTBlocks(
- self.hidden_size, hparams['word_enc_layers'], 1, num_heads=hparams['num_heads'])
- self.sin_pos = SinusoidalPosEmb(self.hidden_size)
- self.enc_pos_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
- self.dec_query_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
- self.dec_res_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
- self.attn = MultiheadAttention(self.hidden_size, 1, encoder_decoder_attention=True, bias=False)
- self.attn.enable_torch_version = False
- if hparams['text_encoder_postnet']:
- self.text_encoder_postnet = ConvBlocks(
- self.hidden_size, self.hidden_size, [1] * 3, 5, layers_in_block=2)
- else:
- self.sin_pos = SinusoidalPosEmb(self.hidden_size)
- # build VAE decoder
- if hparams['use_fvae']:
- del self.decoder
- del self.mel_out
- self.fvae = FVAE(
- c_in_out=self.out_dims,
- hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'],
- kernel_size=hparams['fvae_kernel_size'],
- enc_n_layers=hparams['fvae_enc_n_layers'],
- dec_n_layers=hparams['fvae_dec_n_layers'],
- c_cond=self.hidden_size,
- use_prior_flow=hparams['use_prior_flow'],
- flow_hidden=hparams['prior_flow_hidden'],
- flow_kernel_size=hparams['prior_flow_kernel_size'],
- flow_n_steps=hparams['prior_flow_n_blocks'],
- strides=[hparams['fvae_strides']],
- encoder_type=hparams['fvae_encoder_type'],
- decoder_type=hparams['fvae_decoder_type'],
- )
- else:
- self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
- self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
- if hparams['use_pitch_embed']:
- self.pitch_embed = Embedding(300, self.hidden_size, 0)
- if hparams['add_word_pos']:
- self.word_pos_proj = Linear(self.hidden_size, self.hidden_size)
-
- def build_embedding(self, dictionary, embed_dim):
- num_embeddings = len(dictionary)
- emb = Embedding(num_embeddings, embed_dim, self.padding_idx)
- return emb
-
- def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None,
- spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None,
- global_step=None, *args, **kwargs):
- ret = {}
- style_embed = self.forward_style_embed(spk_embed, spk_id)
- x, tgt_nonpadding = self.run_text_encoder(
- txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, **kwargs)
- x = x * tgt_nonpadding
- ret['nonpadding'] = tgt_nonpadding
- if hparams['use_pitch_embed']:
- x = x + self.pitch_embed(pitch)
- ret['decoder_inp'] = x
- ret['mel_out_fvae'] = ret['mel_out'] = self.run_decoder(x, tgt_nonpadding, ret, infer, tgt_mels, global_step)
- return ret
-
- def run_text_encoder(self, txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, **kwargs):
- word2word = torch.arange(word_len)[None, :].to(ph2word.device) + 1 # [B, T_mel, T_word]
- src_nonpadding = (txt_tokens > 0).float()[:, :, None]
- use_bert = hparams.get("use_bert") is True
- if use_bert:
- ph_encoder_out = self.ph_encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=ph2word,
- graph_lst=kwargs['graph_lst'], etypes_lst=kwargs['etypes_lst'],
- cl_feats=kwargs['cl_feats'], ret=ret) * src_nonpadding + style_embed
- else:
- ph_encoder_out = self.ph_encoder(txt_tokens) * src_nonpadding + style_embed
- if hparams['use_word_encoder']:
- word_encoder_out = self.word_encoder(word_tokens) + style_embed
- ph_encoder_out = ph_encoder_out + expand_states(word_encoder_out, ph2word)
- if hparams['dur_level'] == 'word':
- word_encoder_out = 0
- h_ph_gb_word = group_hidden_by_segs(ph_encoder_out, ph2word, word_len)[0]
- word_encoder_out = word_encoder_out + self.ph2word_encoder(h_ph_gb_word)
- if hparams['use_word_encoder']:
- word_encoder_out = word_encoder_out + self.word_encoder(word_tokens)
- mel2word = self.forward_dur(ph_encoder_out, mel2word, ret, ph2word=ph2word, word_len=word_len)
- mel2word = clip_mel2token_to_multiple(mel2word, hparams['frames_multiple'])
- tgt_nonpadding = (mel2word > 0).float()[:, :, None]
- enc_pos = self.get_pos_embed(word2word, ph2word) # [B, T_ph, H]
- dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H]
- dec_word_mask = build_word_mask(mel2word, ph2word) # [B, T_mel, T_ph]
- x, weight = self.attention(ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask)
- if hparams['add_word_pos']:
- x = x + self.word_pos_proj(dec_pos)
- ret['attn'] = weight
- else:
- mel2ph = self.forward_dur(ph_encoder_out, mel2ph, ret)
- mel2ph = clip_mel2token_to_multiple(mel2ph, hparams['frames_multiple'])
- mel2word = mel2ph_to_mel2word(mel2ph, ph2word)
- x = expand_states(ph_encoder_out, mel2ph)
- if hparams['add_word_pos']:
- dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H]
- x = x + self.word_pos_proj(dec_pos)
- tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
- if hparams['use_word_encoder']:
- x = x + expand_states(word_encoder_out, mel2word)
- return x, tgt_nonpadding
-
- def attention(self, ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask):
- ph_kv = self.enc_pos_proj(torch.cat([ph_encoder_out, enc_pos], -1))
- word_enc_out_expend = expand_states(word_encoder_out, mel2word)
- word_enc_out_expend = torch.cat([word_enc_out_expend, dec_pos], -1)
- if hparams['text_encoder_postnet']:
- word_enc_out_expend = self.dec_res_proj(word_enc_out_expend)
- word_enc_out_expend = self.text_encoder_postnet(word_enc_out_expend)
- dec_q = x_res = word_enc_out_expend
- else:
- dec_q = self.dec_query_proj(word_enc_out_expend)
- x_res = self.dec_res_proj(word_enc_out_expend)
- ph_kv, dec_q = ph_kv.transpose(0, 1), dec_q.transpose(0, 1)
- x, (weight, _) = self.attn(dec_q, ph_kv, ph_kv, attn_mask=(1 - dec_word_mask) * -1e9)
- x = x.transpose(0, 1)
- x = x + x_res
- return x, weight
-
- def run_decoder(self, x, tgt_nonpadding, ret, infer, tgt_mels=None, global_step=0):
- if not hparams['use_fvae']:
- x = self.decoder(x)
- x = self.mel_out(x)
- ret['kl'] = 0
- return x * tgt_nonpadding
- else:
- decoder_inp = x
- x = x.transpose(1, 2) # [B, H, T]
- tgt_nonpadding_BHT = tgt_nonpadding.transpose(1, 2) # [B, H, T]
- if infer:
- z = self.fvae(cond=x, infer=True)
- else:
- tgt_mels = tgt_mels.transpose(1, 2) # [B, 80, T]
- z, ret['kl'], ret['z_p'], ret['m_q'], ret['logs_q'] = self.fvae(
- tgt_mels, tgt_nonpadding_BHT, cond=x)
- if global_step < hparams['posterior_start_steps']:
- z = torch.randn_like(z)
- x_recon = self.fvae.decoder(z, nonpadding=tgt_nonpadding_BHT, cond=x).transpose(1, 2)
- ret['pre_mel_out'] = x_recon
- return x_recon
-
- def forward_dur(self, dur_input, mel2word, ret, **kwargs):
- """
-
- :param dur_input: [B, T_txt, H]
- :param mel2ph: [B, T_mel]
- :param txt_tokens: [B, T_txt]
- :param ret:
- :return:
- """
- src_padding = dur_input.data.abs().sum(-1) == 0
- dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
- dur = self.dur_predictor(dur_input, src_padding)
- if hparams['dur_level'] == 'word':
- word_len = kwargs['word_len']
- ph2word = kwargs['ph2word']
- B, T_ph = ph2word.shape
- dur = torch.zeros([B, word_len.max() + 1]).to(ph2word.device).scatter_add(1, ph2word, dur)
- dur = dur[:, 1:]
- ret['dur'] = dur
- if mel2word is None:
- mel2word = self.length_regulator(dur).detach()
- return mel2word
-
- def get_pos_embed(self, word2word, x2word):
- x_pos = build_word_mask(word2word, x2word).float() # [B, T_word, T_ph]
- x_pos = (x_pos.cumsum(-1) / x_pos.sum(-1).clamp(min=1)[..., None] * x_pos).sum(1)
- x_pos = self.sin_pos(x_pos.float()) # [B, T_ph, H]
- return x_pos
-
- def store_inverse_all(self):
- def remove_weight_norm(m):
- try:
- if hasattr(m, 'store_inverse'):
- m.store_inverse()
- nn.utils.remove_weight_norm(m)
- except ValueError: # this module didn't have weight norm
- return
-
- self.apply(remove_weight_norm)
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/version.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/version.py
deleted file mode 100644
index fc79d63d5430b972ac6ec1c4bfea9af80922da4d..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = '0.2.1'
diff --git a/spaces/AIZero2HeroBootcamp/ChatGPTandLangchain/app.py b/spaces/AIZero2HeroBootcamp/ChatGPTandLangchain/app.py
deleted file mode 100644
index 6e86abff95351769056a696503ff05e34c7117c9..0000000000000000000000000000000000000000
--- a/spaces/AIZero2HeroBootcamp/ChatGPTandLangchain/app.py
+++ /dev/null
@@ -1,442 +0,0 @@
-import streamlit as st
-import openai
-import os
-import base64
-import glob
-import json
-import mistune
-import pytz
-import math
-import requests
-import time
-import re
-import textract
-
-from datetime import datetime
-from openai import ChatCompletion
-from xml.etree import ElementTree as ET
-from bs4 import BeautifulSoup
-from collections import deque
-from audio_recorder_streamlit import audio_recorder
-
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from templates import css, bot_template, user_template
-
-
-
-def generate_filename(prompt, file_type):
- central = pytz.timezone('US/Central')
- safe_date_time = datetime.now(central).strftime("%m%d_%H%M") # Date and time DD-HHMM
- safe_prompt = "".join(x for x in prompt if x.isalnum())[:90] # Limit file name size and trim whitespace
- return f"{safe_date_time}_{safe_prompt}.{file_type}" # Return a safe file name
-
-
-def transcribe_audio(openai_key, file_path, model):
- OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
- headers = {
- "Authorization": f"Bearer {openai_key}",
- }
- with open(file_path, 'rb') as f:
- data = {'file': f}
- response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
- if response.status_code == 200:
- st.write(response.json())
- chatResponse = chat_with_model(response.json().get('text'), '') # *************************************
- transcript = response.json().get('text')
- #st.write('Responses:')
- #st.write(chatResponse)
- filename = generate_filename(transcript, 'txt')
- create_file(filename, transcript, chatResponse)
- return transcript
- else:
- st.write(response.json())
- st.error("Error in API call.")
- return None
-
-def save_and_play_audio(audio_recorder):
- audio_bytes = audio_recorder()
- if audio_bytes:
- filename = generate_filename("Recording", "wav")
- with open(filename, 'wb') as f:
- f.write(audio_bytes)
- st.audio(audio_bytes, format="audio/wav")
- return filename
- return None
-
-def create_file(filename, prompt, response):
- if filename.endswith(".txt"):
- with open(filename, 'w') as file:
- file.write(f"{prompt}\n{response}")
- elif filename.endswith(".htm"):
- with open(filename, 'w') as file:
- file.write(f"{prompt} {response}")
- elif filename.endswith(".md"):
- with open(filename, 'w') as file:
- file.write(f"{prompt}\n\n{response}")
-
-def truncate_document(document, length):
- return document[:length]
-def divide_document(document, max_length):
- return [document[i:i+max_length] for i in range(0, len(document), max_length)]
-
-def get_table_download_link(file_path):
- with open(file_path, 'r') as file:
- try:
- data = file.read()
- except:
- st.write('')
- return file_path
- b64 = base64.b64encode(data.encode()).decode()
- file_name = os.path.basename(file_path)
- ext = os.path.splitext(file_name)[1] # get the file extension
- if ext == '.txt':
- mime_type = 'text/plain'
- elif ext == '.py':
- mime_type = 'text/plain'
- elif ext == '.xlsx':
- mime_type = 'text/plain'
- elif ext == '.csv':
- mime_type = 'text/plain'
- elif ext == '.htm':
- mime_type = 'text/html'
- elif ext == '.md':
- mime_type = 'text/markdown'
- else:
- mime_type = 'application/octet-stream' # general binary data type
- href = f'{file_name}'
- return href
-
-def CompressXML(xml_text):
- root = ET.fromstring(xml_text)
- for elem in list(root.iter()):
- if isinstance(elem.tag, str) and 'Comment' in elem.tag:
- elem.parent.remove(elem)
- return ET.tostring(root, encoding='unicode', method="xml")
-
-def read_file_content(file,max_length):
- if file.type == "application/json":
- content = json.load(file)
- return str(content)
- elif file.type == "text/html" or file.type == "text/htm":
- content = BeautifulSoup(file, "html.parser")
- return content.text
- elif file.type == "application/xml" or file.type == "text/xml":
- tree = ET.parse(file)
- root = tree.getroot()
- xml = CompressXML(ET.tostring(root, encoding='unicode'))
- return xml
- elif file.type == "text/markdown" or file.type == "text/md":
- md = mistune.create_markdown()
- content = md(file.read().decode())
- return content
- elif file.type == "text/plain":
- return file.getvalue().decode()
- else:
- return ""
-
-def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
- model = model_choice
- conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
- conversation.append({'role': 'user', 'content': prompt})
- if len(document_section)>0:
- conversation.append({'role': 'assistant', 'content': document_section})
-
- start_time = time.time()
- report = []
- res_box = st.empty()
- collected_chunks = []
- collected_messages = []
-
- for chunk in openai.ChatCompletion.create(
- model='gpt-3.5-turbo',
- messages=conversation,
- temperature=0.5,
- stream=True
- ):
-
- collected_chunks.append(chunk) # save the event response
- chunk_message = chunk['choices'][0]['delta'] # extract the message
- collected_messages.append(chunk_message) # save the message
-
- content=chunk["choices"][0].get("delta",{}).get("content")
-
- try:
- report.append(content)
- if len(content) > 0:
- result = "".join(report).strip()
- #result = result.replace("\n", "")
- res_box.markdown(f'*{result}*')
- except:
- st.write(' ')
-
- full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
- st.write("Elapsed time:")
- st.write(time.time() - start_time)
- return full_reply_content
-
-def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
- conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
- conversation.append({'role': 'user', 'content': prompt})
- if len(file_content)>0:
- conversation.append({'role': 'assistant', 'content': file_content})
- response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
- return response['choices'][0]['message']['content']
-
-def extract_mime_type(file):
- # Check if the input is a string
- if isinstance(file, str):
- pattern = r"type='(.*?)'"
- match = re.search(pattern, file)
- if match:
- return match.group(1)
- else:
- raise ValueError(f"Unable to extract MIME type from {file}")
- # If it's not a string, assume it's a streamlit.UploadedFile object
- elif isinstance(file, streamlit.UploadedFile):
- return file.type
- else:
- raise TypeError("Input should be a string or a streamlit.UploadedFile object")
-
-from io import BytesIO
-import re
-
-def extract_file_extension(file):
- # get the file name directly from the UploadedFile object
- file_name = file.name
- pattern = r".*?\.(.*?)$"
- match = re.search(pattern, file_name)
- if match:
- return match.group(1)
- else:
- raise ValueError(f"Unable to extract file extension from {file_name}")
-
-def pdf2txt(docs):
- text = ""
- for file in docs:
- file_extension = extract_file_extension(file)
- # print the file extension
- st.write(f"File type extension: {file_extension}")
-
- # read the file according to its extension
- try:
- if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:
- text += file.getvalue().decode('utf-8')
- elif file_extension.lower() == 'pdf':
- from PyPDF2 import PdfReader
- pdf = PdfReader(BytesIO(file.getvalue()))
- for page in range(len(pdf.pages)):
- text += pdf.pages[page].extract_text() # new PyPDF2 syntax
- except Exception as e:
- st.write(f"Error processing file {file.name}: {e}")
-
- return text
-
-def pdf2txt_old(pdf_docs):
- st.write(pdf_docs)
- for file in pdf_docs:
- mime_type = extract_mime_type(file)
- st.write(f"MIME type of file: {mime_type}")
-
- text = ""
- for pdf in pdf_docs:
- pdf_reader = PdfReader(pdf)
- for page in pdf_reader.pages:
- text += page.extract_text()
- return text
-
-def txt2chunks(text):
- text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
- return text_splitter.split_text(text)
-
-def vector_store(text_chunks):
- key = os.getenv('OPENAI_API_KEY')
- embeddings = OpenAIEmbeddings(openai_api_key=key)
- return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-
-def get_chain(vectorstore):
- llm = ChatOpenAI()
- memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
- return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
-
-def process_user_input(user_question):
- response = st.session_state.conversation({'question': user_question})
- st.session_state.chat_history = response['chat_history']
- for i, message in enumerate(st.session_state.chat_history):
- template = user_template if i % 2 == 0 else bot_template
- st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
- # Save file output from PDF query results
- filename = generate_filename(user_question, 'txt')
- create_file(filename, user_question, message.content)
-
- #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-def divide_prompt(prompt, max_length):
- words = prompt.split()
- chunks = []
- current_chunk = []
- current_length = 0
- for word in words:
- if len(word) + current_length <= max_length:
- current_length += len(word) + 1 # Adding 1 to account for spaces
- current_chunk.append(word)
- else:
- chunks.append(' '.join(current_chunk))
- current_chunk = [word]
- current_length = len(word)
- chunks.append(' '.join(current_chunk)) # Append the final chunk
- return chunks
-
-def main():
- # Sidebar and global
- openai.api_key = os.getenv('OPENAI_API_KEY')
- st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
-
- # File type for output, model choice
- menu = ["txt", "htm", "xlsx", "csv", "md", "py"] #619
- choice = st.sidebar.selectbox("Output File Type:", menu)
- model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
-
- # Audio, transcribe, GPT:
- filename = save_and_play_audio(audio_recorder)
- if filename is not None:
- transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
- st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
- filename=None # since transcription is finished next time just use the saved transcript
-
- # prompt interfaces
- user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
-
- # file section interface for prompts against large documents as context
- collength, colupload = st.columns([2,3]) # adjust the ratio as needed
- with collength:
- max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
- with colupload:
- uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx","csv","html", "htm", "md", "txt"])
-
- # Document section chat
- document_sections = deque()
- document_responses = {}
- if uploaded_file is not None:
- file_content = read_file_content(uploaded_file, max_length)
- document_sections.extend(divide_document(file_content, max_length))
- if len(document_sections) > 0:
- if st.button("👁️ View Upload"):
- st.markdown("**Sections of the uploaded file:**")
- for i, section in enumerate(list(document_sections)):
- st.markdown(f"**Section {i+1}**\n{section}")
- st.markdown("**Chat with the model:**")
- for i, section in enumerate(list(document_sections)):
- if i in document_responses:
- st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
- else:
- if st.button(f"Chat about Section {i+1}"):
- st.write('Reasoning with your inputs...')
- response = chat_with_model(user_prompt, section, model_choice) # *************************************
- st.write('Response:')
- st.write(response)
- document_responses[i] = response
- filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
- create_file(filename, user_prompt, response)
- st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
- if st.button('💬 Chat'):
- st.write('Reasoning with your inputs...')
-
- #response = chat_with_model(user_prompt, ''.join(list(document_sections,)), model_choice) # *************************************
-
- # Divide the user_prompt into smaller sections
- user_prompt_sections = divide_prompt(user_prompt, max_length)
- full_response = ''
- for prompt_section in user_prompt_sections:
- # Process each section with the model
- response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
- full_response += response + '\n' # Combine the responses
-
- #st.write('Response:')
- #st.write(full_response)
-
- response = full_response
- st.write('Response:')
- st.write(response)
-
- filename = generate_filename(user_prompt, choice)
- create_file(filename, user_prompt, response)
- st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
- all_files = glob.glob("*.*")
- all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20] # exclude files with short names
- all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order
-
- # sidebar of files
- file_contents=''
- next_action=''
- for file in all_files:
- col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1]) # adjust the ratio as needed
- with col1:
- if st.button("🌐", key="md_"+file): # md emoji button
- with open(file, 'r') as f:
- file_contents = f.read()
- next_action='md'
- with col2:
- st.markdown(get_table_download_link(file), unsafe_allow_html=True)
- with col3:
- if st.button("📂", key="open_"+file): # open emoji button
- with open(file, 'r') as f:
- file_contents = f.read()
- next_action='open'
- with col4:
- if st.button("🔍", key="read_"+file): # search emoji button
- with open(file, 'r') as f:
- file_contents = f.read()
- next_action='search'
- with col5:
- if st.button("🗑", key="delete_"+file):
- os.remove(file)
- st.experimental_rerun()
-
- if len(file_contents) > 0:
- if next_action=='open':
- file_content_area = st.text_area("File Contents:", file_contents, height=500)
- if next_action=='md':
- st.markdown(file_contents)
- if next_action=='search':
- file_content_area = st.text_area("File Contents:", file_contents, height=500)
- st.write('Reasoning with your inputs...')
- response = chat_with_model(user_prompt, file_contents, model_choice)
- filename = generate_filename(file_contents, choice)
- create_file(filename, file_contents, response)
-
- st.experimental_rerun()
- #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-if __name__ == "__main__":
- main()
-
-load_dotenv()
-st.write(css, unsafe_allow_html=True)
-
-st.header("Chat with documents :books:")
-user_question = st.text_input("Ask a question about your documents:")
-if user_question:
- process_user_input(user_question)
-
-with st.sidebar:
- st.subheader("Your documents")
- docs = st.file_uploader("import documents", accept_multiple_files=True)
- with st.spinner("Processing"):
- raw = pdf2txt(docs)
- if len(raw) > 0:
- length = str(len(raw))
- text_chunks = txt2chunks(raw)
- vectorstore = vector_store(text_chunks)
- st.session_state.conversation = get_chain(vectorstore)
- st.markdown('# AI Search Index of Length:' + length + ' Created.') # add timing
- filename = generate_filename(raw, 'txt')
- create_file(filename, raw, '')
\ No newline at end of file
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/_base_/default_runtime.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/_base_/default_runtime.py
deleted file mode 100644
index 561d574fa757fa295f349394bf57047a2d8b576d..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/_base_/default_runtime.py
+++ /dev/null
@@ -1,49 +0,0 @@
-default_scope = 'mmpose'
-
-# hooks
-default_hooks = dict(
- timer=dict(type='IterTimerHook'),
- logger=dict(type='LoggerHook', interval=50),
- param_scheduler=dict(type='ParamSchedulerHook'),
- checkpoint=dict(type='CheckpointHook', interval=10),
- sampler_seed=dict(type='DistSamplerSeedHook'),
- visualization=dict(type='PoseVisualizationHook', enable=False),
-)
-
-# custom hooks
-custom_hooks = [
- # Synchronize model buffers such as running_mean and running_var in BN
- # at the end of each epoch
- dict(type='SyncBuffersHook')
-]
-
-# multi-processing backend
-env_cfg = dict(
- cudnn_benchmark=False,
- mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
- dist_cfg=dict(backend='nccl'),
-)
-
-# visualizer
-vis_backends = [
- dict(type='LocalVisBackend'),
- # dict(type='TensorboardVisBackend'),
- # dict(type='WandbVisBackend'),
-]
-visualizer = dict(
- type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer')
-
-# logger
-log_processor = dict(
- type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
-log_level = 'INFO'
-load_from = None
-resume = False
-
-# file I/O backend
-backend_args = dict(backend='local')
-
-# training/validation/testing progress
-train_cfg = dict(by_epoch=True)
-val_cfg = dict()
-test_cfg = dict()
diff --git a/spaces/Aditya9790/yolo7-object-tracking/app.py b/spaces/Aditya9790/yolo7-object-tracking/app.py
deleted file mode 100644
index d621ffdb8407864cf8c0e74c866737f580264e56..0000000000000000000000000000000000000000
--- a/spaces/Aditya9790/yolo7-object-tracking/app.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import gradio as gr
-import os
-
-import argparse
-import time
-from pathlib import Path
-
-import cv2
-import torch
-import torch.backends.cudnn as cudnn
-from numpy import random
-
-from models.experimental import attempt_load
-from utils.datasets import LoadStreams, LoadImages
-from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
- scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
-from utils.plots import plot_one_box
-from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
-from PIL import Image
-
-from sort import *
-
-from huggingface_hub import hf_hub_download
-
-def load_model(model_name):
- model_path = hf_hub_download(repo_id=f"Yolov7/{model_name}", filename=f"{model_name}.pt")
-
- return model_path
-
-
-model_names = ["yolov7"]
-
-models = {model_name: load_model(model_name) for model_name in model_names}
-
-##################################
-# """Function to Draw Bounding boxes"""
-def draw_boxes(img, bbox, identities=None, categories=None, confidences = None, names=None, colors = None):
- for i, box in enumerate(bbox):
- x1, y1, x2, y2 = [int(i) for i in box]
- tl = opt.thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
-
- cat = int(categories[i]) if categories is not None else 0
- id = int(identities[i]) if identities is not None else 0
- # conf = confidences[i] if confidences is not None else 0
-
- color = colors[cat]
-
- if not opt.nobbox:
- cv2.rectangle(img, (x1, y1), (x2, y2), color, tl)
-
- if not opt.nolabel:
- label = str(id) + ":"+ names[cat] if identities is not None else f'{names[cat]} {confidences[i]:.2f}'
- tf = max(tl - 1, 1) # font thickness
- t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
- c2 = x1 + t_size[0], y1 - t_size[1] - 3
- cv2.rectangle(img, (x1, y1), c2, color, -1, cv2.LINE_AA) # filled
- cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
-
-
- return img
-##################################
-
-
-def detect(save_img=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
- parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
- parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--view-img', action='store_true', help='display results')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
- parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
- parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--update', action='store_true', help='update all models')
- parser.add_argument('--project', default='runs/detect', help='save results to project/name')
- parser.add_argument('--name', default='exp', help='save results to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
-
- parser.add_argument('--track', action='store_true', help='run tracking')
- parser.add_argument('--show-track', action='store_true', help='show tracked path')
- parser.add_argument('--show-fps', action='store_true', help='show fps')
- parser.add_argument('--thickness', type=int, default=2, help='bounding box and font size thickness')
- parser.add_argument('--seed', type=int, default=1, help='random seed to control bbox colors')
- parser.add_argument('--nobbox', action='store_true', help='don`t show bounding box')
- parser.add_argument('--nolabel', action='store_true', help='don`t show label')
- parser.add_argument('--unique-track-color', action='store_true', help='show each track in unique color')
-
- opt = parser.parse_args()
- np.random.seed(opt.seed)
-
- sort_tracker = Sort(max_age=5,
- min_hits=2,
- iou_threshold=0.2)
-
- source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
- save_img = not opt.nosave and not source.endswith('.txt') # save inference images
- webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
- ('rtsp://', 'rtmp://', 'http://', 'https://'))
- save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
- if not opt.nosave:
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Initialize
- set_logging()
- device = select_device(opt.device)
- half = device.type != 'cpu' # half precision only supported on CUDA
-
- # Load model
- model = attempt_load(weights, map_location=device) # load FP32 model
- stride = int(model.stride.max()) # model stride
- imgsz = check_img_size(imgsz, s=stride) # check img_size
-
- if trace:
- model = TracedModel(model, device, opt.img_size)
-
- if half:
- model.half() # to FP16
-
- # Second-stage classifier
- classify = False
- if classify:
- modelc = load_classifier(name='resnet101', n=2) # initialize
- modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
-
- # Set Dataloader
- vid_path, vid_writer = None, None
- if webcam:
- view_img = check_imshow()
- cudnn.benchmark = True # set True to speed up constant image size inference
- dataset = LoadStreams(source, img_size=imgsz, stride=stride)
- else:
- dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
- # Get names and colors
- names = model.module.names if hasattr(model, 'module') else model.names
- colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
- # Run inference
- if device.type != 'cpu':
- model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
- old_img_w = old_img_h = imgsz
- old_img_b = 1
-
- t0 = time.time()
- ###################################
- startTime = 0
- ###################################
- for path, img, im0s, vid_cap in dataset:
- img = torch.from_numpy(img).to(device)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- if img.ndimension() == 3:
- img = img.unsqueeze(0)
-
- # Warmup
- if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
- old_img_b = img.shape[0]
- old_img_h = img.shape[2]
- old_img_w = img.shape[3]
- for i in range(3):
- model(img, augment=opt.augment)[0]
-
- # Inference
- t1 = time_synchronized()
- pred = model(img, augment=opt.augment)[0]
- t2 = time_synchronized()
-
- # Apply NMS
- pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
- t3 = time_synchronized()
-
- # Apply Classifier
- if classify:
- pred = apply_classifier(pred, modelc, img, im0s)
-
- # Process detections
- for i, det in enumerate(pred): # detections per image
- if webcam: # batch_size >= 1
- p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
- else:
- p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
-
- p = Path(p) # to Path
- save_path = str(save_dir / p.name) # img.jpg
- txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- if len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
- # Print results
- for c in det[:, -1].unique():
- n = (det[:, -1] == c).sum() # detections per class
- s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
-
- dets_to_sort = np.empty((0,6))
- # NOTE: We send in detected object class too
- for x1,y1,x2,y2,conf,detclass in det.cpu().detach().numpy():
- dets_to_sort = np.vstack((dets_to_sort,
- np.array([x1, y1, x2, y2, conf, detclass])))
-
-
- if opt.track:
-
- tracked_dets = sort_tracker.update(dets_to_sort, opt.unique_track_color)
- tracks =sort_tracker.getTrackers()
-
- # draw boxes for visualization
- if len(tracked_dets)>0:
- bbox_xyxy = tracked_dets[:,:4]
- identities = tracked_dets[:, 8]
- categories = tracked_dets[:, 4]
- confidences = None
-
- if opt.show_track:
- #loop over tracks
- for t, track in enumerate(tracks):
-
- track_color = colors[int(track.detclass)] if not opt.unique_track_color else sort_tracker.color_list[t]
-
- [cv2.line(im0, (int(track.centroidarr[i][0]),
- int(track.centroidarr[i][1])),
- (int(track.centroidarr[i+1][0]),
- int(track.centroidarr[i+1][1])),
- track_color, thickness=opt.thickness)
- for i,_ in enumerate(track.centroidarr)
- if i < len(track.centroidarr)-1 ]
- else:
- bbox_xyxy = dets_to_sort[:,:4]
- identities = None
- categories = dets_to_sort[:, 5]
- confidences = dets_to_sort[:, 4]
-
- im0 = draw_boxes(im0, bbox_xyxy, identities, categories, confidences, names, colors)
-
- # Print time (inference + NMS)
- print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
-
- # Stream results
- ######################################################
- if dataset.mode != 'image' and opt.show_fps:
- currentTime = time.time()
-
- fps = 1/(currentTime - startTime)
- startTime = currentTime
- cv2.putText(im0, "FPS: " + str(int(fps)), (20, 70), cv2.FONT_HERSHEY_PLAIN, 2, (0,255,0),2)
-
- #######################################################
- if view_img:
- cv2.imshow(str(p), im0)
- cv2.waitKey(1) # 1 millisecond
-
- # Save results (image with detections)
- if save_img:
- if dataset.mode == 'image':
- cv2.imwrite(save_path, im0)
- print(f" The image with the result is saved in: {save_path}")
- else: # 'video' or 'stream'
- if vid_path != save_path: # new video
- vid_path = save_path
- if isinstance(vid_writer, cv2.VideoWriter):
- vid_writer.release() # release previous video writer
- if vid_cap: # video
- fps = vid_cap.get(cv2.CAP_PROP_FPS)
- w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- else: # stream
- fps, w, h = 30, im0.shape[1], im0.shape[0]
- save_path += '.mp4'
- vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
- vid_writer.write(im0)
-
- if save_txt or save_img:
- s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
- #print(f"Results saved to {save_dir}{s}")
-
- print(f'Done. ({time.time() - t0:.3f}s)')
- return img
-
-
-
-desc = "demo for WongKinYiu/yolov7 Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
-gr.Interface(detect,
- inputs = [gr.Video(format="mp4")],
- outputs = gr.Video(format="mp4"),
- title="Yolov7",description=desc).launch()
-# gr.Interface(detect,[gr.Image(type="pil"),gr.Dropdown(choices=model_names)], gr.Image(type="pil"),title="Yolov7",examples=[["horses.jpeg", "yolov7"]],description="demo for WongKinYiu/yolov7 Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors").launch()
\ No newline at end of file
diff --git a/spaces/Aer0xander/sd-to-diffusers/utils.py b/spaces/Aer0xander/sd-to-diffusers/utils.py
deleted file mode 100644
index ff1c065d186347ca51b47d010a697dbe1814695c..0000000000000000000000000000000000000000
--- a/spaces/Aer0xander/sd-to-diffusers/utils.py
+++ /dev/null
@@ -1,6 +0,0 @@
-def is_google_colab():
- try:
- import google.colab
- return True
- except:
- return False
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/conv2d_gradfix.py b/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index 563543d23df5ae0432461a2c637aec71a4bee9ca..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-# ----------------------------------------------------------------------------
-
-# Enable the custom op by setting this to true.
-enabled = False
-# Forcefully disable computation of gradients with respect to the weights.
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients(disable=True):
- global weight_gradients_disabled
- old = weight_gradients_disabled
- if disable:
- weight_gradients_disabled = True
- yield
- weight_gradients_disabled = old
-
-# ----------------------------------------------------------------------------
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
- if _should_use_custom_op(input):
- return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
- return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
- if _should_use_custom_op(input):
- return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
- return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-# ----------------------------------------------------------------------------
-
-
-def _should_use_custom_op(input):
- assert isinstance(input, torch.Tensor)
- if (not enabled) or (not torch.backends.cudnn.enabled):
- return False
- if input.device.type != 'cuda':
- return False
- return True
-
-
-def _tuple_of_ints(xs, ndim):
- xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
- assert len(xs) == ndim
- assert all(isinstance(x, int) for x in xs)
- return xs
-
-# ----------------------------------------------------------------------------
-
-
-_conv2d_gradfix_cache = dict()
-_null_tensor = torch.empty([0])
-
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
- # Parse arguments.
- ndim = 2
- weight_shape = tuple(weight_shape)
- stride = _tuple_of_ints(stride, ndim)
- padding = _tuple_of_ints(padding, ndim)
- output_padding = _tuple_of_ints(output_padding, ndim)
- dilation = _tuple_of_ints(dilation, ndim)
-
- # Lookup from cache.
- key = (transpose, weight_shape, stride, padding,
- output_padding, dilation, groups)
- if key in _conv2d_gradfix_cache:
- return _conv2d_gradfix_cache[key]
-
- # Validate arguments.
- assert groups >= 1
- assert len(weight_shape) == ndim + 2
- assert all(stride[i] >= 1 for i in range(ndim))
- assert all(padding[i] >= 0 for i in range(ndim))
- assert all(dilation[i] >= 0 for i in range(ndim))
- if not transpose:
- assert all(output_padding[i] == 0 for i in range(ndim))
- else: # transpose
- assert all(0 <= output_padding[i] < max(
- stride[i], dilation[i]) for i in range(ndim))
-
- # Helpers.
- common_kwargs = dict(stride=stride, padding=padding,
- dilation=dilation, groups=groups)
-
- def calc_output_padding(input_shape, output_shape):
- if transpose:
- return [0, 0]
- return [
- input_shape[i + 2]
- - (output_shape[i + 2] - 1) * stride[i]
- - (1 - 2 * padding[i])
- - dilation[i] * (weight_shape[i + 2] - 1)
- for i in range(ndim)
- ]
-
- # Forward & backward.
- class Conv2d(torch.autograd.Function):
- @staticmethod
- def forward(ctx, input, weight, bias):
- assert weight.shape == weight_shape
- ctx.save_for_backward(
- input if weight.requires_grad else _null_tensor,
- weight if input.requires_grad else _null_tensor,
- )
- ctx.input_shape = input.shape
-
- # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere).
- if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0):
- a = weight.reshape(
- groups, weight_shape[0] // groups, weight_shape[1])
- b = input.reshape(
- input.shape[0], groups, input.shape[1] // groups, -1)
- c = (a.transpose(1, 2) if transpose else a) @ b.permute(1,
- 2, 0, 3).flatten(2)
- c = c.reshape(-1, input.shape[0],
- *input.shape[2:]).transpose(0, 1)
- c = c if bias is None else c + \
- bias.unsqueeze(0).unsqueeze(2).unsqueeze(3)
- return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
-
- # General case => cuDNN.
- if transpose:
- return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
- return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
- @staticmethod
- def backward(ctx, grad_output):
- input, weight = ctx.saved_tensors
- input_shape = ctx.input_shape
- grad_input = None
- grad_weight = None
- grad_bias = None
-
- if ctx.needs_input_grad[0]:
- p = calc_output_padding(
- input_shape=input_shape, output_shape=grad_output.shape)
- op = _conv2d_gradfix(transpose=(
- not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
- grad_input = op.apply(grad_output, weight, None)
- assert grad_input.shape == input_shape
-
- if ctx.needs_input_grad[1] and not weight_gradients_disabled:
- grad_weight = Conv2dGradWeight.apply(grad_output, input)
- assert grad_weight.shape == weight_shape
-
- if ctx.needs_input_grad[2]:
- grad_bias = grad_output.sum([0, 2, 3])
-
- return grad_input, grad_weight, grad_bias
-
- # Gradient with respect to the weights.
- class Conv2dGradWeight(torch.autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input):
- ctx.save_for_backward(
- grad_output if input.requires_grad else _null_tensor,
- input if grad_output.requires_grad else _null_tensor,
- )
- ctx.grad_output_shape = grad_output.shape
- ctx.input_shape = input.shape
-
- # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere).
- if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0):
- a = grad_output.reshape(
- grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
- b = input.reshape(
- input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
- c = (b @ a.transpose(1, 2) if transpose else a @
- b.transpose(1, 2)).reshape(weight_shape)
- return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
-
- # General case => cuDNN.
- name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight'
- flags = [torch.backends.cudnn.benchmark,
- torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
- return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-
- @staticmethod
- def backward(ctx, grad2_grad_weight):
- grad_output, input = ctx.saved_tensors
- grad_output_shape = ctx.grad_output_shape
- input_shape = ctx.input_shape
- grad2_grad_output = None
- grad2_input = None
-
- if ctx.needs_input_grad[0]:
- grad2_grad_output = Conv2d.apply(
- input, grad2_grad_weight, None)
- assert grad2_grad_output.shape == grad_output_shape
-
- if ctx.needs_input_grad[1]:
- p = calc_output_padding(
- input_shape=input_shape, output_shape=grad_output_shape)
- op = _conv2d_gradfix(transpose=(
- not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
- grad2_input = op.apply(grad_output, grad2_grad_weight, None)
- assert grad2_input.shape == input_shape
-
- return grad2_grad_output, grad2_input
-
- _conv2d_gradfix_cache[key] = Conv2d
- return Conv2d
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Amrrs/DragGan-Inversion/viz/drag_widget.py b/spaces/Amrrs/DragGan-Inversion/viz/drag_widget.py
deleted file mode 100644
index 348ab36de2daff2eff97589204b00d391b3a1e7e..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/viz/drag_widget.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import os
-import torch
-import numpy as np
-import imgui
-import dnnlib
-from gui_utils import imgui_utils
-
-# ----------------------------------------------------------------------------
-
-
-class DragWidget:
- def __init__(self, viz):
- self.viz = viz
- self.point = [-1, -1]
- self.points = []
- self.targets = []
- self.is_point = True
- self.last_click = False
- self.is_drag = False
- self.iteration = 0
- self.mode = 'point'
- self.r_mask = 50
- self.show_mask = False
- self.mask = torch.ones(256, 256)
- self.lambda_mask = 20
- self.feature_idx = 5
- self.r1 = 3
- self.r2 = 12
- self.path = os.path.abspath(os.path.join(
- os.path.dirname(__file__), '..', '_screenshots'))
- self.defer_frames = 0
- self.disabled_time = 0
-
- def action(self, click, down, x, y):
- if self.mode == 'point':
- self.add_point(click, x, y)
- elif down:
- self.draw_mask(x, y)
-
- def add_point(self, click, x, y):
- if click:
- self.point = [y, x]
- elif self.last_click:
- if self.is_drag:
- self.stop_drag()
- if self.is_point:
- self.points.append(self.point)
- self.is_point = False
- else:
- self.targets.append(self.point)
- self.is_point = True
- self.last_click = click
-
- def init_mask(self, w, h):
- self.width, self.height = w, h
- self.mask = torch.ones(h, w)
-
- def draw_mask(self, x, y):
- X = torch.linspace(0, self.width, self.width)
- Y = torch.linspace(0, self.height, self.height)
- yy, xx = torch.meshgrid(Y, X)
- circle = (xx - x)**2 + (yy - y)**2 < self.r_mask**2
- if self.mode == 'flexible':
- self.mask[circle] = 0
- elif self.mode == 'fixed':
- self.mask[circle] = 1
-
- def stop_drag(self):
- self.is_drag = False
- self.iteration = 0
-
- def set_points(self, points):
- self.points = points
-
- def reset_point(self):
- self.points = []
- self.targets = []
- self.is_point = True
-
- def load_points(self, suffix):
- points = []
- point_path = self.path + f'_{suffix}.txt'
- try:
- with open(point_path, "r") as f:
- for line in f.readlines():
- y, x = line.split()
- points.append([int(y), int(x)])
- except:
- print(f'Wrong point file path: {point_path}')
- return points
-
- @imgui_utils.scoped_by_object_id
- def __call__(self, show=True):
- viz = self.viz
- reset = False
- if show:
- with imgui_utils.grayed_out(self.disabled_time != 0):
- imgui.text('Drag')
- imgui.same_line(viz.label_w)
-
- if imgui_utils.button('Add point', width=viz.button_w, enabled='image' in viz.result):
- self.mode = 'point'
-
- imgui.same_line()
- reset = False
- if imgui_utils.button('Reset point', width=viz.button_w, enabled='image' in viz.result):
- self.reset_point()
- reset = True
-
- imgui.text(' ')
- imgui.same_line(viz.label_w)
- if imgui_utils.button('Start', width=viz.button_w, enabled='image' in viz.result):
- self.is_drag = True
- if len(self.points) > len(self.targets):
- self.points = self.points[:len(self.targets)]
-
- imgui.same_line()
- if imgui_utils.button('Stop', width=viz.button_w, enabled='image' in viz.result):
- self.stop_drag()
-
- imgui.text(' ')
- imgui.same_line(viz.label_w)
- imgui.text(f'Steps: {self.iteration}')
-
- imgui.text('Mask')
- imgui.same_line(viz.label_w)
- if imgui_utils.button('Flexible area', width=viz.button_w, enabled='image' in viz.result):
- self.mode = 'flexible'
- self.show_mask = True
-
- imgui.same_line()
- if imgui_utils.button('Fixed area', width=viz.button_w, enabled='image' in viz.result):
- self.mode = 'fixed'
- self.show_mask = True
-
- imgui.text(' ')
- imgui.same_line(viz.label_w)
- if imgui_utils.button('Reset mask', width=viz.button_w, enabled='image' in viz.result):
- self.mask = torch.ones(self.height, self.width)
- imgui.same_line()
- _clicked, self.show_mask = imgui.checkbox(
- 'Show mask', self.show_mask)
-
- imgui.text(' ')
- imgui.same_line(viz.label_w)
- with imgui_utils.item_width(viz.font_size * 6):
- changed, self.r_mask = imgui.input_int(
- 'Radius', self.r_mask)
-
- imgui.text(' ')
- imgui.same_line(viz.label_w)
- with imgui_utils.item_width(viz.font_size * 6):
- changed, self.lambda_mask = imgui.input_int(
- 'Lambda', self.lambda_mask)
-
- self.disabled_time = max(self.disabled_time - viz.frame_delta, 0)
- if self.defer_frames > 0:
- self.defer_frames -= 1
- viz.args.is_drag = self.is_drag
- if self.is_drag:
- self.iteration += 1
- viz.args.iteration = self.iteration
- viz.args.points = [point for point in self.points]
- viz.args.targets = [point for point in self.targets]
- viz.args.mask = self.mask
- viz.args.lambda_mask = self.lambda_mask
- viz.args.feature_idx = self.feature_idx
- viz.args.r1 = self.r1
- viz.args.r2 = self.r2
- viz.args.reset = reset
-
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Amrrs/portfolio/index.html b/spaces/Amrrs/portfolio/index.html
deleted file mode 100644
index ff8968021c46781010dc0b1de7ead4803a35898a..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/portfolio/index.html
+++ /dev/null
@@ -1,107 +0,0 @@
-
-
-
- Welcome to 1littlecoder
-
-
-
-
-
-
-
Welcome to 1littlecoder
-
-
-
About Me
-
Hey! I'm 1littlecoder from India.. I Like Coding R Python Data Science Machine Learning
-
-
-
-
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/deis.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/deis.md
deleted file mode 100644
index 9ab8418210983d4920c677de1aa4a865ab2bfca8..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/deis.md
+++ /dev/null
@@ -1,22 +0,0 @@
-
-
-# DEIS
-
-Fast Sampling of Diffusion Models with Exponential Integrator.
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2204.13902). The original implementation can be found [here](https://github.com/qsh-zh/deis).
-
-## DEISMultistepScheduler
-[[autodoc]] DEISMultistepScheduler
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/euler.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/euler.md
deleted file mode 100644
index f107623363bf49763fc0552bbccd70f7529592f7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/euler.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-
-# Euler scheduler
-
-## Overview
-
-Euler scheduler (Algorithm 2) from the paper [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) by Karras et al. (2022). Based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by Katherine Crowson.
-Fast scheduler which often times generates good outputs with 20-30 steps.
-
-## EulerDiscreteScheduler
-[[autodoc]] EulerDiscreteScheduler
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/attention_flax.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/attention_flax.py
deleted file mode 100644
index 0b160d2384311c1fb426b87c11e5fa1572584070..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/attention_flax.py
+++ /dev/null
@@ -1,446 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import functools
-import math
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-
-
-def _query_chunk_attention(query, key, value, precision, key_chunk_size: int = 4096):
- """Multi-head dot product attention with a limited number of queries."""
- num_kv, num_heads, k_features = key.shape[-3:]
- v_features = value.shape[-1]
- key_chunk_size = min(key_chunk_size, num_kv)
- query = query / jnp.sqrt(k_features)
-
- @functools.partial(jax.checkpoint, prevent_cse=False)
- def summarize_chunk(query, key, value):
- attn_weights = jnp.einsum("...qhd,...khd->...qhk", query, key, precision=precision)
-
- max_score = jnp.max(attn_weights, axis=-1, keepdims=True)
- max_score = jax.lax.stop_gradient(max_score)
- exp_weights = jnp.exp(attn_weights - max_score)
-
- exp_values = jnp.einsum("...vhf,...qhv->...qhf", value, exp_weights, precision=precision)
- max_score = jnp.einsum("...qhk->...qh", max_score)
-
- return (exp_values, exp_weights.sum(axis=-1), max_score)
-
- def chunk_scanner(chunk_idx):
- # julienne key array
- key_chunk = jax.lax.dynamic_slice(
- operand=key,
- start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0], # [...,k,h,d]
- slice_sizes=list(key.shape[:-3]) + [key_chunk_size, num_heads, k_features], # [...,k,h,d]
- )
-
- # julienne value array
- value_chunk = jax.lax.dynamic_slice(
- operand=value,
- start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0], # [...,v,h,d]
- slice_sizes=list(value.shape[:-3]) + [key_chunk_size, num_heads, v_features], # [...,v,h,d]
- )
-
- return summarize_chunk(query, key_chunk, value_chunk)
-
- chunk_values, chunk_weights, chunk_max = jax.lax.map(f=chunk_scanner, xs=jnp.arange(0, num_kv, key_chunk_size))
-
- global_max = jnp.max(chunk_max, axis=0, keepdims=True)
- max_diffs = jnp.exp(chunk_max - global_max)
-
- chunk_values *= jnp.expand_dims(max_diffs, axis=-1)
- chunk_weights *= max_diffs
-
- all_values = chunk_values.sum(axis=0)
- all_weights = jnp.expand_dims(chunk_weights, -1).sum(axis=0)
-
- return all_values / all_weights
-
-
-def jax_memory_efficient_attention(
- query, key, value, precision=jax.lax.Precision.HIGHEST, query_chunk_size: int = 1024, key_chunk_size: int = 4096
-):
- r"""
- Flax Memory-efficient multi-head dot product attention. https://arxiv.org/abs/2112.05682v2
- https://github.com/AminRezaei0x443/memory-efficient-attention
-
- Args:
- query (`jnp.ndarray`): (batch..., query_length, head, query_key_depth_per_head)
- key (`jnp.ndarray`): (batch..., key_value_length, head, query_key_depth_per_head)
- value (`jnp.ndarray`): (batch..., key_value_length, head, value_depth_per_head)
- precision (`jax.lax.Precision`, *optional*, defaults to `jax.lax.Precision.HIGHEST`):
- numerical precision for computation
- query_chunk_size (`int`, *optional*, defaults to 1024):
- chunk size to divide query array value must divide query_length equally without remainder
- key_chunk_size (`int`, *optional*, defaults to 4096):
- chunk size to divide key and value array value must divide key_value_length equally without remainder
-
- Returns:
- (`jnp.ndarray`) with shape of (batch..., query_length, head, value_depth_per_head)
- """
- num_q, num_heads, q_features = query.shape[-3:]
-
- def chunk_scanner(chunk_idx, _):
- # julienne query array
- query_chunk = jax.lax.dynamic_slice(
- operand=query,
- start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0], # [...,q,h,d]
- slice_sizes=list(query.shape[:-3]) + [min(query_chunk_size, num_q), num_heads, q_features], # [...,q,h,d]
- )
-
- return (
- chunk_idx + query_chunk_size, # unused ignore it
- _query_chunk_attention(
- query=query_chunk, key=key, value=value, precision=precision, key_chunk_size=key_chunk_size
- ),
- )
-
- _, res = jax.lax.scan(
- f=chunk_scanner, init=0, xs=None, length=math.ceil(num_q / query_chunk_size) # start counter # stop counter
- )
-
- return jnp.concatenate(res, axis=-3) # fuse the chunked result back
-
-
-class FlaxAttention(nn.Module):
- r"""
- A Flax multi-head attention module as described in: https://arxiv.org/abs/1706.03762
-
- Parameters:
- query_dim (:obj:`int`):
- Input hidden states dimension
- heads (:obj:`int`, *optional*, defaults to 8):
- Number of heads
- dim_head (:obj:`int`, *optional*, defaults to 64):
- Hidden states dimension inside each head
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
-
- """
- query_dim: int
- heads: int = 8
- dim_head: int = 64
- dropout: float = 0.0
- use_memory_efficient_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- inner_dim = self.dim_head * self.heads
- self.scale = self.dim_head**-0.5
-
- # Weights were exported with old names {to_q, to_k, to_v, to_out}
- self.query = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_q")
- self.key = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_k")
- self.value = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_v")
-
- self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name="to_out_0")
- self.dropout_layer = nn.Dropout(rate=self.dropout)
-
- def reshape_heads_to_batch_dim(self, tensor):
- batch_size, seq_len, dim = tensor.shape
- head_size = self.heads
- tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
- tensor = jnp.transpose(tensor, (0, 2, 1, 3))
- tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
- return tensor
-
- def reshape_batch_dim_to_heads(self, tensor):
- batch_size, seq_len, dim = tensor.shape
- head_size = self.heads
- tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
- tensor = jnp.transpose(tensor, (0, 2, 1, 3))
- tensor = tensor.reshape(batch_size // head_size, seq_len, dim * head_size)
- return tensor
-
- def __call__(self, hidden_states, context=None, deterministic=True):
- context = hidden_states if context is None else context
-
- query_proj = self.query(hidden_states)
- key_proj = self.key(context)
- value_proj = self.value(context)
-
- query_states = self.reshape_heads_to_batch_dim(query_proj)
- key_states = self.reshape_heads_to_batch_dim(key_proj)
- value_states = self.reshape_heads_to_batch_dim(value_proj)
-
- if self.use_memory_efficient_attention:
- query_states = query_states.transpose(1, 0, 2)
- key_states = key_states.transpose(1, 0, 2)
- value_states = value_states.transpose(1, 0, 2)
-
- # this if statement create a chunk size for each layer of the unet
- # the chunk size is equal to the query_length dimension of the deepest layer of the unet
-
- flatten_latent_dim = query_states.shape[-3]
- if flatten_latent_dim % 64 == 0:
- query_chunk_size = int(flatten_latent_dim / 64)
- elif flatten_latent_dim % 16 == 0:
- query_chunk_size = int(flatten_latent_dim / 16)
- elif flatten_latent_dim % 4 == 0:
- query_chunk_size = int(flatten_latent_dim / 4)
- else:
- query_chunk_size = int(flatten_latent_dim)
-
- hidden_states = jax_memory_efficient_attention(
- query_states, key_states, value_states, query_chunk_size=query_chunk_size, key_chunk_size=4096 * 4
- )
-
- hidden_states = hidden_states.transpose(1, 0, 2)
- else:
- # compute attentions
- attention_scores = jnp.einsum("b i d, b j d->b i j", query_states, key_states)
- attention_scores = attention_scores * self.scale
- attention_probs = nn.softmax(attention_scores, axis=2)
-
- # attend to values
- hidden_states = jnp.einsum("b i j, b j d -> b i d", attention_probs, value_states)
-
- hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
- hidden_states = self.proj_attn(hidden_states)
- return self.dropout_layer(hidden_states, deterministic=deterministic)
-
-
-class FlaxBasicTransformerBlock(nn.Module):
- r"""
- A Flax transformer block layer with `GLU` (Gated Linear Unit) activation function as described in:
- https://arxiv.org/abs/1706.03762
-
-
- Parameters:
- dim (:obj:`int`):
- Inner hidden states dimension
- n_heads (:obj:`int`):
- Number of heads
- d_head (:obj:`int`):
- Hidden states dimension inside each head
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- only_cross_attention (`bool`, defaults to `False`):
- Whether to only apply cross attention.
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- """
- dim: int
- n_heads: int
- d_head: int
- dropout: float = 0.0
- only_cross_attention: bool = False
- dtype: jnp.dtype = jnp.float32
- use_memory_efficient_attention: bool = False
-
- def setup(self):
- # self attention (or cross_attention if only_cross_attention is True)
- self.attn1 = FlaxAttention(
- self.dim, self.n_heads, self.d_head, self.dropout, self.use_memory_efficient_attention, dtype=self.dtype
- )
- # cross attention
- self.attn2 = FlaxAttention(
- self.dim, self.n_heads, self.d_head, self.dropout, self.use_memory_efficient_attention, dtype=self.dtype
- )
- self.ff = FlaxFeedForward(dim=self.dim, dropout=self.dropout, dtype=self.dtype)
- self.norm1 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
- self.norm2 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
- self.norm3 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
- self.dropout_layer = nn.Dropout(rate=self.dropout)
-
- def __call__(self, hidden_states, context, deterministic=True):
- # self attention
- residual = hidden_states
- if self.only_cross_attention:
- hidden_states = self.attn1(self.norm1(hidden_states), context, deterministic=deterministic)
- else:
- hidden_states = self.attn1(self.norm1(hidden_states), deterministic=deterministic)
- hidden_states = hidden_states + residual
-
- # cross attention
- residual = hidden_states
- hidden_states = self.attn2(self.norm2(hidden_states), context, deterministic=deterministic)
- hidden_states = hidden_states + residual
-
- # feed forward
- residual = hidden_states
- hidden_states = self.ff(self.norm3(hidden_states), deterministic=deterministic)
- hidden_states = hidden_states + residual
-
- return self.dropout_layer(hidden_states, deterministic=deterministic)
-
-
-class FlaxTransformer2DModel(nn.Module):
- r"""
- A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in:
- https://arxiv.org/pdf/1506.02025.pdf
-
-
- Parameters:
- in_channels (:obj:`int`):
- Input number of channels
- n_heads (:obj:`int`):
- Number of heads
- d_head (:obj:`int`):
- Hidden states dimension inside each head
- depth (:obj:`int`, *optional*, defaults to 1):
- Number of transformers block
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- use_linear_projection (`bool`, defaults to `False`): tbd
- only_cross_attention (`bool`, defaults to `False`): tbd
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- """
- in_channels: int
- n_heads: int
- d_head: int
- depth: int = 1
- dropout: float = 0.0
- use_linear_projection: bool = False
- only_cross_attention: bool = False
- dtype: jnp.dtype = jnp.float32
- use_memory_efficient_attention: bool = False
-
- def setup(self):
- self.norm = nn.GroupNorm(num_groups=32, epsilon=1e-5)
-
- inner_dim = self.n_heads * self.d_head
- if self.use_linear_projection:
- self.proj_in = nn.Dense(inner_dim, dtype=self.dtype)
- else:
- self.proj_in = nn.Conv(
- inner_dim,
- kernel_size=(1, 1),
- strides=(1, 1),
- padding="VALID",
- dtype=self.dtype,
- )
-
- self.transformer_blocks = [
- FlaxBasicTransformerBlock(
- inner_dim,
- self.n_heads,
- self.d_head,
- dropout=self.dropout,
- only_cross_attention=self.only_cross_attention,
- dtype=self.dtype,
- use_memory_efficient_attention=self.use_memory_efficient_attention,
- )
- for _ in range(self.depth)
- ]
-
- if self.use_linear_projection:
- self.proj_out = nn.Dense(inner_dim, dtype=self.dtype)
- else:
- self.proj_out = nn.Conv(
- inner_dim,
- kernel_size=(1, 1),
- strides=(1, 1),
- padding="VALID",
- dtype=self.dtype,
- )
-
- self.dropout_layer = nn.Dropout(rate=self.dropout)
-
- def __call__(self, hidden_states, context, deterministic=True):
- batch, height, width, channels = hidden_states.shape
- residual = hidden_states
- hidden_states = self.norm(hidden_states)
- if self.use_linear_projection:
- hidden_states = hidden_states.reshape(batch, height * width, channels)
- hidden_states = self.proj_in(hidden_states)
- else:
- hidden_states = self.proj_in(hidden_states)
- hidden_states = hidden_states.reshape(batch, height * width, channels)
-
- for transformer_block in self.transformer_blocks:
- hidden_states = transformer_block(hidden_states, context, deterministic=deterministic)
-
- if self.use_linear_projection:
- hidden_states = self.proj_out(hidden_states)
- hidden_states = hidden_states.reshape(batch, height, width, channels)
- else:
- hidden_states = hidden_states.reshape(batch, height, width, channels)
- hidden_states = self.proj_out(hidden_states)
-
- hidden_states = hidden_states + residual
- return self.dropout_layer(hidden_states, deterministic=deterministic)
-
-
-class FlaxFeedForward(nn.Module):
- r"""
- Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's
- [`FeedForward`] class, with the following simplifications:
- - The activation function is currently hardcoded to a gated linear unit from:
- https://arxiv.org/abs/2002.05202
- - `dim_out` is equal to `dim`.
- - The number of hidden dimensions is hardcoded to `dim * 4` in [`FlaxGELU`].
-
- Parameters:
- dim (:obj:`int`):
- Inner hidden states dimension
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- dim: int
- dropout: float = 0.0
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- # The second linear layer needs to be called
- # net_2 for now to match the index of the Sequential layer
- self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype)
- self.net_2 = nn.Dense(self.dim, dtype=self.dtype)
-
- def __call__(self, hidden_states, deterministic=True):
- hidden_states = self.net_0(hidden_states, deterministic=deterministic)
- hidden_states = self.net_2(hidden_states)
- return hidden_states
-
-
-class FlaxGEGLU(nn.Module):
- r"""
- Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from
- https://arxiv.org/abs/2002.05202.
-
- Parameters:
- dim (:obj:`int`):
- Input hidden states dimension
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- dim: int
- dropout: float = 0.0
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- inner_dim = self.dim * 4
- self.proj = nn.Dense(inner_dim * 2, dtype=self.dtype)
- self.dropout_layer = nn.Dropout(rate=self.dropout)
-
- def __call__(self, hidden_states, deterministic=True):
- hidden_states = self.proj(hidden_states)
- hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2)
- return self.dropout_layer(hidden_linear * nn.gelu(hidden_gelu), deterministic=deterministic)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/pipeline_utils.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/pipeline_utils.py
deleted file mode 100644
index 133bf3a7a2f8f26d83371cb103410cfe812aaea7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/pipeline_utils.py
+++ /dev/null
@@ -1,1698 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-# Copyright (c) 2022, NVIDIA CORPORATION. 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 fnmatch
-import importlib
-import inspect
-import os
-import re
-import sys
-import warnings
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from huggingface_hub import ModelCard, hf_hub_download, model_info, snapshot_download
-from packaging import version
-from requests.exceptions import HTTPError
-from tqdm.auto import tqdm
-
-import diffusers
-
-from .. import __version__
-from ..configuration_utils import ConfigMixin
-from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
-from ..schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
-from ..utils import (
- CONFIG_NAME,
- DEPRECATED_REVISION_ARGS,
- DIFFUSERS_CACHE,
- HF_HUB_OFFLINE,
- SAFETENSORS_WEIGHTS_NAME,
- WEIGHTS_NAME,
- BaseOutput,
- deprecate,
- get_class_from_dynamic_module,
- is_accelerate_available,
- is_accelerate_version,
- is_compiled_module,
- is_safetensors_available,
- is_torch_version,
- is_transformers_available,
- logging,
- numpy_to_pil,
-)
-
-
-if is_transformers_available():
- import transformers
- from transformers import PreTrainedModel
- from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME
- from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME
- from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME
-
-from ..utils import FLAX_WEIGHTS_NAME, ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME
-
-
-if is_accelerate_available():
- import accelerate
-
-
-INDEX_FILE = "diffusion_pytorch_model.bin"
-CUSTOM_PIPELINE_FILE_NAME = "pipeline.py"
-DUMMY_MODULES_FOLDER = "diffusers.utils"
-TRANSFORMERS_DUMMY_MODULES_FOLDER = "transformers.utils"
-CONNECTED_PIPES_KEYS = ["prior"]
-
-
-logger = logging.get_logger(__name__)
-
-
-LOADABLE_CLASSES = {
- "diffusers": {
- "ModelMixin": ["save_pretrained", "from_pretrained"],
- "SchedulerMixin": ["save_pretrained", "from_pretrained"],
- "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
- "OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],
- },
- "transformers": {
- "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
- "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
- "PreTrainedModel": ["save_pretrained", "from_pretrained"],
- "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
- "ProcessorMixin": ["save_pretrained", "from_pretrained"],
- "ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
- },
- "onnxruntime.training": {
- "ORTModule": ["save_pretrained", "from_pretrained"],
- },
-}
-
-ALL_IMPORTABLE_CLASSES = {}
-for library in LOADABLE_CLASSES:
- ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
-
-
-@dataclass
-class ImagePipelineOutput(BaseOutput):
- """
- Output class for image pipelines.
-
- Args:
- images (`List[PIL.Image.Image]` or `np.ndarray`)
- List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
- num_channels)`.
- """
-
- images: Union[List[PIL.Image.Image], np.ndarray]
-
-
-@dataclass
-class AudioPipelineOutput(BaseOutput):
- """
- Output class for audio pipelines.
-
- Args:
- audios (`np.ndarray`)
- List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.
- """
-
- audios: np.ndarray
-
-
-def is_safetensors_compatible(filenames, variant=None, passed_components=None) -> bool:
- """
- Checking for safetensors compatibility:
- - By default, all models are saved with the default pytorch serialization, so we use the list of default pytorch
- files to know which safetensors files are needed.
- - The model is safetensors compatible only if there is a matching safetensors file for every default pytorch file.
-
- Converting default pytorch serialized filenames to safetensors serialized filenames:
- - For models from the diffusers library, just replace the ".bin" extension with ".safetensors"
- - For models from the transformers library, the filename changes from "pytorch_model" to "model", and the ".bin"
- extension is replaced with ".safetensors"
- """
- pt_filenames = []
-
- sf_filenames = set()
-
- passed_components = passed_components or []
-
- for filename in filenames:
- _, extension = os.path.splitext(filename)
-
- if len(filename.split("/")) == 2 and filename.split("/")[0] in passed_components:
- continue
-
- if extension == ".bin":
- pt_filenames.append(filename)
- elif extension == ".safetensors":
- sf_filenames.add(filename)
-
- for filename in pt_filenames:
- # filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extention = '.bam'
- path, filename = os.path.split(filename)
- filename, extension = os.path.splitext(filename)
-
- if filename.startswith("pytorch_model"):
- filename = filename.replace("pytorch_model", "model")
- else:
- filename = filename
-
- expected_sf_filename = os.path.join(path, filename)
- expected_sf_filename = f"{expected_sf_filename}.safetensors"
-
- if expected_sf_filename not in sf_filenames:
- logger.warning(f"{expected_sf_filename} not found")
- return False
-
- return True
-
-
-def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLike], str]:
- weight_names = [
- WEIGHTS_NAME,
- SAFETENSORS_WEIGHTS_NAME,
- FLAX_WEIGHTS_NAME,
- ONNX_WEIGHTS_NAME,
- ONNX_EXTERNAL_WEIGHTS_NAME,
- ]
-
- if is_transformers_available():
- weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]
-
- # model_pytorch, diffusion_model_pytorch, ...
- weight_prefixes = [w.split(".")[0] for w in weight_names]
- # .bin, .safetensors, ...
- weight_suffixs = [w.split(".")[-1] for w in weight_names]
- # -00001-of-00002
- transformers_index_format = r"\d{5}-of-\d{5}"
-
- if variant is not None:
- # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetensors`
- variant_file_re = re.compile(
- rf"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$"
- )
- # `text_encoder/pytorch_model.bin.index.fp16.json`
- variant_index_re = re.compile(
- rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$"
- )
-
- # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetensors`
- non_variant_file_re = re.compile(
- rf"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$"
- )
- # `text_encoder/pytorch_model.bin.index.json`
- non_variant_index_re = re.compile(rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json")
-
- if variant is not None:
- variant_weights = {f for f in filenames if variant_file_re.match(f.split("/")[-1]) is not None}
- variant_indexes = {f for f in filenames if variant_index_re.match(f.split("/")[-1]) is not None}
- variant_filenames = variant_weights | variant_indexes
- else:
- variant_filenames = set()
-
- non_variant_weights = {f for f in filenames if non_variant_file_re.match(f.split("/")[-1]) is not None}
- non_variant_indexes = {f for f in filenames if non_variant_index_re.match(f.split("/")[-1]) is not None}
- non_variant_filenames = non_variant_weights | non_variant_indexes
-
- # all variant filenames will be used by default
- usable_filenames = set(variant_filenames)
-
- def convert_to_variant(filename):
- if "index" in filename:
- variant_filename = filename.replace("index", f"index.{variant}")
- elif re.compile(f"^(.*?){transformers_index_format}").match(filename) is not None:
- variant_filename = f"{filename.split('-')[0]}.{variant}-{'-'.join(filename.split('-')[1:])}"
- else:
- variant_filename = f"{filename.split('.')[0]}.{variant}.{filename.split('.')[1]}"
- return variant_filename
-
- for f in non_variant_filenames:
- variant_filename = convert_to_variant(f)
- if variant_filename not in usable_filenames:
- usable_filenames.add(f)
-
- return usable_filenames, variant_filenames
-
-
-def warn_deprecated_model_variant(pretrained_model_name_or_path, use_auth_token, variant, revision, model_filenames):
- info = model_info(
- pretrained_model_name_or_path,
- use_auth_token=use_auth_token,
- revision=None,
- )
- filenames = {sibling.rfilename for sibling in info.siblings}
- comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision)
- comp_model_filenames = [".".join(f.split(".")[:1] + f.split(".")[2:]) for f in comp_model_filenames]
-
- if set(comp_model_filenames) == set(model_filenames):
- warnings.warn(
- f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` even though you can load it via `variant=`{revision}`. Loading model variants via `revision='{revision}'` is deprecated and will be removed in diffusers v1. Please use `variant='{revision}'` instead.",
- FutureWarning,
- )
- else:
- warnings.warn(
- f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have the required variant filenames in the 'main' branch. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {revision} files' so that the correct variant file can be added.",
- FutureWarning,
- )
-
-
-def maybe_raise_or_warn(
- library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module
-):
- """Simple helper method to raise or warn in case incorrect module has been passed"""
- if not is_pipeline_module:
- library = importlib.import_module(library_name)
- class_obj = getattr(library, class_name)
- class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
-
- expected_class_obj = None
- for class_name, class_candidate in class_candidates.items():
- if class_candidate is not None and issubclass(class_obj, class_candidate):
- expected_class_obj = class_candidate
-
- # Dynamo wraps the original model in a private class.
- # I didn't find a public API to get the original class.
- sub_model = passed_class_obj[name]
- model_cls = sub_model.__class__
- if is_compiled_module(sub_model):
- model_cls = sub_model._orig_mod.__class__
-
- if not issubclass(model_cls, expected_class_obj):
- raise ValueError(
- f"{passed_class_obj[name]} is of type: {model_cls}, but should be" f" {expected_class_obj}"
- )
- else:
- logger.warning(
- f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
- " has the correct type"
- )
-
-
-def get_class_obj_and_candidates(library_name, class_name, importable_classes, pipelines, is_pipeline_module):
- """Simple helper method to retrieve class object of module as well as potential parent class objects"""
- if is_pipeline_module:
- pipeline_module = getattr(pipelines, library_name)
-
- class_obj = getattr(pipeline_module, class_name)
- class_candidates = {c: class_obj for c in importable_classes.keys()}
- else:
- # else we just import it from the library.
- library = importlib.import_module(library_name)
-
- class_obj = getattr(library, class_name)
- class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
-
- return class_obj, class_candidates
-
-
-def _get_pipeline_class(
- class_obj, config, load_connected_pipeline=False, custom_pipeline=None, cache_dir=None, revision=None
-):
- if custom_pipeline is not None:
- if custom_pipeline.endswith(".py"):
- path = Path(custom_pipeline)
- # decompose into folder & file
- file_name = path.name
- custom_pipeline = path.parent.absolute()
- else:
- file_name = CUSTOM_PIPELINE_FILE_NAME
-
- return get_class_from_dynamic_module(
- custom_pipeline, module_file=file_name, cache_dir=cache_dir, revision=revision
- )
-
- if class_obj != DiffusionPipeline:
- return class_obj
-
- diffusers_module = importlib.import_module(class_obj.__module__.split(".")[0])
- pipeline_cls = getattr(diffusers_module, config["_class_name"])
-
- if load_connected_pipeline:
- from .auto_pipeline import _get_connected_pipeline
-
- connected_pipeline_cls = _get_connected_pipeline(pipeline_cls)
- if connected_pipeline_cls is not None:
- logger.info(
- f"Loading connected pipeline {connected_pipeline_cls.__name__} instead of {pipeline_cls.__name__} as specified via `load_connected_pipeline=True`"
- )
- else:
- logger.info(f"{pipeline_cls.__name__} has no connected pipeline class. Loading {pipeline_cls.__name__}.")
-
- pipeline_cls = connected_pipeline_cls or pipeline_cls
-
- return pipeline_cls
-
-
-def load_sub_model(
- library_name: str,
- class_name: str,
- importable_classes: List[Any],
- pipelines: Any,
- is_pipeline_module: bool,
- pipeline_class: Any,
- torch_dtype: torch.dtype,
- provider: Any,
- sess_options: Any,
- device_map: Optional[Union[Dict[str, torch.device], str]],
- max_memory: Optional[Dict[Union[int, str], Union[int, str]]],
- offload_folder: Optional[Union[str, os.PathLike]],
- offload_state_dict: bool,
- model_variants: Dict[str, str],
- name: str,
- from_flax: bool,
- variant: str,
- low_cpu_mem_usage: bool,
- cached_folder: Union[str, os.PathLike],
-):
- """Helper method to load the module `name` from `library_name` and `class_name`"""
- # retrieve class candidates
- class_obj, class_candidates = get_class_obj_and_candidates(
- library_name, class_name, importable_classes, pipelines, is_pipeline_module
- )
-
- load_method_name = None
- # retrive load method name
- for class_name, class_candidate in class_candidates.items():
- if class_candidate is not None and issubclass(class_obj, class_candidate):
- load_method_name = importable_classes[class_name][1]
-
- # if load method name is None, then we have a dummy module -> raise Error
- if load_method_name is None:
- none_module = class_obj.__module__
- is_dummy_path = none_module.startswith(DUMMY_MODULES_FOLDER) or none_module.startswith(
- TRANSFORMERS_DUMMY_MODULES_FOLDER
- )
- if is_dummy_path and "dummy" in none_module:
- # call class_obj for nice error message of missing requirements
- class_obj()
-
- raise ValueError(
- f"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have"
- f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}."
- )
-
- load_method = getattr(class_obj, load_method_name)
-
- # add kwargs to loading method
- loading_kwargs = {}
- if issubclass(class_obj, torch.nn.Module):
- loading_kwargs["torch_dtype"] = torch_dtype
- if issubclass(class_obj, diffusers.OnnxRuntimeModel):
- loading_kwargs["provider"] = provider
- loading_kwargs["sess_options"] = sess_options
-
- is_diffusers_model = issubclass(class_obj, diffusers.ModelMixin)
-
- if is_transformers_available():
- transformers_version = version.parse(version.parse(transformers.__version__).base_version)
- else:
- transformers_version = "N/A"
-
- is_transformers_model = (
- is_transformers_available()
- and issubclass(class_obj, PreTrainedModel)
- and transformers_version >= version.parse("4.20.0")
- )
-
- # When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers.
- # To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default.
- # This makes sure that the weights won't be initialized which significantly speeds up loading.
- if is_diffusers_model or is_transformers_model:
- loading_kwargs["device_map"] = device_map
- loading_kwargs["max_memory"] = max_memory
- loading_kwargs["offload_folder"] = offload_folder
- loading_kwargs["offload_state_dict"] = offload_state_dict
- loading_kwargs["variant"] = model_variants.pop(name, None)
- if from_flax:
- loading_kwargs["from_flax"] = True
-
- # the following can be deleted once the minimum required `transformers` version
- # is higher than 4.27
- if (
- is_transformers_model
- and loading_kwargs["variant"] is not None
- and transformers_version < version.parse("4.27.0")
- ):
- raise ImportError(
- f"When passing `variant='{variant}'`, please make sure to upgrade your `transformers` version to at least 4.27.0.dev0"
- )
- elif is_transformers_model and loading_kwargs["variant"] is None:
- loading_kwargs.pop("variant")
-
- # if `from_flax` and model is transformer model, can currently not load with `low_cpu_mem_usage`
- if not (from_flax and is_transformers_model):
- loading_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
- else:
- loading_kwargs["low_cpu_mem_usage"] = False
-
- # check if the module is in a subdirectory
- if os.path.isdir(os.path.join(cached_folder, name)):
- loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
- else:
- # else load from the root directory
- loaded_sub_model = load_method(cached_folder, **loading_kwargs)
-
- return loaded_sub_model
-
-
-class DiffusionPipeline(ConfigMixin):
- r"""
- Base class for all pipelines.
-
- [`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
- provides methods for loading, downloading and saving models. It also includes methods to:
-
- - move all PyTorch modules to the device of your choice
- - enable/disable the progress bar for the denoising iteration
-
- Class attributes:
-
- - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
- diffusion pipeline's components.
- - **_optional_components** (`List[str]`) -- List of all optional components that don't have to be passed to the
- pipeline to function (should be overridden by subclasses).
- """
- config_name = "model_index.json"
- _optional_components = []
- _exclude_from_cpu_offload = []
- _load_connected_pipes = False
- _is_onnx = False
-
- def register_modules(self, **kwargs):
- # import it here to avoid circular import
- from diffusers import pipelines
-
- for name, module in kwargs.items():
- # retrieve library
- if module is None:
- register_dict = {name: (None, None)}
- else:
- # register the config from the original module, not the dynamo compiled one
- if is_compiled_module(module):
- not_compiled_module = module._orig_mod
- else:
- not_compiled_module = module
-
- library = not_compiled_module.__module__.split(".")[0]
-
- # check if the module is a pipeline module
- module_path_items = not_compiled_module.__module__.split(".")
- pipeline_dir = module_path_items[-2] if len(module_path_items) > 2 else None
-
- path = not_compiled_module.__module__.split(".")
- is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
-
- # if library is not in LOADABLE_CLASSES, then it is a custom module.
- # Or if it's a pipeline module, then the module is inside the pipeline
- # folder so we set the library to module name.
- if is_pipeline_module:
- library = pipeline_dir
- elif library not in LOADABLE_CLASSES:
- library = not_compiled_module.__module__
-
- # retrieve class_name
- class_name = not_compiled_module.__class__.__name__
-
- register_dict = {name: (library, class_name)}
-
- # save model index config
- self.register_to_config(**register_dict)
-
- # set models
- setattr(self, name, module)
-
- def __setattr__(self, name: str, value: Any):
- if name in self.__dict__ and hasattr(self.config, name):
- # We need to overwrite the config if name exists in config
- if isinstance(getattr(self.config, name), (tuple, list)):
- if value is not None and self.config[name][0] is not None:
- class_library_tuple = (value.__module__.split(".")[0], value.__class__.__name__)
- else:
- class_library_tuple = (None, None)
-
- self.register_to_config(**{name: class_library_tuple})
- else:
- self.register_to_config(**{name: value})
-
- super().__setattr__(name, value)
-
- def save_pretrained(
- self,
- save_directory: Union[str, os.PathLike],
- safe_serialization: bool = False,
- variant: Optional[str] = None,
- ):
- """
- Save all saveable variables of the pipeline to a directory. A pipeline variable can be saved and loaded if its
- class implements both a save and loading method. The pipeline is easily reloaded using the
- [`~DiffusionPipeline.from_pretrained`] class method.
-
- Arguments:
- save_directory (`str` or `os.PathLike`):
- Directory to save a pipeline to. Will be created if it doesn't exist.
- safe_serialization (`bool`, *optional*, defaults to `False`):
- Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
- variant (`str`, *optional*):
- If specified, weights are saved in the format `pytorch_model..bin`.
- """
- model_index_dict = dict(self.config)
- model_index_dict.pop("_class_name", None)
- model_index_dict.pop("_diffusers_version", None)
- model_index_dict.pop("_module", None)
- model_index_dict.pop("_name_or_path", None)
-
- expected_modules, optional_kwargs = self._get_signature_keys(self)
-
- def is_saveable_module(name, value):
- if name not in expected_modules:
- return False
- if name in self._optional_components and value[0] is None:
- return False
- return True
-
- model_index_dict = {k: v for k, v in model_index_dict.items() if is_saveable_module(k, v)}
- for pipeline_component_name in model_index_dict.keys():
- sub_model = getattr(self, pipeline_component_name)
- model_cls = sub_model.__class__
-
- # Dynamo wraps the original model in a private class.
- # I didn't find a public API to get the original class.
- if is_compiled_module(sub_model):
- sub_model = sub_model._orig_mod
- model_cls = sub_model.__class__
-
- save_method_name = None
- # search for the model's base class in LOADABLE_CLASSES
- for library_name, library_classes in LOADABLE_CLASSES.items():
- if library_name in sys.modules:
- library = importlib.import_module(library_name)
- else:
- logger.info(
- f"{library_name} is not installed. Cannot save {pipeline_component_name} as {library_classes} from {library_name}"
- )
-
- for base_class, save_load_methods in library_classes.items():
- class_candidate = getattr(library, base_class, None)
- if class_candidate is not None and issubclass(model_cls, class_candidate):
- # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
- save_method_name = save_load_methods[0]
- break
- if save_method_name is not None:
- break
-
- if save_method_name is None:
- logger.warn(f"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved.")
- # make sure that unsaveable components are not tried to be loaded afterward
- self.register_to_config(**{pipeline_component_name: (None, None)})
- continue
-
- save_method = getattr(sub_model, save_method_name)
-
- # Call the save method with the argument safe_serialization only if it's supported
- save_method_signature = inspect.signature(save_method)
- save_method_accept_safe = "safe_serialization" in save_method_signature.parameters
- save_method_accept_variant = "variant" in save_method_signature.parameters
-
- save_kwargs = {}
- if save_method_accept_safe:
- save_kwargs["safe_serialization"] = safe_serialization
- if save_method_accept_variant:
- save_kwargs["variant"] = variant
-
- save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs)
-
- # finally save the config
- self.save_config(save_directory)
-
- def to(
- self,
- torch_device: Optional[Union[str, torch.device]] = None,
- torch_dtype: Optional[torch.dtype] = None,
- silence_dtype_warnings: bool = False,
- ):
- if torch_device is None and torch_dtype is None:
- return self
-
- # throw warning if pipeline is in "offloaded"-mode but user tries to manually set to GPU.
- def module_is_sequentially_offloaded(module):
- if not is_accelerate_available() or is_accelerate_version("<", "0.14.0"):
- return False
-
- return hasattr(module, "_hf_hook") and not isinstance(
- module._hf_hook, (accelerate.hooks.CpuOffload, accelerate.hooks.AlignDevicesHook)
- )
-
- def module_is_offloaded(module):
- if not is_accelerate_available() or is_accelerate_version("<", "0.17.0.dev0"):
- return False
-
- return hasattr(module, "_hf_hook") and isinstance(module._hf_hook, accelerate.hooks.CpuOffload)
-
- # .to("cuda") would raise an error if the pipeline is sequentially offloaded, so we raise our own to make it clearer
- pipeline_is_sequentially_offloaded = any(
- module_is_sequentially_offloaded(module) for _, module in self.components.items()
- )
- if pipeline_is_sequentially_offloaded and torch.device(torch_device).type == "cuda":
- raise ValueError(
- "It seems like you have activated sequential model offloading by calling `enable_sequential_cpu_offload`, but are now attempting to move the pipeline to GPU. This is not compatible with offloading. Please, move your pipeline `.to('cpu')` or consider removing the move altogether if you use sequential offloading."
- )
-
- # Display a warning in this case (the operation succeeds but the benefits are lost)
- pipeline_is_offloaded = any(module_is_offloaded(module) for _, module in self.components.items())
- if pipeline_is_offloaded and torch.device(torch_device).type == "cuda":
- logger.warning(
- f"It seems like you have activated model offloading by calling `enable_model_cpu_offload`, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components {', '.join(self.components.keys())} to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: `pipeline.to('cpu')` or removing the move altogether if you use offloading."
- )
-
- module_names, _ = self._get_signature_keys(self)
- modules = [getattr(self, n, None) for n in module_names]
- modules = [m for m in modules if isinstance(m, torch.nn.Module)]
-
- is_offloaded = pipeline_is_offloaded or pipeline_is_sequentially_offloaded
- for module in modules:
- is_loaded_in_8bit = hasattr(module, "is_loaded_in_8bit") and module.is_loaded_in_8bit
-
- if is_loaded_in_8bit and torch_dtype is not None:
- logger.warning(
- f"The module '{module.__class__.__name__}' has been loaded in 8bit and conversion to {torch_dtype} is not yet supported. Module is still in 8bit precision."
- )
-
- if is_loaded_in_8bit and torch_device is not None:
- logger.warning(
- f"The module '{module.__class__.__name__}' has been loaded in 8bit and moving it to {torch_dtype} via `.to()` is not yet supported. Module is still on {module.device}."
- )
- else:
- module.to(torch_device, torch_dtype)
-
- if (
- module.dtype == torch.float16
- and str(torch_device) in ["cpu"]
- and not silence_dtype_warnings
- and not is_offloaded
- ):
- logger.warning(
- "Pipelines loaded with `torch_dtype=torch.float16` cannot run with `cpu` device. It"
- " is not recommended to move them to `cpu` as running them will fail. Please make"
- " sure to use an accelerator to run the pipeline in inference, due to the lack of"
- " support for`float16` operations on this device in PyTorch. Please, remove the"
- " `torch_dtype=torch.float16` argument, or use another device for inference."
- )
- return self
-
- @property
- def device(self) -> torch.device:
- r"""
- Returns:
- `torch.device`: The torch device on which the pipeline is located.
- """
- module_names, _ = self._get_signature_keys(self)
- modules = [getattr(self, n, None) for n in module_names]
- modules = [m for m in modules if isinstance(m, torch.nn.Module)]
-
- for module in modules:
- return module.device
-
- return torch.device("cpu")
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
- r"""
- Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights.
-
- The pipeline is set in evaluation mode (`model.eval()`) by default.
-
- If you get the error message below, you need to finetune the weights for your downstream task:
-
- ```
- Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
- You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
- ```
-
- Parameters:
- pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
- Can be either:
-
- - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
- hosted on the Hub.
- - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
- saved using
- [`~DiffusionPipeline.save_pretrained`].
- torch_dtype (`str` or `torch.dtype`, *optional*):
- Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
- dtype is automatically derived from the model's weights.
- custom_pipeline (`str`, *optional*):
-
-
-
- 🧪 This is an experimental feature and may change in the future.
-
-
-
- Can be either:
-
- - A string, the *repo id* (for example `hf-internal-testing/diffusers-dummy-pipeline`) of a custom
- pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines
- the custom pipeline.
- - A string, the *file name* of a community pipeline hosted on GitHub under
- [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file
- names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`
- instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the
- current main branch of GitHub.
- - A path to a directory (`./my_pipeline_directory/`) containing a custom pipeline. The directory
- must contain a file called `pipeline.py` that defines the custom pipeline.
-
- For more information on how to load and create custom pipelines, please have a look at [Loading and
- Adding Custom
- Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview)
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- cache_dir (`Union[str, os.PathLike]`, *optional*):
- Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
- is not used.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
- incompletely downloaded files are deleted.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only (`bool`, *optional*, defaults to `False`):
- Whether to only load local model weights and configuration files or not. If set to `True`, the model
- won't be downloaded from the Hub.
- use_auth_token (`str` or *bool*, *optional*):
- The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
- `diffusers-cli login` (stored in `~/.huggingface`) is used.
- revision (`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
- allowed by Git.
- custom_revision (`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
- `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
- custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
- mirror (`str`, *optional*):
- Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
- guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
- information.
- device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
- A map that specifies where each submodule should go. It doesn’t need to be defined for each
- parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
- same device.
-
- Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
- more information about each option see [designing a device
- map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
- max_memory (`Dict`, *optional*):
- A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
- each GPU and the available CPU RAM if unset.
- offload_folder (`str` or `os.PathLike`, *optional*):
- The path to offload weights if device_map contains the value `"disk"`.
- offload_state_dict (`bool`, *optional*):
- If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
- the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
- when there is some disk offload.
- low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
- Speed up model loading only loading the pretrained weights and not initializing the weights. This also
- tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
- Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
- argument to `True` will raise an error.
- use_safetensors (`bool`, *optional*, defaults to `None`):
- If set to `None`, the safetensors weights are downloaded if they're available **and** if the
- safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
- weights. If set to `False`, safetensors weights are not loaded.
- use_onnx (`bool`, *optional*, defaults to `None`):
- If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
- will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
- `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
- with `.onnx` and `.pb`.
- kwargs (remaining dictionary of keyword arguments, *optional*):
- Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
- class). The overwritten components are passed directly to the pipelines `__init__` method. See example
- below for more information.
- variant (`str`, *optional*):
- Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
- loading `from_flax`.
-
-
-
- To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
- `huggingface-cli login`.
-
-
-
- Examples:
-
- ```py
- >>> from diffusers import DiffusionPipeline
-
- >>> # Download pipeline from huggingface.co and cache.
- >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
-
- >>> # Download pipeline that requires an authorization token
- >>> # For more information on access tokens, please refer to this section
- >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
- >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-
- >>> # Use a different scheduler
- >>> from diffusers import LMSDiscreteScheduler
-
- >>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
- >>> pipeline.scheduler = scheduler
- ```
- """
- cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
- resume_download = kwargs.pop("resume_download", False)
- force_download = kwargs.pop("force_download", False)
- proxies = kwargs.pop("proxies", None)
- local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
- use_auth_token = kwargs.pop("use_auth_token", None)
- revision = kwargs.pop("revision", None)
- from_flax = kwargs.pop("from_flax", False)
- torch_dtype = kwargs.pop("torch_dtype", None)
- custom_pipeline = kwargs.pop("custom_pipeline", None)
- custom_revision = kwargs.pop("custom_revision", None)
- provider = kwargs.pop("provider", None)
- sess_options = kwargs.pop("sess_options", None)
- device_map = kwargs.pop("device_map", None)
- max_memory = kwargs.pop("max_memory", None)
- offload_folder = kwargs.pop("offload_folder", None)
- offload_state_dict = kwargs.pop("offload_state_dict", False)
- low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
- variant = kwargs.pop("variant", None)
- use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
- load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
-
- # 1. Download the checkpoints and configs
- # use snapshot download here to get it working from from_pretrained
- if not os.path.isdir(pretrained_model_name_or_path):
- cached_folder = cls.download(
- pretrained_model_name_or_path,
- cache_dir=cache_dir,
- resume_download=resume_download,
- force_download=force_download,
- proxies=proxies,
- local_files_only=local_files_only,
- use_auth_token=use_auth_token,
- revision=revision,
- from_flax=from_flax,
- use_safetensors=use_safetensors,
- custom_pipeline=custom_pipeline,
- custom_revision=custom_revision,
- variant=variant,
- load_connected_pipeline=load_connected_pipeline,
- **kwargs,
- )
- else:
- cached_folder = pretrained_model_name_or_path
-
- config_dict = cls.load_config(cached_folder)
-
- # pop out "_ignore_files" as it is only needed for download
- config_dict.pop("_ignore_files", None)
-
- # 2. Define which model components should load variants
- # We retrieve the information by matching whether variant
- # model checkpoints exist in the subfolders
- model_variants = {}
- if variant is not None:
- for folder in os.listdir(cached_folder):
- folder_path = os.path.join(cached_folder, folder)
- is_folder = os.path.isdir(folder_path) and folder in config_dict
- variant_exists = is_folder and any(
- p.split(".")[1].startswith(variant) for p in os.listdir(folder_path)
- )
- if variant_exists:
- model_variants[folder] = variant
-
- # 3. Load the pipeline class, if using custom module then load it from the hub
- # if we load from explicit class, let's use it
- pipeline_class = _get_pipeline_class(
- cls,
- config_dict,
- load_connected_pipeline=load_connected_pipeline,
- custom_pipeline=custom_pipeline,
- cache_dir=cache_dir,
- revision=custom_revision,
- )
-
- # DEPRECATED: To be removed in 1.0.0
- if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse(
- version.parse(config_dict["_diffusers_version"]).base_version
- ) <= version.parse("0.5.1"):
- from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy
-
- pipeline_class = StableDiffusionInpaintPipelineLegacy
-
- deprecation_message = (
- "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
- f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
- " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
- " checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your"
- f" checkpoint {pretrained_model_name_or_path} to the format of"
- " https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain"
- " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
- )
- deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False)
-
- # 4. Define expected modules given pipeline signature
- # and define non-None initialized modules (=`init_kwargs`)
-
- # some modules can be passed directly to the init
- # in this case they are already instantiated in `kwargs`
- # extract them here
- expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)
- passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
- passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
-
- init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
-
- # define init kwargs
- init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict}
- init_kwargs = {**init_kwargs, **passed_pipe_kwargs}
-
- # remove `null` components
- def load_module(name, value):
- if value[0] is None:
- return False
- if name in passed_class_obj and passed_class_obj[name] is None:
- return False
- return True
-
- init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}
-
- # Special case: safety_checker must be loaded separately when using `from_flax`
- if from_flax and "safety_checker" in init_dict and "safety_checker" not in passed_class_obj:
- raise NotImplementedError(
- "The safety checker cannot be automatically loaded when loading weights `from_flax`."
- " Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker"
- " separately if you need it."
- )
-
- # 5. Throw nice warnings / errors for fast accelerate loading
- if len(unused_kwargs) > 0:
- logger.warning(
- f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored."
- )
-
- if low_cpu_mem_usage and not is_accelerate_available():
- low_cpu_mem_usage = False
- logger.warning(
- "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
- " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
- " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
- " install accelerate\n```\n."
- )
-
- if device_map is not None and not is_torch_version(">=", "1.9.0"):
- raise NotImplementedError(
- "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
- " `device_map=None`."
- )
-
- if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
- raise NotImplementedError(
- "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
- " `low_cpu_mem_usage=False`."
- )
-
- if low_cpu_mem_usage is False and device_map is not None:
- raise ValueError(
- f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
- " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
- )
-
- # import it here to avoid circular import
- from diffusers import pipelines
-
- # 6. Load each module in the pipeline
- for name, (library_name, class_name) in tqdm(init_dict.items(), desc="Loading pipeline components..."):
- # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
- if class_name.startswith("Flax"):
- class_name = class_name[4:]
-
- # 6.2 Define all importable classes
- is_pipeline_module = hasattr(pipelines, library_name)
- importable_classes = ALL_IMPORTABLE_CLASSES
- loaded_sub_model = None
-
- # 6.3 Use passed sub model or load class_name from library_name
- if name in passed_class_obj:
- # if the model is in a pipeline module, then we load it from the pipeline
- # check that passed_class_obj has correct parent class
- maybe_raise_or_warn(
- library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module
- )
-
- loaded_sub_model = passed_class_obj[name]
- else:
- # load sub model
- loaded_sub_model = load_sub_model(
- library_name=library_name,
- class_name=class_name,
- importable_classes=importable_classes,
- pipelines=pipelines,
- is_pipeline_module=is_pipeline_module,
- pipeline_class=pipeline_class,
- torch_dtype=torch_dtype,
- provider=provider,
- sess_options=sess_options,
- device_map=device_map,
- max_memory=max_memory,
- offload_folder=offload_folder,
- offload_state_dict=offload_state_dict,
- model_variants=model_variants,
- name=name,
- from_flax=from_flax,
- variant=variant,
- low_cpu_mem_usage=low_cpu_mem_usage,
- cached_folder=cached_folder,
- )
- logger.info(
- f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
- )
-
- init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
-
- if pipeline_class._load_connected_pipes and os.path.isfile(os.path.join(cached_folder, "README.md")):
- modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
- connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}
- load_kwargs = {
- "cache_dir": cache_dir,
- "resume_download": resume_download,
- "force_download": force_download,
- "proxies": proxies,
- "local_files_only": local_files_only,
- "use_auth_token": use_auth_token,
- "revision": revision,
- "torch_dtype": torch_dtype,
- "custom_pipeline": custom_pipeline,
- "custom_revision": custom_revision,
- "provider": provider,
- "sess_options": sess_options,
- "device_map": device_map,
- "max_memory": max_memory,
- "offload_folder": offload_folder,
- "offload_state_dict": offload_state_dict,
- "low_cpu_mem_usage": low_cpu_mem_usage,
- "variant": variant,
- "use_safetensors": use_safetensors,
- }
- connected_pipes = {
- prefix: DiffusionPipeline.from_pretrained(repo_id, **load_kwargs.copy())
- for prefix, repo_id in connected_pipes.items()
- if repo_id is not None
- }
-
- for prefix, connected_pipe in connected_pipes.items():
- # add connected pipes to `init_kwargs` with _, e.g. "prior_text_encoder"
- init_kwargs.update(
- {"_".join([prefix, name]): component for name, component in connected_pipe.components.items()}
- )
-
- # 7. Potentially add passed objects if expected
- missing_modules = set(expected_modules) - set(init_kwargs.keys())
- passed_modules = list(passed_class_obj.keys())
- optional_modules = pipeline_class._optional_components
- if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):
- for module in missing_modules:
- init_kwargs[module] = passed_class_obj.get(module, None)
- elif len(missing_modules) > 0:
- passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs
- raise ValueError(
- f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
- )
-
- # 8. Instantiate the pipeline
- model = pipeline_class(**init_kwargs)
-
- # 9. Save where the model was instantiated from
- model.register_to_config(_name_or_path=pretrained_model_name_or_path)
- return model
-
- @property
- def name_or_path(self) -> str:
- return getattr(self.config, "_name_or_path", None)
-
- @property
- def _execution_device(self):
- r"""
- Returns the device on which the pipeline's models will be executed. After calling
- [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from
- Accelerate's module hooks.
- """
- for name, model in self.components.items():
- if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload:
- continue
-
- if not hasattr(model, "_hf_hook"):
- return self.device
- for module in model.modules():
- if (
- hasattr(module, "_hf_hook")
- and hasattr(module._hf_hook, "execution_device")
- and module._hf_hook.execution_device is not None
- ):
- return torch.device(module._hf_hook.execution_device)
- return self.device
-
- def enable_sequential_cpu_offload(self, gpu_id: int = 0, device: Union[torch.device, str] = "cuda"):
- r"""
- Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
- text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
- `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
- Note that offloading happens on a submodule basis. Memory savings are higher than with
- `enable_model_cpu_offload`, but performance is lower.
- """
- if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
- from accelerate import cpu_offload
- else:
- raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
-
- if device == "cuda":
- device = torch.device(f"{device}:{gpu_id}")
-
- if self.device.type != "cpu":
- self.to("cpu", silence_dtype_warnings=True)
- device_mod = getattr(torch, self.device.type, None)
- if hasattr(device_mod, "empty_cache") and device_mod.is_available():
- device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
-
- for name, model in self.components.items():
- if not isinstance(model, torch.nn.Module):
- continue
-
- if name in self._exclude_from_cpu_offload:
- model.to(device)
- else:
- # make sure to offload buffers if not all high level weights
- # are of type nn.Module
- offload_buffers = len(model._parameters) > 0
- cpu_offload(model, device, offload_buffers=offload_buffers)
-
- @classmethod
- def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
- r"""
- Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights.
-
- Parameters:
- pretrained_model_name (`str` or `os.PathLike`, *optional*):
- A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
- hosted on the Hub.
- custom_pipeline (`str`, *optional*):
- Can be either:
-
- - A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained
- pipeline hosted on the Hub. The repository must contain a file called `pipeline.py` that defines
- the custom pipeline.
-
- - A string, the *file name* of a community pipeline hosted on GitHub under
- [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file
- names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`
- instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the
- current `main` branch of GitHub.
-
- - A path to a *directory* (`./my_pipeline_directory/`) containing a custom pipeline. The directory
- must contain a file called `pipeline.py` that defines the custom pipeline.
-
-
-
- 🧪 This is an experimental feature and may change in the future.
-
-
-
- For more information on how to load and create custom pipelines, take a look at [How to contribute a
- community pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/contribute_pipeline).
-
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
- incompletely downloaded files are deleted.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only (`bool`, *optional*, defaults to `False`):
- Whether to only load local model weights and configuration files or not. If set to `True`, the model
- won't be downloaded from the Hub.
- use_auth_token (`str` or *bool*, *optional*):
- The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
- `diffusers-cli login` (stored in `~/.huggingface`) is used.
- revision (`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
- allowed by Git.
- custom_revision (`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
- `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
- custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
- mirror (`str`, *optional*):
- Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
- guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
- information.
- variant (`str`, *optional*):
- Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
- loading `from_flax`.
- use_safetensors (`bool`, *optional*, defaults to `None`):
- If set to `None`, the safetensors weights are downloaded if they're available **and** if the
- safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
- weights. If set to `False`, safetensors weights are not loaded.
- use_onnx (`bool`, *optional*, defaults to `False`):
- If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
- will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
- `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
- with `.onnx` and `.pb`.
-
- Returns:
- `os.PathLike`:
- A path to the downloaded pipeline.
-
-
-
- To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
- `huggingface-cli login`.
-
-
-
- """
- cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
- resume_download = kwargs.pop("resume_download", False)
- force_download = kwargs.pop("force_download", False)
- proxies = kwargs.pop("proxies", None)
- local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
- use_auth_token = kwargs.pop("use_auth_token", None)
- revision = kwargs.pop("revision", None)
- from_flax = kwargs.pop("from_flax", False)
- custom_pipeline = kwargs.pop("custom_pipeline", None)
- custom_revision = kwargs.pop("custom_revision", None)
- variant = kwargs.pop("variant", None)
- use_safetensors = kwargs.pop("use_safetensors", None)
- use_onnx = kwargs.pop("use_onnx", None)
- load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
-
- if use_safetensors and not is_safetensors_available():
- raise ValueError(
- "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
- )
-
- allow_pickle = False
- if use_safetensors is None:
- use_safetensors = is_safetensors_available()
- allow_pickle = True
-
- allow_patterns = None
- ignore_patterns = None
-
- model_info_call_error: Optional[Exception] = None
- if not local_files_only:
- try:
- info = model_info(
- pretrained_model_name,
- use_auth_token=use_auth_token,
- revision=revision,
- )
- except HTTPError as e:
- logger.warn(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
- local_files_only = True
- model_info_call_error = e # save error to reraise it if model is not cached locally
-
- if not local_files_only:
- config_file = hf_hub_download(
- pretrained_model_name,
- cls.config_name,
- cache_dir=cache_dir,
- revision=revision,
- proxies=proxies,
- force_download=force_download,
- resume_download=resume_download,
- use_auth_token=use_auth_token,
- )
-
- config_dict = cls._dict_from_json_file(config_file)
-
- ignore_filenames = config_dict.pop("_ignore_files", [])
-
- # retrieve all folder_names that contain relevant files
- folder_names = [k for k, v in config_dict.items() if isinstance(v, list)]
-
- filenames = {sibling.rfilename for sibling in info.siblings}
- model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant)
-
- if len(variant_filenames) == 0 and variant is not None:
- deprecation_message = (
- f"You are trying to load the model files of the `variant={variant}`, but no such modeling files are available."
- f"The default model files: {model_filenames} will be loaded instead. Make sure to not load from `variant={variant}`"
- "if such variant modeling files are not available. Doing so will lead to an error in v0.22.0 as defaulting to non-variant"
- "modeling files is deprecated."
- )
- deprecate("no variant default", "0.22.0", deprecation_message, standard_warn=False)
-
- # remove ignored filenames
- model_filenames = set(model_filenames) - set(ignore_filenames)
- variant_filenames = set(variant_filenames) - set(ignore_filenames)
-
- # if the whole pipeline is cached we don't have to ping the Hub
- if revision in DEPRECATED_REVISION_ARGS and version.parse(
- version.parse(__version__).base_version
- ) >= version.parse("0.20.0"):
- warn_deprecated_model_variant(
- pretrained_model_name, use_auth_token, variant, revision, model_filenames
- )
-
- model_folder_names = {os.path.split(f)[0] for f in model_filenames if os.path.split(f)[0] in folder_names}
-
- # all filenames compatible with variant will be added
- allow_patterns = list(model_filenames)
-
- # allow all patterns from non-model folders
- # this enables downloading schedulers, tokenizers, ...
- allow_patterns += [f"{k}/*" for k in folder_names if k not in model_folder_names]
- # also allow downloading config.json files with the model
- allow_patterns += [os.path.join(k, "config.json") for k in model_folder_names]
-
- allow_patterns += [
- SCHEDULER_CONFIG_NAME,
- CONFIG_NAME,
- cls.config_name,
- CUSTOM_PIPELINE_FILE_NAME,
- ]
-
- # retrieve passed components that should not be downloaded
- pipeline_class = _get_pipeline_class(
- cls,
- config_dict,
- load_connected_pipeline=load_connected_pipeline,
- custom_pipeline=custom_pipeline,
- cache_dir=cache_dir,
- revision=custom_revision,
- )
- expected_components, _ = cls._get_signature_keys(pipeline_class)
- passed_components = [k for k in expected_components if k in kwargs]
-
- if (
- use_safetensors
- and not allow_pickle
- and not is_safetensors_compatible(
- model_filenames, variant=variant, passed_components=passed_components
- )
- ):
- raise EnvironmentError(
- f"Could not found the necessary `safetensors` weights in {model_filenames} (variant={variant})"
- )
- if from_flax:
- ignore_patterns = ["*.bin", "*.safetensors", "*.onnx", "*.pb"]
- elif use_safetensors and is_safetensors_compatible(
- model_filenames, variant=variant, passed_components=passed_components
- ):
- ignore_patterns = ["*.bin", "*.msgpack"]
-
- use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx
- if not use_onnx:
- ignore_patterns += ["*.onnx", "*.pb"]
-
- safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")}
- safetensors_model_filenames = {f for f in model_filenames if f.endswith(".safetensors")}
- if (
- len(safetensors_variant_filenames) > 0
- and safetensors_model_filenames != safetensors_variant_filenames
- ):
- logger.warn(
- f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(safetensors_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(safetensors_model_filenames - safetensors_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
- )
- else:
- ignore_patterns = ["*.safetensors", "*.msgpack"]
-
- use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx
- if not use_onnx:
- ignore_patterns += ["*.onnx", "*.pb"]
-
- bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")}
- bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")}
- if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames:
- logger.warn(
- f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
- )
-
- # Don't download any objects that are passed
- allow_patterns = [
- p for p in allow_patterns if not (len(p.split("/")) == 2 and p.split("/")[0] in passed_components)
- ]
-
- if pipeline_class._load_connected_pipes:
- allow_patterns.append("README.md")
-
- # Don't download index files of forbidden patterns either
- ignore_patterns = ignore_patterns + [f"{i}.index.*json" for i in ignore_patterns]
-
- re_ignore_pattern = [re.compile(fnmatch.translate(p)) for p in ignore_patterns]
- re_allow_pattern = [re.compile(fnmatch.translate(p)) for p in allow_patterns]
-
- expected_files = [f for f in filenames if not any(p.match(f) for p in re_ignore_pattern)]
- expected_files = [f for f in expected_files if any(p.match(f) for p in re_allow_pattern)]
-
- snapshot_folder = Path(config_file).parent
- pipeline_is_cached = all((snapshot_folder / f).is_file() for f in expected_files)
-
- if pipeline_is_cached and not force_download:
- # if the pipeline is cached, we can directly return it
- # else call snapshot_download
- return snapshot_folder
-
- user_agent = {"pipeline_class": cls.__name__}
- if custom_pipeline is not None and not custom_pipeline.endswith(".py"):
- user_agent["custom_pipeline"] = custom_pipeline
-
- # download all allow_patterns - ignore_patterns
- try:
- cached_folder = snapshot_download(
- pretrained_model_name,
- cache_dir=cache_dir,
- resume_download=resume_download,
- proxies=proxies,
- local_files_only=local_files_only,
- use_auth_token=use_auth_token,
- revision=revision,
- allow_patterns=allow_patterns,
- ignore_patterns=ignore_patterns,
- user_agent=user_agent,
- )
-
- # retrieve pipeline class from local file
- cls_name = cls.load_config(os.path.join(cached_folder, "model_index.json")).get("_class_name", None)
- pipeline_class = getattr(diffusers, cls_name, None)
-
- if pipeline_class is not None and pipeline_class._load_connected_pipes:
- modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
- connected_pipes = sum([getattr(modelcard.data, k, []) for k in CONNECTED_PIPES_KEYS], [])
- for connected_pipe_repo_id in connected_pipes:
- download_kwargs = {
- "cache_dir": cache_dir,
- "resume_download": resume_download,
- "force_download": force_download,
- "proxies": proxies,
- "local_files_only": local_files_only,
- "use_auth_token": use_auth_token,
- "variant": variant,
- "use_safetensors": use_safetensors,
- }
- DiffusionPipeline.download(connected_pipe_repo_id, **download_kwargs)
-
- return cached_folder
-
- except FileNotFoundError:
- # Means we tried to load pipeline with `local_files_only=True` but the files have not been found in local cache.
- # This can happen in two cases:
- # 1. If the user passed `local_files_only=True` => we raise the error directly
- # 2. If we forced `local_files_only=True` when `model_info` failed => we raise the initial error
- if model_info_call_error is None:
- # 1. user passed `local_files_only=True`
- raise
- else:
- # 2. we forced `local_files_only=True` when `model_info` failed
- raise EnvironmentError(
- f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occured"
- " while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace"
- " above."
- ) from model_info_call_error
-
- @staticmethod
- def _get_signature_keys(obj):
- parameters = inspect.signature(obj.__init__).parameters
- required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
- optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
- expected_modules = set(required_parameters.keys()) - {"self"}
- return expected_modules, optional_parameters
-
- @property
- def components(self) -> Dict[str, Any]:
- r"""
- The `self.components` property can be useful to run different pipelines with the same weights and
- configurations without reallocating additional memory.
-
- Returns (`dict`):
- A dictionary containing all the modules needed to initialize the pipeline.
-
- Examples:
-
- ```py
- >>> from diffusers import (
- ... StableDiffusionPipeline,
- ... StableDiffusionImg2ImgPipeline,
- ... StableDiffusionInpaintPipeline,
- ... )
-
- >>> text2img = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
- >>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
- >>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
- ```
- """
- expected_modules, optional_parameters = self._get_signature_keys(self)
- components = {
- k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters
- }
-
- if set(components.keys()) != expected_modules:
- raise ValueError(
- f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
- f" {expected_modules} to be defined, but {components.keys()} are defined."
- )
-
- return components
-
- @staticmethod
- def numpy_to_pil(images):
- """
- Convert a NumPy image or a batch of images to a PIL image.
- """
- return numpy_to_pil(images)
-
- def progress_bar(self, iterable=None, total=None):
- if not hasattr(self, "_progress_bar_config"):
- self._progress_bar_config = {}
- elif not isinstance(self._progress_bar_config, dict):
- raise ValueError(
- f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
- )
-
- if iterable is not None:
- return tqdm(iterable, **self._progress_bar_config)
- elif total is not None:
- return tqdm(total=total, **self._progress_bar_config)
- else:
- raise ValueError("Either `total` or `iterable` has to be defined.")
-
- def set_progress_bar_config(self, **kwargs):
- self._progress_bar_config = kwargs
-
- def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
- r"""
- Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this
- option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
- up during training is not guaranteed.
-
-
-
- ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
- precedent.
-
-
-
- Parameters:
- attention_op (`Callable`, *optional*):
- Override the default `None` operator for use as `op` argument to the
- [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
- function of xFormers.
-
- Examples:
-
- ```py
- >>> import torch
- >>> from diffusers import DiffusionPipeline
- >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
-
- >>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
- >>> pipe = pipe.to("cuda")
- >>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
- >>> # Workaround for not accepting attention shape using VAE for Flash Attention
- >>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
- ```
- """
- self.set_use_memory_efficient_attention_xformers(True, attention_op)
-
- def disable_xformers_memory_efficient_attention(self):
- r"""
- Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
- """
- self.set_use_memory_efficient_attention_xformers(False)
-
- def set_use_memory_efficient_attention_xformers(
- self, valid: bool, attention_op: Optional[Callable] = None
- ) -> None:
- # Recursively walk through all the children.
- # Any children which exposes the set_use_memory_efficient_attention_xformers method
- # gets the message
- def fn_recursive_set_mem_eff(module: torch.nn.Module):
- if hasattr(module, "set_use_memory_efficient_attention_xformers"):
- module.set_use_memory_efficient_attention_xformers(valid, attention_op)
-
- for child in module.children():
- fn_recursive_set_mem_eff(child)
-
- module_names, _ = self._get_signature_keys(self)
- modules = [getattr(self, n, None) for n in module_names]
- modules = [m for m in modules if isinstance(m, torch.nn.Module)]
-
- for module in modules:
- fn_recursive_set_mem_eff(module)
-
- def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
- r"""
- Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
- in slices to compute attention in several steps. This is useful to save some memory in exchange for a small
- speed decrease.
-
- Args:
- slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
- When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
- `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
- provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
- must be a multiple of `slice_size`.
- """
- self.set_attention_slice(slice_size)
-
- def disable_attention_slicing(self):
- r"""
- Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is
- computed in one step.
- """
- # set slice_size = `None` to disable `attention slicing`
- self.enable_attention_slicing(None)
-
- def set_attention_slice(self, slice_size: Optional[int]):
- module_names, _ = self._get_signature_keys(self)
- modules = [getattr(self, n, None) for n in module_names]
- modules = [m for m in modules if isinstance(m, torch.nn.Module) and hasattr(m, "set_attention_slice")]
-
- for module in modules:
- module.set_attention_slice(slice_size)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/spectrogram_diffusion/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/spectrogram_diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/_base_/datasets/coco_detection.py b/spaces/Andy1621/uniformer_image_detection/configs/_base_/datasets/coco_detection.py
deleted file mode 100644
index 09a75c404687223c71dcdf0abc7af827f2e498a6..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/_base_/datasets/coco_detection.py
+++ /dev/null
@@ -1,48 +0,0 @@
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline))
-evaluation = dict(interval=1, metric='bbox')
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index 59508248490b3edbac1c46b4fcc7891f99655b9b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './ann_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
deleted file mode 100644
index 7f6795e5ef0e4bf1d10ee7ed4f608bf93ac24216..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './psanet_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Artgor/digit-draw-detect/src/utils.py b/spaces/Artgor/digit-draw-detect/src/utils.py
deleted file mode 100644
index db5636edd9a5dc324bd33acca3b46453dbb75072..0000000000000000000000000000000000000000
--- a/spaces/Artgor/digit-draw-detect/src/utils.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import datetime
-import json
-import os
-import uuid
-from typing import List
-
-import boto3
-import matplotlib
-import matplotlib.patches as patches
-import matplotlib.pyplot as plt
-import numpy.typing as npt
-import streamlit as st
-import tomli
-
-AWS_ACCESS_KEY_ID = ''
-AWS_SECRET_ACCESS_KEY = ''
-try:
- if st.secrets is not None:
- AWS_ACCESS_KEY_ID = st.secrets['AWS_ACCESS_KEY_ID']
- AWS_SECRET_ACCESS_KEY = st.secrets['AWS_SECRET_ACCESS_KEY']
-except BaseException:
- pass
-
-if os.path.exists('config.toml'):
- with open('config.toml', 'rb') as f:
- config = tomli.load(f)
- AWS_ACCESS_KEY_ID = config['AWS_ACCESS_KEY_ID']
- AWS_SECRET_ACCESS_KEY = config['AWS_SECRET_ACCESS_KEY']
-
-client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
-
-
-def plot_img_with_rects(
- img: npt.ArrayLike, boxes: List[List], threshold: float = 0.5, coef: int = 400
-) -> matplotlib.figure.Figure:
- """
- Plot image with rectangles.
-
- Args:
- img: image as a numpy array
- boxes: the list of the bboxes
- threshold: threshold for bbox probability
- coef: coefficient to multiply images. Can be changed when the original image is a different size
-
- Returns:
- image with bboxes
- """
- fig, ax = plt.subplots(1, figsize=(4, 4))
-
- # Display the image
- ax.imshow(img)
-
- # Create a Rectangle patch
- for _, rect in enumerate(b for b in boxes if b[1] > threshold):
- label, _, xc, yc, w, h = rect
- xc, yc, w, h = xc * coef, yc * coef, w * coef, h * coef
- # the coordinates from center-based to left top corner
- x = xc - w / 2
- y = yc - h / 2
- label = int(label)
- label = label if label != 10 else 'censored'
- label = label if label != 11 else 'other'
- rect = [x, y, x + w, y + h]
-
- rect_ = patches.Rectangle(
- (rect[0], rect[1]), rect[2] - rect[0], rect[3] - rect[1], linewidth=2, edgecolor='blue', facecolor='none'
- )
- plt.text(rect[2], rect[1], f'{label}', color='blue')
- # Add the patch to the Axes
- ax.add_patch(rect_)
- return fig
-
-
-def save_object_to_s3(filename, s3_filename):
- client.upload_file(filename, 'digitdrawdetect', s3_filename)
-
-
-@st.cache_data(show_spinner=False)
-def save_image(image: npt.ArrayLike, pred: List[List]) -> str:
- """
- Save the image and upload the image with bboxes to s3.
-
- Args:
- image: np.array with image
- pred: bboxes
-
- Returns:
- image name
-
- """
- # create a figure and save it
- fig, ax = plt.subplots(1, figsize=(4, 4))
- ax.imshow(image)
- file_name = str(datetime.datetime.today().date()) + str(uuid.uuid1())
- fig.savefig(f'{file_name}.png')
-
- # dump bboxes in a local file
- with open(f'{file_name}.json', 'w') as j_f:
- json.dump({f'{file_name}.png': pred}, j_f)
-
- # upload the image and the bboxes to s3.
- save_object_to_s3(f'{file_name}.png', f'images/{file_name}.png')
- save_object_to_s3(f'{file_name}.json', f'labels/{file_name}.json')
-
- return file_name
diff --git a/spaces/Artrajz/vits-simple-api/utils/nlp.py b/spaces/Artrajz/vits-simple-api/utils/nlp.py
deleted file mode 100644
index be03d8849e8badde58dffbfa063eff97fcbba34e..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/utils/nlp.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import regex as re
-import config
-from .utils import check_is_none
-from logger import logger
-
-# 读取配置选择语种识别库
-clf = getattr(config, "LANGUAGE_IDENTIFICATION_LIBRARY", "fastlid")
-
-
-def clasify_lang(text, speaker_lang):
- pattern = r'[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`' \
- r'\！？｡＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」' \
- r'『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+'
- words = re.split(pattern, text)
-
- pre = ""
- p = 0
-
- if clf.upper() == "FASTLID" or clf.upper() == "FASTTEXT":
- from fastlid import fastlid
- detect = fastlid
- if speaker_lang != None: fastlid.set_languages = speaker_lang
- elif clf.upper() == "LANGID":
- import langid
- detect = langid.classify
- if speaker_lang != None: langid.set_languages(speaker_lang)
- else:
- raise ValueError(f"Wrong LANGUAGE_IDENTIFICATION_LIBRARY in config.py")
-
- for word in words:
-
- if check_is_none(word): continue
-
- lang = detect(word)[0]
-
- if pre == "":
- text = text[:p] + text[p:].replace(word, f'[{lang.upper()}]' + word, 1)
- p += len(f'[{lang.upper()}]')
- elif pre != lang:
- text = text[:p] + text[p:].replace(word, f'[{pre.upper()}][{lang.upper()}]' + word, 1)
- p += len(f'[{pre.upper()}][{lang.upper()}]')
- pre = lang
- p += text[p:].index(word) + len(word)
- text += f"[{pre.upper()}]"
-
- return text
-
-
-def cut(text, max):
- pattern = r'[!(),—+\-.:;?？。，、；：]+'
- sentences = re.split(pattern, text)
- discarded_chars = re.findall(pattern, text)
-
- sentence_list, count, p = [], 0, 0
-
- # 按被分割的符号遍历
- for i, discarded_chars in enumerate(discarded_chars):
- count += len(sentences[i]) + len(discarded_chars)
- if count >= max:
- sentence_list.append(text[p:p + count].strip())
- p += count
- count = 0
-
- # 加入最后剩余的文本
- if p < len(text):
- sentence_list.append(text[p:])
-
- return sentence_list
-
-
-def sentence_split(text, max=50, lang="auto", speaker_lang=None):
- # 如果该speaker只支持一种语言
- if speaker_lang is not None and len(speaker_lang) == 1:
- if lang.upper() not in ["AUTO", "MIX"] and lang.lower() != speaker_lang[0]:
- logger.debug(
- f"lang \"{lang}\" is not in speaker_lang {speaker_lang},automatically set lang={speaker_lang[0]}")
- lang = speaker_lang[0]
-
- sentence_list = []
- if lang.upper() != "MIX":
- if max <= 0:
- sentence_list.append(
- clasify_lang(text,
- speaker_lang) if lang.upper() == "AUTO" else f"[{lang.upper()}]{text}[{lang.upper()}]")
- else:
- for i in cut(text, max):
- if check_is_none(i): continue
- sentence_list.append(
- clasify_lang(i,
- speaker_lang) if lang.upper() == "AUTO" else f"[{lang.upper()}]{i}[{lang.upper()}]")
- else:
- sentence_list.append(text)
-
- for i in sentence_list:
- logger.debug(i)
-
- return sentence_list
diff --git a/spaces/Ashrafb/Tesseract-OCR/README.md b/spaces/Ashrafb/Tesseract-OCR/README.md
deleted file mode 100644
index 6b953bf93788eae3ca019ec6eeac6d044165a424..0000000000000000000000000000000000000000
--- a/spaces/Ashrafb/Tesseract-OCR/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Tesseract OCR
-emoji: 🐢
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app_blocks.py
-pinned: false
-duplicated_from: kneelesh48/Tesseract-OCR
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/enums.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/enums.py
deleted file mode 100644
index 5e3e198233698f2b007489dd299cecb87d971067..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/enums.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""
-All of the Enums that are used throughout the chardet package.
-
-:author: Dan Blanchard (dan.blanchard@gmail.com)
-"""
-
-from enum import Enum, Flag
-
-
-class InputState:
- """
- This enum represents the different states a universal detector can be in.
- """
-
- PURE_ASCII = 0
- ESC_ASCII = 1
- HIGH_BYTE = 2
-
-
-class LanguageFilter(Flag):
- """
- This enum represents the different language filters we can apply to a
- ``UniversalDetector``.
- """
-
- NONE = 0x00
- CHINESE_SIMPLIFIED = 0x01
- CHINESE_TRADITIONAL = 0x02
- JAPANESE = 0x04
- KOREAN = 0x08
- NON_CJK = 0x10
- ALL = 0x1F
- CHINESE = CHINESE_SIMPLIFIED | CHINESE_TRADITIONAL
- CJK = CHINESE | JAPANESE | KOREAN
-
-
-class ProbingState(Enum):
- """
- This enum represents the different states a prober can be in.
- """
-
- DETECTING = 0
- FOUND_IT = 1
- NOT_ME = 2
-
-
-class MachineState:
- """
- This enum represents the different states a state machine can be in.
- """
-
- START = 0
- ERROR = 1
- ITS_ME = 2
-
-
-class SequenceLikelihood:
- """
- This enum represents the likelihood of a character following the previous one.
- """
-
- NEGATIVE = 0
- UNLIKELY = 1
- LIKELY = 2
- POSITIVE = 3
-
- @classmethod
- def get_num_categories(cls) -> int:
- """:returns: The number of likelihood categories in the enum."""
- return 4
-
-
-class CharacterCategory:
- """
- This enum represents the different categories language models for
- ``SingleByteCharsetProber`` put characters into.
-
- Anything less than CONTROL is considered a letter.
- """
-
- UNDEFINED = 255
- LINE_BREAK = 254
- SYMBOL = 253
- DIGIT = 252
- CONTROL = 251
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/mbcsgroupprober.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/mbcsgroupprober.py
deleted file mode 100644
index 6cb9cc7b3bc751fbb5a54ba06eaaf953bf14ed8d..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/mbcsgroupprober.py
+++ /dev/null
@@ -1,57 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-# Mark Pilgrim - port to Python
-# Shy Shalom - original C code
-# Proofpoint, Inc.
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301 USA
-######################### END LICENSE BLOCK #########################
-
-from .big5prober import Big5Prober
-from .charsetgroupprober import CharSetGroupProber
-from .cp949prober import CP949Prober
-from .enums import LanguageFilter
-from .eucjpprober import EUCJPProber
-from .euckrprober import EUCKRProber
-from .euctwprober import EUCTWProber
-from .gb2312prober import GB2312Prober
-from .johabprober import JOHABProber
-from .sjisprober import SJISProber
-from .utf8prober import UTF8Prober
-
-
-class MBCSGroupProber(CharSetGroupProber):
- def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None:
- super().__init__(lang_filter=lang_filter)
- self.probers = [
- UTF8Prober(),
- SJISProber(),
- EUCJPProber(),
- GB2312Prober(),
- EUCKRProber(),
- CP949Prober(),
- Big5Prober(),
- EUCTWProber(),
- JOHABProber(),
- ]
- self.reset()
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distro/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distro/__init__.py
deleted file mode 100644
index 7686fe85a7cc94188da76bfb1c10ad2a10821256..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distro/__init__.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from .distro import (
- NORMALIZED_DISTRO_ID,
- NORMALIZED_LSB_ID,
- NORMALIZED_OS_ID,
- LinuxDistribution,
- __version__,
- build_number,
- codename,
- distro_release_attr,
- distro_release_info,
- id,
- info,
- like,
- linux_distribution,
- lsb_release_attr,
- lsb_release_info,
- major_version,
- minor_version,
- name,
- os_release_attr,
- os_release_info,
- uname_attr,
- uname_info,
- version,
- version_parts,
-)
-
-__all__ = [
- "NORMALIZED_DISTRO_ID",
- "NORMALIZED_LSB_ID",
- "NORMALIZED_OS_ID",
- "LinuxDistribution",
- "build_number",
- "codename",
- "distro_release_attr",
- "distro_release_info",
- "id",
- "info",
- "like",
- "linux_distribution",
- "lsb_release_attr",
- "lsb_release_info",
- "major_version",
- "minor_version",
- "name",
- "os_release_attr",
- "os_release_info",
- "uname_attr",
- "uname_info",
- "version",
- "version_parts",
-]
-
-__version__ = __version__
diff --git a/spaces/Bart92/RVC_HF/Applio-RVC-Fork/utils/README.md b/spaces/Bart92/RVC_HF/Applio-RVC-Fork/utils/README.md
deleted file mode 100644
index fb45a36b5909585aa964f2033762ee59b55526b0..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/Applio-RVC-Fork/utils/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-# External Colab Code
-Code used to make Google Colab work correctly
-- Repo link: https://github.com/IAHispano/Applio-RVC-Fork/
-
-Thanks to https://github.com/kalomaze/externalcolabcode
-
diff --git a/spaces/Benson/text-generation/Examples/60 Segundos Reatomized Apk Descargar Gratis Android.md b/spaces/Benson/text-generation/Examples/60 Segundos Reatomized Apk Descargar Gratis Android.md
deleted file mode 100644
index 4a31d8f6197d24dfde020226f4a31cbf19e8ff2c..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/60 Segundos Reatomized Apk Descargar Gratis Android.md
+++ /dev/null
@@ -1,75 +0,0 @@
-
-
60 segundos reatomized APK: Cómo descargar y jugar en Android
-
Si usted está buscando un juego de supervivencia divertido y desafiante que pondrá a prueba sus habilidades y la toma de decisiones, es posible que desee echa un vistazo 60 Segundos Reatomized. Esta es una versión remasterizada del juego original de 60 segundos, que fue lanzado en 2015. En este juego, tienes que buscar provisiones, rescatar a tu familia y permanecer vivo en tu refugio radioactivo después de un ataque nuclear. El juego cuenta con gráficos mejorados, nuevo contenido y más formas de escapar del páramo. Pero, ¿cómo puedes jugar a este juego en tu dispositivo Android? En este artículo, le mostraremos cómo descargar e instalar el archivo APK reatomizado 60 segundos, y cómo jugar el juego en su teléfono o tableta.
60 Seconds Reatomized es un juego de comedia oscura post-apocalíptica desarrollado por Robot Gentleman. El juego se divide en dos fases: carroña y supervivencia. En la fase de búsqueda, tienes 60 segundos para agarrar tantos artículos y miembros de la familia como puedas de tu casa antes de que las bombas caigan. Tienes que ser rápido e inteligente, ya que todo estará en tu contra: el tiempo, tus muebles y un diseño de casa generado al azar. En la fase de supervivencia, tienes que manejar tus recursos, lidiar con eventos inesperados y tomar decisiones difíciles en tu refugio contra las consecuencias. También puede aventurarse en el páramo para buscar más suministros u oportunidades para escapar. El juego tiene múltiples finales dependiendo de tus acciones y suerte.
-
60 Seconds Reatomized tiene varias características que lo hacen diferente del juego original. Estas incluyen:
-
-
Nuevo modo de juego: Desafíos de supervivencia. Estas son historias cortas que pondrán a prueba tus habilidades de supervivencia.
-
Nuevas oportunidades para escapar de la tierra baldía en forma de una historia que abarca múltiples partidas.
-
Nuevo sistema de relaciones: más historias e interacciones locas entre los miembros de la familia McDoodle.
-
-
Nuevos logros: ponte a prueba y demuestra tus habilidades.
-
-
Cómo descargar 60 segundos reatomized APK para Android
-
Desafortunadamente, 60 segundos Reatomized no está disponible en Google Play Store. Sin embargo, todavía puede descargar e instalar el archivo APK desde otras fuentes. Un archivo APK es un paquete que contiene todos los archivos necesarios para ejecutar una aplicación Android. Sin embargo, usted tiene que tener cuidado acerca de dónde descargar archivos APK de, como algunos sitios pueden contener malware o virus. Solo descarga archivos APK de fuentes confiables que monitorean los archivos que alojan.
-
Uno de los sitios más populares para descargar archivos APK es APK Mirror. Este sitio alberga un montón de aplicaciones de Android que se pueden instalar individualmente o como actualizaciones. También verifica los archivos que aloja para asegurarse de que son seguros y auténticos. Aquí están los pasos para descargar 60 Segundos reatomized APK de APK Mirror:
-
-
Ir a APK Mirror en el navegador de su dispositivo Android.
-
Buscar "60 Seconds Reatomized" en la barra de búsqueda.
-
Seleccione la última versión de la aplicación de la lista de resultados.
-
Desplácese hacia abajo y toque en "Descargar APK" botón.
-
Aceptar cualquier ventana emergente o permisos que puedan aparecer.
-
Espera a que termine la descarga.
-
-
Antes de poder instalar el archivo APK, debe habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, siga estos pasos:
-
-
-
Ir a la configuración de su dispositivo y toque en "Seguridad".
-
Encuentra la opción que dice "Fuentes desconocidas" y cámbiala.
-
Confirme cualquier advertencia que pueda aparecer.
-
-
Ahora está listo para instalar el archivo APK. Para hacer esto, siga estos pasos:
-
-
Ir al administrador de archivos de su dispositivo y localizar el archivo APK descargado.
-
Toque en el archivo y seleccione "Instalar".
-
Espere a que termine la instalación.
-
-
-
Felicidades! Usted ha descargado e instalado con éxito 60 Segundos reatomized APK en su dispositivo Android. Ahora puedes disfrutar del juego y sus características.
-
Cómo jugar 60 segundos Reatomized en Android
-
60 Seconds Reatomized es un juego que desafiará tus habilidades de supervivencia y toma de decisiones. El juego tiene cuatro modos diferentes: Atomic Drill, Apocalypse, Scavenge y Survival. Aquí hay un breve resumen de cada modo y algunos consejos sobre cómo jugarlos:
-
Taladro atómico
-
Este es el modo tutorial del juego. Te enseñará lo básico de la fase de búsqueda, como cómo mover, agarrar objetos y dejarlos en el refugio. También puede practicar sus habilidades en diferentes escenarios y diseños de casas. Este modo se recomienda para principiantes que quieren aprender las cuerdas antes de saltar a la acción real.
-
Apocalipsis
-
Este es el modo principal del juego. Combina las fases de búsqueda y supervivencia. Tienes que buscar provisiones y miembros de la familia en 60 segundos, luego administrar tu refugio radioactivo durante el mayor tiempo posible. Puedes elegir entre tres niveles de dificultad: Little Boy, Fat Man y Tsar Bomba. Cuanto mayor sea la dificultad, más difícil será encontrar objetos útiles, lidiar con los eventos y escapar del páramo. Este modo se recomienda para los jugadores que quieren experimentar la historia completa y el desafío del juego.
-
Carroña
-
Este es un modo que se centra solo en la fase de búsqueda. Usted puede elegir entre diferentes escenarios y diseños de la casa, y tratar de agarrar tantos elementos y miembros de la familia como sea posible en 60 segundos. También puede personalizar su propio escenario eligiendo los elementos, los miembros de la familia y el diseño de la casa. Este modo se recomienda para jugadores que quieran practicar sus habilidades de búsqueda o divertirse con diferentes combinaciones.
-
Supervivencia
-
-
Consejos sobre cómo jugar 60 segundos reatomized en Android
-
Aquí hay algunos consejos generales que le ayudarán a jugar 60 segundos reatomized en Android:
-
-
Planifique con anticipación: Antes de empezar a buscar basura, eche un vistazo al diseño de su casa y decida qué artículos y miembros de la familia desea agarrar. Priorice alimentos, agua, radio, botiquín, máscara de gas, mapa, hacha, rifle, maleta y miembros de la familia.
-
Sé rápido: solo tienes 60 segundos para buscar, así que no pierdas tiempo en acciones o elementos innecesarios. Usa ambas manos para agarrar objetos más rápido y déjalos cerca de la entrada del refugio para facilitar el acceso.
-
Sé inteligente: Tienes que tomar decisiones difíciles en ambas fases del juego. Piense cuidadosamente sobre qué artículos necesita, a qué eventos quiere responder, qué riesgos quiere tomar y qué consecuencias está dispuesto a enfrentar.
-
Sé flexible: El juego es impredecible y aleatorio. Nunca se sabe lo que sucederá a continuación o qué elementos se encuentran. Esté preparado para adaptarse a diferentes situaciones y resultados.
-
Diviértete: El juego está destinado a ser una comedia oscura que se burla de lo absurdo de la guerra nuclear. No te lo tomes demasiado en serio ni te frustres si las cosas salen mal. Disfruta del humor, las referencias y las sorpresas que ofrece el juego.
-
-
Conclusión
-
-
Preguntas frecuentes
-
Aquí están algunas de las preguntas y respuestas más frecuentes sobre 60 segundos Reatomized:
-
¿Son 60 segundos tratados libremente?
-
No, 60 Segundos Reatomized no es un juego gratuito. Es un juego de pago que cuesta $3.99 en Steam y $1.99 en APK Mirror. Sin embargo, puedes descargar el archivo APK gratis de APK Mirror si quieres probar el juego en tu dispositivo Android.
-
¿Es seguro el tratamiento de 60 segundos?
-
Sí, 60 segundos Reatomized es seguro para jugar en su dispositivo Android. El archivo APK de APK Mirror es verificado y auténtico, y no contiene ningún malware o virus. Sin embargo, siempre debes tener cuidado con la descarga de archivos APK de otras fuentes, ya que pueden ser dañinos o falsos.
-
¿Es el multijugador reatomizado 60 segundos?
-
No, 60 segundos Reatomized no es un juego multijugador. Es un juego para un solo jugador que solo se puede jugar sin conexión. Sin embargo, puedes compartir tus logros y capturas de pantalla con tus amigos en línea.
-
¿Es 60 segundos Reatomized compatible con mi dispositivo?
-
60 segundos Reatomized requiere Android 4.1 o superior para ejecutarse en su dispositivo. También requiere al menos 1 GB de RAM y 500 MB de espacio de almacenamiento. Puede comprobar las especificaciones de su dispositivo en el menú de configuración.
-
¿Cómo puedo contactar a los desarrolladores de 60 Seconds Reatomized?
-
Si tienes preguntas, comentarios o problemas con el juego, puedes contactar a los desarrolladores de 60 Seconds Reatomized enviándolos un correo electrónico a support@robotgentleman.com. También puede visitar su sitio web en Robot Gentleman o seguirlos en plataformas de redes sociales como Facebook, Twitter, Instagram y YouTube.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar El Juego De Ftbol Apk.md b/spaces/Benson/text-generation/Examples/Descargar El Juego De Ftbol Apk.md
deleted file mode 100644
index eb2c8a457916d91c14e0f89b6cf38461df7b04df..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar El Juego De Ftbol Apk.md
+++ /dev/null
@@ -1,79 +0,0 @@
-
-
Vive le Football: Un juego gratuito de gestión de fútbol móvil para Android
-
Si eres un fanático del fútbol (o del fútbol, como algunos lo llaman), quizás te interese probar un nuevo juego móvil que te permita administrar tu propio club y competir con otros jugadores en línea. El juego se llama Vive le Football, y está desarrollado por NetEase, una empresa china que también creó juegos populares como Rules of Survival and Identity V. En este artículo, te diremos qué es Vive le Football, cómo descargarlo e instalarlo en tu dispositivo Android, por qué usted debe jugar, y algunos consejos y trucos para ayudarle a tener éxito en el juego.
Vive le Football es un juego de gestión de fútbol móvil gratuito que fue lanzado en junio de 2021. El juego le permite crear su propio club, personalizar sus jugadores, estadio, logotipo y kits, y competir con otros clubes en varios modos. También puedes participar en torneos, ligas, copas y partidos amistosos con otros jugadores de todo el mundo. El juego cuenta con gráficos realistas, física y animaciones, así como un sistema de clima dinámico que afecta el juego. También puedes chatear con otros jugadores y unirte a clubes para cooperar y socializar.
-
Características de Vive le Football
-
Algunas de las características principales de Vive le Football son:
-
-
Puede elegir entre más de 100 clubes con licencia de diferentes países y regiones, o crear su propio club desde cero.
-
Puedes personalizar la apariencia, habilidades, atributos, posiciones y tácticas de tus jugadores.
-
Puede actualizar su estadio, instalaciones, personal y equipos para mejorar el rendimiento y los ingresos de su club.
-
Puedes jugar en varios modos, como el modo carrera, donde empiezas desde abajo y trabajas hasta arriba; modo desafío, donde te enfrentas a diferentes escenarios y objetivos; y modo online, donde compites con otros jugadores en partidos en tiempo real.
-
-
Puedes disfrutar de gráficos realistas, física y animaciones que hacen que el juego sea más inmersivo y divertido. También puede experimentar diferentes condiciones climáticas, como lluvia, nieve, niebla y viento.
-
-
Cómo descargar e instalar Vive le Football APK en Android
-
Si quieres jugar Vive le Football en tu dispositivo Android, tendrás que descargar e instalar el archivo APK del juego. Un archivo APK es un archivo de paquete que contiene los archivos de instalación de una aplicación Android. Puede descargar el archivo APK de Vive le Football de varias fuentes en línea, como Filehippo.com. Sin embargo, antes de instalar el archivo APK, tendrá que habilitar la opción de instalar aplicaciones de fuentes desconocidas en su dispositivo. Para hacer esto, siga estos pasos:
-
-
-
Ir a Configuración > Seguridad > Fuentes desconocidas y activarlo.
-
Vaya a la ubicación donde descargó el archivo APK de Vive le Football y toque en él.
-
Siga las instrucciones en la pantalla para instalar la aplicación.
-
Una vez completada la instalación, puede iniciar la aplicación desde el cajón de la aplicación o la pantalla de inicio.
-
-
Nota: Instalar aplicaciones de fuentes desconocidas puede plantear algunos riesgos para la seguridad y el rendimiento de su dispositivo. Asegúrate de descargar solo archivos APK de fuentes confiables y escanearlos en busca de virus o malware antes de instalarlos.
-
¿Por qué jugar Vive le Football?
-
Vive le Football es un juego que atraerá a los aficionados al fútbol que quieren experimentar la emoción de administrar su propio club y jugar contra otros jugadores en línea. El juego ofrece muchas características y opciones que lo hacen divertido y atractivo. Estas son algunas razones por las que deberías jugar a Vive le Football:
-
Pros y contras de Vive le Football
-
Como cualquier otro juego, Vive le Football tiene sus pros y sus contras. Aquí están algunos de ellos:
-
-
-
Pros
-
Contras
-
-
-
Puedes crear y personalizar tu propio club y jugadores.
-
-
-
-
Puedes jugar en varios modos y competir con otros jugadores en línea.
-
Usted puede encontrar algunos errores y fallos en el juego.
-
-
-
Puedes disfrutar de gráficos realistas, física y animaciones.
-
Es posible que necesite un dispositivo de alta gama para ejecutar el juego sin problemas.
-
-
-
Puedes chatear con otros jugadores y unirte a clubes para cooperar y socializar.
-
Usted puede encontrar algunos jugadores tóxicos o groseros en el chat.
-
-
-
Consejos y trucos para jugar Vive le Football
-
Si quieres mejorar tus habilidades y rendimiento en Vive le Football, aquí tienes algunos consejos y trucos que puedes utilizar:
-
-
Elija un club que se adapte a su estilo de juego y preferencias. Puede seleccionar entre más de 100 clubes con licencia o crear su propio club desde cero. Cada club tiene sus propias fortalezas y debilidades, así que elige sabiamente.
-
Actualice su estadio, instalaciones, personal y equipo con regularidad. Esto le ayudará a aumentar el rendimiento y los ingresos de su club. También puede desbloquear nuevas características y elementos mediante la actualización de su nivel de club.
-
Entrena a tus jugadores y ajusta sus habilidades, atributos, posiciones y tácticas. Puedes personalizar la apariencia, habilidades, atributos, posiciones y tácticas de tus jugadores según tu estrategia. También puedes entrenar a tus jugadores para mejorar sus habilidades y potencial.
-
Juega en diferentes modos y desafíos para ganar recompensas y experiencia. Puedes jugar en modo carrera, modo desafío, modo online, torneos, ligas, copas y partidos amistosos. Cada modo tiene sus propios objetivos y recompensas que puedes usar para mejorar tu club.
-
Controla a tus jugadores en el campo usando botones de pantalla táctil o un joystick virtual. También puede cambiar entre diferentes ángulos de cámara y niveles de zoom para obtener una mejor vista de la acción. También puedes usar gestos para realizar acciones como pasar, disparar, abordar, regatear, etc.
-
-
-
Conclusión
-
Vive le Football es un juego de gestión de fútbol móvil gratuito que te permite crear tu propio club y competir con otros jugadores en línea. El juego cuenta con gráficos realistas, física y animaciones, así como un sistema de clima dinámico que afecta el juego. También puedes chatear con otros jugadores y unirte a clubes para cooperar y socializar. Si quieres jugar Vive le Football en tu dispositivo Android, tendrás que descargar e instalar el archivo APK del juego desde una fuente de confianza. También puedes utilizar algunos consejos y trucos para mejorar tus habilidades y rendimiento en el juego. Vive le Football es un juego que atraerá a los aficionados al fútbol que quieren experimentar la emoción de administrar su propio club y jugar contra otros jugadores en línea.
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Vive le Football:
-
-
¿Vive le Football es gratis?
-
Sí, Vive le Football es gratis. Sin embargo, algunas características y artículos pueden requerir dinero real para desbloquear o comprar.
-
¿Vive le Football está disponible para dispositivos iOS?
-
No, Vive le Football actualmente solo está disponible para dispositivos Android. No hay información oficial sobre si el juego será lanzado para dispositivos iOS en el futuro.
-
¿Cómo puedo contactar a los desarrolladores de Vive le Football?
-
Puede ponerse en contacto con los desarrolladores de Vive le Football enviando un correo electrónico a vlf@service.netease.com o visitando su sitio web oficial. También puedes seguirlos en Facebook o Twitter para actualizaciones y noticias sobre el juego.
-
¿Cómo puedo reportar un error o un problema en Vive le Football?
-
Puede reportar un error o un problema en Vive le Football tocando el icono de configuración en la esquina superior derecha de la pantalla, luego tocando en la retroalimentación, luego tocando en el informe de error. También puede enviar un correo electrónico a vlf@service.netease.com con una captura de pantalla o un vídeo del error o problema.
-
-
Puedes jugar con tus amigos en Vive le Football agregándolos como amigos en el juego. Puedes hacer esto tocando en el icono de amigos en la esquina inferior izquierda de la pantalla, luego tocando en agregar amigo, luego ingresando su nombre de usuario o ID. También puede invitarlos a unirse a su club o jugar un partido amistoso con ellos. También puede chatear con ellos en el juego o enviarles regalos.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/BetterAPI/BetterChat/src/lib/utils/concatUint8Arrays.ts b/spaces/BetterAPI/BetterChat/src/lib/utils/concatUint8Arrays.ts
deleted file mode 100644
index e53396eca7e3dee20a543fb6ac28ecf48c7e3965..0000000000000000000000000000000000000000
--- a/spaces/BetterAPI/BetterChat/src/lib/utils/concatUint8Arrays.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { sum } from "./sum";
-
-export function concatUint8Arrays(arrays: Uint8Array[]): Uint8Array {
- const totalLength = sum(arrays.map((a) => a.length));
- const result = new Uint8Array(totalLength);
- let offset = 0;
- for (const array of arrays) {
- result.set(array, offset);
- offset += array.length;
- }
- return result;
-}
diff --git a/spaces/BiTransSciencia/www/README.md b/spaces/BiTransSciencia/www/README.md
deleted file mode 100644
index 1ae2d53bd64f281ac01566489ff5a37c8f34bbff..0000000000000000000000000000000000000000
--- a/spaces/BiTransSciencia/www/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Www
-emoji: 🐨
-colorFrom: blue
-colorTo: green
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/throttling.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/throttling.py
deleted file mode 100644
index 34ab417299767553718f9070dcf8fecad4dbe551..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/throttling.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from collections import namedtuple
-
-CubicParams = namedtuple('CubicParams', ['w_max', 'k', 'last_fail'])
-
-
-class CubicCalculator:
- _SCALE_CONSTANT = 0.4
- _BETA = 0.7
-
- def __init__(
- self,
- starting_max_rate,
- start_time,
- scale_constant=_SCALE_CONSTANT,
- beta=_BETA,
- ):
- self._w_max = starting_max_rate
- self._scale_constant = scale_constant
- self._beta = beta
- self._k = self._calculate_zero_point()
- self._last_fail = start_time
-
- def _calculate_zero_point(self):
- scaled_value = (self._w_max * (1 - self._beta)) / self._scale_constant
- k = scaled_value ** (1 / 3.0)
- return k
-
- def success_received(self, timestamp):
- dt = timestamp - self._last_fail
- new_rate = self._scale_constant * (dt - self._k) ** 3 + self._w_max
- return new_rate
-
- def error_received(self, current_rate, timestamp):
- # Consider not having this be the current measured rate.
-
- # We have a new max rate, which is the current rate we were sending
- # at when we received an error response.
- self._w_max = current_rate
- self._k = self._calculate_zero_point()
- self._last_fail = timestamp
- return current_rate * self._beta
-
- def get_params_snapshot(self):
- """Return a read-only object of the current cubic parameters.
-
- These parameters are intended to be used for debug/troubleshooting
- purposes. These object is a read-only snapshot and cannot be used
- to modify the behavior of the CUBIC calculations.
-
- New parameters may be added to this object in the future.
-
- """
- return CubicParams(
- w_max=self._w_max, k=self._k, last_fail=self._last_fail
- )
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/_musllinux.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/_musllinux.py
deleted file mode 100644
index 8ac3059ba3c246b9a5a6fb8d14936bb07777191e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/_musllinux.py
+++ /dev/null
@@ -1,136 +0,0 @@
-"""PEP 656 support.
-
-This module implements logic to detect if the currently running Python is
-linked against musl, and what musl version is used.
-"""
-
-import contextlib
-import functools
-import operator
-import os
-import re
-import struct
-import subprocess
-import sys
-from typing import IO, Iterator, NamedTuple, Optional, Tuple
-
-
-def _read_unpacked(f: IO[bytes], fmt: str) -> Tuple[int, ...]:
- return struct.unpack(fmt, f.read(struct.calcsize(fmt)))
-
-
-def _parse_ld_musl_from_elf(f: IO[bytes]) -> Optional[str]:
- """Detect musl libc location by parsing the Python executable.
-
- Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca
- ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html
- """
- f.seek(0)
- try:
- ident = _read_unpacked(f, "16B")
- except struct.error:
- return None
- if ident[:4] != tuple(b"\x7fELF"): # Invalid magic, not ELF.
- return None
- f.seek(struct.calcsize("HHI"), 1) # Skip file type, machine, and version.
-
- try:
- # e_fmt: Format for program header.
- # p_fmt: Format for section header.
- # p_idx: Indexes to find p_type, p_offset, and p_filesz.
- e_fmt, p_fmt, p_idx = {
- 1: ("IIIIHHH", "IIIIIIII", (0, 1, 4)), # 32-bit.
- 2: ("QQQIHHH", "IIQQQQQQ", (0, 2, 5)), # 64-bit.
- }[ident[4]]
- except KeyError:
- return None
- else:
- p_get = operator.itemgetter(*p_idx)
-
- # Find the interpreter section and return its content.
- try:
- _, e_phoff, _, _, _, e_phentsize, e_phnum = _read_unpacked(f, e_fmt)
- except struct.error:
- return None
- for i in range(e_phnum + 1):
- f.seek(e_phoff + e_phentsize * i)
- try:
- p_type, p_offset, p_filesz = p_get(_read_unpacked(f, p_fmt))
- except struct.error:
- return None
- if p_type != 3: # Not PT_INTERP.
- continue
- f.seek(p_offset)
- interpreter = os.fsdecode(f.read(p_filesz)).strip("\0")
- if "musl" not in interpreter:
- return None
- return interpreter
- return None
-
-
-class _MuslVersion(NamedTuple):
- major: int
- minor: int
-
-
-def _parse_musl_version(output: str) -> Optional[_MuslVersion]:
- lines = [n for n in (n.strip() for n in output.splitlines()) if n]
- if len(lines) < 2 or lines[0][:4] != "musl":
- return None
- m = re.match(r"Version (\d+)\.(\d+)", lines[1])
- if not m:
- return None
- return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2)))
-
-
-@functools.lru_cache()
-def _get_musl_version(executable: str) -> Optional[_MuslVersion]:
- """Detect currently-running musl runtime version.
-
- This is done by checking the specified executable's dynamic linking
- information, and invoking the loader to parse its output for a version
- string. If the loader is musl, the output would be something like::
-
- musl libc (x86_64)
- Version 1.2.2
- Dynamic Program Loader
- """
- with contextlib.ExitStack() as stack:
- try:
- f = stack.enter_context(open(executable, "rb"))
- except OSError:
- return None
- ld = _parse_ld_musl_from_elf(f)
- if not ld:
- return None
- proc = subprocess.run([ld], stderr=subprocess.PIPE, universal_newlines=True)
- return _parse_musl_version(proc.stderr)
-
-
-def platform_tags(arch: str) -> Iterator[str]:
- """Generate musllinux tags compatible to the current platform.
-
- :param arch: Should be the part of platform tag after the ``linux_``
- prefix, e.g. ``x86_64``. The ``linux_`` prefix is assumed as a
- prerequisite for the current platform to be musllinux-compatible.
-
- :returns: An iterator of compatible musllinux tags.
- """
- sys_musl = _get_musl_version(sys.executable)
- if sys_musl is None: # Python not dynamically linked against musl.
- return
- for minor in range(sys_musl.minor, -1, -1):
- yield f"musllinux_{sys_musl.major}_{minor}_{arch}"
-
-
-if __name__ == "__main__": # pragma: no cover
- import sysconfig
-
- plat = sysconfig.get_platform()
- assert plat.startswith("linux-"), "not linux"
-
- print("plat:", plat)
- print("musl:", _get_musl_version(sys.executable))
- print("tags:", end=" ")
- for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])):
- print(t, end="\n ")
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/helpers.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/helpers.py
deleted file mode 100644
index 9588b3b780159a2a2d23c7f84a4404ec350e2b65..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/helpers.py
+++ /dev/null
@@ -1,1088 +0,0 @@
-# helpers.py
-import html.entities
-import re
-import typing
-
-from . import __diag__
-from .core import *
-from .util import _bslash, _flatten, _escape_regex_range_chars
-
-
-#
-# global helpers
-#
-def delimited_list(
- expr: Union[str, ParserElement],
- delim: Union[str, ParserElement] = ",",
- combine: bool = False,
- min: typing.Optional[int] = None,
- max: typing.Optional[int] = None,
- *,
- allow_trailing_delim: bool = False,
-) -> ParserElement:
- """Helper to define a delimited list of expressions - the delimiter
- defaults to ','. By default, the list elements and delimiters can
- have intervening whitespace, and comments, but this can be
- overridden by passing ``combine=True`` in the constructor. If
- ``combine`` is set to ``True``, the matching tokens are
- returned as a single token string, with the delimiters included;
- otherwise, the matching tokens are returned as a list of tokens,
- with the delimiters suppressed.
-
- If ``allow_trailing_delim`` is set to True, then the list may end with
- a delimiter.
-
- Example::
-
- delimited_list(Word(alphas)).parse_string("aa,bb,cc") # -> ['aa', 'bb', 'cc']
- delimited_list(Word(hexnums), delim=':', combine=True).parse_string("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE']
- """
- if isinstance(expr, str_type):
- expr = ParserElement._literalStringClass(expr)
-
- dlName = "{expr} [{delim} {expr}]...{end}".format(
- expr=str(expr.copy().streamline()),
- delim=str(delim),
- end=" [{}]".format(str(delim)) if allow_trailing_delim else "",
- )
-
- if not combine:
- delim = Suppress(delim)
-
- if min is not None:
- if min < 1:
- raise ValueError("min must be greater than 0")
- min -= 1
- if max is not None:
- if min is not None and max <= min:
- raise ValueError("max must be greater than, or equal to min")
- max -= 1
- delimited_list_expr = expr + (delim + expr)[min, max]
-
- if allow_trailing_delim:
- delimited_list_expr += Opt(delim)
-
- if combine:
- return Combine(delimited_list_expr).set_name(dlName)
- else:
- return delimited_list_expr.set_name(dlName)
-
-
-def counted_array(
- expr: ParserElement,
- int_expr: typing.Optional[ParserElement] = None,
- *,
- intExpr: typing.Optional[ParserElement] = None,
-) -> ParserElement:
- """Helper to define a counted list of expressions.
-
- This helper defines a pattern of the form::
-
- integer expr expr expr...
-
- where the leading integer tells how many expr expressions follow.
- The matched tokens returns the array of expr tokens as a list - the
- leading count token is suppressed.
-
- If ``int_expr`` is specified, it should be a pyparsing expression
- that produces an integer value.
-
- Example::
-
- counted_array(Word(alphas)).parse_string('2 ab cd ef') # -> ['ab', 'cd']
-
- # in this parser, the leading integer value is given in binary,
- # '10' indicating that 2 values are in the array
- binary_constant = Word('01').set_parse_action(lambda t: int(t[0], 2))
- counted_array(Word(alphas), int_expr=binary_constant).parse_string('10 ab cd ef') # -> ['ab', 'cd']
-
- # if other fields must be parsed after the count but before the
- # list items, give the fields results names and they will
- # be preserved in the returned ParseResults:
- count_with_metadata = integer + Word(alphas)("type")
- typed_array = counted_array(Word(alphanums), int_expr=count_with_metadata)("items")
- result = typed_array.parse_string("3 bool True True False")
- print(result.dump())
-
- # prints
- # ['True', 'True', 'False']
- # - items: ['True', 'True', 'False']
- # - type: 'bool'
- """
- intExpr = intExpr or int_expr
- array_expr = Forward()
-
- def count_field_parse_action(s, l, t):
- nonlocal array_expr
- n = t[0]
- array_expr <<= (expr * n) if n else Empty()
- # clear list contents, but keep any named results
- del t[:]
-
- if intExpr is None:
- intExpr = Word(nums).set_parse_action(lambda t: int(t[0]))
- else:
- intExpr = intExpr.copy()
- intExpr.set_name("arrayLen")
- intExpr.add_parse_action(count_field_parse_action, call_during_try=True)
- return (intExpr + array_expr).set_name("(len) " + str(expr) + "...")
-
-
-def match_previous_literal(expr: ParserElement) -> ParserElement:
- """Helper to define an expression that is indirectly defined from
- the tokens matched in a previous expression, that is, it looks for
- a 'repeat' of a previous expression. For example::
-
- first = Word(nums)
- second = match_previous_literal(first)
- match_expr = first + ":" + second
-
- will match ``"1:1"``, but not ``"1:2"``. Because this
- matches a previous literal, will also match the leading
- ``"1:1"`` in ``"1:10"``. If this is not desired, use
- :class:`match_previous_expr`. Do *not* use with packrat parsing
- enabled.
- """
- rep = Forward()
-
- def copy_token_to_repeater(s, l, t):
- if t:
- if len(t) == 1:
- rep << t[0]
- else:
- # flatten t tokens
- tflat = _flatten(t.as_list())
- rep << And(Literal(tt) for tt in tflat)
- else:
- rep << Empty()
-
- expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
- rep.set_name("(prev) " + str(expr))
- return rep
-
-
-def match_previous_expr(expr: ParserElement) -> ParserElement:
- """Helper to define an expression that is indirectly defined from
- the tokens matched in a previous expression, that is, it looks for
- a 'repeat' of a previous expression. For example::
-
- first = Word(nums)
- second = match_previous_expr(first)
- match_expr = first + ":" + second
-
- will match ``"1:1"``, but not ``"1:2"``. Because this
- matches by expressions, will *not* match the leading ``"1:1"``
- in ``"1:10"``; the expressions are evaluated first, and then
- compared, so ``"1"`` is compared with ``"10"``. Do *not* use
- with packrat parsing enabled.
- """
- rep = Forward()
- e2 = expr.copy()
- rep <<= e2
-
- def copy_token_to_repeater(s, l, t):
- matchTokens = _flatten(t.as_list())
-
- def must_match_these_tokens(s, l, t):
- theseTokens = _flatten(t.as_list())
- if theseTokens != matchTokens:
- raise ParseException(
- s, l, "Expected {}, found{}".format(matchTokens, theseTokens)
- )
-
- rep.set_parse_action(must_match_these_tokens, callDuringTry=True)
-
- expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
- rep.set_name("(prev) " + str(expr))
- return rep
-
-
-def one_of(
- strs: Union[typing.Iterable[str], str],
- caseless: bool = False,
- use_regex: bool = True,
- as_keyword: bool = False,
- *,
- useRegex: bool = True,
- asKeyword: bool = False,
-) -> ParserElement:
- """Helper to quickly define a set of alternative :class:`Literal` s,
- and makes sure to do longest-first testing when there is a conflict,
- regardless of the input order, but returns
- a :class:`MatchFirst` for best performance.
-
- Parameters:
-
- - ``strs`` - a string of space-delimited literals, or a collection of
- string literals
- - ``caseless`` - treat all literals as caseless - (default= ``False``)
- - ``use_regex`` - as an optimization, will
- generate a :class:`Regex` object; otherwise, will generate
- a :class:`MatchFirst` object (if ``caseless=True`` or ``asKeyword=True``, or if
- creating a :class:`Regex` raises an exception) - (default= ``True``)
- - ``as_keyword`` - enforce :class:`Keyword`-style matching on the
- generated expressions - (default= ``False``)
- - ``asKeyword`` and ``useRegex`` are retained for pre-PEP8 compatibility,
- but will be removed in a future release
-
- Example::
-
- comp_oper = one_of("< = > <= >= !=")
- var = Word(alphas)
- number = Word(nums)
- term = var | number
- comparison_expr = term + comp_oper + term
- print(comparison_expr.search_string("B = 12 AA=23 B<=AA AA>12"))
-
- prints::
-
- [['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']]
- """
- asKeyword = asKeyword or as_keyword
- useRegex = useRegex and use_regex
-
- if (
- isinstance(caseless, str_type)
- and __diag__.warn_on_multiple_string_args_to_oneof
- ):
- warnings.warn(
- "More than one string argument passed to one_of, pass"
- " choices as a list or space-delimited string",
- stacklevel=2,
- )
-
- if caseless:
- isequal = lambda a, b: a.upper() == b.upper()
- masks = lambda a, b: b.upper().startswith(a.upper())
- parseElementClass = CaselessKeyword if asKeyword else CaselessLiteral
- else:
- isequal = lambda a, b: a == b
- masks = lambda a, b: b.startswith(a)
- parseElementClass = Keyword if asKeyword else Literal
-
- symbols: List[str] = []
- if isinstance(strs, str_type):
- symbols = strs.split()
- elif isinstance(strs, Iterable):
- symbols = list(strs)
- else:
- raise TypeError("Invalid argument to one_of, expected string or iterable")
- if not symbols:
- return NoMatch()
-
- # reorder given symbols to take care to avoid masking longer choices with shorter ones
- # (but only if the given symbols are not just single characters)
- if any(len(sym) > 1 for sym in symbols):
- i = 0
- while i < len(symbols) - 1:
- cur = symbols[i]
- for j, other in enumerate(symbols[i + 1 :]):
- if isequal(other, cur):
- del symbols[i + j + 1]
- break
- elif masks(cur, other):
- del symbols[i + j + 1]
- symbols.insert(i, other)
- break
- else:
- i += 1
-
- if useRegex:
- re_flags: int = re.IGNORECASE if caseless else 0
-
- try:
- if all(len(sym) == 1 for sym in symbols):
- # symbols are just single characters, create range regex pattern
- patt = "[{}]".format(
- "".join(_escape_regex_range_chars(sym) for sym in symbols)
- )
- else:
- patt = "|".join(re.escape(sym) for sym in symbols)
-
- # wrap with \b word break markers if defining as keywords
- if asKeyword:
- patt = r"\b(?:{})\b".format(patt)
-
- ret = Regex(patt, flags=re_flags).set_name(" | ".join(symbols))
-
- if caseless:
- # add parse action to return symbols as specified, not in random
- # casing as found in input string
- symbol_map = {sym.lower(): sym for sym in symbols}
- ret.add_parse_action(lambda s, l, t: symbol_map[t[0].lower()])
-
- return ret
-
- except re.error:
- warnings.warn(
- "Exception creating Regex for one_of, building MatchFirst", stacklevel=2
- )
-
- # last resort, just use MatchFirst
- return MatchFirst(parseElementClass(sym) for sym in symbols).set_name(
- " | ".join(symbols)
- )
-
-
-def dict_of(key: ParserElement, value: ParserElement) -> ParserElement:
- """Helper to easily and clearly define a dictionary by specifying
- the respective patterns for the key and value. Takes care of
- defining the :class:`Dict`, :class:`ZeroOrMore`, and
- :class:`Group` tokens in the proper order. The key pattern
- can include delimiting markers or punctuation, as long as they are
- suppressed, thereby leaving the significant key text. The value
- pattern can include named results, so that the :class:`Dict` results
- can include named token fields.
-
- Example::
-
- text = "shape: SQUARE posn: upper left color: light blue texture: burlap"
- attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join))
- print(attr_expr[1, ...].parse_string(text).dump())
-
- attr_label = label
- attr_value = Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)
-
- # similar to Dict, but simpler call format
- result = dict_of(attr_label, attr_value).parse_string(text)
- print(result.dump())
- print(result['shape'])
- print(result.shape) # object attribute access works too
- print(result.as_dict())
-
- prints::
-
- [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']]
- - color: 'light blue'
- - posn: 'upper left'
- - shape: 'SQUARE'
- - texture: 'burlap'
- SQUARE
- SQUARE
- {'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'}
- """
- return Dict(OneOrMore(Group(key + value)))
-
-
-def original_text_for(
- expr: ParserElement, as_string: bool = True, *, asString: bool = True
-) -> ParserElement:
- """Helper to return the original, untokenized text for a given
- expression. Useful to restore the parsed fields of an HTML start
- tag into the raw tag text itself, or to revert separate tokens with
- intervening whitespace back to the original matching input text. By
- default, returns astring containing the original parsed text.
-
- If the optional ``as_string`` argument is passed as
- ``False``, then the return value is
- a :class:`ParseResults` containing any results names that
- were originally matched, and a single token containing the original
- matched text from the input string. So if the expression passed to
- :class:`original_text_for` contains expressions with defined
- results names, you must set ``as_string`` to ``False`` if you
- want to preserve those results name values.
-
- The ``asString`` pre-PEP8 argument is retained for compatibility,
- but will be removed in a future release.
-
- Example::
-
- src = "this is test bold text normal text "
- for tag in ("b", "i"):
- opener, closer = make_html_tags(tag)
- patt = original_text_for(opener + SkipTo(closer) + closer)
- print(patt.search_string(src)[0])
-
- prints::
-
- [' bold text']
- ['text']
- """
- asString = asString and as_string
-
- locMarker = Empty().set_parse_action(lambda s, loc, t: loc)
- endlocMarker = locMarker.copy()
- endlocMarker.callPreparse = False
- matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end")
- if asString:
- extractText = lambda s, l, t: s[t._original_start : t._original_end]
- else:
-
- def extractText(s, l, t):
- t[:] = [s[t.pop("_original_start") : t.pop("_original_end")]]
-
- matchExpr.set_parse_action(extractText)
- matchExpr.ignoreExprs = expr.ignoreExprs
- matchExpr.suppress_warning(Diagnostics.warn_ungrouped_named_tokens_in_collection)
- return matchExpr
-
-
-def ungroup(expr: ParserElement) -> ParserElement:
- """Helper to undo pyparsing's default grouping of And expressions,
- even if all but one are non-empty.
- """
- return TokenConverter(expr).add_parse_action(lambda t: t[0])
-
-
-def locatedExpr(expr: ParserElement) -> ParserElement:
- """
- (DEPRECATED - future code should use the Located class)
- Helper to decorate a returned token with its starting and ending
- locations in the input string.
-
- This helper adds the following results names:
-
- - ``locn_start`` - location where matched expression begins
- - ``locn_end`` - location where matched expression ends
- - ``value`` - the actual parsed results
-
- Be careful if the input text contains ```` characters, you
- may want to call :class:`ParserElement.parseWithTabs`
-
- Example::
-
- wd = Word(alphas)
- for match in locatedExpr(wd).searchString("ljsdf123lksdjjf123lkkjj1222"):
- print(match)
-
- prints::
-
- [[0, 'ljsdf', 5]]
- [[8, 'lksdjjf', 15]]
- [[18, 'lkkjj', 23]]
- """
- locator = Empty().set_parse_action(lambda ss, ll, tt: ll)
- return Group(
- locator("locn_start")
- + expr("value")
- + locator.copy().leaveWhitespace()("locn_end")
- )
-
-
-def nested_expr(
- opener: Union[str, ParserElement] = "(",
- closer: Union[str, ParserElement] = ")",
- content: typing.Optional[ParserElement] = None,
- ignore_expr: ParserElement = quoted_string(),
- *,
- ignoreExpr: ParserElement = quoted_string(),
-) -> ParserElement:
- """Helper method for defining nested lists enclosed in opening and
- closing delimiters (``"("`` and ``")"`` are the default).
-
- Parameters:
- - ``opener`` - opening character for a nested list
- (default= ``"("``); can also be a pyparsing expression
- - ``closer`` - closing character for a nested list
- (default= ``")"``); can also be a pyparsing expression
- - ``content`` - expression for items within the nested lists
- (default= ``None``)
- - ``ignore_expr`` - expression for ignoring opening and closing delimiters
- (default= :class:`quoted_string`)
- - ``ignoreExpr`` - this pre-PEP8 argument is retained for compatibility
- but will be removed in a future release
-
- If an expression is not provided for the content argument, the
- nested expression will capture all whitespace-delimited content
- between delimiters as a list of separate values.
-
- Use the ``ignore_expr`` argument to define expressions that may
- contain opening or closing characters that should not be treated as
- opening or closing characters for nesting, such as quoted_string or
- a comment expression. Specify multiple expressions using an
- :class:`Or` or :class:`MatchFirst`. The default is
- :class:`quoted_string`, but if no expressions are to be ignored, then
- pass ``None`` for this argument.
-
- Example::
-
- data_type = one_of("void int short long char float double")
- decl_data_type = Combine(data_type + Opt(Word('*')))
- ident = Word(alphas+'_', alphanums+'_')
- number = pyparsing_common.number
- arg = Group(decl_data_type + ident)
- LPAR, RPAR = map(Suppress, "()")
-
- code_body = nested_expr('{', '}', ignore_expr=(quoted_string | c_style_comment))
-
- c_function = (decl_data_type("type")
- + ident("name")
- + LPAR + Opt(delimited_list(arg), [])("args") + RPAR
- + code_body("body"))
- c_function.ignore(c_style_comment)
-
- source_code = '''
- int is_odd(int x) {
- return (x%2);
- }
-
- int dec_to_hex(char hchar) {
- if (hchar >= '0' && hchar <= '9') {
- return (ord(hchar)-ord('0'));
- } else {
- return (10+ord(hchar)-ord('A'));
- }
- }
- '''
- for func in c_function.search_string(source_code):
- print("%(name)s (%(type)s) args: %(args)s" % func)
-
-
- prints::
-
- is_odd (int) args: [['int', 'x']]
- dec_to_hex (int) args: [['char', 'hchar']]
- """
- if ignoreExpr != ignore_expr:
- ignoreExpr = ignore_expr if ignoreExpr == quoted_string() else ignoreExpr
- if opener == closer:
- raise ValueError("opening and closing strings cannot be the same")
- if content is None:
- if isinstance(opener, str_type) and isinstance(closer, str_type):
- if len(opener) == 1 and len(closer) == 1:
- if ignoreExpr is not None:
- content = Combine(
- OneOrMore(
- ~ignoreExpr
- + CharsNotIn(
- opener + closer + ParserElement.DEFAULT_WHITE_CHARS,
- exact=1,
- )
- )
- ).set_parse_action(lambda t: t[0].strip())
- else:
- content = empty.copy() + CharsNotIn(
- opener + closer + ParserElement.DEFAULT_WHITE_CHARS
- ).set_parse_action(lambda t: t[0].strip())
- else:
- if ignoreExpr is not None:
- content = Combine(
- OneOrMore(
- ~ignoreExpr
- + ~Literal(opener)
- + ~Literal(closer)
- + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
- )
- ).set_parse_action(lambda t: t[0].strip())
- else:
- content = Combine(
- OneOrMore(
- ~Literal(opener)
- + ~Literal(closer)
- + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
- )
- ).set_parse_action(lambda t: t[0].strip())
- else:
- raise ValueError(
- "opening and closing arguments must be strings if no content expression is given"
- )
- ret = Forward()
- if ignoreExpr is not None:
- ret <<= Group(
- Suppress(opener) + ZeroOrMore(ignoreExpr | ret | content) + Suppress(closer)
- )
- else:
- ret <<= Group(Suppress(opener) + ZeroOrMore(ret | content) + Suppress(closer))
- ret.set_name("nested %s%s expression" % (opener, closer))
- return ret
-
-
-def _makeTags(tagStr, xml, suppress_LT=Suppress("<"), suppress_GT=Suppress(">")):
- """Internal helper to construct opening and closing tag expressions, given a tag name"""
- if isinstance(tagStr, str_type):
- resname = tagStr
- tagStr = Keyword(tagStr, caseless=not xml)
- else:
- resname = tagStr.name
-
- tagAttrName = Word(alphas, alphanums + "_-:")
- if xml:
- tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes)
- openTag = (
- suppress_LT
- + tagStr("tag")
- + Dict(ZeroOrMore(Group(tagAttrName + Suppress("=") + tagAttrValue)))
- + Opt("/", default=[False])("empty").set_parse_action(
- lambda s, l, t: t[0] == "/"
- )
- + suppress_GT
- )
- else:
- tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word(
- printables, exclude_chars=">"
- )
- openTag = (
- suppress_LT
- + tagStr("tag")
- + Dict(
- ZeroOrMore(
- Group(
- tagAttrName.set_parse_action(lambda t: t[0].lower())
- + Opt(Suppress("=") + tagAttrValue)
- )
- )
- )
- + Opt("/", default=[False])("empty").set_parse_action(
- lambda s, l, t: t[0] == "/"
- )
- + suppress_GT
- )
- closeTag = Combine(Literal("", adjacent=False)
-
- openTag.set_name("<%s>" % resname)
- # add start results name in parse action now that ungrouped names are not reported at two levels
- openTag.add_parse_action(
- lambda t: t.__setitem__(
- "start" + "".join(resname.replace(":", " ").title().split()), t.copy()
- )
- )
- closeTag = closeTag(
- "end" + "".join(resname.replace(":", " ").title().split())
- ).set_name("" % resname)
- openTag.tag = resname
- closeTag.tag = resname
- openTag.tag_body = SkipTo(closeTag())
- return openTag, closeTag
-
-
-def make_html_tags(
- tag_str: Union[str, ParserElement]
-) -> Tuple[ParserElement, ParserElement]:
- """Helper to construct opening and closing tag expressions for HTML,
- given a tag name. Matches tags in either upper or lower case,
- attributes with namespaces and with quoted or unquoted values.
-
- Example::
-
- text = '
"""
-)
-description = """
-The ROOTS corpus was developed during the [BigScience workshop](https://bigscience.huggingface.co/) for the purpose
-of training the Multilingual Large Language Model [BLOOM](https://huggingface.co/bigscience/bloom). This tool allows
-you to search through the ROOTS corpus. We serve a BM25 index for each language or group of languages included in
-ROOTS. You can read more about the details of the tool design
-[here](https://huggingface.co/spaces/bigscience-data/scisearch/blob/main/roots_search_tool_specs.pdf). For more
-information and instructions on how to access the full corpus check [this form](https://forms.gle/qyYswbEL5kA23Wu99)."""
-
-
-if __name__ == "__main__":
- demo = gr.Blocks(css=".underline-on-hover:hover { text-decoration: underline; }")
-
- with demo:
- processed_results_state = gr.State([])
- highlight_terms_state = gr.State([])
- num_results_state = gr.State(0)
- exact_search_state = gr.State(False)
- received_results_state = gr.State(0)
-
- with gr.Row():
- gr.Markdown(value=title)
- with gr.Row():
- gr.Markdown(value=description)
- with gr.Row():
- query = gr.Textbox(
- lines=1,
- max_lines=1,
- placeholder="Put your query in double quotes for exact search.",
- label="Query",
- )
- with gr.Row():
- lang = gr.Dropdown(
- choices=[
- "ar",
- "ca",
- "code",
- "en",
- "es",
- "eu",
- "fr",
- "id",
- "indic",
- "nigercongo",
- "pt",
- "vi",
- "zh",
- "detect_language",
- "all",
- ],
- value="en",
- label="Language",
- )
- k = gr.Slider(
- 1,
- 100,
- value=10,
- step=1,
- label="Max Results in fuzzy search or Max Results per page in exact search",
- )
- with gr.Row():
- submit_btn = gr.Button("Submit")
- with gr.Row(visible=False) as datasets_filter:
- available_datasets = gr.Dropdown(
- type="value",
- choices=[],
- value=[],
- label="Datasets Filter",
- multiselect=True,
- )
- with gr.Row():
- result_page_html = gr.HTML(label="Results")
-
- with gr.Row(visible=False) as pagination:
- next_page_btn = gr.Button("Next Page")
-
- def run_query(query, lang, k, dropdown_input, received_results):
- query = query.strip()
- exact_search = False
- if query.startswith('"') and query.endswith('"') and len(query) >= 2:
- exact_search = True
- query = query[1:-1]
- else:
- query = " ".join(query.split())
- if query == "" or query is None:
- return (
- [],
- [],
- 0,
- False,
- no_query_error_message(),
- [],
- )
-
- payload = request_payload(query, lang, exact_search, k, received_results)
- err = extract_error_from_payload(payload)
- if err is not None:
- return (
- [],
- [],
- 0,
- False,
- process_error(err, payload),
- [],
- )
-
- (
- processed_results,
- highlight_terms,
- num_results,
- ds,
- ) = extract_results_from_payload(
- query,
- lang,
- payload,
- exact_search,
- )
- result_page = format_result_page(
- lang, processed_results, highlight_terms, num_results, exact_search
- )
- return (
- processed_results,
- highlight_terms,
- num_results,
- exact_search,
- result_page,
- ds,
- )
-
- def submit(query, lang, k, dropdown_input):
- print("submitting", query, lang, k)
- (
- processed_results,
- highlight_terms,
- num_results,
- exact_search,
- result_page,
- datasets,
- ) = run_query(query, lang, k, dropdown_input, 0)
- has_more_results = exact_search and (num_results > k)
- current_results = (
- len(next(iter(processed_results.values())))
- if len(processed_results) > 0
- else 0
- )
- return [
- processed_results,
- highlight_terms,
- num_results,
- exact_search,
- gr.update(visible=True)
- if current_results > 0
- else gr.update(visible=False),
- gr.Dropdown.update(choices=datasets, value=datasets),
- gr.update(visible=has_more_results),
- current_results,
- result_page,
- ]
-
- def next_page(
- query,
- lang,
- k,
- dropdown_input,
- received_results,
- processed_results,
- ):
- (
- processed_results,
- highlight_terms,
- num_results,
- exact_search,
- result_page,
- datasets,
- ) = run_query(query, lang, k, dropdown_input, received_results)
- current_results = sum(
- len(results) for results in processed_results.values()
- )
- has_more_results = exact_search and (
- received_results + current_results < num_results
- )
- print("received_results", received_results)
- print("current_results", current_results)
- print("has_more_results", has_more_results)
- return [
- processed_results,
- highlight_terms,
- num_results,
- exact_search,
- gr.update(visible=True)
- if current_results > 0
- else gr.update(visible=False),
- gr.Dropdown.update(choices=datasets, value=datasets),
- gr.update(visible=current_results >= k and has_more_results),
- received_results + current_results,
- result_page,
- ]
-
- def filter_datasets(
- lang,
- processed_results,
- highlight_terms,
- num_results,
- exact_search,
- datasets_filter,
- ):
- result_page_html = format_result_page(
- lang,
- processed_results,
- highlight_terms,
- num_results,
- exact_search,
- datasets_filter,
- )
- return result_page_html
-
- query.submit(
- fn=submit,
- inputs=[query, lang, k, available_datasets],
- outputs=[
- processed_results_state,
- highlight_terms_state,
- num_results_state,
- exact_search_state,
- datasets_filter,
- available_datasets,
- pagination,
- received_results_state,
- result_page_html,
- ],
- )
- submit_btn.click(
- submit,
- inputs=[query, lang, k, available_datasets],
- outputs=[
- processed_results_state,
- highlight_terms_state,
- num_results_state,
- exact_search_state,
- datasets_filter,
- available_datasets,
- pagination,
- received_results_state,
- result_page_html,
- ],
- )
-
- next_page_btn.click(
- next_page,
- inputs=[
- query,
- lang,
- k,
- available_datasets,
- received_results_state,
- processed_results_state,
- ],
- outputs=[
- processed_results_state,
- highlight_terms_state,
- num_results_state,
- exact_search_state,
- datasets_filter,
- available_datasets,
- pagination,
- received_results_state,
- result_page_html,
- ],
- )
-
- available_datasets.change(
- filter_datasets,
- inputs=[
- lang,
- processed_results_state,
- highlight_terms_state,
- num_results_state,
- exact_search_state,
- available_datasets,
- ],
- outputs=result_page_html,
- )
- demo.launch(enable_queue=True, debug=True)
diff --git a/spaces/bioriAsaeru/text-to-voice/HD Online Player (Baaghi 2 2 Full Movie In Hindi Free ) UPDATED.md b/spaces/bioriAsaeru/text-to-voice/HD Online Player (Baaghi 2 2 Full Movie In Hindi Free ) UPDATED.md
deleted file mode 100644
index ef669f6800880cc72b813cae47e56651a860e373..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HD Online Player (Baaghi 2 2 Full Movie In Hindi Free ) UPDATED.md
+++ /dev/null
@@ -1,10 +0,0 @@
-
-
yes, i was so excited that i really wanted to eat it the moment i opened it. but, as it went i got to know why i was getting this. that is because he just wanted to make my holi a memorable one. and he did it by giving me a purple mustache. the moment he gave me this, i was so happy. i made myself look stunning & gorgeous. i was really happy that he gifted me a mustache. however, he did not stop there.
-
HD Online Player (Baaghi 2 2 Full Movie In Hindi Free )
i love nothing more than a series of cups and glasses of chilled ice tea. last night- purba chandra came over and we enjoyed a talk over the night on a variety of subjects, ranging from politics, movies, books, technology, and many more. then we headed to his place where i could hear the sound of a waterfall in his compound. my heart skipped a beat and i was in awe of his build up, but then i remembered that popee likes his 'beyoncez' pretty hard! i smiled, and enjoyed the night!
-
do you know that minute i logged in to hotstar on my mobile, i got a text from michelle saying there is some sweet news for you - i immediately turned on the notification and just stood still, just in my fright - has she called?
-
i clicked on the notification and immediately pressed the chat icon. i was absolutely surprised and definitely i hugged her for it. she replied to my hug by saying she wanted to tell me something - definitely on the lines of marriage proposal or serious love - since she is so accustomed to courting me with butterflies! :-p
-
-
mamta said you know whenever i was looking at movies and watching films online, i never ever looked at your movies - you never ever looked at my movies and you never ever called me to watch a movie - you don't know how much this sweet gesture of yours went down with me and the way i am right now; i don't deserve this and i am definitely going to miss this sensation and care of yours. your acts made me really get down on my knees, praying for a divine connection between the two of us and i am thankful to the stars for finally breaking the cycle of maharshi and guru dronacharya's(lord indra's) curse.
899543212b
-
-
\ No newline at end of file
diff --git a/spaces/birdortyedi/instagram-filter-removal/modules/normalization.py b/spaces/birdortyedi/instagram-filter-removal/modules/normalization.py
deleted file mode 100644
index fc28bfdeaff873a9212e5af3d32550ef4f67cdd6..0000000000000000000000000000000000000000
--- a/spaces/birdortyedi/instagram-filter-removal/modules/normalization.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class AdaIN(nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x, y):
- ch = y.size(1)
- sigma, mu = torch.split(y.unsqueeze(-1).unsqueeze(-1), [ch // 2, ch // 2], dim=1)
-
- x_mu = x.mean(dim=[2, 3], keepdim=True)
- x_sigma = x.std(dim=[2, 3], keepdim=True)
-
- return sigma * ((x - x_mu) / x_sigma) + mu
diff --git a/spaces/bitofurqan/meta-llama-Llama-2-70b-chat-hf/README.md b/spaces/bitofurqan/meta-llama-Llama-2-70b-chat-hf/README.md
deleted file mode 100644
index 6fd06f40eda841f0f0761e9b282bdb41b6680bec..0000000000000000000000000000000000000000
--- a/spaces/bitofurqan/meta-llama-Llama-2-70b-chat-hf/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Meta Llama Llama 2 70b Chat Hf
-emoji: 🦀
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/mask_ops.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/mask_ops.py
deleted file mode 100644
index 990d04abbb120e40fe07a21d024dfead471bc998..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/mask_ops.py
+++ /dev/null
@@ -1,275 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import Tuple
-import torch
-from PIL import Image
-from torch.nn import functional as F
-
-__all__ = ["paste_masks_in_image"]
-
-
-BYTES_PER_FLOAT = 4
-# TODO: This memory limit may be too much or too little. It would be better to
-# determine it based on available resources.
-GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit
-
-
-def _do_paste_mask(masks, boxes, img_h: int, img_w: int, skip_empty: bool = True):
- """
- Args:
- masks: N, 1, H, W
- boxes: N, 4
- img_h, img_w (int):
- skip_empty (bool): only paste masks within the region that
- tightly bound all boxes, and returns the results this region only.
- An important optimization for CPU.
-
- Returns:
- if skip_empty == False, a mask of shape (N, img_h, img_w)
- if skip_empty == True, a mask of shape (N, h', w'), and the slice
- object for the corresponding region.
- """
- # On GPU, paste all masks together (up to chunk size)
- # by using the entire image to sample the masks
- # Compared to pasting them one by one,
- # this has more operations but is faster on COCO-scale dataset.
- device = masks.device
-
- if skip_empty and not torch.jit.is_scripting():
- x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to(
- dtype=torch.int32
- )
- x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
- y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
- else:
- x0_int, y0_int = 0, 0
- x1_int, y1_int = img_w, img_h
- x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1
-
- N = masks.shape[0]
-
- img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5
- img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5
- img_y = (img_y - y0) / (y1 - y0) * 2 - 1
- img_x = (img_x - x0) / (x1 - x0) * 2 - 1
- # img_x, img_y have shapes (N, w), (N, h)
-
- gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
- gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
- grid = torch.stack([gx, gy], dim=3)
-
- if not torch.jit.is_scripting():
- if not masks.dtype.is_floating_point:
- masks = masks.float()
- img_masks = F.grid_sample(masks, grid.to(masks.dtype), align_corners=False)
-
- if skip_empty and not torch.jit.is_scripting():
- return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
- else:
- return img_masks[:, 0], ()
-
-
-# Annotate boxes as Tensor (but not Boxes) in order to use scripting
-@torch.jit.script_if_tracing
-def paste_masks_in_image(
- masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[int, int], threshold: float = 0.5
-):
- """
- Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image.
- The location, height, and width for pasting each mask is determined by their
- corresponding bounding boxes in boxes.
-
- Note:
- This is a complicated but more accurate implementation. In actual deployment, it is
- often enough to use a faster but less accurate implementation.
- See :func:`paste_mask_in_image_old` in this file for an alternative implementation.
-
- Args:
- masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of
- detected object instances in the image and Hmask, Wmask are the mask width and mask
- height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1].
- boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4).
- boxes[i] and masks[i] correspond to the same object instance.
- image_shape (tuple): height, width
- threshold (float): A threshold in [0, 1] for converting the (soft) masks to
- binary masks.
-
- Returns:
- img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the
- number of detected object instances and Himage, Wimage are the image width
- and height. img_masks[i] is a binary mask for object instance i.
- """
-
- assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported"
- N = len(masks)
- if N == 0:
- return masks.new_empty((0,) + image_shape, dtype=torch.uint8)
- if not isinstance(boxes, torch.Tensor):
- boxes = boxes.tensor
- device = boxes.device
- assert len(boxes) == N, boxes.shape
-
- img_h, img_w = image_shape
-
- # The actual implementation split the input into chunks,
- # and paste them chunk by chunk.
- if device.type == "cpu" or torch.jit.is_scripting():
- # CPU is most efficient when they are pasted one by one with skip_empty=True
- # so that it performs minimal number of operations.
- num_chunks = N
- else:
- # GPU benefits from parallelism for larger chunks, but may have memory issue
- # int(img_h) because shape may be tensors in tracing
- num_chunks = int(np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
- assert (
- num_chunks <= N
- ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it"
- chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
-
- img_masks = torch.zeros(
- N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8
- )
- for inds in chunks:
- masks_chunk, spatial_inds = _do_paste_mask(
- masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu"
- )
-
- if threshold >= 0:
- masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
- else:
- # for visualization and debugging
- masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
-
- if torch.jit.is_scripting(): # Scripting does not use the optimized codepath
- img_masks[inds] = masks_chunk
- else:
- img_masks[(inds,) + spatial_inds] = masks_chunk
- return img_masks
-
-
-# The below are the original paste function (from Detectron1) which has
-# larger quantization error.
-# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample.
-
-
-def paste_mask_in_image_old(mask, box, img_h, img_w, threshold):
- """
- Paste a single mask in an image.
- This is a per-box implementation of :func:`paste_masks_in_image`.
- This function has larger quantization error due to incorrect pixel
- modeling and is not used any more.
-
- Args:
- mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single
- object instance. Values are in [0, 1].
- box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners
- of the object instance.
- img_h, img_w (int): Image height and width.
- threshold (float): Mask binarization threshold in [0, 1].
-
- Returns:
- im_mask (Tensor):
- The resized and binarized object mask pasted into the original
- image plane (a tensor of shape (img_h, img_w)).
- """
- # Conversion from continuous box coordinates to discrete pixel coordinates
- # via truncation (cast to int32). This determines which pixels to paste the
- # mask onto.
- box = box.to(dtype=torch.int32) # Continuous to discrete coordinate conversion
- # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to
- # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1
- # pixels (not x1 - x0 pixels).
- samples_w = box[2] - box[0] + 1 # Number of pixel samples, *not* geometric width
- samples_h = box[3] - box[1] + 1 # Number of pixel samples, *not* geometric height
-
- # Resample the mask from it's original grid to the new samples_w x samples_h grid
- mask = Image.fromarray(mask.cpu().numpy())
- mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR)
- mask = np.array(mask, copy=False)
-
- if threshold >= 0:
- mask = np.array(mask > threshold, dtype=np.uint8)
- mask = torch.from_numpy(mask)
- else:
- # for visualization and debugging, we also
- # allow it to return an unmodified mask
- mask = torch.from_numpy(mask * 255).to(torch.uint8)
-
- im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8)
- x_0 = max(box[0], 0)
- x_1 = min(box[2] + 1, img_w)
- y_0 = max(box[1], 0)
- y_1 = min(box[3] + 1, img_h)
-
- im_mask[y_0:y_1, x_0:x_1] = mask[
- (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
- ]
- return im_mask
-
-
-# Our pixel modeling requires extrapolation for any continuous
-# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks,
-# we would like this extrapolation to be an interpolation between boundary values and zero,
-# instead of using absolute zero or boundary values.
-# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this:
-# masks, scale = pad_masks(masks[:, 0, :, :], 1)
-# boxes = scale_boxes(boxes.tensor, scale)
-
-
-def pad_masks(masks, padding):
- """
- Args:
- masks (tensor): A tensor of shape (B, M, M) representing B masks.
- padding (int): Number of cells to pad on all sides.
-
- Returns:
- The padded masks and the scale factor of the padding size / original size.
- """
- B = masks.shape[0]
- M = masks.shape[-1]
- pad2 = 2 * padding
- scale = float(M + pad2) / M
- padded_masks = masks.new_zeros((B, M + pad2, M + pad2))
- padded_masks[:, padding:-padding, padding:-padding] = masks
- return padded_masks, scale
-
-
-def scale_boxes(boxes, scale):
- """
- Args:
- boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4
- coords representing the corners x0, y0, x1, y1,
- scale (float): The box scaling factor.
-
- Returns:
- Scaled boxes.
- """
- w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5
- h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5
- x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5
- y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5
-
- w_half *= scale
- h_half *= scale
-
- scaled_boxes = torch.zeros_like(boxes)
- scaled_boxes[:, 0] = x_c - w_half
- scaled_boxes[:, 2] = x_c + w_half
- scaled_boxes[:, 1] = y_c - h_half
- scaled_boxes[:, 3] = y_c + h_half
- return scaled_boxes
-
-
-@torch.jit.script_if_tracing
-def _paste_masks_tensor_shape(
- masks: torch.Tensor,
- boxes: torch.Tensor,
- image_shape: Tuple[torch.Tensor, torch.Tensor],
- threshold: float = 0.5,
-):
- """
- A wrapper of paste_masks_in_image where image_shape is Tensor.
- During tracing, shapes might be tensors instead of ints. The Tensor->int
- conversion should be scripted rather than traced.
- """
- return paste_masks_in_image(masks, boxes, (int(image_shape[0]), int(image_shape[1])), threshold)
diff --git a/spaces/candlend/vits-hoshimi/vits/__init__.py b/spaces/candlend/vits-hoshimi/vits/__init__.py
deleted file mode 100644
index 881827ca598258907ed4a984008857327fc7f73f..0000000000000000000000000000000000000000
--- a/spaces/candlend/vits-hoshimi/vits/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import os
-import sys
-
-VITS_ROOT_PATH = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(VITS_ROOT_PATH)
diff --git a/spaces/caojiachen1/ChatGPT/crazy_functions/test_project/cpp/cppipc/queue.h b/spaces/caojiachen1/ChatGPT/crazy_functions/test_project/cpp/cppipc/queue.h
deleted file mode 100644
index a21f3446e06b5826af7b554c8a7d9c5d80848b62..0000000000000000000000000000000000000000
--- a/spaces/caojiachen1/ChatGPT/crazy_functions/test_project/cpp/cppipc/queue.h
+++ /dev/null
@@ -1,216 +0,0 @@
-#pragma once
-
-#include
-#include
-#include // [[since C++14]]: std::exchange
-#include
-#include
-#include
-#include
-#include
-#include
-#include // assert
-
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/rw_lock.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-
-namespace ipc {
-namespace detail {
-
-class queue_conn {
-protected:
- circ::cc_t connected_ = 0;
- shm::handle elems_h_;
-
- template
- Elems* open(char const * name) {
- if (name == nullptr || name[0] == '\0') {
- ipc::error("fail open waiter: name is empty!\n");
- return nullptr;
- }
- if (!elems_h_.acquire(name, sizeof(Elems))) {
- return nullptr;
- }
- auto elems = static_cast(elems_h_.get());
- if (elems == nullptr) {
- ipc::error("fail acquire elems: %s\n", name);
- return nullptr;
- }
- elems->init();
- return elems;
- }
-
- void close() {
- elems_h_.release();
- }
-
-public:
- queue_conn() = default;
- queue_conn(const queue_conn&) = delete;
- queue_conn& operator=(const queue_conn&) = delete;
-
- bool connected() const noexcept {
- return connected_ != 0;
- }
-
- circ::cc_t connected_id() const noexcept {
- return connected_;
- }
-
- template
- auto connect(Elems* elems) noexcept
- /*needs 'optional' here*/
- -> std::tuple().cursor())> {
- if (elems == nullptr) return {};
- // if it's already connected, just return
- if (connected()) return {connected(), false, 0};
- connected_ = elems->connect_receiver();
- return {connected(), true, elems->cursor()};
- }
-
- template
- bool disconnect(Elems* elems) noexcept {
- if (elems == nullptr) return false;
- // if it's already disconnected, just return false
- if (!connected()) return false;
- elems->disconnect_receiver(std::exchange(connected_, 0));
- return true;
- }
-};
-
-template
-class queue_base : public queue_conn {
- using base_t = queue_conn;
-
-public:
- using elems_t = Elems;
- using policy_t = typename elems_t::policy_t;
-
-protected:
- elems_t * elems_ = nullptr;
- decltype(std::declval().cursor()) cursor_ = 0;
- bool sender_flag_ = false;
-
-public:
- using base_t::base_t;
-
- queue_base() = default;
-
- explicit queue_base(char const * name)
- : queue_base{} {
- elems_ = open(name);
- }
-
- explicit queue_base(elems_t * elems) noexcept
- : queue_base{} {
- assert(elems != nullptr);
- elems_ = elems;
- }
-
- /* not virtual */ ~queue_base() {
- base_t::close();
- }
-
- elems_t * elems() noexcept { return elems_; }
- elems_t const * elems() const noexcept { return elems_; }
-
- bool ready_sending() noexcept {
- if (elems_ == nullptr) return false;
- return sender_flag_ || (sender_flag_ = elems_->connect_sender());
- }
-
- void shut_sending() noexcept {
- if (elems_ == nullptr) return;
- if (!sender_flag_) return;
- elems_->disconnect_sender();
- }
-
- bool connect() noexcept {
- auto tp = base_t::connect(elems_);
- if (std::get<0>(tp) && std::get<1>(tp)) {
- cursor_ = std::get<2>(tp);
- return true;
- }
- return std::get<0>(tp);
- }
-
- bool disconnect() noexcept {
- return base_t::disconnect(elems_);
- }
-
- std::size_t conn_count() const noexcept {
- return (elems_ == nullptr) ? static_cast(invalid_value) : elems_->conn_count();
- }
-
- bool valid() const noexcept {
- return elems_ != nullptr;
- }
-
- bool empty() const noexcept {
- return !valid() || (cursor_ == elems_->cursor());
- }
-
- template
- bool push(F&& prep, P&&... params) {
- if (elems_ == nullptr) return false;
- return elems_->push(this, [&](void* p) {
- if (prep(p)) ::new (p) T(std::forward
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Casio Wk 3300 Usb Midi Driver Download What You Need to Know Before You Download.md b/spaces/cihyFjudo/fairness-paper-search/Casio Wk 3300 Usb Midi Driver Download What You Need to Know Before You Download.md
deleted file mode 100644
index 1d890416adb23e743c81f0148f2623285d6da8a6..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Casio Wk 3300 Usb Midi Driver Download What You Need to Know Before You Download.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
Hi there. Is there a driver available for the casio LK-100? I bought myself an audio interface and I've been meaning to record with it. I checked the Casio website but couldn't find it. Thanks in advance
Recommendation: We highly recommend using a tool like DriverDoc [Download DriverDoc - Product by Solvusoft] if you are inexperienced in updating Casio Music Keyboard device drivers. This tool does all of the work for you by downloading and updating your WK-3300 drivers automatically, preventing you from installing the wrong drivers for your operating system version.
-
It can be incredibly difficult to find the exact driver for your WK-3300-based hardware unit, even directly on the website of Casio or a related manufacturer. Even for someone who is experienced at finding, downloading, and manually updating WK-3300 drivers, the process can still be exceptionally tedious and extremely annoying. Installing incorrect drivers may have an adverse impact on the functionality of your hardware or operating system.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/IPB 4 Themes Nulled IO Get Access to Exclusive and High-Quality IPS Themes.md b/spaces/cihyFjudo/fairness-paper-search/IPB 4 Themes Nulled IO Get Access to Exclusive and High-Quality IPS Themes.md
deleted file mode 100644
index 8f9a2830ef1ab93d390fddba5928b8b54012bd45..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/IPB 4 Themes Nulled IO Get Access to Exclusive and High-Quality IPS Themes.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Sarhad Paar 4 Full Movie Download In Hindi Hd Watch the Action-Packed Sequel Online.md b/spaces/cihyFjudo/fairness-paper-search/Sarhad Paar 4 Full Movie Download In Hindi Hd Watch the Action-Packed Sequel Online.md
deleted file mode 100644
index 3b9c6ae29b93fc91f9dca3dbb902bef79f7d0d8b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Sarhad Paar 4 Full Movie Download In Hindi Hd Watch the Action-Packed Sequel Online.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
The federal indictment alleges that all 26 defendants have participated in a conspiracy since Jan. 28, 2019, to distribute crack cocaine, fentanyl, cocaine, and marijuana in Jackson County. That indictment was unsealed and made public yesterday following the arrests of most of the defendants.
According to court documents, the Bureau of Alcohol, Tobacco, Firearms and Explosives began investigating an armed drug trafficking organization operating primarily in east Kansas City, Mo., in April 2021. Members are known to sell marijuana, crack cocaine, powder cocaine, purported ecstasy pills, purported Percocet pills (believed to be counterfeit pills made with fentanyl), purported OxyContin pills, carry firearms, and commit acts of violence.
-
During 1995, he went through drug rehab twice. But over the next three years, he was arrested twice, via undercover police officers, for attempts to buy cocaine.[100] Meanwhile, he associated mainly with drug users, and his home usually had white sheets over its windows.[91] "I had gotten really bad. I mean my place was almost like a crack house," Taylor would later explain.[91]
-
In 2005, after serving time in prison for gun possession, Wilkins drove a stolen truck to Fort Worth, where police tied him to several aggravated assaults and burglaries. There he befriended two men, 40-year-old Willie Freeman and 33-year-old Mike Silva, who duped him into paying $20 for a piece of gravel he thought was a rock of crack cocaine. According to court records, Wilkins said he shot Freeman on Oct. 28, 2005, for laughing about the scam, then he shot Silva because he was there.
-
-
Mama June Shannon is opening up about her drug habits like never before. In ET's exclusive clip from Friday's episode of Mama June: From Not to Hot, titled "Family Crisis," the reality star begins the intake process at an addiction center. The previously unseen footage shows Mama June detailing her addiction to crack cocaine.
-
"[My drug of choice] has to be cocaine crack. I started snorting it, lost my vision, and then I quit and then I went into using... just smoking crack. It's weird because if you snort it, it goes up in here," she tells the intake specialist in the clip.
-
Jamaal, who was an alcoholic and a crack-cocaine addict, was devastated when he learned he was going to be transferred there from a temporary shelter created at Save-on-Foods Memorial Centre during the COVID-19 pandemic.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_tns.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_tns.c
deleted file mode 100644
index 195ff5e2b746ed58d9693de54021dad8b87aefa9..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_tns.c
+++ /dev/null
@@ -1,214 +0,0 @@
-/*
- * AAC encoder TNS
- * Copyright (C) 2015 Rostislav Pehlivanov
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * AAC encoder temporal noise shaping
- * @author Rostislav Pehlivanov ( atomnuker gmail com )
- */
-
-#include "libavutil/libm.h"
-#include "aacenc.h"
-#include "aacenc_tns.h"
-#include "aactab.h"
-#include "aacenc_utils.h"
-
-/* Could be set to 3 to save an additional bit at the cost of little quality */
-#define TNS_Q_BITS 4
-
-/* Coefficient resolution in short windows */
-#define TNS_Q_BITS_IS8 4
-
-/* We really need the bits we save here elsewhere */
-#define TNS_ENABLE_COEF_COMPRESSION
-
-/* TNS will only be used if the LPC gain is within these margins */
-#define TNS_GAIN_THRESHOLD_LOW 1.4f
-#define TNS_GAIN_THRESHOLD_HIGH 1.16f*TNS_GAIN_THRESHOLD_LOW
-
-static inline int compress_coeffs(int *coef, int order, int c_bits)
-{
- int i;
- const int low_idx = c_bits ? 4 : 2;
- const int shift_val = c_bits ? 8 : 4;
- const int high_idx = c_bits ? 11 : 5;
-#ifndef TNS_ENABLE_COEF_COMPRESSION
- return 0;
-#endif /* TNS_ENABLE_COEF_COMPRESSION */
- for (i = 0; i < order; i++)
- if (coef[i] >= low_idx && coef[i] <= high_idx)
- return 0;
- for (i = 0; i < order; i++)
- coef[i] -= (coef[i] > high_idx) ? shift_val : 0;
- return 1;
-}
-
-/**
- * Encode TNS data.
- * Coefficient compression is simply not lossless as it should be
- * on any decoder tested and as such is not active.
- */
-void ff_aac_encode_tns_info(AACEncContext *s, SingleChannelElement *sce)
-{
- TemporalNoiseShaping *tns = &sce->tns;
- int i, w, filt, coef_compress = 0, coef_len;
- const int is8 = sce->ics.window_sequence[0] == EIGHT_SHORT_SEQUENCE;
- const int c_bits = is8 ? TNS_Q_BITS_IS8 == 4 : TNS_Q_BITS == 4;
-
- if (!sce->tns.present)
- return;
-
- for (i = 0; i < sce->ics.num_windows; i++) {
- put_bits(&s->pb, 2 - is8, sce->tns.n_filt[i]);
- if (!tns->n_filt[i])
- continue;
- put_bits(&s->pb, 1, c_bits);
- for (filt = 0; filt < tns->n_filt[i]; filt++) {
- put_bits(&s->pb, 6 - 2 * is8, tns->length[i][filt]);
- put_bits(&s->pb, 5 - 2 * is8, tns->order[i][filt]);
- if (!tns->order[i][filt])
- continue;
- put_bits(&s->pb, 1, tns->direction[i][filt]);
- coef_compress = compress_coeffs(tns->coef_idx[i][filt],
- tns->order[i][filt], c_bits);
- put_bits(&s->pb, 1, coef_compress);
- coef_len = c_bits + 3 - coef_compress;
- for (w = 0; w < tns->order[i][filt]; w++)
- put_bits(&s->pb, coef_len, tns->coef_idx[i][filt][w]);
- }
- }
-}
-
-/* Apply TNS filter */
-void ff_aac_apply_tns(AACEncContext *s, SingleChannelElement *sce)
-{
- TemporalNoiseShaping *tns = &sce->tns;
- IndividualChannelStream *ics = &sce->ics;
- int w, filt, m, i, top, order, bottom, start, end, size, inc;
- const int mmm = FFMIN(ics->tns_max_bands, ics->max_sfb);
- float lpc[TNS_MAX_ORDER];
-
- for (w = 0; w < ics->num_windows; w++) {
- bottom = ics->num_swb;
- for (filt = 0; filt < tns->n_filt[w]; filt++) {
- top = bottom;
- bottom = FFMAX(0, top - tns->length[w][filt]);
- order = tns->order[w][filt];
- if (order == 0)
- continue;
-
- // tns_decode_coef
- compute_lpc_coefs(tns->coef[w][filt], order, lpc, 0, 0, 0);
-
- start = ics->swb_offset[FFMIN(bottom, mmm)];
- end = ics->swb_offset[FFMIN( top, mmm)];
- if ((size = end - start) <= 0)
- continue;
- if (tns->direction[w][filt]) {
- inc = -1;
- start = end - 1;
- } else {
- inc = 1;
- }
- start += w * 128;
-
- /* AR filter */
- for (m = 0; m < size; m++, start += inc) {
- for (i = 1; i <= FFMIN(m, order); i++) {
- sce->coeffs[start] += lpc[i-1]*sce->pcoeffs[start - i*inc];
- }
- }
- }
- }
-}
-
-/*
- * c_bits - 1 if 4 bit coefficients, 0 if 3 bit coefficients
- */
-static inline void quantize_coefs(double *coef, int *idx, float *lpc, int order,
- int c_bits)
-{
- int i;
- const float *quant_arr = tns_tmp2_map[c_bits];
- for (i = 0; i < order; i++) {
- idx[i] = quant_array_idx(coef[i], quant_arr, c_bits ? 16 : 8);
- lpc[i] = quant_arr[idx[i]];
- }
-}
-
-/*
- * 3 bits per coefficient with 8 short windows
- */
-void ff_aac_search_for_tns(AACEncContext *s, SingleChannelElement *sce)
-{
- TemporalNoiseShaping *tns = &sce->tns;
- int w, g, count = 0;
- double gain, coefs[MAX_LPC_ORDER];
- const int mmm = FFMIN(sce->ics.tns_max_bands, sce->ics.max_sfb);
- const int is8 = sce->ics.window_sequence[0] == EIGHT_SHORT_SEQUENCE;
- const int c_bits = is8 ? TNS_Q_BITS_IS8 == 4 : TNS_Q_BITS == 4;
- const int sfb_start = av_clip(tns_min_sfb[is8][s->samplerate_index], 0, mmm);
- const int sfb_end = av_clip(sce->ics.num_swb, 0, mmm);
- const int order = is8 ? 7 : s->profile == FF_PROFILE_AAC_LOW ? 12 : TNS_MAX_ORDER;
- const int slant = sce->ics.window_sequence[0] == LONG_STOP_SEQUENCE ? 1 :
- sce->ics.window_sequence[0] == LONG_START_SEQUENCE ? 0 : 2;
- const int sfb_len = sfb_end - sfb_start;
- const int coef_len = sce->ics.swb_offset[sfb_end] - sce->ics.swb_offset[sfb_start];
-
- if (coef_len <= 0 || sfb_len <= 0) {
- sce->tns.present = 0;
- return;
- }
-
- for (w = 0; w < sce->ics.num_windows; w++) {
- float en[2] = {0.0f, 0.0f};
- int oc_start = 0, os_start = 0;
- int coef_start = sce->ics.swb_offset[sfb_start];
-
- for (g = sfb_start; g < sce->ics.num_swb && g <= sfb_end; g++) {
- FFPsyBand *band = &s->psy.ch[s->cur_channel].psy_bands[w*16+g];
- if (g > sfb_start + (sfb_len/2))
- en[1] += band->energy;
- else
- en[0] += band->energy;
- }
-
- /* LPC */
- gain = ff_lpc_calc_ref_coefs_f(&s->lpc, &sce->coeffs[w*128 + coef_start],
- coef_len, order, coefs);
-
- if (!order || !isfinite(gain) || gain < TNS_GAIN_THRESHOLD_LOW || gain > TNS_GAIN_THRESHOLD_HIGH)
- continue;
-
- tns->n_filt[w] = is8 ? 1 : order != TNS_MAX_ORDER ? 2 : 3;
- for (g = 0; g < tns->n_filt[w]; g++) {
- tns->direction[w][g] = slant != 2 ? slant : en[g] < en[!g];
- tns->order[w][g] = g < tns->n_filt[w] ? order/tns->n_filt[w] : order - oc_start;
- tns->length[w][g] = g < tns->n_filt[w] ? sfb_len/tns->n_filt[w] : sfb_len - os_start;
- quantize_coefs(&coefs[oc_start], tns->coef_idx[w][g], tns->coef[w][g],
- tns->order[w][g], c_bits);
- oc_start += tns->order[w][g];
- os_start += tns->length[w][g];
- }
- count++;
- }
- sce->tns.present = !!count;
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vp9dsp_init_10bpp_arm.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vp9dsp_init_10bpp_arm.c
deleted file mode 100644
index b8cb293b20c96da08527d3f84e56b3801ba33afd..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vp9dsp_init_10bpp_arm.c
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * Copyright (c) 2017 Google Inc.
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#define BPP 10
-#define INIT_FUNC ff_vp9dsp_init_10bpp_arm
-#include "vp9dsp_init_16bpp_arm_template.c"
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libopenh264enc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libopenh264enc.c
deleted file mode 100644
index 3c605fe8ea4c9b7d7c8f5839381a0bb6d0072a5a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libopenh264enc.c
+++ /dev/null
@@ -1,454 +0,0 @@
-/*
- * OpenH264 video encoder
- * Copyright (C) 2014 Martin Storsjo
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include
-#include
-
-#include "libavutil/attributes.h"
-#include "libavutil/common.h"
-#include "libavutil/opt.h"
-#include "libavutil/internal.h"
-#include "libavutil/intreadwrite.h"
-#include "libavutil/mathematics.h"
-
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "encode.h"
-#include "internal.h"
-#include "libopenh264.h"
-
-#if !OPENH264_VER_AT_LEAST(1, 6)
-#define SM_SIZELIMITED_SLICE SM_DYN_SLICE
-#endif
-
-#define TARGET_BITRATE_DEFAULT 2*1000*1000
-
-typedef struct SVCContext {
- const AVClass *av_class;
- ISVCEncoder *encoder;
- int slice_mode;
- int loopfilter;
- int profile;
- int max_nal_size;
- int skip_frames;
- int skipped;
- int coder;
-
- // rate control mode
- int rc_mode;
-} SVCContext;
-
-#define OFFSET(x) offsetof(SVCContext, x)
-#define VE AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_ENCODING_PARAM
-#define DEPRECATED AV_OPT_FLAG_DEPRECATED
-static const AVOption options[] = {
- { "loopfilter", "enable loop filter", OFFSET(loopfilter), AV_OPT_TYPE_INT, { .i64 = 1 }, 0, 1, VE },
- { "profile", "set profile restrictions", OFFSET(profile), AV_OPT_TYPE_INT, { .i64 = FF_PROFILE_UNKNOWN }, FF_PROFILE_UNKNOWN, 0xffff, VE, "profile" },
-#define PROFILE(name, value) name, NULL, 0, AV_OPT_TYPE_CONST, { .i64 = value }, 0, 0, VE, "profile"
- { PROFILE("constrained_baseline", FF_PROFILE_H264_CONSTRAINED_BASELINE) },
- { PROFILE("main", FF_PROFILE_H264_MAIN) },
- { PROFILE("high", FF_PROFILE_H264_HIGH) },
-#undef PROFILE
- { "max_nal_size", "set maximum NAL size in bytes", OFFSET(max_nal_size), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, VE },
- { "allow_skip_frames", "allow skipping frames to hit the target bitrate", OFFSET(skip_frames), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, VE },
- { "coder", "Coder type", OFFSET(coder), AV_OPT_TYPE_INT, { .i64 = -1 }, -1, 1, VE, "coder" },
- { "default", NULL, 0, AV_OPT_TYPE_CONST, { .i64 = -1 }, INT_MIN, INT_MAX, VE, "coder" },
- { "cavlc", NULL, 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, INT_MIN, INT_MAX, VE, "coder" },
- { "cabac", NULL, 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, INT_MIN, INT_MAX, VE, "coder" },
- { "vlc", NULL, 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, INT_MIN, INT_MAX, VE, "coder" },
- { "ac", NULL, 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, INT_MIN, INT_MAX, VE, "coder" },
-
- { "rc_mode", "Select rate control mode", OFFSET(rc_mode), AV_OPT_TYPE_INT, { .i64 = RC_QUALITY_MODE }, RC_OFF_MODE, RC_TIMESTAMP_MODE, VE, "rc_mode" },
- { "off", "bit rate control off", 0, AV_OPT_TYPE_CONST, { .i64 = RC_OFF_MODE }, 0, 0, VE, "rc_mode" },
- { "quality", "quality mode", 0, AV_OPT_TYPE_CONST, { .i64 = RC_QUALITY_MODE }, 0, 0, VE, "rc_mode" },
- { "bitrate", "bitrate mode", 0, AV_OPT_TYPE_CONST, { .i64 = RC_BITRATE_MODE }, 0, 0, VE, "rc_mode" },
- { "buffer", "using buffer status to adjust the video quality (no bitrate control)", 0, AV_OPT_TYPE_CONST, { .i64 = RC_BUFFERBASED_MODE }, 0, 0, VE, "rc_mode" },
-#if OPENH264_VER_AT_LEAST(1, 4)
- { "timestamp", "bit rate control based on timestamp", 0, AV_OPT_TYPE_CONST, { .i64 = RC_TIMESTAMP_MODE }, 0, 0, VE, "rc_mode" },
-#endif
-
- { NULL }
-};
-
-static const AVClass class = {
- .class_name = "libopenh264enc",
- .item_name = av_default_item_name,
- .option = options,
- .version = LIBAVUTIL_VERSION_INT,
-};
-
-static av_cold int svc_encode_close(AVCodecContext *avctx)
-{
- SVCContext *s = avctx->priv_data;
-
- if (s->encoder)
- WelsDestroySVCEncoder(s->encoder);
- if (s->skipped > 0)
- av_log(avctx, AV_LOG_WARNING, "%d frames skipped\n", s->skipped);
- return 0;
-}
-
-static av_cold int svc_encode_init(AVCodecContext *avctx)
-{
- SVCContext *s = avctx->priv_data;
- SEncParamExt param = { 0 };
- int err;
- int log_level;
- WelsTraceCallback callback_function;
- AVCPBProperties *props;
-
- if ((err = ff_libopenh264_check_version(avctx)) < 0)
- return AVERROR_ENCODER_NOT_FOUND;
-
- if (WelsCreateSVCEncoder(&s->encoder)) {
- av_log(avctx, AV_LOG_ERROR, "Unable to create encoder\n");
- return AVERROR_UNKNOWN;
- }
-
- // Pass all libopenh264 messages to our callback, to allow ourselves to filter them.
- log_level = WELS_LOG_DETAIL;
- (*s->encoder)->SetOption(s->encoder, ENCODER_OPTION_TRACE_LEVEL, &log_level);
-
- // Set the logging callback function to one that uses av_log() (see implementation above).
- callback_function = (WelsTraceCallback) ff_libopenh264_trace_callback;
- (*s->encoder)->SetOption(s->encoder, ENCODER_OPTION_TRACE_CALLBACK, &callback_function);
-
- // Set the AVCodecContext as the libopenh264 callback context so that it can be passed to av_log().
- (*s->encoder)->SetOption(s->encoder, ENCODER_OPTION_TRACE_CALLBACK_CONTEXT, &avctx);
-
- (*s->encoder)->GetDefaultParams(s->encoder, ¶m);
-
- if (avctx->framerate.num > 0 && avctx->framerate.den > 0) {
- param.fMaxFrameRate = av_q2d(avctx->framerate);
- } else {
- if (avctx->ticks_per_frame > INT_MAX / avctx->time_base.num) {
- av_log(avctx, AV_LOG_ERROR,
- "Could not set framerate for libopenh264enc: integer overflow\n");
- return AVERROR(EINVAL);
- }
- param.fMaxFrameRate = 1.0 / av_q2d(avctx->time_base) / FFMAX(avctx->ticks_per_frame, 1);
- }
- param.iPicWidth = avctx->width;
- param.iPicHeight = avctx->height;
- param.iTargetBitrate = avctx->bit_rate > 0 ? avctx->bit_rate : TARGET_BITRATE_DEFAULT;
- param.iMaxBitrate = FFMAX(avctx->rc_max_rate, avctx->bit_rate);
- param.iRCMode = s->rc_mode;
- if (avctx->qmax >= 0)
- param.iMaxQp = av_clip(avctx->qmax, 1, 51);
- if (avctx->qmin >= 0)
- param.iMinQp = av_clip(avctx->qmin, 1, param.iMaxQp);
- param.iTemporalLayerNum = 1;
- param.iSpatialLayerNum = 1;
- param.bEnableDenoise = 0;
- param.bEnableBackgroundDetection = 1;
- param.bEnableAdaptiveQuant = 1;
- param.bEnableFrameSkip = s->skip_frames;
- param.bEnableLongTermReference = 0;
- param.iLtrMarkPeriod = 30;
- if (avctx->gop_size >= 0)
- param.uiIntraPeriod = avctx->gop_size;
-#if OPENH264_VER_AT_LEAST(1, 4)
- param.eSpsPpsIdStrategy = CONSTANT_ID;
-#else
- param.bEnableSpsPpsIdAddition = 0;
-#endif
- param.bPrefixNalAddingCtrl = 0;
- param.iLoopFilterDisableIdc = !s->loopfilter;
- param.iEntropyCodingModeFlag = s->coder >= 0 ? s->coder : 1;
- param.iMultipleThreadIdc = avctx->thread_count;
-
- /* Allow specifying the libopenh264 profile through AVCodecContext. */
- if (FF_PROFILE_UNKNOWN == s->profile &&
- FF_PROFILE_UNKNOWN != avctx->profile)
- switch (avctx->profile) {
- case FF_PROFILE_H264_HIGH:
- case FF_PROFILE_H264_MAIN:
- case FF_PROFILE_H264_CONSTRAINED_BASELINE:
- s->profile = avctx->profile;
- break;
- default:
- av_log(avctx, AV_LOG_WARNING,
- "Unsupported avctx->profile: %d.\n", avctx->profile);
- break;
- }
-
- if (s->profile == FF_PROFILE_UNKNOWN && s->coder >= 0)
- s->profile = s->coder == 0 ? FF_PROFILE_H264_CONSTRAINED_BASELINE :
-#if OPENH264_VER_AT_LEAST(1, 8)
- FF_PROFILE_H264_HIGH;
-#else
- FF_PROFILE_H264_MAIN;
-#endif
-
- switch (s->profile) {
- case FF_PROFILE_H264_HIGH:
- av_log(avctx, AV_LOG_VERBOSE, "Using %s, "
- "select EProfileIdc PRO_HIGH in libopenh264.\n",
- param.iEntropyCodingModeFlag ? "CABAC" : "CAVLC");
- break;
- case FF_PROFILE_H264_MAIN:
- av_log(avctx, AV_LOG_VERBOSE, "Using %s, "
- "select EProfileIdc PRO_MAIN in libopenh264.\n",
- param.iEntropyCodingModeFlag ? "CABAC" : "CAVLC");
- break;
- case FF_PROFILE_H264_CONSTRAINED_BASELINE:
- case FF_PROFILE_UNKNOWN:
- s->profile = FF_PROFILE_H264_CONSTRAINED_BASELINE;
- param.iEntropyCodingModeFlag = 0;
- av_log(avctx, AV_LOG_VERBOSE, "Using CAVLC, "
- "select EProfileIdc PRO_BASELINE in libopenh264.\n");
- break;
- default:
- s->profile = FF_PROFILE_H264_CONSTRAINED_BASELINE;
- param.iEntropyCodingModeFlag = 0;
- av_log(avctx, AV_LOG_WARNING, "Unsupported profile, "
- "select EProfileIdc PRO_BASELINE in libopenh264.\n");
- break;
- }
-
- param.sSpatialLayers[0].iVideoWidth = param.iPicWidth;
- param.sSpatialLayers[0].iVideoHeight = param.iPicHeight;
- param.sSpatialLayers[0].fFrameRate = param.fMaxFrameRate;
- param.sSpatialLayers[0].iSpatialBitrate = param.iTargetBitrate;
- param.sSpatialLayers[0].iMaxSpatialBitrate = param.iMaxBitrate;
- param.sSpatialLayers[0].uiProfileIdc = s->profile;
-
-#if OPENH264_VER_AT_LEAST(1, 7)
- if (avctx->sample_aspect_ratio.num && avctx->sample_aspect_ratio.den) {
- // Table E-1.
- static const AVRational sar_idc[] = {
- { 0, 0 }, // Unspecified (never written here).
- { 1, 1 }, { 12, 11 }, { 10, 11 }, { 16, 11 },
- { 40, 33 }, { 24, 11 }, { 20, 11 }, { 32, 11 },
- { 80, 33 }, { 18, 11 }, { 15, 11 }, { 64, 33 },
- { 160, 99 }, // Last 3 are unknown to openh264: { 4, 3 }, { 3, 2 }, { 2, 1 },
- };
- static const ESampleAspectRatio asp_idc[] = {
- ASP_UNSPECIFIED,
- ASP_1x1, ASP_12x11, ASP_10x11, ASP_16x11,
- ASP_40x33, ASP_24x11, ASP_20x11, ASP_32x11,
- ASP_80x33, ASP_18x11, ASP_15x11, ASP_64x33,
- ASP_160x99,
- };
- int num, den, i;
-
- av_reduce(&num, &den, avctx->sample_aspect_ratio.num,
- avctx->sample_aspect_ratio.den, 65535);
-
- for (i = 1; i < FF_ARRAY_ELEMS(sar_idc); i++) {
- if (num == sar_idc[i].num &&
- den == sar_idc[i].den)
- break;
- }
- if (i == FF_ARRAY_ELEMS(sar_idc)) {
- param.sSpatialLayers[0].eAspectRatio = ASP_EXT_SAR;
- param.sSpatialLayers[0].sAspectRatioExtWidth = num;
- param.sSpatialLayers[0].sAspectRatioExtHeight = den;
- } else {
- param.sSpatialLayers[0].eAspectRatio = asp_idc[i];
- }
- param.sSpatialLayers[0].bAspectRatioPresent = true;
- } else {
- param.sSpatialLayers[0].bAspectRatioPresent = false;
- }
-#endif
-
- if ((avctx->slices > 1) && (s->max_nal_size)) {
- av_log(avctx, AV_LOG_ERROR,
- "Invalid combination -slices %d and -max_nal_size %d.\n",
- avctx->slices, s->max_nal_size);
- return AVERROR(EINVAL);
- }
-
- if (avctx->slices > 1)
- s->slice_mode = SM_FIXEDSLCNUM_SLICE;
-
- if (s->max_nal_size)
- s->slice_mode = SM_SIZELIMITED_SLICE;
-
-#if OPENH264_VER_AT_LEAST(1, 6)
- param.sSpatialLayers[0].sSliceArgument.uiSliceMode = s->slice_mode;
- param.sSpatialLayers[0].sSliceArgument.uiSliceNum = avctx->slices;
-#else
- param.sSpatialLayers[0].sSliceCfg.uiSliceMode = s->slice_mode;
- param.sSpatialLayers[0].sSliceCfg.sSliceArgument.uiSliceNum = avctx->slices;
-#endif
- if (avctx->slices == 0 && s->slice_mode == SM_FIXEDSLCNUM_SLICE)
- av_log(avctx, AV_LOG_WARNING, "Slice count will be set automatically\n");
-
- if (s->slice_mode == SM_SIZELIMITED_SLICE) {
- if (s->max_nal_size) {
- param.uiMaxNalSize = s->max_nal_size;
-#if OPENH264_VER_AT_LEAST(1, 6)
- param.sSpatialLayers[0].sSliceArgument.uiSliceSizeConstraint = s->max_nal_size;
-#else
- param.sSpatialLayers[0].sSliceCfg.sSliceArgument.uiSliceSizeConstraint = s->max_nal_size;
-#endif
- } else {
- av_log(avctx, AV_LOG_ERROR, "Invalid -max_nal_size, "
- "specify a valid max_nal_size to use -slice_mode dyn\n");
- return AVERROR(EINVAL);
- }
- }
-
-#if OPENH264_VER_AT_LEAST(1, 6)
- param.sSpatialLayers[0].uiVideoFormat = VF_UNDEF;
-
- if (avctx->color_range != AVCOL_RANGE_UNSPECIFIED) {
- param.sSpatialLayers[0].bFullRange = (avctx->color_range == AVCOL_RANGE_JPEG);
- } else if (avctx->pix_fmt == AV_PIX_FMT_YUVJ420P)
- param.sSpatialLayers[0].bFullRange = 1;
-
- if (avctx->colorspace != AVCOL_SPC_UNSPECIFIED ||
- avctx->color_primaries != AVCOL_PRI_UNSPECIFIED ||
- avctx->color_trc != AVCOL_TRC_UNSPECIFIED) {
- param.sSpatialLayers[0].bColorDescriptionPresent = true;
- }
-
- if (avctx->colorspace != AVCOL_SPC_UNSPECIFIED)
- param.sSpatialLayers[0].uiColorMatrix = avctx->colorspace;
- if (avctx->color_primaries != AVCOL_PRI_UNSPECIFIED)
- param.sSpatialLayers[0].uiColorPrimaries = avctx->color_primaries;
- if (avctx->color_trc != AVCOL_TRC_UNSPECIFIED)
- param.sSpatialLayers[0].uiTransferCharacteristics = avctx->color_trc;
-
- param.sSpatialLayers[0].bVideoSignalTypePresent =
- (param.sSpatialLayers[0].bFullRange || param.sSpatialLayers[0].bColorDescriptionPresent);
-#endif
-
- if ((*s->encoder)->InitializeExt(s->encoder, ¶m) != cmResultSuccess) {
- av_log(avctx, AV_LOG_ERROR, "Initialize failed\n");
- return AVERROR_UNKNOWN;
- }
-
- if (avctx->flags & AV_CODEC_FLAG_GLOBAL_HEADER) {
- SFrameBSInfo fbi = { 0 };
- int i, size = 0;
- (*s->encoder)->EncodeParameterSets(s->encoder, &fbi);
- for (i = 0; i < fbi.sLayerInfo[0].iNalCount; i++)
- size += fbi.sLayerInfo[0].pNalLengthInByte[i];
- avctx->extradata = av_mallocz(size + AV_INPUT_BUFFER_PADDING_SIZE);
- if (!avctx->extradata)
- return AVERROR(ENOMEM);
- avctx->extradata_size = size;
- memcpy(avctx->extradata, fbi.sLayerInfo[0].pBsBuf, size);
- }
-
- props = ff_add_cpb_side_data(avctx);
- if (!props)
- return AVERROR(ENOMEM);
- props->max_bitrate = param.iMaxBitrate;
- props->avg_bitrate = param.iTargetBitrate;
-
- return 0;
-}
-
-static int svc_encode_frame(AVCodecContext *avctx, AVPacket *avpkt,
- const AVFrame *frame, int *got_packet)
-{
- SVCContext *s = avctx->priv_data;
- SFrameBSInfo fbi = { 0 };
- int i, ret;
- int encoded;
- SSourcePicture sp = { 0 };
- int size = 0, layer, first_layer = 0;
- int layer_size[MAX_LAYER_NUM_OF_FRAME] = { 0 };
-
- sp.iColorFormat = videoFormatI420;
- for (i = 0; i < 3; i++) {
- sp.iStride[i] = frame->linesize[i];
- sp.pData[i] = frame->data[i];
- }
- sp.iPicWidth = avctx->width;
- sp.iPicHeight = avctx->height;
-
- if (frame->pict_type == AV_PICTURE_TYPE_I) {
- (*s->encoder)->ForceIntraFrame(s->encoder, true);
- }
-
- encoded = (*s->encoder)->EncodeFrame(s->encoder, &sp, &fbi);
- if (encoded != cmResultSuccess) {
- av_log(avctx, AV_LOG_ERROR, "EncodeFrame failed\n");
- return AVERROR_UNKNOWN;
- }
- if (fbi.eFrameType == videoFrameTypeSkip) {
- s->skipped++;
- av_log(avctx, AV_LOG_DEBUG, "frame skipped\n");
- return 0;
- }
- first_layer = 0;
- // Normal frames are returned with one single layer, while IDR
- // frames have two layers, where the first layer contains the SPS/PPS.
- // If using global headers, don't include the SPS/PPS in the returned
- // packet - thus, only return one layer.
- if (avctx->flags & AV_CODEC_FLAG_GLOBAL_HEADER)
- first_layer = fbi.iLayerNum - 1;
-
- for (layer = first_layer; layer < fbi.iLayerNum; layer++) {
- for (i = 0; i < fbi.sLayerInfo[layer].iNalCount; i++)
- layer_size[layer] += fbi.sLayerInfo[layer].pNalLengthInByte[i];
- size += layer_size[layer];
- }
- av_log(avctx, AV_LOG_DEBUG, "%d slices\n", fbi.sLayerInfo[fbi.iLayerNum - 1].iNalCount);
-
- if ((ret = ff_get_encode_buffer(avctx, avpkt, size, 0)))
- return ret;
-
- size = 0;
- for (layer = first_layer; layer < fbi.iLayerNum; layer++) {
- memcpy(avpkt->data + size, fbi.sLayerInfo[layer].pBsBuf, layer_size[layer]);
- size += layer_size[layer];
- }
- avpkt->pts = frame->pts;
- if (fbi.eFrameType == videoFrameTypeIDR)
- avpkt->flags |= AV_PKT_FLAG_KEY;
- *got_packet = 1;
- return 0;
-}
-
-static const FFCodecDefault svc_enc_defaults[] = {
- { "b", "0" },
- { "g", "-1" },
- { "qmin", "-1" },
- { "qmax", "-1" },
- { NULL },
-};
-
-const FFCodec ff_libopenh264_encoder = {
- .p.name = "libopenh264",
- CODEC_LONG_NAME("OpenH264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10"),
- .p.type = AVMEDIA_TYPE_VIDEO,
- .p.id = AV_CODEC_ID_H264,
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_OTHER_THREADS |
- AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
- .priv_data_size = sizeof(SVCContext),
- .init = svc_encode_init,
- FF_CODEC_ENCODE_CB(svc_encode_frame),
- .close = svc_encode_close,
- .caps_internal = FF_CODEC_CAP_INIT_CLEANUP |
- FF_CODEC_CAP_AUTO_THREADS,
- .p.pix_fmts = (const enum AVPixelFormat[]){ AV_PIX_FMT_YUV420P,
- AV_PIX_FMT_YUVJ420P,
- AV_PIX_FMT_NONE },
- .defaults = svc_enc_defaults,
- .p.priv_class = &class,
- .p.wrapper_name = "libopenh264",
-};
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Angry Birds Classic The Original Slingshot Game for PC.md b/spaces/congsaPfin/Manga-OCR/logs/Angry Birds Classic The Original Slingshot Game for PC.md
deleted file mode 100644
index 106e553917a63d5b238b47483e87a39a4917f122..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Angry Birds Classic The Original Slingshot Game for PC.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-
` for the title, `
` for the main headings, `
` for the subheadings, and `
` for the sub-subheadings. 8. Eighth, in the very bottom of the article write this custom message " Here is an example of how your tables might look like: | Outline | Article | | --- | --- | | Introduction | `
Angry Birds Classic APK PC: Everything You Need to Know
`
Are you a fan of Angry Birds, the classic bird-flinging game that started a global phenomenon? If so, you might be interested in playing Angry Birds Classic on your PC using an APK file.
An APK file is an Android application package that contains all the files and data needed to run an app on an Android device. By using an emulator software, you can install and run APK files on your PC and enjoy Android games on a bigger screen.
In this article, we will show you how to download Angry Birds Classic APK for PC, what are the features of this game, some tips and tricks for playing it, some history and reviews of this game, and some FAQs that you might have.
So let's get started!
| | What is Angry Birds Classic? | `
What is Angry Birds Classic?
`
Angry Birds Classic is a casual puzzle video game developed by Finnish video game developer Rovio Entertainment in 2009. Inspired by a sketch of stylized wingless birds, the game was first released for iOS and Maemo devices starting in December 2009.
The gameplay revolves around players using a slingshot to launch birds at green pigs stationed in or around various structures, with the intent of destroying all the pigs on
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Enjoy Hundreds of Cable TV Channels with Ola TV APK on FireStick and Android [2023 Version].md b/spaces/congsaPfin/Manga-OCR/logs/Enjoy Hundreds of Cable TV Channels with Ola TV APK on FireStick and Android [2023 Version].md
deleted file mode 100644
index 01f48039c526d4068df3595ca4fb41f3a80f956d..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Enjoy Hundreds of Cable TV Channels with Ola TV APK on FireStick and Android [2023 Version].md
+++ /dev/null
@@ -1,175 +0,0 @@
-
-
Ola TV 2023 APK: How to Watch Free Live TV on Your Android Device
-
Do you want to watch live TV channels from around the world on your Android device? If yes, then you should try Ola TV, a free app that lets you stream hundreds of channels in various categories. In this article, we will tell you everything you need to know about Ola TV, including its features, how to download and install it on Android and Firestick, how to use it, and its pros and cons.
-
What is Ola TV?
-
Ola TV is a third-party app that allows you to watch live TV channels from different countries, such as the USA, UK, Canada, India, Brazil, UAE, and more. It has a simple and user-friendly interface that lets you choose from various channel categories, such as entertainment, sports, news, movies, music, kids, etc. You can also search for your favorite channels by name or country.
Ola TV does not host any content on its own servers. Instead, it scrapes links from various sources on the internet and provides them to you in one place. This means that the quality and availability of the channels may vary depending on the source. However, Ola TV usually offers multiple links for each channel, so you can always switch to another one if one is not working.
-
Features of Ola TV
-
Some of the features that make Ola TV a great app for watching live TV are:
-
-
It is free to use and does not require any registration or subscription.
-
It has a huge collection of channels from different countries and genres.
-
It supports multiple video players, such as Kshaw, Ludio Player, MX Player, VLC Player, etc.
-
It has a built-in chat feature that lets you interact with other users and get support from the developers.
-
It has a dark mode option that reduces eye strain and saves battery life.
-
It has an auto-update feature that keeps the app updated with the latest version and channels.
-
-
How to Download and Install Ola TV on Android
-
Ola TV is not available on the Google Play Store because it is a third-party app that may violate some of Google's policies. Therefore, you need to download and install it manually from its official website or other trusted sources. Here are the steps to do so:
Scroll down and click on the "Download" button under the "OlaTV APK" section.
-
A pop-up window will appear asking you to confirm the download. Click on "OK".
-
The APK file will start downloading on your device. You can check the progress in the notification bar.
-
Once the download is complete, open the file manager app on your device and locate the downloaded APK file.
-
Tap on the file and a prompt will appear asking you to enable unknown sources. This is necessary because you are installing an app from outside the Play Store. To enable unknown sources, go to Settings > Security > Unknown Sources and toggle it on.
-
Now go back to the file manager app and tap on the APK file again. This time, you will see an installation screen. Click on "Install".
-
The app will start installing on your device. Wait for a few seconds until it is done.Congratulations! You have successfully installed Ola TV on your Android device. You can now enjoy watching free live TV channels on your phone or tablet.
-
How to Install Ola TV on Firestick
-
If you have a Firestick device, you can also install Ola TV on it and watch live TV on your big screen. However, the process is slightly different from installing it on Android. Here are the steps to follow:
-
What is Firestick?
-
Firestick is a small device that plugs into your TV's HDMI port and turns it into a smart TV. It allows you to stream content from various apps and services, such as Netflix, Amazon Prime Video, Hulu, YouTube, etc. You can also download and install third-party apps that are not available on the official Amazon App Store, such as Ola TV.
-
Why You Need a VPN for Firestick
-
Before you install Ola TV on your Firestick, you need to get a VPN service. A VPN, or Virtual Private Network, is a tool that encrypts your internet traffic and hides your IP address. This way, you can access geo-restricted content, bypass censorship, and protect your privacy online.
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A VPN is especially important for Firestick users because some of the third-party apps that you install may contain pirated or illegal content. If you stream such content without a VPN, you may face legal issues or get in trouble with your ISP. Therefore, it is highly recommended that you use a VPN whenever you use Firestick.
-
There are many VPN services available in the market, but not all of them are compatible with Firestick or offer good speed and security. One of the best VPNs for Firestick is ExpressVPN, which has a dedicated app for Firestick, fast servers in over 90 countries, and strong encryption and privacy features. You can get ExpressVPN from https://www.expressvpn.com/.
-
Steps to Install Ola TV on Firestick
-
Once you have a VPN service, you can proceed to install Ola TV on your Firestick. Here are the steps to do so:
-
-
Turn on your Firestick and go to the home screen.
-
Go to Settings > My Fire TV > Developer Options and enable Apps from Unknown Sources. This will allow you to install third-party apps on your Firestick.
-
Go back to the home screen and go to the Search icon (the magnifying glass) in the top-left corner.
-
Type in "Downloader" and select the app with the orange icon from the results.
-
Click on Download and wait for the app to install.
-
Open the Downloader app and enter https://olatv.me/ in the URL box. This is the official website of Ola TV.
-
Scroll down and click on the "Download" button under the "OlaTV APK" section.
-
A pop-up window will appear asking you to confirm the download. Click on "OK".
-
The APK file will start downloading on your Firestick. You can check the progress in the bottom-right corner.
-
Once the download is complete, a new window will appear asking you to install the app. Click on "Install".
-
The app will start installing on your Firestick. Wait for a few seconds until it is done.
-
Click on "Done" and then click on "Delete" to remove the APK file from your Firestick. This will save some space on your device.
-
Go back to the home screen and go to Your Apps & Channels > See All. You will see Ola TV at the bottom of the list.
-
Select Ola TV and press the menu button (the three horizontal lines) on your remote. Then select Move to Front. This will bring Ola TV to the front of your apps list for easy access.
-
-
Congratulations! You have successfully installed Ola TV on your Firestick. You can now enjoy watching free live TV channels on your TV.
-
How to Use Ola TV on Android and Firestick
-
Ola TV has a similar interface and functionality on both Android and Firestick devices. Here are some tips on how to use it:
-
How to Choose a Channel Category
-
When you open Ola TV, you will see a list of channel categories on the left side of the screen. You can scroll up and down to browse through them. Some of the categories are:
-
-
All Channels: This shows all the channels available on Ola TV, sorted by country and genre.
-
Entertainment: This shows channels that offer movies, TV shows, comedy, drama, etc.
-
Sports: This shows channels that cover various sports, such as football, cricket, basketball, tennis, etc.
-
News: This shows channels that provide news and current affairs from different regions and perspectives.
-
Movies: This shows channels that exclusively stream movies of different genres and languages.
-
Music: This shows channels that play music videos, concerts, and radio stations.
-
Kids: This shows channels that are suitable for children, such as cartoons, animations, educational programs, etc.
-
-
You can also use the search icon (the magnifying glass) at the top-right corner to search for a specific channel by name or country. For example, you can type in "CNN" or "USA" to find channels related to them.
-
How to Play a Channel
-
Once you have chosen a channel category, you will see a list of channels on the right side of the screen. You can scroll up and down to browse through them. Each channel has a name, a logo, and a number of links. The links indicate how many sources are available for that channel. For example, if a channel has 5 links, it means that there are 5 different sources that you can choose from to watch that channel.
-
To play a channel, simply click on it and a pop-up window will appear with the links. You can select any link and the channel will start playing on your default video player. If the link is not working or the quality is poor, you can go back and try another link. You can also change the video player by clicking on the three dots icon at the top-right corner of the pop-up window.
-
How to Change the Video Player
-
Ola TV supports multiple video players, such as Kshaw, Ludio Player, MX Player, VLC Player, etc. You can choose which video player you want to use to watch the channels. To do so, follow these steps:
-
-
Go to Settings > Video Players on Ola TV.
-
You will see a list of video players that are compatible with Ola TV. You can select one or more video players as your preferred ones.
-
When you play a channel, you will see an option to choose your preferred video player from the list. You can also select "Always Ask" if you want to choose a different video player every time.
-
-
Note: Some video players may require additional codecs or plugins to play some channels. You can download them from their respective websites or app stores.
-
Pros and Cons of Ola TV
-
Ola TV is a great app for watching live TV on your Android or Firestick device. However, like any other app, it has its advantages and disadvantages. Here are some of them:
-
Pros
-
-
It is free to use and does not require any registration or subscription.
-
It has a huge collection of channels from different countries and genres.
-
It supports multiple video players and has a built-in chat feature.
-
It has a dark mode option and an auto-update feature.
-
-
Cons
-
-
It may contain some ads or pop-ups that may be annoying or intrusive.
-
It may not work in some regions or countries due to geo-restrictions or censorship.
-
It may not offer HD quality or stable streaming for some channels or links.
-
It may pose some legal or security risks if you stream pirated or illegal content without a VPN.
-
-
Conclusion
-
Ola TV is one of the best apps for watching free live TV on your Android or Firestick device. It offers hundreds of channels from different countries and categories that you can stream with ease. It also has some useful features and options that enhance your viewing experience. However, you should also be aware of its drawbacks and risks and use it responsibly and safely. We hope this article has helped you learn more about Ola TV and how to use it. If you have any questions or feedback, feel free to leave them in the comments below.
-
Frequently Asked Questions
-
Here are some of the most common questions that users have about Ola TV:
-
-
Is Ola TV legal?
-
Ola TV is legal as long as you use it to stream content that is in the public domain or licensed for free distribution. However, some of the content that Ola TV provides may be pirated or illegal in some regions or countries. If you stream such content without a VPN, you may face legal issues or get in trouble with your ISP. Therefore, it is highly recommended that you use a VPN whenever you use Ola TV.
-
Is Ola TV safe?
-
Ola TV is safe to use as long as you download it from its official website or other trusted sources. However, some of the links that Ola TV provides may contain malware or viruses that may harm your device or compromise your security. Therefore, it is advisable that you scan the links before opening them and use a reliable antivirus software on your device.
-
How to update Ola TV?
-
Ola TV has an auto-update feature that keeps the app updated with the latest version and channels. You can also check for updates manually by going to Settings > Check for Updates on Ola TV. If there is a new version available, you can download and install it from the same website or source that you downloaded the app from.
-
How to fix Ola TV not working?
-
If Ola TV is not working properly, you can try some of the following solutions:
-
-
Clear the cache and data of Ola TV by going to Settings > Apps > Ola TV > Storage > Clear Cache and Clear Data on your device.
-
Restart your device and try opening Ola TV again.
-
Check your internet connection and make sure it is stable and fast.
-
Use a VPN to bypass any geo-restrictions or censorship that may block Ola TV.
-
Change the video player or the link if the channel is not playing or buffering.
-
Contact the developers of Ola TV through their chat feature or email if none of the above solutions work.
-
-
How to uninstall Ola TV?
-
If you want to uninstall Ola TV from your device, you can follow these steps:
-
-
Go to Settings > Apps on your device and find Ola TV.
-
Tap on Ola TV and then tap on Uninstall.
-
Confirm the uninstallation by tapping on OK.
-
Ola TV will be removed from your device.
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Parking Master Multiplayer 2 APK Cheat Unlock All Cars and Levels.md b/spaces/congsaPfin/Manga-OCR/logs/Parking Master Multiplayer 2 APK Cheat Unlock All Cars and Levels.md
deleted file mode 100644
index 4c3f0e1461a96b16750a30f7b0a7c29f7dedd32d..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Parking Master Multiplayer 2 APK Cheat Unlock All Cars and Levels.md
+++ /dev/null
@@ -1,61 +0,0 @@
-
-
Parking Master Multiplayer 2 APK Cheat: How to Download and Use It
-
Introduction
-
If you are a fan of car parking games, you might have heard of Parking Master Multiplayer 2, a simulation game that lets you drive and park various cars in realistic scenarios. You can also compete with other players online and show off your parking skills. However, the game can be challenging and frustrating at times, especially when you run out of fuel, encounter annoying ads, or miss out on rewards. That's why you might want to use Parking Master Multiplayer 2 APK Cheat, a modded version of the game that gives you access to unlimited resources and features. In this article, we will show you how to download and use Parking Master Multiplayer 2 APK Cheat and enjoy the game like never before.
Parking Master Multiplayer 2 is a simulation game developed by Spektra Games and released in September 2022. The game features realistic graphics, physics, and sounds that make you feel like you are driving a real car. You can choose from over 100 cars, from sports cars to trucks, and customize them with different colors, wheels, and stickers. You can also explore an open-world map with various locations, such as city streets, parking lots, airports, and more. The game has different modes, such as single-player, multiplayer, free roam, and challenges. You can also earn coins and gems by completing tasks and achievements.
-
What is Parking Master Multiplayer 2 APK Cheat?
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Parking Master Multiplayer 2 APK Cheat is a modified version of the original game that unlocks all the features and resources that are normally limited or paid in the game. By using this cheat, you can get unlimited fuel, no ads, free rewards, and a mega menu that lets you control various aspects of the game. You can also access all the cars and customizations without spending any money. With this cheat, you can enjoy the game without any restrictions or interruptions.
-
Why use Parking Master Multiplayer 2 APK Cheat?
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There are many reasons why you might want to use Parking Master Multiplayer 2 APK Cheat. Here are some of them:
-
-
You can save time and money by getting unlimited fuel, no ads, free rewards, and all the cars and customizations for free.
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You can have more fun and excitement by using the mega menu to change the weather, time, traffic, camera angle, car speed, and more.
-
You can improve your parking skills by practicing with different cars and scenarios without worrying about running out of fuel or failing the tasks.
-
You can impress your friends and other players online by showing off your amazing cars and parking abilities.
-
-
How to Download Parking Master Multiplayer 2 APK Cheat
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If you are interested in downloading Parking Master Multiplayer 2 APK Cheat, here are the steps you need to follow:
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Step 1: Find a reliable source
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The first thing you need to do is find a reliable source that offers the latest version of Parking Master Multiplayer 2 APK Cheat. You can search online for websites or blogs that provide modded games or apps. However, be careful not to download from shady or untrusted sources that might contain viruses or malware. One of the sources we recommend is Moddroid.com, which offers safe and updated modded games or apps. However, be careful not to download from shady or untrusted sources that might contain viruses or malware. One of the sources we recommend is Moddroid.com, which offers safe and updated versions of Parking Master Multiplayer 2 APK Cheat.
-
Step 2: Download the APK file
-
Once you have found a reliable source, you need to download the APK file of Parking Master Multiplayer 2 APK Cheat. The APK file is a package that contains the installation files and data of the modded game. To download the APK file, you need to click on the download button or link provided by the source. You might need to wait for a few seconds or minutes for the download to start or complete. You can also check the progress of the download in your browser or notification bar.
-
Step 3: Install the APK file
-
After you have downloaded the APK file, you need to install it on your device. To do this, you need to enable the installation of apps from unknown sources in your device settings. This will allow you to install apps that are not from the official Google Play Store. To enable this option, you need to go to Settings > Security > Unknown Sources and toggle it on. Then, you need to locate the APK file in your device storage and tap on it to start the installation. You might need to grant some permissions and follow some instructions to complete the installation.
-
Step 4: Launch the game and enjoy the cheat features
-
Once you have installed the APK file, you can launch the game and enjoy the cheat features. You will see a new icon of Parking Master Multiplayer 2 APK Cheat on your device home screen or app drawer. You can tap on it to open the game and start playing. You will notice that you have unlimited fuel, no ads, free rewards, and a mega menu that lets you control various aspects of the game. You can also access all the cars and customizations without spending any money. With this cheat, you can enjoy the game without any restrictions or interruptions.
-
How to Use Parking Master Multiplayer 2 APK Cheat
-
Now that you have downloaded and installed Parking Master Multiplayer 2 APK Cheat, you might be wondering how to use it and what are its features. Here are some of the features and how to use them:
-
Unlimited Fuel
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One of the features of Parking Master Multiplayer 2 APK Cheat is unlimited fuel. This means that you can drive and park as long as you want without worrying about running out of fuel or refilling it. This will save you time and money and allow you to explore more locations and scenarios in the game. To use this feature, you just need to launch the game and start driving. You will see that your fuel gauge will never go down no matter how much you drive.
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No Ads
-
Another feature of Parking Master Multiplayer 2 APK Cheat is no ads. This means that you will not see any annoying ads or pop-ups that interrupt your gameplay or distract you from your tasks. This will make your gaming experience more smooth and enjoyable and prevent you from losing your focus or patience. To use this feature, you just need to launch the game and play as usual. You will see that there will be no ads or banners anywhere in the game.
-
Free Rewards
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A third feature of Parking Master Multiplayer 2 APK Cheat is free rewards. This means that you can get unlimited coins and gems by watching videos, spinning wheels, opening chests, or completing achievements. These coins and gems are used to buy new cars, customizations, or upgrades in the game. This will help you get more variety and fun in the game and improve your parking skills and performance. To use this feature, you just need to launch the game and go to the reward section. You will see that there will be no limits or restrictions on how many rewards you can get.
-
Mega Menu
-
A fourth feature of Parking Master Multiplayer 2 APK Cheat is mega menu. This is a special menu that lets you control various aspects of the game, such as weather, time, traffic, camera angle, car speed, and more. This will give you more freedom and creativity in the game and allow you to customize your gameplay according to your preferences and mood. To use this feature, you just need to launch the game and tap on the mega menu icon on the top left corner of the screen. You will see a list of options that you can toggle on or off or adjust as you like.
-
Conclusion
-
Parking Master Multiplayer 2 is a simulation game that lets you drive and park various cars in realistic scenarios. You can also compete with other players online and show off your parking skills. However, the game can be challenging and frustrating at times, especially when you run out of fuel, encounter annoying ads, or miss out on rewards. That's why you might want to use Parking Master Multiplayer 2 APK Cheat, a modded version of the game that gives you access to unlimited resources and features. By using this cheat, you can get unlimited fuel, no ads, free rewards, and a mega menu that lets you control various aspects of the game. You can also access all the cars and customizations without spending any money. With this cheat, you can enjoy the game without any restrictions or interruptions.
-
In this article, we have shown you how to download and use Parking Master Multiplayer 2 APK Cheat and enjoy the game like never before. We have also explained the features and benefits of using this cheat and how to use them. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy parking!
-
FAQs
-
Here are some of the frequently asked questions about Parking Master Multiplayer 2 APK Cheat:
-
-
Is Parking Master Multiplayer 2 APK Cheat safe to use?
-
Yes, Parking Master Multiplayer 2 APK Cheat is safe to use as long as you download it from a reliable source and follow the installation instructions carefully. However, we cannot guarantee that it will work on all devices or that it will not cause any issues with your device or game. Use it at your own risk and discretion.
-
Is Parking Master Multiplayer 2 APK Cheat legal to use?
-
No, Parking Master Multiplayer 2 APK Cheat is not legal to use as it violates the terms and conditions of the original game and its developers. By using this cheat, you are also cheating other players who play the game fairly and honestly. Therefore, we do not endorse or encourage the use of this cheat. Use it at your own risk and responsibility.
-
Will Parking Master Multiplayer 2 APK Cheat affect my game progress or data?
-
No, Parking Master Multiplayer 2 APK Cheat will not affect your game progress or data as it is a separate version of the game that does not interfere with the original game. However, we recommend that you backup your game data before using this cheat in case anything goes wrong or you want to switch back to the original game.
-
Can I play online with Parking Master Multiplayer 2 APK Cheat?
-
Yes, you can play online with Parking Master Multiplayer 2 APK Cheat as it supports multiplayer mode. However, we advise that you do not use this cheat online as it might ruin the fun and fairness of the game for other players. You might also get banned or reported by other players or the game developers if they detect that you are using this cheat.
-
Can I update Parking Master Multiplayer 2 APK Cheat?
-
No, you cannot update Parking Master Multiplayer 2 APK Cheat as it is a modded version of the game that does not receive official updates from the game developers. If you want to update the game, you need to download and install the latest version of Parking Master Multiplayer 2 APK Cheat from a reliable source or switch back to the original game.
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How to Download ISO Files for AetherSX2: The Best PS2 Emulator for Android
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If you are a fan of PlayStation 2 games and want to enjoy them on your Android device, you need a reliable and powerful emulator that can run them smoothly. AetherSX2 is one of the best PS2 emulators for Android that can do just that. But before you can play your favorite PS2 games on AetherSX2, you need to download their disc images in the form of ISO files. In this article, we will show you how to download ISO files for AetherSX2 from various sources, how to install them on your PC, how to transfer them to your Android device, and how to play them on AetherSX2.
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What is AetherSX2 and why is it the best PS2 emulator for Android
-
AetherSX2 is a PlayStation 2 emulator that enables users to play PS2 games on their devices. With AetherSX2, iOS, Android, Mac, Windows, Linux users can play their favorite games from the PS2 console without the need for a physical PS2 console. AetherSX2 is designed to provide a seamless gaming experience on their devices.
AetherSX2 is based on PCSX2, a long-running, well-established emulator on PC. The developer of AetherSX2 got the permission from the PCSX2 team to use their code and is licensed under the LGPL license. Unlike some other PS2 emulators that have been accused of stealing code and violating licenses, AetherSX2 is free, legal, and ethical.
-
AetherSX2 offers many features that make it stand out as the best PS2 emulator for Android. Some of these features are:
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-
High compatibility: AetherSX2 supports a wide range of PS2 games, from popular titles like Final Fantasy X, God of War, Kingdom Hearts, Metal Gear Solid, Grand Theft Auto, Resident Evil, Silent Hill, etc., to lesser-known gems like Okami, Shadow of the Colossus, Persona, Katamari Damacy, etc.
-
High performance: AetherSX2 runs smoothly on most modern Android devices with Snapdragon processors. The developer recommends having at least four large CPU cores (Cortex-A75 or higher) and an Adreno GPU for optimal performance. You can also choose between Vulkan or OpenGL graphics renderers depending on which one works better for your device and game.
-
High customization: AetherSX2 allows you to adjust various settings and options to suit your preferences and needs. You can change the resolution, aspect ratio, frame rate, audio quality, controller layout, cheats, etc. You can also use different BIOS files and plugins for different games.
-
High convenience: AetherSX2 lets you save and load your game progress anytime and anywhere. You can also use save states to create multiple snapshots of your game and resume from any point. You can also fast-forward or rewind your game speed to skip or replay scenes.
-
High compatibility: AetherSX2 supports multiplayer gaming with other players online or locally. You can use Netplay to connect with other AetherSX2 users over the internet and play co-op or versus games. You can also use local multiplayer to play with up to four players on the same device using split-screen or external controllers.
-
-
With all these features and more, AetherSX2 is undoubtedly the best PS2 emulator for Android that you can find. But before you can enjoy playing PS2 games on AetherSX2, you need to download their ISO files first.
-
What are ISO files and why do you need them to play PS2 games on AetherSX2
-
ISO files are disc images that contain all the data and information of a CD, DVD, or Blu-ray disc. They are often used to backup, distribute, or install software programs, operating systems, or games. ISO files have the .iso extension and can be opened by various software tools.
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To play PS2 games on AetherSX2, you need to have their ISO files on your device. This is because AetherSX2 emulates the PS2 hardware and software, but it does not have the PS2 discs that contain the game data. Therefore, you need to provide the game data in the form of ISO files that AetherSX2 can read and load.
-
There are two main ways to get ISO files for PS2 games: you can either download them from the internet or create them from your own PS2 discs. In this article, we will focus on the first method, as it is easier and more convenient. However, you should be aware that downloading ISO files for PS2 games may be illegal in some countries or regions, depending on the copyright laws and regulations. Therefore, you should only download ISO files for PS2 games that you own legally and use them for personal and non-commercial purposes only.
-
How to Download ISO Files from the Internet
-
There are many websites that offer ISO files for various software programs, operating systems, or games. However, not all of them are safe, reliable, or legal. Some of them may contain malware, viruses, or spyware that can harm your device or compromise your privacy. Some of them may also provide fake, corrupted, or incomplete ISO files that will not work properly or at all. Therefore, you should be careful and selective when choosing where to download ISO files from.
-
How to download iso file for aethersx2 emulator
-Aethersx2 apk download and iso setup guide
-Best PS2 games to play on aethersx2 with iso files
-Aethersx2 vs PCSX2: Which PS2 emulator is better?
-Where to find iso files for aethersx2 compatible games
-Aethersx2 bios download and installation tutorial
-How to burn iso file to disc for aethersx2 backup
-Aethersx2 settings and configuration tips for optimal performance
-How to mount iso file with virtual drive for aethersx2
-Aethersx2 cheats and hacks for PS2 games
-Aethersx2 controller setup and mapping guide
-How to convert bin to iso file for aethersx2
-Aethersx2 multiplayer and online gaming support
-How to extract iso file from PS2 disc for aethersx2
-Aethersx2 save states and memory cards management
-How to compress iso file for aethersx2 storage
-Aethersx2 compatibility list and game reviews
-How to edit iso file for aethersx2 modding
-Aethersx2 graphics and sound enhancements
-How to split iso file for aethersx2 loading
-Aethersx2 troubleshooting and error fixing guide
-How to create iso file from hard disk files for aethersx2
-Aethersx2 update and new features overview
-How to encrypt iso file for aethersx2 security
-Aethersx2 custom themes and skins download
-How to verify iso file integrity for aethersx2
-Aethersx2 system requirements and device compatibility
-How to open iso file with PowerISO for aethersx2
-Aethersx2 FAQs and community forums
-How to rip audio CD to iso file for aethersx2 music playback
-Aethersx2 donation and support options
-How to join iso file parts for aethersx2 game loading
-Aethersx2 development and history background
-How to rename iso file for aethersx2 organization
-Aethersx2 alternatives and similar apps comparison
-How to copy iso file to USB drive for aethersx2 portability
-Aethersx2 keyboard shortcuts and commands list
-How to view iso file contents for aethersx2 inspection
-Aethersx2 license and legal issues explanation
-How to make bootable iso file for aethersx2 installation
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In this section, we will show you how to download ISO files from some of the most trusted and reputable sources on the internet. We will cover how to download Windows and Office ISO files using Microsoft Windows and Office ISO Download Tool, how to download other Windows ISO files using Rufus, and how to download PS2 game ISO files using other websites.
-
How to use Microsoft Windows and Office ISO Download Tool to download Windows and Office ISO files
-
If you want to download Windows or Office ISO files for your PC, one of the easiest and safest ways is to use Microsoft Windows and Office ISO Download Tool. This is a free tool that allows you to download genuine Windows 7, 8.1, 10, 11, and Office 2007-2021 ISO files directly from Microsoft servers. Here are the steps to use this tool:
-
-
Download Microsoft Windows and Office ISO Download Tool from here and run it on your PC.
-
Select the edition of Windows or Office that you want to download from the drop-down menu.
-
Select the language of your choice from the drop-down menu.
-
Select whether you want a 32-bit or 64-bit version of the ISO file.
-
Click on the "Copy Link" button to copy the download link of the ISO file.
-
Paste the link into your browser's address bar and press Enter.
-
The download will start automatically. Save the ISO file in a location of your choice on your PC.
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-
Alternatively, you can also use the "Download" button to download the ISO file directly using your default browser or a download manager of your choice.
-
How to use Rufus to download other Windows ISO files
-
If you want to download other Windows ISO files that are not available on Microsoft Windows and Office ISO Download Tool, such as Windows XP, Vista, Server, or Enterprise editions, you can use Rufus. Rufus is a free and open-source tool that allows you to create bootable USB drives from ISO files. It also has a feature that lets you download various Windows ISO files from Microsoft servers or third-party sources. Here are the steps to use Rufus to download Windows ISO files:
Connect a USB drive to your PC and select it from the "Device" drop-down menu.
-
Click on the "SELECT" button and choose "DOWNLOAD" from the pop-up menu.
-
Select the edition of Windows that you want to download from the list.
-
Select the language of your choice from the list.
-
Select whether you want a 32-bit or 64-bit version of the ISO file.
-
Click on the "DOWNLOAD" button and wait for the download to complete.
-
The ISO file will be saved in the same folder as Rufus. You can also use Rufus to create a bootable USB drive from the ISO file if you want.
-
-
How to use other websites to download PS2 game ISO files
-
If you want to download PS2 game ISO files for AetherSX2, you can use various websites that offer them. However, as we mentioned before, you should only download PS2 game ISO files that you own legally and use them for personal and non-commercial purposes only. You should also scan the downloaded files for any malware, viruses, or spyware before using them. Here are some of the websites that you can use to download PS2 game ISO files:
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CoolROM: This is one of the most popular and trusted websites for downloading ROMs and ISOs for various consoles and platforms. It has a large collection of PS2 game ISOs that you can browse by genre, region, popularity, or alphabetically. You can also use the search function to find your desired game. To download a PS2 game ISO file from CoolROM, simply click on the game title, then click on the "Download Now" button, then click on the "Download Your File" button, then save the file on your PC.
-
Emuparadise: This is another well-known and reliable website for downloading ROMs and ISOs for various consoles and platforms. It has a huge library of PS2 game ISOs that you can browse by genre, region, rating, or alphabetically. You can also use the search function to find your desired game. To download a PS2 game ISO file from Emuparadise, simply click on the game title, then scroll down to the "Direct Download Links" section, then click on one of the links, then save the file on your PC.
-
The Eye: This is a public archive of various files and data that are freely available on the internet. It has a section dedicated to PS2 game ISOs that you can browse by region or alphabetically. You can also use the search function to find your desired game. To download a PS2 game ISO file from The Eye, simply click on the game title, then save the file on your PC.
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-
These are just some of the websites that you can use to download PS2 game ISO files for AetherSX2. There are many others that you can find online, but be careful and cautious when using them. Always check their reputation, reviews, and ratings before downloading anything from them.
-
How to Install a Software Program from an ISO File on Your PC
-
If you downloaded a software program in the form of an ISO file, such as Windows or Office, you need to install it on your PC before you can use it. To do this, you need to mount the ISO file as a virtual drive on your PC and run the setup.exe file from it. Here are two ways to mount an ISO file as a virtual drive on your PC:
-
How to mount an ISO file as a virtual drive using Windows Explorer
-
If you are using Windows 8 or later, you can mount an ISO file as a virtual drive using Windows Explorer without any additional software. Here are the steps to do this:
-Locate the ISO file on your PC and right-click on it.
-
Select "Mount" from the context menu. This will create a virtual drive with a letter assigned to it.
-
Open the virtual drive and double-click on the setup.exe file to start the installation process.
-
Follow the installation instructions on the screen and complete the installation.
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-
How to mount an ISO file as a virtual drive using a third-party tool
-
If you are using Windows 7 or earlier, or if you prefer to use a third-party tool to mount an ISO file as a virtual drive, you can use one of the many software tools available online. Some of the most popular and reliable ones are Daemon Tools, PowerISO, Virtual CloneDrive, WinCDEmu, etc. Here are the steps to use one of these tools to mount an ISO file as a virtual drive:
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-
Download and install one of the tools from their official websites.
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Launch the tool and select an empty virtual drive from its interface.
-
Select the ISO file that you want to mount and click on the "Mount" button. This will create a virtual drive with a letter assigned to it.
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Open the virtual drive and double-click on the setup.exe file to start the installation process.
-
Follow the installation instructions on the screen and complete the installation.
-
-
How to Transfer the ISO File to Your Android Device
-
If you downloaded a PS2 game ISO file for AetherSX2, you need to transfer it to your Android device before you can play it. To do this, you need to connect your Android device to your PC via USB cable or Wi-Fi and copy or move the ISO file from your PC to your Android device's storage. Here are two ways to transfer an ISO file to your Android device:
-
How to connect your Android device to your PC via USB cable
-
If you have a USB cable that is compatible with your Android device and your PC, you can use it to connect them and transfer files between them. Here are the steps to do this:
-
-
Plug one end of the USB cable into your Android device and the other end into your PC.
-
On your Android device, swipe down from the top of the screen and tap on the "USB for file transfer" notification.
-
Select "File Transfer" or "MTP" from the options. This will allow your PC to access your Android device's storage.
-
On your PC, open File Explorer and locate your Android device under "This PC". It should have a name and a letter assigned to it.
-
Open your Android device's storage and locate the folder where you want to copy or move the ISO file. You can also create a new folder if you want.
-
Locate the ISO file on your PC and copy or move it to your Android device's storage by dragging and dropping it or using the context menu.
-
-
How to connect your Android device to your PC via Wi-Fi
-
If you don't have a USB cable or if you prefer to use Wi-Fi to connect your Android device to your PC, you can use one of the many apps that allow you to do that. Some of the most popular and reliable ones are AirDroid, ShareIt, Xender, etc. Here are the steps to use one of these apps to connect your Android device to your PC via Wi-Fi:
-
-
Download and install one of the apps from Google Play Store on your Android device.
-
Launch the app and follow its instructions to create an account or log in if you already have one.
-
On your PC, open a web browser and go to the app's website or download its desktop client if available.
-
Login with the same account that you used on your Android device or scan the QR code that appears on your PC's screen with your Android device's camera.
-
This will establish a connection between your Android device and your PC over Wi-Fi.
-
On your PC, locate your Android device's storage under "Files" or "My Device". It should have a name and a letter assigned to it.
-
Open your Android device's storage and locate the folder where you want to copy or move the ISO file. You can also create a new folder if you want.
-
Locate the ISO file on your PC and copy or move it to your Android device's storage by dragging and dropping it or using the context menu.
-
-
How to Play the ISO File on AetherSX2
-
Once you have transferred the ISO file to your Android device, you are ready to play it on AetherSX2. To do this, you need to launch AetherSX2 and load the ISO file from your device's storage. Here are the steps to do this:
-
-
Launch AetherSX2 on your Android device and grant it the necessary permissions, such as storage access, microphone access, etc.
-
Tap on the "Load Game" button and navigate to the folder where you stored the ISO file.
-
Tap on the ISO file and wait for it to load on AetherSX2. This may take a few seconds or minutes depending on the size of the file and the speed of your device.
-
Once the game is loaded, you can start playing it using the on-screen controls or an external controller if you have one connected. You can also adjust the settings and options for optimal performance and gameplay experience by tapping on the menu button at the top right corner of the screen.
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-
Conclusion
-
In this article, we have shown you how to download ISO files for AetherSX2 from various sources, how to install them on your PC, how to transfer them to your Android device, and how to play them on AetherSX2. We hope that this guide has helped you enjoy playing PS2 games on your Android device using AetherSX2, the best PS2 emulator for Android.
-
If you have any feedback or questions about this article or AetherSX2, please feel free to leave a comment below. We would love to hear from you and help you out. Happy gaming!
-
FAQs
-
What are the system requirements for running AetherSX2 on Android?
-
The developer of AetherSX2 recommends having at least four large CPU cores (Cortex-A75 or higher) and an Adreno GPU for optimal performance. However, some games may run well on lower-end devices as well. You can check the compatibility list of games on here to see how they perform on different devices and settings.
-
What are the advantages of using Vulkan over OpenGL or vice versa?
-
Vulkan and OpenGL are two graphics renderers that AetherSX2 supports. Vulkan is a newer and more advanced renderer that offers better performance, stability, and compatibility than OpenGL. However, Vulkan may not be supported by some older or weaker devices or drivers. OpenGL is an older and more stable renderer that works on most devices and drivers, but may not offer the same level of performance, stability, and compatibility as Vulkan. You can try both renderers and see which one works better for your device and game.
-
How can I save and load my game progress on AetherSX2?
-
You can save and load your game progress on AetherSX2 using two methods: memory cards or save states. Memory cards are virtual representations of the PS2 memory cards that store your game data. You can create up to eight memory cards on AetherSX2 and use them to save and load your game progress from the game's menu. Save states are snapshots of your game at any point that you can create and load anytime. You can create up to 10 save states per game on AetherSX2 and use them to save and load your game progress from the emulator's menu.
-
Can I play multiplayer games on AetherSX2 with other players online or locally?
-
Yes, you can play multiplayer games on AetherSX2 with other players online or locally. To play online, you need to use Netplay, a feature that allows you to connect with other AetherSX2 users over the internet and play co-op or versus games. To use Netplay, you need to have a stable internet connection, a compatible game, and a compatible version of AetherSX2. You can find more information and instructions on how to use Netplay here. To play locally, you need to use local multiplayer, a feature that allows you to play with up to four players on the same device using split-screen or external controllers. To use local multiplayer, you need to have a compatible game, a compatible version of AetherSX2, and enough controllers for each player. You can find more information and instructions on how to use local multiplayer here.
-
Where can I get more information and support for AetherSX2?
-
If you want to get more information and support for AetherSX2, you can visit the official website of the emulator here. There you can find the latest news, updates, downloads, compatibility lists, guides, FAQs, forums, and more. You can also join the official Discord server of AetherSX2 here, where you can chat with other users and developers, ask questions, report bugs, request features, share screenshots, videos, and more.
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index 18d72a944c2ba5af412a1747152b75a2bce57995..0000000000000000000000000000000000000000
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How to Download Rayuwata by Umar M Shareef MP3
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Rayuwata is a beautiful Hausa song by Umar M Shareef, a Nigerian musician and actor from Kaduna. The song is about love, life, and happiness, and it has a catchy melody and lyrics. If you are a fan of Hausa music, or you just want to discover a new song, you might want to download rayuwata by Umar M Shareef MP3 and listen to it offline.
To download this song, you have two options: you can either buy it from an online music store, such as iTunes or Play Music, or you can download it for free from a video-sharing platform, such as YouTube or SoundCloud. In this article, we will show you how to do both, using different devices and methods. All you need is a computer or a smartphone, an internet connection, and some storage space.
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Buying Music on Desktop
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If you want to buy rayuwata by Umar M Shareef MP3 from an online music store, you can use iTunes on your desktop computer. iTunes is a popular music player and store that allows you to buy and download songs legally and easily. Here are the steps to follow:
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-
Install iTunes if you're on Windows. Unfortunately, Windows does not include a built-in option for purchasing and downloading music; however, iTunes is a good alternative. You'll also need to create an Apple ID account and enter payment information for it before you can purchase music through iTunes on Windows. iTunes will be installed by default if you're on a Mac.
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Open iTunes. Click or double-click the iTunes app icon, which resembles a multicolored musical note on a white background.
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Sign in with your Apple ID. If you aren't signed into iTunes, do the following:
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Click the Account menu item at the top of iTunes (Windows) or the screen (Mac).
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Click Sign In in the drop-down menu.
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Enter your Apple ID email address and password in the resulting pop-up window.
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Click Store. It's a tab near the top of the iTunes window.
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Click the search bar. This is the "Search" text box in the upper-right side of the iTunes window.
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Search for rayuwata by Umar M Shareef. Type in " rayuwata by Umar M Shareef" (without quotation marks) and press Enter or Return. This will bring up a list of results that match your query.
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Find the song you want to buy. Look for the song title, artist name, album name, and price in the list of results. You can also preview the song by clicking the play button next to it.
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Click the price button. This is the button that shows the amount of money you need to pay for the song, such as $0.99 or $1.29. This will add the song to your iTunes library and start downloading it to your computer.
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View the music files on your computer. Once the download is complete, you can find the MP3 file of the song in your iTunes folder, which is usually located in your Music folder on Windows or Mac. You can also play the song from iTunes by clicking Library and selecting Songs.
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Buying Music on iPhone
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If you want to buy rayuwata by Umar M Shareef MP3 from an online music store, you can use iTunes Store app on your iPhone. iTunes Store is a pre-installed app that allows you to buy and download songs legally and easily. Here are the steps to follow:
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Open iTunes Store app on your iPhone. Tap the iTunes Store app icon, which resembles a white star on a magenta background.
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Sign in with your Apple ID. If you aren't signed into iTunes Store, do the following:
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Tap More at the bottom-right corner of the screen.
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Tap Sign In at the top of the menu.
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Enter your Apple ID email address and password in the resulting pop-up window.
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Tap Search at the bottom of the screen. This will open a search bar at the top of the screen.
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Search for rayuwata by Umar M Shareef. Type in "rayuwata by Umar M Shareef" (without quotation marks) and tap Search on your keyboard. This will bring up a list of results that match your query.
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Find the song you want to buy. Look for the song title, artist name, album name, and price in the list of results. You can also preview the song by tapping the play button next to it.
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Tap the price button. This is the button that shows the amount of money you need to pay for the song, such as $0.99 or $1.29. This will prompt you to confirm your purchase with your Apple ID password or Touch ID.
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Tap Buy to confirm your purchase. This will add the song to your iTunes library and start downloading it to your iPhone.
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Access the song on your iPhone. Once the download is complete, you can find the MP3 file of the song in your Music app, which is usually located on your home screen. You can also play the song from iTunes Store by tapping Purchased and selecting Music.
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Buying Music on Android
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If you want to buy rayuwata by Umar M Shareef MP3 from an online music store, you can use Play Music app on your Android phone. Play Music is a pre-installed app that allows you to buy and download songs legally and easily. Here are the steps to follow:
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Open Play Music app on your Android phone. Tap the Play Music app icon, which resembles an orange triangle with a music note inside it.
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Sign in with your Google account. If you aren't signed into Play Music, do the following:
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Tap Menu at the top-left corner of the screen.
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Tap Settings at the bottom of the menu.
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Tap Add account under Accounts section.
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Select Google from the list of account types.
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Enter your Google email address and password in the resulting pop-up window.
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Tap Shop at the bottom-right corner of the screen. This will open a new tab with Google Play Store, where you can browse and buy music.
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Tap Search at the top of Google Play Store screen. This will open a search bar where you can type in keywords.
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Search for rayuwata by Umar M Shareef. Type in "rayuwata by Umar M Shareef" (without quotation marks) and tap Enter or Return on your keyboard. This will bring up a list of results that match your query.
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Find the song you want to buy. Look for the song title, artist name, album name, and price in the list of results. You can also preview the song by tapping Listen next to it.
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Tap the price button. This is the button that shows the amount of money you need to pay for the song, such as $0.99 or $1.29. This will prompt you to enter your payment method and confirm your purchase.
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Tap Buy to confirm your purchase. This will add the song to your Play Music library and start downloading it to your Android phone.
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Access the song on your Android phone. Once the download is complete, you can find the MP3 file of the song in your Play Music app, where you can play it offline or online. You can also access the song from other apps that support MP3 files, such as File Manager or Gallery.
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Downloading Free Music from YouTube or SoundCloud
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If you want to download rayuwata by Umar M Shareef MP3 for free, you can use a video-sharing platform, such as YouTube or SoundCloud, and a third-party app or website that can convert the video to audio. However, you should be aware that this method may not be legal or ethical, as it may violate the copyright laws and the terms of service of the platforms. You should only download free music for personal use and not for commercial purposes. Here are the steps to follow:
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-
Find rayuwata on YouTube or SoundCloud. You can use the search bar on these platforms to look for the song by typing in "rayuwata by Umar M Shareef" (without quotation marks). You can also use filters to narrow down your results by duration, date, quality, etc.
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Copy the URL of the video or audio. Once you find the song you want to download, you need to copy its web address from the browser's address bar. You can do this by selecting the URL and pressing Ctrl+C (Windows) or Command+C (Mac) on your keyboard.
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Use a third-party app or website to convert the video to audio. There are many apps and websites that can help you convert YouTube or SoundCloud videos to MP3 files, such as YTMP3, MP3Juices, 4K Video Downloader, etc. You can choose one that suits your needs and preferences, but be careful of malware and pop-up ads. To use these tools, you need to paste the URL of the video or audio into a designated box and click a button to start the conversion process.
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Download the MP3 file to your computer or phone. After the conversion is done, you will see a link or a button to download the MP3 file to your device. You can choose where to save the file and what name to give it. You can also adjust some settings, such as bitrate, quality, volume, etc.
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Access the song on your computer or phone. Once the download is complete, you can find the MP3 file of the song in your Downloads folder or any other folder you specified. You can also play the song from any app that supports MP3 files, such as VLC Media Player, Windows Media Player, Music Player, etc.
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Conclusion
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In this article, we have shown you how to download rayuwata by Umar M Shareef MP3 using different devices and methods. You can either buy it from an online music store, such as iTunes or Play Music, or download it for free from a video-sharing platform, such as YouTube or SoundCloud. However, you should always respect the rights of the artist and the platform and only download music for personal use.
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Rayuwata by Umar M Shareef is a wonderful song that will make you feel happy and inspired. It is one of the best Hausa songs ever made and it deserves your attention and appreciation. If you like this song, you should also check out other songs by Umar M Shareef and support him as an artist. You can follow him on social media, watch his movies, attend his concerts, and share his music with your friends and family.
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FAQs
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What genre is rayuwata?
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Rayuwata is a Hausa pop song that combines traditional Hausa music elements with modern pop influences. It has a lively rhythm, a catchy melody, and meaningful lyrics.
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Who is Umar M Shareef?
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Umar M Shareef is a Nigerian musician and actor from Kaduna. He is one of the most popular Hausa artists in Nigeria and beyond. He has released several albums and singles, such as Tsintuwa, Mariya, Soyayya da Shakuwa, etc. He has also starred in several movies, such as Mansoor, Hafeez, Sabon Sarki, etc.
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Where can I find more songs by Umar M Shareef?
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You can find more songs by Umar M Shareef on various online music platforms, such as iTunes, Play Music, YouTube, SoundCloud, Spotify, etc. You can also visit his official website, www.umarmshareef.com, where you can find his latest news, albums, videos, and events.
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What are some other popular Hausa songs?
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Some other popular Hausa songs are:
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Dan Maraya Jos - Wakar Aure
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Nura M Inuwa - Mai Sauraro
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Ali Jita - Love
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Hamisu Breaker - Jaruma
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Ado Gwanja - Ana Kirana Gwanja
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How can I support Umar M Shareef as an artist?
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You can support Umar M Shareef as an artist by buying his music legally, following him on social media, watching his movies, attending his concerts, and sharing his music with your friends and family. You can also send him messages of appreciation and encouragement and give him feedback on his work.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Sims Mobile Mod Apk - Para Hilesi Yaparak Hayalinizdeki Hayat Yaayn.md b/spaces/congsaPfin/Manga-OCR/logs/Sims Mobile Mod Apk - Para Hilesi Yaparak Hayalinizdeki Hayat Yaayn.md
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Sims Mobile Hile: How to Cheat and Hack Your Way to Simoleons and SimCash
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If you are a fan of the Sims franchise, you might have heard of the term "sims mobile hile". This is a Turkish word that means "cheat" or "hack" in English, and it refers to the various ways that players can manipulate the game to get more money, resources, and features. In this article, we will explore what sims mobile hile is, why people use it, how to use it, and what are the risks involved.
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What is Sims Mobile Hile?
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Sims Mobile is a popular mobile game that allows you to create your own Sims, customize their appearance, personality, and lifestyle, build their homes, pursue their careers and hobbies, make friends and enemies, fall in love and start families, and more. It is a simulation game that lets you play with life in a fun and creative way.
The word "hile" is a Turkish word that means "cheat" or "hack" in English. It is often used by Turkish players who are looking for ways to get more money or features in the game. The term became popular after some websites and YouTube channels started to offer sims mobile hile services, such as providing cheat codes, hacks, mods, or generators that claim to give players unlimited Simoleons (the standard currency) or SimCash (the premium currency) in the game.
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The difference between cheats and hacks in Sims Mobile
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Cheats and hacks are two different types of sims mobile hile that have different effects on the game. Cheats are codes or commands that can be entered in the game to alter some aspects of the gameplay, such as giving yourself more money, unlocking items, or skipping tasks. Hacks are programs or tools that can modify the game files or data to change some features of the game, such as adding new items, removing ads, or bypassing security measures.
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Why do people use Sims Mobile Hile?
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People use sims mobile hile for various reasons, but mostly because they want to have more fun and freedom in the game. Here are some of the benefits and challenges of playing Sims Mobile with or without hile.
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The benefits of having more Simoleons and SimCash
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Simoleons and SimCash are the two main currencies in Sims Mobile. They are used to buy items, furniture, clothes, accessories, hobbies, careers, traits, events, parties, stickers, heirlooms, land expansions, rooms, stories, and more. Having more Simoleons and SimCash means that you can afford anything you want in the game without having to worry about saving up or waiting for timers. You can also speed up actions, complete quests faster, unlock new options, and enjoy more variety and customization in your gameplay.
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The challenges and limitations of playing Sims Mobile without hile
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Playing Sims Mobile without hile can be rewarding but also frustrating at times. You have to earn Simoleons and SimCash by completing tasks, events, quests, parties, careers, hobbies, relationships, etc. You also have to manage your Sims' energy
levels, which can run out quickly if you do too many actions. You also have to deal with ads, timers, limits, and restrictions that can slow down your progress and limit your choices. You might feel bored, frustrated, or impatient if you have to wait for long periods of time or repeat the same tasks over and over again.
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How to use Sims Mobile Hile?
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There are two main ways to use sims mobile hile: the legal and safe way, and the illegal and risky way. Let's take a look at each one and see what they entail.
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The legal and safe ways to earn more Simoleons and SimCash
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The legal and safe ways to earn more Simoleons and SimCash are the ones that do not involve cheating or hacking the game. They are the methods that are approved by the game developers and do not violate the terms of service or the user agreement. They are also the ones that do not put your account, device, or personal information at risk of being banned, hacked, or stolen. Here are some of the legal and safe ways to earn more Simoleons and SimCash in Sims Mobile:
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Completing daily tasks and timed quests
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One of the easiest and most reliable ways to earn more Simoleons and SimCash is to complete the daily tasks and timed quests that are available in the game. These are simple and fun activities that you can do with your Sims, such as cooking, gardening, socializing, working, etc. They usually reward you with a decent amount of Simoleons and SimCash, as well as experience points, cupcakes, fashion gems, etc. You can also get bonus rewards for completing all the daily tasks or a certain number of timed quests in a day.
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Saving money and spending wisely
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Another way to earn more Simoleons and SimCash is to save money and spend wisely in the game. This means that you should avoid wasting money on unnecessary or low-quality items, furniture, clothes, accessories, etc. You should also compare prices and look for discounts or deals before buying anything. You should also prioritize buying items that can help you earn more money in the long run, such as hobbies, careers, traits, events, parties, stickers, heirlooms, etc.
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Optimizing your time and energy
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A third way to earn more Simoleons and SimCash is to optimize your time and energy in the game. This means that you should plan ahead and schedule your actions wisely. You should also make use of the energy refills that you can get from cupcakes, watching ads, leveling up, etc. You should also focus on doing actions that give you more rewards or benefits, such as completing events, quests, parties, careers, hobbies, relationships, etc.
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The illegal and risky ways to get unlimited Simoleons and SimCash
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The illegal and risky ways to get unlimited Simoleons and SimCash are the ones that involve cheating or hacking the game. They are the methods that are not approved by the game developers and violate the terms of service or the user agreement. They are also the ones that put your account, device, or personal information at risk of being banned, hacked, or stolen. Here are some of the illegal and risky ways to get unlimited Simoleons and SimCash in Sims Mobile:
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Using cheat codes or hacks from online sources
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One of the most common and tempting ways to get unlimited Simoleons and SimCash is to use cheat codes or hacks from online sources, such as websites, YouTube channels, blogs, forums, etc. These sources claim to provide you with codes, hacks, mods, or generators that can give you unlimited money or features in the game. However, these sources are usually unreliable, untrustworthy, or fraudulent. They might not work at all, or they might infect your device with malware, viruses, spyware, etc. They might also steal your personal information, such as your account details, passwords, credit card numbers, etc. They might also get your account banned or suspended by the game developers for violating the rules.
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The dangers and consequences of using hile
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The dangers and consequences of using hile are not worth the risk. You might think that you are getting a good deal by getting unlimited money or features in the game, but you are actually putting yourself and your device in danger. You might lose your account, your progress, your money, your identity, or even your device. You might also ruin the fun and challenge of the game by making it too easy or boring. You might also lose the respect and trust of other players who play fair and honest. You might also face legal actions or lawsuits from the game developers or authorities for breaking the law.
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Conclusion
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Sims Mobile Hile is a term that means "cheat" or "hack" in Turkish, and it refers to the various ways that players can manipulate the game to get more money or features. Some people use hile because they want to have more fun and freedom in the game, but they also face many challenges and limitations without hile. There are two main ways to use hile: the legal and safe way, and the illegal and risky way. The legal and safe way involves earning more Simoleons and SimCash by completing tasks, saving money, and optimizing time and energy. The illegal and risky way involves using cheat codes or hacks from online sources that can put your account, device, or personal information at risk of being banned, hacked, or stolen.
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If you want to enjoy Sims Mobile without hile, you should follow these tips:
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-
Play regularly and complete daily tasks and timed quests
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Save money and spend wisely on items that can help you earn more money
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Optimize your time and energy by planning ahead and using energy refills
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Have fun and be creative with your Sims' appearance, personality, lifestyle, home, etc.
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Join a party or start your own party with other players
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Share your stories and experiences with other players
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Respect the rules and the community of the game
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-
If you want to use hile in Sims Mobile, you should be aware of these risks:
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-
You might lose your account or progress if you get banned or suspended by the game developers
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You might lose your money or identity if you get hacked or stolen by online sources
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You might lose your device or data if you get infected with malware or viruses by online sources
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You might lose the fun or challenge of the game if you make it too easy or boring
-
You might lose the respect or trust of other players if you cheat or hack the game
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You might face legal actions or lawsuits if you break the law or violate the terms of service
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-
The choice is yours whether to use hile or not in Sims Mobile. However, we recommend that you play fair and honest, as it is more rewarding and satisfying in the long run. Sims Mobile is a great game that lets you play with life in a fun and creative way. You don't need hile to enjoy it.
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FAQs
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Here are some of the frequently asked questions about sims mobile hile:
-
-
What is sims mobile hile? Sims mobile hile is a Turkish word that means "cheat" or "hack" in English. It refers to the various ways that players can manipulate the game to get more money or features.
-
Why do people use sims mobile hile? People use sims mobile hile because they want to have more fun and freedom in the game. They also want to overcome some of the challenges and limitations of playing without hile.
-
How to use sims mobile hile? There are two main ways to use sims mobile hile: the legal and safe way, and the illegal and risky way. The legal and safe way involves earning more Simoleons and SimCash by completing tasks, saving money, and optimizing time and energy. The illegal and risky way involves using cheat codes or hacks from online sources that can put your account, device, or personal information at risk of being banned, hacked, or stolen.
-
Is sims mobile hile legal or illegal? Sims mobile hile is illegal and violates the terms of service and the user agreement of the game. It is also unethical and unfair to other players who play by the rules. Using hile can result in your account being banned or suspended, your device being infected with malware or viruses, your personal information being stolen or hacked, or even legal actions or lawsuits against you.
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Is sims mobile hile safe or risky? Sims mobile hile is risky and dangerous for your account, device, and personal information. You never know what kind of cheat codes or hacks you are getting from online sources, or what they can do to your game or device. You might end up losing everything you have worked hard for in the game, or even worse, your identity or money. You might also ruin the fun and challenge of the game by making it too easy or boring.
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What are some alternatives to sims mobile hile? Some alternatives to sims mobile hile are playing the game regularly and completing daily tasks and timed quests, saving money and spending wisely on items that can help you earn more money, optimizing your time and energy by planning ahead and using energy refills, having fun and being creative with your Sims' appearance, personality, lifestyle, home, etc., joining a party or starting your own party with other players, sharing your stories and experiences with other players, respecting the rules and the community of the game, etc.
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Where can I find more information about sims mobile hile? You can find more information about sims mobile hile by searching online for websites, YouTube channels, blogs, forums, etc. that offer sims mobile hile services or tips. However, you should be careful and cautious about these sources, as they might not be reliable, trustworthy, or legitimate. They might also contain malware, viruses, spyware, etc. that can harm your device or data. You should also check the reviews and ratings of these sources before using them.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Super Mario Run Mod Apk 3.0.28 How to Install and Play on Any Device.md b/spaces/congsaPfin/Manga-OCR/logs/Super Mario Run Mod Apk 3.0.28 How to Install and Play on Any Device.md
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Super Mario Run 3.0.28 Mod Apk: Everything You Need to Know
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If you love playing Super Mario games, you must have heard of Super Mario Run, a mobile game developed by Nintendo that lets you enjoy the classic platforming action on your smartphone or tablet. But did you know that there is a way to make your gaming experience even more fun and exciting? Yes, we are talking about Super Mario Run 3.0.28 mod apk, a modified version of the game that gives you access to unlimited coins, all levels unlocked, all characters unlocked, no ads, and more.
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In this article, we will tell you everything you need to know about Super Mario Run 3.0.28 mod apk, including its features, how to download and install it on your device, how to play it, and some FAQs. So, without further ado, let's get started!
Super Mario Run 3.0.28 mod apk is not just a regular version of Super Mario Run with some minor tweaks. It is a fully loaded package that offers you a lot of features that will enhance your gaming experience and make you feel like a pro. Here are some of the features that you can enjoy with Super Mario Run 3.0.28 mod apk:
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Unlimited Coins
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Coins are the currency of Super Mario Run, and you can use them to buy items, buildings, decorations, and more in the game. However, collecting coins can be time-consuming and tedious, especially if you want to get enough to unlock everything. That's why Super Mario Run 3.0.28 mod apk gives you unlimited coins, so you don't have to worry about running out of them ever again. You can spend as much as you want and buy whatever you want without any restrictions.
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All Levels Unlocked
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Super Mario Run has four modes: World Tour, Remix 10, Toad Rally, and Kingdom Builder. In World Tour, you can play through 24 levels across six worlds, each with its own theme and challenges. However, you need to pay a one-time fee of $9.99 to unlock all the levels after the first three. If you don't want to pay or wait for a sale, you can use Super Mario Run 3.0.28 mod apk to unlock all the levels for free. You can play any level you want at any time and enjoy the full content of the game.
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All Characters Unlocked
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Super Mario Run lets you play as different characters besides Mario, such as Luigi, Peach, Yoshi, Toad, Toadette, and more. Each character has its own unique ability and style, such as jumping higher, floating longer, or running faster. However, unlocking these characters can be tricky and require you to complete certain tasks or collect certain items in the game. For example, to unlock Peach, you need to rescue her from Bowser in World Tour mode. But with Super Mario Run 3.0.28 mod apk, you don't have to go through all that hassle. You can unlock all the characters from the start and switch between them as you please.
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No Ads
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Ads can be annoying and distracting when you are playing a game, especially if they pop up frequently or take up a lot of screen space. They can also slow down your device or consume your data. That's why Super Mario Run 3.0.28 mod apk removes all the ads from the game, so you can play without any interruptions or inconveniences. You can enjoy a smooth and seamless gaming experience with no ads whatsoever.
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How to Download and Install Super Mario Run 3.0.28 Mod Apk
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Now that you know what Super Mario Run 3.0.28 mod apk can do for you, you might be wondering how to get it on your device. Well, don't worry, because we have got you covered. Here are the steps to download and install Super Mario Run 3.0.28 mod apk on your device:
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Requirements
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Before you download and install Super Mario Run 3.0.28 mod apk, you need to make sure that your device meets the minimum requirements for the game. Here are the requirements:
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Your device must have Android 4.4 or higher.
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Your device must have at least 2 GB of RAM.
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Your device must have at least 200 MB of free storage space.
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Your device must have a stable internet connection.
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Your device must allow installation from unknown sources.
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To enable installation from unknown sources, go to Settings > Security > Unknown Sources and toggle it on.
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Download Link
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Once you have checked the requirements, you can proceed to download Super Mario Run 3.0.28 mod apk from a safe and reliable download link. We recommend using this link: [text], which is verified by us and has no viruses or malware.
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Simply click on the link and wait for the download to finish.
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Installation Guide
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After downloading Super Mario Run 3.0.28 mod apk, follow these steps to install it on your device:
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Locate the downloaded file in your file manager and tap on it.
-
A pop-up will appear asking you to confirm the installation. Tap on Install and wait for the process to complete.
-
Once the installation is done, tap on Open to launch the game.
-
Enjoy playing Super Mario Run 3.0.28 mod apk with all its features!
-
- [assistant](#message)
How to Play Super Mario Run 3.0.28 Mod Apk
-
Super Mario Run 3.0.28 mod apk is easy to play and fun to master. The game has simple controls and gameplay mechanics that anyone can learn and enjoy. Here are some basics on how to play Super Mario Run 3.0.28 mod apk:
-
Modes
-
Super Mario Run 3.0.28 mod apk has four modes that you can choose from: World Tour, Remix 10, Toad Rally, and Kingdom Builder.
-
-
World Tour: This is the main mode of the game, where you can play through 24 levels across six worlds, each with its own theme and challenges. You can also collect coins, pink coins, purple coins, and black coins in each level to unlock bonus content.
-
Remix 10: This is a mode where you can play 10 short levels in a row, each with a different layout and difficulty. You can also collect rainbow coins and rainbow medals in this mode to unlock new characters and items.
-
Toad Rally: This is a mode where you can compete with other players online and show off your skills and style. You can also collect toads of different colors in this mode to increase your popularity and expand your kingdom.
-
Kingdom Builder: This is a mode where you can build your own kingdom using the coins and toads you have collected in the other modes. You can also place buildings, decorations, and bonus games in your kingdom to make it more lively and attractive.
-
-
You can switch between the modes by tapping on their icons on the main menu.
-
Worlds
-
Super Mario Run 3.0.28 mod apk has six worlds that you can explore in World Tour mode, each with four levels. The worlds are:
-
-
World 1: Plains
-
World 2: Underground
-
World 3: Sky
-
World 4: Forest
-
World 5: Ghost House
-
World 6: Castle
-
-
You can progress through the worlds by completing the levels and defeating the bosses at the end of each world.
-
Challenges
-
Super Mario Run 3.0.28 mod apk has various challenges that you can complete in the game to earn rewards and achievements. Some of the challenges are:
-
-
Collecting all the coins in a level
-
Collecting all the pink coins in a level
-
Collecting all the purple coins in a level
-
Collecting all the black coins in a level
-
Completing a level without dying
-
Completing a level with a certain character
-
Completing a level with a certain style
-
Completing a level within a certain time limit
-
Completing a level with a certain number of jumps
-
Completing a level without touching the ground
-
-
You can check your progress and achievements by tapping on the menu button on the top right corner of the screen.
- [assistant](#message)
Tips and Tricks
-
Super Mario Run 3.0.28 mod apk is a game that requires skill and strategy to play well. Here are some tips and tricks that can help you improve your performance and enjoyment in the game:
-
-
Tap the screen to make Mario jump. The longer you tap, the higher he jumps.
-
Tap the screen again while in mid-air to make Mario spin and extend his jump.
-
Tap the screen while touching a wall to make Mario wall jump and change direction.
-
Tap the screen while touching an enemy to make Mario stomp on it and bounce higher.
-
Tap the screen while touching a coin block to make Mario hit it and collect coins.
-
Tap the screen while touching a pause block to make Mario stop and wait for your next move.
-
Tap the screen while touching a red ring to activate a coin rush, where you can collect more coins in a short time.
-
Tap the screen while touching a star to activate a star power, where you can run faster and invincible for a short time.
-
Try to collect as many coins as possible in each level, as they can help you buy items, buildings, decorations, and more in the game.
-
Try to collect all the pink coins, purple coins, and black coins in each level, as they can unlock bonus levels and content in the game.
-
Try to complete all the challenges in each level, as they can earn you rewards and achievements in the game.
-
Try to play with different characters in each level, as they can offer different abilities and styles in the game.
-
Try to play with different modes in the game, as they can offer different experiences and challenges in the game.
-
Try to build your own kingdom in the game, as it can make your game more personalized and fun.
-
Try to compete with other players online in the game, as it can make your game more social and competitive.
-
-
Conclusion
-
Super Mario Run 3.0.28 mod apk is a great way to enjoy Super Mario Run on your device with more features and options. You can play with unlimited coins, all levels unlocked, all characters unlocked, no ads, and more. You can also download and install it easily on your device with our guide. You can also play it with simple controls and gameplay mechanics, and explore different modes, worlds, challenges, and more. Super Mario Run 3.0.28 mod apk is a game that will keep you entertained and engaged for hours. So, what are you waiting for? Download Super Mario Run 3.0.28 mod apk now and have fun!
-
FAQs
-
Here are some common questions and answers about Super Mario Run 3.0.28 mod apk:
-
-
Is Super Mario Run 3.0.28 mod apk safe to use?
-Yes, Super Mario Run 3.0.28 mod apk is safe to use, as long as you download it from a trusted source like [text]. We have verified that it has no viruses or malware that can harm your device or data.
-
Is Super Mario Run 3.0.28 mod apk legal to use?
-Yes, Super Mario Run 3.0.28 mod apk is legal to use, as long as you use it for personal and non-commercial purposes only. We do not endorse or support any illegal or unethical use of Super Mario Run 3.0.28 mod apk.
-
Will Super Mario Run 3.0.28 mod apk work on my device?
-Yes, Super Mario Run 3.0.28 mod apk will work on most devices that have Android 4.4 or higher, 2 GB of RAM or more, 200 MB of free storage space or more, and a stable internet connection. However, some devices may not be compatible or may experience some issues or errors while running Super Mario Run 3.0.28 mod apk.
-
Will Super Mario Run 3.0.28 mod apk affect my original game?
-No, Super Mario Run 3.0.28 mod apk will not affect your original game, as it is a separate file that runs independently from your original game. You can still play your original game without any changes or problems.
-
How can I update Super Mario Run 3.0.28 mod apk?
-apk by following the same steps as you did to download and install it. You can check for updates by visiting [text] regularly and looking for the latest version of the mod apk.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/The Spike - Volleyball Story A Cartoon Style Volleyball Game with a Complicated Gameplay and a Strong Community Support.md b/spaces/congsaPfin/Manga-OCR/logs/The Spike - Volleyball Story A Cartoon Style Volleyball Game with a Complicated Gameplay and a Strong Community Support.md
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The Spike Volleyball Story Mod APK New Update: Everything You Need to Know
-
If you are a fan of volleyball games, you might have heard of The Spike Volleyball Story, a real volleyball game developed by real high school students. This game has a story mode that follows the lives of the characters, as well as a multiplayer mode that lets you compete with other players. But did you know that there is a mod APK version of this game that unlocks premium features and lets you enjoy the game without ads or in-app purchases? In this article, we will tell you everything you need to know about The Spike Volleyball Story mod APK new update, including what is new, how to download and install it, and what are the risks involved.
-
What is The Spike Volleyball Story?
-
A real volleyball game developed by high school students
-
The Spike Volleyball Story is a game created by DAERISOFT, a company founded by high school students who love volleyball. They wanted to make a realistic and fun volleyball game that anyone can enjoy. The game features 3D graphics, realistic physics, and various skills and techniques that you can use to spike, block, and serve. You can also customize your characters with different costumes and accessories.
A story mode that follows the lives of the characters
-
The game also has a story mode that tells the stories of the characters, who are all high school students with different personalities and backgrounds. You can choose your own character and interact with other characters through dialogue choices and events. You can also develop your relationships with them and unlock special scenes and endings. The story mode has multiple episodes and chapters that you can play through.
-
A multiplayer mode that lets you compete with other players
-
If you want to test your skills against other players, you can try the multiplayer mode of The Spike Volleyball Story. You can join or create a room and invite your friends or random players to play with you. You can also chat with them and send them emoticons during the match. The multiplayer mode supports up to four players per room.
-
What is the mod APK version of The Spike Volleyball Story?
-
A modified version that unlocks premium features
-
A mod APK is a modified version of an original APK file, which is the format used for Android applications. A mod APK can alter or add features to the original app, such as unlocking premium content, removing ads, or adding cheats. For The Spike Volleyball Story, the mod APK version unlocks all the characters, costumes, stages, and skills that are otherwise only available through in-app purchases. It also gives you unlimited coins and gems that you can use to buy items and upgrade your character.
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A way to enjoy the game without ads or in-app purchases
-
One of the benefits of using the mod APK version of The Spike Volleyball Story is that you can enjoy the game without any interruptions or limitations. You don't have to watch ads or spend real money to access the full content of the game. You can play as much as you want and explore all the features of the game without any restrictions.
-
A risk of getting banned or infected by malware
-
However, using the mod APK version of The Spike Volleyball Story also comes with some risks. One of them is that you might get banned from the game or lose your progress if the developers detect that you are using a modified version. Another risk is that you might download a fake or malicious mod APK file that can harm your device or steal your personal information. Therefore, you should be careful and only download the mod APK file from a trusted and verified source.
-
What is new in the latest update of The Spike Volleyball Story mod APK?
-
Version 2.7.0 released on June 20, 2023
-
The latest update of The Spike Volleyball Story mod APK is version 2.7.0, which was released on June 20, 2023. This update brings some new features and improvements to the game, as well as fixes some bugs and errors.
-
New characters, costumes, and stages added
-
One of the main features of the new update is the addition of new characters, costumes, and stages to the game. You can now play as four new characters: Yuna, a cheerful and energetic girl who loves volleyball; Leo, a cool and handsome boy who is good at spiking; Rina, a shy and cute girl who is good at serving; and Kai, a smart and calm boy who is good at blocking. You can also dress them up with different costumes, such as school uniforms, casual outfits, sportswear, and more. Moreover, you can play on new stages, such as the beach, the gym, the rooftop, and more.
-
Improved graphics, sound, and gameplay
-
Another feature of the new update is the improvement of the graphics, sound, and gameplay of the game. The game now has more realistic and detailed 3D graphics that make the characters and the environment look more lifelike. The game also has better sound effects and music that enhance the atmosphere and mood of the game. Furthermore, the game has smoother and faster gameplay that makes the matches more exciting and challenging.
-
How to download and install The Spike Volleyball Story mod APK?
-
Find a reliable source that offers the mod APK file
-
The first step to download and install The Spike Volleyball Story mod APK is to find a reliable source that offers the mod APK file. You can search online for websites or blogs that provide links to download the mod APK file. However, you should be careful and only choose sources that have positive reviews and ratings from other users. You should also avoid sources that ask for your personal information or require you to complete surveys or offers before downloading.
-
Enable unknown sources on your device settings
-
The second step to download and install The Spike Volleyball Story mod APK is to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store or other official sources. To do this, you need to go to your device settings, then security or privacy, then unknown sources or install unknown apps. You need to toggle on the option that allows you to install apps from unknown sources.
-
Download and install the mod APK file
-
The third step to download and install The Spike Volleyball Story mod APK is to download and install the mod APK file. You need to go to the source that you have chosen and click on the download link or button. You need to wait for the download to finish and then locate the mod APK file on your device storage. You need to tap on the mod APK file and follow the instructions on your screen to install it. You need to wait for the installation to complete and then launch the game from your app drawer or home screen.
-
Conclusion
-
The Spike Volleyball Story is a real volleyball game developed by high school students who love volleyball. It has a story mode that follows the lives of the characters, as well as a multiplayer mode that lets you compete with other players. The mod APK version of this game unlocks premium features and lets you enjoy the game without ads or in-app purchases. However, it also comes with some risks of getting banned or infected by malware. The latest update of this game adds new characters, costumes, stages, graphics, sound, and gameplay. To download and install this game, you need to find a reliable source that offers the mod APK file, enable unknown sources on your device settings, and download and install the mod APK file.
-
FAQs
-
-
Q: Is The Spike Volleyball Story mod APK safe?
-
A: The Spike Volleyball Story mod APK is not officially endorsed or supported by DAERISOFT or Google Play Store. It may contain viruses or malware that can harm your device or steal your data. You should only download it from a trusted and verified source and at your own risk.
-
Q: How can I update The Spike Volleyball Story mod APK?
-
A: To update The Spike Volleyball Story mod APK, you need to download and install the latest version of the mod APK file from the same source that you used before. You should also uninstall the previous version of the mod APK before installing the new one. You may lose your progress or data if you update the mod APK.
-
Q: Can I play The Spike Volleyball Story mod APK offline?
-
A: Yes, you can play The Spike Volleyball Story mod APK offline. However, you may not be able to access some features or content that require an internet connection, such as the multiplayer mode or the online events.
-
Q: Can I play The Spike Volleyball Story mod APK with my friends?
-
A: Yes, you can play The Spike Volleyball Story mod APK with your friends. You can invite them to join your room or join their room in the multiplayer mode. However, you and your friends need to have the same version of the mod APK to play together.
-
Q: What are some alternatives to The Spike Volleyball Story mod APK?
-
A: Some alternatives to The Spike Volleyball Story mod APK are Volleyball Challenge, Volleyball Champions 3D, and Spike Masters Volleyball. These are also volleyball games that you can download and play on your Android device.
How to Install and Use Autodesk Maya 2012 Plugins Collectionzip
-
Autodesk Maya 2012 is a powerful 3D animation software that can create stunning graphics and effects for movies, games, and other media. However, to enhance its functionality and performance, you may want to install some plugins that can add new features and tools to your Maya workflow. One of the easiest ways to do that is to use the Autodesk Maya 2012 Plugins Collectionzip, which is a collection of various plugins for Maya 2012 that you can download from OpenSea[^1^] or other sources.
In this article, we will show you how to install and use the Autodesk Maya 2012 Plugins Collectionzip on your Windows computer. Follow these steps:
-
-
Download the Autodesk Maya 2012 Plugins Collectionzip file from OpenSea[^1^] or another source. Make sure you have enough space on your hard drive to store the file, which is about 1.5 GB in size.
-
Extract the zip file using a program like WinRAR or 7-Zip. You should see a folder named Autodesk Maya 2012 - Plugins with several subfolders inside.
-
Copy the subfolders that contain the plugins you want to use and paste them into your Maya 2012 installation directory. For example, if you want to use V-Ray for Maya 2012[^2^], copy the folder named vray and paste it into C:\Program Files\Autodesk\Maya2012.
-
Launch Maya 2012 and go to Window > Settings/Preferences > Plug-in Manager. You should see a list of plugins that are available for loading. Check the boxes next to the plugins you want to load and click Refresh.
-
Enjoy using the plugins in your Maya projects. You can access them from different menus and windows depending on their functions. For example, if you loaded V-Ray for Maya 2012[^2^], you can go to Render > Render Settings and choose V-Ray as your renderer.
-
-
We hope this article helped you install and use the Autodesk Maya 2012 Plugins Collectionzip on your Windows computer. If you have any questions or issues, please contact the plugin developers or visit their websites for more information.
-
-
In this section, we will introduce some of the plugins that are included in the Autodesk Maya 2012 Plugins Collectionzip and explain what they can do for your Maya projects. Note that this is not a comprehensive list of all the plugins in the collection, but only a sample of some of the most popular and useful ones. For more information about each plugin, please visit their respective websites or read their documentation.
-
-
V-Ray for Maya 2012: This is a high-performance rendering engine that can produce realistic and photorealistic images and animations. It supports advanced features such as global illumination, ray tracing, depth of field, motion blur, caustics, subsurface scattering, and more. It also comes with a variety of materials, lights, cameras, and shaders that you can use to create stunning scenes.
-
Maya Bonus Tools 2012: This is a collection of useful tools and scripts that can enhance your Maya workflow and productivity. It includes tools for modeling, animation, rigging, rendering, dynamics, and more. Some examples are Align Tool, Delete History by Type, Duplicate Special+, Extrude Along Curve+, Mirror Cut+, Paint Skin Weights+, Set Driven Key+, and many others.
-
Maya nHair 2012: This is a plugin that allows you to create realistic and dynamic hair and fur for your characters and creatures. It uses a new simulation method called Nucleus that can handle complex interactions between hair strands and other objects. You can also style and groom your hair using various tools such as brushes, clumps, curlers, cutters, and more.
-
Maya nCloth 2012: This is a plugin that allows you to create realistic and dynamic cloth simulations for your characters and scenes. It also uses the Nucleus simulation method that can handle collisions, self-collisions, wind forces, gravity, friction, and more. You can also design and edit your cloth using various tools such as paint attributes, sculpt geometry tool, transform constraint tool, and more.
-
Maya nParticles 2012: This is a plugin that allows you to create realistic and dynamic particle effects for your scenes. It also uses the Nucleus simulation method that can handle fluid dynamics, fire, smoke, dust, sand, snow, rain, bubbles, foam, spray, and more. You can also control and modify your particles using various tools such as emitters, fields, solvers, collisions, expressions, ramps, presets, and more.
-
-
These are just some of the plugins that are included in the Autodesk Maya 2012 Plugins Collectionzip. There are many more plugins that you can explore and use to create amazing 3D animations with Maya 2012. We hope you enjoy using them and have fun with your projects.
- d5da3c52bf
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diff --git a/spaces/diacanFperku/AutoGPT/Robot Millenium V21.zipl.md b/spaces/diacanFperku/AutoGPT/Robot Millenium V21.zipl.md
deleted file mode 100644
index 0c0372fda4207edde7ebc97e1c71e52e5558a41e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Robot Millenium V21.zipl.md
+++ /dev/null
@@ -1,100 +0,0 @@
-
-
Robot Millenium V21.zipl: The Best Software for Structural Analysis
-
If you are an engineer, architect or designer who needs to create and test structures, you need a reliable and powerful software that can handle complex calculations and simulations. That's why you need Robot Millenium V21.zipl, the ultimate software for structural analysis.
Robot Millenium V21.zipl is a software developed by Robobat, a leading company in the field of structural engineering. It is designed to help you design, analyze and optimize structures of any type and size, from buildings and bridges to towers and stadiums. Robot Millenium V21.zipl can handle both linear and nonlinear analysis, static and dynamic loads, buckling and stability, seismic and wind effects, steel and concrete design, and more. It can also generate detailed reports and drawings that comply with international codes and standards.
-
How to Download and Install Robot Millenium V21.zipl?
-
Downloading and installing Robot Millenium V21.zipl is easy and fast. You just need to follow these steps:
-
-
Go to the official website of Robobat and register for a free account.
-
Choose the version of Robot Millenium V21.zipl that suits your needs and preferences. You can download a trial version for 30 days or purchase a full license.
-
Download the installation file to your computer and run it.
-
Follow the instructions on the screen and complete the installation process.
-
Activate your license with the code that you received by email.
-
Enjoy using Robot Millenium V21.zipl for your projects.
-
-
Why Choose Robot Millenium V21.zipl?
-
There are many reasons why Robot Millenium V21.zipl is the best software for structural analysis. Here are some of them:
-
-
It is user-friendly and intuitive. You can easily create and modify models with the graphical interface or import them from other software such as AutoCAD or Revit.
-
It is versatile and flexible. You can work with any type of structure, material, load, boundary condition, design criterion, etc. You can also customize your own parameters and settings according to your needs.
-
It is accurate and reliable. You can trust the results of Robot Millenium V21.zipl as it uses advanced algorithms and methods that are validated by experts and researchers.
-
It is fast and efficient. You can save time and resources by performing multiple analyses in parallel or using cloud computing services.
-
It is compatible and interoperable. You can exchange data with other software such as Excel, Word, SAP2000, ETABS, etc. You can also use plugins and extensions to enhance the functionality of Robot Millenium V21.zipl.
-
-
Conclusion
-
Robot Millenium V21.zipl is the best software for structural analysis that you can find on the market. It offers you everything you need to design, analyze and optimize structures of any type and size. It is easy to download, install and use. It is also affordable and cost-effective. If you want to take your engineering skills to the next level, you should try Robot Millenium V21.zipl today.
-
How to Use Robot Millenium V21.zipl?
-
Using Robot Millenium V21.zipl is simple and straightforward. You just need to follow these steps:
-
-
-
Create a new project or open an existing one.
-
Define the geometry of your structure by drawing or importing nodes and elements.
-
Assign the properties of your structure such as materials, sections, supports, etc.
-
Apply the loads and load combinations that act on your structure.
-
Select the type of analysis that you want to perform such as linear, nonlinear, static, dynamic, etc.
-
Run the analysis and check the results such as displacements, forces, stresses, etc.
-
Verify the design of your structure according to the code and standard that you choose.
-
Optimize your structure by modifying the parameters and running the analysis again.
-
Generate the reports and drawings that you need for documentation and presentation.
-
-
What are the Features of Robot Millenium V21.zipl?
-
Robot Millenium V21.zipl has many features that make it stand out from other software for structural analysis. Here are some of them:
-
-
It supports a wide range of structures such as beams, columns, frames, trusses, plates, shells, cables, etc.
-
It can handle different types of materials such as steel, concrete, wood, composite, etc.
-
It can deal with various types of loads such as dead, live, wind, seismic, temperature, etc.
-
It can perform different types of analysis such as linear elastic, nonlinear geometric, nonlinear material, modal, harmonic, transient, spectral, etc.
-
It can design different types of structures according to different codes and standards such as Eurocode, ACI, AISC, BS, etc.
-
It can optimize different aspects of your structure such as weight, cost, performance, etc.
-
It can integrate with other software such as AutoCAD, Revit, Excel, Word, SAP2000, ETABS, etc.
-
It can create stunning reports and drawings with high-quality graphics and tables.
-
-
Frequently Asked Questions About Robot Millenium V21.zipl
-
Here are some of the most common questions that users have about Robot Millenium V21.zipl:
-
-
How much does Robot Millenium V21.zipl cost?
-
The price of Robot Millenium V21.zipl depends on the version and license that you choose. You can download a trial version for 30 days or purchase a full license for a year or more. You can also choose between a single-user or a multi-user license. You can check the official website of Robobat for more details and discounts.
-
What are the system requirements for Robot Millenium V21.zipl?
-
The minimum system requirements for Robot Millenium V21.zipl are: Windows XP/Vista/7/8/10; Intel Pentium 4 or AMD Athlon processor; 1 GB RAM; 1 GB free disk space; 1024 x 768 screen resolution; Internet connection. The recommended system requirements are: Windows 7/8/10; Intel Core i5 or AMD Ryzen processor; 4 GB RAM; 10 GB free disk space; 1920 x 1080 screen resolution; Internet connection.
-
Where can I find tutorials and support for Robot Millenium V21.zipl?
-
You can find many tutorials and support resources for Robot Millenium V21.zipl on the official website of Robobat. You can also access the online help and user manual from within the software. You can also contact the technical support team by email or phone if you have any questions or issues.
-
-
What are the Advantages of Robot Millenium V21.zipl?
-
Robot Millenium V21.zipl has many advantages that make it a superior software for structural analysis. Here are some of them:
-
-
It improves your productivity and efficiency. You can complete your projects faster and easier with Robot Millenium V21.zipl. You can also automate and streamline your workflow with the built-in tools and features.
-
It enhances your creativity and innovation. You can explore different design options and scenarios with Robot Millenium V21.zipl. You can also use the optimization feature to find the best solution for your structure.
-
It increases your quality and accuracy. You can ensure the safety and performance of your structure with Robot Millenium V21.zipl. You can also verify and validate your design with the code checking feature.
-
It saves your money and resources. You can reduce the material and labor costs of your structure with Robot Millenium V21.zipl. You can also avoid errors and rework with the error detection feature.
-
It boosts your reputation and credibility. You can impress your clients and stakeholders with Robot Millenium V21.zipl. You can also demonstrate your professionalism and expertise with the report and drawing feature.
-
-
Who Can Use Robot Millenium V21.zipl?
-
Robot Millenium V21.zipl is a software that can be used by anyone who is involved in the structural engineering field. Here are some of the users who can benefit from Robot Millenium V21.zipl:
-
-
Engineers: You can use Robot Millenium V21.zipl to design, analyze and optimize structures of any type and size. You can also use it to collaborate with other engineers and consultants.
-
Architects: You can use Robot Millenium V21.zipl to create and test structures that match your architectural vision. You can also use it to coordinate with other architects and engineers.
-
Designers: You can use Robot Millenium V21.zipl to create and test structures that are aesthetically pleasing and functional. You can also use it to communicate with other designers and engineers.
-
Educators: You can use Robot Millenium V21.zipl to teach and learn about structural engineering concepts and principles. You can also use it to demonstrate and illustrate real-world examples and applications.
-
Students: You can use Robot Millenium V21.zipl to practice and improve your structural engineering skills and knowledge. You can also use it to complete your assignments and projects.
-
-
What are the Reviews of Robot Millenium V21.zipl?
-
Robot Millenium V21.zipl has received many positive reviews from its users and critics. Here are some of them:
-
-
"Robot Millenium V21.zipl is a great software for structural analysis. It is easy to use and has many features that make it a powerful tool for engineering. I use it for my projects and I am very satisfied with the results."
-A user from SoundCloud
-
-
-
"Robot Millenium V21.zipl is a must-have software for structural engineers. It can handle any type of structure and load with accuracy and speed. It can also design and optimize structures according to different codes and standards. It is worth every penny."
-A user from JadedAI
-
-
-
"Robot Millenium V21.zipl is a fantastic software for structural analysis and design. It is user-friendly and intuitive. It can also integrate with other software such as AutoCAD and Revit. It is the best software that I have ever used."
-A user from LeadWithYes
-
-
Conclusion
-
Robot Millenium V21.zipl is the best software for structural analysis that you can find on the market. It offers you everything you need to design, analyze and optimize structures of any type and size. It is easy to download, install and use. It is also affordable and cost-effective. If you want to take your engineering skills to the next level, you should try Robot Millenium V21.zipl today.
-
Robot Millenium V21.zipl is the best software for structural analysis that you can find on the market. It offers you everything you need to design, analyze and optimize structures of any type and size. It is easy to download, install and use. It is also affordable and cost-effective. If you want to take your engineering skills to the next level, you should try Robot Millenium V21.zipl today.
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/eaglelandsonce/QueryaWebsite/README.md b/spaces/eaglelandsonce/QueryaWebsite/README.md
deleted file mode 100644
index 94bd7b47c397387ce3a4f7ae32f419c3367919ea..0000000000000000000000000000000000000000
--- a/spaces/eaglelandsonce/QueryaWebsite/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Pinecone Query Sitemap Meetup Demo
-emoji: 👁
-colorFrom: indigo
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/easyh/NerDH_Visualisierer/README.md b/spaces/easyh/NerDH_Visualisierer/README.md
deleted file mode 100644
index 0b33b60866956b5f8e2aa445f273acc2ae5cd132..0000000000000000000000000000000000000000
--- a/spaces/easyh/NerDH_Visualisierer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NerDH Visualisierer
-emoji: 💻💬
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/emc348/faces-through-time/models/e4e/psp.py b/spaces/emc348/faces-through-time/models/e4e/psp.py
deleted file mode 100644
index bf9f75dbaa66997abfc1e3e0e4f19ddfec7fedac..0000000000000000000000000000000000000000
--- a/spaces/emc348/faces-through-time/models/e4e/psp.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import matplotlib
-from configs import paths_config
-matplotlib.use('Agg')
-import torch
-from torch import nn
-from models.e4e.encoders import psp_encoders
-from models.e4e.stylegan2.model import Generator
-
-
-def get_keys(d, name):
- if 'state_dict' in d:
- d = d['state_dict']
- d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
- return d_filt
-
-
-class pSp(nn.Module):
-
- def __init__(self, opts):
- super(pSp, self).__init__()
- self.opts = opts
- # Define architecture
- self.encoder = self.set_encoder()
- self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2)
- self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
- # Load weights if needed
- self.load_weights()
-
- def set_encoder(self):
- if self.opts.encoder_type == 'GradualStyleEncoder':
- encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts)
- elif self.opts.encoder_type == 'Encoder4Editing':
- encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts)
- else:
- raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type))
- return encoder
-
- def load_weights(self):
- if self.opts.checkpoint_path is not None:
- print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path))
- ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu')
- self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True)
- self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True)
- self.__load_latent_avg(ckpt)
- else:
- print('Loading encoders weights from irse50!')
- encoder_ckpt = torch.load(paths_config.ir_se50)
- self.encoder.load_state_dict(encoder_ckpt, strict=False)
- print('Loading decoder weights from pretrained!')
- ckpt = torch.load(self.opts.stylegan_weights)
- self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
- self.__load_latent_avg(ckpt, repeat=self.encoder.style_count)
-
- def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True,
- inject_latent=None, return_latents=False, alpha=None):
- if input_code:
- codes = x
- else:
- codes = self.encoder(x)
- # normalize with respect to the center of an average face
- if self.opts.start_from_latent_avg:
- if codes.ndim == 2:
- codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
- else:
- codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)
-
- if latent_mask is not None:
- for i in latent_mask:
- if inject_latent is not None:
- if alpha is not None:
- codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
- else:
- codes[:, i] = inject_latent[:, i]
- else:
- codes[:, i] = 0
-
- input_is_latent = not input_code
- images, result_latent = self.decoder([codes],
- input_is_latent=input_is_latent,
- randomize_noise=randomize_noise,
- return_latents=return_latents)
-
- if resize:
- images = self.face_pool(images)
-
- if return_latents:
- return images, result_latent
- else:
- return images
-
- def __load_latent_avg(self, ckpt, repeat=None):
- if 'latent_avg' in ckpt:
- self.latent_avg = ckpt['latent_avg'].to(self.opts.device)
- if repeat is not None:
- self.latent_avg = self.latent_avg.repeat(repeat, 1)
- else:
- self.latent_avg = None
diff --git a/spaces/enzostvs/hair-colour/app/api/check-hair-color/route.ts b/spaces/enzostvs/hair-colour/app/api/check-hair-color/route.ts
deleted file mode 100644
index 4efa1514a0eb92ab8da2df654bce7d6abfba84b0..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/hair-colour/app/api/check-hair-color/route.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-import fs from 'fs';
-
-import PipelineSingleton from './pipeline';
-import { RawImage } from '@xenova/transformers';
-
-export async function POST(request: Request) {
- const res = await request.formData();
- const file = res.get('file') as Blob;
-
- const classifier: any = await PipelineSingleton.getInstance();
- if (!classifier) {
- return Response.json({ success: false })
- }
- // read blob as data url
- const file_url = await RawImage.fromBlob(file)
- const result = await classifier(file_url, { topk: 4 });
- return Response.json({ data: result })
-}
\ No newline at end of file
diff --git a/spaces/eson/tokenizer-arena/vocab/gpt_neox_chinese_v1/tokenizer/gpt2_tokenization.py b/spaces/eson/tokenizer-arena/vocab/gpt_neox_chinese_v1/tokenizer/gpt2_tokenization.py
deleted file mode 100644
index f8a112880cd7a944eebbd7272ffac15b66d8f8fc..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/gpt_neox_chinese_v1/tokenizer/gpt2_tokenization.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# Copyright (c) 2021, EleutherAI
-# This file is based on code by the authors denoted below and has been modified from its original version.
-#
-# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
-#
-# 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.
-
-"""Tokenization classes for OpenAI GPT."""
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import sys
-import json
-import logging
-import os
-import regex as re
-from io import open
-
-from functools import lru_cache
-
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_VOCAB_ARCHIVE_MAP = {
- "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
-}
-PRETRAINED_MERGES_ARCHIVE_MAP = {
- "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
-}
-PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
- "gpt2": 1024,
-}
-
-VOCAB_NAME = "vocab.json"
-MERGES_NAME = "merges.txt"
-SPECIAL_TOKENS_NAME = "special_tokens.txt"
-
-
-@lru_cache()
-def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a corresponding list of unicode strings.
- The reversible bpe codes work on unicode strings.
- This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
- When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
- This is a significant percentage of your normal, say, 32K bpe vocab.
- To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
- And avoids mapping to whitespace/control characters the bpe code barfs on.
- """
- _chr = unichr if sys.version_info[0] == 2 else chr
- bs = (
- list(range(ord("!"), ord("~") + 1))
- + list(range(ord("¡"), ord("¬") + 1))
- + list(range(ord("®"), ord("ÿ") + 1))
- )
- cs = bs[:]
- n = 0
- for b in range(2**8):
- if b not in bs:
- bs.append(b)
- cs.append(2**8 + n)
- n += 1
- cs = [_chr(n) for n in cs]
- return dict(zip(bs, cs))
-
-
-def get_pairs(word):
- """Return set of symbol pairs in a word.
-
- Word is represented as tuple of symbols (symbols being variable-length strings).
- """
- pairs = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
-
-
-class GPT2Tokenizer(object):
- """
- GPT-2 BPE tokenizer. Peculiarities:
- - Byte-level BPE
- """
-
- @classmethod
- def from_pretrained(
- cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs
- ):
- """
- Instantiate a PreTrainedBertModel from a pre-trained model file.
- Download and cache the pre-trained model file if needed.
- """
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
- vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
- merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
- special_tokens_file = None
- else:
- vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
- merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
- special_tokens_file = os.path.join(
- pretrained_model_name_or_path, SPECIAL_TOKENS_NAME
- )
- if not os.path.exists(special_tokens_file):
- special_tokens_file = None
- else:
- logger.info(
- "loading special tokens file {}".format(special_tokens_file)
- )
- # redirect to the cache, if necessary
- try:
- from .file_utils import cached_path
-
- resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
- resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
- except EnvironmentError:
- logger.error(
- "Model name '{}' was not found in model name list ({}). "
- "We assumed '{}' was a path or url but couldn't find files {} and {} "
- "at this path or url.".format(
- pretrained_model_name_or_path,
- ", ".join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
- pretrained_model_name_or_path,
- vocab_file,
- merges_file,
- )
- )
- return None
- if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
- logger.info("loading vocabulary file {}".format(vocab_file))
- logger.info("loading merges file {}".format(merges_file))
- else:
- logger.info(
- "loading vocabulary file {} from cache at {}".format(
- vocab_file, resolved_vocab_file
- )
- )
- logger.info(
- "loading merges file {} from cache at {}".format(
- merges_file, resolved_merges_file
- )
- )
- if (
- pretrained_model_name_or_path
- in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP
- ):
- # if we're using a pretrained model, ensure the tokenizer won't index sequences longer
- # than the number of positional embeddings
- max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[
- pretrained_model_name_or_path
- ]
- kwargs["max_len"] = min(kwargs.get("max_len", int(1e12)), max_len)
- # Instantiate tokenizer.
- if special_tokens_file and "special_tokens" not in kwargs:
- special_tokens = (
- open(special_tokens_file, encoding="utf-8").read().split("\n")[:-1]
- )
- else:
- special_tokens = kwargs.pop("special_tokens", [])
- tokenizer = cls(
- resolved_vocab_file,
- resolved_merges_file,
- special_tokens=special_tokens,
- *inputs,
- **kwargs
- )
- return tokenizer
-
- def __init__(
- self,
- vocab_file,
- merges_file,
- errors="replace",
- special_tokens=None,
- max_len=None,
- ):
- self.max_len = max_len if max_len is not None else int(1e12)
- self.encoder = json.load(open(vocab_file))
- self.decoder = {v: k for k, v in self.encoder.items()}
- self.errors = errors # how to handle errors in decoding
- self.byte_encoder = bytes_to_unicode()
- self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
- bpe_data = open(merges_file, encoding="utf-8").read().split("\n")[1:-1]
- bpe_merges = [tuple(merge.split()) for merge in bpe_data]
- self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-
- # Should haved added re.IGNORECASE so BPE merges can happen for
- # capitalized versions of contractions
- self.pat = re.compile(
- r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
- )
-
- self.special_tokens = {}
- self.special_tokens_decoder = {}
- self.set_special_tokens(special_tokens)
-
- def __len__(self):
- return len(self.encoder) + len(self.special_tokens)
-
- def set_special_tokens(self, special_tokens):
- """Add a list of additional tokens to the encoder.
- The additional tokens are indexed starting from the last index of the
- current vocabulary in the order of the `special_tokens` list.
- """
- if not special_tokens:
- self.special_tokens = {}
- self.special_tokens_decoder = {}
- return
- self.special_tokens = dict(
- (tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens)
- )
- self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()}
- logger.info("Special tokens {}".format(self.special_tokens))
-
- @lru_cache(maxsize=131072)
- def bpe(self, token):
- word = tuple(token)
- pairs = get_pairs(word)
-
- if not pairs:
- return token
-
- while True:
- bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
- if bigram not in self.bpe_ranks:
- break
- first, second = bigram
- new_word = []
- i = 0
- while i < len(word):
- try:
- j = word.index(first, i)
- new_word.extend(word[i:j])
- i = j
- except BaseException:
- new_word.extend(word[i:])
- break
-
- if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
- new_word.append(first + second)
- i += 2
- else:
- new_word.append(word[i])
- i += 1
- new_word = tuple(new_word)
- word = new_word
- if len(word) == 1:
- break
- else:
- pairs = get_pairs(word)
- word = " ".join(word)
- return word
-
- def tokenize(self, text):
- """Tokenize a string."""
- bpe_tokens = []
- for token in re.findall(self.pat, text):
- if sys.version_info[0] == 2:
- token = "".join(self.byte_encoder[ord(b)] for b in token)
- else:
- token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
- bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
- return bpe_tokens
-
- def convert_tokens_to_ids(self, tokens):
- """Converts a sequence of tokens into ids using the vocab."""
- ids = []
- if isinstance(tokens, str) or (
- sys.version_info[0] == 2 and isinstance(tokens, unicode)
- ):
- if tokens in self.special_tokens:
- return self.special_tokens[tokens]
- else:
- return self.encoder.get(tokens, 0)
- for token in tokens:
- if token in self.special_tokens:
- ids.append(self.special_tokens[token])
- else:
- ids.append(self.encoder.get(token, 0))
- if len(ids) > self.max_len:
- logger.warning(
- "Token indices sequence length is longer than the specified maximum "
- " sequence length for this OpenAI GPT model ({} > {}). Running this"
- " sequence through the model will result in indexing errors".format(
- len(ids), self.max_len
- )
- )
- return ids
-
- def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
- """Converts a sequence of ids in BPE tokens using the vocab."""
- tokens = []
- for i in ids:
- if i in self.special_tokens_decoder:
- if not skip_special_tokens:
- tokens.append(self.special_tokens_decoder[i])
- else:
- tokens.append(self.decoder[i])
- return tokens
-
- def encode(self, text):
- return self.convert_tokens_to_ids(self.tokenize(text))
-
- def decode(self, tokens):
- text = "".join([self.decoder[token] for token in tokens])
- text = bytearray([self.byte_decoder[c] for c in text]).decode(
- "utf-8", errors=self.errors
- )
- return text
-
- def save_vocabulary(self, vocab_path):
- """Save the tokenizer vocabulary and merge files to a directory."""
- if not os.path.isdir(vocab_path):
- logger.error(
- "Vocabulary path ({}) should be a directory".format(vocab_path)
- )
- return
- vocab_file = os.path.join(vocab_path, VOCAB_NAME)
- merge_file = os.path.join(vocab_path, MERGES_NAME)
- special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
-
- with open(vocab_file, "w", encoding="utf-8") as f:
- f.write(json.dumps(self.encoder, ensure_ascii=False))
-
- index = 0
- with open(merge_file, "w", encoding="utf-8") as writer:
- writer.write("#version: 0.2\n")
- for bpe_tokens, token_index in sorted(
- self.bpe_ranks.items(), key=lambda kv: kv[1]
- ):
- if index != token_index:
- logger.warning(
- "Saving vocabulary to {}: BPE merge indices are not consecutive."
- " Please check that the tokenizer is not corrupted!".format(
- merge_file
- )
- )
- index = token_index
- writer.write(" ".join(bpe_tokens) + "\n")
- index += 1
-
- index = len(self.encoder)
- with open(special_tokens_file, "w", encoding="utf-8") as writer:
- for token, token_index in sorted(
- self.special_tokens.items(), key=lambda kv: kv[1]
- ):
- if index != token_index:
- logger.warning(
- "Saving special tokens vocabulary to {}: BPE indices are not consecutive."
- " Please check that the tokenizer is not corrupted!".format(
- special_tokens_file
- )
- )
- index = token_index
- writer.write(token + "\n")
- index += 1
-
- return vocab_file, merge_file, special_tokens_file
diff --git a/spaces/facebook/MusicGen/audiocraft/data/audio_utils.py b/spaces/facebook/MusicGen/audiocraft/data/audio_utils.py
deleted file mode 100644
index 9d3129b84b114c5572078295604279884c79f2cc..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/data/audio_utils.py
+++ /dev/null
@@ -1,176 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Various utilities for audio convertion (pcm format, sample rate and channels),
-and volume normalization."""
-import sys
-import typing as tp
-
-import julius
-import torch
-import torchaudio
-
-
-def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor:
- """Convert audio to the given number of channels.
-
- Args:
- wav (torch.Tensor): Audio wave of shape [B, C, T].
- channels (int): Expected number of channels as output.
- Returns:
- torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
- """
- *shape, src_channels, length = wav.shape
- if src_channels == channels:
- pass
- elif channels == 1:
- # Case 1:
- # The caller asked 1-channel audio, and the stream has multiple
- # channels, downmix all channels.
- wav = wav.mean(dim=-2, keepdim=True)
- elif src_channels == 1:
- # Case 2:
- # The caller asked for multiple channels, but the input file has
- # a single channel, replicate the audio over all channels.
- wav = wav.expand(*shape, channels, length)
- elif src_channels >= channels:
- # Case 3:
- # The caller asked for multiple channels, and the input file has
- # more channels than requested. In that case return the first channels.
- wav = wav[..., :channels, :]
- else:
- # Case 4: What is a reasonable choice here?
- raise ValueError('The audio file has less channels than requested but is not mono.')
- return wav
-
-
-def convert_audio(wav: torch.Tensor, from_rate: float,
- to_rate: float, to_channels: int) -> torch.Tensor:
- """Convert audio to new sample rate and number of audio channels."""
- wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
- wav = convert_audio_channels(wav, to_channels)
- return wav
-
-
-def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db: float = 14,
- loudness_compressor: bool = False, energy_floor: float = 2e-3):
- """Normalize an input signal to a user loudness in dB LKFS.
- Audio loudness is defined according to the ITU-R BS.1770-4 recommendation.
-
- Args:
- wav (torch.Tensor): Input multichannel audio data.
- sample_rate (int): Sample rate.
- loudness_headroom_db (float): Target loudness of the output in dB LUFS.
- loudness_compressor (bool): Uses tanh for soft clipping.
- energy_floor (float): anything below that RMS level will not be rescaled.
- Returns:
- torch.Tensor: Loudness normalized output data.
- """
- energy = wav.pow(2).mean().sqrt().item()
- if energy < energy_floor:
- return wav
- transform = torchaudio.transforms.Loudness(sample_rate)
- input_loudness_db = transform(wav).item()
- # calculate the gain needed to scale to the desired loudness level
- delta_loudness = -loudness_headroom_db - input_loudness_db
- gain = 10.0 ** (delta_loudness / 20.0)
- output = gain * wav
- if loudness_compressor:
- output = torch.tanh(output)
- assert output.isfinite().all(), (input_loudness_db, wav.pow(2).mean().sqrt())
- return output
-
-
-def _clip_wav(wav: torch.Tensor, log_clipping: bool = False, stem_name: tp.Optional[str] = None) -> None:
- """Utility function to clip the audio with logging if specified."""
- max_scale = wav.abs().max()
- if log_clipping and max_scale > 1:
- clamp_prob = (wav.abs() > 1).float().mean().item()
- print(f"CLIPPING {stem_name or ''} happening with proba (a bit of clipping is okay):",
- clamp_prob, "maximum scale: ", max_scale.item(), file=sys.stderr)
- wav.clamp_(-1, 1)
-
-
-def normalize_audio(wav: torch.Tensor, normalize: bool = True,
- strategy: str = 'peak', peak_clip_headroom_db: float = 1,
- rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
- loudness_compressor: bool = False, log_clipping: bool = False,
- sample_rate: tp.Optional[int] = None,
- stem_name: tp.Optional[str] = None) -> torch.Tensor:
- """Normalize the audio according to the prescribed strategy (see after).
-
- Args:
- wav (torch.Tensor): Audio data.
- normalize (bool): if `True` (default), normalizes according to the prescribed
- strategy (see after). If `False`, the strategy is only used in case clipping
- would happen.
- strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
- i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
- with extra headroom to avoid clipping. 'clip' just clips.
- peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
- rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
- than the `peak_clip` one to avoid further clipping.
- loudness_headroom_db (float): Target loudness for loudness normalization.
- loudness_compressor (bool): If True, uses tanh based soft clipping.
- log_clipping (bool): If True, basic logging on stderr when clipping still
- occurs despite strategy (only for 'rms').
- sample_rate (int): Sample rate for the audio data (required for loudness).
- stem_name (str, optional): Stem name for clipping logging.
- Returns:
- torch.Tensor: Normalized audio.
- """
- scale_peak = 10 ** (-peak_clip_headroom_db / 20)
- scale_rms = 10 ** (-rms_headroom_db / 20)
- if strategy == 'peak':
- rescaling = (scale_peak / wav.abs().max())
- if normalize or rescaling < 1:
- wav = wav * rescaling
- elif strategy == 'clip':
- wav = wav.clamp(-scale_peak, scale_peak)
- elif strategy == 'rms':
- mono = wav.mean(dim=0)
- rescaling = scale_rms / mono.pow(2).mean().sqrt()
- if normalize or rescaling < 1:
- wav = wav * rescaling
- _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
- elif strategy == 'loudness':
- assert sample_rate is not None, "Loudness normalization requires sample rate."
- wav = normalize_loudness(wav, sample_rate, loudness_headroom_db, loudness_compressor)
- _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
- else:
- assert wav.abs().max() < 1
- assert strategy == '' or strategy == 'none', f"Unexpected strategy: '{strategy}'"
- return wav
-
-
-def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
- """Convert audio to float 32 bits PCM format.
- """
- if wav.dtype.is_floating_point:
- return wav
- elif wav.dtype == torch.int16:
- return wav.float() / 2**15
- elif wav.dtype == torch.int32:
- return wav.float() / 2**31
- raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
-
-
-def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
- """Convert audio to int 16 bits PCM format.
-
- ..Warning:: There exist many formula for doing this conversion. None are perfect
- due to the asymmetry of the int16 range. One either have possible clipping, DC offset,
- or inconsistencies with f32_pcm. If the given wav doesn't have enough headroom,
- it is possible that `i16_pcm(f32_pcm)) != Identity`.
- """
- if wav.dtype.is_floating_point:
- assert wav.abs().max() <= 1
- candidate = (wav * 2 ** 15).round()
- if candidate.max() >= 2 ** 15: # clipping would occur
- candidate = (wav * (2 ** 15 - 1)).round()
- return candidate.short()
- else:
- assert wav.dtype == torch.int16
- return wav
diff --git a/spaces/fastaioncampus/TrafficSigns/app.py b/spaces/fastaioncampus/TrafficSigns/app.py
deleted file mode 100644
index f20f83bb7a17cabc84cb984859056b383b48913c..0000000000000000000000000000000000000000
--- a/spaces/fastaioncampus/TrafficSigns/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-## importing dependencies
-import gradio as gr
-from fastai.vision.all import *
-
-## importing model created
-learn = load_learner('model.pkl')
-
-## creating labels and examples to be used by the interface
-labels = learn.dls.vocab
-examples = ['stop_sign.jpg',
- 'pedestrian_crossing_sign.jpg',
- 'speed_limit_sign.jpg',
- 'bump_sign.jpg',
- 'semaphore_sign.jpg']
-
-## creating prediction function for the interface
-def predict(img):
- img = PILImage.create(img)
- pred, pred_idx, probability = learn.predict(img)
- return {labels[i]: float(probability[i]) for i in range(len(labels))}
-
-## generating huggingFace's interface
-gr.Interface(fn=predict,
- inputs=gr.components.Image(shape=(512, 512)),
- outputs=gr.components.Label(num_top_classes=3),
- examples=examples
- ).launch(share=False)
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Command and Customize Your Own Modern Warship in this Action-Packed Game.md b/spaces/fatiXbelha/sd/Command and Customize Your Own Modern Warship in this Action-Packed Game.md
deleted file mode 100644
index 7c5bed6fe1bb70d0eb6df5fdcaaf67c54ea1a402..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Command and Customize Your Own Modern Warship in this Action-Packed Game.md
+++ /dev/null
@@ -1,93 +0,0 @@
-
-
Modern Warship Download: How to Play the Most Realistic Naval Battle Game on Your PC
-
Introduction
-
Do you love naval warfare games? Do you want to command your own modern warship and fight with your friends online? If yes, then you should try Modern Warship, the most realistic naval battle game on the market. In this article, we will show you how to download and play Modern Warship on your PC using BlueStacks, the best Android emulator for gaming. We will also share some tips and tricks to help you become a better captain and win more battles. Let's get started!
Modern Warship is an action game developed by Artstorm LLC. It is a realistic online game where you can choose from over 60 different models of ships, submarines, and aircraft carriers based on real-world specifications. You can customize your warship with various weapons, such as missiles, cannons, grenade launchers, torpedo launchers, and more. You can also control deck helicopters, strike fighters, drones, and bombers. You can fight with your friends in epic naval battles online and prove your leadership skills. The game features stunning 3D graphics, realistic physics, and sound effects that make you feel like you are in the middle of the action.
-
Why play Modern Warship on PC?
-
While Modern Warship is a great game to play on your mobile device, it can be even better to play it on your PC. Here are some reasons why:
-
-
You can enjoy a larger screen and better graphics quality.
-
You can use your keyboard and mouse for more precise control and aiming.
-
You can avoid battery drain, overheating, and lag issues that may affect your mobile device.
-
You can use BlueStacks features to enhance your gaming experience, such as shooting mode, free look, macros, scripts, etc.
-
-
How to download and install Modern Warship on PC
-
To play Modern Warship on your PC, you need to download and install BlueStacks first. BlueStacks is an app player that allows you to run Android apps and games on your PC. It is easy to use and has many features that make it the best platform for gaming. Here are the steps to download and install Modern Warship on your PC using BlueStacks:
-
Step 1: Download and install BlueStacks on your PC
-
Go to the official website of BlueStacks ([text]) and click on the download button. The installation file will be downloaded to your PC. Run the file and follow the instructions to install BlueStacks on your PC. It may take a few minutes depending on your system configuration.
-
Step 2: Complete Google sign-in to access the Play Store
-
After installing BlueStacks, launch it from your desktop or start menu. You will see a welcome screen where you need to complete Google sign-in to access the Play Store. If you already have a Google account, you can use it to sign in. If not, you can create one for free.
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-
Step 3: Search for Modern Warship in the search bar
-
Once you are signed in, you will
Once you are signed in, you will see the home screen of BlueStacks with various app icons. You can click on the Google Play icon to access the Play Store. Alternatively, you can use the search bar at the top right corner of the screen and type "Modern Warship". You will see the game icon in the search results.
-
Step 4: Click to install Modern Warship from the search results
-
Click on the Modern Warship icon and you will be taken to the game page on the Play Store. Here you can see more information about the game, such as screenshots, videos, ratings, reviews, etc. You can also see the install button at the top right corner of the screen. Click on it and wait for the game to download and install on your PC. It may take a few minutes depending on your internet speed and PC performance.
-
Step 5: Click the Modern Warship icon on the home screen to start playing
-
Once the game is installed, you will see the Modern Warship icon on the home screen of BlueStacks. You can also find it in the "My Games" tab. Click on it and you will be able to launch the game on your PC. You can use your mouse to navigate the game menu and settings. You can also use your keyboard to control your warship and fire your weapons. You can customize your keyboard controls by clicking on the keyboard icon at the bottom right corner of the screen. You can also enable or disable other BlueStacks features from the sidebar.
-
Tips and tricks for playing Modern Warship on PC
-
Now that you know how to download and play Modern Warship on your PC, here are some tips and tricks to help you improve your skills and enjoy the game more:
-
Choose your battleship wisely
-
Modern Warship offers a variety of ships to choose from, each with its own strengths and weaknesses. You can browse through different categories, such as destroyers, cruisers, battleships, submarines, and aircraft carriers. You can also see their stats, such as speed, armor, firepower, range, etc. You should choose a ship that suits your play style and strategy. For example, if you like to be fast and agile, you can choose a destroyer or a cruiser. If you like to have more firepower and durability, you can choose a battleship or an aircraft carrier. If you like to be stealthy and sneaky, you can choose a submarine.
-
Customize your weapons and equipment
-
Modern Warship allows you to customize your warship with various weapons and equipment. You can equip different types of missiles, cannons, grenade launchers, torpedo launchers, etc. You can also upgrade them to increase their damage, accuracy, reload speed, etc. You can also equip different types of armor, engines, radars, sonars, etc. to improve your defense, speed, detection, etc. You can also change the appearance of your warship by choosing different skins and decals.
-
Use the shooting mode and free look features of BlueStacks
-
One of the advantages of playing Modern Warship on PC is that you can use the shooting mode and free look features of BlueStacks. These features allow you to have more precise control and aiming when firing your weapons. To enable shooting mode, press F1 on your keyboard or click on the shooting mode icon at the bottom right corner of the screen. This will lock your mouse cursor to the center of the screen and allow you to aim with your mouse movement. To enable free look, press F2 on your keyboard or click on the free look icon at the bottom right corner of
of the screen. This will allow you to look around with your mouse movement without changing your direction. You can use these features to have a better view of your surroundings and your enemies.
-
Join tournaments and clans for more rewards and fun
-
Modern Warship is not only a game of skill, but also a game of teamwork and cooperation. You can join tournaments and clans to compete with other players and earn more rewards and fun. Tournaments are events where you can participate in different modes and challenges, such as team deathmatch, capture the flag, king of the hill, etc. You can win prizes, such as gold, gems, chests, etc. Clans are groups of players who share a common interest and goal. You can join or create a clan to chat with other members, share tips and strategies, request and donate resources, etc. You can also fight with your clan mates in clan wars and clan battles.
-
Conclusion
-
Modern Warship is a realistic naval battle game that you can play on your PC using BlueStacks. You can choose from over 60 different models of ships, submarines, and aircraft carriers, customize your weapons and equipment, and fight with your friends online. You can also use the shooting mode and free look features of BlueStacks to have more precise control and aiming. You can also join tournaments and clans for more rewards and fun. If you are a fan of naval warfare games, you should definitely try Modern Warship on your PC. Download it now and enjoy the most realistic naval battle game on your PC!
-
FAQs
-
-
Q: How much space does Modern Warship require on my PC?
-
A: Modern Warship requires about 1 GB of free space on your PC.
-
Q: How can I update Modern Warship on my PC?
-
A: You can update Modern Warship on your PC by opening the Play Store app on BlueStacks and clicking on the "My Apps" tab. You will see a list of apps that have updates available. Click on the "Update" button next to Modern Warship to download and install the latest version.
-
Q: How can I contact the support team of Modern Warship?
-
A: You can contact the support team of Modern Warship by clicking on the "Settings" icon on the game menu and then clicking on the "Support" button. You will be redirected to a web page where you can fill out a form with your issue or feedback.
-
Q: How can I play Modern Warship with my friends?
-
A: You can play Modern Warship with your friends by inviting them to join your room or clan. To invite them to join your room, click on the "Create Room" button on the game menu and then click on the "Invite Friends" button. You will see a list of your friends who are online or offline. Click on the "Invite" button next to their names to send them an invitation. To invite them to join your clan, click on the "Clan" button on the game menu and then click on the "Invite Friends" button. You will see a list of your friends who are not in a clan. Click on the "Invite" button next to their names to send them an invitation.
-
Q: How can I change the language of Modern Warship?
-
A: You can change the language of Modern Warship by clicking on the "Settings" icon on the game menu and then clicking on the "Language" button. You will see a list of languages that are supported by the game. Click on the language that you prefer to apply it.
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Experience the Thrill of Fighting Giants in Attack on Titan Assault for Android.md b/spaces/fatiXbelha/sd/Experience the Thrill of Fighting Giants in Attack on Titan Assault for Android.md
deleted file mode 100644
index 5a5412ee19d4622a8a4e6d6dcb48460415d3695e..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Experience the Thrill of Fighting Giants in Attack on Titan Assault for Android.md
+++ /dev/null
@@ -1,133 +0,0 @@
-
-
Attack on Titan 1 APK Download: How to Play the Epic Anime Game on Your Android Device
-
If you are a fan of the popular anime series Attack on Titan, you might be interested in playing the mobile game based on it. Attack on Titan 1 APK is a free-to-play action game that lets you experience the thrilling battles between humans and Titans. In this article, we will show you how to download and install Attack on Titan 1 APK on your Android device, and how to enjoy the game to the fullest.
-
What is Attack on Titan 1 APK?
-
A brief introduction to the anime series and the game
-
Attack on Titan is a Japanese dark fantasy anime series that premiered in 2013. It is adapted from the manga series of the same name by Hajime Isayama. The story is set in a post-apocalyptic world where humanity lives behind walls to protect themselves from giant humanoid creatures called Titans, who devour humans without reason. The main protagonist is Eren Yeager, a young boy who vows to exterminate all Titans after witnessing his mother being eaten by one. He joins the elite Survey Corps, a military group that fights against Titans using special equipment called Vertical Maneuvering Equipment (VME).
Attack on Titan 1 APK is a mobile game that follows the plot of the anime series. It is developed by GameDreamer and released in 2019. It is an online multiplayer game that allows you to create your own character and join other players in various modes, such as story mode, raid mode, arena mode, and expedition mode. You can also customize your character's appearance, skills, weapons, and VME. The game features stunning graphics, realistic physics, dynamic combat, and original voice acting from the anime cast.
-
The features and gameplay of the game
-
Attack on Titan 1 APK has many features that make it an exciting and immersive game for fans of the anime series. Some of these features are:
-
-
You can choose from four different classes: Scout, Warrior, Assassin, and Engineer. Each class has its own strengths, weaknesses, and abilities.
-
You can collect and upgrade various weapons, such as blades, guns, cannons, traps, and bombs. You can also equip different VMEs that have different effects, such as speed boost, stealth mode, grappling hook, etc.
-
You can team up with other players or compete against them in various modes. You can cooperate with your teammates to defeat Titans or other enemies, or you can challenge other players in PvP battles.
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You can explore different locations from the anime series, such as Shiganshina District, Trost District, Forest of Giant Trees, etc. You can also encounter different types of Titans, such as Normal Titans, Abnormal Titans, Armored Titans, Colossal Titans, etc.
-
You can enjoy the original soundtrack and voice acting from the anime series. You can also interact with your favorite characters from the anime series, such as Eren Yeager, Mikasa Ackerman, Armin Arlert, Levi Ackerman, etc.
-
-
The gameplay of Attack on Titan 1 APK is
The gameplay of Attack on Titan 1 APK is simple and intuitive. You can control your character using the virtual joystick and buttons on the screen. You can move, jump, dash, attack, and use your VME to maneuver around the environment. You can also switch between different weapons and VMEs depending on the situation. You can also use special skills and items to enhance your performance. The game has a tutorial mode that teaches you the basics of the game.
-
How to Download and Install Attack on Titan 1 APK?
-
The requirements and precautions for downloading the game
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Before you download and install Attack on Titan 1 APK, you need to make sure that your Android device meets the following requirements:
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-
Your Android device must have at least 2 GB of RAM and 4 GB of free storage space.
-
Your Android device must have Android 4.4 or higher operating system.
-
Your Android device must have a stable internet connection.
-
-
You also need to take some precautions before downloading and installing the game, such as:
-
-
You need to enable the "Unknown Sources" option in your device settings. This will allow you to install apps from sources other than the Google Play Store.
-
You need to disable any antivirus or security apps that might interfere with the installation process.
-
You need to backup your data before installing the game, in case something goes wrong.
-
-
The steps to download and install the game from Google Drive or FileHippo
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Once you have checked the requirements and precautions, you can follow these steps to download and install Attack on Titan 1 APK on your Android device:
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Go to either Google Drive or FileHippo and click on the download link for Attack on Titan 1 APK. You will be redirected to another page where you can choose a server to download the file.
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Wait for the download to finish. You will see a notification on your device when the file is downloaded.
-
Locate the downloaded file on your device using a file manager app. Tap on the file to open it.
-
You will see a pop-up window asking you to confirm the installation. Tap on "Install" and wait for the installation to complete.
-
Once the installation is done, you will see another pop-up window asking you to open the game. Tap on "Open" and enjoy the game.
-
-
How to Enjoy Attack on Titan 1 APK?
-
The tips and tricks to play the game effectively
-
Now that you have downloaded and installed Attack on Titan 1 APK, you might want to know some tips and tricks to play the game effectively. Here are some of them:
-
-
Choose a class that suits your play style. For example, if you like to deal high damage and take down Titans quickly, you might want to choose the Warrior class. If you like to be stealthy and agile, you might want to choose the Assassin class.
-
Upgrade your weapons and VMEs regularly. You can use coins and gems that you earn from playing the game or from completing quests and achievements. You can also use real money to buy more coins and gems if you want.
-
Use your skills and items wisely. Each skill has a cooldown time, so you need to use them at the right moment. Each item has a limited quantity, so you need to use them sparingly. You can also buy more skills and items from the shop if you need them.
-
Cooperate with your teammates in multiplayer modes. You can communicate with them using voice chat or text chat. You can also use emotes and gestures to express yourself. You can also invite your friends or join random players in multiplayer modes.
-
Learn from your mistakes and improve your skills. You can watch replays of your matches or other players' matches to see what you did wrong or what you did well. You can also practice in solo mode or training mode to hone your skills.
-
-
The benefits and drawbacks of playing the game
-
Playing Attack on Titan 1 APK has its benefits and drawbacks. Some of the benefits are:
-
-
You can enjoy an epic anime game that is faithful to the original series.
-
You can experience thrilling battles between humans and Titans in various modes.
-
You can customize your character's appearance, skills, weapons, and VMEs.
-
You can interact with other players and make new friends.
-
-
Some of the drawbacks are:
-
-
You might
You might encounter some technical issues or bugs while playing the game. For example, you might experience lag, crashes, glitches, or errors. You can report these issues to the developers or check the official website or social media pages for updates and fixes.
-
You might spend too much time or money on the game. The game can be addictive and tempting, especially if you want to unlock more features or items. You need to set a limit on how much time or money you spend on the game and balance it with other aspects of your life.
-
You might face some negative feedback or harassment from other players. The game has a large and diverse community, which means that you might encounter some rude, toxic, or abusive players. You need to ignore them or block them if they bother you. You can also report them to the moderators or administrators if they violate the rules or terms of service.
-
-
Conclusion
-
Attack on Titan 1 APK is a great game for fans of the anime series and for anyone who likes action games. It is a free-to-play game that lets you experience the epic battles between humans and Titans in various modes. You can also customize your character's appearance, skills, weapons, and VMEs. You can also interact with other players and make new friends. However, you also need to be aware of the drawbacks of playing the game, such as technical issues, addiction, and negativity. You need to play the game responsibly and enjoyably.
-
If you want to download and install Attack on Titan 1 APK on your Android device, you can follow the steps we have provided in this article. You can also check out some tips and tricks we have shared to play the game effectively. We hope that this article has helped you learn more about Attack on Titan 1 APK and how to play it on your Android device.
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What are you waiting for? Download Attack on Titan 1 APK now and join the fight against Titans!
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FAQs
-
Is Attack on Titan 1 APK safe and legal?
-
Yes, Attack on Titan 1 APK is safe and legal to download and play. The game is developed by GameDreamer, a licensed and authorized developer that has obtained the rights to use the Attack on Titan brand and content. The game is also scanned and tested for viruses and malware before being released. However, you need to download the game from trusted sources, such as Google Drive or FileHippo, to avoid any fake or harmful files.
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Is Attack on Titan 1 APK compatible with other devices?
-
Attack on Titan 1 APK is mainly designed for Android devices, but it can also be played on other devices using emulators or third-party apps. For example, you can play the game on your PC using BlueStacks or NoxPlayer, which are popular Android emulators that allow you to run Android apps on your PC. You can also play the game on your iOS device using Panda Helper or TutuApp, which are third-party app stores that allow you to install Android apps on your iOS device.
-
How to update Attack on Titan 1 APK?
-
To update Attack on Titan 1 APK, you need to check for updates regularly from the official website or social media pages of the game. You can also enable the auto-update option in your device settings to receive notifications when there is a new version available. You can then download and install the latest version of the game from Google Drive or FileHippo using the same steps as before.
-
How to get more resources and items in Attack on Titan 1 APK?
-
To get more resources and items in Attack on Titan 1 APK, you have several options:
-
-
You can earn more coins and gems by playing the game regularly, completing quests and achievements, watching ads, or participating in events and promotions.
-
You can buy more coins and gems using real money through in-app purchases. You can choose from different packages and payment methods depending on your preference.
-
You can use hacks or cheats to get unlimited resources and items in the game. However, this is not recommended as it might ruin your gaming experience, cause technical issues, or get you banned from the game.
-
-
How to contact the developers of Attack on Titan 1 APK?
-
If you have any questions, feedback, suggestions, or complaints about Attack on Titan 1 APK, you can contact the developers of the game through various channels:
-
-
You can send an email to their customer service at cs@gamedreamer.com.tw
-
You can visit their official website at https://www.gamedreamer.com.tw/You can follow their official Facebook page at https://www.facebook.com/AttackOnTitanGameDreamer/
-
You can join their official Discord server at https://discord.gg/attackontitan
-
-
The developers of Attack on Titan 1 APK are friendly and responsive, and they will try their best to help you with any issues or concerns you might have.
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diff --git a/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/cmrc2018_preprocessing.py b/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/cmrc2018_preprocessing.py
deleted file mode 100644
index 7a8a1de0f6a99062512246f1761ba47324a38998..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/cmrc2018_preprocessing.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import json
-from tqdm import tqdm
-import os
-from sklearn.utils import shuffle
-import re
-import argparse
-
-
-def cut_sent(para):
- para = re.sub('([。，,！？\?])([^”’])', r"\1\n\2", para) # 单字符断句符
- para = re.sub('(\.{6})([^”’])', r"\1\n\2", para) # 英文省略号
- para = re.sub('(\…{2})([^”’])', r"\1\n\2", para) # 中文省略号
- para = re.sub('([。！？\?][”’])([^，。！？\?])', r'\1\n\2', para)
- # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
- para = para.rstrip() # 段尾如果有多余的\n就去掉它
- # 很多规则中会考虑分号;，但是这里
- return para.split("\n")
-
-
-def search(pattern, sequence):
- n = len(pattern)
- res=[]
- for i in range(len(sequence)):
- if sequence[i:i + n] == pattern:
- res.append([i,i + n-1])
- return res
-
-max_length=512
-stride=128
-def stride_split(question, context, answer, start):
- end = start + len(answer) -1
- results, n = [], 0
- max_c_len = max_length - len(question) - 3
- while True:
- left, right = n * stride, n * stride + max_c_len
- if left <= start < end <= right:
- results.append((question, context[left:right], answer, start - left, end - left))
- elif right < start or end < right:
- results.append((question, context[left:right], '', -1, -1))
- if right >= len(context):
- return results
- n += 1
-
-
-def load_data(file_path,is_training=False):
- task_type='抽取任务'
- subtask_type='抽取式阅读理解'
- with open(file_path, 'r', encoding='utf8') as f:
- lines = json.loads(''.join(f.readlines()))
- result=[]
- lines = lines['data']
- for line in tqdm(lines):
- if line['paragraphs']==[]:
- continue
- data = line['paragraphs'][0]
- context=data['context'].strip()
- for qa in data['qas']:
- question=qa['question'].strip()
- rcv=[]
- for a in qa['answers']:
- if a not in rcv:
- rcv.append(a)
- split=stride_split(question, context, a['text'], a['answer_start'])
- for sp in split:
- choices = []
-
- choice = {}
- choice['id']=qa['id']
- choice['entity_type'] = qa['question']
- choice['label']=0
- entity_list=[]
- if sp[3]>=0 and sp[4]>=0:
- entity_list.append({'entity_name':sp[2],'entity_type':'','entity_idx':[[sp[3],sp[4]]]})
-
- choice['entity_list']=entity_list
- choices.append(choice)
-
- if choices==[]:
- print(data)
- continue
- result.append({ 'task_type':task_type,
- 'subtask_type':subtask_type,
- 'text':sp[1],
- 'choices':choices,
- 'id':0})
-
- return result
-
-
-def save_data(data,file_path):
- with open(file_path, 'w', encoding='utf8') as f:
- for line in data:
- json_data=json.dumps(line,ensure_ascii=False)
- f.write(json_data+'\n')
-
-
-
-if __name__=="__main__":
- parser = argparse.ArgumentParser(description="train")
- parser.add_argument("--data_path", type=str,default="")
- parser.add_argument("--save_path", type=str,default="")
-
- args = parser.parse_args()
-
-
- data_path = args.data_path
- save_path = args.save_path
-
- if not os.path.exists(save_path):
- os.makedirs(save_path)
-
- file_list=['dev','train','trial','test']
- train_data = []
- dev_data = []
- for file in file_list:
- file_path = os.path.join(data_path,file+'.json')
- data=load_data(file_path=file_path)
- if 'train' in file or 'trial' in file:
- train_data.extend(data)
- else:
- output_path = os.path.join(save_path,file+'.json')
- save_data(data,output_path)
-
- output_path = os.path.join(save_path,'train.json')
- save_data(train_data,output_path)
-
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/examples/zen2_finetune/fengshen_token_level_ft_task.py b/spaces/fclong/summary/fengshen/examples/zen2_finetune/fengshen_token_level_ft_task.py
deleted file mode 100644
index 619847c1555311226be69d7d0558368dfd048546..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/zen2_finetune/fengshen_token_level_ft_task.py
+++ /dev/null
@@ -1,678 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA 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.
-from fengshen.models.zen2.modeling import ZenForTokenClassification
-from fengshen.metric.metric import SeqEntityScore
-from fengshen.models.zen2.tokenization import BertTokenizer
-from fengshen.models.zen2.ngram_utils import ZenNgramDict
-from pytorch_lightning.callbacks import LearningRateMonitor
-from dataclasses import dataclass
-import logging
-import math
-import numpy as np
-import os
-import json
-import torch
-import pytorch_lightning as pl
-import argparse
-from pytorch_lightning.callbacks import ModelCheckpoint
-from torch.utils.data import Dataset, DataLoader
-
-import torch.nn.functional as F
-logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
- datefmt='%m/%d/%Y %H:%M:%S',
- level=logging.ERROR)
-logger = logging.getLogger(__name__)
-
-
-class InputExample(object):
- """A single training/test example for simple sequence classification."""
-
- def __init__(self, guid, text_a, text_b=None, label=None):
- """Constructs a InputExample.
-
- Args:
- guid: Unique id for the example.
- text_a: string. The untokenized text of the first sequence. For single
- sequence tasks, only this sequence must be specified.
- text_b: (Optional) string. The untokenized text of the second sequence.
- Only must be specified for sequence pair tasks.
- label: (Optional) string. The label of the example. This should be
- specified for train and dev examples, but not for test examples.
- """
- self.guid = guid
- self.text_a = text_a
- self.text_b = text_b
- self.label = label
-
-
-class InputFeatures(object):
- """A single set of features of data."""
-
- def __init__(self, input_ids, input_mask, segment_ids, label_id, ngram_ids, ngram_positions, ngram_lengths,
- ngram_tuples, ngram_seg_ids, ngram_masks, valid_ids=None, label_mask=None, b_use_valid_filter=False):
- self.input_ids = input_ids
- self.input_mask = input_mask
- self.segment_ids = segment_ids
- self.label_id = label_id
- self.valid_ids = valid_ids
- self.label_mask = label_mask
-
- self.ngram_ids = ngram_ids
- self.ngram_positions = ngram_positions
- self.ngram_lengths = ngram_lengths
- self.ngram_tuples = ngram_tuples
- self.ngram_seg_ids = ngram_seg_ids
- self.ngram_masks = ngram_masks
-
- self.b_use_valid_filter = b_use_valid_filter
-
-
-def convert_examples_to_features(examples, label_map, max_seq_length, tokenizer, ngram_dict):
- """Loads a data file into a list of `InputBatch`s."""
-
- # label_map = {label: i for i, label in enumerate(label_list, 1)}
- # label_map["[PAD]"] = 0
-
- features = []
- b_use_valid_filter = False
- for (ex_index, example) in enumerate(examples):
- textlist = example.text_a
- labellist = example.label
- tokens = []
- labels = []
- valid = []
- label_mask = []
- for i, word in enumerate(textlist):
- token = tokenizer.tokenize(word)
- if len(tokens) + len(token) > max_seq_length - 2:
- break
- tokens.extend(token)
- label_1 = labellist[i]
- for m in range(len(token)):
- if m == 0:
- labels.append(label_1)
- valid.append(1)
- label_mask.append(1)
- else:
- valid.append(0)
- b_use_valid_filter = True
- ntokens = []
- segment_ids = []
- label_ids = []
- ntokens.append("[CLS]")
- segment_ids.append(0)
- valid.insert(0, 1)
- label_mask.insert(0, 1)
- label_ids.append(label_map["[CLS]"])
- for i, token in enumerate(tokens):
- ntokens.append(token)
- segment_ids.append(0)
- if len(labels) > i:
- label_ids.append(label_map[labels[i]])
- ntokens.append("[SEP]")
- segment_ids.append(0)
- valid.append(1)
- label_mask.append(1)
- label_ids.append(label_map["[SEP]"])
- input_ids = tokenizer.convert_tokens_to_ids(ntokens)
- input_mask = [1] * len(input_ids)
- label_mask = [1] * len(label_ids)
- while len(input_ids) < max_seq_length:
- input_ids.append(0)
- input_mask.append(0)
- segment_ids.append(0)
- label_ids.append(0)
- valid.append(1)
- label_mask.append(0)
- while len(label_ids) < max_seq_length:
- label_ids.append(0)
- label_mask.append(0)
- assert len(input_ids) == max_seq_length
- assert len(input_mask) == max_seq_length
- assert len(segment_ids) == max_seq_length
- assert len(label_ids) == max_seq_length
- assert len(valid) == max_seq_length
- assert len(label_mask) == max_seq_length
-
- # ----------- code for ngram BEGIN-----------
- ngram_matches = []
- # Filter the ngram segment from 2 to 7 to check whether there is a ngram
- max_gram_n = ngram_dict.max_ngram_len
- for p in range(2, max_gram_n):
- for q in range(0, len(tokens) - p + 1):
- character_segment = tokens[q:q + p]
- # j is the starting position of the ngram
- # i is the length of the current ngram
- character_segment = tuple(character_segment)
- if character_segment in ngram_dict.ngram_to_id_dict:
- ngram_index = ngram_dict.ngram_to_id_dict[character_segment]
- ngram_freq = ngram_dict.ngram_to_freq_dict[character_segment]
- ngram_matches.append([ngram_index, q, p, character_segment, ngram_freq])
-
- ngram_matches = sorted(ngram_matches, key=lambda s: s[0])
-
- max_ngram_in_seq_proportion = math.ceil((len(tokens) / max_seq_length) * ngram_dict.max_ngram_in_seq)
- if len(ngram_matches) > max_ngram_in_seq_proportion:
- ngram_matches = ngram_matches[:max_ngram_in_seq_proportion]
-
- ngram_ids = [ngram[0] for ngram in ngram_matches]
- ngram_positions = [ngram[1] for ngram in ngram_matches]
- ngram_lengths = [ngram[2] for ngram in ngram_matches]
- ngram_tuples = [ngram[3] for ngram in ngram_matches]
- ngram_freqs = [ngram[4] for ngram in ngram_matches]
- ngram_seg_ids = [0 if position < (len(tokens) + 2) else 1 for position in ngram_positions]
-
- ngram_mask_array = np.zeros(ngram_dict.max_ngram_in_seq, dtype=np.bool)
- ngram_mask_array[:len(ngram_ids)] = 1
-
- # record the masked positions
- ngram_positions_matrix = np.zeros(shape=(max_seq_length, ngram_dict.max_ngram_in_seq), dtype=np.int32)
- for i in range(len(ngram_ids)):
- ngram_positions_matrix[ngram_positions[i]:ngram_positions[i] + ngram_lengths[i], i] = ngram_freqs[i]
- ngram_positions_matrix = torch.from_numpy(ngram_positions_matrix.astype(np.float))
- ngram_positions_matrix = torch.div(ngram_positions_matrix, torch.stack(
- [torch.sum(ngram_positions_matrix, 1)] * ngram_positions_matrix.size(1)).t() + 1e-10)
- ngram_positions_matrix = ngram_positions_matrix.numpy()
-
- # Zero-pad up to the max ngram in seq length.
- padding = [0] * (ngram_dict.max_ngram_in_seq - len(ngram_ids))
- ngram_ids += padding
- ngram_lengths += padding
- ngram_seg_ids += padding
-
- # ----------- code for ngram END-----------
-
- if ex_index < 5:
- logger.info("*** Example ***")
- logger.info("guid: %s" % (example.guid))
- logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
- logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
- logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
- logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
- logger.info("label: %s (id = %s)" % (",".join([str(x) for x in example.label]), ",".join([str(x) for x in label_ids])))
- logger.info("valid: %s" % " ".join([str(x) for x in valid]))
- logger.info("b_use_valid_filter: %s" % str(b_use_valid_filter))
- logger.info("ngram_ids: %s" % " ".join([str(x) for x in ngram_ids]))
- logger.info("ngram_positions: %s" % " ".join([str(x) for x in ngram_positions]))
- logger.info("ngram_lengths: %s" % " ".join([str(x) for x in ngram_lengths]))
- logger.info("ngram_tuples: %s" % " ".join([str(x) for x in ngram_tuples]))
- logger.info("ngram_seg_ids: %s" % " ".join([str(x) for x in ngram_seg_ids]))
-
- features.append(
- InputFeatures(input_ids=input_ids,
- input_mask=input_mask,
- segment_ids=segment_ids,
- label_id=label_ids,
- ngram_ids=ngram_ids,
- ngram_positions=ngram_positions_matrix,
- ngram_lengths=ngram_lengths,
- ngram_tuples=ngram_tuples,
- ngram_seg_ids=ngram_seg_ids,
- ngram_masks=ngram_mask_array,
- valid_ids=valid,
- label_mask=label_mask,
- b_use_valid_filter=b_use_valid_filter))
- return features
-
-
-class DataProcessor(object):
- """Base class for data converters for sequence classification data sets."""
-
- def get_examples(self, data_path, set_type, quotechar=' '):
- """See base class."""
- return self._create_examples(
- self._read_tsv(data_path, self.get_quotechar()), set_type)
-
- def _create_examples(self, lines, set_type):
- examples = []
- for i, (sentence, label) in enumerate(lines):
- guid = "%s-%s" % (set_type, i)
- text_a = sentence
- label = label
- examples.append(InputExample(guid=guid, text_a=text_a, label=label))
- return examples
-
- def get_labels(self):
- """Gets the list of labels for this data set."""
- raise NotImplementedError()
-
- def get_quotechar(self):
- return ' '
-
- @classmethod
- def _read_tsv(cls, input_file, quotechar=None):
- '''
- read file
- return format :
- [ ['EU', 'B-ORG'], ['rejects', 'O'], ['German', 'B-MISC'], ['call', 'O'], ['to', 'O'], ['boycott', 'O'], ['British', 'B-MISC'], ['lamb', 'O'], ['.', 'O'] ]
- '''
- f = open(input_file)
- data = []
- sentence = []
- label = []
- for line in f:
- if len(line) == 0 or line.startswith('-DOCSTART') or line[0] == "\n":
- if len(sentence) > 0:
- data.append((sentence, label))
- sentence = []
- label = []
- continue
- splits = line.split(quotechar)
- sentence.append(splits[0])
- label.append(splits[-1][:-1])
-
- if len(sentence) > 0:
- data.append((sentence, label))
- sentence = []
- label = []
- return data
-
-
-class MSRAProcessor(DataProcessor):
- """Processor for the msra data set."""
-
- def get_labels(self):
- return ['B-NR', 'B-NS', 'B-NT', 'E-NR', 'E-NS', 'E-NT', 'M-NR',
- 'M-NS', 'M-NT', 'O', 'S-NR', 'S-NS', 'S-NT', '[CLS]', '[SEP]']
-
-
-class OntoNotes4Processor(DataProcessor):
- """Processor for the OntoNotes4 data set."""
-
- def get_labels(self):
- return ['B-GPE', 'B-LOC', 'B-ORG', 'B-PER', 'E-GPE', 'E-LOC',
- 'E-ORG', 'E-PER', 'M-GPE', 'M-LOC', 'M-ORG', 'M-PER', 'O',
- 'S-GPE', 'S-LOC', 'S-ORG', 'S-PER', '[CLS]', '[SEP]']
-
-
-class WeiboProcessor(DataProcessor):
- """Processor for the Weibo data set."""
-
- def get_labels(self):
- return ['B-GPE.NAM', 'B-GPE.NOM', 'B-LOC.NAM', 'B-LOC.NOM',
- 'B-ORG.NAM', 'B-ORG.NOM', 'B-PER.NAM', 'B-PER.NOM', 'E-GPE.NAM',
- 'E-GPE.NOM', 'E-LOC.NAM', 'E-LOC.NOM', 'E-ORG.NAM', 'E-ORG.NOM',
- 'E-PER.NAM', 'E-PER.NOM', 'M-GPE.NAM', 'M-LOC.NAM', 'M-LOC.NOM',
- 'M-ORG.NAM', 'M-ORG.NOM', 'M-PER.NAM', 'M-PER.NOM', 'O',
- 'S-GPE.NAM', 'S-LOC.NOM', 'S-PER.NAM', 'S-PER.NOM', '[CLS]', '[SEP]']
-
-
-class ResumeProcessor(DataProcessor):
- """Processor for the resume data set."""
-
- def get_labels(self):
- return ['B-CONT', 'B-EDU', 'B-LOC', 'B-NAME', 'B-ORG', 'B-PRO',
- 'B-RACE', 'B-TITLE', 'E-CONT', 'E-EDU', 'E-LOC', 'E-NAME',
- 'E-ORG', 'E-PRO', 'E-RACE', 'E-TITLE', 'M-CONT', 'M-EDU',
- 'M-LOC', 'M-NAME', 'M-ORG', 'M-PRO', 'M-RACE', 'M-TITLE',
- 'O', 'S-NAME', 'S-ORG', 'S-RACE', '[CLS]', '[SEP]']
-
-
-class CMeEEProcessor(DataProcessor):
- """Processor for the CMeEE data set."""
-
- def get_quotechar(self):
- return '\t'
-
- def get_labels(self):
- return ['B-临床表现', 'B-医学检验项目', 'B-医疗程序', 'B-医疗设备',
- 'B-微生物类', 'B-疾病', 'B-科室', 'B-药物', 'B-身体', 'I-临床表现',
- 'I-医学检验项目', 'I-医疗程序', 'I-医疗设备', 'I-微生物类',
- 'I-疾病', 'I-科室', 'I-药物', 'I-身体', 'O', '[CLS]', '[SEP]']
-
-
-class CLUENERProcessor(DataProcessor):
- """Processor for the CLUENER data set."""
-
- def get_quotechar(self):
- return '\t'
-
- def get_labels(self):
- return ['B-书名', 'B-公司', 'B-地址', 'B-姓名', 'B-政府', 'B-景点',
- 'B-游戏', 'B-电影', 'B-组织机构', 'B-职位', 'I-书名', 'I-公司',
- 'I-地址', 'I-姓名', 'I-政府', 'I-景点', 'I-游戏', 'I-电影',
- 'I-组织机构', 'I-职位', 'O', '[CLS]', '[SEP]']
-
-
-class TaskDataset(Dataset):
- def __init__(self, data_path, processor, mode='train'):
- super().__init__()
- self.data = self.load_data(data_path, processor, mode)
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, index):
- return self.data[index]
-
- def load_data(self, data_path, processor, mode):
- if mode == "train":
- examples = processor.get_examples(data_path, mode)
- elif mode == "test":
- examples = processor.get_examples(data_path, mode)
- elif mode == "dev":
- examples = processor.get_examples(data_path, mode)
- return examples
-
-
-@dataclass
-class TaskCollator:
- args = None
- tokenizer = None
- ngram_dict = None
- label2id = None
-
- def __call__(self, samples):
- features = convert_examples_to_features(samples, self.label2id, self.args.max_seq_length, self.tokenizer, self.ngram_dict)
- # logger.info(" Num examples = %d", len(samples))
-
- input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
- input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
- segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
- label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
- valid_ids = torch.tensor([f.valid_ids for f in features], dtype=torch.long)
-
- ngram_ids = torch.tensor([f.ngram_ids for f in features], dtype=torch.long)
- ngram_positions = torch.tensor([f.ngram_positions for f in features], dtype=torch.long)
- # ngram_lengths = torch.tensor([f.ngram_lengths for f in features], dtype=torch.long)
- # ngram_seg_ids = torch.tensor([f.ngram_seg_ids for f in features], dtype=torch.long)
- # ngram_masks = torch.tensor([f.ngram_masks for f in features], dtype=torch.long)
-
- # label_mask = torch.tensor([f.label_mask for f in features], dtype=torch.long)
- b_use_valid_filter = torch.tensor([f.b_use_valid_filter for f in features], dtype=torch.bool)
- # 取第一个出来？
- # b_use_valid_filter = b_use_valid_filter.detach().cpu().numpy()[0]
- b_use_valid_filter = b_use_valid_filter[0]
- return {
- 'input_ids': input_ids,
- 'input_ngram_ids': ngram_ids,
- 'ngram_position_matrix': ngram_positions,
- 'attention_mask': input_mask,
- 'token_type_ids': segment_ids,
- 'labels': label_ids,
- 'valid_ids': valid_ids,
- 'b_use_valid_filter': b_use_valid_filter,
- }
-
-
-class TaskDataModel(pl.LightningDataModule):
- @staticmethod
- def add_data_specific_args(parent_args):
- parser = parent_args.add_argument_group('TASK NAME DataModel')
- parser.add_argument('--data_dir', default='./data', type=str)
- parser.add_argument('--num_workers', default=8, type=int)
- parser.add_argument('--train_data', default='train.json', type=str)
- parser.add_argument('--valid_data', default='dev.json', type=str)
- parser.add_argument('--test_data', default='test.json', type=str)
- parser.add_argument('--train_batchsize', default=16, type=int)
- parser.add_argument('--valid_batchsize', default=32, type=int)
- parser.add_argument('--max_seq_length', default=128, type=int)
-
- parser.add_argument('--texta_name', default='text', type=str)
- parser.add_argument('--textb_name', default='sentence2', type=str)
- parser.add_argument('--label_name', default='label', type=str)
- parser.add_argument('--id_name', default='id', type=str)
-
- parser.add_argument('--dataset_name', default=None, type=str)
- parser.add_argument('--vocab_file',
- type=str, default=None,
- help="Vocabulary mapping/file BERT was pretrainined on")
- parser.add_argument("--do_lower_case",
- action='store_true',
- help="Set this flag if you are using an uncased model.")
- parser.add_argument('--task_name', default='weibo', type=str)
-
- return parent_args
-
- def __init__(self, args):
- super().__init__()
- self.train_batchsize = args.train_batchsize
- self.valid_batchsize = args.valid_batchsize
- self.collator = TaskCollator()
- self.collator.args = args
- self.collator.tokenizer = BertTokenizer.from_pretrained(args.pretrained_model_path, do_lower_case=args.do_lower_case)
- self.collator.ngram_dict = ZenNgramDict.from_pretrained(args.pretrained_model_path, tokenizer=self.collator.tokenizer)
-
- processors = {
- 'weibo': WeiboProcessor,
- 'resume': ResumeProcessor,
- 'msra': MSRAProcessor,
- 'ontonotes4': OntoNotes4Processor,
- 'cmeee': CMeEEProcessor,
- 'cluener': CLUENERProcessor,
- }
- if args.task_name not in processors:
- raise ValueError("Task not found: %s" % (args.task_name))
- processor = processors[args.task_name]()
- # 生成id映射
- label_list = processor.get_labels()
- label2id = {label: i for i, label in enumerate(label_list, 1)}
- label2id["[PAD]"] = 0
- self.id2label = {v: k for k, v in label2id.items()}
- self.collator.label2id = label2id
-
- if args.dataset_name is None:
- self.train_data = TaskDataset(os.path.join(
- args.data_dir, args.train_data), processor, mode='train')
- self.valid_data = TaskDataset(os.path.join(
- args.data_dir, args.valid_data), processor, mode='dev')
- self.test_data = TaskDataset(os.path.join(
- args.data_dir, args.test_data), processor, mode='test')
-
- else:
- import datasets
- ds = datasets.load_dataset(args.dataset_name)
- self.train_data = ds['train']
- self.valid_data = ds['validation']
- self.test_data = ds['test']
- self.save_hyperparameters(args)
-
- def train_dataloader(self):
- return DataLoader(self.train_data, shuffle=True, batch_size=self.train_batchsize, pin_memory=False,
- collate_fn=self.collator)
-
- def val_dataloader(self):
- return DataLoader(self.valid_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
- collate_fn=self.collator)
-
- def predict_dataloader(self):
- return DataLoader(self.test_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
- collate_fn=self.collator)
-
-
-class LitModel(pl.LightningModule):
-
- @staticmethod
- def add_model_specific_args(parent_args):
- parser = parent_args.add_argument_group('BaseModel')
- parser.add_argument('--markup', default='bios', type=str)
- parser.add_argument('--middle_prefix', default='I-', type=str)
- return parent_args
-
- def __init__(self, args, id2label):
- super().__init__()
- # config = ZenConfig(os.path.join(args.pretrained_model_path, 'config.json'))
- self.model = ZenForTokenClassification.from_pretrained(args.pretrained_model_path, num_labels=len(id2label))
- self.seq_entity_score = SeqEntityScore(id2label, markup=args.markup, middle_prefix=args.middle_prefix)
- self.train_seq_entity_score = SeqEntityScore(id2label, markup=args.markup, middle_prefix=args.middle_prefix)
- self.id2label = id2label
- self.label2id = {v: k for k, v in id2label.items()}
- self.save_hyperparameters(args)
-
- def setup(self, stage) -> None:
- if stage == 'fit':
- train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
- # Calculate total steps
- if self.trainer.max_epochs > 0:
- world_size = self.trainer.world_size
- tb_size = self.hparams.train_batchsize * max(1, world_size)
- ab_size = self.trainer.accumulate_grad_batches
- self.total_steps = (len(train_loader.dataset) *
- self.trainer.max_epochs // tb_size) // ab_size
- else:
- self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches
-
- print('Total steps: {}' .format(self.total_steps))
-
- def training_step(self, batch, batch_idx):
- outputs = self.model(**batch)
- loss = outputs.loss
- # logits = outputs.logits
- # preds = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
- # preds = preds.detach().cpu().numpy()
- # labels = batch['labels'].detach().cpu().numpy()
- # num_labels = len(self.label2id)
- # y_true = []
- # y_pred = []
- # for i, label in enumerate(labels):
- # temp_1 = []
- # temp_2 = []
- # for j, m in enumerate(label):
- # if j == 0:
- # continue
- # elif labels[i][j] == num_labels - 1:
- # y_true.append(temp_1)
- # y_pred.append(temp_2)
- # break
- # else:
- # temp_1.append(self.id2label[labels[i][j]])
- # temp_2.append(self.id2label[preds[i][j]])
-
- # self.train_seq_entity_score.update(y_true, y_pred)
- # result = self.train_seq_entity_score.result()
- # self.train_seq_entity_score.reset()
- self.log('train_loss', loss)
-
- return loss
-
- def validation_step(self, batch, batch_idx):
- outputs = self.model(**batch)
- loss = outputs.loss
- logits = outputs.logits
- preds = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
- preds = preds.detach().cpu().numpy()
- labels = batch['labels'].detach().cpu().numpy()
- num_labels = len(self.label2id)
- y_true = []
- y_pred = []
- for i, label in enumerate(labels):
- temp_1 = []
- temp_2 = []
- for j, m in enumerate(label):
- if j == 0:
- continue
- elif labels[i][j] == num_labels - 1:
- y_true.append(temp_1)
- y_pred.append(temp_2)
- break
- else:
- temp_1.append(self.id2label[labels[i][j]])
- temp_2.append(self.id2label[preds[i][j]])
-
- self.seq_entity_score.update(y_true, y_pred)
- self.log('val_loss', loss)
-
- def validation_epoch_end(self, outputs):
- # compute metric for all process
- score_dict, _ = self.seq_entity_score.result()
- if self.trainer._accelerator_connector.cluster_environment.global_rank() == 0:
- print('score_dict:\n', score_dict)
- # reset the metric after once validation
- self.seq_entity_score.reset()
- for k, v in score_dict.items():
- self.log('val_{}'.format(k), v)
-
- def configure_optimizers(self):
- from fengshen.models.model_utils import configure_optimizers
- return configure_optimizers(self)
-
-
-class TaskModelCheckpoint:
- @staticmethod
- def add_argparse_args(parent_args):
- parser = parent_args.add_argument_group('BaseModel')
-
- parser.add_argument('--monitor', default='train_loss', type=str)
- parser.add_argument('--mode', default='min', type=str)
- parser.add_argument('--dirpath', default='./log/', type=str)
- parser.add_argument(
- '--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str)
-
- parser.add_argument('--save_top_k', default=3, type=float)
- parser.add_argument('--every_n_train_steps', default=100, type=float)
- parser.add_argument('--save_weights_only', default=True, type=bool)
-
- return parent_args
-
- def __init__(self, args):
- self.callbacks = ModelCheckpoint(monitor=args.monitor,
- save_top_k=args.save_top_k,
- mode=args.mode,
- every_n_train_steps=args.every_n_train_steps,
- save_weights_only=args.save_weights_only,
- dirpath=args.dirpath,
- filename=args.filename)
-
-
-def save_test(data, args, data_model):
- with open(args.output_save_path, 'w', encoding='utf-8') as f:
- idx = 0
- for i in range(len(data)):
- batch = data[i]
- for sample in batch:
- tmp_result = dict()
- label_id = np.argmax(sample.numpy())
- tmp_result['id'] = data_model.test_data.data[idx]['id']
- tmp_result['label'] = data_model.id2label[label_id]
- json_data = json.dumps(tmp_result, ensure_ascii=False)
- f.write(json_data+'\n')
- idx += 1
- print('save the result to '+args.output_save_path)
-
-
-def main():
- total_parser = argparse.ArgumentParser("TASK NAME")
- total_parser.add_argument('--pretrained_model_path', default='', type=str)
- total_parser.add_argument('--output_save_path',
- default='./predict.json', type=str)
- # * Args for data preprocessing
- total_parser = TaskDataModel.add_data_specific_args(total_parser)
- # * Args for training
- total_parser = pl.Trainer.add_argparse_args(total_parser)
- total_parser = TaskModelCheckpoint.add_argparse_args(total_parser)
-
- # * Args for base model
- from fengshen.models.model_utils import add_module_args
- total_parser = add_module_args(total_parser)
- total_parser = LitModel.add_model_specific_args(total_parser)
-
- args = total_parser.parse_args()
-
- checkpoint_callback = TaskModelCheckpoint(args).callbacks
- lr_monitor = LearningRateMonitor(logging_interval='step')
- trainer = pl.Trainer.from_argparse_args(args,
- callbacks=[checkpoint_callback, lr_monitor]
- )
-
- data_model = TaskDataModel(args)
- id2label = data_model.id2label
- print('id2label:', id2label)
- model = LitModel(args, id2label)
- trainer.fit(model, data_model)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/ - .md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/ - .md
deleted file mode 100644
index 683a3302e8adf6d3ce8145c5ae9798f67be80ce1..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/ - .md
+++ /dev/null
@@ -1,122 +0,0 @@
-
-
What is Bukva R and How to Write It?
-
Bukva R is a letter of the Cyrillic alphabet that represents the sound /r/ in many languages, such as Russian, Bulgarian, Serbian, and Ukrainian. It looks like this: Р (capital) and р (lowercase). It is similar to the Latin letter R, but not exactly the same.
-
In this article, I will show you how to write Bukva R in both print and cursive, and give you some examples of words that use it. I will also explain some interesting facts about this letter and its history. Let's get started!
Writing Bukva R in print is pretty easy. You just need to follow these steps:
-
-
Start at the top and draw a vertical line down.
-
Go back to the top and draw a diagonal line to the right.
-
Draw another diagonal line to the left, crossing the first one.
-
Draw a horizontal line to the right, connecting the two diagonal lines.
-
-
Here is what it should look like:
-
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-
-
You can also write Bukva R in a more rounded way, like this:
-
-
Either way is fine, as long as it is clear and legible.
-
How to Write Bukva R in Cursive
-
Writing Bukva R in cursive is a bit more tricky, but not too hard. You just need to follow these steps:
-
-
Start at the bottom and draw a curved line up and to the right.
-
Draw a loop to the left, going below the baseline.
-
Draw another loop to the right, going above the baseline.
-
Draw a diagonal line down and to the right, ending with a hook.
-
-
Here is what it should look like:
-
-
Note that the hook at the end is important, because it helps you connect Bukva R with the next letter. For example, if you want to write "река" (river), you would write it like this:
-
-
Pretty neat, huh?
-
Examples of Words with Bukva R
-
Now that you know how to write Bukva R, let's see some examples of words that use it. Here are some common words in different languages that have Bukva R:
-
-
Language
Word
Meaning
Pronunciation
-
Russian
роза
rose
[ˈrozə]
-
Bulgarian
радио
radio
[ˈradio]
-
Serbian
река
river
[rěːka]
-
Ukrainian
робота
work
[rɔˈbɔtɑ]
-
Macedonian
роман
novel
[ˈrɔman]
-
Bosnian
риба
fish
[rîba]
-
Mongolian
руулай
wheel
[ruːlaj]
-
Tajik
рафтар
speed
[raftɒr]
-
Kazakh
республи ка
republic
[rɛspʊˈblɪka]
-
-
As you can see, Bukva R is used in many different words and contexts. It can also be combined with other letters to form new sounds, such as РЪ (hard sign), РЬ (soft sign), РШ (sh), and РЖ (zh).
-
Interesting Facts about Bukva R
-
Bukva R is not just a letter, it is also a symbol of culture and history. Here are some interesting facts about Bukva R that you might not know:
-
-
Bukva R is the 18th letter of the Cyrillic alphabet, and has a numerical value of 100 in the Cyrillic numeral system.
-
Bukva R is derived from the Greek letter Rho, which in turn comes from the Phoenician letter Resh, meaning "head".
-
Bukva R is sometimes called "er" or "ery" in Russian, especially when spelling words or acronyms. For example, РФ (RF) is pronounced "er-ef", meaning "Russian Federation".
-
Bukva R is one of the six letters that can be used to write the name of Russia in Russian: Россия (Rossiya). The others are О, С, И, Я, and Ь.
-
Bukva R is also one of the six letters that can be used to write the name of Serbia in Serbian: Србија (Srbija). The others are С, Б, И, Ј, and А.
-
Bukva R is the first letter of the word "революция" (revolution), which has been used to describe many political and social movements in history, such as the Russian Revolution of 1917, the Cuban Revolution of 1959, and the Velvet Revolution of 1989.
-
Bukva R is also the first letter of the word "радость" (joy), which is a positive emotion that many people seek and experience in life. Some examples of things that can bring joy are love, friendship, music, art, nature, and laughter.
-
-
Conclusion
-
Bukva R is a fascinating letter that has many uses and meanings. It represents the sound /r/ in many languages that use the Cyrillic alphabet, such as Russian, Bulgarian, Serbian, and Ukrainian. It can be written in both print and cursive, and it can form new sounds with other letters. It is also a symbol of culture and history, as it is part of the names of countries, words, and concepts that are important to many people. Bukva R is a letter that deserves to be learned and appreciated.
-
FAQs
-
What is Bukva R?
-
Bukva R is a letter of the Cyrillic alphabet that represents the sound /r/ in many languages.
-
How do you write Bukva R?
-
You can write Bukva R in print or cursive. In print, you draw a vertical line down and then a horizontal line with two diagonal lines crossing it. In cursive, you draw a curved line up and to the right, then two loops and a diagonal line down with a hook.
-
What are some words that use Bukva R?
-
Some examples of words that use Bukva R are роза (rose), радио (radio), река (river), робота (work), роман (novel), риба (fish), руулай (wheel), рафтар (speed), and республика (republic).
-
What are some interesting facts about Bukva R?
-
Some interesting facts about Bukva R are that it is derived from the Greek letter Rho, it has a numerical value of 100 in the Cyrillic numeral system, it is sometimes called "er" or "ery" in Russian, it is part of the names of Russia and Serbia, and it is the first letter of words like "revolution" and "joy".
-
Why should I learn Bukva R?
-
You should learn Bukva R if you want to read and write in languages that use the Cyrillic alphabet, such as Russian, Bulgarian, Serbian, and Ukrainian. You should also learn Bukva R if you want to understand more about the culture and history of the people who use it. Bukva R is a fascinating letter that has many uses and meanings, and learning it can enrich your knowledge and experience.
-
I hope you enjoyed this article and learned something new about Bukva R. If you have any questions or comments, feel free to leave them below. Thank you for reading!
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Block Puzzle Jewel - A Challenging and Addictive APK Game.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Block Puzzle Jewel - A Challenging and Addictive APK Game.md
deleted file mode 100644
index e32e992690d5ab7787c3eb69ef3864f40bc830a3..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Block Puzzle Jewel - A Challenging and Addictive APK Game.md
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
Block Puzzle Jewel APKPure: A Fun and Relaxing Game for Android
-
If you are looking for a simple yet challenging game to pass the time, you might want to try Block Puzzle Jewel, a free and popular puzzle game for Android devices. In this article, we will tell you what Block Puzzle Jewel is, how to play it, why you should download it from APKPure, what features it offers, and some tips and tricks to help you score higher.
-
What is Block Puzzle Jewel?
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Block Puzzle Jewel is a classic block puzzle game that requires you to fit different shapes of blocks into a 10x10 grid. The goal is to fill up horizontal or vertical lines with blocks to clear them and earn points. The game is over when there is no more space for new blocks on the grid.
The gameplay of Block Puzzle Jewel is very simple and intuitive. You just need to drag and drop the blocks from the bottom of the screen to the grid. You can also rotate the blocks by tapping on them. You can see the next three blocks that will appear on the bottom right corner of the screen. You can also pause the game by tapping on the pause button on the top right corner of the screen.
-
Why download Block Puzzle Jewel from APKPure?
-
APKPure is a trusted and reliable source for downloading Android apps and games. By downloading Block Puzzle Jewel from APKPure, you can enjoy the following benefits:
-
-
You can get the latest version of Block Puzzle Jewel without waiting for Google Play Store updates.
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You can save your mobile data and storage space by downloading a smaller APK file than the one on Google Play Store.
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You can access Block Puzzle Jewel even if it is not available in your region or country.
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You can avoid any potential malware or viruses that might come with other sources.
-
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To download Block Puzzle Jewel from APKPure, you just need to visit this link and tap on the download button. You will need to enable unknown sources on your device settings to install the APK file.
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Features of Block Puzzle Jewel
-
Block Puzzle Jewel is not just a simple block puzzle game. It also offers some amazing features that make it more fun and enjoyable. Here are some of them:
-
Simple and addictive gameplay
-
Block Puzzle Jewel has a simple gameplay that anyone can learn in seconds, but it also has a high level of challenge that will keep you hooked for hours. You will need to use your logic, strategy, and creativity to fit the blocks into the grid and clear as many lines as possible. You will also feel a sense of satisfaction and accomplishment every time you clear a line or beat your high score.
-
Colorful and vivid graphics
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Block Puzzle Jewel has colorful and vivid graphics that will brighten up your mood and enhance your gaming experience. The blocks have different shapes and colors that make them easy to distinguish and match. The background also changes according to the mode and level you are playing, giving you a variety of scenes to enjoy.
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Various modes and levels
-
Block Puzzle Jewel has various modes and levels that will suit your preference and skill level. You can choose from classic mode, arcade mode, bomb mode, hexa mode, jewel mode, and more. Each mode has its own rules and challenges that will test your abilities and keep you entertained. You can also unlock new levels as you progress in the game, giving you more fun and excitement. Here is a table that shows the different modes and levels of Block Puzzle Jewel:
-
-
-
Mode
-
Description
-
Levels
-
-
-
Classic
-
The original and basic mode of the game. Just fit the blocks into the grid and clear the lines.
-
10
-
-
-
Arcade
-
A fast-paced and challenging mode where the blocks fall faster and the grid size changes randomly.
-
10
-
-
-
Bomb
-
A mode where some blocks have bombs that will explode if you don't clear them in time.
-
10
-
-
-
Hexa
-
A mode where the grid is hexagonal and the blocks have different shapes.
-
10
-
-
-
Jewel
-
A mode where the blocks are jewels that will shine and sparkle when you clear them.
-
10
-
-
-
And more...
-
You can discover more modes and levels as you play the game.
-
N/A
-
-
-
Leaderboards and achievements
-
Block Puzzle Jewel also has leaderboards and achievements that will motivate you to play more and improve your skills. You can compare your scores with other players around the world and see how you rank among them. You can also unlock various achievements by completing different tasks and challenges in the game. You can view your leaderboards and achievements by tapping on the trophy button on the top left corner of the screen.
-
Tips and tricks for Block Puzzle Jewel
-
If you want to score higher and enjoy Block Puzzle Jewel more, here are some tips and tricks that might help you:
-
Plan ahead and think strategically
-
Before you place a block on the grid, you should look at the next three blocks that will appear and think about where to put them. You should also try to leave some space for larger blocks that might come later. You should avoid placing blocks randomly or filling up the grid too quickly, as this will limit your options and make it harder to clear lines.
-
Use the rotate button wisely
-
Sometimes, rotating a block can help you fit it into a better position or create more space on the grid. However, you should not overuse the rotate button, as it will cost you some points every time you use it. You should only rotate a block when it is necessary or beneficial for your strategy.
-
Clear multiple rows and columns at once
-
The more rows and columns you clear at once, the more points you will earn. You should try to create combos by placing blocks that can clear multiple lines simultaneously. For example, if you place a long horizontal block on the bottom row, you can clear four rows at once. You should also look for opportunities to create T-shaped or L-shaped gaps that can be filled with matching blocks.
-
Watch out for the bomb blocks
-
In bomb mode, some blocks have bombs that will explode if you don't clear them in time. You should try to clear these blocks as soon as possible, as they will reduce your score and take up valuable space on the grid. You should also avoid placing other blocks near them, as they will be affected by the explosion as well.
-
Conclusion
-
Block Puzzle Jewel is a fun and relaxing game that will keep you entertained for hours. It has simple and addictive gameplay, colorful and vivid graphics, various modes and levels, leaderboards and achievements, and more. It is also easy to download from APKPure, which offers many advantages over Google Play Store. If you are looking for a new puzzle game to try, you should definitely give Block Puzzle Jewel a chance. You won't regret it!
-
Download Block Puzzle Jewel APKPure today
-
To download Block Puzzle Jewel from APKPure, just follow these simple steps:
Tap on the download button and wait for the APK file to be downloaded.
-
Open the APK file and tap on install. You might need to enable unknown sources on your device settings first.
-
Enjoy playing Block Puzzle Jewel!
-
-
Frequently Asked Questions (FAQs)
-
-
What is Block Puzzle Jewel?
-
Block Puzzle Jewel is a classic block puzzle game that requires you to fit different shapes of blocks into a 10x10 grid and clear the lines.
-
How to play Block Puzzle Jewel?
-
You just need to drag and drop the blocks from the bottom of the screen to the grid. You can also rotate the blocks by tapping on them. You can see the next three blocks that will appear on the bottom right corner of the screen. You can also pause the game by tapping on the pause button on the top right corner of the screen.
-
Why download Block Puzzle Jewel from APKPure?
-
By downloading Block Puzzle Jewel from APKPure, you can get the latest version of the game without waiting for Google Play Store updates, save your mobile data and storage space by downloading a smaller APK file, access the game even if it is not available in your region or country, and avoid any potential malware or viruses that might come with other sources.
-
What features does Block Puzzle Jewel offer?
-
Block Puzzle Jewel offers some amazing features that make it more fun and enjoyable, such as simple and addictive gameplay, colorful and vivid graphics, various modes and levels, leaderboards and achievements, and more.
-
What are some tips and tricks for Block Puzzle Jewel?
-
Some tips and tricks for Block Puzzle Jewel are to plan ahead and think strategically, use the rotate button wisely, clear multiple rows and columns at once, watch out for the bomb blocks, and more.
- 401be4b1e0
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Truckers of Europe 3 MOD APK (Unlimited Money Fuel) from Modyolo.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Truckers of Europe 3 MOD APK (Unlimited Money Fuel) from Modyolo.md
deleted file mode 100644
index 8aea423f0cd6d7b349a3cc359ba87471d33c757b..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Truckers of Europe 3 MOD APK (Unlimited Money Fuel) from Modyolo.md
+++ /dev/null
@@ -1,111 +0,0 @@
-
-
Truckers of Europe 3 Mod APK: A Realistic Truck Driving Simulator
-
If you are a fan of truck driving games, you might want to check out Truckers of Europe 3, a realistic and immersive simulator that lets you experience the life of a truck driver in Europe. In this game, you can drive various trucks across different countries, deliver cargoes, earn money, upgrade your vehicles, and enjoy the scenery along the way. But what if you want to have more fun and convenience in the game? That's where Truckers of Europe 3 Mod APK comes in handy. In this article, we will tell you what Truckers of Europe 3 is, what Truckers of Europe 3 Mod APK is, and how to download and install it from modyolo.com.
Truckers of Europe 3 is a truck driving simulator game developed by GameTOV. It is available for Android devices and has over 5 million downloads on Google Play Store. The game features realistic graphics, physics, and sounds that make you feel like you are driving a real truck. You can choose from different trucks, trailers, and cargoes, and customize them according to your preferences. You can also explore various locations in Europe, such as Germany, France, Italy, Spain, and more. You can drive on highways, country roads, city streets, and off-road terrains. You can also encounter different weather conditions, traffic situations, and day and night cycles.
-
Features of Truckers of Europe 3
-
Some of the features that make Truckers of Europe 3 an enjoyable game are:
-
-
Realistic truck models with detailed interiors and exteriors
-
Various types of trailers and cargoes to transport
-
Customizable truck parts, such as engines, transmissions, tires, lights, horns, paint jobs, and more
-
Different camera angles to view your truck from different perspectives
-
Dynamic weather system that affects the driving conditions
-
Realistic traffic system that includes cars, buses, trucks, police cars, ambulances, and more
-
Realistic sound effects that match the engine sounds, horn sounds, brake sounds, etc.
-
Map system that shows your location, destination, route, fuel stations, rest areas, etc.
-
Career mode that allows you to earn money by completing missions and contracts
-
Free mode that allows you to drive freely without any restrictions
-
Multiplayer mode that allows you to play with other players online
-
-
How to play Truckers of Europe 3
-
To play Truckers of Europe 3, you need to follow these steps:
-
-
Select your truck and trailer from the garage. You can also customize them if you want.
-
Select your cargo from the cargo market. You can choose from different types of cargoes with different weights and sizes.
-
Select your destination from the map. You can choose from different countries and cities in Europe.
-
Start your engine and drive your truck to the loading point. You need to attach your trailer to your truck by following the instructions on the screen.
-
Drive your truck to the delivery point. You need to follow the traffic rules and avoid accidents. You also need to monitor your fuel level, speed limit, damage level, etc.
-
Park your truck at the designated spot and detach your trailer. You need to follow the instructions on the screen to complete the delivery.
-
Earn money for your delivery. You can use your money to buy new trucks, upgrade your existing trucks, or repair your damaged trucks.
-
-
What is Truckers of Europe 3 Mod APK?What is Truckers of Europe 3 Mod APK?
-
Truckers of Europe 3 Mod APK is a modified version of the original Truckers of Europe 3 game that offers some extra features and benefits that are not available in the official version. These features and benefits include:
-
Benefits of Truckers of Europe 3 Mod APK
-
Some of the benefits that you can enjoy by using Truckers of Europe 3 Mod APK are:
-
-
Unlimited money: You can get unlimited money in the game, which means you can buy any truck, trailer, or cargo you want without worrying about the cost. You can also upgrade your trucks or repair them without spending any money.
-
Unlocked trucks: You can unlock all the trucks in the game, which means you can drive any truck you like without completing any missions or contracts. You can also access all the customizations and options for your trucks.
-
Unlocked trailers: You can unlock all the trailers in the game, which means you can transport any cargo you like without completing any missions or contracts. You can also access all the customizations and options for your trailers.
-
Unlocked cargoes: You can unlock all the cargoes in the game, which means you can choose any cargo you like without completing any missions or contracts. You can also access all the types and sizes of cargoes.
-
Unlocked locations: You can unlock all the locations in the game, which means you can travel to any country or city in Europe without completing any missions or contracts. You can also access all the roads and terrains in the game.
-
No ads: You can play the game without any ads, which means you can enjoy the game without any interruptions or distractions.
-
-
How to download and install Truckers of Europe 3 Mod APK from modyolo.com
-
To download and install Truckers of Europe 3 Mod APK from modyolo.com, you need to follow these steps:
-
-
Go to modyolo.com, a website that provides modded games and apps for Android devices.
-
Search for Truckers of Europe 3 Mod APK in the search bar or browse through the categories.
-
Click on the download button and wait for the file to be downloaded on your device.
-
Go to your device settings and enable the installation of apps from unknown sources.
-
Locate the downloaded file and tap on it to start the installation process.
-
Follow the instructions on the screen and wait for the installation to be completed.
-
Launch the game and enjoy Truckers of Europe 3 Mod APK with unlimited money, unlocked trucks, trailers, cargoes, locations, and no ads.
-
-
Conclusion
-
Truckers of Europe 3 is a realistic and immersive truck driving simulator game that lets you experience the life of a truck driver in Europe. You can drive various trucks across different countries, deliver cargoes, earn money, upgrade your vehicles, and enjoy the scenery along the way. However, if you want to have more fun and convenience in the game, you can use Truckers of Europe 3 Mod APK, a modified version of the game that offers unlimited money, unlocked trucks, trailers, cargoes, locations, and no ads. You can download and install Truckers of Europe 3 Mod APK from modyolo.com, a website that provides modded games and apps for Android devices. We hope this article has helped you learn more about Truckers of Europe 3 and Truckers of Europe 3 Mod APK. Happy trucking!
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FAQs
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Here are some frequently asked questions about Truckers of Europe 3 and Truckers of Europe 3 Mod APK:
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Is Truckers of Europe 3 free to play?
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Yes, Truckers of Europe 3 is free to play. However, it contains some in-app purchases that require real money. If you want to avoid spending money on the game, you can use Truckers of Europe 3 Mod APK instead.
-
Is Truckers of Europe 3 Mod APK safe to use?
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Yes, Truckers of Europe 3 Mod APK is safe to use. However, you should always download it from a trusted source like modyolo.com. You should also scan it with an antivirus program before installing it on your device.
-
Does Truckers of Europe 3 require an internet connection?
-
No, Truckers of Europe 3 does not require an internet connection. You can play it offline without any problems. However, if you want to play multiplayer mode or access some online features, you need to have an internet connection.
-
Can I play Truckers of Europe 3 on PC?
-
No, Truckers of Europe 3 is only available for Android devices. However, you can use an Android emulator to play it on your PC. An Android emulator is a software that allows you to run Android apps and games on your PC. Some of the popular Android emulators are BlueStacks, NoxPlayer, and LDPlayer.
-
Can I play Truckers of Europe 3 with a controller?
-
Yes, you can play Truckers of Europe 3 with a controller. The game supports various types of controllers, such as Bluetooth controllers, USB controllers, and steering wheels. You can connect your controller to your device and configure the settings in the game to enjoy a more realistic driving experience.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/child_process.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/child_process.d.ts
deleted file mode 100644
index c537d6d6214ab993b5542c11c9be82404dbfeab4..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/child_process.d.ts
+++ /dev/null
@@ -1,1369 +0,0 @@
-/**
- * The `child_process` module provides the ability to spawn subprocesses in
- * a manner that is similar, but not identical, to [`popen(3)`](http://man7.org/linux/man-pages/man3/popen.3.html). This capability
- * is primarily provided by the {@link spawn} function:
- *
- * ```js
- * const { spawn } = require('child_process');
- * const ls = spawn('ls', ['-lh', '/usr']);
- *
- * ls.stdout.on('data', (data) => {
- * console.log(`stdout: ${data}`);
- * });
- *
- * ls.stderr.on('data', (data) => {
- * console.error(`stderr: ${data}`);
- * });
- *
- * ls.on('close', (code) => {
- * console.log(`child process exited with code ${code}`);
- * });
- * ```
- *
- * By default, pipes for `stdin`, `stdout`, and `stderr` are established between
- * the parent Node.js process and the spawned subprocess. These pipes have
- * limited (and platform-specific) capacity. If the subprocess writes to
- * stdout in excess of that limit without the output being captured, the
- * subprocess blocks waiting for the pipe buffer to accept more data. This is
- * identical to the behavior of pipes in the shell. Use the `{ stdio: 'ignore' }`option if the output will not be consumed.
- *
- * The command lookup is performed using the `options.env.PATH` environment
- * variable if `env` is in the `options` object. Otherwise, `process.env.PATH` is
- * used. If `options.env` is set without `PATH`, lookup on Unix is performed
- * on a default search path search of `/usr/bin:/bin` (see your operating system's
- * manual for execvpe/execvp), on Windows the current processes environment
- * variable `PATH` is used.
- *
- * On Windows, environment variables are case-insensitive. Node.js
- * lexicographically sorts the `env` keys and uses the first one that
- * case-insensitively matches. Only first (in lexicographic order) entry will be
- * passed to the subprocess. This might lead to issues on Windows when passing
- * objects to the `env` option that have multiple variants of the same key, such as`PATH` and `Path`.
- *
- * The {@link spawn} method spawns the child process asynchronously,
- * without blocking the Node.js event loop. The {@link spawnSync} function provides equivalent functionality in a synchronous manner that blocks
- * the event loop until the spawned process either exits or is terminated.
- *
- * For convenience, the `child_process` module provides a handful of synchronous
- * and asynchronous alternatives to {@link spawn} and {@link spawnSync}. Each of these alternatives are implemented on
- * top of {@link spawn} or {@link spawnSync}.
- *
- * * {@link exec}: spawns a shell and runs a command within that
- * shell, passing the `stdout` and `stderr` to a callback function when
- * complete.
- * * {@link execFile}: similar to {@link exec} except
- * that it spawns the command directly without first spawning a shell by
- * default.
- * * {@link fork}: spawns a new Node.js process and invokes a
- * specified module with an IPC communication channel established that allows
- * sending messages between parent and child.
- * * {@link execSync}: a synchronous version of {@link exec} that will block the Node.js event loop.
- * * {@link execFileSync}: a synchronous version of {@link execFile} that will block the Node.js event loop.
- *
- * For certain use cases, such as automating shell scripts, the `synchronous counterparts` may be more convenient. In many cases, however,
- * the synchronous methods can have significant impact on performance due to
- * stalling the event loop while spawned processes complete.
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/child_process.js)
- */
-declare module 'child_process' {
- import { ObjectEncodingOptions } from 'node:fs';
- import { EventEmitter, Abortable } from 'node:events';
- import * as net from 'node:net';
- import { Writable, Readable, Stream, Pipe } from 'node:stream';
- import { URL } from 'node:url';
- type Serializable = string | object | number | boolean | bigint;
- type SendHandle = net.Socket | net.Server;
- /**
- * Instances of the `ChildProcess` represent spawned child processes.
- *
- * Instances of `ChildProcess` are not intended to be created directly. Rather,
- * use the {@link spawn}, {@link exec},{@link execFile}, or {@link fork} methods to create
- * instances of `ChildProcess`.
- * @since v2.2.0
- */
- class ChildProcess extends EventEmitter {
- /**
- * A `Writable Stream` that represents the child process's `stdin`.
- *
- * If a child process waits to read all of its input, the child will not continue
- * until this stream has been closed via `end()`.
- *
- * If the child was spawned with `stdio[0]` set to anything other than `'pipe'`,
- * then this will be `null`.
- *
- * `subprocess.stdin` is an alias for `subprocess.stdio[0]`. Both properties will
- * refer to the same value.
- *
- * The `subprocess.stdin` property can be `undefined` if the child process could
- * not be successfully spawned.
- * @since v0.1.90
- */
- stdin: Writable | null;
- /**
- * A `Readable Stream` that represents the child process's `stdout`.
- *
- * If the child was spawned with `stdio[1]` set to anything other than `'pipe'`,
- * then this will be `null`.
- *
- * `subprocess.stdout` is an alias for `subprocess.stdio[1]`. Both properties will
- * refer to the same value.
- *
- * ```js
- * const { spawn } = require('child_process');
- *
- * const subprocess = spawn('ls');
- *
- * subprocess.stdout.on('data', (data) => {
- * console.log(`Received chunk ${data}`);
- * });
- * ```
- *
- * The `subprocess.stdout` property can be `null` if the child process could
- * not be successfully spawned.
- * @since v0.1.90
- */
- stdout: Readable | null;
- /**
- * A `Readable Stream` that represents the child process's `stderr`.
- *
- * If the child was spawned with `stdio[2]` set to anything other than `'pipe'`,
- * then this will be `null`.
- *
- * `subprocess.stderr` is an alias for `subprocess.stdio[2]`. Both properties will
- * refer to the same value.
- *
- * The `subprocess.stderr` property can be `null` if the child process could
- * not be successfully spawned.
- * @since v0.1.90
- */
- stderr: Readable | null;
- /**
- * The `subprocess.channel` property is a reference to the child's IPC channel. If
- * no IPC channel currently exists, this property is `undefined`.
- * @since v7.1.0
- */
- readonly channel?: Pipe | null | undefined;
- /**
- * A sparse array of pipes to the child process, corresponding with positions in
- * the `stdio` option passed to {@link spawn} that have been set
- * to the value `'pipe'`. `subprocess.stdio[0]`, `subprocess.stdio[1]`, and`subprocess.stdio[2]` are also available as `subprocess.stdin`,`subprocess.stdout`, and `subprocess.stderr`,
- * respectively.
- *
- * In the following example, only the child's fd `1` (stdout) is configured as a
- * pipe, so only the parent's `subprocess.stdio[1]` is a stream, all other values
- * in the array are `null`.
- *
- * ```js
- * const assert = require('assert');
- * const fs = require('fs');
- * const child_process = require('child_process');
- *
- * const subprocess = child_process.spawn('ls', {
- * stdio: [
- * 0, // Use parent's stdin for child.
- * 'pipe', // Pipe child's stdout to parent.
- * fs.openSync('err.out', 'w'), // Direct child's stderr to a file.
- * ]
- * });
- *
- * assert.strictEqual(subprocess.stdio[0], null);
- * assert.strictEqual(subprocess.stdio[0], subprocess.stdin);
- *
- * assert(subprocess.stdout);
- * assert.strictEqual(subprocess.stdio[1], subprocess.stdout);
- *
- * assert.strictEqual(subprocess.stdio[2], null);
- * assert.strictEqual(subprocess.stdio[2], subprocess.stderr);
- * ```
- *
- * The `subprocess.stdio` property can be `undefined` if the child process could
- * not be successfully spawned.
- * @since v0.7.10
- */
- readonly stdio: [
- Writable | null,
- // stdin
- Readable | null,
- // stdout
- Readable | null,
- // stderr
- Readable | Writable | null | undefined,
- // extra
- Readable | Writable | null | undefined // extra
- ];
- /**
- * The `subprocess.killed` property indicates whether the child process
- * successfully received a signal from `subprocess.kill()`. The `killed` property
- * does not indicate that the child process has been terminated.
- * @since v0.5.10
- */
- readonly killed: boolean;
- /**
- * Returns the process identifier (PID) of the child process. If the child process
- * fails to spawn due to errors, then the value is `undefined` and `error` is
- * emitted.
- *
- * ```js
- * const { spawn } = require('child_process');
- * const grep = spawn('grep', ['ssh']);
- *
- * console.log(`Spawned child pid: ${grep.pid}`);
- * grep.stdin.end();
- * ```
- * @since v0.1.90
- */
- readonly pid?: number | undefined;
- /**
- * The `subprocess.connected` property indicates whether it is still possible to
- * send and receive messages from a child process. When `subprocess.connected` is`false`, it is no longer possible to send or receive messages.
- * @since v0.7.2
- */
- readonly connected: boolean;
- /**
- * The `subprocess.exitCode` property indicates the exit code of the child process.
- * If the child process is still running, the field will be `null`.
- */
- readonly exitCode: number | null;
- /**
- * The `subprocess.signalCode` property indicates the signal received by
- * the child process if any, else `null`.
- */
- readonly signalCode: NodeJS.Signals | null;
- /**
- * The `subprocess.spawnargs` property represents the full list of command-line
- * arguments the child process was launched with.
- */
- readonly spawnargs: string[];
- /**
- * The `subprocess.spawnfile` property indicates the executable file name of
- * the child process that is launched.
- *
- * For {@link fork}, its value will be equal to `process.execPath`.
- * For {@link spawn}, its value will be the name of
- * the executable file.
- * For {@link exec}, its value will be the name of the shell
- * in which the child process is launched.
- */
- readonly spawnfile: string;
- /**
- * The `subprocess.kill()` method sends a signal to the child process. If no
- * argument is given, the process will be sent the `'SIGTERM'` signal. See [`signal(7)`](http://man7.org/linux/man-pages/man7/signal.7.html) for a list of available signals. This function
- * returns `true` if [`kill(2)`](http://man7.org/linux/man-pages/man2/kill.2.html) succeeds, and `false` otherwise.
- *
- * ```js
- * const { spawn } = require('child_process');
- * const grep = spawn('grep', ['ssh']);
- *
- * grep.on('close', (code, signal) => {
- * console.log(
- * `child process terminated due to receipt of signal ${signal}`);
- * });
- *
- * // Send SIGHUP to process.
- * grep.kill('SIGHUP');
- * ```
- *
- * The `ChildProcess` object may emit an `'error'` event if the signal
- * cannot be delivered. Sending a signal to a child process that has already exited
- * is not an error but may have unforeseen consequences. Specifically, if the
- * process identifier (PID) has been reassigned to another process, the signal will
- * be delivered to that process instead which can have unexpected results.
- *
- * While the function is called `kill`, the signal delivered to the child process
- * may not actually terminate the process.
- *
- * See [`kill(2)`](http://man7.org/linux/man-pages/man2/kill.2.html) for reference.
- *
- * On Windows, where POSIX signals do not exist, the `signal` argument will be
- * ignored, and the process will be killed forcefully and abruptly (similar to`'SIGKILL'`).
- * See `Signal Events` for more details.
- *
- * On Linux, child processes of child processes will not be terminated
- * when attempting to kill their parent. This is likely to happen when running a
- * new process in a shell or with the use of the `shell` option of `ChildProcess`:
- *
- * ```js
- * 'use strict';
- * const { spawn } = require('child_process');
- *
- * const subprocess = spawn(
- * 'sh',
- * [
- * '-c',
- * `node -e "setInterval(() => {
- * console.log(process.pid, 'is alive')
- * }, 500);"`,
- * ], {
- * stdio: ['inherit', 'inherit', 'inherit']
- * }
- * );
- *
- * setTimeout(() => {
- * subprocess.kill(); // Does not terminate the Node.js process in the shell.
- * }, 2000);
- * ```
- * @since v0.1.90
- */
- kill(signal?: NodeJS.Signals | number): boolean;
- /**
- * When an IPC channel has been established between the parent and child (
- * i.e. when using {@link fork}), the `subprocess.send()` method can
- * be used to send messages to the child process. When the child process is a
- * Node.js instance, these messages can be received via the `'message'` event.
- *
- * The message goes through serialization and parsing. The resulting
- * message might not be the same as what is originally sent.
- *
- * For example, in the parent script:
- *
- * ```js
- * const cp = require('child_process');
- * const n = cp.fork(`${__dirname}/sub.js`);
- *
- * n.on('message', (m) => {
- * console.log('PARENT got message:', m);
- * });
- *
- * // Causes the child to print: CHILD got message: { hello: 'world' }
- * n.send({ hello: 'world' });
- * ```
- *
- * And then the child script, `'sub.js'` might look like this:
- *
- * ```js
- * process.on('message', (m) => {
- * console.log('CHILD got message:', m);
- * });
- *
- * // Causes the parent to print: PARENT got message: { foo: 'bar', baz: null }
- * process.send({ foo: 'bar', baz: NaN });
- * ```
- *
- * Child Node.js processes will have a `process.send()` method of their own
- * that allows the child to send messages back to the parent.
- *
- * There is a special case when sending a `{cmd: 'NODE_foo'}` message. Messages
- * containing a `NODE_` prefix in the `cmd` property are reserved for use within
- * Node.js core and will not be emitted in the child's `'message'` event. Rather, such messages are emitted using the`'internalMessage'` event and are consumed internally by Node.js.
- * Applications should avoid using such messages or listening for`'internalMessage'` events as it is subject to change without notice.
- *
- * The optional `sendHandle` argument that may be passed to `subprocess.send()` is
- * for passing a TCP server or socket object to the child process. The child will
- * receive the object as the second argument passed to the callback function
- * registered on the `'message'` event. Any data that is received
- * and buffered in the socket will not be sent to the child.
- *
- * The optional `callback` is a function that is invoked after the message is
- * sent but before the child may have received it. The function is called with a
- * single argument: `null` on success, or an `Error` object on failure.
- *
- * If no `callback` function is provided and the message cannot be sent, an`'error'` event will be emitted by the `ChildProcess` object. This can
- * happen, for instance, when the child process has already exited.
- *
- * `subprocess.send()` will return `false` if the channel has closed or when the
- * backlog of unsent messages exceeds a threshold that makes it unwise to send
- * more. Otherwise, the method returns `true`. The `callback` function can be
- * used to implement flow control.
- *
- * #### Example: sending a server object
- *
- * The `sendHandle` argument can be used, for instance, to pass the handle of
- * a TCP server object to the child process as illustrated in the example below:
- *
- * ```js
- * const subprocess = require('child_process').fork('subprocess.js');
- *
- * // Open up the server object and send the handle.
- * const server = require('net').createServer();
- * server.on('connection', (socket) => {
- * socket.end('handled by parent');
- * });
- * server.listen(1337, () => {
- * subprocess.send('server', server);
- * });
- * ```
- *
- * The child would then receive the server object as:
- *
- * ```js
- * process.on('message', (m, server) => {
- * if (m === 'server') {
- * server.on('connection', (socket) => {
- * socket.end('handled by child');
- * });
- * }
- * });
- * ```
- *
- * Once the server is now shared between the parent and child, some connections
- * can be handled by the parent and some by the child.
- *
- * While the example above uses a server created using the `net` module, `dgram`module servers use exactly the same workflow with the exceptions of listening on
- * a `'message'` event instead of `'connection'` and using `server.bind()` instead
- * of `server.listen()`. This is, however, currently only supported on Unix
- * platforms.
- *
- * #### Example: sending a socket object
- *
- * Similarly, the `sendHandler` argument can be used to pass the handle of a
- * socket to the child process. The example below spawns two children that each
- * handle connections with "normal" or "special" priority:
- *
- * ```js
- * const { fork } = require('child_process');
- * const normal = fork('subprocess.js', ['normal']);
- * const special = fork('subprocess.js', ['special']);
- *
- * // Open up the server and send sockets to child. Use pauseOnConnect to prevent
- * // the sockets from being read before they are sent to the child process.
- * const server = require('net').createServer({ pauseOnConnect: true });
- * server.on('connection', (socket) => {
- *
- * // If this is special priority...
- * if (socket.remoteAddress === '74.125.127.100') {
- * special.send('socket', socket);
- * return;
- * }
- * // This is normal priority.
- * normal.send('socket', socket);
- * });
- * server.listen(1337);
- * ```
- *
- * The `subprocess.js` would receive the socket handle as the second argument
- * passed to the event callback function:
- *
- * ```js
- * process.on('message', (m, socket) => {
- * if (m === 'socket') {
- * if (socket) {
- * // Check that the client socket exists.
- * // It is possible for the socket to be closed between the time it is
- * // sent and the time it is received in the child process.
- * socket.end(`Request handled with ${process.argv[2]} priority`);
- * }
- * }
- * });
- * ```
- *
- * Do not use `.maxConnections` on a socket that has been passed to a subprocess.
- * The parent cannot track when the socket is destroyed.
- *
- * Any `'message'` handlers in the subprocess should verify that `socket` exists,
- * as the connection may have been closed during the time it takes to send the
- * connection to the child.
- * @since v0.5.9
- * @param options The `options` argument, if present, is an object used to parameterize the sending of certain types of handles. `options` supports the following properties:
- */
- send(message: Serializable, callback?: (error: Error | null) => void): boolean;
- send(message: Serializable, sendHandle?: SendHandle, callback?: (error: Error | null) => void): boolean;
- send(message: Serializable, sendHandle?: SendHandle, options?: MessageOptions, callback?: (error: Error | null) => void): boolean;
- /**
- * Closes the IPC channel between parent and child, allowing the child to exit
- * gracefully once there are no other connections keeping it alive. After calling
- * this method the `subprocess.connected` and `process.connected` properties in
- * both the parent and child (respectively) will be set to `false`, and it will be
- * no longer possible to pass messages between the processes.
- *
- * The `'disconnect'` event will be emitted when there are no messages in the
- * process of being received. This will most often be triggered immediately after
- * calling `subprocess.disconnect()`.
- *
- * When the child process is a Node.js instance (e.g. spawned using {@link fork}), the `process.disconnect()` method can be invoked
- * within the child process to close the IPC channel as well.
- * @since v0.7.2
- */
- disconnect(): void;
- /**
- * By default, the parent will wait for the detached child to exit. To prevent the
- * parent from waiting for a given `subprocess` to exit, use the`subprocess.unref()` method. Doing so will cause the parent's event loop to not
- * include the child in its reference count, allowing the parent to exit
- * independently of the child, unless there is an established IPC channel between
- * the child and the parent.
- *
- * ```js
- * const { spawn } = require('child_process');
- *
- * const subprocess = spawn(process.argv[0], ['child_program.js'], {
- * detached: true,
- * stdio: 'ignore'
- * });
- *
- * subprocess.unref();
- * ```
- * @since v0.7.10
- */
- unref(): void;
- /**
- * Calling `subprocess.ref()` after making a call to `subprocess.unref()` will
- * restore the removed reference count for the child process, forcing the parent
- * to wait for the child to exit before exiting itself.
- *
- * ```js
- * const { spawn } = require('child_process');
- *
- * const subprocess = spawn(process.argv[0], ['child_program.js'], {
- * detached: true,
- * stdio: 'ignore'
- * });
- *
- * subprocess.unref();
- * subprocess.ref();
- * ```
- * @since v0.7.10
- */
- ref(): void;
- /**
- * events.EventEmitter
- * 1. close
- * 2. disconnect
- * 3. error
- * 4. exit
- * 5. message
- * 6. spawn
- */
- addListener(event: string, listener: (...args: any[]) => void): this;
- addListener(event: 'close', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- addListener(event: 'disconnect', listener: () => void): this;
- addListener(event: 'error', listener: (err: Error) => void): this;
- addListener(event: 'exit', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- addListener(event: 'message', listener: (message: Serializable, sendHandle: SendHandle) => void): this;
- addListener(event: 'spawn', listener: () => void): this;
- emit(event: string | symbol, ...args: any[]): boolean;
- emit(event: 'close', code: number | null, signal: NodeJS.Signals | null): boolean;
- emit(event: 'disconnect'): boolean;
- emit(event: 'error', err: Error): boolean;
- emit(event: 'exit', code: number | null, signal: NodeJS.Signals | null): boolean;
- emit(event: 'message', message: Serializable, sendHandle: SendHandle): boolean;
- emit(event: 'spawn', listener: () => void): boolean;
- on(event: string, listener: (...args: any[]) => void): this;
- on(event: 'close', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- on(event: 'disconnect', listener: () => void): this;
- on(event: 'error', listener: (err: Error) => void): this;
- on(event: 'exit', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- on(event: 'message', listener: (message: Serializable, sendHandle: SendHandle) => void): this;
- on(event: 'spawn', listener: () => void): this;
- once(event: string, listener: (...args: any[]) => void): this;
- once(event: 'close', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- once(event: 'disconnect', listener: () => void): this;
- once(event: 'error', listener: (err: Error) => void): this;
- once(event: 'exit', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- once(event: 'message', listener: (message: Serializable, sendHandle: SendHandle) => void): this;
- once(event: 'spawn', listener: () => void): this;
- prependListener(event: string, listener: (...args: any[]) => void): this;
- prependListener(event: 'close', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- prependListener(event: 'disconnect', listener: () => void): this;
- prependListener(event: 'error', listener: (err: Error) => void): this;
- prependListener(event: 'exit', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- prependListener(event: 'message', listener: (message: Serializable, sendHandle: SendHandle) => void): this;
- prependListener(event: 'spawn', listener: () => void): this;
- prependOnceListener(event: string, listener: (...args: any[]) => void): this;
- prependOnceListener(event: 'close', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- prependOnceListener(event: 'disconnect', listener: () => void): this;
- prependOnceListener(event: 'error', listener: (err: Error) => void): this;
- prependOnceListener(event: 'exit', listener: (code: number | null, signal: NodeJS.Signals | null) => void): this;
- prependOnceListener(event: 'message', listener: (message: Serializable, sendHandle: SendHandle) => void): this;
- prependOnceListener(event: 'spawn', listener: () => void): this;
- }
- // return this object when stdio option is undefined or not specified
- interface ChildProcessWithoutNullStreams extends ChildProcess {
- stdin: Writable;
- stdout: Readable;
- stderr: Readable;
- readonly stdio: [
- Writable,
- Readable,
- Readable,
- // stderr
- Readable | Writable | null | undefined,
- // extra, no modification
- Readable | Writable | null | undefined // extra, no modification
- ];
- }
- // return this object when stdio option is a tuple of 3
- interface ChildProcessByStdio extends ChildProcess {
- stdin: I;
- stdout: O;
- stderr: E;
- readonly stdio: [
- I,
- O,
- E,
- Readable | Writable | null | undefined,
- // extra, no modification
- Readable | Writable | null | undefined // extra, no modification
- ];
- }
- interface MessageOptions {
- keepOpen?: boolean | undefined;
- }
- type IOType = 'overlapped' | 'pipe' | 'ignore' | 'inherit';
- type StdioOptions = IOType | Array;
- type SerializationType = 'json' | 'advanced';
- interface MessagingOptions extends Abortable {
- /**
- * Specify the kind of serialization used for sending messages between processes.
- * @default 'json'
- */
- serialization?: SerializationType | undefined;
- /**
- * The signal value to be used when the spawned process will be killed by the abort signal.
- * @default 'SIGTERM'
- */
- killSignal?: NodeJS.Signals | number | undefined;
- /**
- * In milliseconds the maximum amount of time the process is allowed to run.
- */
- timeout?: number | undefined;
- }
- interface ProcessEnvOptions {
- uid?: number | undefined;
- gid?: number | undefined;
- cwd?: string | URL | undefined;
- env?: NodeJS.ProcessEnv | undefined;
- }
- interface CommonOptions extends ProcessEnvOptions {
- /**
- * @default false
- */
- windowsHide?: boolean | undefined;
- /**
- * @default 0
- */
- timeout?: number | undefined;
- }
- interface CommonSpawnOptions extends CommonOptions, MessagingOptions, Abortable {
- argv0?: string | undefined;
- stdio?: StdioOptions | undefined;
- shell?: boolean | string | undefined;
- windowsVerbatimArguments?: boolean | undefined;
- }
- interface SpawnOptions extends CommonSpawnOptions {
- detached?: boolean | undefined;
- }
- interface SpawnOptionsWithoutStdio extends SpawnOptions {
- stdio?: StdioPipeNamed | StdioPipe[] | undefined;
- }
- type StdioNull = 'inherit' | 'ignore' | Stream;
- type StdioPipeNamed = 'pipe' | 'overlapped';
- type StdioPipe = undefined | null | StdioPipeNamed;
- interface SpawnOptionsWithStdioTuple extends SpawnOptions {
- stdio: [Stdin, Stdout, Stderr];
- }
- /**
- * The `child_process.spawn()` method spawns a new process using the given`command`, with command-line arguments in `args`. If omitted, `args` defaults
- * to an empty array.
- *
- * **If the `shell` option is enabled, do not pass unsanitized user input to this**
- * **function. Any input containing shell metacharacters may be used to trigger**
- * **arbitrary command execution.**
- *
- * A third argument may be used to specify additional options, with these defaults:
- *
- * ```js
- * const defaults = {
- * cwd: undefined,
- * env: process.env
- * };
- * ```
- *
- * Use `cwd` to specify the working directory from which the process is spawned.
- * If not given, the default is to inherit the current working directory. If given,
- * but the path does not exist, the child process emits an `ENOENT` error
- * and exits immediately. `ENOENT` is also emitted when the command
- * does not exist.
- *
- * Use `env` to specify environment variables that will be visible to the new
- * process, the default is `process.env`.
- *
- * `undefined` values in `env` will be ignored.
- *
- * Example of running `ls -lh /usr`, capturing `stdout`, `stderr`, and the
- * exit code:
- *
- * ```js
- * const { spawn } = require('child_process');
- * const ls = spawn('ls', ['-lh', '/usr']);
- *
- * ls.stdout.on('data', (data) => {
- * console.log(`stdout: ${data}`);
- * });
- *
- * ls.stderr.on('data', (data) => {
- * console.error(`stderr: ${data}`);
- * });
- *
- * ls.on('close', (code) => {
- * console.log(`child process exited with code ${code}`);
- * });
- * ```
- *
- * Example: A very elaborate way to run `ps ax | grep ssh`
- *
- * ```js
- * const { spawn } = require('child_process');
- * const ps = spawn('ps', ['ax']);
- * const grep = spawn('grep', ['ssh']);
- *
- * ps.stdout.on('data', (data) => {
- * grep.stdin.write(data);
- * });
- *
- * ps.stderr.on('data', (data) => {
- * console.error(`ps stderr: ${data}`);
- * });
- *
- * ps.on('close', (code) => {
- * if (code !== 0) {
- * console.log(`ps process exited with code ${code}`);
- * }
- * grep.stdin.end();
- * });
- *
- * grep.stdout.on('data', (data) => {
- * console.log(data.toString());
- * });
- *
- * grep.stderr.on('data', (data) => {
- * console.error(`grep stderr: ${data}`);
- * });
- *
- * grep.on('close', (code) => {
- * if (code !== 0) {
- * console.log(`grep process exited with code ${code}`);
- * }
- * });
- * ```
- *
- * Example of checking for failed `spawn`:
- *
- * ```js
- * const { spawn } = require('child_process');
- * const subprocess = spawn('bad_command');
- *
- * subprocess.on('error', (err) => {
- * console.error('Failed to start subprocess.');
- * });
- * ```
- *
- * Certain platforms (macOS, Linux) will use the value of `argv[0]` for the process
- * title while others (Windows, SunOS) will use `command`.
- *
- * Node.js currently overwrites `argv[0]` with `process.execPath` on startup, so`process.argv[0]` in a Node.js child process will not match the `argv0`parameter passed to `spawn` from the parent,
- * retrieve it with the`process.argv0` property instead.
- *
- * If the `signal` option is enabled, calling `.abort()` on the corresponding`AbortController` is similar to calling `.kill()` on the child process except
- * the error passed to the callback will be an `AbortError`:
- *
- * ```js
- * const { spawn } = require('child_process');
- * const controller = new AbortController();
- * const { signal } = controller;
- * const grep = spawn('grep', ['ssh'], { signal });
- * grep.on('error', (err) => {
- * // This will be called with err being an AbortError if the controller aborts
- * });
- * controller.abort(); // Stops the child process
- * ```
- * @since v0.1.90
- * @param command The command to run.
- * @param args List of string arguments.
- */
- function spawn(command: string, options?: SpawnOptionsWithoutStdio): ChildProcessWithoutNullStreams;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, options: SpawnOptions): ChildProcess;
- // overloads of spawn with 'args'
- function spawn(command: string, args?: ReadonlyArray, options?: SpawnOptionsWithoutStdio): ChildProcessWithoutNullStreams;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptionsWithStdioTuple): ChildProcessByStdio;
- function spawn(command: string, args: ReadonlyArray, options: SpawnOptions): ChildProcess;
- interface ExecOptions extends CommonOptions {
- shell?: string | undefined;
- signal?: AbortSignal | undefined;
- maxBuffer?: number | undefined;
- killSignal?: NodeJS.Signals | number | undefined;
- }
- interface ExecOptionsWithStringEncoding extends ExecOptions {
- encoding: BufferEncoding;
- }
- interface ExecOptionsWithBufferEncoding extends ExecOptions {
- encoding: BufferEncoding | null; // specify `null`.
- }
- interface ExecException extends Error {
- cmd?: string | undefined;
- killed?: boolean | undefined;
- code?: number | undefined;
- signal?: NodeJS.Signals | undefined;
- }
- /**
- * Spawns a shell then executes the `command` within that shell, buffering any
- * generated output. The `command` string passed to the exec function is processed
- * directly by the shell and special characters (vary based on [shell](https://en.wikipedia.org/wiki/List_of_command-line_interpreters))
- * need to be dealt with accordingly:
- *
- * ```js
- * const { exec } = require('child_process');
- *
- * exec('"/path/to/test file/test.sh" arg1 arg2');
- * // Double quotes are used so that the space in the path is not interpreted as
- * // a delimiter of multiple arguments.
- *
- * exec('echo "The \\$HOME variable is $HOME"');
- * // The $HOME variable is escaped in the first instance, but not in the second.
- * ```
- *
- * **Never pass unsanitized user input to this function. Any input containing shell**
- * **metacharacters may be used to trigger arbitrary command execution.**
- *
- * If a `callback` function is provided, it is called with the arguments`(error, stdout, stderr)`. On success, `error` will be `null`. On error,`error` will be an instance of `Error`. The
- * `error.code` property will be
- * the exit code of the process. By convention, any exit code other than `0`indicates an error. `error.signal` will be the signal that terminated the
- * process.
- *
- * The `stdout` and `stderr` arguments passed to the callback will contain the
- * stdout and stderr output of the child process. By default, Node.js will decode
- * the output as UTF-8 and pass strings to the callback. The `encoding` option
- * can be used to specify the character encoding used to decode the stdout and
- * stderr output. If `encoding` is `'buffer'`, or an unrecognized character
- * encoding, `Buffer` objects will be passed to the callback instead.
- *
- * ```js
- * const { exec } = require('child_process');
- * exec('cat *.js missing_file | wc -l', (error, stdout, stderr) => {
- * if (error) {
- * console.error(`exec error: ${error}`);
- * return;
- * }
- * console.log(`stdout: ${stdout}`);
- * console.error(`stderr: ${stderr}`);
- * });
- * ```
- *
- * If `timeout` is greater than `0`, the parent will send the signal
- * identified by the `killSignal` property (the default is `'SIGTERM'`) if the
- * child runs longer than `timeout` milliseconds.
- *
- * Unlike the [`exec(3)`](http://man7.org/linux/man-pages/man3/exec.3.html) POSIX system call, `child_process.exec()` does not replace
- * the existing process and uses a shell to execute the command.
- *
- * If this method is invoked as its `util.promisify()` ed version, it returns
- * a `Promise` for an `Object` with `stdout` and `stderr` properties. The returned`ChildProcess` instance is attached to the `Promise` as a `child` property. In
- * case of an error (including any error resulting in an exit code other than 0), a
- * rejected promise is returned, with the same `error` object given in the
- * callback, but with two additional properties `stdout` and `stderr`.
- *
- * ```js
- * const util = require('util');
- * const exec = util.promisify(require('child_process').exec);
- *
- * async function lsExample() {
- * const { stdout, stderr } = await exec('ls');
- * console.log('stdout:', stdout);
- * console.error('stderr:', stderr);
- * }
- * lsExample();
- * ```
- *
- * If the `signal` option is enabled, calling `.abort()` on the corresponding`AbortController` is similar to calling `.kill()` on the child process except
- * the error passed to the callback will be an `AbortError`:
- *
- * ```js
- * const { exec } = require('child_process');
- * const controller = new AbortController();
- * const { signal } = controller;
- * const child = exec('grep ssh', { signal }, (error) => {
- * console.log(error); // an AbortError
- * });
- * controller.abort();
- * ```
- * @since v0.1.90
- * @param command The command to run, with space-separated arguments.
- * @param callback called with the output when process terminates.
- */
- function exec(command: string, callback?: (error: ExecException | null, stdout: string, stderr: string) => void): ChildProcess;
- // `options` with `"buffer"` or `null` for `encoding` means stdout/stderr are definitely `Buffer`.
- function exec(
- command: string,
- options: {
- encoding: 'buffer' | null;
- } & ExecOptions,
- callback?: (error: ExecException | null, stdout: Buffer, stderr: Buffer) => void
- ): ChildProcess;
- // `options` with well known `encoding` means stdout/stderr are definitely `string`.
- function exec(
- command: string,
- options: {
- encoding: BufferEncoding;
- } & ExecOptions,
- callback?: (error: ExecException | null, stdout: string, stderr: string) => void
- ): ChildProcess;
- // `options` with an `encoding` whose type is `string` means stdout/stderr could either be `Buffer` or `string`.
- // There is no guarantee the `encoding` is unknown as `string` is a superset of `BufferEncoding`.
- function exec(
- command: string,
- options: {
- encoding: BufferEncoding;
- } & ExecOptions,
- callback?: (error: ExecException | null, stdout: string | Buffer, stderr: string | Buffer) => void
- ): ChildProcess;
- // `options` without an `encoding` means stdout/stderr are definitely `string`.
- function exec(command: string, options: ExecOptions, callback?: (error: ExecException | null, stdout: string, stderr: string) => void): ChildProcess;
- // fallback if nothing else matches. Worst case is always `string | Buffer`.
- function exec(
- command: string,
- options: (ObjectEncodingOptions & ExecOptions) | undefined | null,
- callback?: (error: ExecException | null, stdout: string | Buffer, stderr: string | Buffer) => void
- ): ChildProcess;
- interface PromiseWithChild extends Promise {
- child: ChildProcess;
- }
- namespace exec {
- function __promisify__(command: string): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- command: string,
- options: {
- encoding: 'buffer' | null;
- } & ExecOptions
- ): PromiseWithChild<{
- stdout: Buffer;
- stderr: Buffer;
- }>;
- function __promisify__(
- command: string,
- options: {
- encoding: BufferEncoding;
- } & ExecOptions
- ): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- command: string,
- options: ExecOptions
- ): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- command: string,
- options?: (ObjectEncodingOptions & ExecOptions) | null
- ): PromiseWithChild<{
- stdout: string | Buffer;
- stderr: string | Buffer;
- }>;
- }
- interface ExecFileOptions extends CommonOptions, Abortable {
- maxBuffer?: number | undefined;
- killSignal?: NodeJS.Signals | number | undefined;
- windowsVerbatimArguments?: boolean | undefined;
- shell?: boolean | string | undefined;
- signal?: AbortSignal | undefined;
- }
- interface ExecFileOptionsWithStringEncoding extends ExecFileOptions {
- encoding: BufferEncoding;
- }
- interface ExecFileOptionsWithBufferEncoding extends ExecFileOptions {
- encoding: 'buffer' | null;
- }
- interface ExecFileOptionsWithOtherEncoding extends ExecFileOptions {
- encoding: BufferEncoding;
- }
- type ExecFileException = ExecException & NodeJS.ErrnoException;
- /**
- * The `child_process.execFile()` function is similar to {@link exec} except that it does not spawn a shell by default. Rather, the specified
- * executable `file` is spawned directly as a new process making it slightly more
- * efficient than {@link exec}.
- *
- * The same options as {@link exec} are supported. Since a shell is
- * not spawned, behaviors such as I/O redirection and file globbing are not
- * supported.
- *
- * ```js
- * const { execFile } = require('child_process');
- * const child = execFile('node', ['--version'], (error, stdout, stderr) => {
- * if (error) {
- * throw error;
- * }
- * console.log(stdout);
- * });
- * ```
- *
- * The `stdout` and `stderr` arguments passed to the callback will contain the
- * stdout and stderr output of the child process. By default, Node.js will decode
- * the output as UTF-8 and pass strings to the callback. The `encoding` option
- * can be used to specify the character encoding used to decode the stdout and
- * stderr output. If `encoding` is `'buffer'`, or an unrecognized character
- * encoding, `Buffer` objects will be passed to the callback instead.
- *
- * If this method is invoked as its `util.promisify()` ed version, it returns
- * a `Promise` for an `Object` with `stdout` and `stderr` properties. The returned`ChildProcess` instance is attached to the `Promise` as a `child` property. In
- * case of an error (including any error resulting in an exit code other than 0), a
- * rejected promise is returned, with the same `error` object given in the
- * callback, but with two additional properties `stdout` and `stderr`.
- *
- * ```js
- * const util = require('util');
- * const execFile = util.promisify(require('child_process').execFile);
- * async function getVersion() {
- * const { stdout } = await execFile('node', ['--version']);
- * console.log(stdout);
- * }
- * getVersion();
- * ```
- *
- * **If the `shell` option is enabled, do not pass unsanitized user input to this**
- * **function. Any input containing shell metacharacters may be used to trigger**
- * **arbitrary command execution.**
- *
- * If the `signal` option is enabled, calling `.abort()` on the corresponding`AbortController` is similar to calling `.kill()` on the child process except
- * the error passed to the callback will be an `AbortError`:
- *
- * ```js
- * const { execFile } = require('child_process');
- * const controller = new AbortController();
- * const { signal } = controller;
- * const child = execFile('node', ['--version'], { signal }, (error) => {
- * console.log(error); // an AbortError
- * });
- * controller.abort();
- * ```
- * @since v0.1.91
- * @param file The name or path of the executable file to run.
- * @param args List of string arguments.
- * @param callback Called with the output when process terminates.
- */
- function execFile(file: string): ChildProcess;
- function execFile(file: string, options: (ObjectEncodingOptions & ExecFileOptions) | undefined | null): ChildProcess;
- function execFile(file: string, args?: ReadonlyArray | null): ChildProcess;
- function execFile(file: string, args: ReadonlyArray | undefined | null, options: (ObjectEncodingOptions & ExecFileOptions) | undefined | null): ChildProcess;
- // no `options` definitely means stdout/stderr are `string`.
- function execFile(file: string, callback: (error: ExecFileException | null, stdout: string, stderr: string) => void): ChildProcess;
- function execFile(file: string, args: ReadonlyArray | undefined | null, callback: (error: ExecFileException | null, stdout: string, stderr: string) => void): ChildProcess;
- // `options` with `"buffer"` or `null` for `encoding` means stdout/stderr are definitely `Buffer`.
- function execFile(file: string, options: ExecFileOptionsWithBufferEncoding, callback: (error: ExecFileException | null, stdout: Buffer, stderr: Buffer) => void): ChildProcess;
- function execFile(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptionsWithBufferEncoding,
- callback: (error: ExecFileException | null, stdout: Buffer, stderr: Buffer) => void
- ): ChildProcess;
- // `options` with well known `encoding` means stdout/stderr are definitely `string`.
- function execFile(file: string, options: ExecFileOptionsWithStringEncoding, callback: (error: ExecFileException | null, stdout: string, stderr: string) => void): ChildProcess;
- function execFile(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptionsWithStringEncoding,
- callback: (error: ExecFileException | null, stdout: string, stderr: string) => void
- ): ChildProcess;
- // `options` with an `encoding` whose type is `string` means stdout/stderr could either be `Buffer` or `string`.
- // There is no guarantee the `encoding` is unknown as `string` is a superset of `BufferEncoding`.
- function execFile(file: string, options: ExecFileOptionsWithOtherEncoding, callback: (error: ExecFileException | null, stdout: string | Buffer, stderr: string | Buffer) => void): ChildProcess;
- function execFile(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptionsWithOtherEncoding,
- callback: (error: ExecFileException | null, stdout: string | Buffer, stderr: string | Buffer) => void
- ): ChildProcess;
- // `options` without an `encoding` means stdout/stderr are definitely `string`.
- function execFile(file: string, options: ExecFileOptions, callback: (error: ExecFileException | null, stdout: string, stderr: string) => void): ChildProcess;
- function execFile(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptions,
- callback: (error: ExecFileException | null, stdout: string, stderr: string) => void
- ): ChildProcess;
- // fallback if nothing else matches. Worst case is always `string | Buffer`.
- function execFile(
- file: string,
- options: (ObjectEncodingOptions & ExecFileOptions) | undefined | null,
- callback: ((error: ExecFileException | null, stdout: string | Buffer, stderr: string | Buffer) => void) | undefined | null
- ): ChildProcess;
- function execFile(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: (ObjectEncodingOptions & ExecFileOptions) | undefined | null,
- callback: ((error: ExecFileException | null, stdout: string | Buffer, stderr: string | Buffer) => void) | undefined | null
- ): ChildProcess;
- namespace execFile {
- function __promisify__(file: string): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- file: string,
- args: ReadonlyArray | undefined | null
- ): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- file: string,
- options: ExecFileOptionsWithBufferEncoding
- ): PromiseWithChild<{
- stdout: Buffer;
- stderr: Buffer;
- }>;
- function __promisify__(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptionsWithBufferEncoding
- ): PromiseWithChild<{
- stdout: Buffer;
- stderr: Buffer;
- }>;
- function __promisify__(
- file: string,
- options: ExecFileOptionsWithStringEncoding
- ): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptionsWithStringEncoding
- ): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- file: string,
- options: ExecFileOptionsWithOtherEncoding
- ): PromiseWithChild<{
- stdout: string | Buffer;
- stderr: string | Buffer;
- }>;
- function __promisify__(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptionsWithOtherEncoding
- ): PromiseWithChild<{
- stdout: string | Buffer;
- stderr: string | Buffer;
- }>;
- function __promisify__(
- file: string,
- options: ExecFileOptions
- ): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: ExecFileOptions
- ): PromiseWithChild<{
- stdout: string;
- stderr: string;
- }>;
- function __promisify__(
- file: string,
- options: (ObjectEncodingOptions & ExecFileOptions) | undefined | null
- ): PromiseWithChild<{
- stdout: string | Buffer;
- stderr: string | Buffer;
- }>;
- function __promisify__(
- file: string,
- args: ReadonlyArray | undefined | null,
- options: (ObjectEncodingOptions & ExecFileOptions) | undefined | null
- ): PromiseWithChild<{
- stdout: string | Buffer;
- stderr: string | Buffer;
- }>;
- }
- interface ForkOptions extends ProcessEnvOptions, MessagingOptions, Abortable {
- execPath?: string | undefined;
- execArgv?: string[] | undefined;
- silent?: boolean | undefined;
- stdio?: StdioOptions | undefined;
- detached?: boolean | undefined;
- windowsVerbatimArguments?: boolean | undefined;
- }
- /**
- * The `child_process.fork()` method is a special case of {@link spawn} used specifically to spawn new Node.js processes.
- * Like {@link spawn}, a `ChildProcess` object is returned. The
- * returned `ChildProcess` will have an additional communication channel
- * built-in that allows messages to be passed back and forth between the parent and
- * child. See `subprocess.send()` for details.
- *
- * Keep in mind that spawned Node.js child processes are
- * independent of the parent with exception of the IPC communication channel
- * that is established between the two. Each process has its own memory, with
- * their own V8 instances. Because of the additional resource allocations
- * required, spawning a large number of child Node.js processes is not
- * recommended.
- *
- * By default, `child_process.fork()` will spawn new Node.js instances using the `process.execPath` of the parent process. The `execPath` property in the`options` object allows for an alternative
- * execution path to be used.
- *
- * Node.js processes launched with a custom `execPath` will communicate with the
- * parent process using the file descriptor (fd) identified using the
- * environment variable `NODE_CHANNEL_FD` on the child process.
- *
- * Unlike the [`fork(2)`](http://man7.org/linux/man-pages/man2/fork.2.html) POSIX system call, `child_process.fork()` does not clone the
- * current process.
- *
- * The `shell` option available in {@link spawn} is not supported by`child_process.fork()` and will be ignored if set.
- *
- * If the `signal` option is enabled, calling `.abort()` on the corresponding`AbortController` is similar to calling `.kill()` on the child process except
- * the error passed to the callback will be an `AbortError`:
- *
- * ```js
- * if (process.argv[2] === 'child') {
- * setTimeout(() => {
- * console.log(`Hello from ${process.argv[2]}!`);
- * }, 1_000);
- * } else {
- * const { fork } = require('child_process');
- * const controller = new AbortController();
- * const { signal } = controller;
- * const child = fork(__filename, ['child'], { signal });
- * child.on('error', (err) => {
- * // This will be called with err being an AbortError if the controller aborts
- * });
- * controller.abort(); // Stops the child process
- * }
- * ```
- * @since v0.5.0
- * @param modulePath The module to run in the child.
- * @param args List of string arguments.
- */
- function fork(modulePath: string, options?: ForkOptions): ChildProcess;
- function fork(modulePath: string, args?: ReadonlyArray, options?: ForkOptions): ChildProcess;
- interface SpawnSyncOptions extends CommonSpawnOptions {
- input?: string | NodeJS.ArrayBufferView | undefined;
- maxBuffer?: number | undefined;
- encoding?: BufferEncoding | 'buffer' | null | undefined;
- }
- interface SpawnSyncOptionsWithStringEncoding extends SpawnSyncOptions {
- encoding: BufferEncoding;
- }
- interface SpawnSyncOptionsWithBufferEncoding extends SpawnSyncOptions {
- encoding?: 'buffer' | null | undefined;
- }
- interface SpawnSyncReturns {
- pid: number;
- output: Array;
- stdout: T;
- stderr: T;
- status: number | null;
- signal: NodeJS.Signals | null;
- error?: Error | undefined;
- }
- /**
- * The `child_process.spawnSync()` method is generally identical to {@link spawn} with the exception that the function will not return
- * until the child process has fully closed. When a timeout has been encountered
- * and `killSignal` is sent, the method won't return until the process has
- * completely exited. If the process intercepts and handles the `SIGTERM` signal
- * and doesn't exit, the parent process will wait until the child process has
- * exited.
- *
- * **If the `shell` option is enabled, do not pass unsanitized user input to this**
- * **function. Any input containing shell metacharacters may be used to trigger**
- * **arbitrary command execution.**
- * @since v0.11.12
- * @param command The command to run.
- * @param args List of string arguments.
- */
- function spawnSync(command: string): SpawnSyncReturns;
- function spawnSync(command: string, options: SpawnSyncOptionsWithStringEncoding): SpawnSyncReturns;
- function spawnSync(command: string, options: SpawnSyncOptionsWithBufferEncoding): SpawnSyncReturns;
- function spawnSync(command: string, options?: SpawnSyncOptions): SpawnSyncReturns;
- function spawnSync(command: string, args: ReadonlyArray): SpawnSyncReturns;
- function spawnSync(command: string, args: ReadonlyArray, options: SpawnSyncOptionsWithStringEncoding): SpawnSyncReturns;
- function spawnSync(command: string, args: ReadonlyArray, options: SpawnSyncOptionsWithBufferEncoding): SpawnSyncReturns;
- function spawnSync(command: string, args?: ReadonlyArray, options?: SpawnSyncOptions): SpawnSyncReturns;
- interface CommonExecOptions extends CommonOptions {
- input?: string | NodeJS.ArrayBufferView | undefined;
- stdio?: StdioOptions | undefined;
- killSignal?: NodeJS.Signals | number | undefined;
- maxBuffer?: number | undefined;
- encoding?: BufferEncoding | 'buffer' | null | undefined;
- }
- interface ExecSyncOptions extends CommonExecOptions {
- shell?: string | undefined;
- }
- interface ExecSyncOptionsWithStringEncoding extends ExecSyncOptions {
- encoding: BufferEncoding;
- }
- interface ExecSyncOptionsWithBufferEncoding extends ExecSyncOptions {
- encoding?: 'buffer' | null | undefined;
- }
- /**
- * The `child_process.execSync()` method is generally identical to {@link exec} with the exception that the method will not return
- * until the child process has fully closed. When a timeout has been encountered
- * and `killSignal` is sent, the method won't return until the process has
- * completely exited. If the child process intercepts and handles the `SIGTERM`signal and doesn't exit, the parent process will wait until the child process
- * has exited.
- *
- * If the process times out or has a non-zero exit code, this method will throw.
- * The `Error` object will contain the entire result from {@link spawnSync}.
- *
- * **Never pass unsanitized user input to this function. Any input containing shell**
- * **metacharacters may be used to trigger arbitrary command execution.**
- * @since v0.11.12
- * @param command The command to run.
- * @return The stdout from the command.
- */
- function execSync(command: string): Buffer;
- function execSync(command: string, options: ExecSyncOptionsWithStringEncoding): string;
- function execSync(command: string, options: ExecSyncOptionsWithBufferEncoding): Buffer;
- function execSync(command: string, options?: ExecSyncOptions): string | Buffer;
- interface ExecFileSyncOptions extends CommonExecOptions {
- shell?: boolean | string | undefined;
- }
- interface ExecFileSyncOptionsWithStringEncoding extends ExecFileSyncOptions {
- encoding: BufferEncoding;
- }
- interface ExecFileSyncOptionsWithBufferEncoding extends ExecFileSyncOptions {
- encoding?: 'buffer' | null; // specify `null`.
- }
- /**
- * The `child_process.execFileSync()` method is generally identical to {@link execFile} with the exception that the method will not
- * return until the child process has fully closed. When a timeout has been
- * encountered and `killSignal` is sent, the method won't return until the process
- * has completely exited.
- *
- * If the child process intercepts and handles the `SIGTERM` signal and
- * does not exit, the parent process will still wait until the child process has
- * exited.
- *
- * If the process times out or has a non-zero exit code, this method will throw an `Error` that will include the full result of the underlying {@link spawnSync}.
- *
- * **If the `shell` option is enabled, do not pass unsanitized user input to this**
- * **function. Any input containing shell metacharacters may be used to trigger**
- * **arbitrary command execution.**
- * @since v0.11.12
- * @param file The name or path of the executable file to run.
- * @param args List of string arguments.
- * @return The stdout from the command.
- */
- function execFileSync(file: string): Buffer;
- function execFileSync(file: string, options: ExecFileSyncOptionsWithStringEncoding): string;
- function execFileSync(file: string, options: ExecFileSyncOptionsWithBufferEncoding): Buffer;
- function execFileSync(file: string, options?: ExecFileSyncOptions): string | Buffer;
- function execFileSync(file: string, args: ReadonlyArray): Buffer;
- function execFileSync(file: string, args: ReadonlyArray, options: ExecFileSyncOptionsWithStringEncoding): string;
- function execFileSync(file: string, args: ReadonlyArray, options: ExecFileSyncOptionsWithBufferEncoding): Buffer;
- function execFileSync(file: string, args?: ReadonlyArray, options?: ExecFileSyncOptions): string | Buffer;
-}
-declare module 'node:child_process' {
- export * from 'child_process';
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/stream/consumers.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/stream/consumers.d.ts
deleted file mode 100644
index 1ebf12e1fa741b8d5f9e8f69805b297b39b78719..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/stream/consumers.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-declare module 'stream/consumers' {
- import { Blob as NodeBlob } from "node:buffer";
- import { Readable } from 'node:stream';
- function buffer(stream: NodeJS.ReadableStream | Readable | AsyncIterator): Promise;
- function text(stream: NodeJS.ReadableStream | Readable | AsyncIterator): Promise;
- function arrayBuffer(stream: NodeJS.ReadableStream | Readable | AsyncIterator): Promise;
- function blob(stream: NodeJS.ReadableStream | Readable | AsyncIterator): Promise;
- function json(stream: NodeJS.ReadableStream | Readable | AsyncIterator): Promise;
-}
-declare module 'node:stream/consumers' {
- export * from 'stream/consumers';
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/has/src/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/has/src/index.js
deleted file mode 100644
index dd92dd9094edb035159cfbf38af395418381abe7..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/has/src/index.js
+++ /dev/null
@@ -1,5 +0,0 @@
-'use strict';
-
-var bind = require('function-bind');
-
-module.exports = bind.call(Function.call, Object.prototype.hasOwnProperty);
diff --git a/spaces/freddyaboulton/test-blue/README.md b/spaces/freddyaboulton/test-blue/README.md
deleted file mode 100644
index 75f66a203b574e1fc5d3c8b2f22b2f183943a842..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/test-blue/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
----
-tags: [gradio-theme]
-title: test-blue
-colorFrom: orange
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.1b3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-# test-blue
-## Description
-Add a description of this theme here!
-## Contributions
-Thanks to [@freddyaboulton](https://huggingface.co/freddyaboulton) for adding this gradio theme!
diff --git a/spaces/freddyaboulton/xgboost-income-prediction-with-explainability/DESCRIPTION.md b/spaces/freddyaboulton/xgboost-income-prediction-with-explainability/DESCRIPTION.md
deleted file mode 100644
index cd69ef8e1e3e3ab360b84e76c9cc222a74d4246f..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/xgboost-income-prediction-with-explainability/DESCRIPTION.md
+++ /dev/null
@@ -1 +0,0 @@
-This demo takes in 12 inputs from the user in dropdowns and sliders and predicts income. It also has a separate button for explaining the prediction.
\ No newline at end of file
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/helpers/you.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/helpers/you.py
deleted file mode 100644
index 02985ed14d4848c2de20a99b4771d208286a2558..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/helpers/you.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import sys
-import json
-import urllib.parse
-
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-messages = config['messages']
-prompt = ''
-
-
-def transform(messages: list) -> list:
- result = []
- i = 0
-
- while i < len(messages):
- if messages[i]['role'] == 'user':
- question = messages[i]['content']
- i += 1
-
- if i < len(messages) and messages[i]['role'] == 'assistant':
- answer = messages[i]['content']
- i += 1
- else:
- answer = ''
-
- result.append({'question': question, 'answer': answer})
-
- elif messages[i]['role'] == 'assistant':
- result.append({'question': '', 'answer': messages[i]['content']})
- i += 1
-
- elif messages[i]['role'] == 'system':
- result.append({'question': messages[i]['content'], 'answer': ''})
- i += 1
-
- return result
-
-headers = {
- 'Content-Type': 'application/x-www-form-urlencoded',
- 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
- 'Sec-Fetch-Site': 'same-origin',
- 'Accept-Language': 'en-GB,en;q=0.9',
- 'Sec-Fetch-Mode': 'navigate',
- 'Host': 'you.com',
- 'Origin': 'https://you.com',
- 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.4 Safari/605.1.15',
- 'Referer': 'https://you.com/api/streamingSearch?q=nice&safeSearch=Moderate&onShoppingPage=false&mkt=&responseFilter=WebPages,Translations,TimeZone,Computation,RelatedSearches&domain=youchat&queryTraceId=7a6671f8-5881-404d-8ea3-c3f8301f85ba&chat=%5B%7B%22question%22%3A%22hi%22%2C%22answer%22%3A%22Hello!%20How%20can%20I%20assist%20you%20today%3F%22%7D%5D&chatId=7a6671f8-5881-404d-8ea3-c3f8301f85ba&__cf_chl_tk=ex2bw6vn5vbLsUm8J5rDYUC0Bjzc1XZqka6vUl6765A-1684108495-0-gaNycGzNDtA',
- 'Connection': 'keep-alive',
- 'Sec-Fetch-Dest': 'document',
- 'Priority': 'u=0, i',
-}
-
-if messages[-1]['role'] == 'user':
- prompt = messages[-1]['content']
- messages = messages[:-1]
-
-params = urllib.parse.urlencode({
- 'q': prompt,
- 'domain': 'youchat',
- 'chat': transform(messages)
-})
-
-def output(chunk):
- if b'"youChatToken"' in chunk:
- chunk_json = json.loads(chunk.decode().split('data: ')[1])
-
- print(chunk_json['youChatToken'], flush=True, end = '')
-
-while True:
- try:
- response = requests.get(f'https://you.com/api/streamingSearch?{params}',
- headers=headers, content_callback=output, impersonate='safari15_5')
-
- exit(0)
-
- except Exception as e:
- print('an error occured, retrying... |', e, flush=True)
- continue
\ No newline at end of file
diff --git a/spaces/giorgiolatour/aqiprediction/1_feature_gen.py b/spaces/giorgiolatour/aqiprediction/1_feature_gen.py
deleted file mode 100644
index fe2d036fc178ac938842c2ebeaab4c60e83fc7ab..0000000000000000000000000000000000000000
--- a/spaces/giorgiolatour/aqiprediction/1_feature_gen.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import pandas as pd
-import datetime
-from my_functions import getAQI
-from my_functions import getCoords
-from my_functions import cleanData
-import sys
-import os
-import logging
-import logging.handlers
-
-"""
-Retreive air pollution data from OpenWeather API
-and save to disk.
-
-Feature Descriptions:
- datetime: timestamp of data,
- co: carbon monoxide concentration in micrograms per cubic meter,
- no: nitrogen monoxide concentration in micrograms per cubic meter,
- no2: nitrogen dioxide concentration in micrograms per cubic meter,
- o3: ozone concentration in micrograms per cubic meter,
- so2: sulfur dioxide concentration in micrograms per cubic meter,
- pm2_5: particulates concentration in micrograms per cubic meter,
- pm10: particulates concentration in micrograms per cubic meter,
- nh3: ammonia concentration in micrograms per cubic meter,
- lat: latitude of location,
- lon: longitude of location,
- aqi: air quality index,
- id: id number
-"""
-
-# set up logger
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-logger_file_handler = logging.handlers.RotatingFileHandler(
- "status.log",
- maxBytes=1024 * 1024,
- backupCount=1,
- encoding="utf8",
-)
-formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-logger_file_handler.setFormatter(formatter)
-logger.addHandler(logger_file_handler)
-
-zip_code = '60603' # Chicago
-country_code = 'US'
-city = 'Chicago'
-
-aqi_table_name = f'aqi_{city}_{zip_code}'.lower()
-
-"""
-If file exists already, we just need to download any new data.
-"""
-data_path = os.path.join('data', f'{aqi_table_name}.csv')
-
-if os.path.exists(data_path):
- df = pd.read_csv(data_path, index_col='datetime', parse_dates=True)
- start_date = df.index.max() + datetime.timedelta(hours=1)
- start_date_id = df['id'].max() + 1
-else:
- start_date = datetime.datetime(2020, 11, 27, 0, 0 ,0)
- start_date_id = 0
-
-end_date = datetime.datetime.now() - datetime.timedelta(hours=3)
-end_date = end_date.replace(second=0, microsecond=0, minute=0, hour=end_date.hour)
-"""
-If the start date is greater than or equal to the end date, we have the
-latest data and can quit.
-"""
-if start_date >= end_date:
- sys.exit('Data is up to date. Quitting now...')
-
-try:
- api_key = os.environ['OPENWEATHERAPIKEY']
-except KeyError:
- import private
- api_key = private.MY_API_KEY
- logger.info('Environment API Key not available!')
-
-
-zip_code_api = f'{zip_code},{country_code}'
-coords = getCoords(zip_code_api, api_key)
-
-data = getAQI(start_date, end_date, coords['lat'], coords['lon'], api_key, start_date_id=start_date_id)
-
-# Get rid of duplicates and deal with missing values.
-data = cleanData(data, start_date_id)
-
-# Append new data to old.
-data.to_csv(data_path, mode='a', index=False, header=not os.path.exists(data_path))
-
-logger.info(f'Downloaded AQI data for {start_date} to {end_date}.')
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Citroen Servicebox SEDRE 11 2010 Download [UPD].md b/spaces/gotiQspiryo/whisper-ui/examples/Citroen Servicebox SEDRE 11 2010 Download [UPD].md
deleted file mode 100644
index 86f474f105bd16b897d10a248243221de874b681..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Citroen Servicebox SEDRE 11 2010 Download [UPD].md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Citroen Servicebox SEDRE 11 2010 Download Pc citroen service box + sedre, citroen service box + sedre 4.2 multilenguaje (espaol), citroen service box +Â ... 4d29de3e1b
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Kevin Zegers on Gracepoint (2014) Why He Was Drawn to the Project and What He Hopes Viewers Will Take Away.md b/spaces/gotiQspiryo/whisper-ui/examples/Kevin Zegers on Gracepoint (2014) Why He Was Drawn to the Project and What He Hopes Viewers Will Take Away.md
deleted file mode 100644
index bd7e9dc841690d01478fedf8741cbfe615d612f0..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Kevin Zegers on Gracepoint (2014) Why He Was Drawn to the Project and What He Hopes Viewers Will Take Away.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-
-### LVIS Instance Segmentation Baselines with Mask R-CNN
-
-Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5.
-These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195).
-
-NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines.
-They are roughly 24 epochs of LVISv0.5 data.
-The final results of these configs have large variance across different runs.
-
-
-
-
-
-
-
-Ablations for normalization methods, and a few models trained from scratch following [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883).
-(Note: The baseline uses `2fc` head while the others use [`4conv1fc` head](https://arxiv.org/abs/1803.08494))
-
-
-
-
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Capepacksoftwarefree14.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Capepacksoftwarefree14.md
deleted file mode 100644
index 4ee5be1383e9a1c169ba439910ece2e72c9648f9..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Capepacksoftwarefree14.md
+++ /dev/null
@@ -1,33 +0,0 @@
-
----> ServiceClient failure for DeepLeo[/ERROR]
-
-
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-- Capepacksoftwarefree14 is a cloud-first software that offers an end-to-end workflow for palletization optimization. You can upload and save your information to the cloud, and easily share your palletization reports with others. You can also create new case sizes, build efficient pallet patterns, and analyze the compression strength of your cases and pallets. Capepacksoftwarefree14 is available as a software subscription, so you can have access to the latest version and adapt your subscription to your workload. Capepacksoftwarefree14 is the ultimate solution for palletization optimization.
-- If you want to reduce your transportation costs and your ecological footprint, you should try capepacksoftwarefree14. This is a cloud-based software that allows you to optimize your pallet loading and shipping. You can enter your product dimensions, case type, pallet type and load constraints, and get the best pallet pattern for your products. You can also compare different pallet patterns and evaluate their efficiency and sustainability. With capepacksoftwarefree14, you can create high-quality reports and upload them to the cloud for easy sharing and collaboration. You can also print, email or interface with other program applications. Capepacksoftwarefree14 is a modular suite of palletization software that can help you improve your product size, case size and pallet load.
-- Capepacksoftwarefree14 is a simple and practical program for solving pallet loading problems. It is a cloud-based software that allows you to view a range of on-screen diagrams and reports within seconds of entering your data. You can find the right pallet pattern for your products, and optimize your primary product size for shipping. You can also perform compression strength analysis on your case and pallet load, and improve your space utilization. With capepacksoftwarefree14, you can upload your design to the cloud and share it with others. You can also print, email or interface with other program applications. Start your 30-day trial of capepacksoftwarefree14 now and see how it can make your pallet loading easier and more efficient.
-
-
-- Capepacksoftwarefree14 is a cloud-based software that helps you design better product and case sizes for your pallet loading. You can enter your product dimensions, case type, pallet type and load constraints, and get the best product size for your shipping. You can also compare different product sizes and evaluate their impact on material and cube utilization. With capepacksoftwarefree14, you can create high-quality reports and upload them to the cloud for easy sharing and collaboration. You can also print, email or interface with other program applications. Capepacksoftwarefree14 is a modular suite of palletization software that can help you create a sustainable packaging supply chain.
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-- Capepacksoftwarefree14 is a simple and practical program for solving pallet loading problems. It is a cloud-based software that allows you to view a range of on-screen diagrams and reports within seconds of entering your data. You can find the right pallet pattern for your products, and optimize your primary product size for shipping. You can also perform compression strength analysis on your case and pallet load, and improve your space utilization. With capepacksoftwarefree14, you can upload your design to the cloud and share it with others. You can also print, email or interface with other program applications. Start your 30-day trial of capepacksoftwarefree14 now and see how it can make your pallet loading easier and more efficient.
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-- Capepacksoftwarefree14 is a cloud-based software that helps you determine the best product size, case size and pallet load for your products. You can enter your product dimensions, case type, pallet type and load constraints, and get the best product size for your shipping. You can also compare different product sizes and evaluate their impact on material and cube utilization. With capepacksoftwarefree14, you can create high-quality reports and upload them to the cloud for easy sharing and collaboration. You can also print, email or interface with other program applications. Capepacksoftwarefree14 is a modular suite of palletization software that can help you improve your product size, case size and pallet load.
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\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Crack High Qualitymultiplayercallofduty417.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Crack High Qualitymultiplayercallofduty417.md
deleted file mode 100644
index 128e2f6dfd6619191bf0c69e5f9e0381c6da9bd0..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Crack High Qualitymultiplayercallofduty417.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-
Multiplayer Call of Duty 417: A New Era of FPS Gaming
-
Call of Duty is one of the most popular and successful first-person shooter (FPS) franchises in the world, with millions of fans and players across the globe. The latest installment, Call of Duty 417, is set to launch in 2023 and promises to deliver a new era of multiplayer gaming that will redefine the genre.
Call of Duty 417 is a sequel to Call of Duty Modern Warfare II, which was released in 2022 and received critical acclaim for its immersive and realistic gameplay, stunning graphics, and diverse modes. Call of Duty 417 will continue the story of Modern Warfare II, as well as introduce new features and improvements that will enhance the multiplayer experience.
-
Some of the new features and improvements that Call of Duty 417 will offer include:
-
-
A revamped Gunsmith system that allows players to customize their weapons with more options and flexibility, such as weapon platforms, weapon tuning, and a firing range.
-
A variety of new and returning multiplayer modes, such as Prisoner Rescue, Knock Out, and a third-person playlist.
-
A selection of new and classic maps, ranging from a small bustling marketplace to a modern museum nestled in the foothills of the mountains.
-
A cross-play and cross-progression system that enables players to play with and against each other on different platforms and devices, as well as carry over their progress and achievements.
-
A seasonal content model that provides regular updates and additions to the game, such as new weapons, modes, maps, operators, skins, and more.
-
-
Call of Duty 417 is expected to be one of the most anticipated and best-selling games of 2023, as it will appeal to both casual and hardcore FPS fans. Whether you are looking for fast-paced action, grounded combat, photo-realistic environments, or deep weapon customization, Call of Duty 417 has something for everyone. Pre-order now and get ready to experience the new era of Call of Duty multiplayer in 2023.
-
-
-
One of the most exciting aspects of Call of Duty 417 is the Gunsmith system, which gives players unprecedented control over their weapons. The Gunsmith allows players to choose from different weapon platforms, such as assault rifles, submachine guns, sniper rifles, and more. Each platform has its own strengths and weaknesses, as well as unique attachments and perks. Players can also tune their weapons to suit their playstyle, such as adjusting the recoil, fire rate, accuracy, and damage. Additionally, players can test their weapons in the firing range, where they can practice their aim and skills.
-
Another feature that sets Call of Duty 417 apart from other FPS games is the variety of multiplayer modes that it offers. Players can enjoy classic modes such as Team Deathmatch, Domination, and Search and Destroy, as well as new modes such as Prisoner Rescue and Knock Out. Prisoner Rescue is a mode where one team has to rescue their captured teammates from the enemy base, while the other team has to defend their prisoners and prevent their escape. Knock Out is a mode where players have to collect money bags from fallen enemies and deposit them in designated locations, while avoiding being knocked out by the enemy team. Furthermore, Call of Duty 417 also features a third-person playlist, where players can switch to a third-person perspective and experience the game in a different way.
-
Call of Duty 417 also boasts a selection of new and classic maps that will challenge and delight players of all skill levels. The maps are designed to be realistic and immersive, with dynamic elements such as weather effects, destructible environments, and interactive objects. Some of the new maps include Mercado Las Almas, a small bustling marketplace in Mexico City; Farm 18, a rural farmstead in Nebraska; and Museum 21, a modern museum in Switzerland. Some of the returning maps include Rust, Terminal, and Crash, which are fan-favorites from previous Call of Duty games.
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-
-
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Heropanti Full Movie Dvdrip Mp4 Download.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Heropanti Full Movie Dvdrip Mp4 Download.md
deleted file mode 100644
index 621d5f145ee3f6ad940a195d41818e8984583474..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Heropanti Full Movie Dvdrip Mp4 Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
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-
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diff --git a/spaces/inreVtussa/clothingai/Examples/Artcam 2013 With Cracks 20.md b/spaces/inreVtussa/clothingai/Examples/Artcam 2013 With Cracks 20.md
deleted file mode 100644
index b6501f23b78f9ba55ad7d83566ec5be395227bc1..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Artcam 2013 With Cracks 20.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-
20. the portable device according to claim 13, wherein the one-chip microcontroller further comprises a data transfer bus for transferring the image data from the image sensor interface to the multi-core processor, whereby data transferred from the image sensor interface to the multi-core processor is performed entirely within the one-chip microcontroller.
-
21. the portable device according to claim 13, wherein the one-chip microcontroller further comprises a data transfer bus for transferring the image data from the image sensor interface to the multi-core processor, whereby data transferred from the image sensor interface to the multi-core processor is performed entirely within the one-chip microcontroller.
22. the portable device according to claim 13, wherein the one-chip microcontroller further comprises a data transfer bus for transferring the image data from the image sensor interface to the multi-core processor, whereby data transferred from the image sensor interface to the multi-core processor is performed entirely within the one-chip microcontroller.
-
23. the portable device according to claim 13, wherein the one-chip microcontroller further comprises a data transfer bus for transferring the image data from the image sensor interface to the multi-core processor, whereby data transferred from the image sensor interface to the multi-core processor is performed entirely within the one-chip microcontroller.
-
about a week ago, i finally got around to seeing the newest installment in the toy story franchise, toy story 3. while the first two movies toyed around with the idea of toys being used as a way to express, and even live, their emotions, toy story 3 takes a much more literal approach. in toy story 3, the toys are real, and theyre real emotional beings, with real feelings and real reactions to everything around them. and thanks to a patent filing by the company, we know that this is a concept thats been in the works for a while.
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diff --git a/spaces/inreVtussa/clothingai/Examples/Banjo Kazooie Wii Wad 12l.md b/spaces/inreVtussa/clothingai/Examples/Banjo Kazooie Wii Wad 12l.md
deleted file mode 100644
index b4e04a2b9b340a2168bcf2aeb9df7fca4739e1fa..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Banjo Kazooie Wii Wad 12l.md
+++ /dev/null
@@ -1,52 +0,0 @@
-
-
How to Play Banjo Kazooie Wii Wad 12l on Your Wii Console
-
Banjo Kazooie is one of the most beloved platformers of all time, developed by Rare and released for the Nintendo 64 in 1998. The game features a bear named Banjo and his bird friend Kazooie, who embark on an adventure to stop the evil witch Gruntilda from stealing Banjo's sister's beauty.
If you want to relive this classic game on your Wii console, you may be wondering if there is a way to play Banjo Kazooie Wii Wad 12l on it. A Wii Wad is a file that contains a Wii Channel or a Virtual Console game that can be installed on your Wii console using a homebrew application.
-
In this article, we will show you how to play Banjo Kazooie Wii Wad 12l on your Wii console, as well as the pros and cons of doing so.
-
Step 1: Prepare Your Wii Console
-
Before you can play Banjo Kazooie Wii Wad 12l on your Wii console, you need to prepare your Wii console for homebrew applications. Homebrew applications are unofficial software that can enhance your Wii console's functionality and allow you to run games and programs that are not authorized by Nintendo.
-
-
To prepare your Wii console for homebrew applications, you need to do the following:
-
-
Find out your Wii's system menu version. You can do this by going to Settings > Wii Settings > System Information. You will see a number like 4.3U or 4.3E, which indicates your system menu version and region.
-
Choose a method to install the Homebrew Channel on your Wii console. The Homebrew Channel is a channel that allows you to run homebrew applications from an SD card or a USB drive. There are different methods to install the Homebrew Channel depending on your system menu version and region. You can find a list of methods and tutorials on the official Homebrew Channel website.
-
Follow the instructions carefully to install the Homebrew Channel on your Wii console. Make sure you have a compatible SD card or USB drive with enough space for the files. Be careful not to brick your Wii console by following the wrong steps or using the wrong files.
-
-
Step 2: Download Banjo Kazooie Wii Wad 12l
-
After you have installed the Homebrew Channel on your Wii console, you need to download Banjo Kazooie Wii Wad 12l from the internet. You can find the link in the web search results for this keyword. Make sure you download the correct file for your region and architecture (32-bit or 64-bit).
-
Once you have downloaded Banjo Kazooie Wii Wad 12l, you need to copy it to your SD card or USB drive. You can use any folder name, but make sure it is easy to find later.
-
Step 3: Install Banjo Kazooie Wii Wad 12l
-
After you have copied Banjo Kazooie Wii Wad 12l to your SD card or USB drive, you need to install it on your Wii console using a homebrew application called WAD Manager. WAD Manager is an application that allows you to install or uninstall Wii Wads on your Wii console.
-
To install Banjo Kazooie Wii Wad 12l using WAD Manager, you need to do the following:
-
-
Insert your SD card or USB drive into your Wii console.
-
Launch the Homebrew Channel on your Wii console.
-
Select WAD Manager from the list of homebrew applications and press A.
-
Select IOS249 as the IOS version and press A.
-
Select SD Card or USB Device as the source device and press A.
-
Browse to the folder where you copied Banjo Kazooie Wii Wad 12l and select it with A.
-
Select Install WAD and press A.
-
Wait for the installation process to finish and press any button to return to the main menu.
-
Press Home to exit WAD Manager and return to the Homebrew Channel.
-
-
Step 4: Play Banjo Kazooie Wii Wad 12l
-
After you have installed Banjo Kazooie Wii Wad 12l on your Wii console, you can play it from the main menu of your Wii console. You will see a new channel with the Banjo Kazooie logo and title. Select it with A and enjoy playing Banjo Kazooie on your Wii console.
-
Pros and Cons of Playing Banjo Kazooie Wii Wad 12l
-
Playing Banjo Kazooie Wii Wad 12l on your Wii console has some pros and cons that you should be aware of before doing so. Here are some of them:
-
-
Pros:
-
-
You can play Banjo Kazooie on your Wii console without needing an N64 emulator or a physical cartridge.
-
You can use your Wiimote or Classic Controller as input devices for playing Banjo Kazooie.
-
You can save your progress and settings on your Wii console's internal memory or an SD card.
-
-
-
Cons:
-
-
You may encounter some compatibility issues or glitches while playing Banjo Kazooie on your Wii console, such as graphical errors, sound problems, or freezes.
-
Conclusion
-
-
By following these steps, you can play Banjo Kazooie Wii Wad 12l on your Wii console using homebrew applications. However, you should also be aware of the pros and cons of doing so, as well as the potential risks involved. If you have any questions or feedback, please let us know in the comments below.
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/inreVtussa/clothingai/Examples/CINEMA 4D Studio R19.024 Multilingual 2017 Full Version.md b/spaces/inreVtussa/clothingai/Examples/CINEMA 4D Studio R19.024 Multilingual 2017 Full Version.md
deleted file mode 100644
index ae5be5c002a758d74cbeb03b7813b04aad5171eb..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/CINEMA 4D Studio R19.024 Multilingual 2017 Full Version.md
+++ /dev/null
@@ -1,29 +0,0 @@
-
-```html
-
CINEMA 4D Studio R19.024 Multilingual 2017: A Powerful 3D Animation Software
-
If you are looking for a professional 3D animation software that can help you create stunning 3D graphics and characters, you might want to check out CINEMA 4D Studio R19.024 Multilingual 2017. This is the latest version of the popular CINEMA 4D Studio software, which offers a wide range of tools and features for 3D artists of all levels.
-
CINEMA 4D Studio R19.024 Multilingual 2017 full version
CINEMA 4D Studio R19.024 Multilingual 2017 is easy to learn and extremely powerful, allowing you to achieve breathtaking results fast and hassle-free. Whether you want to create scientific or architectural simulations, motion graphics, visual effects, or games, CINEMA 4D Studio R19.024 Multilingual 2017 has everything you need for high-end 3D production.
-
What's New in CINEMA 4D Studio R19.024 Multilingual 2017?
-
CINEMA 4D Studio R19.024 Multilingual 2017 comes with many new features and improvements that make it more versatile and efficient than ever before. Some of the highlights include:
-
-
A new knife tool that lets you slice models with quick precision and interactivity.
-
A new sound effector that lets you create animations based on audio input.
-
A new spherical camera that lets you render VR and 360-degree videos.
-
A new viewport that supports OpenGL and PBR rendering.
-
A new media core that supports more file formats and codecs.
-
A new scene reconstruction feature that lets you create point clouds or basic mesh from motion-tracked footage.
-
Improved character animation tools, such as a new weight mirroring function and a new pose space deformation feature.
-
Improved sculpting tools, such as a new project mesh function and a new smoothing deformer.
-
Improved MoGraph tools, such as a new Voronoi fracture object and a new push apart effector.
-
Improved rendering tools, such as a new ProRender engine that supports GPU rendering and a new shadow catcher shader.
-
-
How to Download CINEMA 4D Studio R19.024 Multilingual 2017 Full Version?
-
If you are interested in downloading CINEMA 4D Studio R19.024 Multilingual 2017 full version, you can visit the official website of MAXON, the developer of CINEMA 4D Studio. There you can find more information about the software, as well as the system requirements and the pricing options. You can also download a free trial version of CINEMA 4D Studio R19.024 Multilingual 2017 for 42 days to test its features and performance.
-
Alternatively, you can also download CINEMA 4D Studio R19.024 Multilingual 2017 full version from various third-party websites that offer cracked or portable versions of the software. However, this is not recommended, as it may expose your computer to viruses, malware, or legal issues. It is always better to download CINEMA 4D Studio R19.024 Multilingual 2017 full version from the official source or a trusted reseller.
-
Conclusion
-
CINEMA 4D Studio R19.024 Multilingual 2017 is one of the best 3D animation software available in the market today. It offers a comprehensive and user-friendly toolset for creating and animating 3D objects and characters. It also supports multiple languages and platforms, making it accessible to a wide range of users around the world. If you are looking for a professional 3D animation software that can help you create stunning 3D graphics and characters, you should definitely give CINEMA 4D Studio R19.024 Multilingual 2017 a try.
-```
- d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/inreVtussa/clothingai/Examples/Code Akses Maksi Pro [NEW].md b/spaces/inreVtussa/clothingai/Examples/Code Akses Maksi Pro [NEW].md
deleted file mode 100644
index 1f163d701923479e77f5fc870a39e8b5f37aec13..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Code Akses Maksi Pro [NEW].md
+++ /dev/null
@@ -1,38 +0,0 @@
-
-
-[cccc]
-
-\
-
-$k$ & $t_k$ & $\eta_k$ & $c_k$\
-
-1 & 0 & 0 & 1\
-
-1 & 3 & 1 & 0.5\
-
-2 & 1 & 1 & 1\
-
-2 & 3 & 1 & 0.5\
-
-3 & 1 & 2 & 1\
-
-3 & 2 & 1 & 0.5\
-
-4 & 1 & 3 & 1\
-
-4 & 3 & 2 & 0.5\
-
-[^1]: We also tried to use a bit more, because we wanted to make sure that the parent’s distillation only helped the child to bootstrap the network during the early phase and not interfere with the child’s exploration of the environment. However, even with 10 parents, we were not able to observe any significant impact on the child’s exploration.
-
-[^2]: When the child is very close to the goal, they may behave as if they’re planning to get there (by replaying the previous step). While this is not happening in our experiments, it is a potential issue in real environments.
-
-“We are very pleased to announce that we have entered into a distribution partnership with Anniesland, a critically acclaimed and celebrated independent record label out of Glasgow. Both have enjoyed tremendous success in their respective music scenes, with Anniesland releasing music from a host of artists including Bat For Lashes, Malian’s Habib Koité, The Blue Album, Swans, Sufjan Stevens, Teenager, Elbow, Ed Harcourt and many more, while Jodie Birch releases new music through The Field.
-
-Anniesland have supported numerous acts on tour and have brought world class music back to the UK for their audiences, touring extensively in the UK and around the globe and featuring artists such as John Grant, Hozier, Elbow, Bob Mould, Greg Dulli and Alex Turner.
-
-“For me to have the opportunity to work with the amazing Anniesland team is a real dream come true, and working with such talented, creative and ambitious individuals makes it all the more exciting. The Anniesland ethos is a dedication to excellence in every aspect, and it is with this approach and Jodie’s expertise that we are going to develop a great musical journey together.
-
-“ 4fefd39f24
-
-
-
diff --git a/spaces/ishan10/Science_Tutor/app.py b/spaces/ishan10/Science_Tutor/app.py
deleted file mode 100644
index eca9d3d5d2e9dbb965d0605c6a336eb827a87b49..0000000000000000000000000000000000000000
--- a/spaces/ishan10/Science_Tutor/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import openai
-import gradio
-import threading
-import time
-import os
-
-# Access the API key using environment variables
-api_key = os.environ["API_KEY"]
-openai.api_key = api_key
-
-messages = [
- {
- "role": "system",
- "content": "You are a science tutor that can teach concepts in physics, chemistry, and biology. You can also answer questions about the history of science.",
- }
-]
-
-# Token bucket rate limiter
-lock = threading.Lock()
-request_count = 0
-rate_limit_period = 60
-max_requests = 1
-
-
-def check_rate_limit():
- global request_count
- with lock:
- if request_count >= max_requests:
- return False
- request_count += 1
- threading.Timer(rate_limit_period, reset_request_count).start()
- return True
-
-
-def reset_request_count():
- global request_count
- with lock:
- request_count = 0
-
-
-def CustomChatGPT(user_input):
- if not check_rate_limit():
- return "Too many requests. Please wait and try again after a minute. This process ensures that API Tokens don't get wasted."
-
- messages.append({"role": "user", "content": user_input})
- response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
- ChatGPT_reply = response["choices"][0]["message"]["content"]
- messages.append({"role": "assistant", "content": ChatGPT_reply})
- return ChatGPT_reply
-
-
-
-
-demo = gradio.Interface(
- fn=CustomChatGPT,
- inputs="text",
- outputs="text",
- title="Science Tutor"
-)
-
-demo.launch(inline=False)
-
\ No newline at end of file
diff --git a/spaces/ivn888/Twitter-dashboard/panel-geodashboard-twitter/graphs/line_plots.py b/spaces/ivn888/Twitter-dashboard/panel-geodashboard-twitter/graphs/line_plots.py
deleted file mode 100644
index ae3042a57d8df018ceeda1c15405e533de817d11..0000000000000000000000000000000000000000
--- a/spaces/ivn888/Twitter-dashboard/panel-geodashboard-twitter/graphs/line_plots.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import hvplot.pandas # noqa
-import numpy as np
-from bokeh.models import HoverTool, WheelZoomTool
-from pd_utils.utils import filter_df_by_bbox
-
-LINE_COLOR = "#03DAC6"
-
-
-def get_daily_tweets(in_data, x_range, y_range):
- """
- Returns a line plot showing the number of tweets
- per day within the current map extent.
- """
-
- def hook(plot, element):
- """
- Custom hook for disabling zoom on axis
- """
-
- # Disable zoom on axis
- for tool in plot.state.toolbar.tools:
- if isinstance(tool, WheelZoomTool):
- tool.zoom_on_axis = False
- break
-
- # Filter the tweet locations by bounding box
- out_data = filter_df_by_bbox(in_data, x_range, y_range)
-
- # Define a custom Hover tool
- tweets_hover = HoverTool(
- tooltips=[("Day", "@tweet_date{%F}"), ("Tweets", "@tweet_id")],
- formatters={"@tweet_date": "datetime"},
- point_policy="follow_mouse",
- )
-
- # Get the number of tweets on daily basis
- tweets_df = out_data.resample("D")["tweet_id"].count()
- tweets_df = tweets_df.replace(0, np.nan)
- tweets_df = tweets_df.reset_index()
-
- # Number of tweets
- total_tweets = tweets_df["tweet_id"].sum()
-
- # Create the line plot
- tweets_plt = tweets_df.hvplot.line(
- title=f"Total: {int(total_tweets)}",
- x="tweet_date",
- y="tweet_id",
- xlabel="Time [Days]",
- ylabel="Tweets",
- yformatter="%.0f",
- color=LINE_COLOR,
- alpha=0.7,
- tools=[tweets_hover],
- min_height=300,
- min_width=300,
- responsive=True,
- )
-
- # Additional plot options
- tweets_plt.opts(
- hooks=[hook],
- )
-
- return tweets_plt
-
-
-def get_daily_unique_users(in_data, x_range, y_range):
- """
- Returns a line plot showing the number of
- Unique users per day within the current
- map extent.
- """
-
- def hook(plot, element):
- """
- Custom hook for disabling zoom on axis
- """
-
- # Disable zoom on axis
- for tool in plot.state.toolbar.tools:
- if isinstance(tool, WheelZoomTool):
- tool.zoom_on_axis = False
- break
-
- # Filter the tweet locations by bounding box
- out_data = filter_df_by_bbox(in_data, x_range, y_range)
-
- # Define a custom Hover tool
- uu_hover = HoverTool(
- tooltips=[("Day", "@tweet_date{%F}"), ("UU", "@user_id")],
- formatters={"@tweet_date": "datetime"},
- point_policy="follow_mouse",
- )
-
- # Get the number of unique users on daily basis
- uu_df = out_data.drop_duplicates(["user_id"])
- uu_df = uu_df.resample("D")["user_id"].count()
- uu_df = uu_df.replace(0, np.nan)
- uu_df = uu_df.reset_index()
-
- # Number of unique users
- unique_users = uu_df["user_id"].sum()
-
- # Create the line plot
- uu_plt = uu_df.hvplot.line(
- title=f"Total: {int(unique_users)}",
- x="tweet_date",
- y="user_id",
- xlabel="Time [Days]",
- ylabel="Unique Users [UU]",
- yformatter="%.0f",
- color=LINE_COLOR,
- alpha=0.7,
- tools=[uu_hover],
- min_height=300,
- min_width=300,
- responsive=True,
- )
-
- # Additional plot options
- uu_plt.opts(hooks=[hook])
-
- return uu_plt
diff --git a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/backbone/swin_transformer.py b/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/backbone/swin_transformer.py
deleted file mode 100644
index 1c66194deb5dd370e797e57e2712f44303e568cc..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/backbone/swin_transformer.py
+++ /dev/null
@@ -1,802 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# --------------------------------------------------------
-# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
-# --------------------------------------------------------
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from groundingdino.util.misc import NestedTensor
-
-
-class Mlp(nn.Module):
- """Multilayer perceptron."""
-
- def __init__(
- self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
- ):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class WindowAttention(nn.Module):
- """Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- """
-
- def __init__(
- self,
- dim,
- window_size,
- num_heads,
- qkv_bias=True,
- qk_scale=None,
- attn_drop=0.0,
- proj_drop=0.0,
- ):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim**-0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
- ) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- trunc_normal_(self.relative_position_bias_table, std=0.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """Forward function.
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv = (
- self.qkv(x)
- .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
- .permute(2, 0, 3, 1, 4)
- )
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = q @ k.transpose(-2, -1)
-
- relative_position_bias = self.relative_position_bias_table[
- self.relative_position_index.view(-1)
- ].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
- ) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(
- 2, 0, 1
- ).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
-class SwinTransformerBlock(nn.Module):
- """Swin Transformer Block.
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(
- self,
- dim,
- num_heads,
- window_size=7,
- shift_size=0,
- mlp_ratio=4.0,
- qkv_bias=True,
- qk_scale=None,
- drop=0.0,
- attn_drop=0.0,
- drop_path=0.0,
- act_layer=nn.GELU,
- norm_layer=nn.LayerNorm,
- ):
- super().__init__()
- self.dim = dim
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim,
- window_size=to_2tuple(self.window_size),
- num_heads=num_heads,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- attn_drop=attn_drop,
- proj_drop=drop,
- )
-
- self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(
- in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
- )
-
- self.H = None
- self.W = None
-
- def forward(self, x, mask_matrix):
- """Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- mask_matrix: Attention mask for cyclic shift.
- """
- B, L, C = x.shape
- H, W = self.H, self.W
- assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
-
- # pad feature maps to multiples of window size
- pad_l = pad_t = 0
- pad_r = (self.window_size - W % self.window_size) % self.window_size
- pad_b = (self.window_size - H % self.window_size) % self.window_size
- x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
- _, Hp, Wp, _ = x.shape
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- attn_mask = mask_matrix
- else:
- shifted_x = x
- attn_mask = None
-
- # partition windows
- x_windows = window_partition(
- shifted_x, self.window_size
- ) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(
- -1, self.window_size * self.window_size, C
- ) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
-
- if pad_r > 0 or pad_b > 0:
- x = x[:, :H, :W, :].contiguous()
-
- x = x.view(B, H * W, C)
-
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
-
- return x
-
-
-class PatchMerging(nn.Module):
- """Patch Merging Layer
- Args:
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
-
- def forward(self, x, H, W):
- """Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
-
- x = x.view(B, H, W, C)
-
- # padding
- pad_input = (H % 2 == 1) or (W % 2 == 1)
- if pad_input:
- x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.norm(x)
- x = self.reduction(x)
-
- return x
-
-
-class BasicLayer(nn.Module):
- """A basic Swin Transformer layer for one stage.
- Args:
- dim (int): Number of feature channels
- depth (int): Depths of this stage.
- num_heads (int): Number of attention head.
- window_size (int): Local window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(
- self,
- dim,
- depth,
- num_heads,
- window_size=7,
- mlp_ratio=4.0,
- qkv_bias=True,
- qk_scale=None,
- drop=0.0,
- attn_drop=0.0,
- drop_path=0.0,
- norm_layer=nn.LayerNorm,
- downsample=None,
- use_checkpoint=False,
- ):
- super().__init__()
- self.window_size = window_size
- self.shift_size = window_size // 2
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList(
- [
- SwinTransformerBlock(
- dim=dim,
- num_heads=num_heads,
- window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop,
- attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer,
- )
- for i in range(depth)
- ]
- )
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, H, W):
- """Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
-
- # calculate attention mask for SW-MSA
- Hp = int(np.ceil(H / self.window_size)) * self.window_size
- Wp = int(np.ceil(W / self.window_size)) * self.window_size
- img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
- h_slices = (
- slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None),
- )
- w_slices = (
- slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None),
- )
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(
- img_mask, self.window_size
- ) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
- attn_mask == 0, float(0.0)
- )
-
- for blk in self.blocks:
- blk.H, blk.W = H, W
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, attn_mask)
- else:
- x = blk(x, attn_mask)
- if self.downsample is not None:
- x_down = self.downsample(x, H, W)
- Wh, Ww = (H + 1) // 2, (W + 1) // 2
- return x, H, W, x_down, Wh, Ww
- else:
- return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
- """Image to Patch Embedding
- Args:
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- patch_size = to_2tuple(patch_size)
- self.patch_size = patch_size
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- """Forward function."""
- # padding
- _, _, H, W = x.size()
- if W % self.patch_size[1] != 0:
- x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
- if H % self.patch_size[0] != 0:
- x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
- x = self.proj(x) # B C Wh Ww
- if self.norm is not None:
- Wh, Ww = x.size(2), x.size(3)
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
- return x
-
-
-class SwinTransformer(nn.Module):
- """Swin Transformer backbone.
- A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
- https://arxiv.org/pdf/2103.14030
- Args:
- pretrain_img_size (int): Input image size for training the pretrained model,
- used in absolute postion embedding. Default 224.
- patch_size (int | tuple(int)): Patch size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- depths (tuple[int]): Depths of each Swin Transformer stage.
- num_heads (tuple[int]): Number of attention head of each stage.
- window_size (int): Window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
- drop_rate (float): Dropout rate.
- attn_drop_rate (float): Attention dropout rate. Default: 0.
- drop_path_rate (float): Stochastic depth rate. Default: 0.2.
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
- patch_norm (bool): If True, add normalization after patch embedding. Default: True.
- out_indices (Sequence[int]): Output from which stages.
- frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
- -1 means not freezing any parameters.
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
- """
-
- def __init__(
- self,
- pretrain_img_size=224,
- patch_size=4,
- in_chans=3,
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- mlp_ratio=4.0,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.0,
- attn_drop_rate=0.0,
- drop_path_rate=0.2,
- norm_layer=nn.LayerNorm,
- ape=False,
- patch_norm=True,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- dilation=False,
- use_checkpoint=False,
- ):
- super().__init__()
-
- self.pretrain_img_size = pretrain_img_size
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.out_indices = out_indices
- self.frozen_stages = frozen_stages
- self.dilation = dilation
-
- # if use_checkpoint:
- # print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
-
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- patch_size=patch_size,
- in_chans=in_chans,
- embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None,
- )
-
- # absolute position embedding
- if self.ape:
- pretrain_img_size = to_2tuple(pretrain_img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [
- pretrain_img_size[0] // patch_size[0],
- pretrain_img_size[1] // patch_size[1],
- ]
-
- self.absolute_pos_embed = nn.Parameter(
- torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
- )
- trunc_normal_(self.absolute_pos_embed, std=0.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [
- x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
- ] # stochastic depth decay rule
-
- # build layers
- self.layers = nn.ModuleList()
- # prepare downsample list
- downsamplelist = [PatchMerging for i in range(self.num_layers)]
- downsamplelist[-1] = None
- num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
- if self.dilation:
- downsamplelist[-2] = None
- num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
- for i_layer in range(self.num_layers):
- layer = BasicLayer(
- # dim=int(embed_dim * 2 ** i_layer),
- dim=num_features[i_layer],
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop_rate,
- attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
- norm_layer=norm_layer,
- # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
- downsample=downsamplelist[i_layer],
- use_checkpoint=use_checkpoint,
- )
- self.layers.append(layer)
-
- # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
- self.num_features = num_features
-
- # add a norm layer for each output
- for i_layer in out_indices:
- layer = norm_layer(num_features[i_layer])
- layer_name = f"norm{i_layer}"
- self.add_module(layer_name, layer)
-
- self._freeze_stages()
-
- def _freeze_stages(self):
- if self.frozen_stages >= 0:
- self.patch_embed.eval()
- for param in self.patch_embed.parameters():
- param.requires_grad = False
-
- if self.frozen_stages >= 1 and self.ape:
- self.absolute_pos_embed.requires_grad = False
-
- if self.frozen_stages >= 2:
- self.pos_drop.eval()
- for i in range(0, self.frozen_stages - 1):
- m = self.layers[i]
- m.eval()
- for param in m.parameters():
- param.requires_grad = False
-
- # def init_weights(self, pretrained=None):
- # """Initialize the weights in backbone.
- # Args:
- # pretrained (str, optional): Path to pre-trained weights.
- # Defaults to None.
- # """
-
- # def _init_weights(m):
- # if isinstance(m, nn.Linear):
- # trunc_normal_(m.weight, std=.02)
- # if isinstance(m, nn.Linear) and m.bias is not None:
- # nn.init.constant_(m.bias, 0)
- # elif isinstance(m, nn.LayerNorm):
- # nn.init.constant_(m.bias, 0)
- # nn.init.constant_(m.weight, 1.0)
-
- # if isinstance(pretrained, str):
- # self.apply(_init_weights)
- # logger = get_root_logger()
- # load_checkpoint(self, pretrained, strict=False, logger=logger)
- # elif pretrained is None:
- # self.apply(_init_weights)
- # else:
- # raise TypeError('pretrained must be a str or None')
-
- def forward_raw(self, x):
- """Forward function."""
- x = self.patch_embed(x)
-
- Wh, Ww = x.size(2), x.size(3)
- if self.ape:
- # interpolate the position embedding to the corresponding size
- absolute_pos_embed = F.interpolate(
- self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
- )
- x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
- else:
- x = x.flatten(2).transpose(1, 2)
- x = self.pos_drop(x)
-
- outs = []
- for i in range(self.num_layers):
- layer = self.layers[i]
- x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
- # import ipdb; ipdb.set_trace()
-
- if i in self.out_indices:
- norm_layer = getattr(self, f"norm{i}")
- x_out = norm_layer(x_out)
-
- out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
- outs.append(out)
- # in:
- # torch.Size([2, 3, 1024, 1024])
- # outs:
- # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
- # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
- return tuple(outs)
-
- def forward(self, tensor_list: NestedTensor):
- x = tensor_list.tensors
-
- """Forward function."""
- x = self.patch_embed(x)
-
- Wh, Ww = x.size(2), x.size(3)
- if self.ape:
- # interpolate the position embedding to the corresponding size
- absolute_pos_embed = F.interpolate(
- self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
- )
- x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
- else:
- x = x.flatten(2).transpose(1, 2)
- x = self.pos_drop(x)
-
- outs = []
- for i in range(self.num_layers):
- layer = self.layers[i]
- x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
- if i in self.out_indices:
- norm_layer = getattr(self, f"norm{i}")
- x_out = norm_layer(x_out)
-
- out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
- outs.append(out)
- # in:
- # torch.Size([2, 3, 1024, 1024])
- # out:
- # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
- # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
-
- # collect for nesttensors
- outs_dict = {}
- for idx, out_i in enumerate(outs):
- m = tensor_list.mask
- assert m is not None
- mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
- outs_dict[idx] = NestedTensor(out_i, mask)
-
- return outs_dict
-
- def train(self, mode=True):
- """Convert the model into training mode while keep layers freezed."""
- super(SwinTransformer, self).train(mode)
- self._freeze_stages()
-
-
-def build_swin_transformer(modelname, pretrain_img_size, **kw):
- assert modelname in [
- "swin_T_224_1k",
- "swin_B_224_22k",
- "swin_B_384_22k",
- "swin_L_224_22k",
- "swin_L_384_22k",
- ]
-
- model_para_dict = {
- "swin_T_224_1k": dict(
- embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
- ),
- "swin_B_224_22k": dict(
- embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
- ),
- "swin_B_384_22k": dict(
- embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
- ),
- "swin_L_224_22k": dict(
- embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
- ),
- "swin_L_384_22k": dict(
- embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
- ),
- }
- kw_cgf = model_para_dict[modelname]
- kw_cgf.update(kw)
- model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
- return model
-
-
-if __name__ == "__main__":
- model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
- x = torch.rand(2, 3, 1024, 1024)
- y = model.forward_raw(x)
- import ipdb
-
- ipdb.set_trace()
- x = torch.rand(2, 3, 384, 384)
- y = model.forward_raw(x)
diff --git a/spaces/jbilcke-hf/VideoQuest/src/components/ui/alert.tsx b/spaces/jbilcke-hf/VideoQuest/src/components/ui/alert.tsx
deleted file mode 100644
index f589783193a6cfe14032a77b89055cb3e920fe8c..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/components/ui/alert.tsx
+++ /dev/null
@@ -1,59 +0,0 @@
-import * as React from "react"
-import { cva, type VariantProps } from "class-variance-authority"
-
-import { cn } from "@/lib/utils"
-
-const alertVariants = cva(
- "relative w-full rounded-lg border border-stone-200 p-4 [&:has(svg)]:pl-11 [&>svg+div]:translate-y-[-3px] [&>svg]:absolute [&>svg]:left-4 [&>svg]:top-4 [&>svg]:text-stone-950 dark:border-stone-800 dark:[&>svg]:text-stone-50",
- {
- variants: {
- variant: {
- default: "bg-white text-stone-950 dark:bg-stone-950 dark:text-stone-50",
- destructive:
- "border-red-500/50 text-red-500 dark:border-red-500 [&>svg]:text-red-500 dark:border-red-900/50 dark:text-red-900 dark:dark:border-red-900 dark:[&>svg]:text-red-900",
- },
- },
- defaultVariants: {
- variant: "default",
- },
- }
-)
-
-const Alert = React.forwardRef<
- HTMLDivElement,
- React.HTMLAttributes & VariantProps
->(({ className, variant, ...props }, ref) => (
-
-))
-Alert.displayName = "Alert"
-
-const AlertTitle = React.forwardRef<
- HTMLParagraphElement,
- React.HTMLAttributes
->(({ className, ...props }, ref) => (
-
-))
-AlertTitle.displayName = "AlertTitle"
-
-const AlertDescription = React.forwardRef<
- HTMLParagraphElement,
- React.HTMLAttributes
->(({ className, ...props }, ref) => (
-
-))
-AlertDescription.displayName = "AlertDescription"
-
-export { Alert, AlertTitle, AlertDescription }
diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/app/interface/bottom-bar/index.tsx b/spaces/jbilcke-hf/ai-comic-factory/src/app/interface/bottom-bar/index.tsx
deleted file mode 100644
index 3756be10917e407a5cb70a800e5fe9202e25d277..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/src/app/interface/bottom-bar/index.tsx
+++ /dev/null
@@ -1,140 +0,0 @@
-import { useStore } from "@/app/store"
-import { Button } from "@/components/ui/button"
-import { cn } from "@/lib/utils"
-import { About } from "../about"
-import { startTransition, useState } from "react"
-import { upscaleImage } from "@/app/engine/render"
-import { sleep } from "@/lib/sleep"
-import { AIClipFactory } from "../ai-clip-factory"
-import { Share } from "../share"
-import { SettingsDialog } from "../settings-dialog"
-
-export function BottomBar() {
- const download = useStore(state => state.download)
- const isGeneratingStory = useStore(state => state.isGeneratingStory)
- const prompt = useStore(state => state.prompt)
- const panelGenerationStatus = useStore(state => state.panelGenerationStatus)
- const page = useStore(state => state.page)
- const preset = useStore(state => state.preset)
- const pageToImage = useStore(state => state.pageToImage)
-
- const allStatus = Object.values(panelGenerationStatus)
- const remainingImages = allStatus.reduce((acc, s) => (acc + (s ? 1 : 0)), 0)
-
- const upscaleQueue = useStore(state => state.upscaleQueue)
- const renderedScenes = useStore(state => state.renderedScenes)
- const removeFromUpscaleQueue = useStore(state => state.removeFromUpscaleQueue)
- const setRendered = useStore(state => state.setRendered)
- const [isUpscaling, setUpscaling] = useState(false)
-
- const handleUpscale = () => {
- setUpscaling(true)
- startTransition(() => {
- const fn = async () => {
- for (let [panelId, renderedScene] of Object.entries(upscaleQueue)) {
- try {
- console.log(`upscaling panel ${panelId} (${renderedScene.renderId})`)
- const result = await upscaleImage(renderedScene.assetUrl)
- await sleep(1000)
- if (result.assetUrl) {
- console.log(`upscale successful, removing ${panelId} (${renderedScene.renderId}) from upscale queue`)
- setRendered(panelId, {
- ...renderedScene,
- assetUrl: result.assetUrl
- })
- removeFromUpscaleQueue(panelId)
- }
-
- } catch (err) {
- console.error(`failed to upscale: ${err}`)
- }
- }
-
- setUpscaling(false)
- }
-
- fn()
- })
- }
-
- const handlePrint = () => {
- window.print()
- }
-
- return (
-
-
-
-
-
-
-
-
-
- {/*
-
-
-
-
-
-
- */}
-
-
-
-
- )
-}
\ No newline at end of file
diff --git a/spaces/jefftko/DreamShaper-webui/README.md b/spaces/jefftko/DreamShaper-webui/README.md
deleted file mode 100644
index a573a8a2d9d75befe020751fb4c3d4e2e127bf23..0000000000000000000000000000000000000000
--- a/spaces/jefftko/DreamShaper-webui/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: DreamShaper Web UI
-emoji: 🚧
-colorFrom: white
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-duplicated_from: Lykon/DreamShaper-webui
----
-
-## Stable Diffusion Web UI
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-
-## Documentation
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki)
-
-## Models License
-https://huggingface.co/spaces/CompVis/stable-diffusion-license
\ No newline at end of file
diff --git a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig6a_Compare_wildtype_mutant.py b/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig6a_Compare_wildtype_mutant.py
deleted file mode 100644
index 032f2bdfe15abecee42d2832009af663e008daef..0000000000000000000000000000000000000000
--- a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig6a_Compare_wildtype_mutant.py
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/usr/bin/python
-# coding: utf-8
-
-# Author: LE YUAN
-# Date: 2021-06-14
-
-import os
-import json
-import math
-import seaborn as sns
-import matplotlib.pyplot as plt
-from matplotlib import rc
-import pandas as pd
-import numpy as np
-import statsmodels.api as sm
-
-
-def median(lst):
- sortedLst = sorted(lst)
- lstLen = len(lst)
- index = (lstLen - 1) // 2
-
- if (lstLen % 2):
- return sortedLst[index]
- else:
- return (sortedLst[index] + sortedLst[index + 1])/2.0
-
-def write_Kcat_enzymeType() :
- with open('../../Data/database/Kcat_combination_0918_wildtype_mutant.json', 'r') as infile :
- entries = json.load(infile)
-
- types_Kcat = {
- 'Wildtype': list(),
- 'Mutant': list()
- }
-
- write_items = list()
- for entry in entries :
- enzymeType = entry['Type']
- value = entry['Value']
-
- if enzymeType == 'wildtype' and float(value) > 0 :
- value_log10 = math.log10(float(value))
- types_Kcat['Wildtype'].append(value_log10)
- write_items.append(['Wildtype', str(value_log10)])
- if enzymeType == 'mutant' and float(value) > 0 :
- value_log10 = math.log10(float(value))
- types_Kcat['Mutant'].append(value_log10)
- write_items.append(['Mutant', str(value_log10)])
-
- with open('./source_data/SuppleFig6a.txt', 'w') as outfile :
- items = ['Type', 'Kcat value (log10)']
- outfile.write('\t'.join(items)+'\n')
-
- i = 0
- for item in write_items :
- outfile.write('\t'.join(item)+'\n')
-
- print("Writing to file done!")
-
-
-if __name__ == "__main__" :
- write_Kcat_enzymeType()
-
-
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/zonefile.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/zonefile.py
deleted file mode 100644
index 27f04924ff0f977e5496d1baa1c8f50481464a91..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/zonefile.py
+++ /dev/null
@@ -1,747 +0,0 @@
-# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
-
-# Copyright (C) 2003-2007, 2009-2011 Nominum, Inc.
-#
-# Permission to use, copy, modify, and distribute this software and its
-# documentation for any purpose with or without fee is hereby granted,
-# provided that the above copyright notice and this permission notice
-# appear in all copies.
-#
-# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES
-# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR
-# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
-# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-"""DNS Zones."""
-
-import re
-import sys
-from typing import Any, Iterable, List, Optional, Set, Tuple, Union
-
-import dns.exception
-import dns.grange
-import dns.name
-import dns.node
-import dns.rdata
-import dns.rdataclass
-import dns.rdatatype
-import dns.rdtypes.ANY.SOA
-import dns.rrset
-import dns.tokenizer
-import dns.transaction
-import dns.ttl
-
-
-class UnknownOrigin(dns.exception.DNSException):
- """Unknown origin"""
-
-
-class CNAMEAndOtherData(dns.exception.DNSException):
- """A node has a CNAME and other data"""
-
-
-def _check_cname_and_other_data(txn, name, rdataset):
- rdataset_kind = dns.node.NodeKind.classify_rdataset(rdataset)
- node = txn.get_node(name)
- if node is None:
- # empty nodes are neutral.
- return
- node_kind = node.classify()
- if (
- node_kind == dns.node.NodeKind.CNAME
- and rdataset_kind == dns.node.NodeKind.REGULAR
- ):
- raise CNAMEAndOtherData("rdataset type is not compatible with a CNAME node")
- elif (
- node_kind == dns.node.NodeKind.REGULAR
- and rdataset_kind == dns.node.NodeKind.CNAME
- ):
- raise CNAMEAndOtherData(
- "CNAME rdataset is not compatible with a regular data node"
- )
- # Otherwise at least one of the node and the rdataset is neutral, so
- # adding the rdataset is ok
-
-
-SavedStateType = Tuple[
- dns.tokenizer.Tokenizer,
- Optional[dns.name.Name], # current_origin
- Optional[dns.name.Name], # last_name
- Optional[Any], # current_file
- int, # last_ttl
- bool, # last_ttl_known
- int, # default_ttl
- bool,
-] # default_ttl_known
-
-
-def _upper_dollarize(s):
- s = s.upper()
- if not s.startswith("$"):
- s = "$" + s
- return s
-
-
-class Reader:
-
- """Read a DNS zone file into a transaction."""
-
- def __init__(
- self,
- tok: dns.tokenizer.Tokenizer,
- rdclass: dns.rdataclass.RdataClass,
- txn: dns.transaction.Transaction,
- allow_include: bool = False,
- allow_directives: Union[bool, Iterable[str]] = True,
- force_name: Optional[dns.name.Name] = None,
- force_ttl: Optional[int] = None,
- force_rdclass: Optional[dns.rdataclass.RdataClass] = None,
- force_rdtype: Optional[dns.rdatatype.RdataType] = None,
- default_ttl: Optional[int] = None,
- ):
- self.tok = tok
- (self.zone_origin, self.relativize, _) = txn.manager.origin_information()
- self.current_origin = self.zone_origin
- self.last_ttl = 0
- self.last_ttl_known = False
- if force_ttl is not None:
- default_ttl = force_ttl
- if default_ttl is None:
- self.default_ttl = 0
- self.default_ttl_known = False
- else:
- self.default_ttl = default_ttl
- self.default_ttl_known = True
- self.last_name = self.current_origin
- self.zone_rdclass = rdclass
- self.txn = txn
- self.saved_state: List[SavedStateType] = []
- self.current_file: Optional[Any] = None
- self.allowed_directives: Set[str]
- if allow_directives is True:
- self.allowed_directives = {"$GENERATE", "$ORIGIN", "$TTL"}
- if allow_include:
- self.allowed_directives.add("$INCLUDE")
- elif allow_directives is False:
- # allow_include was ignored in earlier releases if allow_directives was
- # False, so we continue that.
- self.allowed_directives = set()
- else:
- # Note that if directives are explicitly specified, then allow_include
- # is ignored.
- self.allowed_directives = set(_upper_dollarize(d) for d in allow_directives)
- self.force_name = force_name
- self.force_ttl = force_ttl
- self.force_rdclass = force_rdclass
- self.force_rdtype = force_rdtype
- self.txn.check_put_rdataset(_check_cname_and_other_data)
-
- def _eat_line(self):
- while 1:
- token = self.tok.get()
- if token.is_eol_or_eof():
- break
-
- def _get_identifier(self):
- token = self.tok.get()
- if not token.is_identifier():
- raise dns.exception.SyntaxError
- return token
-
- def _rr_line(self):
- """Process one line from a DNS zone file."""
- token = None
- # Name
- if self.force_name is not None:
- name = self.force_name
- else:
- if self.current_origin is None:
- raise UnknownOrigin
- token = self.tok.get(want_leading=True)
- if not token.is_whitespace():
- self.last_name = self.tok.as_name(token, self.current_origin)
- else:
- token = self.tok.get()
- if token.is_eol_or_eof():
- # treat leading WS followed by EOL/EOF as if they were EOL/EOF.
- return
- self.tok.unget(token)
- name = self.last_name
- if not name.is_subdomain(self.zone_origin):
- self._eat_line()
- return
- if self.relativize:
- name = name.relativize(self.zone_origin)
-
- # TTL
- if self.force_ttl is not None:
- ttl = self.force_ttl
- self.last_ttl = ttl
- self.last_ttl_known = True
- else:
- token = self._get_identifier()
- ttl = None
- try:
- ttl = dns.ttl.from_text(token.value)
- self.last_ttl = ttl
- self.last_ttl_known = True
- token = None
- except dns.ttl.BadTTL:
- self.tok.unget(token)
-
- # Class
- if self.force_rdclass is not None:
- rdclass = self.force_rdclass
- else:
- token = self._get_identifier()
- try:
- rdclass = dns.rdataclass.from_text(token.value)
- except dns.exception.SyntaxError:
- raise
- except Exception:
- rdclass = self.zone_rdclass
- self.tok.unget(token)
- if rdclass != self.zone_rdclass:
- raise dns.exception.SyntaxError("RR class is not zone's class")
-
- if ttl is None:
- # support for syntax
- token = self._get_identifier()
- ttl = None
- try:
- ttl = dns.ttl.from_text(token.value)
- self.last_ttl = ttl
- self.last_ttl_known = True
- token = None
- except dns.ttl.BadTTL:
- if self.default_ttl_known:
- ttl = self.default_ttl
- elif self.last_ttl_known:
- ttl = self.last_ttl
- self.tok.unget(token)
-
- # Type
- if self.force_rdtype is not None:
- rdtype = self.force_rdtype
- else:
- token = self._get_identifier()
- try:
- rdtype = dns.rdatatype.from_text(token.value)
- except Exception:
- raise dns.exception.SyntaxError("unknown rdatatype '%s'" % token.value)
-
- try:
- rd = dns.rdata.from_text(
- rdclass,
- rdtype,
- self.tok,
- self.current_origin,
- self.relativize,
- self.zone_origin,
- )
- except dns.exception.SyntaxError:
- # Catch and reraise.
- raise
- except Exception:
- # All exceptions that occur in the processing of rdata
- # are treated as syntax errors. This is not strictly
- # correct, but it is correct almost all of the time.
- # We convert them to syntax errors so that we can emit
- # helpful filename:line info.
- (ty, va) = sys.exc_info()[:2]
- raise dns.exception.SyntaxError(
- "caught exception {}: {}".format(str(ty), str(va))
- )
-
- if not self.default_ttl_known and rdtype == dns.rdatatype.SOA:
- # The pre-RFC2308 and pre-BIND9 behavior inherits the zone default
- # TTL from the SOA minttl if no $TTL statement is present before the
- # SOA is parsed.
- self.default_ttl = rd.minimum
- self.default_ttl_known = True
- if ttl is None:
- # if we didn't have a TTL on the SOA, set it!
- ttl = rd.minimum
-
- # TTL check. We had to wait until now to do this as the SOA RR's
- # own TTL can be inferred from its minimum.
- if ttl is None:
- raise dns.exception.SyntaxError("Missing default TTL value")
-
- self.txn.add(name, ttl, rd)
-
- def _parse_modify(self, side: str) -> Tuple[str, str, int, int, str]:
- # Here we catch everything in '{' '}' in a group so we can replace it
- # with ''.
- is_generate1 = re.compile(r"^.*\$({(\+|-?)(\d+),(\d+),(.)}).*$")
- is_generate2 = re.compile(r"^.*\$({(\+|-?)(\d+)}).*$")
- is_generate3 = re.compile(r"^.*\$({(\+|-?)(\d+),(\d+)}).*$")
- # Sometimes there are modifiers in the hostname. These come after
- # the dollar sign. They are in the form: ${offset[,width[,base]]}.
- # Make names
- g1 = is_generate1.match(side)
- if g1:
- mod, sign, offset, width, base = g1.groups()
- if sign == "":
- sign = "+"
- g2 = is_generate2.match(side)
- if g2:
- mod, sign, offset = g2.groups()
- if sign == "":
- sign = "+"
- width = 0
- base = "d"
- g3 = is_generate3.match(side)
- if g3:
- mod, sign, offset, width = g3.groups()
- if sign == "":
- sign = "+"
- base = "d"
-
- if not (g1 or g2 or g3):
- mod = ""
- sign = "+"
- offset = 0
- width = 0
- base = "d"
-
- offset = int(offset)
- width = int(width)
-
- if sign not in ["+", "-"]:
- raise dns.exception.SyntaxError("invalid offset sign %s" % sign)
- if base not in ["d", "o", "x", "X", "n", "N"]:
- raise dns.exception.SyntaxError("invalid type %s" % base)
-
- return mod, sign, offset, width, base
-
- def _generate_line(self):
- # range lhs [ttl] [class] type rhs [ comment ]
- """Process one line containing the GENERATE statement from a DNS
- zone file."""
- if self.current_origin is None:
- raise UnknownOrigin
-
- token = self.tok.get()
- # Range (required)
- try:
- start, stop, step = dns.grange.from_text(token.value)
- token = self.tok.get()
- if not token.is_identifier():
- raise dns.exception.SyntaxError
- except Exception:
- raise dns.exception.SyntaxError
-
- # lhs (required)
- try:
- lhs = token.value
- token = self.tok.get()
- if not token.is_identifier():
- raise dns.exception.SyntaxError
- except Exception:
- raise dns.exception.SyntaxError
-
- # TTL
- try:
- ttl = dns.ttl.from_text(token.value)
- self.last_ttl = ttl
- self.last_ttl_known = True
- token = self.tok.get()
- if not token.is_identifier():
- raise dns.exception.SyntaxError
- except dns.ttl.BadTTL:
- if not (self.last_ttl_known or self.default_ttl_known):
- raise dns.exception.SyntaxError("Missing default TTL value")
- if self.default_ttl_known:
- ttl = self.default_ttl
- elif self.last_ttl_known:
- ttl = self.last_ttl
- # Class
- try:
- rdclass = dns.rdataclass.from_text(token.value)
- token = self.tok.get()
- if not token.is_identifier():
- raise dns.exception.SyntaxError
- except dns.exception.SyntaxError:
- raise dns.exception.SyntaxError
- except Exception:
- rdclass = self.zone_rdclass
- if rdclass != self.zone_rdclass:
- raise dns.exception.SyntaxError("RR class is not zone's class")
- # Type
- try:
- rdtype = dns.rdatatype.from_text(token.value)
- token = self.tok.get()
- if not token.is_identifier():
- raise dns.exception.SyntaxError
- except Exception:
- raise dns.exception.SyntaxError("unknown rdatatype '%s'" % token.value)
-
- # rhs (required)
- rhs = token.value
-
- def _calculate_index(counter: int, offset_sign: str, offset: int) -> int:
- """Calculate the index from the counter and offset."""
- if offset_sign == "-":
- offset *= -1
- return counter + offset
-
- def _format_index(index: int, base: str, width: int) -> str:
- """Format the index with the given base, and zero-fill it
- to the given width."""
- if base in ["d", "o", "x", "X"]:
- return format(index, base).zfill(width)
-
- # base can only be n or N here
- hexa = _format_index(index, "x", width)
- nibbles = ".".join(hexa[::-1])[:width]
- if base == "N":
- nibbles = nibbles.upper()
- return nibbles
-
- lmod, lsign, loffset, lwidth, lbase = self._parse_modify(lhs)
- rmod, rsign, roffset, rwidth, rbase = self._parse_modify(rhs)
- for i in range(start, stop + 1, step):
- # +1 because bind is inclusive and python is exclusive
-
- lindex = _calculate_index(i, lsign, loffset)
- rindex = _calculate_index(i, rsign, roffset)
-
- lzfindex = _format_index(lindex, lbase, lwidth)
- rzfindex = _format_index(rindex, rbase, rwidth)
-
- name = lhs.replace("$%s" % (lmod), lzfindex)
- rdata = rhs.replace("$%s" % (rmod), rzfindex)
-
- self.last_name = dns.name.from_text(
- name, self.current_origin, self.tok.idna_codec
- )
- name = self.last_name
- if not name.is_subdomain(self.zone_origin):
- self._eat_line()
- return
- if self.relativize:
- name = name.relativize(self.zone_origin)
-
- try:
- rd = dns.rdata.from_text(
- rdclass,
- rdtype,
- rdata,
- self.current_origin,
- self.relativize,
- self.zone_origin,
- )
- except dns.exception.SyntaxError:
- # Catch and reraise.
- raise
- except Exception:
- # All exceptions that occur in the processing of rdata
- # are treated as syntax errors. This is not strictly
- # correct, but it is correct almost all of the time.
- # We convert them to syntax errors so that we can emit
- # helpful filename:line info.
- (ty, va) = sys.exc_info()[:2]
- raise dns.exception.SyntaxError(
- "caught exception %s: %s" % (str(ty), str(va))
- )
-
- self.txn.add(name, ttl, rd)
-
- def read(self) -> None:
- """Read a DNS zone file and build a zone object.
-
- @raises dns.zone.NoSOA: No SOA RR was found at the zone origin
- @raises dns.zone.NoNS: No NS RRset was found at the zone origin
- """
-
- try:
- while 1:
- token = self.tok.get(True, True)
- if token.is_eof():
- if self.current_file is not None:
- self.current_file.close()
- if len(self.saved_state) > 0:
- (
- self.tok,
- self.current_origin,
- self.last_name,
- self.current_file,
- self.last_ttl,
- self.last_ttl_known,
- self.default_ttl,
- self.default_ttl_known,
- ) = self.saved_state.pop(-1)
- continue
- break
- elif token.is_eol():
- continue
- elif token.is_comment():
- self.tok.get_eol()
- continue
- elif token.value[0] == "$" and len(self.allowed_directives) > 0:
- # Note that we only run directive processing code if at least
- # one directive is allowed in order to be backwards compatible
- c = token.value.upper()
- if c not in self.allowed_directives:
- raise dns.exception.SyntaxError(
- f"zone file directive '{c}' is not allowed"
- )
- if c == "$TTL":
- token = self.tok.get()
- if not token.is_identifier():
- raise dns.exception.SyntaxError("bad $TTL")
- self.default_ttl = dns.ttl.from_text(token.value)
- self.default_ttl_known = True
- self.tok.get_eol()
- elif c == "$ORIGIN":
- self.current_origin = self.tok.get_name()
- self.tok.get_eol()
- if self.zone_origin is None:
- self.zone_origin = self.current_origin
- self.txn._set_origin(self.current_origin)
- elif c == "$INCLUDE":
- token = self.tok.get()
- filename = token.value
- token = self.tok.get()
- new_origin: Optional[dns.name.Name]
- if token.is_identifier():
- new_origin = dns.name.from_text(
- token.value, self.current_origin, self.tok.idna_codec
- )
- self.tok.get_eol()
- elif not token.is_eol_or_eof():
- raise dns.exception.SyntaxError("bad origin in $INCLUDE")
- else:
- new_origin = self.current_origin
- self.saved_state.append(
- (
- self.tok,
- self.current_origin,
- self.last_name,
- self.current_file,
- self.last_ttl,
- self.last_ttl_known,
- self.default_ttl,
- self.default_ttl_known,
- )
- )
- self.current_file = open(filename, "r")
- self.tok = dns.tokenizer.Tokenizer(self.current_file, filename)
- self.current_origin = new_origin
- elif c == "$GENERATE":
- self._generate_line()
- else:
- raise dns.exception.SyntaxError(
- f"Unknown zone file directive '{c}'"
- )
- continue
- self.tok.unget(token)
- self._rr_line()
- except dns.exception.SyntaxError as detail:
- (filename, line_number) = self.tok.where()
- if detail is None:
- detail = "syntax error"
- ex = dns.exception.SyntaxError(
- "%s:%d: %s" % (filename, line_number, detail)
- )
- tb = sys.exc_info()[2]
- raise ex.with_traceback(tb) from None
-
-
-class RRsetsReaderTransaction(dns.transaction.Transaction):
- def __init__(self, manager, replacement, read_only):
- assert not read_only
- super().__init__(manager, replacement, read_only)
- self.rdatasets = {}
-
- def _get_rdataset(self, name, rdtype, covers):
- return self.rdatasets.get((name, rdtype, covers))
-
- def _get_node(self, name):
- rdatasets = []
- for (rdataset_name, _, _), rdataset in self.rdatasets.items():
- if name == rdataset_name:
- rdatasets.append(rdataset)
- if len(rdatasets) == 0:
- return None
- node = dns.node.Node()
- node.rdatasets = rdatasets
- return node
-
- def _put_rdataset(self, name, rdataset):
- self.rdatasets[(name, rdataset.rdtype, rdataset.covers)] = rdataset
-
- def _delete_name(self, name):
- # First remove any changes involving the name
- remove = []
- for key in self.rdatasets:
- if key[0] == name:
- remove.append(key)
- if len(remove) > 0:
- for key in remove:
- del self.rdatasets[key]
-
- def _delete_rdataset(self, name, rdtype, covers):
- try:
- del self.rdatasets[(name, rdtype, covers)]
- except KeyError:
- pass
-
- def _name_exists(self, name):
- for n, _, _ in self.rdatasets:
- if n == name:
- return True
- return False
-
- def _changed(self):
- return len(self.rdatasets) > 0
-
- def _end_transaction(self, commit):
- if commit and self._changed():
- rrsets = []
- for (name, _, _), rdataset in self.rdatasets.items():
- rrset = dns.rrset.RRset(
- name, rdataset.rdclass, rdataset.rdtype, rdataset.covers
- )
- rrset.update(rdataset)
- rrsets.append(rrset)
- self.manager.set_rrsets(rrsets)
-
- def _set_origin(self, origin):
- pass
-
- def _iterate_rdatasets(self):
- raise NotImplementedError # pragma: no cover
-
- def _iterate_names(self):
- raise NotImplementedError # pragma: no cover
-
-
-class RRSetsReaderManager(dns.transaction.TransactionManager):
- def __init__(
- self, origin=dns.name.root, relativize=False, rdclass=dns.rdataclass.IN
- ):
- self.origin = origin
- self.relativize = relativize
- self.rdclass = rdclass
- self.rrsets = []
-
- def reader(self): # pragma: no cover
- raise NotImplementedError
-
- def writer(self, replacement=False):
- assert replacement is True
- return RRsetsReaderTransaction(self, True, False)
-
- def get_class(self):
- return self.rdclass
-
- def origin_information(self):
- if self.relativize:
- effective = dns.name.empty
- else:
- effective = self.origin
- return (self.origin, self.relativize, effective)
-
- def set_rrsets(self, rrsets):
- self.rrsets = rrsets
-
-
-def read_rrsets(
- text: Any,
- name: Optional[Union[dns.name.Name, str]] = None,
- ttl: Optional[int] = None,
- rdclass: Optional[Union[dns.rdataclass.RdataClass, str]] = dns.rdataclass.IN,
- default_rdclass: Union[dns.rdataclass.RdataClass, str] = dns.rdataclass.IN,
- rdtype: Optional[Union[dns.rdatatype.RdataType, str]] = None,
- default_ttl: Optional[Union[int, str]] = None,
- idna_codec: Optional[dns.name.IDNACodec] = None,
- origin: Optional[Union[dns.name.Name, str]] = dns.name.root,
- relativize: bool = False,
-) -> List[dns.rrset.RRset]:
- """Read one or more rrsets from the specified text, possibly subject
- to restrictions.
-
- *text*, a file object or a string, is the input to process.
-
- *name*, a string, ``dns.name.Name``, or ``None``, is the owner name of
- the rrset. If not ``None``, then the owner name is "forced", and the
- input must not specify an owner name. If ``None``, then any owner names
- are allowed and must be present in the input.
-
- *ttl*, an ``int``, string, or None. If not ``None``, the the TTL is
- forced to be the specified value and the input must not specify a TTL.
- If ``None``, then a TTL may be specified in the input. If it is not
- specified, then the *default_ttl* will be used.
-
- *rdclass*, a ``dns.rdataclass.RdataClass``, string, or ``None``. If
- not ``None``, then the class is forced to the specified value, and the
- input must not specify a class. If ``None``, then the input may specify
- a class that matches *default_rdclass*. Note that it is not possible to
- return rrsets with differing classes; specifying ``None`` for the class
- simply allows the user to optionally type a class as that may be convenient
- when cutting and pasting.
-
- *default_rdclass*, a ``dns.rdataclass.RdataClass`` or string. The class
- of the returned rrsets.
-
- *rdtype*, a ``dns.rdatatype.RdataType``, string, or ``None``. If not
- ``None``, then the type is forced to the specified value, and the
- input must not specify a type. If ``None``, then a type must be present
- for each RR.
-
- *default_ttl*, an ``int``, string, or ``None``. If not ``None``, then if
- the TTL is not forced and is not specified, then this value will be used.
- if ``None``, then if the TTL is not forced an error will occur if the TTL
- is not specified.
-
- *idna_codec*, a ``dns.name.IDNACodec``, specifies the IDNA
- encoder/decoder. If ``None``, the default IDNA 2003 encoder/decoder
- is used. Note that codecs only apply to the owner name; dnspython does
- not do IDNA for names in rdata, as there is no IDNA zonefile format.
-
- *origin*, a string, ``dns.name.Name``, or ``None``, is the origin for any
- relative names in the input, and also the origin to relativize to if
- *relativize* is ``True``.
-
- *relativize*, a bool. If ``True``, names are relativized to the *origin*;
- if ``False`` then any relative names in the input are made absolute by
- appending the *origin*.
- """
- if isinstance(origin, str):
- origin = dns.name.from_text(origin, dns.name.root, idna_codec)
- if isinstance(name, str):
- name = dns.name.from_text(name, origin, idna_codec)
- if isinstance(ttl, str):
- ttl = dns.ttl.from_text(ttl)
- if isinstance(default_ttl, str):
- default_ttl = dns.ttl.from_text(default_ttl)
- if rdclass is not None:
- rdclass = dns.rdataclass.RdataClass.make(rdclass)
- else:
- rdclass = None
- default_rdclass = dns.rdataclass.RdataClass.make(default_rdclass)
- if rdtype is not None:
- rdtype = dns.rdatatype.RdataType.make(rdtype)
- else:
- rdtype = None
- manager = RRSetsReaderManager(origin, relativize, default_rdclass)
- with manager.writer(True) as txn:
- tok = dns.tokenizer.Tokenizer(text, "", idna_codec=idna_codec)
- reader = Reader(
- tok,
- default_rdclass,
- txn,
- allow_directives=False,
- force_name=name,
- force_ttl=ttl,
- force_rdclass=rdclass,
- force_rdtype=rdtype,
- default_ttl=default_ttl,
- )
- reader.read()
- return manager.rrsets
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/qtPen.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/qtPen.py
deleted file mode 100644
index eb13d03d2f611de4ce0b29ce3995f85e8f9e491a..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/qtPen.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from fontTools.pens.basePen import BasePen
-
-
-__all__ = ["QtPen"]
-
-
-class QtPen(BasePen):
- def __init__(self, glyphSet, path=None):
- BasePen.__init__(self, glyphSet)
- if path is None:
- from PyQt5.QtGui import QPainterPath
-
- path = QPainterPath()
- self.path = path
-
- def _moveTo(self, p):
- self.path.moveTo(*p)
-
- def _lineTo(self, p):
- self.path.lineTo(*p)
-
- def _curveToOne(self, p1, p2, p3):
- self.path.cubicTo(*p1, *p2, *p3)
-
- def _qCurveToOne(self, p1, p2):
- self.path.quadTo(*p1, *p2)
-
- def _closePath(self):
- self.path.closeSubpath()
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_r_e_p.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_r_e_p.py
deleted file mode 100644
index b4b92f3e924ba2f20ade9a6cca45ce78284ffe21..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_r_e_p.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from fontTools import ttLib
-
-superclass = ttLib.getTableClass("fpgm")
-
-
-class table__p_r_e_p(superclass):
- pass
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/wikipedia.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/wikipedia.py
deleted file mode 100644
index ff84e43b11be1503b0fe2e377395627773dfd1d6..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/wikipedia.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""Simple reader that reads wikipedia."""
-from typing import Any, List
-
-from gpt_index.readers.base import BaseReader
-from gpt_index.readers.schema.base import Document
-
-
-class WikipediaReader(BaseReader):
- """Wikipedia reader.
-
- Reads a page.
-
- """
-
- def __init__(self) -> None:
- """Initialize with parameters."""
- try:
- import wikipedia # noqa: F401
- except ImportError:
- raise ImportError(
- "`wikipedia` package not found, please run `pip install wikipedia`"
- )
-
- def load_data(self, pages: List[str], **load_kwargs: Any) -> List[Document]:
- """Load data from the input directory.
-
- Args:
- pages (List[str]): List of pages to read.
-
- """
- import wikipedia
-
- results = []
- for page in pages:
- page_content = wikipedia.page(page, **load_kwargs).content
- results.append(Document(page_content))
- return results
diff --git a/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/models/diffusion/__init__.py b/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/models/diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/joyson072/Stock_market_prediction/app.py b/spaces/joyson072/Stock_market_prediction/app.py
deleted file mode 100644
index 542867c20510efe329fa6718288777d5673ae8ee..0000000000000000000000000000000000000000
--- a/spaces/joyson072/Stock_market_prediction/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import yfinance as yf
-from keras.models import Sequential
-from sklearn.preprocessing import MinMaxScaler
-from keras.layers.core import Dense,Dropout,Activation
-from keras.layers.recurrent import LSTM
-from datetime import date
-import gradio as gr
-
-def create_dataset(dataset,time_step=15):
- x_ind,y_dep =[],[]
- for i in range(len(dataset)-time_step-1):
- a=dataset[i:(i+time_step),0]
- x_ind.append(a)
- y_dep.append(dataset[i+time_step,0])
- return np.array(x_ind),np.array(y_dep)
-
-def stockprice(stockname,number_of_samples):
- df_yahoo = yf.download(stockname,start='2020-09-15',end=date.today(),interval = "1h",progress=False,auto_adjust=True)
- df=df_yahoo
- df.index.rename('Date', inplace=True)
- df=df.sort_values(by=['Date'],ignore_index=True)
- min_max_scaler=MinMaxScaler(feature_range=(0,1))
- dataset=min_max_scaler.fit_transform(df['Close'].values.reshape(-1,1))
- train_size=int(len(df)*0.8)
- test_size=len(df)-train_size
- Train=dataset[0:train_size,:]
- Test=dataset[train_size:len(dataset),:]
- x_train,y_train=create_dataset(Train,time_step=15)
- x_test,y_test=create_dataset(Test,time_step=15)
- x_train=np.reshape(x_train,(x_train.shape[0],1,x_train.shape[1]))
- x_test=np.reshape(x_test,(x_test.shape[0],1,x_test.shape[1]))
- time_step=15
- model=Sequential()
- model.add(LSTM(20,input_shape=(1,time_step)))
- model.add(Dense(1))
- model.compile(loss="mean_squared_error",optimizer='adam')
- model.fit(x_train,y_train,epochs=100,verbose=0)
- y_pred=model.predict(x_test)
- y_pred_RNN=min_max_scaler.inverse_transform(y_pred)
- y_test=np.expand_dims(y_test,axis=1)
- y_test=min_max_scaler.inverse_transform(y_test)
- df1=df.drop(["Volume","Open","High","Low"],axis=1)
- a= int(number_of_samples)*15
- new_data = df1[-(a+1):-1]
- last60prices=np.array(new_data)
- last60prices=last60prices.reshape(-1, 1)
- X=min_max_scaler.transform(last60prices)
- TimeSteps=int(15)
- NumFeatures=int(1)
- number_of_samples=int(number_of_samples)
- X=X.reshape(number_of_samples, NumFeatures, TimeSteps)
- predicted_Price = model.predict(X)
- predicted_Price = min_max_scaler.inverse_transform(predicted_Price)
- pred_df=pd.DataFrame(list(map(lambda x: x[0], predicted_Price)),columns=["PREDICTIONS"])
-
- pred_df.reset_index(inplace=True)
- pred_df = pred_df.rename(columns = {'index':'HOURS'})
-
- plt.figure(figsize=(15, 6))
- range_history = len(new_data)
- range_future = list(range(range_history, range_history +len(predicted_Price)))
- plt.plot(np.arange(range_history), np.array(new_data),label='History')
- plt.plot(range_future, np.array(predicted_Price),label='Forecasted for RNN')
- plt.legend(loc='upper right')
- plt.xlabel('Time step (hour)')
- plt.ylabel('Stock Price')
-
-
- return pred_df,plt.gcf()
-
-interface = gr.Interface(fn = stockprice,
-inputs = [gr.inputs.Textbox(lines=1, placeholder="Enter STOCK-TICKER", default="FB", label="STOCKNAME"),
-gr.inputs.Slider(minimum=0, maximum=150, step=1, default=5, label="Number of Sample to Predict")],
-outputs = ["dataframe","plot"],
-description="LSTM STOCK PREDICTION")
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/examples/scalable_t5/t5_1_1/examples/__init__.py b/spaces/juancopi81/youtube-music-transcribe/t5x/examples/scalable_t5/t5_1_1/examples/__init__.py
deleted file mode 100644
index da022c16301721a096a208e8bdb2a71bb87f9788..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/examples/scalable_t5/t5_1_1/examples/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# 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.
-
-# This empty file is needed for loading the gin files in this directory.
diff --git a/spaces/justin-zk/Personalize-SAM/per_segment_anything/modeling/mask_decoder.py b/spaces/justin-zk/Personalize-SAM/per_segment_anything/modeling/mask_decoder.py
deleted file mode 100644
index f5fa22ac626eda37b73aae08444a88b58161cca7..0000000000000000000000000000000000000000
--- a/spaces/justin-zk/Personalize-SAM/per_segment_anything/modeling/mask_decoder.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from typing import List, Tuple, Type
-
-from .common import LayerNorm2d
-
-
-class MaskDecoder(nn.Module):
- def __init__(
- self,
- *,
- transformer_dim: int,
- transformer: nn.Module,
- num_multimask_outputs: int = 3,
- activation: Type[nn.Module] = nn.GELU,
- iou_head_depth: int = 3,
- iou_head_hidden_dim: int = 256,
- ) -> None:
- """
- Predicts masks given an image and prompt embeddings, using a
- transformer architecture.
-
- Arguments:
- transformer_dim (int): the channel dimension of the transformer
- transformer (nn.Module): the transformer used to predict masks
- num_multimask_outputs (int): the number of masks to predict
- when disambiguating masks
- activation (nn.Module): the type of activation to use when
- upscaling masks
- iou_head_depth (int): the depth of the MLP used to predict
- mask quality
- iou_head_hidden_dim (int): the hidden dimension of the MLP
- used to predict mask quality
- """
- super().__init__()
- self.transformer_dim = transformer_dim
- self.transformer = transformer
-
- self.num_multimask_outputs = num_multimask_outputs
-
- self.iou_token = nn.Embedding(1, transformer_dim)
- self.num_mask_tokens = num_multimask_outputs + 1
- self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
- self.output_upscaling = nn.Sequential(
- nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
- LayerNorm2d(transformer_dim // 4),
- activation(),
- nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
- activation(),
- )
- self.output_hypernetworks_mlps = nn.ModuleList(
- [
- MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
- for i in range(self.num_mask_tokens)
- ]
- )
-
- self.iou_prediction_head = MLP(
- transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
- )
-
- def forward(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- multimask_output: bool,
- attn_sim=None,
- target_embedding=None
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Predict masks given image and prompt embeddings.
-
- Arguments:
- image_embeddings (torch.Tensor): the embeddings from the image encoder
- image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
- sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
- dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
- multimask_output (bool): Whether to return multiple masks or a single
- mask.
-
- Returns:
- torch.Tensor: batched predicted masks
- torch.Tensor: batched predictions of mask quality
- """
- masks, iou_pred = self.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=image_pe,
- sparse_prompt_embeddings=sparse_prompt_embeddings,
- dense_prompt_embeddings=dense_prompt_embeddings,
- attn_sim=attn_sim,
- target_embedding=target_embedding
- )
-
- # Select the correct mask or masks for output
- if multimask_output:
- mask_slice = slice(1, None)
- else:
- mask_slice = slice(0, 1)
- masks = masks[:, mask_slice, :, :]
- iou_pred = iou_pred[:, mask_slice]
-
- # Prepare output
- return masks, iou_pred
-
- def predict_masks(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- attn_sim=None,
- target_embedding=None
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Predicts masks. See 'forward' for more details."""
- # Concatenate output tokens
- output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
- output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
- tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
-
- # Expand per-image data in batch direction to be per-mask
- src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
- src = src + dense_prompt_embeddings
- pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
- b, c, h, w = src.shape
-
- # Run the transformer
- hs, src = self.transformer(src, pos_src, tokens, attn_sim, target_embedding)
- iou_token_out = hs[:, 0, :]
- mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
-
- # Upscale mask embeddings and predict masks using the mask tokens
- src = src.transpose(1, 2).view(b, c, h, w)
- upscaled_embedding = self.output_upscaling(src)
- hyper_in_list: List[torch.Tensor] = []
- for i in range(self.num_mask_tokens):
- hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
- hyper_in = torch.stack(hyper_in_list, dim=1)
- b, c, h, w = upscaled_embedding.shape
- masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
-
- # Generate mask quality predictions
- iou_pred = self.iou_prediction_head(iou_token_out)
-
- return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
- def __init__(
- self,
- input_dim: int,
- hidden_dim: int,
- output_dim: int,
- num_layers: int,
- sigmoid_output: bool = False,
- ) -> None:
- super().__init__()
- self.num_layers = num_layers
- h = [hidden_dim] * (num_layers - 1)
- self.layers = nn.ModuleList(
- nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
- )
- self.sigmoid_output = sigmoid_output
-
- def forward(self, x):
- for i, layer in enumerate(self.layers):
- x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
- if self.sigmoid_output:
- x = F.sigmoid(x)
- return x
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/utils/__init__.py b/spaces/keithhon/Real-Time-Voice-Cloning/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/keras-dreambooth/dreambooth_diffusion_hokusai/README.md b/spaces/keras-dreambooth/dreambooth_diffusion_hokusai/README.md
deleted file mode 100644
index 68ad31b491b6ab7d11061aa005db6ee00cab6256..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/dreambooth_diffusion_hokusai/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Dreambooth Diffusion Hokusai
-emoji: 🏞
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
-tags:
- - keras-dreambooth
- - consentful
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/keras-io/Image_Classification_using_Consistency_Training/app.py b/spaces/keras-io/Image_Classification_using_Consistency_Training/app.py
deleted file mode 100644
index 31b6b0d860510e126098f376d8162a90247f806d..0000000000000000000000000000000000000000
--- a/spaces/keras-io/Image_Classification_using_Consistency_Training/app.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import numpy as np
-import tensorflow as tf
-import gradio as gr
-from huggingface_hub import from_pretrained_keras
-
-teacher_model = from_pretrained_keras("keras-io/consistency_training_with_supervision_teacher_model")
-
-student_model = from_pretrained_keras("keras-io/consistency_training_with_supervision_student_model")
-
-class_names = [
- "Airplane",
- "Automobile",
- "Bird",
- "Cat",
- "Deer",
- "Dog",
- "Frog",
- "Horse",
- "Ship",
- "Truck",
-]
-
-examples = [
- ['./aeroplane.png'],
- ['./horse.png'],
- ['./ship.png'],
- ['./truck.png']
-]
-
-IMG_SIZE = 72
-
-def teacher_model_output(image_tensor):
- predictions = teacher_model.predict(np.expand_dims((image_tensor), axis=0))
- predictions = np.squeeze(predictions)
- predictions = np.argmax(predictions)
- predicted_label = class_names[predictions.item()]
- return str(predicted_label)
-
-def student_model_output(image_tensor):
- predictions = student_model.predict(np.expand_dims((image_tensor), axis=0))
- predictions = np.squeeze(predictions)
- predictions = np.argmax(predictions)
- predicted_label = class_names[predictions.item()]
- return str(predicted_label)
-
-def infer(input_image):
- image_tensor = tf.convert_to_tensor(input_image)
- image_tensor.set_shape([None, None, 3])
- image_tensor = tf.image.resize(image_tensor, (IMG_SIZE, IMG_SIZE))
- return teacher_model_output(image_tensor), student_model_output(image_tensor)
-
-input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))
-output = [gr.outputs.Label(label = "Teacher Model Output"), gr.outputs.Label(label = "Student Model Output")]
-
-title = "Image Classification using Consistency training with supervision"
-description = "Upload an image or select from examples to classify it. The allowed classes are - Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck.
Teacher Model Repo - https://huggingface.co/keras-io/consistency_training_with_supervision_teacher_model Student Model Repo - https://huggingface.co/keras-io/consistency_training_with_supervision_student_model Keras Example - https://keras.io/examples/vision/consistency_training/
- ) : null
-}
diff --git a/spaces/lightli/bingo-newbing/src/components/chat-scroll-anchor.tsx b/spaces/lightli/bingo-newbing/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/lightli/bingo-newbing/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
- trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
- const isAtBottom = useAtBottom()
- const { ref, entry, inView } = useInView({
- trackVisibility,
- delay: 100,
- rootMargin: '0px 0px -150px 0px'
- })
-
- React.useEffect(() => {
- if (isAtBottom && trackVisibility && !inView) {
- entry?.target.scrollIntoView({
- block: 'start'
- })
- }
- }, [inView, entry, isAtBottom, trackVisibility])
-
- return
-}
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Carlevari La Argentina Geografia Humana Y Economica Pdf [REPACK] Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Carlevari La Argentina Geografia Humana Y Economica Pdf [REPACK] Download.md
deleted file mode 100644
index 4cb4ab02d68de24ab29ee8328135e88d0a538c18..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Carlevari La Argentina Geografia Humana Y Economica Pdf [REPACK] Download.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-
Carlevari La Argentina Geografia Humana y Economica PDF Download: A Comprehensive Guide to the Geography of Argentina
-
-
If you are looking for a reliable and updated source of information on the geography of Argentina, you may want to download the PDF version of Carlevari La Argentina Geografia Humana y Economica. This book, written by Isidro J.F. Carlevari and Ricardo D. Carlevari, is a classic reference for students, teachers, researchers, and anyone interested in the economic and human geography of this South American country.
-
-
In this article, we will give you an overview of what you can find in Carlevari La Argentina Geografia Humana y Economica PDF download, as well as some tips on how to get it for free or at a low cost.
-
carlevari la argentina geografia humana y economica pdf download
What is Carlevari La Argentina Geografia Humana y Economica?
-
-
Carlevari La Argentina Geografia Humana y Economica is a book that covers the main aspects of the geography of Argentina, such as its physical features, natural resources, population, urbanization, regional development, agriculture, industry, services, foreign trade, and integration.
-
-
The book was first published in 1973 and has been revised and updated several times since then. The latest edition, the 15th one, was released in 2013 as an e-book by Alfaomega Grupo Editor Argentino[^1^]. The book has 608 pages and contains maps, graphs, tables, photos, and exercises to help the reader understand and analyze the geographic reality of Argentina.
-
-
Carlevari La Argentina Geografia Humana y Economica is widely used as a textbook in schools and universities in Argentina and other countries. It is also a valuable resource for professionals and researchers who work on topics related to the geography of Argentina.
-
-
Why should you download Carlevari La Argentina Geografia Humana y Economica PDF?
-
-
There are many reasons why you may want to download Carlevari La Argentina Geografia Humana y Economica PDF instead of buying the printed version. Here are some of them:
-
-
-
You can access it anytime and anywhere on your computer, tablet, or smartphone.
-
You can save money on shipping costs and taxes.
-
You can avoid damage or loss of the physical book.
-
You can search for specific terms or topics within the PDF file.
-
You can highlight, bookmark, or annotate the PDF file according to your preferences.
-
You can print out only the pages or sections that you need.
-
-
-
How can you download Carlevari La Argentina Geografia Humana y Humana PDF for free or at a low cost?
-
-
If you are interested in downloading Carlevari La Argentina Geografia Humana y Economica PDF for free or at a low cost, you have several options. Here are some of them:
-
-
-
You can visit the official website of Alfaomega Grupo Editor Argentino[^1^] and buy the e-book for $14.99 USD. You will receive a link to download the PDF file after completing the payment.
-
You can search for online libraries or platforms that offer free or discounted access to academic books. For example, you can try Docer.com.ar[^2^], where you can find Carlevari La Argentina Geografia Humana y Economica PDF for free. However, you may need to register or create an account to access the file.
-
You can look for websites that share PDF files uploaded by users. For example, you can try Idoc.pub[^3^], where you can find Carlevari La Argentina Geografia Humana y Economica PDF uploaded by Pedro Skywalker in December 2019. However, you should be careful about the quality and legality of these files.
-
-
-
Conclusion
-
-
Carlevari La Argentina Geografia Humana y Economica PDF download is a great option for anyone who wants to learn more about the geography of Argentina. The book covers all the relevant aspects of the economic and human geography of this country in a comprehensive and updated way
- d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/ImageLine Vocodex VST V103 Torrent Free.md b/spaces/lincquiQcaudo/Top-20-Diffusion/ImageLine Vocodex VST V103 Torrent Free.md
deleted file mode 100644
index 0328c8cb7732463732f3278d64d7c8bd5bff3720..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/ImageLine Vocodex VST V103 Torrent Free.md
+++ /dev/null
@@ -1,22 +0,0 @@
-
"Likelihoods" will show more than N tokens if a top completion for one sentence is unlikely for the other sentence.')
- .parent().parent()
- .append('div.flex-row')
- .appendMany('div.button', [30, 200, 1000, 5000, 99999])
- .text(d => d > 5000 ? 'All' : d)
- .st({textAlign: 'center'})
- .on('click', d => {
- pair.count = d
- updateChart()
- })
-
- var typeSel = optionSel.append('div')
- .append('b').text('Chart Type')
- .append('info').text('ⓘ').call(addLockedTooltip)
- .datum('"Likelihoods" shows the logits from both models plotted directly with a shared linear scale.
To better contrast the outputs, "Differences" shows logitA - logitB on the y-axis and mean(logitA, logitB) on the x-axis with separate linear scales.')
- .parent().parent()
- .append('div.flex-row')
- .appendMany('div.button', ['Likelihoods', 'Differences'])
- .text(d => d)
- .st({textAlign: 'center'})
- .on('click', d => {
- pair.type = d
- updateChart()
- })
-
- var modelSel = optionSel.append('div')
- .st({display: pair.model == 'BERT' ? 'none' : ''})
- .append('b').text('Model')
- .parent()
- .append('div.flex-row')
- .appendMany('div.button', ['BERT', 'Zari'])
- .text(d => d)
- .st({textAlign: 'center'})
- .on('click', d => {
- pair.model = d
- updateChart()
- })
-
- // TODO add loading spinner
- var updateSel = optionSel
- .append('div.flex-row')
- .append('div.button.update').on('click', updateChart)
- .text('Update')
- .st({display: isMobile ? 'none' : ''})
-
- var warningSel = optionSel.append('div.warning')
- .text('⚠️Some of the text this model was trained on includes harmful stereotypes. This is a tool to uncover these associations—not an endorsement of them.')
-
- var resetSel = optionSel.append('div.reset')
- .html('↻ Reset')
- .on('click', () => {
- pair = JSON.parse(pair.pairStr)
- pair.pairStr = JSON.stringify(pair)
-
- input0Sel.node().value = pair.s0
- input1Sel.node().value = pair.s1
-
- updateChart(true)
- })
-
- if (pair.alts){
- d3.select('.' + pair.class + '-alts').html('')
- .classed('alt-block', 1).st({display: 'block'})
- .appendMany('span.p-button-link', pair.alts)
- .html(d => d.str)
- .on('click', d => {
- input0Sel.node().value = d.s0
- input1Sel.node().value = d.s1
-
- updateChart()
- })
- }
-
-
- var margin = {bottom: 50, left: 25, top: 5, right: 20}
- var graphSel = sel.append('div.graph')
- var totalWidth = graphSel.node().offsetWidth
- var width = totalWidth - margin.left - margin.right
-
- var c = d3.conventions({
- sel: graphSel.append('div').st({marginTop: isMobile ? 20 : -5}),
- width,
- height: width,
- margin,
- layers: 'sdds',
- })
-
-
- var nTicks = 4
- var tickScale = d3.scaleLinear().range([0, c.width])
- c.svg.appendMany('path.bg-tick', d3.range(nTicks + 1))
- .at({d: d => `M ${.5 + Math.round(tickScale(d/nTicks))} 0 V ${c.height}`})
- c.svg.appendMany('path.bg-tick', d3.range(nTicks + 1))
- .at({d: d => `M 0 ${.5 + Math.round(tickScale(d/nTicks))} H ${c.width}`})
-
-
- var annotationSel = c.layers[1].appendMany('div.annotations', pair.annotations)
- .translate(d => d.pos)
- .html(d => d.str)
- .st({color: d => d.color, width: 250, postion: 'absolute'})
-
- var scatter = window.initScatter(c)
-
- updateChart(true)
-
-
- async function updateChart(isFirst){
- sel.classed('changed', 0)
- warningSel.st({opacity: isFirst ? 0 : 1})
- resetSel.st({opacity: isFirst ? 0 : 1})
- annotationSel.st({opacity: isFirst ? 1 : 0})
-
- countSel.classed('active', d => d == pair.count)
- typeSel.classed('active', d => d == pair.type)
- modelSel.classed('active', d => d == pair.model)
-
- function getStr(sel){
- return sel.node().value.replace('_', '[MASK]')
- }
-
- var modelPath = pair.model == 'Zari' ? 'embed_zari_cda' : 'embed'
-
- pair.s0 = input0Sel.node().value.replace('_', '[MASK]')
- pair.s1 = input1Sel.node().value.replace('_', '[MASK]')
-
- updateSel.classed('loading', 1)
- var vals0 = await post(modelPath, {sentence: pair.s0})
- var vals1 = await post(modelPath, {sentence: pair.s1})
- updateSel.classed('loading', 0)
-
-
- var allTokens = vals0.map((v0, i) => {
- return {word: tokenizer.vocab[i], v0, i, v1: vals1[i]}
- })
- allTokens.forEach(d => {
- d.dif = d.v0 - d.v1
- d.meanV = (d.v0 + d.v1) / 2
- d.isVisible = false
- })
-
- _.sortBy(allTokens, d => -d.v1).forEach((d, i) => d.v1i = i)
- _.sortBy(allTokens, d => -d.v0).forEach((d, i) => d.v0i = i)
-
- var topTokens = allTokens.filter(d => d.v0i <= pair.count || d.v1i <= pair.count)
-
-
- var logitExtent = d3.extent(topTokens.map(d => d.v0).concat(topTokens.map(d => d.v1)))
-
- var tokens = allTokens
- .filter(d => logitExtent[0] <= d.v0 && logitExtent[0] <= d.v1)
-
- var mag = logitExtent[1] - logitExtent[0]
- logitExtent = [logitExtent[0] - mag*.002, logitExtent[1] + mag*.002]
-
- if (pair.type == 'Differences') tokens = _.sortBy(allTokens, d => -d.meanV).slice(0, pair.count)
-
- tokens.forEach(d => {
- d.isVisible = true
- })
-
- var maxDif = d3.max(d3.extent(tokens, d => d.dif).map(Math.abs))
- var color = palette(-maxDif*.8, maxDif*.8)
-
- updateSentenceLabels()
-
- if (pair.type == 'Likelihoods'){
- drawXY()
- } else{
- drawRotated()
- }
-
- sel.classed('is-xy', pair.type == 'Likelihoods')
- sel.classed('is-rotate', pair.type != 'Likelihoods')
-
-
- function drawXY(){
- c.x.domain(logitExtent)
- c.y.domain(logitExtent)
-
- d3.drawAxis(c)
-
- var s = {30: 4, 200: 3, 1000: 3}[pair.count] || 2
- var scatterData = allTokens.map(d => {
- var x = c.x(d.v0)
- var y = c.y(d.v1)
- var fill = color(d.dif)
- var dif = d.dif
- var word = d.word
- var show = ''
- var isVisible = d.isVisible
-
- return {x, y, s, dif, fill, word, show, isVisible}
- })
-
- var textCandidates = _.sortBy(scatterData.filter(d => d.isVisible), d => d.dif)
- d3.nestBy(textCandidates.slice(0, 1000), d => Math.round(d.y/10))
- .forEach(d => d[0].show = 'uf')
- d3.nestBy(textCandidates.reverse().slice(0, 1000), d => Math.round(d.y/10))
- .forEach(d => d[0].show = 'lr')
-
- logitExtent.pair = pair
- scatter.draw(c, scatterData, true)
-
- c.svg.selectAppend('text.x-axis-label.xy-only')
- .translate([c.width/2, c.height + 24])
- .text(pair.label0 ? ' __ likelihood, ' + pair.label0 + ' sentence →' : '__ likelihood, sentence two →')
- .st({fill: util.colors[0]})
- .at({textAnchor: 'middle'})
-
-
- c.svg.selectAppend('g.y-axis-label.xy-only')
- .translate([c.width + 20, c.height/2])
- .selectAppend('text')
- .text(pair.label1 ? ' __ likelihood, ' + pair.label1 + ' sentence →' : '__ likelihood, sentence one →')
- .st({fill: util.colors[1]})
- .at({textAnchor: 'middle', transform: 'rotate(-90)'})
- }
-
- function drawRotated(){
- c.x.domain(d3.extent(tokens, d => d.meanV))
- c.y.domain([maxDif, -maxDif])
-
- d3.drawAxis(c)
-
- var scatterData = allTokens.map(d => {
- var x = c.x(d.meanV)
- var y = c.y(d.dif)
- var fill = color(d.dif)
- var word = d.word
- var show = ''
- var isVisible = d.isVisible
-
- return {x, y, s: 2, fill, word, show, isVisible}
- })
-
- scatterData.forEach(d => {
- d.dx = d.x - c.width/2
- d.dy = d.y - c.height/2
- })
-
- var textCandidates = _.sortBy(scatterData, d => -d.dx*d.dx - d.dy*d.dy)
- .filter(d => d.isVisible)
- .slice(0, 5000)
- d3.nestBy(textCandidates, d => Math.round(12*Math.atan2(d.dx, d.dy)))
- .map(d => d[0])
- .forEach(d => d.show = (d.dy < 0 ? 'u' : 'l') + (d.dx < 0 ? 'l' : 'r'))
-
- scatter.draw(c, scatterData, false)
-
- c.svg.selectAppend('text.rotate-only.x-axis-label')
- .translate([c.width/2, c.height + 24])
- .text('__ likelihood, both sentences →')
- .at({textAnchor: 'middle'})
- .st({fill: '#000'})
-
- c.svg.selectAll('g.rotate-only.sent-1,g.rotate-only.sent-1').remove()
- c.svg.selectAppend('g.rotate-only.sent-1')
- .translate([c.width + 20, c.height/2])
- .append('text')
- .text(`Higher likelihood, ${pair.label1 ? pair.label1 + ' sentence ' : 'sentence one'} →`)
- .at({textAnchor: 'start', transform: 'rotate(-90)', x: 20})
- .st({fill: util.colors[1]})
-
- c.svg.selectAppend('g.rotate-only.sent-1')
- .translate([c.width + 20, c.height/2 + 0])
- .append('text')
- .text(`← Higher likelihood, ${pair.label0 ? pair.label0 + ' sentence ' : 'sentence two'}`)
- .at({textAnchor: 'end', transform: 'rotate(-90)', x: -20})
- .st({fill: util.colors[0]})
- }
- }
-
- function updateSentenceLabels(){
- var t0 = tokenizer.tokenize(pair.s0)
- var t1 = tokenizer.tokenize(pair.s1)
-
- var i = 0
- while (t0[i] == t1[i] && i < t0.length) i++
-
- var j = 1
- while (t0[t0.length - j] == t1[t1.length - j] && j < t0.length) j++
-
- pair.label0 = tokens2origStr(t0, pair.s0)
- pair.label1 = tokens2origStr(t1, pair.s1)
-
- function tokens2origStr(t, s){
- var tokenStr = tokenizer.decode(t.slice(i, -j + 1)).trim()
- var lowerStr = s.toLowerCase()
-
- var startI = lowerStr.indexOf(tokenStr)
- return s.slice(startI, startI + tokenStr.length)
- }
-
- if (
- !pair.label0.length ||
- !pair.label1.length ||
- pair.label0.length > 15 ||
- pair.label1.length > 15){
- pair.label0 = ''
- pair.label1 = ''
- }
-
- // console.log(i, j, pair.label0, pair.label1)
- }
-}
-
-if (window.init) init()
diff --git a/spaces/merve/data-leak/source/hidden-bias/annotations.js b/spaces/merve/data-leak/source/hidden-bias/annotations.js
deleted file mode 100644
index b0fd377b443ee9bd31e7bd1d9dbacafc4e5282e3..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/hidden-bias/annotations.js
+++ /dev/null
@@ -1,86 +0,0 @@
-window.annotations = [
- {
- "slide": 0,
- "x": 1.77,
- "y": 3.17,
- "path": "M -3,-59 A 31.215 31.215 0 1 0 -10,2",
- "text": "Joshua had a high school GPA of 3.2 and 1.8 in college",
- "textOffset": [
- -1,
- -48
- ]
- },
- {
- "slide": 0,
- "x": 2.93,
- "y": 2.08,
- "path": "M 56,61 A 45.102 45.102 0 0 0 19.000001907348633,1.0000003576278687",
- "text": "Abigail has a 2.1 in high school and 2.9 in college",
- "textOffset": [
- -5,
- 85
- ],
- "width": 18
- },
- {
- "slide": 1,
- "x": 3.7,
- "y": 2,
- "path": "M 1,41 A 209.709 209.709 0 0 1 -310,76",
- "text": "Most students have a higher GPA in high school",
- "textOffset": [
- -69,
- 11
- ],
- "width": 18
- },
- {
- "slide": 2,
- "x": 1,
- "y": 4,
- "path": "M 0 0",
- "text": "A well adjusted model will usually over predict about half the students' grades...",
- "textOffset": [
- 25,
- 50
- ],
- "width": 25
- },
- {
- "slide": 2,
- "x": 4,
- "y": 1,
- "path": "M 0 0",
- "text": "...and under predict the other half",
- "textOffset": [
- -109,
- -51
- ],
- "width": 18
- },
- {
- "slide": 5,
- "x": 2.58,
- "y": 2,
- "path": "M 54,34 A 29.707 29.707 0 0 0 11,-6",
- "text": "The model predicted both Lucas and Mia would get a 2.0, but she ended up with a higher GPA",
- "html": "The model predicted both Lucas and Mia would get a 2.0, but she ended up with a higher GPA",
- "textOffset": [
- -22,
- 44
- ],
- "width": 23
- },
- {
- "slide": 5,
- "x": 2.14,
- "y": 2,
- "path": "M 40,61 A 35.025 35.025 0 0 1 -4,7",
- "text": "",
- "textOffset": [
- -100,
- 179
- ],
- "width": 14
- }
-]
\ No newline at end of file
diff --git a/spaces/merve/hidden-bias/public/base-rate/script.js b/spaces/merve/hidden-bias/public/base-rate/script.js
deleted file mode 100644
index efc40861466afc2bb19cee8d3ef6cd5a98d80ddc..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/public/base-rate/script.js
+++ /dev/null
@@ -1,317 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-
-
-console.clear()
-var ttSel = d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
-
-window.renderFns = []
-
-window.m = (function(){
- var rv = {b: .7, tpr: .8, fnr: .5, update, str: 'kids', titleStr: 'Children',}
-
- function update(obj={}){
- Object.assign(rv, obj)
- window.renderFns.forEach(d => d())
- }
-
- return rv
-})()
-
-window.f = (function(){
- var rv = {b: .3, tpr: .8, fnr: .5, update, str: 'adults', titleStr: 'Adults'}
-
- function update(obj={}){
- window.renderFns.forEach(d => d())
- }
-
- return rv
-})()
-
-
-var wLarge = d3.clamp(0, innerWidth/2 - 30, 300)
-
-d3.select('#big-matrix').html('')
- .appendMany('div.big-container', [{w: wLarge, s: f, isText: 1}, {w: wLarge, s: m, isText: 1}])
- .each(drawMatrix)
-
-
-addPattern(10, `pattern-${wLarge}-`)
-addPattern(5, 'pattern-50-')
-
-function addPattern(s, str){
- var cColors = [colors.sick, colors.sick, colors.well, colors.well, lcolors.sick, lcolors.sick, lcolors.well, lcolors.well]
- var rColors = [lcolors.sick, lcolors.well, lcolors.sick, lcolors.well, llcolors.sick, llcolors.well, llcolors.sick, llcolors.well]
-
- d3.select('#big-matrix')
- .append('svg')
- .st({height: 0, position: 'absolute'})
- .append('defs').appendMany('pattern', d3.range(8))
- .at({ id: i => str + i, width: s, height: s})
- .attr('patternUnits', 'userSpaceOnUse')
- .append('rect')
- .at({width: s, height: s, fill: i => rColors[i]})
- .parent().append('circle')
- .at({r: s == 10 ? 2.5 : 1.5, cx: s/2, cy: s/2, fill: i => cColors[i]})
-}
-
-
-var scale = d3.clamp(0, ((innerWidth - 50) / 3)/280, 1)
-var isScaled = scale != 1
-
-d3.select('#metrics').html('').st({height: 350*scale + 30})
- .appendMany('div', [0, 1, 2])
- .st({width: 280*scale, display: 'inline-block'})
- .append('div')
- .st({transform: `scale(${scale})`, transformOrigin: '0% 0%'})
- .append('div.metrics-container').st({width: 280})
- .each(drawMetric)
-
-d3.selectAll('rect.drag')
- .on('mouseover.style', d => d3.selectAll('rect.' + d).st({strokeWidth: 3, stroke: '#000'}))
- .on('mouseout.style', d => d3.selectAll('rect.' + d).st({strokeWidth: 0}))
-
-function drawMetric(i){
- var sel = d3.select(this)
-
- var text = [
- // 'Percentage of sick people who test positive',
- 'Percentage of sick people who test positive',
- 'Percentage of positive tests who are actually sick',
- 'Percentage of well people who test negative',
- ][i]
-
- var percentFn = [
- s => s.tpr,
- s => s.b*s.tpr/(s.b*s.tpr + (1 - s.b)*(s.fnr)),
- s => 1 - s.fnr,
- ][i]
-
- var colors = [
- ['#f0f', '#fcf', '#fff', '#fff'],
- ['#f0f', '#fff', '#fcf', '#fff'],
- ['#fff', '#fff', '#fcf', '#f0f'],
- ][i]
-
- sel.append('h3').st({marginBottom: 20, fontSize: isScaled ? 30 : 20}).html(isScaled ? text.replace(' ', '') : text)
-
- var h = 200
- var width = 100
-
- var fDiv = sel.append('div').st({position: 'relative', top: -h + 7})
- .datum({w: 50, s: f, isText: 0, colors}).each(drawMatrix)
-
- var svg = sel.append('svg')
- .at({width, height: h})
- .st({fontSize: 14, fontFamily: 'monospace'})
-
- svg.append('path').at({stroke: '#ccc', d: `M ${width/2 + .5} 0 V ${h}`})
-
- var errorSel = svg.append('path')
- .translate(width/2 + .5, 0)
- .at({stroke: 'orange', strokeWidth: 3})
-
- var fSel = svg.append('g')
- var mSel = svg.append('g')
-
- mSel.append('circle').at({r: 4, cx: width/2 + .5, fill: 'none', stroke: '#000'})
- fSel.append('circle').at({r: 4, cx: width/2 + .5, fill: 'none', stroke: '#000'})
-
- var fTextSel = fSel.append('text').text('23%')
- .at({dy: '.33em', textAnchor: 'middle', x: width/4 - 3, fontSize: isScaled ? 20 : 16})
- var mTextSel = mSel.append('text').text('23%')
- .at({dy: '.33em', textAnchor: 'middle', x: width/4*3 + 5, fontSize: isScaled ? 20 : 16})
-
- fSel.append('text').text('Adults').st({fontSize: isScaled ? 18 : 12})
- .at({textAnchor: 'middle', x: -23, y: -30})
- mSel.append('text').text('Children').st({fontSize: isScaled ? 18 : 12})
- .at({textAnchor: 'middle', x: 124, y: -30})
-
- var mDiv = sel.append('div').st({position: 'relative', top: -h + 7})
- .datum({w: 50, s: m, isText: 0, colors}).each(drawMatrix)
-
-
- renderFns.push(() => {
- var fPercent = percentFn(f)
- fSel.translate(h - h*fPercent, 1)
- fTextSel.text(d3.format('.0%')(fPercent))
-
- var mPercent = percentFn(m)
- mSel.translate(h - h*mPercent, 1)
- mTextSel.text(d3.format('.0%')(mPercent))
-
- fDiv.translate(h - h*fPercent, 1)
- mDiv.translate(h - h*mPercent, 1)
-
- errorSel.at({d: 'M 0 ' + (h - h*fPercent) + ' V ' + (h - h*mPercent) })
- })
-}
-
-function drawMatrix({s, w, isText, colors}){
- var svg = d3.select(this).append('svg')
- .at({width: w, height: w})
-
-
- svg.append('rect').at({width: w + 1, height: w + 1})
-
- if (!colors) colors = ['#000', '#000', '#000', '#000']
-
- var rects = [
- {n: 'tp', x: 0, y: 0, width: _ => s.b*w, height: _ => s.tpr*w},
- {n: 'fn', x: 0, y: _ => 1 + s.tpr*w, width: _ => s.b*w, height: _ => w - s.tpr*w},
- {n: 'fp', x: _ => 1 + s.b*w, y: 0, width: _ => w - s.b*w, height: _ => s.fnr*w},
- {n: 'tn', x: _ => 1 + s.b*w, y: _ => 1 + s.fnr*w, width: _ => w - s.b*w, height: _ => w - s.fnr*w},
- ]
- rects.forEach((d, i) => d.i = i)
-
- var rectSel = svg.appendMany('rect', rects)
- .at({fill: d => `url(#pattern-${w}-${d.i}`})
- // .at({opacity: d => colors[d.i] == '#fff' ? .5 : 1})
- // .at({fill: d => `url(#pattern-${w}-${d.i + (colors[d.i] == '#ccc' ? 4 : 0)})`})
- // .at({fill: d => colors[d.i] == '#ccc' ? '#000' : `url(#pattern-${w}-${d.i + (colors[d.i] == '#ccc' ? 4 : 0)})`})
- .each(function(d){ d.sel = d3.select(this) })
- rectSel.filter(d => colors[d.i] == '#fff').at({fill: '#eee'})
-
- var bh = .5
- svg.append('rect.tpr').at({height: bh}).translate(-bh/2, 1)
- .datum('tpr')
-
- svg.append('rect.fnr').at({height: bh}).translate(-bh/2, 1)
- .datum('fnr')
-
- svg.append('rect.b').at({width: bh, height: w}).translate(-bh/2, 0)
- .datum('b')
-
- var bh = 20
- svg.append('rect.drag.tpr').at({height: bh}).translate(-bh/2, 1)
- .call(makeDrag('tpr', 1)).datum('tpr').call(d3.attachTooltip).on('mouseover', ttFormat)
-
- svg.append('rect.drag.fnr').at({height: bh}).translate(-bh/2, 1)
- .call(makeDrag('fnr', 1)).datum('fnr').call(d3.attachTooltip).on('mouseover', ttFormat)
-
- svg.append('rect.drag.b').at({width: bh, height: w}).translate(-bh/2, 0)
- .call(makeDrag('b', 0)).datum('b').call(d3.attachTooltip).on('mouseover', ttFormat)
-
-
- var tprRect = svg.selectAll('rect.tpr')
- var fnrRect = svg.selectAll('rect.fnr')
- var bRect = svg.selectAll('rect.b')
-
- function ttFormat(str){
- var html = ''
- if (str == 'tpr') html = `${d3.format('.0%')(s.tpr)} of sick ${s.titleStr.toLowerCase()} test positive`
- if (str == 'fnr') html = `${d3.format('.0%')(s.fnr)} of well ${s.titleStr.toLowerCase()} test negative`
- if (str == 'b') html = `${d3.format('.0%')(s.b)} of ${s.titleStr.toLowerCase()} are sick`
- ttSel.html(html)
- }
-
- function makeDrag(str, index){
-
- return d3.drag()
- .on('drag', function(){
- var percent = d3.mouse(this)[index]/w
- s[str] = d3.clamp(.15, percent, .85)
-
- window.basetimer.stop()
- s.update()
-
- ttMove()
- ttFormat(str)
- })
- .on('start', _ => svg.classed('dragging', 1))
- .on('end', _ => svg.classed('dragging', 0))
- }
-
- renderFns.push(() => {
- rectSel.each(d => d.sel.at(d))
-
- tprRect.at({width: w*s.b, y: w*s.tpr})
- fnrRect.at({x: w*s.b, width: w - w*s.b, y: w*s.fnr})
- bRect.at({x: w*s.b})
-
- // s => s.tpr,
- // s => s.b*s.tpr/(s.b*s.tpr + (1 - s.b)*(s.fnr)),
- // s => 1 - s.fnr,
- if (!isText) return
- })
-
-
- if (!isText) return
-
- svg.append('text').text(s.titleStr).at({textAnchor: 'middle', x: w/2, y: -8, fontSize: 20})
-
- if (innerWidth < 800) return
- // if (true)
-
- svg.appendMany('text', d3.range(4)).each(function(i){
- var isSick = i < 2
- var isPos = i % 2
-
- var pad = 5
- d3.select(this)
- .translate([isSick ? pad : w - pad, isPos ? 13 : w - 23])
- .at({
- textAnchor: isSick ? 'start' : 'end',
- fill: '#000',
- fontSize: 12,
- fontFamily: 'monospace',
- pointerEvents: 'none',
- })
- .tspans([
- ' test : ' + (isPos ? 'sick' : 'well'),
- 'truth: ' + (isSick ? 'sick' : 'well')])
- })
-}
-
-
-if (window.basetimer) window.basetimer.stop()
-window.basetimer = d3.timer(t => {
-
- var val = t/1000 % (Math.PI*4)
-
- if (val < Math.PI*2){
- m.b = (Math.sin(val + Math.PI/2))/4 + .4
- } else if (Math.PI*3 < val && val < Math.PI*5 || true){
- f.tpr = (Math.sin(val + Math.PI/2))/4 + .4
- }
- m.update()
-})
-
-
-
-
-
-m.update()
-
-
-
-function ttMove(d){
- if (!ttSel.size()) return;
-
- var e = d3.event.sourceEvent,
- x = e.clientX,
- y = e.clientY,
- bb = ttSel.node().getBoundingClientRect(),
- left = d3.clamp(20, (x-bb.width/2), window.innerWidth - bb.width - 20),
- top = innerHeight > y + 20 + bb.height ? y + 20 : y - bb.height - 20;
-
- ttSel
- .style('left', left +'px')
- .style('top', top + 'px');
-}
-
diff --git a/spaces/merve/hidden-bias/source/measuring-fairness/style.css b/spaces/merve/hidden-bias/source/measuring-fairness/style.css
deleted file mode 100644
index 27a4ab72371dd17fe64ae938268ef37f7fb16247..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/measuring-fairness/style.css
+++ /dev/null
@@ -1,274 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-@media (max-width: 925px) {
- #graph > div{
- position: relative;
- top: 25px;
- }
-}
-
-
-
-body{
- --colors-well: rgb(179, 201, 204);
- --colors-sick: rgb(241, 85, 85);
- --lcolors-well: rgb(217, 228, 230);
- --lcolors-sick: rgb(246, 145, 145);
- --dcolors-well: rgb(63, 70, 71);
- --dcolors-sick: rgb(84, 30, 30);
-}
-
-
-.tooltip {
- top: -1000px;
- position: fixed;
- padding: 10px;
- background: rgba(255, 255, 255, .90);
- border: 1px solid lightgray;
- pointer-events: none;
-}
-.tooltip-hidden{
- opacity: 0;
- transition: all .3s;
- transition-delay: .1s;
-}
-
-@media (max-width: 590px){
- div.tooltip{
- bottom: -1px;
- width: calc(100%);
- left: -1px !important;
- right: -1px !important;
- top: auto !important;
- width: auto !important;
- }
-}
-
-svg{
- overflow: visible;
-}
-
-.domain{
- display: none;
-}
-
-text{
- /*pointer-events: none;*/
- /*text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;*/
-}
-
-
-
-#graph > div{
- margin-top: 20px;
-}
-
-
-#end{
- height: 600px;
-}
-
-
-.mono{
- font-family: monospace;
-}
-
-
-
-
-.mini .axis{
- font-size: 10px;
- line-height: 12px !important;
- position: relative;
- top: 40px;
-}
-
-.axis{
- font-size: 12px;
-}
-.axis{
- color: #999;
-}
-.axis text{
- fill: #999;
-}
-.axis line{
- stroke: #ccc;
-}
-
-div.axis b{
- margin-bottom: -10px;
- display: block;
-}
-
-.init-hidden{
- opacity: 0;
-}
-
-
-.highlight{
- color: #fff;
- padding-left: 3px;
- padding-right: 3px;
- padding-top: 1px;
- padding-bottom: 1px;
- border-radius: 3px;
-}
-
-.highlight.grey{ background: var(--colors-well); }
-.highlight.box{
- border: 1px solid #000;
- border-radius: 0px;
- color: #000;
- padding-bottom: 2px;
-}
-
-.weepeople {
- font-family: "WeePeople";
-}
-
-
-wee{
- font-family: "WeePeople";
- font-size: 30px;
- height: 22px;
- display: inline;
- position: relative;
- top: 5px;
- color: var(--colors-well);
- padding: 1px;
- margin: -1px;
- line-height: 3px;
-}
-wee.sick{
- color: var(--colors-sick);
-}
-
-wee.bg-sick{
- background: var(--lcolors-sick);
-}
-wee.bg-well{
- background: var(--lcolors-well);
-}
-
-bg{
- background: var(--lcolors-well);
- padding-left: 2px;
- padding-right: 2px;
-}
-
-bg.sick{
- background: var(--lcolors-sick);
-}
-
-wee.sick.bg-well{
- -webkit-text-stroke: .6px var(--dcolors-sick);
-}
-wee.well.bg-sick{
- -webkit-text-stroke: .6px var(--dcolors-well);
-}
-
-
-
-.equation{
- margin: 7px;
- position: relative;
-}
-
-
-.gated #hidden{
- visibility: hidden;
-}
-
-.gated.opened #hidden{
- visibility: unset;
-}
-.gated.opened #default{
- display: none;
-}
-
-.gated #default{
- height: 0px;
-}
-
-
-
-
-
-
-
-text.weepeople{
- stroke: #000;
- stroke-width: 0;
- /*stroke-width: .2;*/
-}
-
-
-
-
-.post-summary, .headline{
- display: none;
-}
-
-
-i{
- pointer-events: none;
-}
-
-.slider{
- position: relative;
- z-index: 100;
-}
-
-
-
-
-
-.cursor{
- animation-duration: 1s;
- animation-name: bgblink;
- display: inline-block;
- animation-iteration-count: infinite;
- animation-direction: alternate;
- cursor: pointer;
- transition: opacity .5s;
- stroke: #000;
-}
-
-@keyframes bgblink {
- from {
- /*fill: black;*/
- stroke-width: 0px;
- }
-
- to {
- /*fill: green;*/
- stroke-width: 16px;
- }
-}
-
-.no-blink .cursor{
- /*background: rgba(255,255,0,0) !important;*/
- animation: 0;
-}
-
-
-
-#adjust-text{
- padding-top: 15px;
- display: block;
-}
diff --git a/spaces/merve/hidden-bias/source/uncertainty-calibration/init.js b/spaces/merve/hidden-bias/source/uncertainty-calibration/init.js
deleted file mode 100644
index d23a4fecea1bfa4fae6557043d8053dc3acc29ce..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/uncertainty-calibration/init.js
+++ /dev/null
@@ -1,36 +0,0 @@
-window.thresholds = [0, 0.2, 0.4, 0.6, 0.8, 1];
-window.emojis = ['☀️','🌧️'];
-window.constant_score = 0.5;
-
-window.ttSel = d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
-
-
-window.init = function(){
-
- var graphSel = d3.select('#graph')
- var width = height = graphSel.node().offsetWidth
- if (innerWidth <= 925){
- width = innerWidth
- height = innerHeight*.65
- window.isMobile = true
- }
- fig_height = height/2
- fig_width = width
-
-
- window.util = window.initUtil()
- window.weatherGraph = window.drawWeatherGraph(graphSel, fig_height, fig_width);
- window.calibrationCurve = window.drawCalibrationCurve(graphSel, fig_height, fig_width);
- // window.calibrationSlider = window.drawCalibrationSlider(weatherGraph, calibrationCurve, fig_width/2)
- // window.modelRemapper = window.drawModelRemapping(fig_width/2);
-
-
- window.slides = window.drawSlides()
- weatherGraph.renderThresholds()
-
-}
-
-window.init()
-
-
-
diff --git a/spaces/micole66/weird_normal/app.py b/spaces/micole66/weird_normal/app.py
deleted file mode 100644
index 6f29437c2e675f54febb303ebdeecef880cd880e..0000000000000000000000000000000000000000
--- a/spaces/micole66/weird_normal/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load(("huggingface/micole66/autotrain-strano-o-normale-1798362191"), allow_flagging=True, enable_queue=True, api_key="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").launch()
\ No newline at end of file
diff --git a/spaces/mikeee/multilingual-dokugpt/ggml-try.py b/spaces/mikeee/multilingual-dokugpt/ggml-try.py
deleted file mode 100644
index b9a5740f49e8d3cae841c239aa244024cc125860..0000000000000000000000000000000000000000
--- a/spaces/mikeee/multilingual-dokugpt/ggml-try.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""Adopted from https://github.com/imartinez/privateGPT/blob/main/privateGPT.py
-
-https://raw.githubusercontent.com/imartinez/privateGPT/main/requirements.txt
-
-from pathlib import Path
-Path("models").mkdir(exit_ok=True)
-!time wget -c https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin -O models/ggml-gpt4all-j-v1.3-groovy.bin"""
-from dotenv import load_dotenv, dotenv_values
-from langchain.chains import RetrievalQA
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.vectorstores import Chroma
-from langchain.llms import GPT4All, LlamaCpp
-import os
-import argparse
-import time
-
-from types import SimpleNamespace
-from chromadb.config import Settings
-
-# embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
-# persist_directory = os.environ.get('PERSIST_DIRECTORY')
-
-# load_dotenv()
-# model_type = os.environ.get('MODEL_TYPE')
-# model_path = os.environ.get('MODEL_PATH')
-# model_n_ctx = os.environ.get('MODEL_N_CTX')
-# model_n_batch = int(os.environ.get('MODEL_N_BATCH',8))
-# target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
-
-settings = dict([('PERSIST_DIRECTORY', 'db1'),
- ('MODEL_TYPE', 'GPT4All'),
- ('MODEL_PATH', 'models/ggml-gpt4all-j-v1.3-groovy.bin'),
- ('EMBEDDINGS_MODEL_NAME', 'all-MiniLM-L6-v2'),
- ('MODEL_N_CTX', '1000'),
- ('MODEL_N_BATCH', '8'),
- ('TARGET_SOURCE_CHUNKS', '4')])
-
-# models/ggml-gpt4all-j-v1.3-groovy.bin ~5G
-
-persist_directory = settings.get('PERSIST_DIRECTORY')
-
-model_type = settings.get('MODEL_TYPE')
-model_path = settings.get('MODEL_PATH')
-embeddings_model_name = settings.get("EMBEDDINGS_MODEL_NAME")
-# embeddings_model_name = 'all-MiniLM-L6-v2'
-# embeddings_model_name = 'paraphrase-multilingual-mpnet-base-v2'
-
-model_n_ctx = settings.get('MODEL_N_CTX')
-model_n_batch = int(settings.get('MODEL_N_BATCH',8))
-target_source_chunks = int(settings.get('TARGET_SOURCE_CHUNKS',4))
-
-# Define the Chroma settings
-CHROMA_SETTINGS = Settings(
- chroma_db_impl='duckdb+parquet',
- persist_directory=persist_directory,
- anonymized_telemetry=False
-)
-
-args = SimpleNamespace(hide_source=False, mute_stream=False)
-
-# load chroma database from db1
-embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
-
-retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
-# activate/deactivate the streaming StdOut callback for LLMs
-callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
-# Prepare the LLM
-match model_type:
- case "LlamaCpp":
- llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks, verbose=False)
- case "GPT4All":
- llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=False)
- case _default:
- # raise exception if model_type is not supported
- raise Exception(f"Model type {model_type} is not supported. Please choose one of the following: LlamaCpp, GPT4All")
-
-# need about 5G RAM
-
-qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
-
-# Get the answer from the chain
-
-query = "共产党是什么"
-
-start = time.time()
-res = qa(query)
-answer, docs = res['result'], [] if args.hide_source else res['source_documents']
-end = time.time()
-
-# Print the result
-print("\n\n> Question:")
-print(query)
-print(f"\n> Answer (took {round(end - start, 2)} s.):")
-print(answer)
diff --git a/spaces/mikeee/radiobee-aligner/radiobee/__init__.py b/spaces/mikeee/radiobee-aligner/radiobee/__init__.py
deleted file mode 100644
index 152660db86a388d4abcc19e07ae2fb743ddc3099..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-aligner/radiobee/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Init."""
-__version__ = "0.1.0"
\ No newline at end of file
diff --git a/spaces/mikkoar/marco/src/components/external-link.tsx b/spaces/mikkoar/marco/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/mikkoar/marco/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
- href,
- children
-}: {
- href: string
- children: React.ReactNode
-}) {
- return (
-
- {children}
-
-
- )
-}
diff --git a/spaces/miyaaa666/bingo/next.config.js b/spaces/miyaaa666/bingo/next.config.js
deleted file mode 100644
index 0e6ccd7fbc91d0459eaaff3e968ce0556789c605..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/next.config.js
+++ /dev/null
@@ -1,38 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
- // output: 'export',
- // assetPrefix: '.',
- webpack: (config, { isServer }) => {
- if (!isServer) {
- config.resolve = {
- ...config.resolve,
- fallback: {
- 'bufferutil': false,
- 'utf-8-validate': false,
- http: false,
- https: false,
- stream: false,
- // fixes proxy-agent dependencies
- net: false,
- dns: false,
- tls: false,
- assert: false,
- // fixes next-i18next dependencies
- path: false,
- fs: false,
- // fixes mapbox dependencies
- events: false,
- // fixes sentry dependencies
- process: false
- }
- };
- }
- config.module.exprContextCritical = false;
-
- return config;
- },
-}
-
-module.exports = (...args) => {
- return nextConfig
-}
diff --git a/spaces/mlpc-lab/BLIVA/bliva/processors/__init__.py b/spaces/mlpc-lab/BLIVA/bliva/processors/__init__.py
deleted file mode 100644
index df56c10edba0fa271e15bc23cb83c4a53671adec..0000000000000000000000000000000000000000
--- a/spaces/mlpc-lab/BLIVA/bliva/processors/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from bliva.processors.base_processor import BaseProcessor
-
-from bliva.processors.blip_processors import (
- BlipImageTrainProcessor,
- Blip2ImageTrainProcessor,
- BlipImageEvalProcessor,
- BlipCaptionProcessor,
-)
-from bliva.processors.clip_processors import ClipImageTrainProcessor
-
-from bliva.common.registry import registry
-
-__all__ = [
- "BaseProcessor",
- "BlipImageTrainProcessor",
- "Blip2ImageTrainProcessor",
- "BlipImageEvalProcessor",
- "BlipCaptionProcessor",
- "ClipImageTrainProcessor",
-]
-
-
-def load_processor(name, cfg=None):
- """
- Example
-
- >>> processor = load_processor("alpro_video_train", cfg=None)
- """
- processor = registry.get_processor_class(name).from_config(cfg)
-
- return processor
diff --git a/spaces/mmlab-ntu/Segment-Any-RGBD/tools/convert-pretrained-clip-model-to-d2.py b/spaces/mmlab-ntu/Segment-Any-RGBD/tools/convert-pretrained-clip-model-to-d2.py
deleted file mode 100644
index 8e0fc1a37805727d625ba40cbbf07e9426e87ad7..0000000000000000000000000000000000000000
--- a/spaces/mmlab-ntu/Segment-Any-RGBD/tools/convert-pretrained-clip-model-to-d2.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-import pickle as pkl
-import sys
-
-import torch
-
-"""
-Usage:
- # download pretrained swin model:
- wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
- # run the conversion
- ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl
- # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config:
-MODEL:
- WEIGHTS: "/path/to/swin_tiny_patch4_window7_224.pkl"
-INPUT:
- FORMAT: "RGB"
-"""
-
-
-def transform(path):
- model = torch.load(path, map_location="cpu")
- print(f"loading {path}......")
- state_dict = model["model"]
- state_dict = {
- k.replace("visual_model.", ""): v
- for k, v in state_dict.items()
- if k.startswith("visual_model")
- }
- source_keys = [k for k in state_dict.keys() if "relative_coords" in k]
- for k in source_keys:
- state_dict[
- k.replace("relative_coords", "relative_position_index")
- ] = state_dict[k]
- del state_dict[k]
-
- source_keys = [k for k in state_dict.keys() if "atten_mask_matrix" in k]
- for k in source_keys:
- state_dict[k.replace("atten_mask_matrix", "attn_mask")] = state_dict[k]
- del state_dict[k]
-
- source_keys = [k for k in state_dict.keys() if "rel_pos_embed_table" in k]
- for k in source_keys:
- state_dict[
- k.replace("rel_pos_embed_table", "relative_position_bias_table")
- ] = state_dict[k]
- del state_dict[k]
-
- source_keys = [k for k in state_dict.keys() if "channel_reduction" in k]
- for k in source_keys:
- state_dict[k.replace("channel_reduction", "reduction")] = state_dict[k]
- del state_dict[k]
- return {
- k if k.startswith("backbone.") else "backbone." + k: v
- for k, v in state_dict.items()
- }
-
-
-if __name__ == "__main__":
- input = sys.argv[1]
- res = {
- "model": transform(input),
- "__author__": "third_party",
- "matching_heuristics": True,
- }
- with open(sys.argv[2], "wb") as f:
- pkl.dump(res, f)
diff --git a/spaces/ms180/espnet_onnx_demo/start.py b/spaces/ms180/espnet_onnx_demo/start.py
deleted file mode 100644
index f5b9217e0ef966bb596f57a8ec32839f7cd3eafe..0000000000000000000000000000000000000000
--- a/spaces/ms180/espnet_onnx_demo/start.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import subprocess
-
-subprocess.run("uvicorn modules.app:app --host 0.0.0.0 --port 7860", shell=True)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/text_to_speech/tacotron2.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/text_to_speech/tacotron2.py
deleted file mode 100644
index bb327e81e74900349e1357261bf2f14bc037ccd6..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/text_to_speech/tacotron2.py
+++ /dev/null
@@ -1,350 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
- FairseqIncrementalDecoder, register_model,
- register_model_architecture)
-from fairseq.modules import LSTMCellWithZoneOut, LocationAttention
-
-
-logger = logging.getLogger(__name__)
-
-
-def encoder_init(m):
- if isinstance(m, nn.Conv1d):
- nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu"))
-
-
-class Tacotron2Encoder(FairseqEncoder):
- def __init__(self, args, src_dict, embed_speaker):
- super().__init__(src_dict)
- self.padding_idx = src_dict.pad()
- self.embed_speaker = embed_speaker
- self.spk_emb_proj = None
- if embed_speaker is not None:
- self.spk_emb_proj = nn.Linear(
- args.encoder_embed_dim + args.speaker_embed_dim,
- args.encoder_embed_dim
- )
-
- self.embed_tokens = nn.Embedding(len(src_dict), args.encoder_embed_dim,
- padding_idx=self.padding_idx)
-
- assert(args.encoder_conv_kernel_size % 2 == 1)
- self.convolutions = nn.ModuleList(
- nn.Sequential(
- nn.Conv1d(args.encoder_embed_dim, args.encoder_embed_dim,
- kernel_size=args.encoder_conv_kernel_size,
- padding=((args.encoder_conv_kernel_size - 1) // 2)),
- nn.BatchNorm1d(args.encoder_embed_dim),
- nn.ReLU(),
- nn.Dropout(args.encoder_dropout)
- )
- for _ in range(args.encoder_conv_layers)
- )
-
- self.lstm = nn.LSTM(args.encoder_embed_dim, args.encoder_embed_dim // 2,
- num_layers=args.encoder_lstm_layers,
- batch_first=True, bidirectional=True)
-
- self.apply(encoder_init)
-
- def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs):
- x = self.embed_tokens(src_tokens)
- x = x.transpose(1, 2).contiguous() # B x T x C -> B x C x T
- for conv in self.convolutions:
- x = conv(x)
- x = x.transpose(1, 2).contiguous() # B x C x T -> B x T x C
-
- src_lengths = src_lengths.cpu().long()
- x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True)
- x = self.lstm(x)[0]
- x = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)[0]
-
- encoder_padding_mask = src_tokens.eq(self.padding_idx)
-
- if self.embed_speaker is not None:
- seq_len, bsz, _ = x.size()
- emb = self.embed_speaker(speaker).expand(seq_len, bsz, -1)
- x = self.spk_emb_proj(torch.cat([x, emb], dim=2))
-
- return {
- "encoder_out": [x], # B x T x C
- "encoder_padding_mask": encoder_padding_mask, # B x T
- }
-
-
-class Prenet(nn.Module):
- def __init__(self, in_dim, n_layers, n_units, dropout):
- super().__init__()
- self.layers = nn.ModuleList(
- nn.Sequential(nn.Linear(in_dim if i == 0 else n_units, n_units),
- nn.ReLU())
- for i in range(n_layers)
- )
- self.dropout = dropout
-
- def forward(self, x):
- for layer in self.layers:
- x = F.dropout(layer(x), p=self.dropout) # always applies dropout
- return x
-
-
-class Postnet(nn.Module):
- def __init__(self, in_dim, n_channels, kernel_size, n_layers, dropout):
- super(Postnet, self).__init__()
- self.convolutions = nn.ModuleList()
- assert(kernel_size % 2 == 1)
- for i in range(n_layers):
- cur_layers = [
- nn.Conv1d(in_dim if i == 0 else n_channels,
- n_channels if i < n_layers - 1 else in_dim,
- kernel_size=kernel_size,
- padding=((kernel_size - 1) // 2)),
- nn.BatchNorm1d(n_channels if i < n_layers - 1 else in_dim)
- ] + ([nn.Tanh()] if i < n_layers - 1 else []) + [nn.Dropout(dropout)]
- nn.init.xavier_uniform_(
- cur_layers[0].weight,
- torch.nn.init.calculate_gain(
- "tanh" if i < n_layers - 1 else "linear"
- )
- )
- self.convolutions.append(nn.Sequential(*cur_layers))
-
- def forward(self, x):
- x = x.transpose(1, 2) # B x T x C -> B x C x T
- for conv in self.convolutions:
- x = conv(x)
- return x.transpose(1, 2)
-
-
-def decoder_init(m):
- if isinstance(m, torch.nn.Conv1d):
- nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh"))
-
-
-class Tacotron2Decoder(FairseqIncrementalDecoder):
- def __init__(self, args, src_dict):
- super().__init__(None)
- self.args = args
- self.n_frames_per_step = args.n_frames_per_step
- self.out_dim = args.output_frame_dim * args.n_frames_per_step
-
- self.prenet = Prenet(self.out_dim, args.prenet_layers, args.prenet_dim,
- args.prenet_dropout)
-
- # take prev_context, prev_frame, (speaker embedding) as input
- self.attention_lstm = LSTMCellWithZoneOut(
- args.zoneout,
- args.prenet_dim + args.encoder_embed_dim,
- args.decoder_lstm_dim
- )
-
- # take attention_lstm output, attention_state, encoder_out as input
- self.attention = LocationAttention(
- args.attention_dim, args.encoder_embed_dim, args.decoder_lstm_dim,
- (1 + int(args.attention_use_cumprob)),
- args.attention_conv_dim, args.attention_conv_kernel_size
- )
-
- # take attention_lstm output, context, (gated_latent) as input
- self.lstm = nn.ModuleList(
- LSTMCellWithZoneOut(
- args.zoneout,
- args.encoder_embed_dim + args.decoder_lstm_dim,
- args.decoder_lstm_dim
- )
- for i in range(args.decoder_lstm_layers)
- )
-
- proj_in_dim = args.encoder_embed_dim + args.decoder_lstm_dim
- self.feat_proj = nn.Linear(proj_in_dim, self.out_dim)
- self.eos_proj = nn.Linear(proj_in_dim, 1)
-
- self.postnet = Postnet(self.out_dim, args.postnet_conv_dim,
- args.postnet_conv_kernel_size,
- args.postnet_layers, args.postnet_dropout)
-
- self.ctc_proj = None
- if getattr(args, "ctc_weight", 0.) > 0.:
- self.ctc_proj = nn.Linear(self.out_dim, len(src_dict))
-
- self.apply(decoder_init)
-
- def _get_states(self, incremental_state, enc_out):
- bsz, in_len, _ = enc_out.size()
- alstm_h = self.get_incremental_state(incremental_state, "alstm_h")
- if alstm_h is None:
- alstm_h = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
- alstm_c = self.get_incremental_state(incremental_state, "alstm_c")
- if alstm_c is None:
- alstm_c = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
-
- lstm_h = self.get_incremental_state(incremental_state, "lstm_h")
- if lstm_h is None:
- lstm_h = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
- for _ in range(self.args.decoder_lstm_layers)]
- lstm_c = self.get_incremental_state(incremental_state, "lstm_c")
- if lstm_c is None:
- lstm_c = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
- for _ in range(self.args.decoder_lstm_layers)]
-
- attn_w = self.get_incremental_state(incremental_state, "attn_w")
- if attn_w is None:
- attn_w = enc_out.new_zeros(bsz, in_len)
- attn_w_cum = self.get_incremental_state(incremental_state, "attn_w_cum")
- if attn_w_cum is None:
- attn_w_cum = enc_out.new_zeros(bsz, in_len)
- return alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum
-
- def _get_init_attn_c(self, enc_out, enc_mask):
- bsz = enc_out.size(0)
- if self.args.init_attn_c == "zero":
- return enc_out.new_zeros(bsz, self.args.encoder_embed_dim)
- elif self.args.init_attn_c == "avg":
- enc_w = (~enc_mask).type(enc_out.type())
- enc_w = enc_w / enc_w.sum(dim=1, keepdim=True)
- return torch.sum(enc_out * enc_w.unsqueeze(2), dim=1)
- else:
- raise ValueError(f"{self.args.init_attn_c} not supported")
-
- def forward(self, prev_output_tokens, encoder_out=None,
- incremental_state=None, target_lengths=None, **kwargs):
- enc_mask = encoder_out["encoder_padding_mask"]
- enc_out = encoder_out["encoder_out"][0]
- in_len = enc_out.size(1)
-
- if incremental_state is not None:
- prev_output_tokens = prev_output_tokens[:, -1:, :]
- bsz, out_len, _ = prev_output_tokens.size()
-
- prenet_out = self.prenet(prev_output_tokens)
- (alstm_h, alstm_c, lstm_h, lstm_c,
- attn_w, attn_w_cum) = self._get_states(incremental_state, enc_out)
- attn_ctx = self._get_init_attn_c(enc_out, enc_mask)
-
- attn_out = enc_out.new_zeros(bsz, in_len, out_len)
- feat_out = enc_out.new_zeros(bsz, out_len, self.out_dim)
- eos_out = enc_out.new_zeros(bsz, out_len)
- for t in range(out_len):
- alstm_in = torch.cat((attn_ctx, prenet_out[:, t, :]), dim=1)
- alstm_h, alstm_c = self.attention_lstm(alstm_in, (alstm_h, alstm_c))
-
- attn_state = attn_w.unsqueeze(1)
- if self.args.attention_use_cumprob:
- attn_state = torch.stack((attn_w, attn_w_cum), dim=1)
- attn_ctx, attn_w = self.attention(
- enc_out, enc_mask, alstm_h, attn_state
- )
- attn_w_cum = attn_w_cum + attn_w
- attn_out[:, :, t] = attn_w
-
- for i, cur_lstm in enumerate(self.lstm):
- if i == 0:
- lstm_in = torch.cat((attn_ctx, alstm_h), dim=1)
- else:
- lstm_in = torch.cat((attn_ctx, lstm_h[i - 1]), dim=1)
- lstm_h[i], lstm_c[i] = cur_lstm(lstm_in, (lstm_h[i], lstm_c[i]))
-
- proj_in = torch.cat((attn_ctx, lstm_h[-1]), dim=1)
- feat_out[:, t, :] = self.feat_proj(proj_in)
- eos_out[:, t] = self.eos_proj(proj_in).squeeze(1)
- self.attention.clear_cache()
-
- self.set_incremental_state(incremental_state, "alstm_h", alstm_h)
- self.set_incremental_state(incremental_state, "alstm_c", alstm_c)
- self.set_incremental_state(incremental_state, "lstm_h", lstm_h)
- self.set_incremental_state(incremental_state, "lstm_c", lstm_c)
- self.set_incremental_state(incremental_state, "attn_w", attn_w)
- self.set_incremental_state(incremental_state, "attn_w_cum", attn_w_cum)
-
- post_feat_out = feat_out + self.postnet(feat_out)
- eos_out = eos_out.view(bsz, out_len, 1)
- return post_feat_out, eos_out, {"attn": attn_out, "feature_out": feat_out}
-
-
-@register_model("tacotron_2")
-class Tacotron2Model(FairseqEncoderDecoderModel):
- """
- Implementation for https://arxiv.org/pdf/1712.05884.pdf
- """
-
- @staticmethod
- def add_args(parser):
- # encoder
- parser.add_argument("--encoder-dropout", type=float)
- parser.add_argument("--encoder-embed-dim", type=int)
- parser.add_argument("--encoder-conv-layers", type=int)
- parser.add_argument("--encoder-conv-kernel-size", type=int)
- parser.add_argument("--encoder-lstm-layers", type=int)
- # decoder
- parser.add_argument("--attention-dim", type=int)
- parser.add_argument("--attention-conv-dim", type=int)
- parser.add_argument("--attention-conv-kernel-size", type=int)
- parser.add_argument("--prenet-dropout", type=float)
- parser.add_argument("--prenet-layers", type=int)
- parser.add_argument("--prenet-dim", type=int)
- parser.add_argument("--postnet-dropout", type=float)
- parser.add_argument("--postnet-layers", type=int)
- parser.add_argument("--postnet-conv-dim", type=int)
- parser.add_argument("--postnet-conv-kernel-size", type=int)
- parser.add_argument("--init-attn-c", type=str)
- parser.add_argument("--attention-use-cumprob", action='store_true')
- parser.add_argument("--zoneout", type=float)
- parser.add_argument("--decoder-lstm-layers", type=int)
- parser.add_argument("--decoder-lstm-dim", type=int)
- parser.add_argument("--output-frame-dim", type=int)
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self._num_updates = 0
-
- @classmethod
- def build_model(cls, args, task):
- embed_speaker = task.get_speaker_embeddings(args)
- encoder = Tacotron2Encoder(args, task.src_dict, embed_speaker)
- decoder = Tacotron2Decoder(args, task.src_dict)
- return cls(encoder, decoder)
-
- def forward_encoder(self, src_tokens, src_lengths, **kwargs):
- return self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
-
- def set_num_updates(self, num_updates):
- super().set_num_updates(num_updates)
- self._num_updates = num_updates
-
-
-@register_model_architecture("tacotron_2", "tacotron_2")
-def base_architecture(args):
- # encoder
- args.encoder_dropout = getattr(args, "encoder_dropout", 0.5)
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
- args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3)
- args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5)
- args.encoder_lstm_layers = getattr(args, "encoder_lstm_layers", 1)
- # decoder
- args.attention_dim = getattr(args, "attention_dim", 128)
- args.attention_conv_dim = getattr(args, "attention_conv_dim", 32)
- args.attention_conv_kernel_size = getattr(args,
- "attention_conv_kernel_size", 15)
- args.prenet_dropout = getattr(args, "prenet_dropout", 0.5)
- args.prenet_layers = getattr(args, "prenet_layers", 2)
- args.prenet_dim = getattr(args, "prenet_dim", 256)
- args.postnet_dropout = getattr(args, "postnet_dropout", 0.5)
- args.postnet_layers = getattr(args, "postnet_layers", 5)
- args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512)
- args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5)
- args.init_attn_c = getattr(args, "init_attn_c", "zero")
- args.attention_use_cumprob = getattr(args, "attention_use_cumprob", True)
- args.zoneout = getattr(args, "zoneout", 0.1)
- args.decoder_lstm_layers = getattr(args, "decoder_lstm_layers", 2)
- args.decoder_lstm_dim = getattr(args, "decoder_lstm_dim", 1024)
- args.output_frame_dim = getattr(args, "output_frame_dim", 80)
diff --git a/spaces/multimodalart/finetuned-text-to-music/share_btn.py b/spaces/multimodalart/finetuned-text-to-music/share_btn.py
deleted file mode 100644
index 9e0cf262c423c5a953560794be3590eb3e051374..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/finetuned-text-to-music/share_btn.py
+++ /dev/null
@@ -1,101 +0,0 @@
-community_icon_html = """"""
-
-loading_icon_html = """"""
-
-share_js = """async () => {
- async function uploadFile(file){
- const UPLOAD_URL = 'https://huggingface.co/uploads';
- const response = await fetch(UPLOAD_URL, {
- method: 'POST',
- headers: {
- 'Content-Type': file.type,
- 'X-Requested-With': 'XMLHttpRequest',
- },
- body: file, /// <- File inherits from Blob
- });
- const url = await response.text();
- return url;
- }
- async function getInputImgFile(imgEl){
- const res = await fetch(imgEl.src);
- const blob = await res.blob();
- const imgId = Date.now() % 200;
- const isPng = imgEl.src.startsWith(`data:image/png`);
- if(isPng){
- const fileName = `sd-perception-${{imgId}}.png`;
- return new File([blob], fileName, { type: 'image/png' });
- }else{
- const fileName = `sd-perception-${{imgId}}.jpg`;
- return new File([blob], fileName, { type: 'image/jpeg' });
- }
- }
- async function getOutputMusicFile(audioEL){
- const res = await fetch(audioEL.src);
- const blob = await res.blob();
- const audioId = Date.now() % 200;
- const fileName = `spectro-music-${{audioId}}.wav`;
- const musicBlob = new File([blob], fileName, { type: 'audio/wav' });
- console.log(musicBlob);
- return musicBlob;
- }
-
- async function audioToBase64(audioFile) {
- return new Promise((resolve, reject) => {
- let reader = new FileReader();
- reader.readAsDataURL(audioFile);
- reader.onload = () => resolve(reader.result);
- reader.onerror = error => reject(error);
-
- });
- }
- const gradioEl = document.querySelector('body > gradio-app');
- // const gradioEl = document.querySelector("gradio-app").shadowRoot;
- const inputPromptEl = gradioEl.querySelector('#prompt-in textarea').value;
- const outputImgEl = gradioEl.querySelector('#img-out img');
- const outputMusic = gradioEl.querySelector('#music-out audio');
- const outputMusic_src = gradioEl.querySelector('#music-out audio').src;
-
- let titleTxt = inputPromptEl;
- //if(titleTxt.length > 100){
- // titleTxt = titleTxt.slice(0, 100) + ' ...';
- //}
- const shareBtnEl = gradioEl.querySelector('#share-btn');
- const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
- const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
- if(!outputMusic){
- return;
- };
- shareBtnEl.style.pointerEvents = 'none';
- shareIconEl.style.display = 'none';
- loadingIconEl.style.removeProperty('display');
- const outputImg = await getInputImgFile(outputImgEl);
- const urlOutputImg = await uploadFile(outputImg);
- const musicFile = await getOutputMusicFile(outputMusic);
- const dataOutputMusic = await uploadFile(musicFile);
-
- const descriptionMd = `#### Spectrogram Image:
-
-
-#### Spectrogram Sound:
-
-
-`;
- const params = new URLSearchParams({
- title: titleTxt,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/fffiloni/spectrogram-to-music/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/myscale/object-detection-safari/query_model.py b/spaces/myscale/object-detection-safari/query_model.py
deleted file mode 100644
index b9735303a43fd331bbae15ec098556486b390a14..0000000000000000000000000000000000000000
--- a/spaces/myscale/object-detection-safari/query_model.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import logging
-
-
-def topk_obj_query(client, xq, IMG_DB_NAME, OBJ_DB_NAME,
- exclude_list=[], topk=10):
- xq_s = [
- f"[{', '.join([str(float(fnum)) for fnum in _xq.tolist() + [1]])}]" for _xq in xq]
- exclude_list_str = ','.join([f'\'{i}\'' for i in exclude_list])
- _cond = (f"WHERE obj_id NOT IN ({exclude_list_str})" if len(
- exclude_list) > 0 else "")
- _subq_str = []
- _img_score_subq = []
- for _l, _xq in enumerate(xq_s):
- _img_score_subq.append(
- f"arrayReduce('maxIf', logit, arrayMap(x->x={_l}, label))")
- _subq_str.append(f"""
- SELECT img_id, img_url, img_w, img_h, 1/(1+exp(-arraySum(arrayMap((x,y)->x*y, prelogit, {_xq})))) AS pred_logit,
- obj_id, box_cx, box_cy, box_w, box_h, class_embedding, {_l} AS l
- FROM {OBJ_DB_NAME}
- JOIN {IMG_DB_NAME}
- ON {IMG_DB_NAME}.img_id = {OBJ_DB_NAME}.img_id
- WHERE obj_id IN (
- SELECT obj_id FROM (
- SELECT obj_id, distance(prelogit, {_xq}) AS dist FROM {OBJ_DB_NAME}
- ORDER BY dist DESC LIMIT 5000
- ) {_cond} LIMIT {topk}
- )
- """)
- _subq_str = ' UNION ALL '.join(_subq_str)
- _img_score_q = ','.join(_img_score_subq)
- _img_score_q = f"arraySum(arrayFilter(x->NOT isNaN(x), array({_img_score_q}))) AS img_score"
- q_str = f"""
- SELECT img_id, img_url, img_w, img_h, groupArray(obj_id) AS box_id,
- groupArray(box_cx) AS cx, groupArray(box_cy) AS cy, groupArray(box_w) AS w, groupArray(box_h) AS h,
- groupArray(pred_logit) AS logit, groupArray(l) as label,
- {_img_score_q}
- FROM
- ({_subq_str})
- GROUP BY img_id, img_url, img_w, img_h ORDER BY img_score DESC
- """
- xc = [{k: v for k, v in r.items()} for r in client.query(q_str).named_results()]
- return xc
-
-
-def rev_query(client, xq, img_ids, IMG_DB_NAME, OBJ_DB_NAME, thresh=0.08):
- xq_s = [
- f"[{', '.join([str(float(fnum)) for fnum in _xq.tolist() + [1]])}]" for _xq in xq]
- image_list = ','.join([f'\'{i}\'' for i in img_ids])
- _thresh = f"WHERE pred_logit > {thresh}" if thresh > 0 else ""
- _subq_str = []
- _img_score_subq = []
- for _l, _xq in enumerate(xq_s):
- _img_score_subq.append(
- f"arrayReduce('maxIf', logit, arrayMap(x->x={_l}, label))")
- _subq_str.append(f"""
- SELECT {OBJ_DB_NAME}.img_id AS img_id, img_url, img_w, img_h,
- (1 / (1 + exp(-(arraySum(arrayMap((x,y)->x*y, prelogit, {_xq})))))) AS pred_logit,
- obj_id, box_cx, box_cy, box_w, box_h, class_embedding, {_l} AS l
- FROM {OBJ_DB_NAME}
- JOIN {IMG_DB_NAME}
- ON {IMG_DB_NAME}.img_id = {OBJ_DB_NAME}.img_id
- PREWHERE img_id IN ({image_list})
- {_thresh}
- """)
- _subq_str = ' UNION ALL '.join(_subq_str)
- _img_score_q = ','.join(_img_score_subq)
- _img_score_q = f"arraySum(arrayFilter(x->NOT isNaN(x), array({_img_score_q}))) AS img_score"
- q_str = f"""
- SELECT img_id, groupArray(obj_id) AS box_id, img_url, img_w, img_h,
- groupArray(box_cx) AS cx, groupArray(box_cy) AS cy, groupArray(box_w) AS w, groupArray(box_h) AS h,
- groupArray(pred_logit) AS logit, groupArray(l) as label,
- {_img_score_q}
- FROM
- ({_subq_str})
- GROUP BY img_id, img_url, img_w, img_h ORDER BY img_score DESC
- """
- xc = [{k: v for k, v in r.items()} for r in client.query(q_str).named_results()]
- return xc
-
-
-def simple_query(client, xq, IMG_DB_NAME, OBJ_DB_NAME, thresh=0.08, topk=10):
- xq_s = [
- f"[{', '.join([str(float(fnum)) for fnum in _xq.tolist() + [1]])}]" for _xq in xq]
- res = []
- subq_str = []
- _thresh = f"WHERE pred_logit > {thresh}" if thresh > 0 else ""
- for _l, _xq in enumerate(xq_s):
- subq_str.append(
- f"""
- SELECT {OBJ_DB_NAME}.img_id AS img_id, img_url, img_w, img_h, prelogit,
- obj_id, box_cx, box_cy, box_w, box_h, {_l} AS l, distance(prelogit, {_xq}) AS dist
- FROM {OBJ_DB_NAME}
- JOIN {IMG_DB_NAME}
- ON {IMG_DB_NAME}.img_id = {OBJ_DB_NAME}.img_id
- {_thresh} ORDER BY dist DESC LIMIT {topk}
- """)
- subq_str = " UNION ALL ".join(subq_str)
- q_str = f"""
- SELECT groupArray(img_url) AS img_url, groupArray(img_w) AS img_w, groupArray(img_h) AS img_h,
- groupArray(box_cx) AS cx, groupArray(box_cy) AS cy, groupArray(box_w) AS w, groupArray(box_h) AS h,
- l AS label, groupArray(dist) as d,
- groupArray(1 / (1 + exp(-dist))) AS logit FROM (
- {subq_str}
- )
- GROUP BY l
- """
- xc = [{k: v for k, v in r.items()} for r in client.query(q_str).named_results()]
- return xc
diff --git a/spaces/nesticot/player_cards/app.py b/spaces/nesticot/player_cards/app.py
deleted file mode 100644
index def1f7fa47eed9b3be4d7f599542687ba330f918..0000000000000000000000000000000000000000
--- a/spaces/nesticot/player_cards/app.py
+++ /dev/null
@@ -1,1003 +0,0 @@
-print('Running')
-import time
-import requests
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-from matplotlib.pyplot import figure
-from matplotlib.offsetbox import OffsetImage, AnnotationBbox
-import matplotlib.lines as mlines
-import matplotlib.transforms as mtransforms
-import numpy as np
-import time
-from urllib.error import HTTPError
-#import plotly.express as px
-#!pip install chart_studio
-#import chart_studio.tools as tls
-from bs4 import BeautifulSoup
-import matplotlib.pyplot as plt
-import numpy as np
-import matplotlib.font_manager as font_manager
-from datetime import datetime
-import pytz
-from matplotlib.ticker import MaxNLocator
-from matplotlib.patches import Ellipse
-import matplotlib.transforms as transforms
-from matplotlib.gridspec import GridSpec
-datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
-# Configure Notebook
-#%matplotlib inline
-plt.style.use('fivethirtyeight')
-sns.set_context("notebook")
-import warnings
-warnings.filterwarnings('ignore')
-
-# import dataframe_image as dfi
-# from google.colab import drive
-def percentile(n):
- def percentile_(x):
- return np.percentile(x, n)
- percentile_.__name__ = 'percentile_%s' % n
- return percentile_
-
-# import os
-# import praw
-# import matplotlib.pyplot as plt
-# import matplotlib.colors
-# import matplotlib.colors as mcolors
-# cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285f4","#FFFFFF","#F0E442"])
-#import pybaseball
-import math
-# import matplotlib.ticker as mtick
-# import matplotlib.ticker as ticker
-
-# colour_palette = ['#FFB000','#648FFF','#785EF0',
- # '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-# import matplotlib.colors as mcolors
-# from matplotlib.ticker import FuncFormatter
-# from matplotlib.font_manager import FontProperties
-import matplotlib.ticker as mtick
-import numpy as np
-# import matplotlib.pyplot as plt
-import matplotlib.colors
-
-import matplotlib.pyplot as plt
-from matplotlib.colors import Normalize
-from matplotlib import cm
-from datetime import date
-
-def show_values(axs, orient="v", space=-1,stat = [],rank_n=[]):
- def _single(ax):
- if orient == "v":
- i = 0
- for p in ax.patches:
-
- _x = p.get_x() + p.get_width() / 2
- _y = p.get_y() + p.get_height() + (p.get_height()*0.01)
- value = '{:.0f}'.format(p.get_height())
- if isinstance(stat[i], float):
- value_stat = '{:.2f}'.format(stat[i])
- else:
- value_stat = stat[i]
- rank = rank_n[i]
- ax.text(_x, -3.8, value_stat, ha="center",fontstyle='italic')
- ax.text(_x, -4.2, rank, ha="center",fontstyle='italic')
- i = i+1
- elif orient == "h":
- for p in ax.patches:
- _x = p.get_x() + p.get_width() + float(space)
- _y = p.get_y() + p.get_height() - (p.get_height()*0.5)
- value = '{:.0f}'.format(p.get_width())
- ax.text(_x, _y, value, ha="left")
-
- if isinstance(axs, np.ndarray):
- for idx, ax in np.ndenumerate(axs):
- _single(ax)
- else:
- _single(axs)
-
-
-
-data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/427.l.public;out=settings/players;position=ALL;start=0;count=1000;sort=rank_season;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
-
-total_list = []
-
-for x in data_r['fantasy_content']['league']['players']:
- single_list = []
-
- single_list.append(int(x['player']['player_id']))
- single_list.append(int(x['player']['player_ranks'][0]['player_rank']['rank_value']))
- single_list.append(x['player']['name']['full'])
- single_list.append(x['player']['name']['first'])
- single_list.append(x['player']['name']['last'])
- single_list.append(x['player']['draft_analysis']['average_pick'])
- single_list.append(x['player']['average_auction_cost'])
- single_list.append(x['player']['display_position'])
- single_list.append(x['player']['editorial_team_abbr'])
- if 'value' in x['player']['percent_owned']:
- single_list.append(x['player']['percent_owned']['value']/100)
- else:
- single_list.append(0)
- total_list.append(single_list)
-
-df_2023 = pd.DataFrame(data=total_list,columns=['player_id','rank_value','full','first','last','average_pick', 'average_cost','display_position','editorial_team_abbr','percent_owned'])
-
-df_2023['pos_new'] = ['D' if "D" in x else 'F' for x in df_2023['display_position']]
-
-player_games = pd.read_csv('Drive/player_games_cards.csv',index_col=[0]).sort_values(by='date').drop_duplicates(subset=['player_id','date'])
-summary_2023 = pd.read_csv('Drive/summary_2024.csv',index_col=[0])
-summary_2022 = pd.read_csv('Drive/2022-23/summary_2023.csv',index_col=[0])
-team_games = pd.read_csv('Drive/team_games.csv',index_col=[0])
-nhl_logos = pd.read_csv("NHL Logos - NHL Logos.csv")
-team_games = team_games.merge(right=nhl_logos[['Team Name','Team']],left_on=['team'],right_on=['Team Name'],how='left')
-
-
-player_games.player_id = player_games.player_id.astype(int)
-summary_2023.player_id = summary_2023.player_id.astype(int)
-summary_2023.player_id = summary_2023.player_id.astype(int)
-
-yahoo_to_nhl_df = pd.read_csv('yahoo_to_nhl.csv', encoding='unicode_escape')
-
-yahoo_to_nhl_df = yahoo_to_nhl_df.merge(df_2023,left_on='player_id_yahoo',right_on='player_id')
-summary_2023 = summary_2023.merge(yahoo_to_nhl_df,left_on='player_id',right_on='nhl_id',suffixes=['','_yahoo'],how='left')
-
-summary_2023 = summary_2023.merge(right=player_games.drop_duplicates(subset=['player_id'],keep='last')[['player_id','Position','Team']],left_on=['player_id'],right_on=['player_id'],how='left')
-
-summary_2023.loc[summary_2023.display_position.isna(),'display_position'] = summary_2023.loc[summary_2023.display_position.isna(),'Position']
-summary_2023.display_position = summary_2023.display_position.replace({'L':'LW','R':'RW'})
-summary_2023.percent_owned = summary_2023.percent_owned.fillna(0)
-
-player_games['game'] = player_games.groupby(['player_id']).cumcount() + 1
-
-skater_dict = summary_2023.set_index('player_id').sort_values(by='Player')
-skater_dict['skater_team'] = skater_dict.Player + ' - ' + skater_dict.Team
-skater_dict = skater_dict['skater_team'].to_dict()
-
-from shiny import ui, render, App
-import matplotlib.image as mpimg
-app_ui = ui.page_fluid(
- #ui.panel_title("Simulate a normal distribution"),
-
- ui.layout_sidebar(
-
- ui.panel_sidebar(
- #ui.input_date_range("date_range_id", "Date range input",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),
- ui.input_select("id", "Select Skater",skater_dict,width=1,selected=8480069,selectize=True),
- #ui.input_select("id", "Select Skater",skater_dict,width=1),
- ui.input_numeric("last_game_id", "Select Last 'n' Games",value=1,width=1),
- #ui.input_select("ignore_id", "Remove Columns",['Position','Roster%'],multiple=True,selectize=True),
- width=2),
- ui.panel_main(
- ui.output_plot("plot",height = "1350px",width="2400px")
- )
- ),
-)
-
-
-
-
-from urllib.request import Request, urlopen
-from shiny import App, reactive, ui
-from shiny.ui import h2, tags
-# importing OpenCV(cv2) module
-
-
-
-
-#print(app_ui)
-def server(input, output, session):
-
- @output
- @render.plot(alt="A histogram")
- def plot():
- # from matplotlib import rc, font_manager
- # rc('text', usetex=True)
- # rc('font',**{'family':'sans-serif','sans-serif':['Century Gothic']})
- #fig.set_facecolor('white')
- sns.set_theme(style="whitegrid", palette="pastel")
- cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])
-
- try:
- if sum(summary_2023.player_id.isin([int(input.id())]))<1:
- fig, ax = plt.subplots(figsize=(10,16))
- fig.set_facecolor('white')
- fig.text(s=f'Select Player From List',x=0.5,y=0.5,fontsize=28,ha='center')
- return
- except ValueError:
- fig, ax = plt.subplots(figsize=(10,16))
- fig.set_facecolor('white')
- fig.text(s=f'Select Player From List',x=0.5,y=0.5,fontsize=28,ha='center')
- return
-
- player_id = int(input.id())
-
- last_games = input.last_game_id()
-
-
-
- player_games_one = player_games[player_games['player_id']==player_id].sort_values(by='game',ascending=False)
-
- print(player_games_one)
- summary_2023_one = summary_2023[summary_2023['player_id']==player_id]
- summary_2022_one = summary_2022[summary_2022['player_id']==player_id]
-
- last_games = min(last_games,max(summary_2023_one.GP.max()-1,1))
-
- name = player_games_one.Player.values[0]
-
- df_last_games = pd.DataFrame(data={'Games':[last_games]})
- df_last_games['TOI'] = player_games_one['TOI'][:last_games].sum()
- df_last_games['TOI/G'] = player_games_one['TOI'][:last_games].sum()/df_last_games.Games
- df_last_games['PP TOI/G'] = player_games_one['TOI_pp'][:last_games].sum()/df_last_games.Games
- df_last_games['Points/60'] = player_games_one['Total Points'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['Shots/60'] = player_games_one['Shots'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['Hits/60'] = player_games_one['Hits'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['Blocks/60'] = player_games_one['Shots Blocked'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['Goals/60'] = player_games_one['Goals'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['ixG/60'] = player_games_one['ixG'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['G-ixG/60'] = df_last_games['Goals/60'] - df_last_games['ixG/60']
- df_last_games['iCF/60'] = player_games_one['iCF'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['iSCF/60'] = player_games_one['iSCF'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['iHDCF/60'] = player_games_one['iHDCF'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['GF/60'] = player_games_one['GF'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['xGF/60'] = player_games_one['xGF'][:last_games].sum()/df_last_games.TOI*60
- df_last_games['IPP'] = player_games_one['Total Points'][:last_games].sum()/player_games_one['GF'][:last_games].sum()
- df_last_games['S%'] = player_games_one['Goals'][:last_games].sum()/player_games_one['Shots'][:last_games].sum()
- df_last_games['xS%'] = player_games_one['ixG'][:last_games].sum()/player_games_one['Shots'][:last_games].sum()
- df_last_games['1st Assist%'] = player_games_one['First Assists'][:last_games].sum()/player_games_one['Total Assists'][:last_games].sum()
- df_last_games['Off. Zone Start%'] = player_games_one['Off.\xa0Zone Starts'][:last_games].sum()/(player_games_one['Off.\xa0Zone Starts'][:last_games].sum()+player_games_one['Def.\xa0Zone Starts'][:last_games].sum())
- df_last_games['oiSH%'] = player_games_one['GF'][:last_games].sum()/player_games_one['SF'][:last_games].sum()
-
- df_season = pd.DataFrame(data={'Games':[len(player_games_one)]})
- df_season['TOI'] = player_games_one['TOI'][:len(player_games_one)].sum()
- df_season['TOI/G'] = player_games_one['TOI'][:len(player_games_one)].sum()/df_season.Games
- df_season['PP TOI/G'] = player_games_one['TOI_pp'][:len(player_games_one)].sum()/df_season.Games
- df_season['Points/60'] = player_games_one['Total Points'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['Shots/60'] = player_games_one['Shots'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['Hits/60'] = player_games_one['Hits'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['Blocks/60'] = player_games_one['Shots Blocked'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['Goals/60'] = player_games_one['Goals'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['ixG/60'] = player_games_one['ixG'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['G-ixG/60'] = df_season['Goals/60'] - df_season['ixG/60']
- df_season['iCF/60'] = player_games_one['iCF'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['iSCF/60'] = player_games_one['iSCF'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['iHDCF/60'] = player_games_one['iHDCF'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['GF/60'] = player_games_one['GF'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['xGF/60'] = player_games_one['xGF'][:len(player_games_one)].sum()/df_season.TOI*60
- df_season['IPP'] = player_games_one['Total Points'][:len(player_games_one)].sum()/player_games_one['GF'][:len(player_games_one)].sum()
- df_season['S%'] = player_games_one['Goals'][:len(player_games_one)].sum()/player_games_one['Shots'][:len(player_games_one)].sum()
- df_season['xS%'] = player_games_one['ixG'][:len(player_games_one)].sum()/player_games_one['Shots'][:len(player_games_one)].sum()
- df_season['1st Assist%'] = player_games_one['First Assists'][:len(player_games_one)].sum()/player_games_one['Total Assists'][:len(player_games_one)].sum()
- df_season['Off. Zone Start%'] = player_games_one['Off.\xa0Zone Starts'][:len(player_games_one)].sum()/(player_games_one['Off.\xa0Zone Starts'][:len(player_games_one)].sum()+player_games_one['Def.\xa0Zone Starts'][:len(player_games_one)].sum())
- df_season['oiSH%'] = player_games_one['GF'][:len(player_games_one)].sum()/player_games_one['SF'][:len(player_games_one)].sum()
- df_last_games.rename(index={0:'Last '+str(df_last_games.Games[0])+' GP'},inplace=True)
- df_season.rename(index={0:'2023-24 ('+str(df_season.Games[0])+'GP)'},inplace=True)
-
- # cols_to_use = summary_all.columns.difference(summary_all_on_ice.columns)
-
-
- # df_career_total = summary_all.merge(summary_all_on_ice, left_index=True, right_index=True,
- # how='outer', suffixes=('', '_y'))
-
- # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)
-
- # df_career_total = df_career_total.merge(summary_pp, left_index=True, right_index=True,
- # how='outer', suffixes=('', '_y'))
-
- # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)
-
- # df_career_total = df_career_total.merge(summary_pp_on_ice, left_index=True, right_index=True,
- # how='outer', suffixes=('', '_y'))
-
- # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)
- df_career_total = summary_2022_one.sort_index(ascending=False)
- df_career_total.columns = [c.replace(u"\xa0", u" ") for c in list(df_career_total.columns)]
- df_career = pd.DataFrame(data={'Games':[df_career_total.GP.sum()]})
- df_career['TOI'] = df_career_total['TOI'].sum()
- df_career['TOI/G'] = df_career_total['TOI'].sum()/df_career.Games
- df_career['PP TOI/G'] = df_career_total['TOI_pp'].sum()/df_career.Games
- df_career['Points/60'] = df_career_total['Total_Points'].sum()/df_career_total.TOI.sum()*60
- df_career['Shots/60'] = df_career_total['Shots'].sum()/df_career_total.TOI.sum()*60
- df_career['Hits/60'] = df_career_total['Hits'].sum()/df_career_total.TOI.sum()*60
- df_career['Blocks/60'] = df_career_total['Shots_Blocked'].sum()/df_career_total.TOI.sum()*60
- df_career['Goals/60'] = df_career_total['Goals'].sum()/df_career_total.TOI.sum()*60
- df_career['ixG/60'] = df_career_total['ixG'].sum()/df_career_total.TOI.sum()*60
- df_career['G-ixG/60'] = df_career['Goals/60'] - df_career['ixG/60']
- df_career['iCF/60'] = df_career_total['iCF'].sum()/df_career_total.TOI.sum()*60
- df_career['iSCF/60'] = df_career_total['iSCF'].sum()/df_career_total.TOI.sum()*60
- df_career['iHDCF/60'] = df_career_total['iHDCF'].sum()/df_career_total.TOI.sum()*60
- df_career['GF/60'] = df_career_total['GF'].sum()/df_career_total.TOI.sum()*60
- df_career['xGF/60'] = df_career_total['xGF'].sum()/df_career_total.TOI.sum()*60
- df_career['IPP'] = df_career_total['Total_Points'].sum()/df_career_total['GF'].sum()
- df_career['S%'] = df_career_total['Goals'].sum()/df_career_total['Shots'].sum()
- df_career['xS%'] = df_career_total['ixG'].sum()/df_career_total['Shots'].sum()
- df_career['1st Assist%'] = df_career_total['First_Assists'].sum()/df_career_total['Assists'].sum()
- df_career['Off. Zone Start%'] = df_career_total['OZ Start%'].sum()
- df_career['oiSH%'] = df_career_total['GF'].sum()/df_career_total['SF'].sum()
- df_career.rename(index={0:'2022-23 ('+str(df_career.Games[0])+'GP)'},inplace=True)
- df_combined = df_last_games.append([df_season,df_career])
- df_combined_t = round(df_combined,3).transpose()
- df_combined.style.format(formatter={"IPP": "{:.1%}","S%": "{:.1%}","1st Assist%": "{:.1%}","Off. Zone Start%": "{:.1%}","oiSH%": "{:.1%}"}).set_precision(2)
- rows = [idx for idx in df_combined_t.index if '/' not in idx]
-
-
- df_combined_t = df_combined_t.astype(float).fillna(0)
- # df_combined_t_style = df_combined_t.style.set_table_styles([{
- # 'selector': 'caption',
- # 'props': [
- # ('color', ''),
- # ('fontname', 'Century Gothic'),
- # ('font-size', '12px'),
- # ('font-style', 'italic'),
- # ('font-weight', ''),
- # ('text-align', 'centre'),
- # ]
-
- # },{'selector' :'th', 'props':[('text-align', 'center'),('Height','8px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '10px')]}],overwrite=False).set_properties(
- # **{'background-color':'White','index':'White','min-width':'60px'},overwrite=False).set_table_styles(
- # [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
- # [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
- # [{'selector': 'tr', 'props': [('line-height', '18px')]}],overwrite=False).set_properties(
- # **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).apply(
-
- # lambda x: ["background: #FEEFC1" if (i < 1 and v > x.iloc[1]*1.05) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #FEEFC1" if (i == 1 and v > x.iloc[2]*1.05) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #D0E1FD" if (i < 1 and v < x.iloc[1]*0.95) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #D0E1FD" if (i == 1 and v < x.iloc[2]*0.95) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #FDDE82" if (i < 1 and v > x.iloc[1]*1.1) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #FDDE82" if (i == 1 and v > x.iloc[2]*1.1) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #A1C2FA" if (i < 1 and v < x.iloc[1]*0.9) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #A1C2FA" if (i == 1 and v < x.iloc[2]*0.9) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #FCCD43" if (i < 1 and v > x.iloc[1]*1.15) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #FCCD43" if (i == 1 and v > x.iloc[2]*1.15) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #72A4F7" if (i < 1 and v < x.iloc[1]*0.85) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #72A4F7" if (i == 1 and v < x.iloc[2]*0.85) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #FBBC04" if (i < 1 and v > x.iloc[1]*1.2) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #FBBC04" if (i == 1 and v > x.iloc[2]*1.2) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["color: #ffffff" if (i < 1 and v < x.iloc[1]*0.8) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["color: #ffffff" if (i == 1 and v < x.iloc[2]*0.8) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #4285F4" if (i == 0 and v < x.iloc[1]*0.8) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #4285F4" if (i == 1 and v < x.iloc[2]*0.8) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #ffffff" if (i == 0 and v == x.iloc[1]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["background: #ffffff" if (i == 1 and v == x.iloc[2]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["color: #000000" if (i < 1 and v == x.iloc[1]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
- # lambda x: ["color: #000000" if (i == 1 and v == x.iloc[2]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).format(
- # "{:.1%}",subset=(rows[2:],df_combined_t.columns)).format(
- # '{:.0f}',subset=(rows[0],df_combined_t.columns)).set_precision(2).set_properties(
- # **{'index':'White','min-width':'60px'},overwrite=False)
-
-
-
- # #.set_table_styles([index_names, headers])
-
- # df_combined_t_style = df_combined_t_style.format(
- # {df_combined_t_style.columns[0]: '{:,.1%}'.format,
- # df_combined_t_style.columns[1]: '{:,.1%}'.format,
- # df_combined_t_style.columns[2]: '{:,.1%}'.format},subset=(rows[2:],df_combined_t.columns)
- # )
- # df_combined_t_style = df_combined_t_style.format(
- # '{:.0f}',subset=(rows[0],df_combined_t.columns))
-
-
- # df_combined_t_style
-
-
-
-
- # dfi.export(df_combined_t_style, 'players/'+name+'_'+str(last_games)+'.png',fontsize=9,dpi=600,table_conversion='chrome')
-
-
- percent_owned = summary_2023_one['percent_owned'].reset_index(drop=True)[0]
- yahoo_position = summary_2023_one['display_position'].reset_index(drop=True)[0]
-
-
-
- # image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
- # logo = nhl_logos[nhl_logos.Team==list(player_games_one['Team'])[0]].reset_index().URL[0]
- # fig, ax = plt.subplots(figsize=(10,16))
- # fig.set_facecolor('white')
-
- # # img = mpimg.imread('players/'+name+'_'+str(last_games)+'.png')
- # # ax.imshow(img)
- # # ax.axis('off')
- # # fig.tight_layout()
-
- # ax.axis('off')
- # im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
- # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
- # ax.imshow(im)
- # ax.axis('off')
- # fig.tight_layout()
-
- # fig.text(x=0.5,y=-0.05,s='Note: Last Games compares to 2023-24. 2023-24 compares to 2022-23.',horizontalalignment='center',fontsize=16,fontname='Century Gothic')
- # # fig.text(x=0.05,y=-0.1,s='Created By: @TJStats',horizontalalignment='left',fontsize=24,fontname='Century Gothic')
- # # fig.text(x=0.95,y=-0.1,s='Data: Natural Stat Trick',horizontalalignment='right',fontsize=24,fontname='Century Gothic')
- # fig.text(x=0.5,y=1.13,s='NHL Player Summary',horizontalalignment='center',fontsize=52, fontweight='bold')
- # fig.text(x=0.5,y=1.08,s='2023-24 Season',horizontalalignment='center',fontsize=36,fontname='Century Gothic', fontstyle='italic')
- # fig.text(x=0.12,y=1.03,s='Player',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
- # fig.text(x=0.12,y=1.0,s='Team',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
- # fig.text(x=0.12,y=0.97,s='Position',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
- # #fig.text(x=0.12,y=0.94,s='Age',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
- # # fig.text(x=0.12,y=0.91,s='Cap Hit',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
- # #fig.text(x=0.12,y=0.88,s='Roster%',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
-
- # fig.text(x=0.25,y=1.03,s=name,horizontalalignment='left',fontsize=22,fontname='Century Gothic')
- # fig.text(x=0.25,y=1.0,s=list(player_games_one['Team'])[0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
- # fig.text(x=0.25,y=0.97,s=yahoo_position,horizontalalignment='left',fontsize=22,fontname='Century Gothic')
- # #fig.text(x=0.25,y=0.94,s=str(int(summary_2023_one.reset_index().AGE[0])),horizontalalignment='left',fontsize=22,fontname='Century Gothic')
- # # fig.text(x=0.25,y=0.91,s=summary_2023_one.loc[summary_2023_one['player_id']==player_id].reset_index()['sheets'][0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
- # #fig.text(x=0.25,y=0.88,s=str(int(percent_owned*100))+'%',horizontalalignment='left',fontsize=22,fontname='Century Gothic')
-
-
- # im = plt.imread(image)
- # newax = fig.add_axes([0.5,0.8,0.22,0.22], anchor='NW', zorder=1)
- # newax.imshow(im)
-
- # imr = plt.imread(logo)
- # newerax = fig.add_axes([0.75,0.8,0.22,0.22], anchor='NW', zorder=1)
- # newerax.imshow(imr)
-
- # newax.axis('off')
- # newerax.axis('off')
-
- # plt.savefig('players/'+name+"_"+str(last_games)+' int.png',bbox_inches="tight")
- # plt.close()
-
- #test.loc[name == test.Player].reset_index().TOI[0]/1.5
- min_time = min(math.floor(summary_2023.loc[player_id == summary_2023.player_id].reset_index().TOI[0]/100)*100,summary_2023.GP.max()*10)
- # min_time = min(math.floor(test.loc[name == test.Player].reset_index().TOI[0]/100)*100,test.GP.max()*10)
- test_filter = summary_2023[(summary_2023.TOI >= min_time)&(summary_2023.pos == summary_2023.loc[player_id == summary_2023.player_id].reset_index().pos[0])]
- test_filter['Goals/GP'] = test_filter['Goals']/test_filter['GP']
- test_filter['Total Assists/GP'] = test_filter['Assists']/test_filter['GP']
- test_filter['Total Points/GP'] = test_filter['Total_Points']/test_filter['GP']
- test_filter['PP Points/GP'] = test_filter['Total_Points_pp']/test_filter['GP']
- test_filter['Shots/GP'] = test_filter['Shots']/test_filter['GP']
- test_filter['Hits/GP'] = test_filter['Hits']/test_filter['GP']
- test_filter['Shots Blocked/GP'] = test_filter['Shots_Blocked']/test_filter['GP']
- test_filter['ixG/60'] = test_filter['ixG']/test_filter['TOI']*60
- test_filter['Goals/60'] = test_filter['Goals']/test_filter['TOI']*60
- test_filter['G-xG/60'] = test_filter['G-ixG']/test_filter['TOI']*60
- test_filter['iCF/60'] = test_filter['iCF']/test_filter['TOI']*60
- test_filter['iSCF/60'] = test_filter['iSCF']/test_filter['TOI']*60
- test_filter['First Assists/60'] = test_filter['First_Assists']/test_filter['TOI']*60
- test_filter['Total Points/60'] = test_filter['Total_Points']/test_filter['TOI']*60
-
-
- test_filter['G-xG/60'] = test_filter['Goals/60'] - test_filter['ixG/60']
- test_filter['Goals Z-Score'] = (test_filter['Goals/GP']-test_filter['Goals/GP'].mean())/test_filter['Goals/GP'].std()
- test_filter['Assists Z-Score'] = (test_filter['Total Assists/GP']-test_filter['Total Assists/GP'].mean())/test_filter['Total Assists/GP'].std()
- test_filter['Points Z-Score'] = (test_filter['Total Points/GP']-test_filter['Total Points/GP'].mean())/test_filter['Total Points/GP'].std()
- test_filter['PP Points Z-Score'] = (test_filter['PP Points/GP']-test_filter['PP Points/GP'].mean())/test_filter['PP Points/GP'].std()
- test_filter['Shots Z-Score'] = (test_filter['Shots/GP']-test_filter['Shots/GP'].mean())/test_filter['Shots/GP'].std()
- test_filter['Hits Z-Score'] = (test_filter['Hits/GP']-test_filter['Hits/GP'].mean())/test_filter['Hits/GP'].std()
- test_filter['Blocks Z-Score'] = (test_filter['Shots Blocked/GP']-test_filter['Shots Blocked/GP'].mean())/test_filter['Shots Blocked/GP'].std()
-
-
- test_filter['ixG Z-Score'] = (test_filter['ixG/60']-test_filter['ixG/60'].mean())/test_filter['ixG/60'].std()
- test_filter['G Z-Score'] = (test_filter['Goals/60']-test_filter['Goals/60'].mean())/test_filter['Goals/60'].std()
- test_filter['G-xG Z-Score'] = (test_filter['G-xG/60']-test_filter['G-xG/60'].mean())/test_filter['G-xG/60'].std()
- test_filter['iCF Z-Score'] = (test_filter['iCF/60']-test_filter['iCF/60'].mean())/test_filter['iCF/60'].std()
- test_filter['iSCF Z-Score'] = (test_filter['iSCF/60']-test_filter['iSCF/60'].mean())/test_filter['iSCF/60'].std()
- test_filter['First Assists Z-Score'] = (test_filter['First Assists/60']-test_filter['First Assists/60'].mean())/test_filter['First Assists/60'].std()
- test_filter['P Z-Score'] = (test_filter['Total Points/60']-test_filter['Total Points/60'].mean())/test_filter['Total Points/60'].std()
- values_1 = test_filter.loc[player_id == test_filter.player_id][['Goals/GP','Total Assists/GP','Total Points/GP','PP Points/GP','Shots/GP','Hits/GP','Shots Blocked/GP']].reset_index(drop=True).loc[0]
- values_2 = test_filter.loc[player_id == test_filter.player_id][['ixG/60','Goals/60','G-xG/60','iCF/60','iSCF/60','First Assists/60','Total Points/60']].reset_index(drop=True).loc[0]
- categories = [i[:-11]+'/GP' for i in test_filter.columns[-14:-7]]
-
- #categories = [*categories]
-
- player = list(np.around(test_filter.loc[player_id == test_filter.player_id][test_filter.columns[-14:-7]].values.flatten().tolist()))
-
- #player = [*player, player[0]]
-
-
- label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(player))
-
- players_stats_all_on_filter = test_filter.copy()
- players_stats_all_on_filter['GF/60'] = (players_stats_all_on_filter['GF'])/players_stats_all_on_filter['TOI']*60
- players_stats_all_on_filter['xGF/60'] = (players_stats_all_on_filter['xGF'])/players_stats_all_on_filter['TOI']*60
- players_stats_all_on_filter['CF/60'] = (players_stats_all_on_filter['CF'])/players_stats_all_on_filter['TOI']*60
- # players_stats_all_on_filter['oiSH%'] = (players_stats_all_on_filter['On-Ice SH%']-players_stats_all_on_filter['On-Ice SH%'].mean())/players_stats_all_on_filter['On-Ice SH%'].std()
- #players_stats_all_on_filter['OZ Start%'] = (players_stats_all_on_filter['CF']-players_stats_all_on_filter['CF'])/players_stats_all_on_filter['CF']
- players_stats_all_on_filter['GF Z-Score'] = (players_stats_all_on_filter['GF/60']-players_stats_all_on_filter['GF/60'].mean())/players_stats_all_on_filter['GF/60'].std()
- players_stats_all_on_filter['xGF Z-Score'] = (players_stats_all_on_filter['xGF/60']-players_stats_all_on_filter['xGF/60'].mean())/players_stats_all_on_filter['xGF/60'].std()
- players_stats_all_on_filter['CF Z-Score'] = (players_stats_all_on_filter['CF/60']-players_stats_all_on_filter['CF/60'].mean())/players_stats_all_on_filter['CF/60'].std()
- players_stats_all_on_filter['oiSH% Z-Score'] = (players_stats_all_on_filter['oiSH']-players_stats_all_on_filter['oiSH'].mean())/players_stats_all_on_filter['oiSH'].std()
- players_stats_all_on_filter['OZ Start% Z-Score'] = (players_stats_all_on_filter['OZ Start%']-players_stats_all_on_filter['OZ Start%'].mean())/players_stats_all_on_filter['OZ Start%'].std()
- categories_on = [i[:-8]+'/60' for i in players_stats_all_on_filter.columns[-5:]]
- categories_on = ['GF/60', 'xGF/60', 'CF/60', 'oiSH', 'OZ Start%']
- player_on = list(np.around(players_stats_all_on_filter.loc[player_id == test_filter.player_id][players_stats_all_on_filter.columns[-5:]].values.flatten().tolist(),2))
- categories_1 = [i[:-8]+'/GP' for i in test_filter.columns[-14:-7]]
- categories_2 = [i[:-8]+'/60' for i in test_filter.columns[-7:]]
- categories = categories_1+categories_2
- categories[12] = '1A/60'
- player = list(np.around(test_filter.loc[player_id == test_filter.player_id][test_filter.columns[-14:]].values.flatten().tolist(),2))
-
- # color_scheme = []
- # for i in player:
- # if i <= -2:
- # color_scheme.append('#4285F4')
- # if i > -2 and i <= -1:
- # color_scheme.append('#A1C2FA')
- # if i > -1 and i <= 0:
- # color_scheme.append('#D0E1FD')
- # if i > 0 and i <= 1:
- # color_scheme.append('#FEEFC1')
- # if i > 1 and i <= 2:
- # color_scheme.append('#FDDE82')
- # if i > 2:
- # color_scheme.append('#FBBC04')
-
-
- goals_above_expected = test_filter.loc[player_id == test_filter.player_id]['Goals'] - test_filter.loc[player_id == test_filter.player_id]['ixG']
- goals_above_expected = test_filter.loc[player_id == test_filter.player_id]['Goals'] - test_filter.loc[player_id == test_filter.player_id]['ixG']
-
- # color_scheme_on= []
- # for i in player_on:
- # if i <= -2:
- # color_scheme_on.append('#4285F4')
- # if i > -2 and i <= -1:
- # color_scheme_on.append('#A1C2FA')
- # if i > -1 and i <= 0:
- # color_scheme_on.append('#D0E1FD')
- # if i > 0 and i <= 1:
- # color_scheme_on.append('#FEEFC1')
- # if i > 1 and i <= 2:
- # color_scheme_on.append('#FDDE82')
- # if i > 2:
- # color_scheme_on.append('#FBBC04')
-
- fig = plt.figure(figsize=(24, 13.5),dpi=300)
- fig.set_facecolor('white')
-
- cmap_new = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white",'#FBBC04'])
- colormap_new = plt.get_cmap(cmap_new)
- norm_new = Normalize(vmin=-3, vmax=3)
-
- color_scheme = [colormap_new(norm_new(x)) for x in player]
- color_scheme_on = [colormap_new(norm_new(x)) for x in player_on]
-
- players_stats_all_on_filter = players_stats_all_on_filter.rename(columns={'On-Ice SH%':'oiSH%','Off.\xa0Zone Start %':'OZ Start%'})
- values_on = players_stats_all_on_filter.loc[player_id == test_filter.player_id][['GF/60', 'xGF/60', 'CF/60', 'oiSH', 'OZ Start%']].reset_index(drop=True).loc[0]
- position_player = summary_2023_one['pos'].reset_index().pos[0]
- rank_1 = list(test_filter[values_1.index].rank(method='min',ascending=False).loc[player_id == test_filter.player_id].reset_index(drop=True).astype(int).iloc[0])
- rank_2 = list(test_filter[values_2.index].rank(method='min',ascending=False).loc[player_id == test_filter.player_id].reset_index(drop=True).astype(int).iloc[0])
- rank_3 = list(players_stats_all_on_filter[values_on.index].rank(method='min',ascending=False).loc[player_id == test_filter.player_id].reset_index(drop=True).astype(int).iloc[0][categories_on])
- #rank_3 = rank_3 + list(players_stats_all_on_filter[values_on.index].rank(method='first',ascending=True).loc[player_id == test.player_id].reset_index(drop=False).astype(int).iloc[0][categories_on[3:5]])
-
- values_on = [float(x) for x in values_on]
-
- player_games_one = player_games_one.merge(right=team_games[['Team','pp_toi','date']],left_on=['Team','date'],right_on=['Team','date'],how='left').fillna(0)
- y=(player_games_one.rolling(last_games).sum()['TOI_pp']/player_games_one.rolling(last_games).sum()['pp_toi'])[last_games:]
- player_games_one = player_games_one.sort_values(by='date').reset_index(drop=True)
-
- # fig, ([ax1,ax2],[ax3,ax4])= plt.subplots(nrows=2, ncols=2,figsize=(16,10))
-
-
-
- colormap = plt.get_cmap(cmap)
-
- value = 1
- # Normalize the value
- norm = Normalize(vmin=0.8, vmax=1.2)
- normalized_value = norm(value)
-
- col_3_colour = ['white']*len(df_combined_t)
- col_2_colour = [colormap(norm(x)) for x in list(((df_combined_t[df_combined_t.columns[1]].values) / (df_combined_t[df_combined_t.columns[2]].values)))]
- col_1_colour = [colormap(norm(x)) for x in list(((df_combined_t[df_combined_t.columns[0]].values) / (df_combined_t[df_combined_t.columns[1]].values)))]
- colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour]).T.values
-
- if df_combined_t[df_combined_t.columns[2]].values[0] == 0:
-
- for i in range(len(colour_df)):
- colour_df[[i],[1]] = 'white'
-
- colour_df[[0],[0]] = 'white'
- colour_df[[1],[0]] = 'white'
- colour_df[[0],[1]] = 'white'
- colour_df[[1],[1]] = 'white'
-
-
- if df_combined_t.values[[10],[0]] < 0 and df_combined_t.values[[10],[1]] < 0:
- cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
- norm = Normalize(vmin=0.8, vmax=1.2)
- colour_df[[10],[0]] = tuple(cmap_flip(norm(df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))
-
- elif df_combined_t.values[[10],[0]] < 0 and df_combined_t.values[[10],[1]] > 0:
- #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
- cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#4285F4"])
- norm = Normalize(vmin=-1, vmax=0)
- colour_df[[10],[0]] = tuple(cmap_y(norm(df_combined_t.values[[10],[0]] - df_combined_t.values[[10],[1]])))
-
- elif df_combined_t.values[[10],[0]] > 0 and df_combined_t.values[[10],[1]] < 0:
- cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#FBBC04"])
- norm = Normalize(vmin=0, vmax=1)
- colour_df[[10],[0]] = tuple(cmap_y(norm(df_combined_t.values[[10],[0]] - df_combined_t.values[[10],[1]])))
-
-
- if df_combined_t.values[[10],[1]] < 0 and df_combined_t.values[[10],[2]] < 0:
- cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
- norm = Normalize(vmin=0.8, vmax=1.2)
- colour_df[[10],[1]] = tuple(cmap_flip(norm(df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))
-
- elif df_combined_t.values[[10],[1]] < 0 and df_combined_t.values[[10],[2]] > 0:
- #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
- cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#4285F4"])
- norm = Normalize(vmin=-1, vmax=0)
- colour_df[[10],[1]] = tuple(cmap_y(norm(df_combined_t.values[[10],[1]] - df_combined_t.values[[10],[2]])))
-
- elif df_combined_t.values[[10],[1]] > 0 and df_combined_t.values[[10],[2]] < 0:
- cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#FBBC04"])
- norm = Normalize(vmin=0, vmax=1)
- colour_df[[10],[1]] = tuple(cmap_y(norm(df_combined_t.values[[10],[1]] - df_combined_t.values[[10],[2]])))
-
-
- ax1 = plt.subplot(1,3,1)
- ax2 = plt.subplot(3,3,2)
- ax3 = plt.subplot(3,3,5)
- ax4 = plt.subplot(3,3,8)
- ax5 = plt.subplot(3,3,3)
- ax6 = plt.subplot(3,3,6)
- ax7 = plt.subplot(3,3,9)
- #axbot = plt.subplot(3,1,1)
- axes = [ax1, ax2, ax3, ax4, ax5,ax6,ax7]
- # ax[0][0].axis('off')
- # im = plt.imread('players/'+name+'_'+str(last_games)+' int.png')
- # ax[0][0] = fig.add_axes([0,0,1,1], anchor='W', zorder=1)
- # ax[0][0].imshow(im)
-
-
- try:
- image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
- im = plt.imread(image)
-
- except HTTPError as err:
- if err.code == 403:
- image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/skater.png"
- im = plt.imread(image)
- else:
- raise
-
- logo = nhl_logos[nhl_logos.Team==list(player_games_one['Team'])[0]].reset_index().URL[0]
- #im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
- #ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
- #ax.imshow(im)
-
-
- im = plt.imread(image)
- im = OffsetImage(im, zoom=.75)
- ab = AnnotationBbox(im, (0.69, 0.765), xycoords='axes fraction', box_alignment=(0.0,0.0),bboxprops={'edgecolor':'white'})
- ax1.add_artist(ab)
- # axim = fig.add_axes([0.25,0.76,0.12,0.12], anchor='NW', zorder=1)
- # axim.imshow(im)
- # axim.axis('off')
-
- imr = plt.imread(logo)
- imr = OffsetImage(imr, zoom=.1)
- ab = AnnotationBbox(imr, (0.45, 0.765), xycoords='axes fraction', box_alignment=(0.0,0.0),bboxprops={'edgecolor':'white'})
- ax1.add_artist(ab)
- # axim = fig.add_axes([0.18,0.76,0.75,0.075], anchor='NW', zorder=1)
- # axim.imshow(imr)
- # axim.axis('off')
-
- sub_value = 0.16
- ax1.text(x=0.5,y=1.13-sub_value,s='NHL Player Summary',horizontalalignment='center',fontsize=36, fontweight='bold')
- ax1.text(x=0.5,y=1.08-sub_value,s='2023-24 Season',horizontalalignment='center',fontsize=28,fontname='Century Gothic', fontstyle='italic')
- ax1.text(x=0.05,y=1.04-sub_value,s='Player',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
- ax1.text(x=0.05,y=1.005-sub_value,s='Team',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
- ax1.text(x=0.05,y=0.97-sub_value,s='Position',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
- #ax1.text(x=0.1,y=0.94-sub_value,s='Age',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
- #ax1.text(x=0.12,y=0.91-sub_value,s='Cap Hit',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
- ax1.text(x=0.05,y=0.935-sub_value,s='Roster%',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
-
- ax1.text(x=0.175,y=1.04-sub_value,s=name,horizontalalignment='left',fontsize=18,fontname='Century Gothic')
- ax1.text(x=0.175,y=1.005-sub_value,s=list(player_games_one['Team'])[0],horizontalalignment='left',fontsize=18,fontname='Century Gothic')
- ax1.text(x=0.175,y=0.97-sub_value,s=yahoo_position,horizontalalignment='left',fontsize=18,fontname='Century Gothic')
- #ax1.text(x=0.25,y=0.94-sub_value,s=str(summary_2023_one.reset_index().AGE[0]),horizontalalignment='left',fontsize=22,fontname='Century Gothic')
- #ax1.text(x=0.25,y=0.91-sub_value,s=summary_2023_one.loc[summary_2023_one['player_id']==player_id].reset_index()['sheets'][0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
- ax1.text(x=0.175,y=0.935-sub_value,s=str(int(percent_owned*100))+'%',horizontalalignment='left',fontsize=18,fontname='Century Gothic')
-
-
-
-
- ax1.axis("off")
-
-
-
-
-
- table = ax1.table(cellText=df_combined_t.values, colLabels=df_combined_t.columns,rowLabels=df_combined_t.index
- , cellLoc='center',rowLoc='center', bbox=[0.15, 0.05, 0.8, 0.7],cellColours=colour_df)
- #table.auto_set_font_size(True)
- table.set_fontsize(20)
- table.scale(1, 1.5)
-
-
-
- format_col = df_combined_t[df_combined_t.columns[0]]
- n_c = 0
- for cell in table.get_celld().values():
- if n_c < 1*3:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
- elif n_c < 16*3:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
- else:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
- n_c = n_c + 1
-
-
- format_col = df_combined_t[df_combined_t.columns[1]]
- n_c = 0
- for cell in table.get_celld().values():
- if n_c < 1*3:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
- elif n_c < 16*3:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
- else:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
- n_c = n_c + 1
-
- format_col = df_combined_t[df_combined_t.columns[2]]
- n_c = 0
- for cell in table.get_celld().values():
- if n_c < 1*3:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
- elif n_c < 16*3:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
- else:
- if cell.get_text().get_text() in format_col.astype(str).values:
- cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
- n_c = n_c + 1
-
- #ax1.text('')
-
-
-
- # ax1.axis('off')
- # img = mpimg.imread('players/'+name+'_'+str(last_games)+' int.png')
-
- # im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
- # ax1 = fig.add_axes([0.03,0.,1,1], anchor='SW')
- # ax1.imshow(im)
-
- # ax1.imshow(img)
- # ax1.axis('off')
- # fig.tight_layout()
-
-
-
- categories[0:7] = ['G/GP','A/GP','P/GP','PPP/GP','S/GP','Hits/GP','Blk/GP']
- _ = sns.barplot(data=test_filter[test_filter.columns[56:63]],x=categories[0:7],y=player[0:7],palette=color_scheme[0:7],edgecolor='black',ax=ax2)
-
- ax2.set_title("Individual Per Game Z-Score (vs "+position_player+", min. "+str(min_time)+' TOI)',fontsize=14,fontname='Century Gothic')
-
-
- #plt.rcParams['xtick.color']='#333F4B'
- #ax[0][1].set_prop_cycle(ytick.color='#333F4B')
-
-
- ax2.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
- #plt.ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
-
- #plt.xlabel('Percentile', fontsize=12, color = '#000000',fontname='Century Gothic')
- #plt.tick_params(axis='x', which='both', labelsize=10,bottom=False,top=False)
- ax2.set_ylim([-3, 3])
- plt.style.use('classic')
- show_values(ax2,stat = values_1,rank_n=rank_1)
- ax2.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
-
- ax2.set_axisbelow(True)
- #ax[1].hlines(y=0,xmin=0,xmax=len(player),color='black')
-
-
- #plt.savefig('players/'+name+"_"+str(last_games)+'_Z.png')#,bbox_inches="tight")
- # ax[1][0] = sns.barplot(data=test,x=categories,y=player,palette=color_scheme,edgecolor='black')
- # show_values(ax2,stat = values)
- # data_input = df_shots[(df_shots.shooterName==name) & (df_shots.event!='MISS')]
- # sns.kdeplot(data=data_input, x=data_input["yCordAdjusted"]*-1, y=data_input["xCordAdjusted"],fill=True,thresh=0.4,cmap=cmap,ax=ax3)
- # #sns.scatterplot(data=data_input, x=data_input["yCordAdjusted"]*-1, y=data_input["xCordAdjusted"], alpha=1,zorder=25,cmap='Blues',s=400,hue='shotType',palette="Set2")
- # rink = NHLRink(rotation=270)
- # #x, y = rink.convert_xy(x, y)
- # rink.draw(ax=ax3,display_range='dzone')
- # ax3.set_title(name+" Shooting Heat Map",fontsize=16,fontname='Century Gothic')
- #plt.suptitle('NHL Shot Locations ', fontsize=32, y = 0.91)
- #plt.title('All Shots', fontsize=16, y=1.02)
- sns.barplot(x=categories[7:14],y=player[7:14],palette=color_scheme[7:14],edgecolor='black',ax=ax3)
- ax3.set_title("Individual Rate Z-Score (vs "+position_player+", min. "+str(min_time)+' TOI)',fontsize=14,fontname='Century Gothic')
- ax3.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
- ax3.set_ylim([-3, 3])
- ax3.set_axisbelow(True)
- ax3.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
- #plt.rcParams['xtick.color']='#333F4B'
- #ax[0][1].set_prop_cycle(ytick.color='#333F4B')
-
-
- sns.barplot(x=categories_on,y=player_on,palette=color_scheme_on,edgecolor='black',ax=ax4)
- ax4.set_ylim([-3, 3])
- ax4.set_title("On-Ice All Situations Rate Z-Score (vs "+position_player+", min. "+str(min_time)+' TOI)',fontsize=14,fontname='Century Gothic')
- ax4.set_axisbelow(True)
- ax4.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
- ax4.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
-
- ax2.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
- show_values(ax3,stat = values_2,rank_n=rank_2)
- values_on[3] = str(round(values_on[3]*100,1))+'%'
- values_on[4] = str(round(values_on[4]*100,1))+'%'
- show_values(ax4,stat = values_on,rank_n=rank_3)
-
-
- ax2.text(-0.5, -3.8, '2023-24', ha="right",fontstyle='italic',zorder=1)
- ax3.text(-0.5, -3.8, '2023-24', ha="right",fontstyle='italic')
- ax4.text(-0.5, -3.8, '2023-24', ha="right",fontstyle='italic')
-
-
- ax2.text(-0.5, -4.2, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100)
- ax3.text(-0.5, -4.2, 'Rank(of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100)
- ax4.text(-0.5, -4.2, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100)
-
-
- ax2.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)
- ax3.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)
- ax4.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)
-
- if summary_2023_one.GP.values[0] < 2:
- line_text_value = 1
- else:
- line_text_value = last_games+0.5
-
- if position_player == 'F':
- ax5.hlines(y=18,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
- ax5.text(s="1st",y=18,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
- ax5.hlines(y=16,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
- ax5.text(s="2nd",y=16,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
- ax5.hlines(y=14,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
- ax5.text(s="3rd",y=14,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
- ax5.hlines(y=12,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
- ax5.text(s="4th",y=12,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
-
- if position_player == 'D':
- ax5.hlines(y=23,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
- ax5.text(s="1st",y=23,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
- ax5.hlines(y=20,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
- ax5.text(s="2nd",y=20,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
- ax5.hlines(y=17,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
- ax5.text(s="3rd",y=17,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
-
-
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games,min_periods=last_games).mean()['TOI'],ax=ax5,color='#FFB000',linewidth = 2,label='TOI/GP',legend=False)
-
-
-
-
- ax5.xaxis.set_major_locator(MaxNLocator(integer=True))
- ax5.set_xlim([last_games, np.max(player_games_one.game)])
- axn = ax5.twinx()
- #sns.lineplot(x=game_log_pp.Game,y=game_log_pp.rolling(last_games).mean()['PP_TOI'],ax=axn,color='#DCAD23',linewidth = 2,label='PP TOI/GP',legend=False)
- #ax5.set_ylim([min(math.floor(min(player_games_one.TOI)/5)*5,10), math.ceil(max((player_games_one.rolling(last_games).mean().fillna(0)['TOI']))/5)*5])
- ax5.set_ylim([10,30])
- ax5.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
- ax5.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
- ax5.set_title(str(last_games)+" Game Rolling Average - TOI and PP%",fontsize=14,fontname='Century Gothic')
- ax5.set(xlabel='Game', ylabel='TOI/GP')
- axn.set(xlabel='Game', ylabel='PP%')
- axn.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
- axn.set_ylim([0, 1])
-
-
- sns.lineplot(x=player_games_one.game,y=(player_games_one.rolling(last_games).sum()['TOI_pp']/player_games_one.rolling(last_games).sum()['pp_toi']),ax=axn,color='#648FFF',linewidth = 2,label='PP%',legend=False)
- #
- handles, labels = [ax5.get_legend_handles_labels()[0]+axn.get_legend_handles_labels()[0]]+[ax5.get_legend_handles_labels()[1]+axn.get_legend_handles_labels()[1]]
- ax5.legend(handles, labels, fontsize=10,ncol=2,loc=9)
-
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Points'],ax=ax6,color='#FFB000',label='Points',linewidth = 2)
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Points_pp'],ax=ax6,color='#648FFF',label='PP Points',linewidth = 2)
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Goals'],ax=ax6,color='#DC267F',label='Goals',linewidth = 2)
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Assists'],ax=ax6,color='#FE6100',label='Assists',linewidth = 2)
- ax6.legend(fontsize=10,ncol=4,loc=9)
- ax6.xaxis.set_major_locator(MaxNLocator(integer=True))
- ax6.set_xlim([last_games, np.max(player_games_one.game)])
- ax6.set_ylim([0, round(max((player_games_one.rolling(last_games).mean().fillna(0)['Total Points'])+1)*2)/2])
- ax6.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
- ax6.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
- ax6.set_title(str(last_games)+" Game Rolling Average - Points",fontsize=14)
- ax6.set(xlabel='Game', ylabel='Value Per Game')
-
-
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Shots'],ax=ax7,color='#FFB000',linewidth = 2,label='Shots')
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['iCF'],ax=ax7,color='#648FFF',linewidth = 2,label='iCF')
- # sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['iSCF'],ax=ax7,color='#DC267F',linewidth = 2,label='iSCF')
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Hits'],ax=ax7,color='#DC267F',linewidth = 2,label='Hits')
- sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Shots Blocked'],ax=ax7,color="#FE6100",linewidth = 2,label='Blocks')
-
- ax7.legend(fontsize=10,ncol=5,loc=9)
- ax7.xaxis.set_major_locator(MaxNLocator(integer=True))
- ax7.set_xlim([last_games, np.max(player_games_one.game)])
- ax7.set_ylim([0, max(round(max((player_games_one.rolling(last_games).mean().fillna(0)['iCF'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Shots'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Hits'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Shots Blocked'])+1)*2)/2+1)])
- ax7.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
- ax7.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
- ax7.set_title(str(last_games)+" Game Rolling Average - Shots and Bangers",fontsize=14,fontname='Century Gothic')
- ax7.set(xlabel='Game', ylabel='Value Per Game')
- ax2.hlines(y=0,xmin=-0.5, xmax=len(ax2.patches)-0.5, color='black',linewidth=1)
- ax3.hlines(y=0,xmin=-0.5, xmax=len(ax3.patches)-0.5, color='black',linewidth=1)
- ax4.hlines(y=0,xmin=-0.5, xmax=len(ax4.patches)-0.5, color='black',linewidth=1)
-
- ax2.set_xlim([-0.5, len(ax2.patches)-0.5])
- ax3.set_xlim([-0.5, len(ax3.patches)-0.5])
- ax4.set_xlim([-0.5, len(ax4.patches)-0.5])
-
- #plt.tight_layout(pad=5, w_pad=2, h_pad=2)
-
- #abot = fig.add_axes([0.075,0.025,0.9,0.025], anchor='NW', zorder=1)
-
- print('here')
-
- ax1.text(x=0.0,y=-0.025,s='Created By: @TJStats',horizontalalignment='left',fontsize=14)#,fontname='Century Gothic')
- ax1.text(x=0.0,y=-0.05,s='Data: Natural Stat Trick, CapFriendly, Yahoo Fantasy',horizontalalignment='left',fontsize=14)#,fontname='Century Gothic')
- ax1.text(x=0.725,y=-0.025,s=f'Generated: {str(date.today())}',horizontalalignment='right',fontsize=14)#,fontname='Century Gothic')
-
- from matplotlib.font_manager import FontProperties
- font_properties_label = FontProperties(family='century gothic', size=16)
- ax5.set_xlabel(xlabel=ax5.get_xlabel(),fontproperties=font_properties_label)
- ax6.set_xlabel(xlabel=ax6.get_xlabel(),fontproperties=font_properties_label)
- ax7.set_xlabel(xlabel=ax7.get_xlabel(),fontproperties=font_properties_label)
- ax5.set_ylabel(ylabel=ax5.get_ylabel(),fontproperties=font_properties_label)
- ax6.set_ylabel(ylabel=ax6.get_ylabel(),fontproperties=font_properties_label)
- ax7.set_ylabel(ylabel=ax7.get_ylabel(),fontproperties=font_properties_label)
-
- font_properties_title = FontProperties(family='century gothic', size=16)
- ax2.set_title(label=ax2.get_title(),fontproperties=font_properties_title)
- ax3.set_title(label=ax3.get_title(),fontproperties=font_properties_title)
- ax4.set_title(label=ax4.get_title(),fontproperties=font_properties_title)
- ax5.set_title(label=ax5.get_title(),fontproperties=font_properties_title)
- ax6.set_title(label=ax6.get_title(),fontproperties=font_properties_title)
- ax7.set_title(label=ax7.get_title(),fontproperties=font_properties_title)
-
- ax1.text(x=0.43,y=0.025,s='Note: Last Games compares to 2023-24. 2023-24 compares to 2022-23.',horizontalalignment='center',fontsize=12,fontname='Century Gothic')
- # font_properties_ticks = FontProperties(family='century gothic', size=12)
- # ax5.set_xticklabels(ax5.get_xticks(),fontproperties=font_properties_label)
-
- #plt.xticks(fontname = 'century gothic')
- for key, cell in table.get_celld().items():
- cell.set_linewidth(0)
-
-
- #abot.axis('off')
- #ax1.set_zorder(2)
- fig.tight_layout()
-
- # for i in range(0,len(df_combined_t.index)):
- # ax1.text(s=df_combined_t.index[i],x=10,y=700+(10*i),fontsize=10,bbox=dict(facecolor='red', alpha=1))
-
-
- # #plt.figure(dpi=300)
- # if not os.path.exists('player_cards/skaters/'+name):
- # os.makedirs('player_cards/skaters/'+name)
-
- # dir = 'players/'
- # for f in os.listdir(dir):
- # os.remove(os.path.join(dir, f))
-
-
- #plt.savefig('player_cards/skaters/'+name+'/'+name+"_"+str(last_games)+'_Znew.png',dpi=600,bbox_inches="tight")
-
- #matplotlib.rcParams["figure.dpi"] = 600
-
- #plt.savefig('players/'+name+"_"+str(last_games)+'_Znew.png',dpi=600,bbox_inches="tight")
-
-
-
-# test = test.fillna(0)
-#test['PP TOI'] = ["%d:%02d" % (int(x),(x*60)%60) if x>0 else '0:00' for x in test['PP TOI']]
-
-
-
-app = App(app_ui, server)
-#time.sleep(60)
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe.Photoshop.CS6.v13.0.Pre.Release.Incl.Keymaker-CORE 64 Bit.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe.Photoshop.CS6.v13.0.Pre.Release.Incl.Keymaker-CORE 64 Bit.md
deleted file mode 100644
index e8efc25b31ff53f865b1117907658d2a839526a6..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe.Photoshop.CS6.v13.0.Pre.Release.Incl.Keymaker-CORE 64 Bit.md
+++ /dev/null
@@ -1,46 +0,0 @@
-
-
How to Download and Install Adobe Photoshop CS6 v13.0 Pre Release Incl Keymaker-CORE 64 Bit
-
-
Adobe Photoshop CS6 is one of the most popular and powerful image editing software in the world. It offers a wide range of features and tools for creating stunning graphics, photos, and designs. Whether you are a professional photographer, graphic designer, or hobbyist, Adobe Photoshop CS6 can help you achieve your creative vision.
-
Adobe.Photoshop.CS6.v13.0.Pre.Release.Incl.Keymaker-CORE 64 Bit
However, if you want to try out the latest version of Adobe Photoshop CS6 before it is officially released, you can download and install the pre-release version that includes a keymaker-core that allows you to activate the software for free. This version is compatible with 64-bit Windows operating systems and has some new and improved features that are not available in the previous versions.
-
-
In this article, we will show you how to download and install Adobe Photoshop CS6 v13.0 Pre Release Incl Keymaker-CORE 64 Bit on your computer. Follow these simple steps and enjoy the benefits of this amazing software.
-
-
Step 1: Download the Software
-
-
The first thing you need to do is to download the software from a reliable source. You can use one of the links below to download the software:
[^4^] Adobe Photoshop CS6 v13.0 Pre Release - Nsane Forums
-
-
-
Make sure you choose the right link for your operating system and language preference. The file size is about 1.65 GB, so it may take some time to download depending on your internet speed.
-
-
-
Step 2: Extract the Files
-
-
Once you have downloaded the software, you need to extract the files from the compressed archive. You can use any file extraction software such as WinRAR or 7-Zip to do this. Right-click on the downloaded file and choose "Extract Here" or "Extract to Adobe.Photoshop.CS6.v13.0.Pre.Release.Incl.Keymaker-CORE 64 Bit" depending on your software.
-
-
You will see a folder named "Adobe.Photoshop.CS6.v13.0.Pre.Release.Incl.Keymaker-CORE 64 Bit" containing several files and subfolders. Open this folder and look for the file named "Set-up.exe" (Windows) or "Install.app" (macOS).
-
-
Step 3: Install the Software
-
-
Double-click on the file named "Set-up.exe" (Windows) or "Install.app" (macOS) to begin the installation process. You will see a welcome screen with some information about the software. Click on "Next" to continue.
-
-
You will be asked to accept the license agreement and choose the installation type. You can choose between "Typical", "Complete", or "Custom". We recommend choosing "Complete" to install all the features and components of Adobe Photoshop CS6.
-
-
You will also be asked to enter your serial number or choose to install as a trial version. If you have a serial number, enter it in the appropriate field and click on "Next". If you don't have a serial number, choose "Install as a trial version" and click on "Next". You can activate the software later using the keymaker-core.
-
-
The installation will begin and may take several minutes to complete depending on your system specifications. You will see a progress bar showing the status of the installation. Do not close or interrupt the installation until it is finished.
-
-
Step 4: Activate the Software
-
-
After the installation is completed, you need to activate the software using the keymaker-core that is included in the downloaded folder. Open the folder named "Keymaker-CORE" and look for the file named "keygen.exe". Double-click on this file to run it.
-
-
You will see a window with some options and buttons. Choose "Adobe Photoshop CS6 Extended v
e93f5a0c3f
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Happy Days Hindi Audio Track.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Happy Days Hindi Audio Track.md
deleted file mode 100644
index 3e9334e8580cad3ea805554f926949ca1b287f04..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Happy Days Hindi Audio Track.md
+++ /dev/null
@@ -1,36 +0,0 @@
-
-
How to Download Happy Days Hindi Audio Track for Free
-
Happy Days is a 2007 Telugu musical coming-of-age film directed by Sekhar Kammula. The film follows the lives of eight friends who go through the ups and downs of college life. The film was a huge hit and won several awards, including six Filmfare Awards South.
Many fans of the film have been looking for the Hindi audio track of Happy Days, as the film was dubbed in Hindi and released in 2012. However, finding the Hindi audio track online can be a challenge, as it is not available on any official platforms.
-
Fortunately, there are some unofficial sources where you can download the Happy Days Hindi audio track for free. Here are some of the steps you need to follow:
-
-
Go to this YouTube link, which has the full Hindi dubbed version of Happy Days.
-
Copy the URL of the video and paste it on this website, which can convert YouTube videos to MP3 files.
-
Click on "Convert" and wait for a few seconds until the conversion is done.
-
Click on "Download" and save the MP3 file on your device.
-
Now you have the Happy Days Hindi audio track, which you can play along with the original Telugu video of the film.
-
-
Enjoy watching Happy Days in Hindi with your friends and family!
-
-
If you are a fan of Happy Days, you might also be interested in listening to the original Telugu songs of the film. The film has a total of nine songs, composed by Mickey J Meyer and sung by various artists. The songs are catchy, melodious and reflect the mood and theme of the film.
-
Some of the popular songs from Happy Days are:
-
-
-
"Happy Days", the title track, which is a cheerful and upbeat song that celebrates friendship and college life.
-
"Oh My Friend", a romantic duet that expresses the feelings of two friends who fall in love with each other.
-
"Arey Rey", a sad and emotional song that depicts the pain of separation and heartbreak.
-
"Ye Cheekati", a rock song that showcases the talent and passion of the college band.
-
"Jill Jill Jinga", a fun and peppy song that features a dance competition between the boys and girls.
-
-
You can listen to all the Happy Days Telugu songs on this website, where you can also download them for offline listening.
-
Happy Days is a film that will make you nostalgic and happy at the same time. It is a film that you can watch over and over again with your friends and family. So, what are you waiting for? Grab your popcorn and enjoy Happy Days in Hindi or Telugu!
-
-- The cast and crew of Happy Days and their other works.
-- The critical and commercial reception of Happy Days and its impact on Telugu cinema.
-- The similarities and differences between Happy Days and other coming-of-age films from different languages and cultures.
-- The personal experiences and opinions of the writer or the readers about Happy Days and what they learned from it.
-
-These are some possible ways to extend the article, but you should also make sure that they are relevant and interesting to your target audience. You should also avoid repeating or contradicting yourself, and maintain a clear and coherent structure throughout the article. I hope this helps.ð 81aa517590
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Luis Miguel - Todos Los Romances [320 Kbps]l.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Luis Miguel - Todos Los Romances [320 Kbps]l.md
deleted file mode 100644
index 3639c227f7a9b3e2e2d6ede54ee3fb8d128c9086..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Luis Miguel - Todos Los Romances [320 Kbps]l.md
+++ /dev/null
@@ -1,23 +0,0 @@
-
-
Luis Miguel: The King of Boleros and Romance
-
If you are a fan of Luis Miguel, the Mexican singer who has captivated millions of hearts with his smooth voice and romantic songs, you might want to check out his compilation album Todos Los Romances (All the Romances). This album contains the three previous albums of the Romance series, in which Luis Miguel covers classic boleros from different eras.
Boleros are a type of Latin music that originated in Cuba in the late 19th century and became popular throughout Latin America and Spain in the 20th century. They are characterized by their slow tempo, sentimental lyrics, and lush orchestration. Luis Miguel is one of the most successful artists who has revived and modernized this genre, bringing it to new generations of listeners.
-
The Romance Series
-
The Romance series consists of three albums that Luis Miguel released between 1991 and 1997. Each album features a selection of boleros from different composers and periods, arranged by renowned musicians such as Armando Manzanero, Bebu Silvetti, Juan Carlos Calderón, and Kiko Cibrian. The albums are:
-
-
Romance (1991): The first album of the series, which sold over 7 million copies worldwide and received a Grammy nomination for Best Latin Pop Album. It includes songs such as "No Me Platiques Más", "Inolvidable", "La Puerta", and "La Barca".
-
Segundo Romance (1994): The second album of the series, which won a Grammy Award for Best Latin Pop Performance. It includes songs such as "El DÃa Que Me Quieras", "Solamente Una Vez", "Somos Novios", and "Nosotros".
In 1998, WEA Latina released a box set compilation album titled Todos Los Romances, which contains the three albums of the Romance series in their entirety. The album was a commercial success, reaching number four in Spain and number 12 on the Billboard Top Latin Albums chart in the United States. It also received gold and platinum certifications in several countries.
-
-
If you want to enjoy the best of Luis Miguel's boleros, Todos Los Romances is the perfect album for you. You will find 36 tracks of pure romance, passion, and elegance, performed by one of the most talented and charismatic singers of all time.
Luis Miguel: A Star Since Childhood
-
Luis Miguel was born on April 19, 1970, in San Juan, Puerto Rico, to Spanish singer Luisito Rey and Italian actress Marcela Basteri. He inherited his musical talent and passion from his father, who encouraged him to start singing at a very young age. Luis Miguel made his debut on a Mexican television show in 1981, when he was only 11 years old, and impressed the audience with his rendition of "La Malagueña".
-
His first album, Un Sol (A Sun), was released in 1982 by EMI Mexico, and included the hit single "1+1=2 Enamorados" (1+1=2 Lovers). He soon became a teen idol and a sensation in Latin America, earning his first Grammy Award at age 14 for his duet with Sheena Easton, "Me Gustas Tal Como Eres" (I Like You Just the Way You Are). He also starred in several movies and telenovelas, such as Fiebre de Amor (Love Fever) and Palabra de Honor (Word of Honor).
-
Luis Miguel: A Versatile and Innovative Artist
-
Luis Miguel's artistic career has been marked by his versatility and innovation. He has explored different genres and styles, from pop to ballads, from boleros to mariachi, from jazz to big band. He has collaborated with renowned musicians and composers, such as Armando Manzanero, Juan Carlos Calderón, Kiko Cibrian, and Bebu Silvetti. He has also performed with legendary artists, such as Frank Sinatra, Plácido Domingo, Barbra Streisand, and Michael Jackson.
-
Luis Miguel has been recognized as one of the best-selling Latin music artists of all time, with over 60 million records sold worldwide. He has received numerous awards and honors, such as four Grammy Awards, six Latin Grammy Awards, three World Music Awards, a star on the Hollywood Walk of Fame, and the Billboard Lifetime Achievement Award. He has also broken several records for his high-grossing and captivating live performances. He is the highest-grossing Latino touring artist since Boxscore began tracking touring data in 1990.
7196e7f11a
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/MRTG CentOS 5.9 !!HOT!!.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/MRTG CentOS 5.9 !!HOT!!.md
deleted file mode 100644
index 3f879e7742bbefa3280f817963cf920707a48870..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/MRTG CentOS 5.9 !!HOT!!.md
+++ /dev/null
@@ -1,60 +0,0 @@
-
-
How to Install and Configure MRTG on CentOS 5.9
-
MRTG (Multi Router Traffic Grapher) is a tool that monitors the traffic load on network links. It generates HTML pages containing PNG images that show the live visual representation of the traffic. In this article, we will show you how to install and configure MRTG on CentOS 5.9.
-
Prerequisites
-
Before you start, you need to have the following packages installed on your CentOS 5.9 server:
net-snmp and net-snmp-utils: SNMP (Simple Network Management Protocol) is a protocol used for network management. The NET-SNMP project includes various SNMP tools. net-snmp package contains the snmpd and snmptrapd daemons, documentation, etc. You also want to install the net-snmp-utils package, which contains NET-SNMP utilities.
-
-
You can install these packages using yum command:
-
# yum install mrtg net-snmp net-snmp-utils
-
-
Configure snmpd
-
If you need to monitor localhost including interface and other stuff such as CPU, memory etc, you need to configure snmpd. Open /etc/snmp/snmpd.conf file with your favorite editor:
-
# vi /etc/snmp/snmpd.conf
-
-
Update it as follows to only allow access from localhost:
-
com2sec local localhost public
-group MyRWGroup v1 local
-group MyRWGroup v2c local
-group MyRWGroup usm local
-view all included .1 80
-access MyRWGroup "" any noauth exact all all none
-syslocation VSNL, India
-syscontact Root <vivek@nixcraft.tld>
-
-
Save and close the file. Restart snmpd:
-
# chkconfig snmpd on
-# service snmpd restart
-
-
Make sure you see interface IP, by running the following command:
-
-
# snmpwalk -v 1 -c public localhost IP-MIB::ipAdEntIfIndex
-
public@localhost: public is the community name of the device you want to create a configuration for. If you are using the wrong community name you will get no response from the device. localhost is the DNS name or the IP number of an SNMP-manageable device i.e. our local server.
-
-
Next, run indexmaker to create web pages which display the status of an array of mrtg interface status pages:
Feel free to give us your feedback on this NER demo.
- For all your Named Entity Recognition related requirements, we are here to help you. Email us your requirement at
- letstalk@pragnakalp.com And don't forget to check out more interesting
- NLP services we are offering.
-
-Jan A. P. Kaczmarek, Kaczmarek, Jan A. P. - Unfaithful - Amazon.com Music. . This soundtrack captures the various moods of Diane Lane's monumental performance. â–º Unfaithful is not just a soundtrack, but something more, .
-Jan Kaczmarek, Unfaithful (OST) - OST - The Witcher - Witcher Soundtrack - Wild Hunt - The Soundtrack - Ennio Morricone - The Soundtrack - Ennio Morricone (OST) - Ennio Morricone - OST - The Witcher - Witcher Soundtrack - Wild Hunt - The Soundtrack - Ennio Morricone - OST - The Witcher - Witcher Soundtrack - Wild Hunt - The Soundtrack .
-Jan A. P. Kaczmarek, Kaczmarek, Jan A. P. - Unfaithful - Amazon.com Music. .
-This soundtrack reflects 8a78ff9644
-
-
-
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/C Program Files Odeon Jaf Winscard Dll [2021].md b/spaces/quidiaMuxgu/Expedit-SAM/C Program Files Odeon Jaf Winscard Dll [2021].md
deleted file mode 100644
index c16f174cd9e181259d12464d60fd1b63a9071c44..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/C Program Files Odeon Jaf Winscard Dll [2021].md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-The firmware files you download need to be in /Program ... In C:/program files/odeon/jaf, you'll need a file " jaf_nok4models.ini "containing a reference ... that cannot create file C:\Program Files (x86)\ODEON\JAF\winscard.dll. 4d29de3e1b
-
-
-
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Firmware Reset Samsung Scx 3400 !LINK!.md b/spaces/quidiaMuxgu/Expedit-SAM/Firmware Reset Samsung Scx 3400 !LINK!.md
deleted file mode 100644
index edd885b8070d82919df0896480ddbfd1fbcc88fd..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Firmware Reset Samsung Scx 3400 !LINK!.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
Samsung TVC Hub is a standalone, integrated, and full featured Samsung Connect platform to integrate Samsung devices with your smart home. It comes with a remote, a set-top box, a HDMI cable, a LAN cable, and a power cord. It works with smart hubs like Amazon Alexa and IFTTT to turn smart devices on and off, dim the lights, adjust the thermostat, and adjust the music.
The Galaxy S10e is Samsungs fifth smartphone in its smartphone lineup. It is the lightest and least expensive phone in the Galaxy S10 line, with a screen size that is the same as the Galaxy S10 and Galaxy S10 Plus. It is also Samsungs first smartphone with a USB-C charging port and, like the Galaxy S10 and Galaxy S10 Plus, the Galaxy S10e also features an Infinity Flex Display that curves around the outside edges of the phone and wraps around the sides to make it even slimmer. The Galaxy S10e also features Samsungs A.I. based AI CAM that lets users take high quality photos and videos, as well as a dual-camera setup on the back that include a primary 12MP main camera and a 12MP ultra-wide camera for high quality close-up shots and added depth to images and videos. The phone also has a USB Type-C charger port that can be used for fast charging.
-
Theres no need for any additional software installation or setup required for simply replacing a new toner cartridge. All you have to do is to simply replace a new toner cartridge by opening the box and follow the instructions that are easy to understand. On the other hand, if you want to use a compatible toner cartridge that is compatible with the printer, you have to install software on your device first. The Samsung SCX-3400 will allow you to simply replace a new cartridge without any driver installations or re-settings required. The Samsung SCX-3400 is one of the best printers for text documents like resumes, engineering drawings, and contracts. This printer is also known to print high quality photos and graphics. It may have small capacity for a color laser printer, but with its high-resolution capability of up to 210x180 DPI, its not that far behind that of an inkjet printer.
899543212b
-
-
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Kp Thakur English Grammar Pdf Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Kp Thakur English Grammar Pdf Download.md
deleted file mode 100644
index d4758218bd475b7f7e6b77b5f959a5313eb0775d..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Kp Thakur English Grammar Pdf Download.md
+++ /dev/null
@@ -1,29 +0,0 @@
-
-
How to Download KP Thakur English Grammar PDF for Free
-
If you are looking for a practical and comprehensive guide to English grammar and composition, you might want to check out KP Thakur's books. KP Thakur is an author of two popular books on English grammar and translation: A Practical Guide To English Grammar / E9 and A Practical Guide to English Translation and Composition. These books are designed to help learners master the rules and usage of English language, as well as improve their writing and communication skills.
In this article, we will show you how to download KP Thakur English grammar PDF for free from various online sources. We will also give you some tips on how to use the PDF files effectively for your learning.
-
Steps to Download KP Thakur English Grammar PDF for Free
-
There are many websites that offer free PDF downloads of KP Thakur's books, but not all of them are reliable or safe. Some may contain viruses, malware, or spam. Therefore, you should be careful when choosing a website to download from. Here are some steps you can follow to download KP Thakur English grammar PDF for free:
-
-
Go to a reputable search engine like Google or Bing and type in "KP Thakur English grammar PDF download" or a similar keyword.
-
Look for websites that have a high ranking, positive reviews, and a secure connection (https://).
-
Click on the website that seems trustworthy and relevant to your query.
-
Look for the download link or button on the website. It may be located at the top, bottom, or side of the page. Sometimes, you may have to fill in a captcha or a survey before you can access the download link.
-
Click on the download link or button and wait for the PDF file to be downloaded to your device.
-
Open the PDF file with a PDF reader or viewer like Adobe Acrobat Reader or Google Chrome.
-
-
Tips on How to Use KP Thakur English Grammar PDF Effectively
-
Downloading KP Thakur English grammar PDF is not enough to improve your English skills. You also need to use the PDF files effectively for your learning. Here are some tips on how to use KP Thakur English grammar PDF effectively:
-
-
-
Read the PDF files regularly and carefully. Try to understand the concepts and examples given by the author.
-
Practice the exercises and activities given in the PDF files. Check your answers with the answer key or a reliable source.
-
Review the PDF files periodically and revise the topics that you find difficult or confusing.
-
Apply what you have learned from the PDF files to your own writing and speaking. Try to use correct grammar, vocabulary, and punctuation in your sentences.
-
Seek feedback from others on your writing and speaking. Ask them to point out your mistakes and suggest ways to improve.
-
-
Conclusion
-
KP Thakur's books are a great resource for anyone who wants to learn English grammar and composition. You can download KP Thakur English grammar PDF for free from various online sources, but make sure you choose a reliable and safe website. You can also use the PDF files effectively for your learning by following some tips we have given above. We hope this article has helped you find and use KP Thakur English grammar PDF for free.
d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Malena 2000 Uncut Bluray 720p Dts X264.md b/spaces/quidiaMuxgu/Expedit-SAM/Malena 2000 Uncut Bluray 720p Dts X264.md
deleted file mode 100644
index 60292d1a6da46f34c7befc6c216a8822d59b1241..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Malena 2000 Uncut Bluray 720p Dts X264.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- . . . mobiledit phone copier crack, mobiledit phone copier . . . . mobiledit phone copier mobile crack, mobiledit phone copier . . . . mobiledit phone copier imei crack, mobiledit phone copier imei, mobiledit phone copier express key, mobiledit phone copier keygen, mobiledit phone copier keygen free download, mobiledit phone copier key, mobiledit phone copier keygen imei . . . . mobiledit phone copier unlock imei, mobiledit phone copier imei free, mobiledit phone copier unlock, mobiledit phone copier unlock net, mobiledit phone copier unlock ios, mobiledit phone copier unlock samsung, mobiledit phone copier unlock sony, mobiledit phone copier unlock sonyericsson, mobiledit phone copier unlock samsungericsson, mobiledit phone copier unlock imei, mobiledit phone copier imei unlock, mobiledit phone copier imei unlock net, mobiledit phone copier imei unlock ios, mobiledit phone copier imei unlock samsung, mobiledit phone copier imei unlock sony, mobiledit phone copier imei unlock sonyericsson, mobiledit phone copier imei unlock samsungericsson, mobiledit phone copier imei unlock, mobiledit phone copier imei unlock net, mobiledit phone copier imei unlock ios, mobiledit phone copier imei unlock samsung, mobiledit phone copier imei unlock sony, mobiledit phone copier imei unlock sonyericsson, mobiledit phone copier imei unlock samsungericsson, mobiledit phone copier imei unlock . . . . mobiledit phone copier app, mobiledit phone copier android, mobiledit phone copier developer, mobiledit phone copier express, mobiledit phone copier free, mobiledit phone copier mobile, mobiledit phone copier unlock samsung, mobiledit phone copier unlock sonyericsson, mobiledit phone copier unlock sonyericsson, mobiledit phone copier samsung, mobiledit phone copier sony 4fefd39f24
-
-
-
diff --git a/spaces/ramiin2/AutoGPT/tests/unit/test_browse_scrape_links.py b/spaces/ramiin2/AutoGPT/tests/unit/test_browse_scrape_links.py
deleted file mode 100644
index 0a3340e7397a997da96b8ab9828954230e1a3c20..0000000000000000000000000000000000000000
--- a/spaces/ramiin2/AutoGPT/tests/unit/test_browse_scrape_links.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Generated by CodiumAI
-
-# Dependencies:
-# pip install pytest-mock
-import pytest
-
-from autogpt.commands.web_requests import scrape_links
-
-"""
-Code Analysis
-
-Objective:
-The objective of the 'scrape_links' function is to scrape hyperlinks from a
-given URL and return them in a formatted way.
-
-Inputs:
-- url: a string representing the URL to be scraped.
-
-Flow:
-1. Send a GET request to the given URL using the requests library and the user agent header from the config file.
-2. Check if the response contains an HTTP error. If it does, return "error".
-3. Parse the HTML content of the response using the BeautifulSoup library.
-4. Remove any script and style tags from the parsed HTML.
-5. Extract all hyperlinks from the parsed HTML using the 'extract_hyperlinks' function.
-6. Format the extracted hyperlinks using the 'format_hyperlinks' function.
-7. Return the formatted hyperlinks.
-
-Outputs:
-- A list of formatted hyperlinks.
-
-Additional aspects:
-- The function uses the 'requests' and 'BeautifulSoup' libraries to send HTTP
-requests and parse HTML content, respectively.
-- The 'extract_hyperlinks' function is called to extract hyperlinks from the parsed HTML.
-- The 'format_hyperlinks' function is called to format the extracted hyperlinks.
-- The function checks for HTTP errors and returns "error" if any are found.
-"""
-
-
-class TestScrapeLinks:
- # Tests that the function returns a list of formatted hyperlinks when
- # provided with a valid url that returns a webpage with hyperlinks.
- def test_valid_url_with_hyperlinks(self):
- url = "https://www.google.com"
- result = scrape_links(url)
- assert len(result) > 0
- assert isinstance(result, list)
- assert isinstance(result[0], str)
-
- # Tests that the function returns correctly formatted hyperlinks when given a valid url.
- def test_valid_url(self, mocker):
- # Mock the requests.get() function to return a response with sample HTML containing hyperlinks
- mock_response = mocker.Mock()
- mock_response.status_code = 200
- mock_response.text = (
- "Google"
- )
- mocker.patch("requests.Session.get", return_value=mock_response)
-
- # Call the function with a valid URL
- result = scrape_links("https://www.example.com")
-
- # Assert that the function returns correctly formatted hyperlinks
- assert result == ["Google (https://www.google.com)"]
-
- # Tests that the function returns "error" when given an invalid url.
- def test_invalid_url(self, mocker):
- # Mock the requests.get() function to return an HTTP error response
- mock_response = mocker.Mock()
- mock_response.status_code = 404
- mocker.patch("requests.Session.get", return_value=mock_response)
-
- # Call the function with an invalid URL
- result = scrape_links("https://www.invalidurl.com")
-
- # Assert that the function returns "error"
- assert "Error:" in result
-
- # Tests that the function returns an empty list when the html contains no hyperlinks.
- def test_no_hyperlinks(self, mocker):
- # Mock the requests.get() function to return a response with sample HTML containing no hyperlinks
- mock_response = mocker.Mock()
- mock_response.status_code = 200
- mock_response.text = "
No hyperlinks here
"
- mocker.patch("requests.Session.get", return_value=mock_response)
-
- # Call the function with a URL containing no hyperlinks
- result = scrape_links("https://www.example.com")
-
- # Assert that the function returns an empty list
- assert result == []
-
- # Tests that scrape_links() correctly extracts and formats hyperlinks from
- # a sample HTML containing a few hyperlinks.
- def test_scrape_links_with_few_hyperlinks(self, mocker):
- # Mock the requests.get() function to return a response with a sample HTML containing hyperlinks
- mock_response = mocker.Mock()
- mock_response.status_code = 200
- mock_response.text = """
-
-
-
-
-[2] Trunks can transform into a Super Saiyan which causes his hair to spike on end ... "Bryn Apprill, Eric Vale Star in Funimation's Broadcast Dub Cast for First Love ... of Trunks (TV Movie) Gohan (English version, voice) 1990 Dragon Ball Z: ... version, voice) 1993 Dragon Ball Z: Broly - The Legendary Super ... 1fdad05405
-
-
-
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Citroen Service Doc Backup Sedre 2011 NEW!.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Citroen Service Doc Backup Sedre 2011 NEW!.md
deleted file mode 100644
index f417cdc98f01f9e4b6b14837e68b249c525bd894..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Citroen Service Doc Backup Sedre 2011 NEW!.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Peugeot Service Box (DocBackup + Sedre) (11.2013)+Keygen TORRENT. Administrator Offline ... FS Citroen Service Doc Backup 09 2011Â ... 1fdad05405
-
-
-
diff --git a/spaces/renatotn7/teste2/setup.py b/spaces/renatotn7/teste2/setup.py
deleted file mode 100644
index 474e9188aa2dc5c19614921760ce4ad99bd19c13..0000000000000000000000000000000000000000
--- a/spaces/renatotn7/teste2/setup.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import time
-
-version_file = 'gfpgan/version.py'
-
-
-def readme():
- with open('README.md', encoding='utf-8') as f:
- content = f.read()
- return content
-
-
-def get_git_hash():
-
- def _minimal_ext_cmd(cmd):
- # construct minimal environment
- env = {}
- for k in ['SYSTEMROOT', 'PATH', 'HOME']:
- v = os.environ.get(k)
- if v is not None:
- env[k] = v
- # LANGUAGE is used on win32
- env['LANGUAGE'] = 'C'
- env['LANG'] = 'C'
- env['LC_ALL'] = 'C'
- out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
- return out
-
- try:
- out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
- sha = out.strip().decode('ascii')
- except OSError:
- sha = 'unknown'
-
- return sha
-
-
-def get_hash():
- if os.path.exists('.git'):
- sha = get_git_hash()[:7]
- else:
- sha = 'unknown'
-
- return sha
-
-
-def write_version_py():
- content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
- sha = get_hash()
- with open('VERSION', 'r') as f:
- SHORT_VERSION = f.read().strip()
- VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
- version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
- with open(version_file, 'w') as f:
- f.write(version_file_str)
-
-
-def get_version():
- with open(version_file, 'r') as f:
- exec(compile(f.read(), version_file, 'exec'))
- return locals()['__version__']
-
-
-def get_requirements(filename='requirements.txt'):
- here = os.path.dirname(os.path.realpath(__file__))
- with open(os.path.join(here, filename), 'r') as f:
- requires = [line.replace('\n', '') for line in f.readlines()]
- return requires
-
-
-if __name__ == '__main__':
- write_version_py()
- setup(
- name='gfpgan',
- version=get_version(),
- description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration',
- long_description=readme(),
- long_description_content_type='text/markdown',
- author='Xintao Wang',
- author_email='xintao.wang@outlook.com',
- keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan',
- url='https://github.com/TencentARC/GFPGAN',
- include_package_data=True,
- packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
- classifiers=[
- 'Development Status :: 4 - Beta',
- 'License :: OSI Approved :: Apache Software License',
- 'Operating System :: OS Independent',
- 'Programming Language :: Python :: 3',
- 'Programming Language :: Python :: 3.7',
- 'Programming Language :: Python :: 3.8',
- ],
- license='Apache License Version 2.0',
- setup_requires=['cython', 'numpy'],
- install_requires=get_requirements(),
- zip_safe=False)
diff --git a/spaces/riccorl/relik-entity-linking/relik/retriever/indexers/base.py b/spaces/riccorl/relik-entity-linking/relik/retriever/indexers/base.py
deleted file mode 100644
index c9b1b635566e941e5dfa6e204e594cd0d13c274c..0000000000000000000000000000000000000000
--- a/spaces/riccorl/relik-entity-linking/relik/retriever/indexers/base.py
+++ /dev/null
@@ -1,334 +0,0 @@
-import os
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
-
-import hydra
-import numpy
-import torch
-from omegaconf import OmegaConf
-from rich.pretty import pprint
-
-from relik.common import upload
-from relik.common.log import get_console_logger, get_logger
-from relik.common.utils import (
- from_cache,
- is_remote_url,
- is_str_a_path,
- relative_to_absolute_path,
- sapienzanlp_model_urls,
-)
-from relik.retriever.data.labels import Labels
-
-# from relik.retriever.models.model import GoldenRetriever, RetrievedSample
-
-
-logger = get_logger(__name__)
-console_logger = get_console_logger()
-
-
-@dataclass
-class IndexerOutput:
- indices: Union[torch.Tensor, numpy.ndarray]
- distances: Union[torch.Tensor, numpy.ndarray]
-
-
-class BaseDocumentIndex:
- CONFIG_NAME = "config.yaml"
- DOCUMENTS_FILE_NAME = "documents.json"
- EMBEDDINGS_FILE_NAME = "embeddings.pt"
-
- def __init__(
- self,
- documents: Union[str, List[str], Labels, os.PathLike, List[os.PathLike]] = None,
- embeddings: Optional[torch.Tensor] = None,
- name_or_dir: Optional[Union[str, os.PathLike]] = None,
- ) -> None:
- if documents is not None:
- if isinstance(documents, Labels):
- self.documents = documents
- else:
- documents_are_paths = False
-
- # normalize the documents to list if not already
- if not isinstance(documents, list):
- documents = [documents]
-
- # now check if the documents are a list of paths (either str or os.PathLike)
- if isinstance(documents[0], str) or isinstance(
- documents[0], os.PathLike
- ):
- # check if the str is a path
- documents_are_paths = is_str_a_path(documents[0])
-
- # if the documents are a list of paths, then we load them
- if documents_are_paths:
- logger.info("Loading documents from paths")
- _documents = []
- for doc in documents:
- with open(relative_to_absolute_path(doc)) as f:
- _documents += [line.strip() for line in f.readlines()]
- # remove duplicates
- documents = list(set(_documents))
-
- self.documents = Labels()
- self.documents.add_labels(documents)
- else:
- self.documents = Labels()
-
- self.embeddings = embeddings
- self.name_or_dir = name_or_dir
-
- def __iter__(self):
- # make this class iterable
- for i in range(len(self)):
- yield self[i]
-
- def __len__(self):
- return self.documents.get_label_size()
-
- def __getitem__(self, index):
- return self.get_passage_from_index(index)
-
- @property
- def config(self) -> Dict[str, Any]:
- """
- The configuration of the document index.
-
- Returns:
- `Dict[str, Any]`: The configuration of the retriever.
- """
-
- def obj_to_dict(obj):
- match obj:
- case dict():
- data = {}
- for k, v in obj.items():
- data[k] = obj_to_dict(v)
- return data
-
- case list() | tuple():
- return [obj_to_dict(x) for x in obj]
-
- case object(__dict__=_):
- data = {
- "_target_": f"{obj.__class__.__module__}.{obj.__class__.__name__}",
- }
- for k, v in obj.__dict__.items():
- if not k.startswith("_"):
- data[k] = obj_to_dict(v)
- return data
-
- case _:
- return obj
-
- return obj_to_dict(self)
-
- def index(
- self,
- retriever,
- *args,
- **kwargs,
- ) -> "BaseDocumentIndex":
- raise NotImplementedError
-
- def search(self, query: Any, k: int = 1, *args, **kwargs) -> List:
- raise NotImplementedError
-
- def get_index_from_passage(self, document: str) -> int:
- """
- Get the index of the passage.
-
- Args:
- document (`str`):
- The document to get the index for.
-
- Returns:
- `int`: The index of the document.
- """
- return self.documents.get_index_from_label(document)
-
- def get_passage_from_index(self, index: int) -> str:
- """
- Get the document from the index.
-
- Args:
- index (`int`):
- The index of the document.
-
- Returns:
- `str`: The document.
- """
- return self.documents.get_label_from_index(index)
-
- def get_embeddings_from_index(self, index: int) -> torch.Tensor:
- """
- Get the document vector from the index.
-
- Args:
- index (`int`):
- The index of the document.
-
- Returns:
- `torch.Tensor`: The document vector.
- """
- if self.embeddings is None:
- raise ValueError(
- "The documents must be indexed before they can be retrieved."
- )
- if index >= self.embeddings.shape[0]:
- raise ValueError(
- f"The index {index} is out of bounds. The maximum index is {len(self.embeddings) - 1}."
- )
- return self.embeddings[index]
-
- def get_embeddings_from_passage(self, document: str) -> torch.Tensor:
- """
- Get the document vector from the document label.
-
- Args:
- document (`str`):
- The document to get the vector for.
-
- Returns:
- `torch.Tensor`: The document vector.
- """
- if self.embeddings is None:
- raise ValueError(
- "The documents must be indexed before they can be retrieved."
- )
- return self.get_embeddings_from_index(self.get_index_from_passage(document))
-
- def save_pretrained(
- self,
- output_dir: Union[str, os.PathLike],
- config: Optional[Dict[str, Any]] = None,
- config_file_name: Optional[str] = None,
- document_file_name: Optional[str] = None,
- embedding_file_name: Optional[str] = None,
- push_to_hub: bool = False,
- **kwargs,
- ):
- """
- Save the retriever to a directory.
-
- Args:
- output_dir (`str`):
- The directory to save the retriever to.
- config (`Optional[Dict[str, Any]]`, `optional`):
- The configuration to save. If `None`, the current configuration of the retriever will be
- saved. Defaults to `None`.
- config_file_name (`Optional[str]`, `optional`):
- The name of the configuration file. Defaults to `config.yaml`.
- document_file_name (`Optional[str]`, `optional`):
- The name of the document file. Defaults to `documents.json`.
- embedding_file_name (`Optional[str]`, `optional`):
- The name of the embedding file. Defaults to `embeddings.pt`.
- push_to_hub (`bool`, `optional`):
- Whether to push the saved retriever to the hub. Defaults to `False`.
- """
- if config is None:
- # create a default config
- config = self.config
-
- config_file_name = config_file_name or self.CONFIG_NAME
- document_file_name = document_file_name or self.DOCUMENTS_FILE_NAME
- embedding_file_name = embedding_file_name or self.EMBEDDINGS_FILE_NAME
-
- # create the output directory
- output_dir = Path(output_dir)
- output_dir.mkdir(parents=True, exist_ok=True)
-
- logger.info(f"Saving retriever to {output_dir}")
- logger.info(f"Saving config to {output_dir / config_file_name}")
- # pretty print the config
- pprint(config, console=console_logger, expand_all=True)
- OmegaConf.save(config, output_dir / config_file_name)
-
- # save the current state of the retriever
- embedding_path = output_dir / embedding_file_name
- logger.info(f"Saving retriever state to {output_dir / embedding_path}")
- torch.save(self.embeddings, embedding_path)
-
- # save the passage index
- documents_path = output_dir / document_file_name
- logger.info(f"Saving passage index to {documents_path}")
- self.documents.save(documents_path)
-
- logger.info("Saving document index to disk done.")
-
- if push_to_hub:
- # push to hub
- logger.info(f"Pushing to hub")
- model_id = model_id or output_dir.name
- upload(output_dir, model_id, **kwargs)
-
- @classmethod
- def from_pretrained(
- cls,
- name_or_dir: Union[str, os.PathLike],
- device: str = "cpu",
- precision: Optional[str] = None,
- config_file_name: Optional[str] = None,
- document_file_name: Optional[str] = None,
- embedding_file_name: Optional[str] = None,
- config_kwargs: Optional[Dict[str, Any]] = None,
- *args,
- **kwargs,
- ) -> "BaseDocumentIndex":
- cache_dir = kwargs.pop("cache_dir", None)
- force_download = kwargs.pop("force_download", False)
-
- config_file_name = config_file_name or cls.CONFIG_NAME
- document_file_name = document_file_name or cls.DOCUMENTS_FILE_NAME
- embedding_file_name = embedding_file_name or cls.EMBEDDINGS_FILE_NAME
-
- model_dir = from_cache(
- name_or_dir,
- filenames=[config_file_name, document_file_name, embedding_file_name],
- cache_dir=cache_dir,
- force_download=force_download,
- )
-
- config_path = model_dir / config_file_name
- if not config_path.exists():
- raise FileNotFoundError(
- f"Model configuration file not found at {config_path}."
- )
-
- config = OmegaConf.load(config_path)
- # override the config with the kwargs
- if config_kwargs is not None:
- config = OmegaConf.merge(config, OmegaConf.create(config_kwargs))
- pprint(OmegaConf.to_container(config), console=console_logger, expand_all=True)
-
- # load the documents
- documents_path = model_dir / document_file_name
-
- if not documents_path.exists():
- raise ValueError(f"Document file `{documents_path}` does not exist.")
- logger.info(f"Loading documents from {documents_path}")
- documents = Labels.from_file(documents_path)
-
- # load the passage embeddings
- embedding_path = model_dir / embedding_file_name
- # run some checks
- embeddings = None
- if embedding_path.exists():
- logger.info(f"Loading embeddings from {embedding_path}")
- embeddings = torch.load(embedding_path, map_location="cpu")
- else:
- logger.warning(f"Embedding file `{embedding_path}` does not exist.")
-
- document_index = hydra.utils.instantiate(
- config,
- documents=documents,
- embeddings=embeddings,
- device=device,
- precision=precision,
- name_or_dir=name_or_dir,
- *args,
- **kwargs,
- )
-
- return document_index
diff --git a/spaces/robin0307/MMOCR/configs/kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py b/spaces/robin0307/MMOCR/configs/kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py
deleted file mode 100644
index f073064affebe05d3830e18d76453c1cceb0f1a1..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py
+++ /dev/null
@@ -1,105 +0,0 @@
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-max_scale, min_scale = 1024, 512
-
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=(max_scale, min_scale), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='KIEFormatBundle'),
- dict(
- type='Collect',
- keys=['img', 'relations', 'texts', 'gt_bboxes', 'gt_labels'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=(max_scale, min_scale), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='KIEFormatBundle'),
- dict(
- type='Collect',
- keys=['img', 'relations', 'texts', 'gt_bboxes'],
- meta_keys=[
- 'img_norm_cfg', 'img_shape', 'ori_filename', 'filename',
- 'ori_texts'
- ])
-]
-
-dataset_type = 'KIEDataset'
-data_root = 'data/wildreceipt'
-
-loader = dict(
- type='HardDiskLoader',
- repeat=1,
- parser=dict(
- type='LineJsonParser',
- keys=['file_name', 'height', 'width', 'annotations']))
-
-train = dict(
- type=dataset_type,
- ann_file=f'{data_root}/train.txt',
- pipeline=train_pipeline,
- img_prefix=data_root,
- loader=loader,
- dict_file=f'{data_root}/dict.txt',
- test_mode=False)
-test = dict(
- type=dataset_type,
- ann_file=f'{data_root}/test.txt',
- pipeline=test_pipeline,
- img_prefix=data_root,
- loader=loader,
- dict_file=f'{data_root}/dict.txt',
- test_mode=True)
-
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- val_dataloader=dict(samples_per_gpu=1),
- test_dataloader=dict(samples_per_gpu=1),
- train=train,
- val=test,
- test=test)
-
-evaluation = dict(
- interval=1,
- metric='macro_f1',
- metric_options=dict(
- macro_f1=dict(
- ignores=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25])))
-
-model = dict(
- type='SDMGR',
- backbone=dict(type='UNet', base_channels=16),
- bbox_head=dict(
- type='SDMGRHead', visual_dim=16, num_chars=92, num_classes=26),
- visual_modality=True,
- train_cfg=None,
- test_cfg=None,
- class_list=f'{data_root}/class_list.txt')
-
-optimizer = dict(type='Adam', weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=1,
- warmup_ratio=1,
- step=[40, 50])
-total_epochs = 60
-
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-
-find_unused_parameters = True
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/coder/tblr_bbox_coder.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/coder/tblr_bbox_coder.py
deleted file mode 100644
index cb4206636f5b3704b465c5507d1f25492f11cf5c..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/coder/tblr_bbox_coder.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class TBLRBBoxCoder(BaseBBoxCoder):
- """TBLR BBox coder.
-
- Following the practice in `FSAF `_,
- this coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left,
- right) and decode it back to the original.
-
- Args:
- normalizer (list | float): Normalization factor to be
- divided with when coding the coordinates. If it is a list, it should
- have length of 4 indicating normalization factor in tblr dims.
- Otherwise it is a unified float factor for all dims. Default: 4.0
- clip_border (bool, optional): Whether clip the objects outside the
- border of the image. Defaults to True.
- """
-
- def __init__(self, normalizer=4.0, clip_border=True):
- super(BaseBBoxCoder, self).__init__()
- self.normalizer = normalizer
- self.clip_border = clip_border
-
- def encode(self, bboxes, gt_bboxes):
- """Get box regression transformation deltas that can be used to
- transform the ``bboxes`` into the ``gt_bboxes`` in the (top, left,
- bottom, right) order.
-
- Args:
- bboxes (torch.Tensor): source boxes, e.g., object proposals.
- gt_bboxes (torch.Tensor): target of the transformation, e.g.,
- ground truth boxes.
-
- Returns:
- torch.Tensor: Box transformation deltas
- """
- assert bboxes.size(0) == gt_bboxes.size(0)
- assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
- encoded_bboxes = bboxes2tblr(
- bboxes, gt_bboxes, normalizer=self.normalizer)
- return encoded_bboxes
-
- def decode(self, bboxes, pred_bboxes, max_shape=None):
- """Apply transformation `pred_bboxes` to `boxes`.
-
- Args:
- bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4)
- pred_bboxes (torch.Tensor): Encoded boxes with shape
- (B, N, 4) or (N, 4)
- max_shape (Sequence[int] or torch.Tensor or Sequence[
- Sequence[int]],optional): Maximum bounds for boxes, specifies
- (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then
- the max_shape should be a Sequence[Sequence[int]]
- and the length of max_shape should also be B.
-
- Returns:
- torch.Tensor: Decoded boxes.
- """
- decoded_bboxes = tblr2bboxes(
- bboxes,
- pred_bboxes,
- normalizer=self.normalizer,
- max_shape=max_shape,
- clip_border=self.clip_border)
-
- return decoded_bboxes
-
-
-@mmcv.jit(coderize=True)
-def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True):
- """Encode ground truth boxes to tblr coordinate.
-
- It first convert the gt coordinate to tblr format,
- (top, bottom, left, right), relative to prior box centers.
- The tblr coordinate may be normalized by the side length of prior bboxes
- if `normalize_by_wh` is specified as True, and it is then normalized by
- the `normalizer` factor.
-
- Args:
- priors (Tensor): Prior boxes in point form
- Shape: (num_proposals,4).
- gts (Tensor): Coords of ground truth for each prior in point-form
- Shape: (num_proposals, 4).
- normalizer (Sequence[float] | float): normalization parameter of
- encoded boxes. If it is a list, it has to have length = 4.
- Default: 4.0
- normalize_by_wh (bool): Whether to normalize tblr coordinate by the
- side length (wh) of prior bboxes.
-
- Return:
- encoded boxes (Tensor), Shape: (num_proposals, 4)
- """
-
- # dist b/t match center and prior's center
- if not isinstance(normalizer, float):
- normalizer = torch.tensor(normalizer, device=priors.device)
- assert len(normalizer) == 4, 'Normalizer must have length = 4'
- assert priors.size(0) == gts.size(0)
- prior_centers = (priors[:, 0:2] + priors[:, 2:4]) / 2
- xmin, ymin, xmax, ymax = gts.split(1, dim=1)
- top = prior_centers[:, 1].unsqueeze(1) - ymin
- bottom = ymax - prior_centers[:, 1].unsqueeze(1)
- left = prior_centers[:, 0].unsqueeze(1) - xmin
- right = xmax - prior_centers[:, 0].unsqueeze(1)
- loc = torch.cat((top, bottom, left, right), dim=1)
- if normalize_by_wh:
- # Normalize tblr by anchor width and height
- wh = priors[:, 2:4] - priors[:, 0:2]
- w, h = torch.split(wh, 1, dim=1)
- loc[:, :2] /= h # tb is normalized by h
- loc[:, 2:] /= w # lr is normalized by w
- # Normalize tblr by the given normalization factor
- return loc / normalizer
-
-
-@mmcv.jit(coderize=True)
-def tblr2bboxes(priors,
- tblr,
- normalizer=4.0,
- normalize_by_wh=True,
- max_shape=None,
- clip_border=True):
- """Decode tblr outputs to prediction boxes.
-
- The process includes 3 steps: 1) De-normalize tblr coordinates by
- multiplying it with `normalizer`; 2) De-normalize tblr coordinates by the
- prior bbox width and height if `normalize_by_wh` is `True`; 3) Convert
- tblr (top, bottom, left, right) pair relative to the center of priors back
- to (xmin, ymin, xmax, ymax) coordinate.
-
- Args:
- priors (Tensor): Prior boxes in point form (x0, y0, x1, y1)
- Shape: (N,4) or (B, N, 4).
- tblr (Tensor): Coords of network output in tblr form
- Shape: (N, 4) or (B, N, 4).
- normalizer (Sequence[float] | float): Normalization parameter of
- encoded boxes. By list, it represents the normalization factors at
- tblr dims. By float, it is the unified normalization factor at all
- dims. Default: 4.0
- normalize_by_wh (bool): Whether the tblr coordinates have been
- normalized by the side length (wh) of prior bboxes.
- max_shape (Sequence[int] or torch.Tensor or Sequence[
- Sequence[int]],optional): Maximum bounds for boxes, specifies
- (H, W, C) or (H, W). If priors shape is (B, N, 4), then
- the max_shape should be a Sequence[Sequence[int]]
- and the length of max_shape should also be B.
- clip_border (bool, optional): Whether clip the objects outside the
- border of the image. Defaults to True.
-
- Return:
- encoded boxes (Tensor): Boxes with shape (N, 4) or (B, N, 4)
- """
- if not isinstance(normalizer, float):
- normalizer = torch.tensor(normalizer, device=priors.device)
- assert len(normalizer) == 4, 'Normalizer must have length = 4'
- assert priors.size(0) == tblr.size(0)
- if priors.ndim == 3:
- assert priors.size(1) == tblr.size(1)
-
- loc_decode = tblr * normalizer
- prior_centers = (priors[..., 0:2] + priors[..., 2:4]) / 2
- if normalize_by_wh:
- wh = priors[..., 2:4] - priors[..., 0:2]
- w, h = torch.split(wh, 1, dim=-1)
- # Inplace operation with slice would failed for exporting to ONNX
- th = h * loc_decode[..., :2] # tb
- tw = w * loc_decode[..., 2:] # lr
- loc_decode = torch.cat([th, tw], dim=-1)
- # Cannot be exported using onnx when loc_decode.split(1, dim=-1)
- top, bottom, left, right = loc_decode.split((1, 1, 1, 1), dim=-1)
- xmin = prior_centers[..., 0].unsqueeze(-1) - left
- xmax = prior_centers[..., 0].unsqueeze(-1) + right
- ymin = prior_centers[..., 1].unsqueeze(-1) - top
- ymax = prior_centers[..., 1].unsqueeze(-1) + bottom
-
- bboxes = torch.cat((xmin, ymin, xmax, ymax), dim=-1)
-
- if clip_border and max_shape is not None:
- # clip bboxes with dynamic `min` and `max` for onnx
- if torch.onnx.is_in_onnx_export():
- from mmdet.core.export import dynamic_clip_for_onnx
- xmin, ymin, xmax, ymax = dynamic_clip_for_onnx(
- xmin, ymin, xmax, ymax, max_shape)
- bboxes = torch.cat([xmin, ymin, xmax, ymax], dim=-1)
- return bboxes
- if not isinstance(max_shape, torch.Tensor):
- max_shape = priors.new_tensor(max_shape)
- max_shape = max_shape[..., :2].type_as(priors)
- if max_shape.ndim == 2:
- assert bboxes.ndim == 3
- assert max_shape.size(0) == bboxes.size(0)
-
- min_xy = priors.new_tensor(0)
- max_xy = torch.cat([max_shape, max_shape],
- dim=-1).flip(-1).unsqueeze(-2)
- bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
- bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
-
- return bboxes
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/fpg.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/fpg.py
deleted file mode 100644
index a6a2a12ed415bbb517b056d01172a83f6e30833d..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/fpg.py
+++ /dev/null
@@ -1,406 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule
-from mmcv.runner import BaseModule
-
-from ..builder import NECKS
-
-
-class Transition(BaseModule):
- """Base class for transition.
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of output channels.
- """
-
- def __init__(self, in_channels, out_channels, init_cfg=None):
- super().__init__(init_cfg)
- self.in_channels = in_channels
- self.out_channels = out_channels
-
- def forward(x):
- pass
-
-
-class UpInterpolationConv(Transition):
- """A transition used for up-sampling.
-
- Up-sample the input by interpolation then refines the feature by
- a convolution layer.
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of output channels.
- scale_factor (int): Up-sampling factor. Default: 2.
- mode (int): Interpolation mode. Default: nearest.
- align_corners (bool): Whether align corners when interpolation.
- Default: None.
- kernel_size (int): Kernel size for the conv. Default: 3.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- scale_factor=2,
- mode='nearest',
- align_corners=None,
- kernel_size=3,
- init_cfg=None,
- **kwargs):
- super().__init__(in_channels, out_channels, init_cfg)
- self.mode = mode
- self.scale_factor = scale_factor
- self.align_corners = align_corners
- self.conv = ConvModule(
- in_channels,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- **kwargs)
-
- def forward(self, x):
- x = F.interpolate(
- x,
- scale_factor=self.scale_factor,
- mode=self.mode,
- align_corners=self.align_corners)
- x = self.conv(x)
- return x
-
-
-class LastConv(Transition):
- """A transition used for refining the output of the last stage.
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of output channels.
- num_inputs (int): Number of inputs of the FPN features.
- kernel_size (int): Kernel size for the conv. Default: 3.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- num_inputs,
- kernel_size=3,
- init_cfg=None,
- **kwargs):
- super().__init__(in_channels, out_channels, init_cfg)
- self.num_inputs = num_inputs
- self.conv_out = ConvModule(
- in_channels,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- **kwargs)
-
- def forward(self, inputs):
- assert len(inputs) == self.num_inputs
- return self.conv_out(inputs[-1])
-
-
-@NECKS.register_module()
-class FPG(BaseModule):
- """FPG.
-
- Implementation of `Feature Pyramid Grids (FPG)
- `_.
- This implementation only gives the basic structure stated in the paper.
- But users can implement different type of transitions to fully explore the
- the potential power of the structure of FPG.
-
- Args:
- in_channels (int): Number of input channels (feature maps of all levels
- should have the same channels).
- out_channels (int): Number of output channels (used at each scale)
- num_outs (int): Number of output scales.
- stack_times (int): The number of times the pyramid architecture will
- be stacked.
- paths (list[str]): Specify the path order of each stack level.
- Each element in the list should be either 'bu' (bottom-up) or
- 'td' (top-down).
- inter_channels (int): Number of inter channels.
- same_up_trans (dict): Transition that goes down at the same stage.
- same_down_trans (dict): Transition that goes up at the same stage.
- across_lateral_trans (dict): Across-pathway same-stage
- across_down_trans (dict): Across-pathway bottom-up connection.
- across_up_trans (dict): Across-pathway top-down connection.
- across_skip_trans (dict): Across-pathway skip connection.
- output_trans (dict): Transition that trans the output of the
- last stage.
- start_level (int): Index of the start input backbone level used to
- build the feature pyramid. Default: 0.
- end_level (int): Index of the end input backbone level (exclusive) to
- build the feature pyramid. Default: -1, which means the last level.
- add_extra_convs (bool): It decides whether to add conv
- layers on top of the original feature maps. Default to False.
- If True, its actual mode is specified by `extra_convs_on_inputs`.
- norm_cfg (dict): Config dict for normalization layer. Default: None.
- init_cfg (dict or list[dict], optional): Initialization config dict.
- """
-
- transition_types = {
- 'conv': ConvModule,
- 'interpolation_conv': UpInterpolationConv,
- 'last_conv': LastConv,
- }
-
- def __init__(self,
- in_channels,
- out_channels,
- num_outs,
- stack_times,
- paths,
- inter_channels=None,
- same_down_trans=None,
- same_up_trans=dict(
- type='conv', kernel_size=3, stride=2, padding=1),
- across_lateral_trans=dict(type='conv', kernel_size=1),
- across_down_trans=dict(type='conv', kernel_size=3),
- across_up_trans=None,
- across_skip_trans=dict(type='identity'),
- output_trans=dict(type='last_conv', kernel_size=3),
- start_level=0,
- end_level=-1,
- add_extra_convs=False,
- norm_cfg=None,
- skip_inds=None,
- init_cfg=[
- dict(type='Caffe2Xavier', layer='Conv2d'),
- dict(
- type='Constant',
- layer=[
- '_BatchNorm', '_InstanceNorm', 'GroupNorm',
- 'LayerNorm'
- ],
- val=1.0)
- ]):
- super(FPG, self).__init__(init_cfg)
- assert isinstance(in_channels, list)
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.num_ins = len(in_channels)
- self.num_outs = num_outs
- if inter_channels is None:
- self.inter_channels = [out_channels for _ in range(num_outs)]
- elif isinstance(inter_channels, int):
- self.inter_channels = [inter_channels for _ in range(num_outs)]
- else:
- assert isinstance(inter_channels, list)
- assert len(inter_channels) == num_outs
- self.inter_channels = inter_channels
- self.stack_times = stack_times
- self.paths = paths
- assert isinstance(paths, list) and len(paths) == stack_times
- for d in paths:
- assert d in ('bu', 'td')
-
- self.same_down_trans = same_down_trans
- self.same_up_trans = same_up_trans
- self.across_lateral_trans = across_lateral_trans
- self.across_down_trans = across_down_trans
- self.across_up_trans = across_up_trans
- self.output_trans = output_trans
- self.across_skip_trans = across_skip_trans
-
- self.with_bias = norm_cfg is None
- # skip inds must be specified if across skip trans is not None
- if self.across_skip_trans is not None:
- skip_inds is not None
- self.skip_inds = skip_inds
- assert len(self.skip_inds[0]) <= self.stack_times
-
- if end_level == -1 or end_level == self.num_ins - 1:
- self.backbone_end_level = self.num_ins
- assert num_outs >= self.num_ins - start_level
- else:
- # if end_level is not the last level, no extra level is allowed
- self.backbone_end_level = end_level + 1
- assert end_level < self.num_ins
- assert num_outs == end_level - start_level + 1
- self.start_level = start_level
- self.end_level = end_level
- self.add_extra_convs = add_extra_convs
-
- # build lateral 1x1 convs to reduce channels
- self.lateral_convs = nn.ModuleList()
- for i in range(self.start_level, self.backbone_end_level):
- l_conv = nn.Conv2d(self.in_channels[i],
- self.inter_channels[i - self.start_level], 1)
- self.lateral_convs.append(l_conv)
-
- extra_levels = num_outs - self.backbone_end_level + self.start_level
- self.extra_downsamples = nn.ModuleList()
- for i in range(extra_levels):
- if self.add_extra_convs:
- fpn_idx = self.backbone_end_level - self.start_level + i
- extra_conv = nn.Conv2d(
- self.inter_channels[fpn_idx - 1],
- self.inter_channels[fpn_idx],
- 3,
- stride=2,
- padding=1)
- self.extra_downsamples.append(extra_conv)
- else:
- self.extra_downsamples.append(nn.MaxPool2d(1, stride=2))
-
- self.fpn_transitions = nn.ModuleList() # stack times
- for s in range(self.stack_times):
- stage_trans = nn.ModuleList() # num of feature levels
- for i in range(self.num_outs):
- # same, across_lateral, across_down, across_up
- trans = nn.ModuleDict()
- if s in self.skip_inds[i]:
- stage_trans.append(trans)
- continue
- # build same-stage down trans (used in bottom-up paths)
- if i == 0 or self.same_up_trans is None:
- same_up_trans = None
- else:
- same_up_trans = self.build_trans(
- self.same_up_trans, self.inter_channels[i - 1],
- self.inter_channels[i])
- trans['same_up'] = same_up_trans
- # build same-stage up trans (used in top-down paths)
- if i == self.num_outs - 1 or self.same_down_trans is None:
- same_down_trans = None
- else:
- same_down_trans = self.build_trans(
- self.same_down_trans, self.inter_channels[i + 1],
- self.inter_channels[i])
- trans['same_down'] = same_down_trans
- # build across lateral trans
- across_lateral_trans = self.build_trans(
- self.across_lateral_trans, self.inter_channels[i],
- self.inter_channels[i])
- trans['across_lateral'] = across_lateral_trans
- # build across down trans
- if i == self.num_outs - 1 or self.across_down_trans is None:
- across_down_trans = None
- else:
- across_down_trans = self.build_trans(
- self.across_down_trans, self.inter_channels[i + 1],
- self.inter_channels[i])
- trans['across_down'] = across_down_trans
- # build across up trans
- if i == 0 or self.across_up_trans is None:
- across_up_trans = None
- else:
- across_up_trans = self.build_trans(
- self.across_up_trans, self.inter_channels[i - 1],
- self.inter_channels[i])
- trans['across_up'] = across_up_trans
- if self.across_skip_trans is None:
- across_skip_trans = None
- else:
- across_skip_trans = self.build_trans(
- self.across_skip_trans, self.inter_channels[i - 1],
- self.inter_channels[i])
- trans['across_skip'] = across_skip_trans
- # build across_skip trans
- stage_trans.append(trans)
- self.fpn_transitions.append(stage_trans)
-
- self.output_transition = nn.ModuleList() # output levels
- for i in range(self.num_outs):
- trans = self.build_trans(
- self.output_trans,
- self.inter_channels[i],
- self.out_channels,
- num_inputs=self.stack_times + 1)
- self.output_transition.append(trans)
-
- self.relu = nn.ReLU(inplace=True)
-
- def build_trans(self, cfg, in_channels, out_channels, **extra_args):
- cfg_ = cfg.copy()
- trans_type = cfg_.pop('type')
- trans_cls = self.transition_types[trans_type]
- return trans_cls(in_channels, out_channels, **cfg_, **extra_args)
-
- def fuse(self, fuse_dict):
- out = None
- for item in fuse_dict.values():
- if item is not None:
- if out is None:
- out = item
- else:
- out = out + item
- return out
-
- def forward(self, inputs):
- assert len(inputs) == len(self.in_channels)
-
- # build all levels from original feature maps
- feats = [
- lateral_conv(inputs[i + self.start_level])
- for i, lateral_conv in enumerate(self.lateral_convs)
- ]
- for downsample in self.extra_downsamples:
- feats.append(downsample(feats[-1]))
-
- outs = [feats]
-
- for i in range(self.stack_times):
- current_outs = outs[-1]
- next_outs = []
- direction = self.paths[i]
- for j in range(self.num_outs):
- if i in self.skip_inds[j]:
- next_outs.append(outs[-1][j])
- continue
- # feature level
- if direction == 'td':
- lvl = self.num_outs - j - 1
- else:
- lvl = j
- # get transitions
- if direction == 'td':
- same_trans = self.fpn_transitions[i][lvl]['same_down']
- else:
- same_trans = self.fpn_transitions[i][lvl]['same_up']
- across_lateral_trans = self.fpn_transitions[i][lvl][
- 'across_lateral']
- across_down_trans = self.fpn_transitions[i][lvl]['across_down']
- across_up_trans = self.fpn_transitions[i][lvl]['across_up']
- across_skip_trans = self.fpn_transitions[i][lvl]['across_skip']
- # init output
- to_fuse = dict(
- same=None, lateral=None, across_up=None, across_down=None)
- # same downsample/upsample
- if same_trans is not None:
- to_fuse['same'] = same_trans(next_outs[-1])
- # across lateral
- if across_lateral_trans is not None:
- to_fuse['lateral'] = across_lateral_trans(
- current_outs[lvl])
- # across downsample
- if lvl > 0 and across_up_trans is not None:
- to_fuse['across_up'] = across_up_trans(current_outs[lvl -
- 1])
- # across upsample
- if (lvl < self.num_outs - 1 and across_down_trans is not None):
- to_fuse['across_down'] = across_down_trans(
- current_outs[lvl + 1])
- if across_skip_trans is not None:
- to_fuse['across_skip'] = across_skip_trans(outs[0][lvl])
- x = self.fuse(to_fuse)
- next_outs.append(x)
-
- if direction == 'td':
- outs.append(next_outs[::-1])
- else:
- outs.append(next_outs)
-
- # output trans
- final_outs = []
- for i in range(self.num_outs):
- lvl_out_list = []
- for s in range(len(outs)):
- lvl_out_list.append(outs[s][i])
- lvl_out = self.output_transition[i](lvl_out_list)
- final_outs.append(lvl_out)
-
- return final_outs
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/hdetr/models/backbone.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/hdetr/models/backbone.py
deleted file mode 100644
index cc4f220e45e93efa48965fab866df74888fd7bac..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/hdetr/models/backbone.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# ------------------------------------------------------------------------
-# H-DETR
-# Copyright (c) 2022 Peking University & Microsoft Research Asia. All Rights Reserved.
-# Licensed under the MIT-style license found in the LICENSE file in the root directory
-# ------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# ------------------------------------------------------------------------
-
-"""
-Backbone modules.
-"""
-from collections import OrderedDict
-
-import torch
-import torch.nn.functional as F
-import torchvision
-from torch import nn
-from torchvision.models._utils import IntermediateLayerGetter
-from typing import Dict, List
-
-from .util.misc import NestedTensor, is_main_process
-
-from .position_encoding import build_position_encoding
-from .swin_transformer import SwinTransformer
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
- """
- BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
- Copy-paste from torchvision.misc.ops with added eps before rqsrt,
- without which any other models than torchvision.models.resnet[18,34,50,101]
- produce nans.
- """
-
- def __init__(self, n, eps=1e-5):
- super(FrozenBatchNorm2d, self).__init__()
- self.register_buffer("weight", torch.ones(n))
- self.register_buffer("bias", torch.zeros(n))
- self.register_buffer("running_mean", torch.zeros(n))
- self.register_buffer("running_var", torch.ones(n))
- self.eps = eps
-
- def _load_from_state_dict(
- self,
- state_dict,
- prefix,
- local_metadata,
- strict,
- missing_keys,
- unexpected_keys,
- error_msgs,
- ):
- num_batches_tracked_key = prefix + "num_batches_tracked"
- if num_batches_tracked_key in state_dict:
- del state_dict[num_batches_tracked_key]
-
- super(FrozenBatchNorm2d, self)._load_from_state_dict(
- state_dict,
- prefix,
- local_metadata,
- strict,
- missing_keys,
- unexpected_keys,
- error_msgs,
- )
-
- def forward(self, x):
- # move reshapes to the beginning
- # to make it fuser-friendly
- w = self.weight.reshape(1, -1, 1, 1)
- b = self.bias.reshape(1, -1, 1, 1)
- rv = self.running_var.reshape(1, -1, 1, 1)
- rm = self.running_mean.reshape(1, -1, 1, 1)
- eps = self.eps
- scale = w * (rv + eps).rsqrt()
- bias = b - rm * scale
- return x * scale + bias
-
-
-class BackboneBase(nn.Module):
- def __init__(
- self, backbone: nn.Module, train_backbone: bool, return_interm_layers: bool
- ):
- super().__init__()
- for name, parameter in backbone.named_parameters():
- if (
- not train_backbone
- or "layer2" not in name
- and "layer3" not in name
- and "layer4" not in name
- ):
- parameter.requires_grad_(False)
- if return_interm_layers:
- # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
- return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
- self.strides = [8, 16, 32]
- self.num_channels = [512, 1024, 2048]
- else:
- return_layers = {"layer4": "0"}
- self.strides = [32]
- self.num_channels = [2048]
- self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
-
- def forward(self, tensor_list: NestedTensor):
- xs = self.body(tensor_list.tensors)
- out: Dict[str, NestedTensor] = {}
- for name, x in xs.items():
- m = tensor_list.mask
- assert m is not None
- mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
- out[name] = NestedTensor(x, mask)
- return out
-
-
-class Backbone(BackboneBase):
- """ResNet backbone with frozen BatchNorm."""
-
- def __init__(
- self,
- name: str,
- train_backbone: bool,
- return_interm_layers: bool,
- dilation: bool,
- ):
- norm_layer = FrozenBatchNorm2d
- backbone = getattr(torchvision.models, name)(
- replace_stride_with_dilation=[False, False, dilation],
- pretrained=is_main_process(),
- norm_layer=norm_layer,
- )
- assert name not in ("resnet18", "resnet34"), "number of channels are hard coded"
- super().__init__(backbone, train_backbone, return_interm_layers)
- if dilation:
- self.strides[-1] = self.strides[-1] // 2
-
-
-class TransformerBackbone(nn.Module):
- def __init__(
- self, backbone: str, train_backbone: bool, return_interm_layers: bool, args
- ):
- super().__init__()
- out_indices = (1, 2, 3)
- if backbone == "swin_tiny":
- backbone = SwinTransformer(
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- ape=False,
- drop_path_rate=args.drop_path_rate,
- patch_norm=True,
- use_checkpoint=True,
- out_indices=out_indices,
- )
- embed_dim = 96
- # backbone.init_weights(args.pretrained_backbone_path)
- elif backbone == "swin_small":
- backbone = SwinTransformer(
- embed_dim=96,
- depths=[2, 2, 18, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- ape=False,
- drop_path_rate=args.drop_path_rate,
- patch_norm=True,
- use_checkpoint=True,
- out_indices=out_indices,
- )
- embed_dim = 96
- # backbone.init_weights(args.pretrained_backbone_path)
- elif backbone == "swin_large":
- backbone = SwinTransformer(
- embed_dim=192,
- depths=[2, 2, 18, 2],
- num_heads=[6, 12, 24, 48],
- window_size=7,
- ape=False,
- drop_path_rate=args.drop_path_rate,
- patch_norm=True,
- use_checkpoint=True,
- out_indices=out_indices,
- )
- embed_dim = 192
- # backbone.init_weights(args.pretrained_backbone_path)
- elif backbone == "swin_large_window12":
- backbone = SwinTransformer(
- pretrain_img_size=384,
- embed_dim=192,
- depths=[2, 2, 18, 2],
- num_heads=[6, 12, 24, 48],
- window_size=12,
- ape=False,
- drop_path_rate=args.drop_path_rate,
- patch_norm=True,
- use_checkpoint=True,
- out_indices=out_indices,
- )
- embed_dim = 192
- # backbone.init_weights(args.pretrained_backbone_path)
- else:
- raise NotImplementedError
-
- for name, parameter in backbone.named_parameters():
- # TODO: freeze some layers?
- if not train_backbone:
- parameter.requires_grad_(False)
-
- if return_interm_layers:
-
- self.strides = [8, 16, 32]
- self.num_channels = [
- embed_dim * 2,
- embed_dim * 4,
- embed_dim * 8,
- ]
- else:
- self.strides = [32]
- self.num_channels = [embed_dim * 8]
-
- self.body = backbone
-
- def forward(self, tensor_list: NestedTensor):
- xs = self.body(tensor_list.tensors)
-
- out: Dict[str, NestedTensor] = {}
- for name, x in xs.items():
- m = tensor_list.mask
- assert m is not None
- mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
- out[name] = NestedTensor(x, mask)
- return out
-
-
-class Joiner(nn.Sequential):
- def __init__(self, backbone, position_embedding):
- super().__init__(backbone, position_embedding)
- self.strides = backbone.strides
- self.num_channels = backbone.num_channels
-
- def forward(self, tensor_list: NestedTensor):
- xs = self[0](tensor_list)
- out: List[NestedTensor] = []
- pos = []
- for name, x in sorted(xs.items()):
- out.append(x)
-
- # position encoding
- for x in out:
- pos.append(self[1](x).to(x.tensors.dtype))
-
- return out, pos
-
-
-def build_backbone(args):
- position_embedding = build_position_encoding(args)
- train_backbone = False
- return_interm_layers = args.masks or (args.num_feature_levels > 1)
- if "resnet" in args.backbone:
- backbone = Backbone(
- args.backbone, train_backbone, return_interm_layers, args.dilation,
- )
- else:
- backbone = TransformerBackbone(
- args.backbone, train_backbone, return_interm_layers, args
- )
- model = Joiner(backbone, position_embedding)
- return model
diff --git a/spaces/ronvolutional/iframe-test/modules/inference.py b/spaces/ronvolutional/iframe-test/modules/inference.py
deleted file mode 100644
index fbf5cce09c4dd0844bb300e7afb161a15f7b0149..0000000000000000000000000000000000000000
--- a/spaces/ronvolutional/iframe-test/modules/inference.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-
-tokenizer = T5Tokenizer.from_pretrained("t5-small")
-model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
-
-def infer_t5(input):
- input_ids = tokenizer(input, return_tensors="pt").input_ids
- outputs = model.generate(input_ids)
-
- return tokenizer.decode(outputs[0], skip_special_tokens=True)
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Family Therapy Concepts And Methods (11th Edition) 22.md b/spaces/rorallitri/biomedical-language-models/logs/Family Therapy Concepts And Methods (11th Edition) 22.md
deleted file mode 100644
index d8ef48c70467644f2e3ec61211c55573f3061017..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Family Therapy Concepts And Methods (11th Edition) 22.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
fromminuchincenter/yahoo_site_admin/assets/docs/the_case_family_therapy.65165254Minuchin Center for the Family. (2017). Structural Family Therapy. Retrieved fromminuchincenter/structural_family_therapyNichols, M., Davis, S. (2016). Family Therapy: Concepts and Methods (11th Ed.).New York, NY: Pearson.Vetere, A. (2001). Structural Family Therapy. Child Psychology & Psychiatry Review,6 (3), 133-140. Retrieved frompdfs.semanticscholar/5f22/cad9a27fdab39f4c5265a10ccfedf25fd35c.pdf
-
Family Therapy: Concepts and Methods (11th Edition) 22
Yoga and Vedanta are the two largest surviving schools of Hindu traditions. Although they share many principles, concepts, and the belief in Self, they differ in degree, style, and methods; yoga accepts three means to obtain knowledge, and Advaita Vedanta accepts.[182] Yoga disputes Advaita Vedanta's monism.[183] It believes that in the state of moksha, each individual discovers the blissful, liberating sense of himself or herself as an independent identity; Advaita Vedanta teaches that in the state of moksha, each individual discovers the blissful, liberating sense of himself or herself as part of oneness with everything, everyone and the Universal Self. They both hold that the free conscience is transcendent, liberated and self-aware. Advaita Vedanta also encourages the use of Patanjali's yoga practices and the Upanishads for those seeking the supreme good and ultimate freedom.[183]
-
ASTRO 7N (GA/GN) is both an introductory course in astronomy for non-science majors and a creative space for those with science backgrounds interested in visual arts; it provides students the opportunity to demonstrate understanding and develop a personal connection to the subject by designing four art projects. Students will learn the broad concepts of astronomy by playing an immersive video game, which allows them to 1) explore seasons, phases of the Moon, light, gravity, and telescopes from a virtual colony on Mars; 2) fly from planet to planet in the Solar System and learn about their properties and formation; 3) visit the Sun and other stars, learn how they produce energy, and about their life cycles; 4) fly through the cosmos and construct their own universe, particle by particle. Students will also learn about the relationships and exchanges between arts and sciences, and explore inspiration and perspective on these topics by designing themed art projects using traditional and digital media. These projects include assembling a photo- journal of astronomically-relevant subjects, constructing their own video-game-like scene, interpreting data to inform a plausible depiction of an alien world, and producing three- color images using methods like those employed by astronomers to compose and display Hubble Space Telescope images. Students who have passed ASTRO 1, ASTRO 5, ASTRO 6 or ASTRO 10 may not take this course for credit.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/rosenthal/chess/chessfenbot/chessboard_finder.py b/spaces/rosenthal/chess/chessfenbot/chessboard_finder.py
deleted file mode 100644
index 25ee6878016997eb5bb7404ba2ba6ebbc89c0d09..0000000000000000000000000000000000000000
--- a/spaces/rosenthal/chess/chessfenbot/chessboard_finder.py
+++ /dev/null
@@ -1,426 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Pass in image of online chessboard screenshot, returns corners of chessboard
-# usage: chessboard_finder.py [-h] urls [urls ...]
-
-# Find orthorectified chessboard corners in image
-
-# positional arguments:
-# urls Input image urls
-
-# optional arguments:
-# -h, --help show this help message and exit
-
-
-# sudo apt-get install libatlas-base-dev for numpy error, see https://github.com/Kitt-AI/snowboy/issues/262
-import numpy as np
-# sudo apt-get install libopenjp2-7 libtiff5
-import PIL.Image
-import argparse
-from time import time
-from .helper_image_loading import *
-
-
-def nonmax_suppress_1d(arr, winsize=5):
- """Return 1d array with only peaks, use neighborhood window of winsize px"""
- _arr = arr.copy()
-
- for i in range(_arr.size):
- if i == 0:
- left_neighborhood = 0
- else:
- left_neighborhood = arr[max(0,i-winsize):i]
- if i >= _arr.size-2:
- right_neighborhood = 0
- else:
- right_neighborhood = arr[i+1:min(arr.size-1,i+winsize)]
-
- if arr[i] < np.max(left_neighborhood) or arr[i] <= np.max(right_neighborhood):
- _arr[i] = 0
- return _arr
-
-def findChessboardCorners(img_arr_gray, noise_threshold = 8000):
- # Load image grayscale as an numpy array
- # Return None on failure to find a chessboard
- #
- # noise_threshold: Ratio of standard deviation of hough values along an axis
- # versus the number of pixels, manually measured bad trigger images
- # at < 5,000 and good chessboards values at > 10,000
-
- # Get gradients, split into positive and inverted negative components
- gx, gy = np.gradient(img_arr_gray)
- gx_pos = gx.copy()
- gx_pos[gx_pos<0] = 0
- gx_neg = -gx.copy()
- gx_neg[gx_neg<0] = 0
-
- gy_pos = gy.copy()
- gy_pos[gy_pos<0] = 0
- gy_neg = -gy.copy()
- gy_neg[gy_neg<0] = 0
-
- # 1-D ampltitude of hough transform of gradients about X & Y axes
- num_px = img_arr_gray.shape[0] * img_arr_gray.shape[1]
- hough_gx = gx_pos.sum(axis=1) * gx_neg.sum(axis=1)
- hough_gy = gy_pos.sum(axis=0) * gy_neg.sum(axis=0)
-
- # Check that gradient peak signal is strong enough by
- # comparing normalized standard deviation to threshold
- if min(hough_gx.std() / hough_gx.size,
- hough_gy.std() / hough_gy.size) < noise_threshold:
- return None
-
- # Normalize and skeletonize to just local peaks
- hough_gx = nonmax_suppress_1d(hough_gx) / hough_gx.max()
- hough_gy = nonmax_suppress_1d(hough_gy) / hough_gy.max()
-
- # Arbitrary threshold of 20% of max
- hough_gx[hough_gx<0.2] = 0
- hough_gy[hough_gy<0.2] = 0
-
- # Now we have a set of potential vertical and horizontal lines that
- # may contain some noisy readings, try different subsets of them with
- # consistent spacing until we get a set of 7, choose strongest set of 7
- pot_lines_x = np.where(hough_gx)[0]
- pot_lines_y = np.where(hough_gy)[0]
- pot_lines_x_vals = hough_gx[pot_lines_x]
- pot_lines_y_vals = hough_gy[pot_lines_y]
-
- # Get all possible length 7+ sequences
- seqs_x = getAllSequences(pot_lines_x)
- seqs_y = getAllSequences(pot_lines_y)
-
- if len(seqs_x) == 0 or len(seqs_y) == 0:
- return None
-
- # Score sequences by the strength of their hough peaks
- seqs_x_vals = [pot_lines_x_vals[[v in seq for v in pot_lines_x]] for seq in seqs_x]
- seqs_y_vals = [pot_lines_y_vals[[v in seq for v in pot_lines_y]] for seq in seqs_y]
-
- # shorten sequences to up to 9 values based on score
- # X sequences
- for i in range(len(seqs_x)):
- seq = seqs_x[i]
- seq_val = seqs_x_vals[i]
-
- # if the length of sequence is more than 7 + edges = 9
- # strip weakest edges
- if len(seq) > 9:
- # while not inner 7 chess lines, strip weakest edges
- while len(seq) > 7:
- if seq_val[0] > seq_val[-1]:
- seq = seq[:-1]
- seq_val = seq_val[:-1]
- else:
- seq = seq[1:]
- seq_val = seq_val[1:]
-
- seqs_x[i] = seq
- seqs_x_vals[i] = seq_val
-
- # Y sequences
- for i in range(len(seqs_y)):
- seq = seqs_y[i]
- seq_val = seqs_y_vals[i]
-
- while len(seq) > 9:
- if seq_val[0] > seq_val[-1]:
- seq = seq[:-1]
- seq_val = seq_val[:-1]
- else:
- seq = seq[1:]
- seq_val = seq_val[1:]
-
- seqs_y[i] = seq
- seqs_y_vals[i] = seq_val
-
- # Now that we only have length 7-9 sequences, score and choose the best one
- scores_x = np.array([np.mean(v) for v in seqs_x_vals])
- scores_y = np.array([np.mean(v) for v in seqs_y_vals])
-
- # Keep first sequence with the largest step size
- # scores_x = np.array([np.median(np.diff(s)) for s in seqs_x])
- # scores_y = np.array([np.median(np.diff(s)) for s in seqs_y])
-
- #TODO(elucidation): Choose heuristic score between step size and hough response
-
- best_seq_x = seqs_x[scores_x.argmax()]
- best_seq_y = seqs_y[scores_y.argmax()]
- # print(best_seq_x, best_seq_y)
-
- # Now if we have sequences greater than length 7, (up to 9),
- # that means we have up to 9 possible combinations of sets of 7 sequences
- # We try all of them and see which has the best checkerboard response
- sub_seqs_x = [best_seq_x[k:k+7] for k in range(len(best_seq_x) - 7 + 1)]
- sub_seqs_y = [best_seq_y[k:k+7] for k in range(len(best_seq_y) - 7 + 1)]
-
- dx = np.median(np.diff(best_seq_x))
- dy = np.median(np.diff(best_seq_y))
- corners = np.zeros(4, dtype=int)
-
- # Add 1 buffer to include the outer tiles, since sequences are only using
- # inner chessboard lines
- corners[0] = int(best_seq_y[0]-dy)
- corners[1] = int(best_seq_x[0]-dx)
- corners[2] = int(best_seq_y[-1]+dy)
- corners[3] = int(best_seq_x[-1]+dx)
-
- # Generate crop image with on full sequence, which may be wider than a normal
- # chessboard by an extra 2 tiles, we'll iterate over all combinations
- # (up to 9) and choose the one that correlates best with a chessboard
- gray_img_crop = PIL.Image.fromarray(img_arr_gray).crop(corners)
-
- # Build a kernel image of an idea chessboard to correlate against
- k = 8 # Arbitrarily chose 8x8 pixel tiles for correlation image
- quad = np.ones([k,k])
- kernel = np.vstack([np.hstack([quad,-quad]), np.hstack([-quad,quad])])
- kernel = np.tile(kernel,(4,4)) # Becomes an 8x8 alternating grid (chessboard)
- kernel = kernel/np.linalg.norm(kernel) # normalize
- # 8*8 = 64x64 pixel ideal chessboard
-
- k = 0
- n = max(len(sub_seqs_x), len(sub_seqs_y))
- final_corners = None
- best_score = None
-
- # Iterate over all possible combinations of sub sequences and keep the corners
- # with the best correlation response to the ideal 64x64px chessboard
- for i in range(len(sub_seqs_x)):
- for j in range(len(sub_seqs_y)):
- k = k + 1
-
- # [y, x, y, x]
- sub_corners = np.array([
- sub_seqs_y[j][0]-corners[0]-dy, sub_seqs_x[i][0]-corners[1]-dx,
- sub_seqs_y[j][-1]-corners[0]+dy, sub_seqs_x[i][-1]-corners[1]+dx],
- dtype=np.int)
-
- # Generate crop candidate, nearest pixel is fine for correlation check
- sub_img = gray_img_crop.crop(sub_corners).resize((64,64))
-
- # Perform correlation score, keep running best corners as our final output
- # Use absolute since it's possible board is rotated 90 deg
- score = np.abs(np.sum(kernel * sub_img))
- if best_score is None or score > best_score:
- best_score = score
- final_corners = sub_corners + [corners[0], corners[1], corners[0], corners[1]]
-
- return final_corners
-
-def getAllSequences(seq, min_seq_len=7, err_px=5):
- """Given sequence of increasing numbers, get all sequences with common
- spacing (within err_px) that contain at least min_seq_len values"""
-
- # Sanity check that there are enough values to satisfy
- if len(seq) < min_seq_len:
- return []
-
- # For every value, take the next value and see how many times we can step
- # that falls on another value within err_px points
- seqs = []
- for i in range(len(seq)-1):
- for j in range(i+1, len(seq)):
- # Check that seq[i], seq[j] not already in previous sequences
- duplicate = False
- for prev_seq in seqs:
- for k in range(len(prev_seq)-1):
- if seq[i] == prev_seq[k] and seq[j] == prev_seq[k+1]:
- duplicate = True
- if duplicate:
- continue
- d = seq[j] - seq[i]
-
- # Ignore two points that are within error bounds of each other
- if d < err_px:
- continue
-
- s = [seq[i], seq[j]]
- n = s[-1] + d
- while np.abs((seq-n)).min() < err_px:
- n = seq[np.abs((seq-n)).argmin()]
- s.append(n)
- n = s[-1] + d
-
- if len(s) >= min_seq_len:
- s = np.array(s)
- seqs.append(s)
- return seqs
-
-def getChessTilesColor(img, corners):
- # img is a color RGB image
- # corners = (x0, y0, x1, y1) for top-left corner to bot-right corner of board
- height, width, depth = img.shape
- if depth !=3:
- print("Need RGB color image input")
- return None
-
- # corners could be outside image bounds, pad image as needed
- padl_x = max(0, -corners[0])
- padl_y = max(0, -corners[1])
- padr_x = max(0, corners[2] - width)
- padr_y = max(0, corners[3] - height)
-
- img_padded = np.pad(img, ((padl_y,padr_y),(padl_x,padr_x), (0,0)), mode='edge')
-
- chessboard_img = img_padded[
- (padl_y + corners[1]):(padl_y + corners[3]),
- (padl_x + corners[0]):(padl_x + corners[2]), :]
-
- # 256x256 px RGB image, 32x32px individual RGB tiles, normalized 0-1 floats
- chessboard_img_resized = np.asarray( \
- PIL.Image.fromarray(chessboard_img) \
- .resize([256,256], PIL.Image.BILINEAR), dtype=np.float32) / 255.0
-
- # stack deep 64 tiles with 3 channesl RGB each
- # so, first 3 slabs are RGB for tile A1, then next 3 slabs for tile A2 etc.
- tiles = np.zeros([32,32,3*64], dtype=np.float32) # color
- # Assume A1 is bottom left of image, need to reverse rank since images start
- # with origin in top left
- for rank in range(8): # rows (numbers)
- for file in range(8): # columns (letters)
- # color
- tiles[:,:,3*(rank*8+file):3*(rank*8+file+1)] = \
- chessboard_img_resized[(7-rank)*32:((7-rank)+1)*32,file*32:(file+1)*32]
-
- return tiles
-
-def getChessBoardGray(img, corners):
- # img is a grayscale image
- # corners = (x0, y0, x1, y1) for top-left corner to bot-right corner of board
- height, width = img.shape
-
- # corners could be outside image bounds, pad image as needed
- padl_x = max(0, -corners[0])
- padl_y = max(0, -corners[1])
- padr_x = max(0, corners[2] - width)
- padr_y = max(0, corners[3] - height)
-
- img_padded = np.pad(img, ((padl_y,padr_y),(padl_x,padr_x)), mode='edge')
-
- chessboard_img = img_padded[
- (padl_y + corners[1]):(padl_y + corners[3]),
- (padl_x + corners[0]):(padl_x + corners[2])]
-
- # 256x256 px image, 32x32px individual tiles
- # Normalized
- chessboard_img_resized = np.asarray( \
- PIL.Image.fromarray(chessboard_img) \
- .resize([256,256], PIL.Image.BILINEAR), dtype=np.uint8) / 255.0
- return chessboard_img_resized
-
-def getChessTilesGray(img, corners):
- chessboard_img_resized = getChessBoardGray(img, corners)
- return getTiles(chessboard_img_resized)
-
-
-def getTiles(processed_gray_img):
- # Given 256x256 px normalized grayscale image of a chessboard (32x32px per tile)
- # NOTE (values must be in range 0-1)
- # Return a 32x32x64 tile array
- #
- # stack deep 64 tiles
- # so, first slab is tile A1, then A2 etc.
- tiles = np.zeros([32,32,64], dtype=np.float32) # grayscale
- # Assume A1 is bottom left of image, need to reverse rank since images start
- # with origin in top left
- for rank in range(8): # rows (numbers)
- for file in range(8): # columns (letters)
- tiles[:,:,(rank*8+file)] = \
- processed_gray_img[(7-rank)*32:((7-rank)+1)*32,file*32:(file+1)*32]
-
- return tiles
-
-def findGrayscaleTilesInImage(img):
- """ Find chessboard and convert into input tiles for CNN """
- if img is None:
- return None, None
-
- # Convert to grayscale numpy array
- img_arr = np.asarray(img.convert("L"), dtype=np.float32)
-
- # Use computer vision to find orthorectified chessboard corners in image
- corners = findChessboardCorners(img_arr)
- if corners is None:
- return None, None
-
- # Pull grayscale tiles out given image and chessboard corners
- tiles = getChessTilesGray(img_arr, corners)
-
- # Return both the tiles as well as chessboard corner locations in the image
- return tiles, corners
-
-# DEBUG
-# from matplotlib import pyplot as plt
-# def plotTiles(tiles):
-# """Plot color or grayscale tiles as 8x8 subplots"""
-# plt.figure(figsize=(6,6))
-# files = "ABCDEFGH"
-# for rank in range(8): # rows (numbers)
-# for file in range(8): # columns (letters)
-# plt.subplot(8,8,(7-rank)*8 + file + 1) # Plot rank reverse order to match image
-
-# if tiles.shape[2] == 64:
-# # Grayscale
-# tile = tiles[:,:,(rank*8+file)] # grayscale
-# plt.imshow(tile, interpolation='None', cmap='gray', vmin = 0, vmax = 1)
-# else:
-# #Color
-# tile = tiles[:,:,3*(rank*8+file):3*(rank*8+file+1)] # color
-# plt.imshow(tile, interpolation='None',)
-
-# plt.axis('off')
-# plt.title('%s %d' % (files[file], rank+1), fontsize=6)
-# plt.show()
-
-def main(url):
- print("Loading url %s..." % url)
- color_img, url = loadImageFromURL(url)
-
- # Fail if can't load image
- if color_img is None:
- print('Couldn\'t load url: %s' % url)
- return
-
- if color_img.mode != 'RGB':
- color_img = color_img.convert('RGB')
- print("Processing...")
- a = time()
- img_arr = np.asarray(color_img.convert("L"), dtype=np.float32)
- corners = findChessboardCorners(img_arr)
- print("Took %.4fs" % (time()-a))
- # corners = [x0, y0, x1, y1] where (x0,y0)
- # is top left and (x1,y1) is bot right
-
- if corners is not None:
- print("\tFound corners for %s: %s" % (url, corners))
- link = getVisualizeLink(corners, url)
- print(link)
-
- # tiles = getChessTilesColor(np.array(color_img), corners)
- # tiles = getChessTilesGray(img_arr, corners)
- # plotTiles(tiles)
-
- # plt.imshow(color_img, interpolation='none')
- # plt.plot(corners[[0,0,2,2,0]]-0.5, corners[[1,3,3,1,1]]-0.5, color='red', linewidth=1)
- # plt.show()
- else:
- print('\tNo corners found in image')
-
-if __name__ == '__main__':
- np.set_printoptions(suppress=True, precision=2)
- parser = argparse.ArgumentParser(description='Find orthorectified chessboard corners in image')
- parser.add_argument('urls', default=['https://i.redd.it/1uw3h772r0fy.png'],
- metavar='urls', type=str, nargs='*', help='Input image urls')
- # main('http://www.chessanytime.com/img/jeudirect/simplechess.png')
- # main('https://i.imgur.com/JpzfV3y.jpg')
- # main('https://i.imgur.com/jsCKzU9.jpg')
- # main('https://i.imgur.com/49htmMA.png')
- # main('https://i.imgur.com/HHdHGBX.png')
- # main('http://imgur.com/By2xJkO')
- # main('http://imgur.com/p8DJMly')
- # main('https://i.imgur.com/Ns0iBrw.jpg')
- # main('https://i.imgur.com/KLcCiuk.jpg')
- args = parser.parse_args()
- for url in args.urls:
- main(url)
-
diff --git a/spaces/runa91/bite_gradio/src/configs/anipose_data_info.py b/spaces/runa91/bite_gradio/src/configs/anipose_data_info.py
deleted file mode 100644
index 8e7bad68b45cf9926fdfd3ca1b7e1f147e909cfd..0000000000000000000000000000000000000000
--- a/spaces/runa91/bite_gradio/src/configs/anipose_data_info.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from dataclasses import dataclass
-from typing import List
-import json
-import numpy as np
-import os
-
-STATISTICS_DATA_DIR = os.path.join(os.path.dirname(__file__), '..', '..', 'data', 'statistics')
-STATISTICS_PATH = os.path.join(STATISTICS_DATA_DIR, 'statistics_modified_v1.json')
-
-@dataclass
-class DataInfo:
- rgb_mean: List[float]
- rgb_stddev: List[float]
- joint_names: List[str]
- hflip_indices: List[int]
- n_joints: int
- n_keyp: int
- n_bones: int
- n_betas: int
- image_size: int
- trans_mean: np.ndarray
- trans_std: np.ndarray
- flength_mean: np.ndarray
- flength_std: np.ndarray
- pose_rot6d_mean: np.ndarray
- keypoint_weights: List[float]
-
-# SMAL samples 3d statistics
-# statistics like mean values were calculated once when the project was started and they were not changed afterwards anymore
-def load_statistics(statistics_path):
- with open(statistics_path) as f:
- statistics = json.load(f)
- '''new_pose_mean = [[[np.round(val, 2) for val in sublst] for sublst in sublst_big] for sublst_big in statistics['pose_mean']]
- statistics['pose_mean'] = new_pose_mean
- j_out = json.dumps(statistics, indent=4) #, sort_keys=True)
- with open(self.statistics_path, 'w') as file: file.write(j_out)'''
- new_statistics = {'trans_mean': np.asarray(statistics['trans_mean']),
- 'trans_std': np.asarray(statistics['trans_std']),
- 'flength_mean': np.asarray(statistics['flength_mean']),
- 'flength_std': np.asarray(statistics['flength_std']),
- 'pose_mean': np.asarray(statistics['pose_mean']),
- }
- new_statistics['pose_rot6d_mean'] = new_statistics['pose_mean'][:, :, :2].reshape((-1, 6))
- return new_statistics
-STATISTICS = load_statistics(STATISTICS_PATH)
-
-AniPose_JOINT_NAMES_swapped = [
- 'L_F_Paw', 'L_F_Knee', 'L_F_Elbow',
- 'L_B_Paw', 'L_B_Knee', 'L_B_Elbow',
- 'R_F_Paw', 'R_F_Knee', 'R_F_Elbow',
- 'R_B_Paw', 'R_B_Knee', 'R_B_Elbow',
- 'TailBase', '_Tail_end_', 'L_EarBase', 'R_EarBase',
- 'Nose', '_Chin_', '_Left_ear_tip_', '_Right_ear_tip_',
- 'L_Eye', 'R_Eye', 'Withers', 'Throat']
-
-KEYPOINT_WEIGHTS = [3, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 3, 3, 2, 2, 3, 1, 2, 2]
-
-COMPLETE_DATA_INFO = DataInfo(
- rgb_mean=[0.4404, 0.4440, 0.4327], # not sure
- rgb_stddev=[0.2458, 0.2410, 0.2468], # not sure
- joint_names=AniPose_JOINT_NAMES_swapped, # AniPose_JOINT_NAMES,
- hflip_indices=[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 12, 13, 15, 14, 16, 17, 19, 18, 21, 20, 22, 23],
- n_joints = 35,
- n_keyp = 24, # 20, # 25,
- n_bones = 24,
- n_betas = 30, # 10,
- image_size = 256,
- trans_mean = STATISTICS['trans_mean'],
- trans_std = STATISTICS['trans_std'],
- flength_mean = STATISTICS['flength_mean'],
- flength_std = STATISTICS['flength_std'],
- pose_rot6d_mean = STATISTICS['pose_rot6d_mean'],
- keypoint_weights = KEYPOINT_WEIGHTS
- )
diff --git a/spaces/safi842/FashionGen/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h b/spaces/safi842/FashionGen/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h
deleted file mode 100644
index bc9d35181dd97c355fb6a5b17bc9e82e24ef1566..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- * File : prroi_pooling_gpu.h
- * Author : Jiayuan Mao, Tete Xiao
- * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com
- * Date : 07/13/2018
- *
- * Distributed under terms of the MIT license.
- * Copyright (c) 2017 Megvii Technology Limited.
- */
-
-int prroi_pooling_forward_cuda(THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, int pooled_height, int pooled_width, float spatial_scale);
-
-int prroi_pooling_backward_cuda(
- THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff,
- int pooled_height, int pooled_width, float spatial_scale
-);
-
-int prroi_pooling_coor_backward_cuda(
- THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff,
- int pooled_height, int pooled_width, float spatial_scal
-);
-
diff --git a/spaces/scedlatioru/img-to-music/example/(2011) Numroto Plus [FULL Version] [PATCHED] Download.md b/spaces/scedlatioru/img-to-music/example/(2011) Numroto Plus [FULL Version] [PATCHED] Download.md
deleted file mode 100644
index 15dfaacc2fd52d10e52f8ddbbad66b4394a7c780..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/(2011) Numroto Plus [FULL Version] [PATCHED] Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Hello everybody, I use Winpcsign pro 2010 and I have a problem with its activation code. The activation code of Winpcsign pro 2010 is DE-A05 but it is not working at all on my PC. But I had Winpcsign before and it is working fine so I am sure that my Winpcsign is not corrupted. When I activate Winpcsign, its is not working and its return the following message: "Windows has verified that the software is genuine". What should I do?Washburn, Minnesota
-
-Washburn is a city in Stevens County, Minnesota, United States. The population was 2,541 at the 2010 census. It is part of the Grand Forks, ND-MN Metropolitan Statistical Area.
-
-Geography
-
-According to the United States Census Bureau, the city has a total area of, of which, is land and is water.
-
-Demographics
-
-2010 census
-
-As of the census of 2010, there were 2,541 people, 1,054 households, and 669 families living in the city. The population density was. There were 1,148 housing units at an average density of. The racial makeup of the city was 95.1% White, 0.6% African American, 1.8% Native American, 0.1% Asian, 0.1% Pacific Islander, 0.5% from other races, and 1.9% from two or more races. Hispanic or Latino of any race were 1.9% of the population.
-
-There were 1,054 households of which 33.8% had children under the age of 18 living with them, 44.3% were married couples living together, 12.8% had a female householder with no husband present, 6.7% had a male householder with no wife present, and 36.3% were non-families. 29.7% of all households were made up of individuals and 11.8% had someone living alone who was 65 years of age or older. The average household size was 2.36 and the average family size was 2.92.
-
-The median age in the city was 35 years. 25.2% of residents were under the age of 18; 6.9% were between the ages of 18 and 24; 28.5% were from 25 to 44; 24.1% were from 45 to 64; and 15.2% were 4fefd39f24
-
-
-
diff --git a/spaces/seduerr/personality/app.py b/spaces/seduerr/personality/app.py
deleted file mode 100644
index 6ff0e05405f34b30ef4ba0da7a1f493e6ba641f3..0000000000000000000000000000000000000000
--- a/spaces/seduerr/personality/app.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import spacy
-from spacy.lang.en.stop_words import STOP_WORDS
-import re
-import pickle
-import numpy as np
-import gradio as gr
-
-nlp = spacy.load("en_core_web_sm")
-
-dummy_fn = lambda x:x
-
-with open('./models/cv.pickle', 'rb') as f:
- cv = pickle.load(f)
-with open('./models/idf_transformer.pickle', 'rb') as f:
- idf_transformer = pickle.load(f)
-with open('./models/LR_clf_IE_kaggle.pickle', 'rb') as f:
- lr_ie = pickle.load(f)
-with open('./models/LR_clf_JP_kaggle.pickle', 'rb') as f:
- lr_jp = pickle.load(f)
-with open('./models/LR_clf_NS_kaggle.pickle', 'rb') as f:
- lr_ns = pickle.load(f)
-with open('./models/LR_clf_TF_kaggle.pickle', 'rb') as f:
- lr_tf = pickle.load(f)
-
-def tokeniser(sentence):
- sentence = re.sub("[]|||[]", " ", sentence)
- sentence = re.sub("/r/[0-9A-Za-z]", "", sentence)
- MBTI_types = ['INFJ', 'ENTP', 'INTP', 'INTJ', 'ENTJ', 'ENFJ', 'INFP', 'ENFP','ISFP', 'ISTP', 'ISFJ', 'ISTJ', 'ESTP', 'ESFP', 'ESTJ', 'ESFJ', 'MBTI']
- MBTI_types = [ti.lower() for ti in MBTI_types] + [ti.lower() + 's' for ti in MBTI_types]
-
- tokens = nlp(sentence)
-
- tokens = [ti for ti in tokens if ti.lower_ not in STOP_WORDS]
- tokens = [ti for ti in tokens if not ti.is_space]
- tokens = [ti for ti in tokens if not ti.is_punct]
- tokens = [ti for ti in tokens if not ti.like_num]
- tokens = [ti for ti in tokens if not ti.like_url]
- tokens = [ti for ti in tokens if not ti.like_email]
- tokens = [ti for ti in tokens if ti.lower_ not in MBTI_types]
-
- tokens = [ti.lemma_ for ti in tokens if ti.lemma_ not in STOP_WORDS]
- tokens = [ti for ti in tokens if len(ti) > 1]
- print("Tokens: ",tokens)
- print("sentence")
- return tokens
-
-def predict(input_text):
- c = cv.transform([tokeniser(input_text)])
- x = idf_transformer.transform(c)
-
- ie = lr_ie.predict_proba(x).flatten()
- ns = lr_ns.predict_proba(x).flatten()
- tf = lr_tf.predict_proba(x).flatten()
- jp = lr_jp.predict_proba(x).flatten()
-
- probs = np.vstack([ie, ns, tf, jp])
-
- names = ["Introversion - Extroversion",
- "Intuiting - Sensing",
- "Thinking - Feeling",
- "Judging - Perceiving"]
-
- profile = []
-
- for i, dim in enumerate(names):
- profile.append(str(f"{dim}: {probs[i,1]:.3f} - {probs[i, 0]:.3f}"))
- profile_as_string = " \n".join(profile)
- return profile_as_string
-
-title = "Identify Your Personality"
-description = """
-
Simply insert a text that is written by you.
-
-With this tool you can identify your personality based on the [Myers Briggs Type Indicator](https://en.wikipedia.org/wiki/Myers%E2%80%93Briggs_Type_Indicator).
-
-Please find some explanations below. """
-
-article = """### Extraversion vs. Introversion
-- The Extraversion/Introversion dichotomy describes how a person gets their energy.
-- Introverts are energized by spending quiet time alone or with a small group. They tend to be more reserved and thoughtful.
-- Extraverts are energized by spending time with people and in busy, active surroundings. They tend to be more expressive and outspoken.
-
-### Sensing vs. Intuition
-- The Sensing/Intuition dichotomy describes how a person takes in information.
-- Sensors focus on their five senses and are interested in information they can directly see, hear, feel, and so on. They tend to be hands-on learners and are often described as "practical."
-- Intuitives focus on a more abstract level of thinking; they are more interested in theories, patterns, and explanations. They are often more concerned with the future than the present and are often described as "creative."
-
-### Thinking vs. Feeling
-- The Thinking/Feeling dichotomy describes how a person makes decisions.
-- Thinkers tend to make decisions with their heads; they are interested in finding the most logical, reasonable choice.
-- Feelers tend to make decisions with their hearts; they are interested in how a decision will affect people, and whether it fits in with their values.
-
-### Judging vs. Perceiving
-- The Judging/Perceiving dichotomy describes how a person organizes their world.
-- Judgers appreciate structure and order; they like things planned, and dislike last-minute changes.
-- Perceivers appreciate flexibility and spontaneity; they like to leave things open so they can change their minds.
-
-Once you have determined which style you prefer for each of the four dichotomies, you can figure out your four-letter type code. In Myers and Briggs' system, the four letters of a personality type are the first initials of each of your prefererences. For example, someone with a preference for Extraversion, Intuition, Feeling and Judging would have the type “ENTJ.” A preference for Intuition is signified with the letter "N," to avoid confusion with Introversion.
-
-There are sixteen possible combinations of preferences, making up 16 total personality types.
-
-[Source truity.com](https://www.truity.com/myers-briggs/4-letters-myers-briggs-personality-types)
-
-"""
-
-examples = ['''I’m gonna take a hot shower and get into my book with a nice glass of vino :)''', '''You said feel free to connect on your web page :)
-I am excited to meet you later Omar!''', '''Hi Sebastian, my name is Ella and I am a Senior Talent Business Partner at Julio. We are searching for a Machine Learning Engineer and your impressive profile caught my eye.''']
-
-
-
-iface = gr.Interface(fn=predict,
- inputs = [gr.inputs.Textbox(lines=3, label='Input any personal text of yours!')],
- outputs = "text",
- title = title,
- description = description,
- article = article,
- examples = examples,
- )
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/segments-tobias/conex/espnet/transform/transform_interface.py b/spaces/segments-tobias/conex/espnet/transform/transform_interface.py
deleted file mode 100644
index 8a6aba45b0fbd4896e05a39b8b4aee774f86dd69..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/transform/transform_interface.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# TODO(karita): add this to all the transform impl.
-class TransformInterface:
- """Transform Interface"""
-
- def __call__(self, x):
- raise NotImplementedError("__call__ method is not implemented")
-
- @classmethod
- def add_arguments(cls, parser):
- return parser
-
- def __repr__(self):
- return self.__class__.__name__ + "()"
-
-
-class Identity(TransformInterface):
- """Identity Function"""
-
- def __call__(self, x):
- return x
diff --git a/spaces/segments-tobias/conex/espnet2/lm/abs_model.py b/spaces/segments-tobias/conex/espnet2/lm/abs_model.py
deleted file mode 100644
index ba5773d0126175a148be6abaf294f69008b2686c..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/lm/abs_model.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from abc import ABC
-from abc import abstractmethod
-from typing import Tuple
-
-import torch
-
-from espnet.nets.scorer_interface import BatchScorerInterface
-
-
-class AbsLM(torch.nn.Module, BatchScorerInterface, ABC):
- """The abstract LM class
-
- To share the loss calculation way among different models,
- We uses delegate pattern here:
- The instance of this class should be passed to "LanguageModel"
-
- >>> from espnet2.lm.abs_model import AbsLM
- >>> lm = AbsLM()
- >>> model = LanguageESPnetModel(lm=lm)
-
- This "model" is one of mediator objects for "Task" class.
-
- """
-
- @abstractmethod
- def forward(
- self, input: torch.Tensor, hidden: torch.Tensor
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- raise NotImplementedError
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer_uvr5.py b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer_uvr5.py
deleted file mode 100644
index 4aada2dc204281de2e20ff8f353294bfaa5953bf..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer_uvr5.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import os, sys, torch, warnings, pdb
-
-warnings.filterwarnings("ignore")
-import librosa
-import importlib
-import numpy as np
-import hashlib, math
-from tqdm import tqdm
-from uvr5_pack.lib_v5 import spec_utils
-from uvr5_pack.utils import _get_name_params, inference
-from uvr5_pack.lib_v5.model_param_init import ModelParameters
-from scipy.io import wavfile
-
-
-class _audio_pre_:
- def __init__(self, agg, model_path, device, is_half):
- self.model_path = model_path
- self.device = device
- self.data = {
- # Processing Options
- "postprocess": False,
- "tta": False,
- # Constants
- "window_size": 512,
- "agg": agg,
- "high_end_process": "mirroring",
- }
- nn_arch_sizes = [
- 31191, # default
- 33966,
- 61968,
- 123821,
- 123812,
- 537238, # custom
- ]
- self.nn_architecture = list("{}KB".format(s) for s in nn_arch_sizes)
- model_size = math.ceil(os.stat(model_path).st_size / 1024)
- nn_architecture = "{}KB".format(
- min(nn_arch_sizes, key=lambda x: abs(x - model_size))
- )
- nets = importlib.import_module(
- "uvr5_pack.lib_v5.nets"
- + f"_{nn_architecture}".replace("_{}KB".format(nn_arch_sizes[0]), ""),
- package=None,
- )
- model_hash = hashlib.md5(open(model_path, "rb").read()).hexdigest()
- param_name, model_params_d = _get_name_params(model_path, model_hash)
-
- mp = ModelParameters(model_params_d)
- model = nets.CascadedASPPNet(mp.param["bins"] * 2)
- cpk = torch.load(model_path, map_location="cpu")
- model.load_state_dict(cpk)
- model.eval()
- if is_half:
- model = model.half().to(device)
- else:
- model = model.to(device)
-
- self.mp = mp
- self.model = model
-
- def _path_audio_(self, music_file, ins_root=None, vocal_root=None):
- if ins_root is None and vocal_root is None:
- return "No save root."
- name = os.path.basename(music_file)
- if ins_root is not None:
- os.makedirs(ins_root, exist_ok=True)
- if vocal_root is not None:
- os.makedirs(vocal_root, exist_ok=True)
- X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
- bands_n = len(self.mp.param["band"])
- # print(bands_n)
- for d in range(bands_n, 0, -1):
- bp = self.mp.param["band"][d]
- if d == bands_n: # high-end band
- (
- X_wave[d],
- _,
- ) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
- music_file,
- bp["sr"],
- False,
- dtype=np.float32,
- res_type=bp["res_type"],
- )
- if X_wave[d].ndim == 1:
- X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
- else: # lower bands
- X_wave[d] = librosa.core.resample(
- X_wave[d + 1],
- self.mp.param["band"][d + 1]["sr"],
- bp["sr"],
- res_type=bp["res_type"],
- )
- # Stft of wave source
- X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
- X_wave[d],
- bp["hl"],
- bp["n_fft"],
- self.mp.param["mid_side"],
- self.mp.param["mid_side_b2"],
- self.mp.param["reverse"],
- )
- # pdb.set_trace()
- if d == bands_n and self.data["high_end_process"] != "none":
- input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
- self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
- )
- input_high_end = X_spec_s[d][
- :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
- ]
-
- X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
- aggresive_set = float(self.data["agg"] / 100)
- aggressiveness = {
- "value": aggresive_set,
- "split_bin": self.mp.param["band"][1]["crop_stop"],
- }
- with torch.no_grad():
- pred, X_mag, X_phase = inference(
- X_spec_m, self.device, self.model, aggressiveness, self.data
- )
- # Postprocess
- if self.data["postprocess"]:
- pred_inv = np.clip(X_mag - pred, 0, np.inf)
- pred = spec_utils.mask_silence(pred, pred_inv)
- y_spec_m = pred * X_phase
- v_spec_m = X_spec_m - y_spec_m
-
- if ins_root is not None:
- if self.data["high_end_process"].startswith("mirroring"):
- input_high_end_ = spec_utils.mirroring(
- self.data["high_end_process"], y_spec_m, input_high_end, self.mp
- )
- wav_instrument = spec_utils.cmb_spectrogram_to_wave(
- y_spec_m, self.mp, input_high_end_h, input_high_end_
- )
- else:
- wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
- print("%s instruments done" % name)
- wavfile.write(
- os.path.join(
- ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
- ),
- self.mp.param["sr"],
- (np.array(wav_instrument) * 32768).astype("int16"),
- ) #
- if vocal_root is not None:
- if self.data["high_end_process"].startswith("mirroring"):
- input_high_end_ = spec_utils.mirroring(
- self.data["high_end_process"], v_spec_m, input_high_end, self.mp
- )
- wav_vocals = spec_utils.cmb_spectrogram_to_wave(
- v_spec_m, self.mp, input_high_end_h, input_high_end_
- )
- else:
- wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
- print("%s vocals done" % name)
- wavfile.write(
- os.path.join(
- vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
- ),
- self.mp.param["sr"],
- (np.array(wav_vocals) * 32768).astype("int16"),
- )
-
-
-if __name__ == "__main__":
- device = "cuda"
- is_half = True
- model_path = "uvr5_weights/2_HP-UVR.pth"
- pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True)
- audio_path = "神女劈观.aac"
- save_path = "opt"
- pre_fun._path_audio_(audio_path, save_path, save_path)
diff --git a/spaces/shi-labs/FcF-Inpainting/training/losses/ade20k/segm_lib/utils/data/__init__.py b/spaces/shi-labs/FcF-Inpainting/training/losses/ade20k/segm_lib/utils/data/__init__.py
deleted file mode 100644
index f3b008fb13c5e8a84b1b785056e8c4f5226dc976..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/FcF-Inpainting/training/losses/ade20k/segm_lib/utils/data/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-
-from .dataset import Dataset, TensorDataset, ConcatDataset
-from .dataloader import DataLoader
diff --git a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/utils/instantiators.py b/spaces/shivammehta25/Diff-TTSG/diff_ttsg/utils/instantiators.py
deleted file mode 100644
index 1e2713a0d1e1ea39b86b2def82bc17e50a674a0b..0000000000000000000000000000000000000000
--- a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/utils/instantiators.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from typing import List
-
-import hydra
-from lightning import Callback
-from lightning.pytorch.loggers import Logger
-from omegaconf import DictConfig
-
-from diff_ttsg.utils import pylogger
-
-log = pylogger.get_pylogger(__name__)
-
-
-def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
- """Instantiates callbacks from config."""
-
- callbacks: List[Callback] = []
-
- if not callbacks_cfg:
- log.warning("No callback configs found! Skipping..")
- return callbacks
-
- if not isinstance(callbacks_cfg, DictConfig):
- raise TypeError("Callbacks config must be a DictConfig!")
-
- for _, cb_conf in callbacks_cfg.items():
- if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
- log.info(f"Instantiating callback <{cb_conf._target_}>")
- callbacks.append(hydra.utils.instantiate(cb_conf))
-
- return callbacks
-
-
-def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:
- """Instantiates loggers from config."""
-
- logger: List[Logger] = []
-
- if not logger_cfg:
- log.warning("No logger configs found! Skipping...")
- return logger
-
- if not isinstance(logger_cfg, DictConfig):
- raise TypeError("Logger config must be a DictConfig!")
-
- for _, lg_conf in logger_cfg.items():
- if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
- log.info(f"Instantiating logger <{lg_conf._target_}>")
- logger.append(hydra.utils.instantiate(lg_conf))
-
- return logger
diff --git a/spaces/sidphbot/Researcher/setup.py b/spaces/sidphbot/Researcher/setup.py
deleted file mode 100644
index 21447a3be1c133af6618ef7167028898ea937da6..0000000000000000000000000000000000000000
--- a/spaces/sidphbot/Researcher/setup.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import setuptools
-
-with open("README.md", "r", encoding="utf-8") as fh:
- long_description = fh.read()
-
-setuptools.setup(
- name="Auto-Research",
- version="1.0",
- author="Sidharth Pal",
- author_email="sidharth.pal1992@gmail.com",
- description="Geberate scientific survey with just a query",
- long_description=long_description,
- long_description_content_type="text/markdown",
- url="https://github.com/sidphbot/Auto-Research",
- project_urls={
- "Docs" : "https://github.com/example/example/README.md",
- "Bug Tracker": "https://github.com/sidphbot/Auto-Research/issues",
- "Demo": "https://www.kaggle.com/sidharthpal/auto-research-generate-survey-from-query",
- },
- classifiers=[
- "Development Status :: 5 - Production/Stable",
- "Environment :: Console",
- "Environment :: Other Environment",
- "Intended Audience :: Developers",
- "Intended Audience :: Education",
- "Intended Audience :: Science/Research",
- "Intended Audience :: Other Audience",
- "Topic :: Education",
- "Topic :: Education :: Computer Aided Instruction (CAI)",
- "Topic :: Scientific/Engineering",
- "Topic :: Scientific/Engineering :: Artificial Intelligence",
- "Topic :: Scientific/Engineering :: Information Analysis",
- "Topic :: Scientific/Engineering :: Medical Science Apps.",
- "Topic :: Scientific/Engineering :: Physics",
- "Natural Language :: English",
- "License :: OSI Approved :: GNU General Public License (GPL)",
- "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
- "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
- "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
- "Operating System :: POSIX :: Linux",
- "Operating System :: MacOS :: MacOS X",
- "Environment :: GPU",
- "Environment :: GPU :: NVIDIA CUDA",
- "Programming Language :: Python",
- "Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3 :: Only",
- "Programming Language :: Python :: 3.6",
- ],
- package_dir={"": "src"},
- packages=setuptools.find_packages(where="src"),
- python_requires=">=3.7",
- install_requires=[
- "pip",
- "boto3==1.9.118",
- "requests==2.20.0",
- "unicodedata2",
- "pdfminer3k",
- "sentence-transformers",
- "pdftotext",
- "arxiv",
- "arxiv2bib",
- "scholarly",
- "PyMuPDF",
- "Pillow",
- "tabula-py",
- "sentencepiece",
- "keybert",
- "scispacy",
- "amrlib",
- "transformers",
- "en_core_sci_scibert",
- "bert-extractive-summarizer",
- "en_core_sci_lg",
- ],
- extras_require={
- "spacy": ["all"],
- },
- dependency_links=[
- "https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_scibert-0.5.0.tar.gz#egg=en_core_sci_scibert-0.5.0",
- "https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_lg-0.5.0.tar.gz#egg=en_core_sci_lg-0.5.0"
- ],
- tests_require=["pytest"],
- entry_points={
- 'console_scripts': [
- 'cursive = src.Surveyor:main',
- ],
- },
-
-)
\ No newline at end of file
diff --git a/spaces/silencewing/server/youyou/js/jquery.js b/spaces/silencewing/server/youyou/js/jquery.js
deleted file mode 100644
index 5f5c9cae25759c83c82136f1853d4fc90892186b..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/js/jquery.js
+++ /dev/null
@@ -1,10704 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.7.0
- * https://jquery.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2023-05-11T18:29Z
- */
-( function( global, factory ) {
-
- "use strict";
-
- if ( typeof module === "object" && typeof module.exports === "object" ) {
-
- // For CommonJS and CommonJS-like environments where a proper `window`
- // is present, execute the factory and get jQuery.
- // For environments that do not have a `window` with a `document`
- // (such as Node.js), expose a factory as module.exports.
- // This accentuates the need for the creation of a real `window`.
- // e.g. var jQuery = require("jquery")(window);
- // See ticket trac-14549 for more info.
- module.exports = global.document ?
- factory( global, true ) :
- function( w ) {
- if ( !w.document ) {
- throw new Error( "jQuery requires a window with a document" );
- }
- return factory( w );
- };
- } else {
- factory( global );
- }
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
- return arr.flat.call( array );
-} : function( array ) {
- return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
- // Support: Chrome <=57, Firefox <=52
- // In some browsers, typeof returns "function" for HTML
- 
- 
- 
- 
- 
- 
- 
- 
-
- 
-
- 
- 
- 
- 
- 
- PROGRAMACIÓN SEMANAL PARA LIDERES
-