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-## Matlab R2008a Crack Keygen Free
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-# How to Download and Install Matlab R2008a Crack Keygen Free
-Matlab R2008a is a powerful software for mathematical computing, visualization, and programming. It can be used for various applications such as data analysis, algorithm development, simulation, and modeling. However, Matlab R2008a is not a free software and requires a license to activate it. If you want to use Matlab R2008a without paying for a license, you can try to download and install a cracked version with a keygen. Here are the steps to do that:
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-4. After the installation is complete, do not run Matlab yet. Copy the libmwservices.dll file from the crack folder and paste it into the bin folder of your Matlab installation directory (usually C:\Program Files\MATLAB\R2008a\bin).
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-Note: This method is illegal and may violate the terms of use of Matlab. It may also expose your system to viruses or malware. Use it at your own risk. We do not recommend or endorse this method and we are not responsible for any consequences that may arise from using it.
-## What is Matlab R2008a?
-Matlab R2008a is the seventh release of Matlab, which was launched in March 2008. It introduced several new features and improvements, such as:
-- A new object-oriented programming model based on classes and inheritance.
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-- Enhanced performance and memory management for large data sets and arrays.
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-Matlab R2008a is a popular and widely used software for scientific and engineering applications. However, it is also a costly software that requires a valid license to activate and use. A license for Matlab R2008a can cost up to $2,150 for a single user or $10,000 for a network license. For many students, researchers, and hobbyists, this price may be too high to afford.
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-<h1>Abacre Restaurant Point of Sale: A Complete Solution for Your Restaurant Business</h1>
- <p>If you are looking for a restaurant management software that can help you achieve full control over your business operations, you should consider Abacre Restaurant Point of Sale (POS). This software is a new generation of restaurant management software for Windows that offers a complete solution from taking orders from patrons to billing and tax reports. In this article, we will review what Abacre Restaurant Point of Sale is, what are its main features, how to download and install it, and how to buy it.</p>
- <h2>What is Abacre Restaurant Point of Sale?</h2>
- <h3>A new generation of restaurant management software</h3>
- <p>Abacre Restaurant Point of Sale is a new generation of restaurant management software for Windows that uses the latest technologies and concepts. It is designed to work on multiple computers and devices, such as touch screen monitors, tablets, laptops, desktops, or even smartphones. It can also work offline without an internet connection, which makes it reliable and secure.</p>
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- <h3>A complete solution from taking orders to billing and tax reports</h3>
- <p>Abacre Restaurant Point of Sale is a complete solution that covers all aspects of restaurant management. It allows you to take orders from patrons using different methods, such as keyboard, mouse, touch screen, or handheld devices. It also allows you to print orders to kitchen printers or send them to kitchen displays. You can also manage your inventory, menu items, recipes, ingredients, modifiers, and prices. You can also generate bills for your guests with customizable layouts and print them or email them. You can also handle payments with cash, credit cards, checks, or gift cards. You can also generate various reports that show you the performance of your restaurant, such as sales, profits, taxes, tips, discounts, refunds, reservations, hours of operation, busiest tables, most active employees, and more.</p>
- <h3>A user-friendly interface optimized for high speed input and error prevention</h3>
- <p>Abacre Restaurant Point of Sale has a user-friendly interface that is carefully optimized for high speed input of a patron's order and the prevention of common mistakes. It has large buttons and icons that are easy to see and use. It also has color-coded categories and items that help you find what you need quickly. It also has smart features that help you avoid errors, such as automatic detection of duplicate orders, confirmation dialogs before deleting or modifying orders or payments, warning messages when inventory levels are low or when prices are changed.</p>
- <h2>What are the main features of Abacre Restaurant Point of Sale?</h2>
- <h3>Reliable and secure authorization levels</h3>
- <p>Abacre Restaurant Point of Sale has reliable and secure authorization levels that allow you to control who can access what functions in the software. You can create different user roles with different permissions, such as owner, manager, cashier, waiter, cook, etc. You can also assign passwords or use fingerprint scanners to log in users. You can also track the actions of each user in the software with audit logs.</p>
- <h3>Customizable guest bill layouts and currency, tax, and gratuity settings</h3>
- <p>Abacre Restaurant Point of Sale allows you to customize the guest bill layouts according to your preferences and needs. You can choose from different templates or create your own using a built-in editor. You can also add your logo, address, phone number, website URL, and other information to your bills. You can also set up different currency formats, tax rates, and gratuity options for your bills. You can also apply discounts, coupons, or surcharges to your bills.</p>
- <h3>Multiple payment methods and automatic tax calculations</h3>
- <p>Abacre Restaurant Point of Sale supports multiple payment methods for your guests, such as cash, credit cards, checks, or gift cards. You can also split bills among guests or merge bills from different tables. You can also accept partial payments or deposits for reservations or catering orders. You can also integrate with various payment processors, such as PayPal, Stripe, Square, or Authorize.Net. Abacre Restaurant Point of Sale also calculates taxes automatically based on your tax settings and applies them to your bills.</p>
- <h3>Rich set of reports for managers and owners</h3>
- <p>Abacre Restaurant Point of Sale provides a rich set of reports that show you a complete picture of your restaurant operations and life cycles. You can generate reports on various aspects of your business, such as sales, profits, taxes, tips, discounts, refunds, reservations, hours of operation, busiest tables, most active employees, payment methods, and more. You can also filter and sort the reports by date range, time period, location, employee, table, item category, or any other criteria. You can also export the reports to Excel, PDF, HTML, or text formats or email them to yourself or others.</p>
- <h3>Standardized restaurant management process and improved serving speed</h3>
- <p>By standardizing the entire restaurant management process with Abacre Restaurant Point of Sale, you can improve the efficiency and quality of your service. You can reduce the waiting time for your guests by taking orders faster and sending them directly to the kitchen. You can also avoid errors and confusion by printing clear and accurate bills and receipts. You can also increase customer satisfaction by offering discounts, coupons, or loyalty programs. You can also handle complaints and refunds quickly and professionally.</p>
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- <h3>Support for serial and kitchen matrix printers, poles, and cash drawers</h3>
- <p>Abacre Restaurant Point of Sale supports various hardware devices that enhance your restaurant operations. You can use serial or kitchen matrix printers to print orders to the kitchen or bar. You can use poles (line and graphic displays) to show order information or promotional messages to your guests. You can use cash drawers to store cash payments securely. You can also use barcode scanners, scales, magnetic card readers, fingerprint scanners, or touch screen monitors.</p>
- <h2>How to download  and install Abacre Restaurant Point  of Sale?</h2>
- <h3>Download  the full-featured 30-day trial version  from the official website</h3>
- <p>If you want  to try Abacre Restaurant Point  of Sale before buying it, you can download  the full-featured 30-day trial version  from the official website. The trial version  is fully functional  and has no limitations  except  the time limit. You can use  the trial version  on any number  of computers  or devices.</p>
- <h3>Install  the software  on multiple computers  using the setup wizard</h3>
- <p>To install  the software  on your computer  or device, you need  to run  the setup wizard  that guides you through  the installation process. You need  to agree  to the end user license agreement (EULA), choose  the installation folder  and components, and create shortcuts. You can also choose  to install  the software  on multiple computers  using the network installation option.</p>
- <h3>Uninstall  the software easily if not satisfied</h3>
- <p>If you are not satisfied with Abacre Restaurant Point of Sale or if you want to remove it from your computer or device for any reason, you can uninstall it easily using the Windows Control Panel or the uninstaller program that comes with the software. You can also delete the installation folder and Here is the continuation of the article. <h2>How to buy Abacre Restaurant Point of Sale?</h2>
- <h3>Choose from three license types: Lite, Standard, or Professional</h3>
- <p>Abacre Restaurant Point of Sale offers three license types for different needs and budgets: Lite, Standard, and Professional. Each license type allows you to use the software on one workstation (computer or device). You can also buy additional licenses for more workstations at discounted prices. The main difference between the license types is the number of features and functions they include. You can compare the features and prices of each license type using the feature matrix.</p>
- <h3>Compare the features and prices of each license type</h3>
- <p>The Lite license is the most affordable option, but it has the least features and functions. It costs $149.99 for one workstation. It includes basic features such as taking orders, printing bills, accepting payments, and generating sales reports. It does not include advanced features such as inventory management, menu engineering, reservations, delivery, loyalty programs, gift cards, barcode scanners, fingerprint scanners, or touch screen monitors.</p>
- <p>The Standard license is the most popular option, as it has more features and functions than the Lite license. It costs $299.99 for one workstation. It includes all the features of the Lite license plus inventory management, menu engineering, reservations, delivery, loyalty programs, gift cards, barcode scanners, fingerprint scanners, and touch screen monitors. It does not include some features such as kitchen displays, kitchen printers, poles, cash drawers, scales, or magnetic card readers.</p>
- <p>The Professional license is the most comprehensive option, as it has all the features and functions of the software. It costs $449.99 for one workstation. It includes all the features of the Standard license plus kitchen displays, kitchen printers, poles, cash drawers, scales, and magnetic card readers. It also includes some exclusive features such as multi-location support, cloud backup and restore, web interface access, remote database access, and email notifications.</p>
- <h3>Order online using a secure payment system</h3>
- <p>If you have decided which license type you want to buy, you can order online using a secure payment system. You can pay by credit card, PayPal, check, bank wire transfer, purchase order, phone call, or fax over a secure web server. You can also choose the currency and language of your order. You can order online from the official website or from one of the resellers. After you place your order, you will receive a confirmation email with your registration key and instructions on how to enter it into the software. You will also receive free email support and updates for one year.</p>
- <h2>Conclusion</h2>
- <p>Abacre Restaurant Point of Sale is a new generation of restaurant management software for Windows that offers a complete solution from taking orders from patrons to billing and tax reports. It has a user-friendly interface optimized for high speed input and error prevention. It has reliable and secure authorization levels and customizable guest bill layouts and currency, tax, and gratuity settings. It supports multiple payment methods and automatic tax calculations. It provides a rich set of reports for managers and owners. It standardizes restaurant management process and improves serving speed. It supports various hardware devices such as serial and kitchen matrix printers, poles, and cash drawers. You can download and install Abacre Restaurant Point of Sale easily from the official website. You can also buy Abacre Restaurant Point of Sale online using a secure payment system. You can choose from three license types: Lite, Standard, or Professional, depending on your needs and budget. You can also compare the features and prices of each license type using the feature matrix. Abacre Restaurant Point of Sale is a great software that can help you achieve full control over your restaurant business.</p>
- <h4>FAQs</h4>
- <p>Q: What are the system requirements for Abacre Restaurant Point of Sale?</p>
-<p>A: Abacre Restaurant Point of Sale works perfectly on Windows XP/2003/Vista/2008/Windows 7/8/10. It requires at least 512 MB of RAM and 100 MB of free disk space.</p>
- <p>Q: How can I learn how to use Abacre Restaurant Point of Sale?</p>
-<p>A: You can learn how to use Abacre Restaurant Point of Sale by viewing a 5-minute interactive flash movie that shows you the main features and functions of the software. You can also read the manual that provides detailed instructions on how to use the software. You can also watch video tutorials that demonstrate how to use the software in different scenarios.</p>
- <p>Q: How can I get technical support for Abacre Restaurant Point of Sale?</p>
-<p>A: You can get technical support for Abacre Restaurant Point of Sale by sending an email to [email protected] or by posting your questions on the discussion forums. You can also find answers to frequently asked questions on the support page. You can also contact Abacre Limited by phone or fax using the contact information on their website.</p>
- <p>Q: How can I update Abacre Restaurant Point of Sale?</p>
-<p>A: You can update Abacre Restaurant Point of Sale by downloading the latest version from the official website or by using the built-in update feature in the software. You can also subscribe to RSS feeds or free newsletter to get notified about new versions and updates.</p>
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-<p>AutoCAD is a popular software for designing and drafting in various fields such as architecture, engineering, and construction. However, the official version of AutoCAD 2016 is not free and requires a license to activate. If you want to use AutoCAD 2016 without paying for it, you might be tempted to download a cracked version from the internet. But is it safe and legal to do so?</p>
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-<p>In this article, we will explain what a crack is, why you should avoid downloading AutoCAD 2016 32 bit crack, and how you can get a free trial or a student version of AutoCAD instead.</p>
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-<h1>Geografía e Historia 1 ESO Santillana.pdf: A Comprehensive Guide</h1>
- <p>If you are a student of first year of secondary education (ESO) in Spain, you probably have to use Geografía e Historia 1 ESO Santillana.pdf as your textbook for geography and history. But what is this book exactly? Why is it important to study it? And how can you use it effectively to improve your knowledge and skills? In this article, we will answer these questions and more. We will provide you with an overview of the book, a detailed analysis of its contents, and some recommendations for further learning and practice. By the end of this article, you will have a better understanding of Geografía e Historia 1 ESO Santillana.pdf and how to make the most of it.</p>
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- <h2>Introduction</h2>
- <h3>What is Geografía e Historia 1 ESO Santillana.pdf?</h3>
- <p>Geografía e Historia 1 ESO Santillana.pdf is a textbook for geography and history for first year of secondary education (ESO) in Spain. It is published by Santillana, one of the leading educational publishers in Spain and Latin America. It is part of the project "Saber hacer contigo", which aims to provide students with a comprehensive and integrated learning experience that develops their competencies and values.</p>
- <h3>Why is it important to study Geografía e Historia 1 ESO Santillana.pdf?</h3>
- <p>Geography and history are essential subjects that help students understand the world they live in, its diversity, its complexity, its evolution, and its challenges. They also help students develop critical thinking, communication, research, and problem-solving skills that are useful for their personal and professional lives. Geografía e Historia 1 ESO Santillana.pdf is designed to help students achieve these goals by providing them with relevant, updated, and engaging content that covers the key aspects of geography and history from a global and local perspective.</p>
- <h3>How to use Geografía e Historia 1 ESO Santillana.pdf effectively?</h3>
- <p>To use Geografía e Historia 1 ESO Santillana.pdf effectively, you need to follow some basic steps:</p>
-<ul>
-<li>Read the introduction of each chapter carefully to get an overview of the main objectives, contents, and activities.</li>
-<li>Study the text and the images attentively to understand the concepts and facts presented.</li>
-<li>Do the exercises and activities proposed in each section to check your comprehension and apply your knowledge.</li>
-<li>Review the summary and the key words at the end of each chapter to reinforce your learning.</li>
-<li>Use the additional resources available online or in other formats to deepen your understanding and practice your skills.</li>
-</ul>
-<p>In the next sections, we will explain more in detail what you can find in each chapter of Geografía e Historia 1 ESO Santillana.pdf and how to use it effectively.</p>
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- <h2>Main Content</h2>
- <h3>Geografía e Historia 1 ESO Santillana.pdf: An Overview</h3>
- <h4>The structure and features of the book</h4>
- <p>Geografía e Historia 1 ESO Santillana.pdf consists of eight chapters that cover different topics related to geography and history. Each chapter is divided into several sections that present the information in a clear and organized way. The book also has some features that make it more attractive and user-friendly:</p>
-<ul>
-<li>The cover page of each chapter shows an image related to the topic, a title that summarizes the main idea, a question that sparks curiosity, and a QR code that links to online resources.</li>
-<li>The introduction page of each chapter explains the main objectives, contents, and activities that students will find in the chapter. It also includes a map or a timeline that provides a visual overview of the topic.</li>
-<li>The text pages present the information in a concise and accessible way, using different types of fonts, colors, boxes, icons, graphs, maps, images, etc. to highlight the most important points.</li>
-<li>The activity pages propose different types of exercises and tasks that help students check their comprehension, apply their knowledge, develop their skills, express their opinions, etc. They also include some documents that provide additional information or sources related to the topic.</li>
-<li>The summary page at the end of each chapter summarizes the main points covered in the chapter using bullet points, key words, images, etc. It also includes some questions that help students review their learning.</li>
-<li>The appendix pages at the end of the book provide some useful tools for students such as a glossary, an index, a bibliography, etc.</li>
-</ul>
- <h4>The main topics and themes covered in the book</h4>
- <p>The eight chapters of Geografía e Historia 1 ESO Santillana.pdf cover different topics related to geography and history from a global and local perspective. The topics are:</p>
-<ol>
-<li>The Earth: its origin, structure, movements, representation methods.</li>
-<li>The relief: its formation processes, types, characteristics.</li>
-<li>The waters: their distribution, properties, uses.</li>
-<li>The climate: its elements, factors, types.</li>
-<li>The landscapes: their classification, characteristics.</li>
-<li>The continents: their location, physical features.</li>
-<h4>The benefits and challenges of using the book</h4>
- <p>Geografía e Historia 1 ESO Santillana.pdf is a book that offers many benefits for students who want to learn geography and history in a comprehensive and integrated way. Some of the benefits are:</p>
-<ul>
-<li>It provides students with relevant, updated, and engaging content that covers the key aspects of geography and history from a global and local perspective.</li>
-<li>It helps students develop competencies and values that are essential for their personal and professional lives, such as critical thinking, communication, research, problem-solving, etc.</li>
-<li>It offers students a variety of exercises and activities that help them check their comprehension, apply their knowledge, develop their skills, express their opinions, etc.</li>
-<li>It supports students with additional resources available online or in other formats that help them deepen their understanding and practice their skills.</li>
-</ul>
-<p>However, Geografía e Historia 1 ESO Santillana.pdf also poses some challenges for students who want to use it effectively. Some of the challenges are:</p>
-<ul>
-<li>It requires students to be motivated and interested in the topics covered in the book.</li>
-<li>It demands students to be attentive and focused when reading the text and the images.</li>
-<li>It expects students to be active and responsible when doing the exercises and activities.</li>
-<li>It encourages students to be curious and creative when using the additional resources.</li>
-</ul>
- <h3>Geografía e Historia 1 ESO Santillana.pdf: A Detailed Analysis</h3>
- <h4>The key concepts and skills learned in each chapter</h4>
- <p>In this section, we will analyze each chapter of Geografía e Historia 1 ESO Santillana.pdf in more detail and explain what are the key concepts and skills that students can learn from each chapter. We will also provide some examples of how these concepts and skills can be applied in real-life situations.</p>
- <p><strong>Chapter 1: The Earth</strong></p>
- <p>In this chapter, students can learn about the origin, structure, movements, and representation methods of the Earth. Some of the key concepts and skills are:</p>
-<ul>
-<li>The origin of the Earth: how the Earth was formed from a cloud of dust and gas about 4.6 billion years ago.</li>
-<li>The structure of the Earth: how the Earth is composed of different layers (crust, mantle, core) with different characteristics (thickness, temperature, density).</li>
-<li>The movements of the Earth: how the Earth rotates around its axis (rotation) and revolves around the Sun (revolution) causing phenomena such as day and night, seasons, etc.</li>
-<li>The representation methods of the Earth: how the Earth can be represented using different models (globe, map) with different advantages and disadvantages (accuracy, distortion).</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-<li>The origin of the Earth: understanding how life evolved on Earth over time.</li>
-<li>The structure of the Earth: knowing how natural disasters such as earthquakes or volcanoes occur.</li>
-<li>The movements of the Earth: planning activities according to the time of day or the season of the year.</li>
-<li>The representation methods of the Earth: using maps or globes to locate places or calculate distances.</li>
-</ul>
- <p><strong>Chapter 2: The relief</strong></p>
- <p>In this chapter, students can learn about the formation processes, types, and characteristics of the relief. Some of the key concepts and skills are:</p>
-<ul>
-<li>The formation processes of the relief: how the relief is shaped by internal forces (tectonic plates) and external forces (erosion).</li>
-<li>The types of relief: how the relief can be classified into different types according to its height (mountains, hills, plains) or its origin (continental, oceanic).</li>
-<li>The characteristics of the relief: how the relief can be described using different criteria such as altitude (high, low), slope (steep, gentle), orientation (north-facing, south-facing), etc.</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-<li>The formation processes of the relief: understanding how landscapes change over time.</li>
-<li>The types of relief: knowing how different types of relief affect climate, vegetation, wildlife, human activities, etc.</li>
-<li>The characteristics of the relief: using maps or graphs to analyze or compare different regions or countries.</li>
-</ul>
- <p><strong>Chapter 3: The waters</strong></p>
- properties, and uses of the waters. Some of the key concepts and skills are:</p>
-<ul>
-<li>The distribution of the waters: how the waters are distributed on Earth in different forms (solid, liquid, gas) and in different places (oceans, seas, rivers, lakes, glaciers, groundwater).</li>
-<li>The properties of the waters: how the waters have different properties such as salinity (freshwater, saltwater), temperature (cold, warm), density (light, heavy), etc.</li>
-<li>The uses of the waters: how the waters are used by humans for different purposes such as drinking, irrigation, transportation, energy, recreation, etc.</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-<li>The distribution of the waters: knowing how much water is available on Earth and where it is located.</li>
-<li>The properties of the waters: understanding how water affects climate, weather, currents, tides, etc.</li>
-<li>The uses of the waters: managing water resources wisely and sustainably.</li>
-</ul>
- <p><strong>Chapter 4: The climate</strong></p>
- <p>In this chapter, students can learn about the elements, factors, types, and influence of the climate. Some of the key concepts and skills are:</p>
-<ul>
-<li>The elements of the climate: how the climate is determined by different elements such as temperature, precipitation, humidity, pressure, wind, etc.</li>
-<li>The factors of the climate: how the climate is influenced by different factors such as latitude, altitude, distance from the sea, relief, ocean currents, etc.</li>
-<li>The types of climate: how the climate can be classified into different types according to its characteristics such as tropical, temperate, polar, etc.</li>
-<li>The influence of the climate: how the climate affects living beings (plants, animals, humans) and their activities (agriculture, industry, tourism, etc.).</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-barometers, anemometers, etc.</li>
-<li>The factors of the climate: comparing and contrasting the climates of different regions or countries using maps or graphs.</li>
-<li>The types of climate: identifying and describing the main features and examples of each type of climate.</li>
-<li>The influence of the climate: explaining and evaluating how climate affects living beings and their activities.</li>
-</ul>
- <p><strong>Chapter 5: The landscapes</strong></p>
- <p>In this chapter, students can learn about the classification and characteristics of the landscapes. Some of the key concepts and skills are:</p>
-<ul>
-<li>The classification of the landscapes: how the landscapes can be classified into natural or humanized according to their degree of transformation by human action.</li>
-<li>The characteristics of the landscapes: how the landscapes can be described using different criteria such as physical (relief, climate, water, vegetation, wildlife), human (population, settlement, activities, culture), or aesthetic (beauty, harmony, diversity).</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-<li>The classification of the landscapes: recognizing and categorizing different types of landscapes using images or field trips.</li>
-<li>The characteristics of the landscapes: observing and analyzing different aspects of the landscapes using maps or photographs.</li>
-</ul>
- <p><strong>Chapter 6: The continents</strong></p>
- <p>In this chapter, students can learn about the location and physical features of the continents. Some of the key concepts and skills are:</p>
-<ul>
-<li>The location of the continents: how the continents are located on Earth according to their position (north, south, east, west) and their hemispheres (northern, southern, eastern, western).</li>
-<li>The physical features of the continents: how the continents have different physical features such as size (area), shape (outline), relief (mountains, plains), coasts (peninsulas, islands), waters (rivers, lakes), climate (types), vegetation (forests, grasslands), wildlife (species).</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-<li>The location of the continents: locating and naming the continents on a map or a globe.</li>
-<li>The physical features of the continents: comparing and contrasting the physical features of different continents using tables or charts.</li>
-</ul>
- <p><strong>Chapter 7: The physical geography of Spain</strong></p>
- students can learn about the relief, coasts, rivers, and natural environments of Spain. Some of the key concepts and skills are:</p>
-<ul>
-<li>The relief of Spain: how Spain has a varied and complex relief that can be divided into three main units: the Meseta Central (central plateau), the mountain ranges, and the coastal plains.</li>
-<li>The coasts of Spain: how Spain has a long and diverse coastline that can be divided into four main sections: the Cantabrian coast, the Atlantic coast, the Mediterranean coast, and the island coasts.</li>
-<li>The rivers of Spain: how Spain has a dense and irregular river network that can be divided into three main basins: the Atlantic basin, the Mediterranean basin, and the endorheic basin.</li>
-<li>The natural environments of Spain: how Spain has a rich and varied natural environment that can be classified into six main types: the oceanic environment, the Mediterranean environment, the continental environment, the mountain environment, the arid environment, and the island environment.</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-<li>The relief of Spain: identifying and describing the main features and examples of each relief unit.</li>
-<li>The coasts of Spain: recognizing and explaining the main characteristics and examples of each coast section.</li>
-<li>The rivers of Spain: naming and locating the main rivers and basins of Spain.</li>
-<li>The natural environments of Spain: distinguishing and illustrating the main elements and examples of each natural environment.</li>
-</ul>
- <p><strong>Chapter 8: The Prehistory</strong></p>
- <p>In this chapter, students can learn about the stages, processes, and cultures of the Prehistory. Some of the key concepts and skills are:</p>
-<ul>
-<li>The stages of the Prehistory: how the Prehistory is divided into three main stages according to the technological development of humans: Paleolithic (Old Stone Age), Neolithic (New Stone Age), and Metal Age.</li>
-<li>The processes of the Prehistory: how humans evolved physically and culturally during the Prehistory through two main processes: hominization (the appearance and diversification of human species) and civilization (the development of agriculture, livestock, trade, art, etc.).</li>
-<li>The cultures of the Prehistory: how different human groups created different cultures during the Prehistory that can be identified by their material remains (tools, weapons, pottery, etc.) and their artistic expressions (paintings, sculptures, etc.).</li>
-</ul>
-<p>Some examples of how these concepts and skills can be applied in real-life situations are:</p>
-<ul>
-<li>The stages of the Prehistory: ordering and describing the main events and characteristics of each stage.</li>
-<li>The processes of the Prehistory: explaining and comparing the main changes and achievements of humans during each process.</li>
-<li>The cultures of the Prehistory: recognizing and appreciating the diversity and creativity of human cultures during each stage.</li>
-</ul>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, we have provided you with a comprehensive guide on Geografía e Historia 1 ESO Santillana.pdf. We have explained what is this book, why is it important to study it, and how to use it effectively. We have also given you an overview of its structure and features, a detailed analysis of its contents, and some recommendations for further learning and practice. We hope that this article has helped you to understand Geografía e Historia 1 ESO Santillana.pdf better and to make the most of it.</p>
- <h3>Recommendations for further learning and practice</h3>
- <p>If you want to learn more about geography and history or to practice your skills further, we suggest you to:</p>
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-<p>If you want to download and install a barcode scanner APK for Android, you need to follow these steps:</p>
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-<li>Scan from gallery: The app can scan barcodes and QR codes from images stored in your device's gallery or other sources.</li>
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- <h3>Examples of how to scan different types of barcodes and QR codes</h3>
-<p>There are different types of barcodes and QR codes that you can scan with a barcode scanner app. Some of the common types are:</p>
-<table>
-<tr><th>Type</th><th>Description</th><th>Example</th></tr>
-<tr><td>EAN-13</td><td>A 13-digit barcode that is used for retail products worldwide.</td><td><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/9b/EAN-13-ISBN-13.svg/1200px-EAN-13-ISBN-13.svg.png" alt="EAN-13 barcode" width="200"></td></tr>
-<tr><td>UPC-A</td><td>A 12-digit barcode that is used for retail products in North America.</td><td><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/44/UPC-A-036000291452.svg/1200px-UPC-A-036000291452.svg.png" alt="UPC-A barcode" width="200"></td></tr>
-<tr><td>Code 39</td><td>A variable-length barcode that can encode alphanumeric characters. It is used for industrial and military applications.</td><td><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/6d/Code_39_wikipedia.svg/1200px-Code_39_wikipedia.svg.png" alt="Code 39 barcode" width="200"></td></tr>
-<tr><td>QR code</td><td>A two-dimensional barcode that can encode various types of information, such as text, URL, contact, etc. It is widely used for mobile applications and marketing campaigns.</td><td><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/d0/QR_code_for_mobile_English_Wikipedia.svg/1200px-QR_code_for_mobile_English_Wikipedia.svg.png" alt="QR code" width="200"></td></tr>
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-<ol>
-<li>Open the barcode scanner app on your Android device.</li>
-<li>Point the camera at the barcode or QR code that you want to scan. Make sure that the barcode or QR code is within the frame of the camera.</li>
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spaces/1toTree/lora_test/ppdiffusers/modeling_utils.py DELETED Viewed

@@ -1,619 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-# 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 os
-import tempfile
-from functools import partial
-from typing import Callable, Optional, Union
-import paddle
-import paddle.nn as nn
-from huggingface_hub import (
-    create_repo,
-    get_hf_file_metadata,
-    hf_hub_download,
-    hf_hub_url,
-    repo_type_and_id_from_hf_id,
-    upload_folder,
-)
-from huggingface_hub.utils import EntryNotFoundError
-from requests import HTTPError
-from .download_utils import ppdiffusers_bos_download
-from .utils import (
-    CONFIG_NAME,
-    DOWNLOAD_SERVER,
-    HF_CACHE,
-    PPDIFFUSERS_CACHE,
-    WEIGHTS_NAME,
-    logging,
-)
-from .version import VERSION as __version__
-logger = logging.get_logger(__name__)
-def unfreeze_params(params):
-    for param in params:
-        param.stop_gradient = False
-def freeze_params(params):
-    for param in params:
-        param.stop_gradient = True
-# device
-def get_parameter_device(parameter: nn.Layer):
-    try:
-        return next(parameter.named_parameters())[1].place
-    except StopIteration:
-        return paddle.get_device()
-def get_parameter_dtype(parameter: nn.Layer):
-    try:
-        return next(parameter.named_parameters())[1].dtype
-    except StopIteration:
-        return paddle.get_default_dtype()
-def load_dict(checkpoint_file: Union[str, os.PathLike], map_location: str = "cpu"):
-    """
-    Reads a Paddle checkpoint file, returning properly formatted errors if they arise.
-    """
-    try:
-        if map_location == "cpu":
-            with paddle.device_scope("cpu"):
-                state_dict = paddle.load(checkpoint_file)
-        else:
-            state_dict = paddle.load(checkpoint_file)
-        return state_dict
-    except Exception as e:
-        try:
-            with open(checkpoint_file) as f:
-                if f.read().startswith("version"):
-                    raise OSError(
-                        "You seem to have cloned a repository without having git-lfs installed. Please install "
-                        "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
-                        "you cloned."
-                    )
-                else:
-                    raise ValueError(
-                        f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained "
-                        "model. Make sure you have saved the model properly."
-                    ) from e
-        except (UnicodeDecodeError, ValueError):
-            raise OSError(
-                f"Unable to load weights from Paddle checkpoint file for '{checkpoint_file}' "
-                f"at '{checkpoint_file}'. "
-                "If you tried to load a Paddle model from a TF 2.0 checkpoint, please set from_tf=True."
-            )
-class ModelMixin(nn.Layer):
-    r"""
-    Base class for all models.
-    [`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading
-    and saving models.
-        - **config_name** ([`str`]) -- A filename under which the model should be stored when calling
-          [`~modeling_utils.ModelMixin.save_pretrained`].
-    """
-    config_name = CONFIG_NAME
-    _automatically_saved_args = ["_ppdiffusers_version", "_class_name", "_name_or_path"]
-    _supports_gradient_checkpointing = False
-    def __init__(self):
-        super().__init__()
-    @property
-    def is_gradient_checkpointing(self) -> bool:
-        """
-        Whether gradient checkpointing is activated for this model or not.
-        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
-        activations".
-        """
-        return any(
-            hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing
-            for m in self.sublayers(include_self=True)
-        )
-    def enable_gradient_checkpointing(self):
-        """
-        Activates gradient checkpointing for the current model.
-        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
-        activations".
-        """
-        if not self._supports_gradient_checkpointing:
-            raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
-        self.apply(partial(self._set_gradient_checkpointing, value=True))
-    def disable_gradient_checkpointing(self):
-        """
-        Deactivates gradient checkpointing for the current model.
-        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
-        activations".
-        """
-        if self._supports_gradient_checkpointing:
-            self.apply(partial(self._set_gradient_checkpointing, value=False))
-    def save_pretrained(
-        self,
-        save_directory: Union[str, os.PathLike],
-        is_main_process: bool = True,
-        save_function: Callable = paddle.save,
-    ):
-        """
-        Save a model and its configuration file to a directory, so that it can be re-loaded using the
-        `[`~modeling_utils.ModelMixin.from_pretrained`]` class method.
-        Arguments:
-            save_directory (`str` or `os.PathLike`):
-                Directory to which to save. Will be created if it doesn't exist.
-            is_main_process (`bool`, *optional*, defaults to `True`):
-                Whether the process calling this is the main process or not. Useful when in distributed training like
-                TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
-                the main process to avoid race conditions.
-            save_function (`Callable`):
-                The function to use to save the state dictionary. Useful on distributed training like TPUs when one
-                need to replace `paddle.save` by another method.
-        """
-        if os.path.isfile(save_directory):
-            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
-            return
-        os.makedirs(save_directory, exist_ok=True)
-        model_to_save = self
-        # Attach architecture to the config
-        # Save the config
-        if is_main_process:
-            model_to_save.save_config(save_directory)
-        # Save the model
-        state_dict = model_to_save.state_dict()
-        # Clean the folder from a previous save
-        for filename in os.listdir(save_directory):
-            full_filename = os.path.join(save_directory, filename)
-            # If we have a shard file that is not going to be replaced, we delete it, but only from the main process
-            # in distributed settings to avoid race conditions.
-            if filename.startswith(WEIGHTS_NAME[:-4]) and os.path.isfile(full_filename) and is_main_process:
-                os.remove(full_filename)
-        # Save the model
-        save_function(state_dict, os.path.join(save_directory, WEIGHTS_NAME))
-        logger.info(f"Model weights saved in {os.path.join(save_directory, WEIGHTS_NAME)}")
-    def save_to_hf_hub(
-        self,
-        repo_id: str,
-        private: Optional[bool] = None,
-        subfolder: Optional[str] = None,
-        commit_message: Optional[str] = None,
-        revision: Optional[str] = None,
-        create_pr: bool = False,
-    ):
-        """
-        Uploads all elements of this model to a new HuggingFace Hub repository.
-        Args:
-            repo_id (str): Repository name for your model/tokenizer in the Hub.
-            private (bool, optional): Whether the model/tokenizer is set to private
-            subfolder (str, optional): Push to a subfolder of the repo instead of the root
-            commit_message (str, optional) — The summary / title / first line of the generated commit. Defaults to: f"Upload {path_in_repo} with huggingface_hub"
-            revision (str, optional) — The git revision to commit from. Defaults to the head of the "main" branch.
-            create_pr (boolean, optional) — Whether or not to create a Pull Request with that commit. Defaults to False.
-                If revision is not set, PR is opened against the "main" branch. If revision is set and is a branch, PR is opened against this branch.
-                If revision is set and is not a branch name (example: a commit oid), an RevisionNotFoundError is returned by the server.
-        Returns: The url of the commit of your model in the given repository.
-        """
-        repo_url = create_repo(repo_id, private=private, exist_ok=True)
-        # Infer complete repo_id from repo_url
-        # Can be different from the input `repo_id` if repo_owner was implicit
-        _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)
-        repo_id = f"{repo_owner}/{repo_name}"
-        # Check if README file already exist in repo
-        try:
-            get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
-            has_readme = True
-        except EntryNotFoundError:
-            has_readme = False
-        with tempfile.TemporaryDirectory() as root_dir:
-            if subfolder is not None:
-                save_dir = os.path.join(root_dir, subfolder)
-            else:
-                save_dir = root_dir
-            # save model
-            self.save_pretrained(save_dir)
-            # Add readme if does not exist
-            logger.info("README.md not found, adding the default README.md")
-            if not has_readme:
-                with open(os.path.join(root_dir, "README.md"), "w") as f:
-                    f.write(f"---\nlibrary_name: ppdiffusers\n---\n# {repo_id}")
-            # Upload model and return
-            logger.info(f"Pushing to the {repo_id}. This might take a while")
-            return upload_folder(
-                repo_id=repo_id,
-                repo_type="model",
-                folder_path=root_dir,
-                commit_message=commit_message,
-                revision=revision,
-                create_pr=create_pr,
-            )
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
-        r"""
-        Instantiate a pretrained paddle model from a pre-trained model configuration.
-        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
-        the model, you should first set it back in training mode with `model.train()`.
-        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
-        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
-        task.
-        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
-        weights are discarded.
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
-                      Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
-                    - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
-                      `./my_model_directory/`.
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory in which a downloaded pretrained model configuration should be cached if the
-                standard cache should not be used.
-            paddle_dtype (`str` or `paddle.dtype`, *optional*):
-                Override the default `paddle.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
-                will be automatically derived from the model's weights.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            subfolder (`str`, *optional*, defaults to `""`):
-                In case the relevant files are located inside a subfolder of the model repo (either remote in
-                huggingface.co or downloaded locally), you can specify the folder name here.
-            from_hf_hub (bool, *optional*):
-                Whether to load from Hugging Face Hub. Defaults to False
-        """
-        from_hf_hub = kwargs.pop("from_hf_hub", False)
-        if from_hf_hub:
-            cache_dir = kwargs.pop("cache_dir", HF_CACHE)
-        else:
-            cache_dir = kwargs.pop("cache_dir", PPDIFFUSERS_CACHE)
-        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
-        output_loading_info = kwargs.pop("output_loading_info", False)
-        paddle_dtype = kwargs.pop("paddle_dtype", None)
-        subfolder = kwargs.pop("subfolder", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        # Load config if we don't provide a configuration
-        config_path = pretrained_model_name_or_path
-        model_file = None
-        if model_file is None:
-            model_file = _get_model_file(
-                pretrained_model_name_or_path,
-                weights_name=WEIGHTS_NAME,
-                cache_dir=cache_dir,
-                subfolder=subfolder,
-                from_hf_hub=from_hf_hub,
-            )
-        config, unused_kwargs = cls.load_config(
-            config_path,
-            cache_dir=cache_dir,
-            return_unused_kwargs=True,
-            subfolder=subfolder,
-            from_hf_hub=from_hf_hub,
-            **kwargs,
-        )
-        model = cls.from_config(config, **unused_kwargs)
-        state_dict = load_dict(model_file, map_location="cpu")
-        keys = list(state_dict.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    logger.warning("Deleting key {} from state_dict.".format(k))
-                    del state_dict[k]
-        dtype = set(v.dtype for v in state_dict.values())
-        if len(dtype) > 1 and paddle.float32 not in dtype:
-            raise ValueError(
-                f"The weights of the model file {model_file} have a mixture of incompatible dtypes {dtype}. Please"
-                f" make sure that {model_file} weights have only one dtype."
-            )
-        elif len(dtype) > 1 and paddle.float32 in dtype:
-            dtype = paddle.float32
-        else:
-            dtype = dtype.pop()
-        # move model to correct dtype
-        model = model.to(dtype=dtype)
-        model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
-            model,
-            state_dict,
-            model_file,
-            pretrained_model_name_or_path,
-            ignore_mismatched_sizes=ignore_mismatched_sizes,
-        )
-        loading_info = {
-            "missing_keys": missing_keys,
-            "unexpected_keys": unexpected_keys,
-            "mismatched_keys": mismatched_keys,
-            "error_msgs": error_msgs,
-        }
-        if paddle_dtype is not None and not isinstance(paddle_dtype, paddle.dtype):
-            raise ValueError(
-                f"{paddle_dtype} needs to be of type `paddle.dtype`, e.g. `paddle.float16`, but is {type(paddle_dtype)}."
-            )
-        elif paddle_dtype is not None:
-            model = model.to(dtype=paddle_dtype)
-        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
-        # Set model in evaluation mode to deactivate DropOut modules by default
-        model.eval()
-        if output_loading_info:
-            return model, loading_info
-        return model
-    @classmethod
-    def _load_pretrained_model(
-        cls,
-        model,
-        state_dict,
-        resolved_archive_file,
-        pretrained_model_name_or_path,
-        ignore_mismatched_sizes=False,
-    ):
-        # Retrieve missing & unexpected_keys
-        model_state_dict = model.state_dict()
-        loaded_keys = [k for k in state_dict.keys()]
-        expected_keys = list(model_state_dict.keys())
-        original_loaded_keys = loaded_keys
-        missing_keys = list(set(expected_keys) - set(loaded_keys))
-        unexpected_keys = list(set(loaded_keys) - set(expected_keys))
-        # Make sure we are able to load base models as well as derived models (with heads)
-        model_to_load = model
-        def _find_mismatched_keys(
-            state_dict,
-            model_state_dict,
-            loaded_keys,
-            ignore_mismatched_sizes,
-        ):
-            mismatched_keys = []
-            if ignore_mismatched_sizes:
-                for checkpoint_key in loaded_keys:
-                    model_key = checkpoint_key
-                    if model_key in model_state_dict and list(state_dict[checkpoint_key].shape) != list(
-                        model_state_dict[model_key].shape
-                    ):
-                        mismatched_keys.append(
-                            (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
-                        )
-                        del state_dict[checkpoint_key]
-            return mismatched_keys
-        if state_dict is not None:
-            # Whole checkpoint
-            mismatched_keys = _find_mismatched_keys(
-                state_dict,
-                model_state_dict,
-                original_loaded_keys,
-                ignore_mismatched_sizes,
-            )
-            error_msgs = ""
-            model_to_load.load_dict(state_dict)
-        if len(error_msgs) > 0:
-            error_msg = "\n\t".join(error_msgs)
-            if "size mismatch" in error_msg:
-                error_msg += (
-                    "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
-                )
-            raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
-        if len(unexpected_keys) > 0:
-            logger.warning(
-                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
-                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
-                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
-                " or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
-                " BertForPreTraining model).\n- This IS NOT expected if you are initializing"
-                f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
-                " identical (initializing a BertForSequenceClassification model from a"
-                " BertForSequenceClassification model)."
-            )
-        else:
-            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
-        if len(missing_keys) > 0:
-            logger.warning(
-                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
-                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
-                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
-            )
-        elif len(mismatched_keys) == 0:
-            logger.info(
-                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
-                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
-                f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
-                " without further training."
-            )
-        if len(mismatched_keys) > 0:
-            mismatched_warning = "\n".join(
-                [
-                    f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
-                    for key, shape1, shape2 in mismatched_keys
-                ]
-            )
-            logger.warning(
-                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
-                f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
-                f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
-                " able to use it for predictions and inference."
-            )
-        return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs
-    @property
-    def device(self):
-        """
-        `paddle.place`: The device on which the module is (assuming that all the module parameters are on the same
-        device).
-        """
-        return get_parameter_device(self)
-    @property
-    def dtype(self) -> paddle.dtype:
-        """
-        `paddle.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
-        """
-        return get_parameter_dtype(self)
-    def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
-        """
-        Get number of (optionally, trainable or non-embeddings) parameters in the module.
-        Args:
-            only_trainable (`bool`, *optional*, defaults to `False`):
-                Whether or not to return only the number of trainable parameters
-            exclude_embeddings (`bool`, *optional*, defaults to `False`):
-                Whether or not to return only the number of non-embeddings parameters
-        Returns:
-            `int`: The number of parameters.
-        """
-        if exclude_embeddings:
-            embedding_param_names = [
-                f"{name}.weight"
-                for name, module_type in self.named_sublayers(include_self=True)
-                if isinstance(module_type, nn.Embedding)
-            ]
-            non_embedding_parameters = [
-                parameter for name, parameter in self.named_parameters() if name not in embedding_param_names
-            ]
-            return sum(p.numel() for p in non_embedding_parameters if not p.stop_gradient or not only_trainable)
-        else:
-            return sum(p.numel() for p in self.parameters() if not p.stop_gradient or not only_trainable)
-def unwrap_model(model: nn.Layer) -> nn.Layer:
-    """
-    Recursively unwraps a model from potential containers (as used in distributed training).
-    Args:
-        model (`nn.Layer`): The model to unwrap.
-    """
-    # since there could be multiple levels of wrapping, unwrap recursively
-    if hasattr(model, "_layers"):
-        return unwrap_model(model._layers)
-    else:
-        return model
-def _get_model_file(
-    pretrained_model_name_or_path,
-    *,
-    weights_name,
-    subfolder,
-    cache_dir,
-    from_hf_hub,
-):
-    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-    if os.path.isdir(pretrained_model_name_or_path):
-        if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
-            # Load from a PyTorch checkpoint
-            model_file = os.path.join(pretrained_model_name_or_path, weights_name)
-        elif subfolder is not None and os.path.isfile(
-            os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
-        ):
-            model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
-        else:
-            raise EnvironmentError(
-                f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
-            )
-        return model_file
-    elif from_hf_hub:
-        model_file = hf_hub_download(
-            repo_id=pretrained_model_name_or_path,
-            filename=weights_name,
-            cache_dir=cache_dir,
-            subfolder=subfolder,
-            library_name="PPDiffusers",
-            library_version=__version__,
-        )
-        return model_file
-    else:
-        try:
-            # Load from URL or cache if already cached
-            model_file = ppdiffusers_bos_download(
-                pretrained_model_name_or_path,
-                filename=weights_name,
-                subfolder=subfolder,
-                cache_dir=cache_dir,
-            )
-        except HTTPError as err:
-            raise EnvironmentError(
-                "There was a specific connection error when trying to load" f" {pretrained_model_name_or_path}:\n{err}"
-            )
-        except ValueError:
-            raise EnvironmentError(
-                f"We couldn't connect to '{DOWNLOAD_SERVER}' to load this model, couldn't find it"
-                f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
-                f" directory containing a file named {weights_name} or"
-                " \nCheckout your internet connection or see how to run the library in"
-                " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
-            )
-        except EnvironmentError:
-            raise EnvironmentError(
-                f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
-                "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
-                f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
-                f"containing a file named {weights_name}"
-            )
-        return model_file

spaces/7Vivek/Next-Word-Prediction-Streamlit/README.md DELETED Viewed

@@ -1,37 +0,0 @@
----
-title: Next Word Prediction Streamlit
-emoji: 😻
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-# Configuration
-`title`: _string_
-Display title for the Space
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/7hao/bingo/src/components/ui/textarea.tsx DELETED Viewed

@@ -1,24 +0,0 @@
-import * as React from 'react'
-import { cn } from '@/lib/utils'
-export interface TextareaProps
-  extends React.TextareaHTMLAttributes<HTMLTextAreaElement> {}
-const Textarea = React.forwardRef<HTMLTextAreaElement, TextareaProps>(
-  ({ className, ...props }, ref) => {
-    return (
-      <textarea
-        className={cn(
-          'flex min-h-[80px] w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm ring-offset-background placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Textarea.displayName = 'Textarea'
-export { Textarea }

spaces/801artistry/RVC801/train/process_ckpt.py DELETED Viewed

@@ -1,259 +0,0 @@
-import torch, traceback, os, pdb, sys
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from collections import OrderedDict
-from i18n import I18nAuto
-i18n = I18nAuto()
-def savee(ckpt, sr, if_f0, name, epoch, version, hps):
-    try:
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        opt["config"] = [
-            hps.data.filter_length // 2 + 1,
-            32,
-            hps.model.inter_channels,
-            hps.model.hidden_channels,
-            hps.model.filter_channels,
-            hps.model.n_heads,
-            hps.model.n_layers,
-            hps.model.kernel_size,
-            hps.model.p_dropout,
-            hps.model.resblock,
-            hps.model.resblock_kernel_sizes,
-            hps.model.resblock_dilation_sizes,
-            hps.model.upsample_rates,
-            hps.model.upsample_initial_channel,
-            hps.model.upsample_kernel_sizes,
-            hps.model.spk_embed_dim,
-            hps.model.gin_channels,
-            hps.data.sampling_rate,
-        ]
-        opt["info"] = "%sepoch" % epoch
-        opt["sr"] = sr
-        opt["f0"] = if_f0
-        opt["version"] = version
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-def show_info(path):
-    try:
-        a = torch.load(path, map_location="cpu")
-        return "Epochs: %s\nSample rate: %s\nPitch guidance: %s\nRVC Version: %s" % (
-            a.get("info", "None"),
-            a.get("sr", "None"),
-            a.get("f0", "None"),
-            a.get("version", "None"),
-        )
-    except:
-        return traceback.format_exc()
-def extract_small_model(path, name, sr, if_f0, info, version):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        if "model" in ckpt:
-            ckpt = ckpt["model"]
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        if sr == "40k":
-            opt["config"] = [
-                1025,
-                32,
-                192,
-                192,
-                768,
-                2,
-                6,
-                3,
-                0,
-                "1",
-                [3, 7, 11],
-                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                [10, 10, 2, 2],
-                512,
-                [16, 16, 4, 4],
-                109,
-                256,
-                40000,
-            ]
-        elif sr == "48k":
-            if version == "v1":
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 6, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-            else:
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [12, 10, 2, 2],
-                    512,
-                    [24, 20, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-        elif sr == "32k":
-            if version == "v1":
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 4, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-            else:
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 8, 2, 2],
-                    512,
-                    [20, 16, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-        if info == "":
-            info = "Extracted model."
-        opt["info"] = info
-        opt["version"] = version
-        opt["sr"] = sr
-        opt["f0"] = int(if_f0)
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-def change_info(path, info, name):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        ckpt["info"] = info
-        if name == "":
-            name = os.path.basename(path)
-        torch.save(ckpt, "weights/%s" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-def merge(path1, path2, alpha1, sr, f0, info, name, version):
-    try:
-        def extract(ckpt):
-            a = ckpt["model"]
-            opt = OrderedDict()
-            opt["weight"] = {}
-            for key in a.keys():
-                if "enc_q" in key:
-                    continue
-                opt["weight"][key] = a[key]
-            return opt
-        ckpt1 = torch.load(path1, map_location="cpu")
-        ckpt2 = torch.load(path2, map_location="cpu")
-        cfg = ckpt1["config"]
-        if "model" in ckpt1:
-            ckpt1 = extract(ckpt1)
-        else:
-            ckpt1 = ckpt1["weight"]
-        if "model" in ckpt2:
-            ckpt2 = extract(ckpt2)
-        else:
-            ckpt2 = ckpt2["weight"]
-        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
-            return "Fail to merge the models. The model architectures are not the same."
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt1.keys():
-            # try:
-            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
-                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key][:min_shape0].float())
-                    + (1 - alpha1) * (ckpt2[key][:min_shape0].float())
-                ).half()
-            else:
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
-                ).half()
-        # except:
-        #     pdb.set_trace()
-        opt["config"] = cfg
-        """
-        if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
-        elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
-        elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
-        """
-        opt["sr"] = sr
-        opt["f0"] = 1 if f0 else 0
-        opt["version"] = version
-        opt["info"] = info
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()

spaces/AI-Dashboards/Memory-Chat-Story-Generator-ChatGPT/README.md DELETED Viewed

@@ -1,17 +0,0 @@
----
-title: Memory Chat Story Generator ChatGPT
-emoji: 📚
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: awacke1/Memory-Chat-Story-Generator-ChatGPT
----
-1. Aaron Wacker
-2. Colton Eckenrode
-3. Kene Onyeachonam
-4. Furqan Kassa

spaces/AIConsultant/MusicGen/tests/models/test_encodec_model.py DELETED Viewed

@@ -1,60 +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 random
-import numpy as np
-import torch
-from audiocraft.models import EncodecModel
-from audiocraft.modules import SEANetEncoder, SEANetDecoder
-from audiocraft.quantization import DummyQuantizer
-class TestEncodecModel:
-    def _create_encodec_model(self,
-                              sample_rate: int,
-                              channels: int,
-                              dim: int = 5,
-                              n_filters: int = 3,
-                              n_residual_layers: int = 1,
-                              ratios: list = [5, 4, 3, 2],
-                              **kwargs):
-        frame_rate = np.prod(ratios)
-        encoder = SEANetEncoder(channels=channels, dimension=dim, n_filters=n_filters,
-                                n_residual_layers=n_residual_layers, ratios=ratios)
-        decoder = SEANetDecoder(channels=channels, dimension=dim, n_filters=n_filters,
-                                n_residual_layers=n_residual_layers, ratios=ratios)
-        quantizer = DummyQuantizer()
-        model = EncodecModel(encoder, decoder, quantizer, frame_rate=frame_rate,
-                             sample_rate=sample_rate, channels=channels, **kwargs)
-        return model
-    def test_model(self):
-        random.seed(1234)
-        sample_rate = 24_000
-        channels = 1
-        model = self._create_encodec_model(sample_rate, channels)
-        for _ in range(10):
-            length = random.randrange(1, 10_000)
-            x = torch.randn(2, channels, length)
-            res = model(x)
-            assert res.x.shape == x.shape
-    def test_model_renorm(self):
-        random.seed(1234)
-        sample_rate = 24_000
-        channels = 1
-        model_nonorm = self._create_encodec_model(sample_rate, channels, renormalize=False)
-        model_renorm = self._create_encodec_model(sample_rate, channels, renormalize=True)
-        for _ in range(10):
-            length = random.randrange(1, 10_000)
-            x = torch.randn(2, channels, length)
-            codes, scales = model_nonorm.encode(x)
-            codes, scales = model_renorm.encode(x)
-            assert scales is not None

spaces/AIFILMS/generate_human_motion/VQ-Trans/visualization/plot_3d_global.py DELETED Viewed

@@ -1,129 +0,0 @@
-import torch
-import matplotlib.pyplot as plt
-import numpy as np
-import io
-import matplotlib
-from mpl_toolkits.mplot3d.art3d import Poly3DCollection
-import mpl_toolkits.mplot3d.axes3d as p3
-from textwrap import wrap
-import imageio
-def plot_3d_motion(args, figsize=(10, 10), fps=120, radius=4):
-    matplotlib.use('Agg')
-    joints, out_name, title = args
-    data = joints.copy().reshape(len(joints), -1, 3)
-    nb_joints = joints.shape[1]
-    smpl_kinetic_chain = [[0, 11, 12, 13, 14, 15], [0, 16, 17, 18, 19, 20], [0, 1, 2, 3, 4], [3, 5, 6, 7], [3, 8, 9, 10]] if nb_joints == 21 else [[0, 2, 5, 8, 11], [0, 1, 4, 7, 10], [0, 3, 6, 9, 12, 15], [9, 14, 17, 19, 21], [9, 13, 16, 18, 20]]
-    limits = 1000 if nb_joints == 21 else 2
-    MINS = data.min(axis=0).min(axis=0)
-    MAXS = data.max(axis=0).max(axis=0)
-    colors = ['red', 'blue', 'black', 'red', 'blue',
-              'darkblue', 'darkblue', 'darkblue', 'darkblue', 'darkblue',
-              'darkred', 'darkred', 'darkred', 'darkred', 'darkred']
-    frame_number = data.shape[0]
-    #     print(data.shape)
-    height_offset = MINS[1]
-    data[:, :, 1] -= height_offset
-    trajec = data[:, 0, [0, 2]]
-    data[..., 0] -= data[:, 0:1, 0]
-    data[..., 2] -= data[:, 0:1, 2]
-    def update(index):
-        def init():
-            ax.set_xlim(-limits, limits)
-            ax.set_ylim(-limits, limits)
-            ax.set_zlim(0, limits)
-            ax.grid(b=False)
-        def plot_xzPlane(minx, maxx, miny, minz, maxz):
-            ## Plot a plane XZ
-            verts = [
-                [minx, miny, minz],
-                [minx, miny, maxz],
-                [maxx, miny, maxz],
-                [maxx, miny, minz]
-            ]
-            xz_plane = Poly3DCollection([verts])
-            xz_plane.set_facecolor((0.5, 0.5, 0.5, 0.5))
-            ax.add_collection3d(xz_plane)
-        fig = plt.figure(figsize=(480/96., 320/96.), dpi=96) if nb_joints == 21 else plt.figure(figsize=(10, 10), dpi=96)
-        if title is not None :
-            wraped_title = '\n'.join(wrap(title, 40))
-            fig.suptitle(wraped_title, fontsize=16)
-        ax = p3.Axes3D(fig)
-        init()
-        ax.lines = []
-        ax.collections = []
-        ax.view_init(elev=110, azim=-90)
-        ax.dist = 7.5
-        #         ax =
-        plot_xzPlane(MINS[0] - trajec[index, 0], MAXS[0] - trajec[index, 0], 0, MINS[2] - trajec[index, 1],
-                     MAXS[2] - trajec[index, 1])
-        #         ax.scatter(data[index, :22, 0], data[index, :22, 1], data[index, :22, 2], color='black', s=3)
-        if index > 1:
-            ax.plot3D(trajec[:index, 0] - trajec[index, 0], np.zeros_like(trajec[:index, 0]),
-                      trajec[:index, 1] - trajec[index, 1], linewidth=1.0,
-                      color='blue')
-        #             ax = plot_xzPlane(ax, MINS[0], MAXS[0], 0, MINS[2], MAXS[2])
-        for i, (chain, color) in enumerate(zip(smpl_kinetic_chain, colors)):
-            #             print(color)
-            if i < 5:
-                linewidth = 4.0
-            else:
-                linewidth = 2.0
-            ax.plot3D(data[index, chain, 0], data[index, chain, 1], data[index, chain, 2], linewidth=linewidth,
-                      color=color)
-        #         print(trajec[:index, 0].shape)
-        plt.axis('off')
-        ax.set_xticklabels([])
-        ax.set_yticklabels([])
-        ax.set_zticklabels([])
-        if out_name is not None :
-            plt.savefig(out_name, dpi=96)
-            plt.close()
-        else :
-            io_buf = io.BytesIO()
-            fig.savefig(io_buf, format='raw', dpi=96)
-            io_buf.seek(0)
-            # print(fig.bbox.bounds)
-            arr = np.reshape(np.frombuffer(io_buf.getvalue(), dtype=np.uint8),
-                                newshape=(int(fig.bbox.bounds[3]), int(fig.bbox.bounds[2]), -1))
-            io_buf.close()
-            plt.close()
-            return arr
-    out = []
-    for i in range(frame_number) :
-        out.append(update(i))
-    out = np.stack(out, axis=0)
-    return torch.from_numpy(out)
-def draw_to_batch(smpl_joints_batch, title_batch=None, outname=None) :
-    batch_size = len(smpl_joints_batch)
-    out = []
-    for i in range(batch_size) :
-        out.append(plot_3d_motion([smpl_joints_batch[i], None, title_batch[i] if title_batch is not None else None]))
-        if outname is not None:
-            imageio.mimsave(outname[i], np.array(out[-1]), fps=20)
-    out = torch.stack(out, axis=0)
-    return out

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/__init__.py DELETED Viewed

File without changes

spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/loss.py DELETED Viewed

@@ -1,307 +0,0 @@
-from multiprocessing.sharedctypes import Value
-import torch
-import torch.distributed.nn
-from torch import distributed as dist, nn as nn
-from torch.nn import functional as F
-import numpy as np
-from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
-try:
-    import horovod.torch as hvd
-except ImportError:
-    hvd = None
-def gather_features(
-        audio_features,
-        text_features,
-        audio_features_mlp=None,
-        text_features_mlp=None,
-        local_loss=False,
-        gather_with_grad=False,
-        rank=0,
-        world_size=1,
-        use_horovod=False,
-        mlp_loss=False
-):
-    if use_horovod:
-        assert hvd is not None, 'Please install horovod'
-        if gather_with_grad:
-            all_audio_features = hvd.allgather(audio_features)
-            all_text_features = hvd.allgather(text_features)
-            if mlp_loss:
-                all_audio_features_mlp = hvd.allgather(audio_features_mlp)
-                all_text_features_mlp = hvd.allgather(text_features_mlp)
-        else:
-            with torch.no_grad():
-                all_audio_features = hvd.allgather(audio_features)
-                all_text_features = hvd.allgather(text_features)
-                if mlp_loss:
-                    all_audio_features_mlp = hvd.allgather(audio_features_mlp)
-                    all_text_features_mlp = hvd.allgather(text_features_mlp)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_audio_features = list(all_audio_features.chunk(world_size, dim=0))
-                gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
-                gathered_audio_features[rank] = audio_features
-                gathered_text_features[rank] = text_features
-                all_audio_features = torch.cat(gathered_audio_features, dim=0)
-                all_text_features = torch.cat(gathered_text_features, dim=0)
-                if mlp_loss:
-                    gathered_audio_features_mlp = list(all_audio_features_mlp.chunk(world_size, dim=0))
-                    gathered_text_features_mlp = list(all_text_features_mlp.chunk(world_size, dim=0))
-                    gathered_audio_features_mlp[rank] = audio_features_mlp
-                    gathered_text_features_mlp[rank] = text_features_mlp
-                    all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
-                    all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
-    else:
-        # We gather tensors from all gpus
-        if gather_with_grad:
-            all_audio_features = torch.cat(torch.distributed.nn.all_gather(audio_features), dim=0)
-            all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
-            if mlp_loss:
-                all_audio_features_mlp = torch.cat(torch.distributed.nn.all_gather(audio_features_mlp), dim=0)
-                all_text_features_mlp = torch.cat(torch.distributed.nn.all_gather(text_features_mlp), dim=0)
-        else:
-            gathered_audio_features = [torch.zeros_like(audio_features) for _ in range(world_size)]
-            gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
-            dist.all_gather(gathered_audio_features, audio_features)
-            dist.all_gather(gathered_text_features, text_features)
-            if mlp_loss:
-                gathered_audio_features_mlp = [torch.zeros_like(audio_features_mlp) for _ in range(world_size)]
-                gathered_text_features_mlp = [torch.zeros_like(text_features_mlp) for _ in range(world_size)]
-                dist.all_gather(gathered_audio_features_mlp, audio_features_mlp)
-                dist.all_gather(gathered_text_features_mlp, text_features_mlp)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_audio_features[rank] = audio_features
-                gathered_text_features[rank] = text_features
-                if mlp_loss:
-                    gathered_audio_features_mlp[rank] = audio_features_mlp
-                    gathered_text_features_mlp[rank] = text_features_mlp
-            all_audio_features = torch.cat(gathered_audio_features, dim=0)
-            all_text_features = torch.cat(gathered_text_features, dim=0)
-            if mlp_loss:
-                all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
-                all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
-    if mlp_loss:
-        return all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp
-    else:
-        return all_audio_features, all_text_features
-class ClipLoss(nn.Module):
-    def __init__(
-            self,
-            local_loss=False,
-            gather_with_grad=False,
-            cache_labels=False,
-            rank=0,
-            world_size=1,
-            use_horovod=False,
-            mlp_loss=False,
-            weight_loss_kappa=0,
-    ):
-        super().__init__()
-        self.local_loss = local_loss
-        self.gather_with_grad = gather_with_grad
-        self.cache_labels = cache_labels
-        self.rank = rank
-        self.world_size = world_size
-        self.use_horovod = use_horovod
-        self.mlp_loss = mlp_loss
-        self.weighted_loss = bool(weight_loss_kappa!=0)
-        self.weight_loss_kappa = weight_loss_kappa
-        # cache state
-        self.prev_num_logits = 0
-        self.labels = {}
-    def forward(self, audio_features, text_features, logit_scale_a, logit_scale_t=None, audio_features_mlp=None, text_features_mlp=None):
-        device = audio_features.device
-        if self.mlp_loss:
-            if self.world_size > 1:
-                all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp = gather_features(
-                    audio_features=audio_features,text_features=text_features,
-                    audio_features_mlp=audio_features_mlp,text_features_mlp=text_features_mlp,
-                    local_loss=self.local_loss,gather_with_grad=self.gather_with_grad,
-                    rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod,
-                    mlp_loss=self.mlp_loss
-                )
-                if self.local_loss:
-                    a_logits_per_audio = logit_scale_a * audio_features @ all_text_features_mlp.T
-                    a_logits_per_text = logit_scale_a * text_features_mlp @ all_audio_features.T
-                    t_logits_per_audio = logit_scale_t * audio_features_mlp @ all_text_features.T
-                    t_logits_per_text = logit_scale_t * text_features @ all_audio_features_mlp.T
-                else:
-                    a_logits_per_audio = logit_scale_a * all_audio_features @ all_text_features_mlp.T
-                    a_logits_per_text = a_logits_per_audio.T
-                    t_logits_per_audio = logit_scale_t * all_audio_features_mlp @ all_text_features.T
-                    t_logits_per_text = t_logits_per_audio.T
-            else:
-                a_logits_per_audio = logit_scale_a * audio_features @ text_features_mlp.T
-                a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T
-                t_logits_per_audio = logit_scale_t * audio_features_mlp @ text_features.T
-                t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T
-            # calculated ground-truth and cache if enabled
-            num_logits = a_logits_per_audio.shape[0]
-            if self.prev_num_logits != num_logits or device not in self.labels:
-                labels = torch.arange(num_logits, device=device, dtype=torch.long)
-                if self.world_size > 1 and self.local_loss:
-                    labels = labels + num_logits * self.rank
-                if self.cache_labels:
-                    self.labels[device] = labels
-                    self.prev_num_logits = num_logits
-            else:
-                labels = self.labels[device]
-            if not self.weighted_loss:
-                total_loss = (
-                    F.cross_entropy(a_logits_per_audio, labels) +
-                    F.cross_entropy(a_logits_per_text, labels) +
-                    F.cross_entropy(t_logits_per_audio, labels) +
-                    F.cross_entropy(t_logits_per_text, labels)
-                    ) / 4
-            else:
-                audio_weight = (audio_features@audio_features.T).detach()
-                audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(audio_weight)))).detach()
-                text_weight = (text_features@text_features.T).detach()
-                text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(text_features)))).detach()
-                total_loss = (
-                    F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight) +
-                    F.cross_entropy(a_logits_per_text, labels, weight=audio_weight) +
-                    F.cross_entropy(t_logits_per_audio, labels, weight=text_weight) +
-                    F.cross_entropy(t_logits_per_text, labels, weight=text_weight)
-                    ) / 4
-        else:
-            if self.world_size > 1:
-                all_audio_features, all_text_features = gather_features(
-                    audio_features=audio_features,text_features=text_features,
-                    local_loss=self.local_loss,gather_with_grad=self.gather_with_grad,
-                    rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod,
-                    mlp_loss=self.mlp_loss
-                )
-                if self.local_loss:
-                    logits_per_audio = logit_scale_a * audio_features @ all_text_features.T
-                    logits_per_text = logit_scale_a * text_features @ all_audio_features.T
-                else:
-                    logits_per_audio = logit_scale_a * all_audio_features @ all_text_features.T
-                    logits_per_text = logits_per_audio.T
-            else:
-                logits_per_audio = logit_scale_a * audio_features @ text_features.T
-                logits_per_text = logit_scale_a * text_features @ audio_features.T
-            # calculated ground-truth and cache if enabled
-            num_logits = logits_per_audio.shape[0]
-            if self.prev_num_logits != num_logits or device not in self.labels:
-                labels = torch.arange(num_logits, device=device, dtype=torch.long)
-                if self.world_size > 1 and self.local_loss:
-                    labels = labels + num_logits * self.rank
-                if self.cache_labels:
-                    self.labels[device] = labels
-                    self.prev_num_logits = num_logits
-            else:
-                labels = self.labels[device]
-            if not self.weighted_loss:
-                total_loss = (
-                    F.cross_entropy(logits_per_audio, labels) +
-                    F.cross_entropy(logits_per_text, labels)
-                    ) / 2
-            else:
-                audio_weight = (all_audio_features@all_audio_features.T).detach()
-                audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(all_audio_features)))).detach()
-                text_weight = (all_text_features@all_text_features.T).detach()
-                text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(all_text_features)))).detach()
-                total_loss = (
-                    F.cross_entropy(logits_per_audio, labels, weight=text_weight) +
-                    F.cross_entropy(logits_per_text, labels, weight=audio_weight)
-                    ) / 2
-        return total_loss
-def lp_gather_features(
-        pred,
-        target,
-        world_size=1,
-        use_horovod=False
-):
-    if use_horovod:
-        assert hvd is not None, 'Please install horovod'
-        with torch.no_grad():
-            all_preds = hvd.allgather(pred)
-            all_targets = hvd.allgath(target)
-    else:
-        gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)]
-        gathered_targets = [torch.zeros_like(target) for _ in range(world_size)]
-        dist.all_gather(gathered_preds, pred)
-        dist.all_gather(gathered_targets, target)
-        all_preds = torch.cat(gathered_preds, dim=0)
-        all_targets = torch.cat(gathered_targets, dim=0)
-    return all_preds, all_targets
-def get_map(pred, target):
-    pred = torch.sigmoid(pred).numpy()
-    target = target.numpy()
-    return np.mean(average_precision_score(target, pred, average=None))
-def get_acc(pred, target):
-    pred = torch.argmax(pred,1).numpy()
-    target = torch.argmax(target,1).numpy()
-    return accuracy_score(target, pred)
-def get_mauc(pred, target):
-    pred = torch.sigmoid(pred).numpy()
-    target = target.numpy()
-    return np.mean(roc_auc_score(target, pred, average=None))
-class LPMetrics(object):
-    def __init__(self, metric_names = ['map','acc','mauc']):
-        self.metrics = []
-        for name in metric_names:
-            self.metrics.append(self.get_metric(name))
-        self.metric_names = metric_names
-    def get_metric(self,name):
-        if name == 'map':
-            return get_map
-        elif name == 'acc':
-            return get_acc
-        elif name == 'mauc':
-            return get_mauc
-        else:
-            raise ValueError(f'the metric should be at least one of [map, acc, mauc]')
-    def evaluate_mertics(self, pred, target):
-        metric_dict = {}
-        for i in range(len(self.metric_names)):
-            metric_dict[self.metric_names[i]] = self.metrics[i](pred, target)
-        return metric_dict
-def calc_celoss(pred, target):
-    target = torch.argmax(target, 1).long()
-    return nn.CrossEntropyLoss()(pred, target)
-class LPLoss(nn.Module):
-    def __init__(self, loss_name):
-        super().__init__()
-        if loss_name == 'bce':
-            self.loss_func = nn.BCEWithLogitsLoss()
-        elif loss_name == 'ce':
-            self.loss_func = calc_celoss
-        elif loss_name == 'mse':
-            self.loss_func = nn.MSELoss()
-        else:
-            raise ValueError(f'the loss func should be at least one of [bce, ce, mse]')
-    def forward(self, pred, target):
-        loss = self.loss_func(pred, target)
-        return loss

spaces/AIZ2H/03-Streamlit-Video-ASR-NLP/app.py DELETED Viewed

@@ -1,119 +0,0 @@
-from collections import deque
-import streamlit as st
-import torch
-from streamlit_player import st_player
-from transformers import AutoModelForCTC, Wav2Vec2Processor
-from streaming import ffmpeg_stream
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-player_options = {
-    "events": ["onProgress"],
-    "progress_interval": 200,
-    "volume": 1.0,
-    "playing": True,
-    "loop": False,
-    "controls": False,
-    "muted": False,
-    "config": {"youtube": {"playerVars": {"start": 1}}},
-}
-# disable rapid fading in and out on `st.code` updates
-st.markdown("<style>.element-container{opacity:1 !important}</style>", unsafe_allow_html=True)
-@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
-def load_model(model_path="facebook/wav2vec2-large-robust-ft-swbd-300h"):
-    processor = Wav2Vec2Processor.from_pretrained(model_path)
-    model = AutoModelForCTC.from_pretrained(model_path).to(device)
-    return processor, model
-processor, model = load_model()
-def stream_text(url, chunk_duration_ms, pad_duration_ms):
-    sampling_rate = processor.feature_extractor.sampling_rate
-    # calculate the length of logits to cut from the sides of the output to account for input padding
-    output_pad_len = model._get_feat_extract_output_lengths(int(sampling_rate * pad_duration_ms / 1000))
-    # define the audio chunk generator
-    stream = ffmpeg_stream(url, sampling_rate, chunk_duration_ms=chunk_duration_ms, pad_duration_ms=pad_duration_ms)
-    leftover_text = ""
-    for i, chunk in enumerate(stream):
-        input_values = processor(chunk, sampling_rate=sampling_rate, return_tensors="pt").input_values
-        with torch.no_grad():
-            logits = model(input_values.to(device)).logits[0]
-            if i > 0:
-                logits = logits[output_pad_len : len(logits) - output_pad_len]
-            else:  # don't count padding at the start of the clip
-                logits = logits[: len(logits) - output_pad_len]
-            predicted_ids = torch.argmax(logits, dim=-1).cpu().tolist()
-            if processor.decode(predicted_ids).strip():
-                leftover_ids = processor.tokenizer.encode(leftover_text)
-                # concat the last word (or its part) from the last frame with the current text
-                text = processor.decode(leftover_ids + predicted_ids)
-                # don't return the last word in case it's just partially recognized
-                text, leftover_text = text.rsplit(" ", 1)
-                yield text
-            else:
-                yield leftover_text
-                leftover_text = ""
-    yield leftover_text
-def main():
-    state = st.session_state
-    st.header("Video ASR Streamlit from Youtube Link")
-    with st.form(key="inputs_form"):
-        # Our worlds best teachers on subjects of AI, Cognitive, Neuroscience for our Behavioral and Medical Health
-        ytJoschaBach="https://youtu.be/cC1HszE5Hcw?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=8984"
-        ytSamHarris="https://www.youtube.com/watch?v=4dC_nRYIDZU&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=2"
-        ytJohnAbramson="https://www.youtube.com/watch?v=arrokG3wCdE&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=3"
-        ytElonMusk="https://www.youtube.com/watch?v=DxREm3s1scA&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=4"
-        ytJeffreyShainline="https://www.youtube.com/watch?v=EwueqdgIvq4&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=5"
-        ytJeffHawkins="https://www.youtube.com/watch?v=Z1KwkpTUbkg&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=6"
-        ytSamHarris="https://youtu.be/Ui38ZzTymDY?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytTimelapseAI="https://www.youtube.com/watch?v=63yr9dlI0cU&list=PLHgX2IExbFovQybyfltywXnqZi5YvaSS-"
-        state.youtube_url = st.text_input("YouTube URL", ytTimelapseAI)
-        state.chunk_duration_ms = st.slider("Audio chunk duration (ms)", 2000, 10000, 3000, 100)
-        state.pad_duration_ms = st.slider("Padding duration (ms)", 100, 5000, 1000, 100)
-        submit_button = st.form_submit_button(label="Submit")
-    if submit_button or "asr_stream" not in state:
-        # a hack to update the video player on value changes
-        state.youtube_url = (
-            state.youtube_url.split("&hash=")[0]
-            + f"&hash={state.chunk_duration_ms}-{state.pad_duration_ms}"
-        )
-        state.asr_stream = stream_text(
-            state.youtube_url, state.chunk_duration_ms, state.pad_duration_ms
-        )
-        state.chunks_taken = 0
-        state.lines = deque([], maxlen=100)  # limit to the last n lines of subs
-    player = st_player(state.youtube_url, **player_options, key="youtube_player")
-    if "asr_stream" in state and player.data and player.data["played"] < 1.0:
-        # check how many seconds were played, and if more than processed - write the next text chunk
-        processed_seconds = state.chunks_taken * (state.chunk_duration_ms / 1000)
-        if processed_seconds < player.data["playedSeconds"]:
-            text = next(state.asr_stream)
-            state.lines.append(text)
-            state.chunks_taken += 1
-    if "lines" in state:
-        # print the lines of subs
-        st.code("\n".join(state.lines))
-if __name__ == "__main__":
-    main()

spaces/AIZerotoHero-Health4All/02-ClinicalTerminology/app.py DELETED Viewed

@@ -1,63 +0,0 @@
-import os
-import pandas as pd
-import gradio as gr
-# SNOMEDCT Download https://www.nlm.nih.gov/healthit/snomedct/us_edition.html
-# LOINC Download https://loinc.org/downloads/
-# ECQM for Value Set Measures and Quality Reporting: https://vsac.nlm.nih.gov/download/ecqm?rel=20220505&res=eh_only.unique_vs.20220505.txt
-# SNOMED Nurse Subset https://www.nlm.nih.gov/healthit/snomedct/index.html?_gl=1*36x5pi*_ga*MTI0ODMyNjkxOS4xNjY1NTY3Mjcz*_ga_P1FPTH9PL4*MTY2Nzk4OTI1My41LjEuMTY2Nzk4OTY5Ni4wLjAuMA..
-def MatchLOINC(name):
-    basedir = os.path.dirname(__file__)
-    pd.set_option("display.max_rows", None)
-    data = pd.read_csv(f'LoincTableCore.csv')
-    swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
-    return swith
-def MatchLOINCPanelsandForms(name):
-    basedir = os.path.dirname(__file__)
-    data = pd.read_csv(f'PanelsAndForms.csv')
-    swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
-    return swith
-def MatchSNOMED(name):
-    basedir = os.path.dirname(__file__)
-    data = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')
-    swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
-    #swith = data[data['term'].str.match(name)]
-    return swith
-def MatchOMS(name):
-    basedir = os.path.dirname(__file__)
-    data = pd.read_csv(f'SnomedOMS.csv')
-    swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
-    #swith = data[data['SNOMED CT'].str.match(name)]
-    return swith
-with gr.Blocks() as demo:
-    with gr.Row():
-        name = gr.Textbox(label="Enter a term or word to match and find LOINC, SNOMED and OMS clinical terminologies.")
-    with gr.Row():
-        button1 = gr.Button("LOINC Terminology")
-        button2 = gr.Button("LOINC Panels and Forms")
-        button3 = gr.Button("SNOMED Clinical Terminology")
-        button4 = gr.Button("SNOMED and OMS Clinical Terminology")
-    with gr.Row():
-        output1 = gr.DataFrame(label="LOINC Terminology")
-    with gr.Row():
-        output2 = gr.DataFrame(label="LOINC Assessment Panels")
-    with gr.Row():
-        output3 = gr.DataFrame(label="SNOMED Terminology")
-    with gr.Row():
-        output4 = gr.DataFrame(label="SNOMED and OMS Terminology")
-    button1.click(fn=MatchLOINC, inputs=name, outputs=output1)
-    button2.click(fn=MatchLOINCPanelsandForms, inputs=name, outputs=output2)
-    button3.click(fn=MatchSNOMED, inputs=name, outputs=output3)
-    button4.click(fn=MatchOMS, inputs=name, outputs=output4)
-demo.launch(debug=True)

spaces/Aaaaaaaabdualh/poetry/README.md DELETED Viewed

@@ -1,14 +0,0 @@
----
-title: Arabic Poetry Generator
-emoji: 🐠
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: true
-license: cc-by-nc-4.0
-duplicated_from: akhooli/poetry
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AchyuthGamer/OpenGPT/g4f/Provider/ChatForAi.py DELETED Viewed

@@ -1,53 +0,0 @@
-from __future__ import annotations
-from ..typing import AsyncGenerator
-from ..requests import StreamSession
-from .base_provider import AsyncGeneratorProvider
-class ChatForAi(AsyncGeneratorProvider):
-    url                   = "https://chatforai.com"
-    supports_gpt_35_turbo = True
-    working               = True
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        timeout: int = 30,
-        **kwargs
-    ) -> AsyncGenerator:
-        async with StreamSession(impersonate="chrome107", timeout=timeout) as session:
-            prompt = messages[-1]["content"]
-            data = {
-                "conversationId": "temp",
-                "conversationType": "chat_continuous",
-                "botId": "chat_continuous",
-                "globalSettings":{
-                    "baseUrl": "https://api.openai.com",
-                    "model": model if model else "gpt-3.5-turbo",
-                    "messageHistorySize": 5,
-                    "temperature": 0.7,
-                    "top_p": 1,
-                    **kwargs
-                },
-                "botSettings": {},
-                "prompt": prompt,
-                "messages": messages,
-            }
-            async with session.post(f"{cls.url}/api/handle/provider-openai", json=data) as response:
-                response.raise_for_status()
-                async for chunk in response.iter_content():
-                    yield chunk.decode()
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/Adapter/CoAdapter/ldm/modules/extra_condition/model_edge.py DELETED Viewed

@@ -1,653 +0,0 @@
-"""
-Author: Zhuo Su, Wenzhe Liu
-Date: Feb 18, 2021
-"""
-import math
-import cv2
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from basicsr.utils import img2tensor
-nets = {
-    'baseline': {
-        'layer0':  'cv',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'c-v15': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'a-v15': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'r-v15': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cvvv4': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'avvv4': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'rvvv4': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cccv4': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cv',
-        },
-    'aaav4': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'cv',
-        },
-    'rrrv4': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    'c16': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cd',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cd',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cd',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cd',
-        },
-    'a16': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'ad',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'ad',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'ad',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'ad',
-        },
-    'r16': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'rd',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'rd',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'rd',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'rd',
-        },
-    'carv4': {
-        'layer0':  'cd',
-        'layer1':  'ad',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'ad',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'ad',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'ad',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    }
-def createConvFunc(op_type):
-    assert op_type in ['cv', 'cd', 'ad', 'rd'], 'unknown op type: %s' % str(op_type)
-    if op_type == 'cv':
-        return F.conv2d
-    if op_type == 'cd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for cd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for cd_conv should be 3x3'
-            assert padding == dilation, 'padding for cd_conv set wrong'
-            weights_c = weights.sum(dim=[2, 3], keepdim=True)
-            yc = F.conv2d(x, weights_c, stride=stride, padding=0, groups=groups)
-            y = F.conv2d(x, weights, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y - yc
-        return func
-    elif op_type == 'ad':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for ad_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for ad_conv should be 3x3'
-            assert padding == dilation, 'padding for ad_conv set wrong'
-            shape = weights.shape
-            weights = weights.view(shape[0], shape[1], -1)
-            weights_conv = (weights - weights[:, :, [3, 0, 1, 6, 4, 2, 7, 8, 5]]).view(shape) # clock-wise
-            y = F.conv2d(x, weights_conv, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    elif op_type == 'rd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for rd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for rd_conv should be 3x3'
-            padding = 2 * dilation
-            shape = weights.shape
-            if weights.is_cuda:
-                buffer = torch.cuda.FloatTensor(shape[0], shape[1], 5 * 5).fill_(0)
-            else:
-                buffer = torch.zeros(shape[0], shape[1], 5 * 5)
-            weights = weights.view(shape[0], shape[1], -1)
-            buffer[:, :, [0, 2, 4, 10, 14, 20, 22, 24]] = weights[:, :, 1:]
-            buffer[:, :, [6, 7, 8, 11, 13, 16, 17, 18]] = -weights[:, :, 1:]
-            buffer[:, :, 12] = 0
-            buffer = buffer.view(shape[0], shape[1], 5, 5)
-            y = F.conv2d(x, buffer, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    else:
-        print('impossible to be here unless you force that')
-        return None
-class Conv2d(nn.Module):
-    def __init__(self, pdc, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False):
-        super(Conv2d, self).__init__()
-        if in_channels % groups != 0:
-            raise ValueError('in_channels must be divisible by groups')
-        if out_channels % groups != 0:
-            raise ValueError('out_channels must be divisible by groups')
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.groups = groups
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
-        if bias:
-            self.bias = nn.Parameter(torch.Tensor(out_channels))
-        else:
-            self.register_parameter('bias', None)
-        self.reset_parameters()
-        self.pdc = pdc
-    def reset_parameters(self):
-        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-        if self.bias is not None:
-            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
-            bound = 1 / math.sqrt(fan_in)
-            nn.init.uniform_(self.bias, -bound, bound)
-    def forward(self, input):
-        return self.pdc(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
-class CSAM(nn.Module):
-    """
-    Compact Spatial Attention Module
-    """
-    def __init__(self, channels):
-        super(CSAM, self).__init__()
-        mid_channels = 4
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0)
-        self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1, bias=False)
-        self.sigmoid = nn.Sigmoid()
-        nn.init.constant_(self.conv1.bias, 0)
-    def forward(self, x):
-        y = self.relu1(x)
-        y = self.conv1(y)
-        y = self.conv2(y)
-        y = self.sigmoid(y)
-        return x * y
-class CDCM(nn.Module):
-    """
-    Compact Dilation Convolution based Module
-    """
-    def __init__(self, in_channels, out_channels):
-        super(CDCM, self).__init__()
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)
-        self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=5, padding=5, bias=False)
-        self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=7, padding=7, bias=False)
-        self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=9, padding=9, bias=False)
-        self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=11, padding=11, bias=False)
-        nn.init.constant_(self.conv1.bias, 0)
-    def forward(self, x):
-        x = self.relu1(x)
-        x = self.conv1(x)
-        x1 = self.conv2_1(x)
-        x2 = self.conv2_2(x)
-        x3 = self.conv2_3(x)
-        x4 = self.conv2_4(x)
-        return x1 + x2 + x3 + x4
-class MapReduce(nn.Module):
-    """
-    Reduce feature maps into a single edge map
-    """
-    def __init__(self, channels):
-        super(MapReduce, self).__init__()
-        self.conv = nn.Conv2d(channels, 1, kernel_size=1, padding=0)
-        nn.init.constant_(self.conv.bias, 0)
-    def forward(self, x):
-        return self.conv(x)
-class PDCBlock(nn.Module):
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock, self).__init__()
-        self.stride=stride
-        self.stride=stride
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        self.conv1 = Conv2d(pdc, inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-class PDCBlock_converted(nn.Module):
-    """
-    CPDC, APDC can be converted to vanilla 3x3 convolution
-    RPDC can be converted to vanilla 5x5 convolution
-    """
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock_converted, self).__init__()
-        self.stride=stride
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        if pdc == 'rd':
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding=2, groups=inplane, bias=False)
-        else:
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-class PiDiNet(nn.Module):
-    def __init__(self, inplane, pdcs, dil=None, sa=False, convert=False):
-        super(PiDiNet, self).__init__()
-        self.sa = sa
-        if dil is not None:
-            assert isinstance(dil, int), 'dil should be an int'
-        self.dil = dil
-        self.fuseplanes = []
-        self.inplane = inplane
-        if convert:
-            if pdcs[0] == 'rd':
-                init_kernel_size = 5
-                init_padding = 2
-            else:
-                init_kernel_size = 3
-                init_padding = 1
-            self.init_block = nn.Conv2d(3, self.inplane,
-                    kernel_size=init_kernel_size, padding=init_padding, bias=False)
-            block_class = PDCBlock_converted
-        else:
-            self.init_block = Conv2d(pdcs[0], 3, self.inplane, kernel_size=3, padding=1)
-            block_class = PDCBlock
-        self.block1_1 = block_class(pdcs[1], self.inplane, self.inplane)
-        self.block1_2 = block_class(pdcs[2], self.inplane, self.inplane)
-        self.block1_3 = block_class(pdcs[3], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # C
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block2_1 = block_class(pdcs[4], inplane, self.inplane, stride=2)
-        self.block2_2 = block_class(pdcs[5], self.inplane, self.inplane)
-        self.block2_3 = block_class(pdcs[6], self.inplane, self.inplane)
-        self.block2_4 = block_class(pdcs[7], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 2C
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block3_1 = block_class(pdcs[8], inplane, self.inplane, stride=2)
-        self.block3_2 = block_class(pdcs[9], self.inplane, self.inplane)
-        self.block3_3 = block_class(pdcs[10], self.inplane, self.inplane)
-        self.block3_4 = block_class(pdcs[11], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-        self.block4_1 = block_class(pdcs[12], self.inplane, self.inplane, stride=2)
-        self.block4_2 = block_class(pdcs[13], self.inplane, self.inplane)
-        self.block4_3 = block_class(pdcs[14], self.inplane, self.inplane)
-        self.block4_4 = block_class(pdcs[15], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-        self.conv_reduces = nn.ModuleList()
-        if self.sa and self.dil is not None:
-            self.attentions = nn.ModuleList()
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.attentions.append(CSAM(self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        elif self.sa:
-            self.attentions = nn.ModuleList()
-            for i in range(4):
-                self.attentions.append(CSAM(self.fuseplanes[i]))
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-        elif self.dil is not None:
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        else:
-            for i in range(4):
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-        self.classifier = nn.Conv2d(4, 1, kernel_size=1) # has bias
-        nn.init.constant_(self.classifier.weight, 0.25)
-        nn.init.constant_(self.classifier.bias, 0)
-        # print('initialization done')
-    def get_weights(self):
-        conv_weights = []
-        bn_weights = []
-        relu_weights = []
-        for pname, p in self.named_parameters():
-            if 'bn' in pname:
-                bn_weights.append(p)
-            elif 'relu' in pname:
-                relu_weights.append(p)
-            else:
-                conv_weights.append(p)
-        return conv_weights, bn_weights, relu_weights
-    def forward(self, x):
-        H, W = x.size()[2:]
-        x = self.init_block(x)
-        x1 = self.block1_1(x)
-        x1 = self.block1_2(x1)
-        x1 = self.block1_3(x1)
-        x2 = self.block2_1(x1)
-        x2 = self.block2_2(x2)
-        x2 = self.block2_3(x2)
-        x2 = self.block2_4(x2)
-        x3 = self.block3_1(x2)
-        x3 = self.block3_2(x3)
-        x3 = self.block3_3(x3)
-        x3 = self.block3_4(x3)
-        x4 = self.block4_1(x3)
-        x4 = self.block4_2(x4)
-        x4 = self.block4_3(x4)
-        x4 = self.block4_4(x4)
-        x_fuses = []
-        if self.sa and self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](self.dilations[i](xi)))
-        elif self.sa:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](xi))
-        elif self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.dilations[i](xi))
-        else:
-            x_fuses = [x1, x2, x3, x4]
-        e1 = self.conv_reduces[0](x_fuses[0])
-        e1 = F.interpolate(e1, (H, W), mode="bilinear", align_corners=False)
-        e2 = self.conv_reduces[1](x_fuses[1])
-        e2 = F.interpolate(e2, (H, W), mode="bilinear", align_corners=False)
-        e3 = self.conv_reduces[2](x_fuses[2])
-        e3 = F.interpolate(e3, (H, W), mode="bilinear", align_corners=False)
-        e4 = self.conv_reduces[3](x_fuses[3])
-        e4 = F.interpolate(e4, (H, W), mode="bilinear", align_corners=False)
-        outputs = [e1, e2, e3, e4]
-        output = self.classifier(torch.cat(outputs, dim=1))
-        #if not self.training:
-        #    return torch.sigmoid(output)
-        outputs.append(output)
-        outputs = [torch.sigmoid(r) for r in outputs]
-        return outputs
-def config_model(model):
-    model_options = list(nets.keys())
-    assert model in model_options, \
-        'unrecognized model, please choose from %s' % str(model_options)
-    # print(str(nets[model]))
-    pdcs = []
-    for i in range(16):
-        layer_name = 'layer%d' % i
-        op = nets[model][layer_name]
-        pdcs.append(createConvFunc(op))
-    return pdcs
-def pidinet():
-    pdcs = config_model('carv4')
-    dil = 24 #if args.dil else None
-    return PiDiNet(60, pdcs, dil=dil, sa=True)
-if __name__ == '__main__':
-    model = pidinet()
-    ckp = torch.load('table5_pidinet.pth')['state_dict']
-    model.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()})
-    im = cv2.imread('examples/test_my/cat_v4.png')
-    im = img2tensor(im).unsqueeze(0)/255.
-    res = model(im)[-1]
-    res = res>0.5
-    res = res.float()
-    res = (res[0,0].cpu().data.numpy()*255.).astype(np.uint8)
-    print(res.shape)
-    cv2.imwrite('edge.png', res)

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/visibility/classroom.py DELETED Viewed

@@ -1,84 +0,0 @@
-from __future__ import annotations
-import random
-from typing import TYPE_CHECKING, Any, List, Union
-from . import visibility_registry as VisibilityRegistry
-from .base import BaseVisibility
-if TYPE_CHECKING:
-    from agentverse.environments import BaseEnvironment
-@VisibilityRegistry.register("classroom")
-class ClassroomVisibility(BaseVisibility):
-    """
-    Visibility function for classroom, supports group discussion.
-    Args:
-        student_per_group:
-            The number of students per group.
-        num_discussion_turn:
-            The number of turns for group discussion.
-        grouping:
-            The grouping information. If it is a string, then it should be a
-            grouping method, options are ["random", "sequential"]. If it is a
-            list of list of int, then it should be the grouping information.
-    """
-    grouping: Union[str, List[List[int]]]
-    student_per_group: int = 4
-    num_discussion_turn: int = 5
-    current_turn: int = 0
-    def update_visible_agents(self, environment: BaseEnvironment):
-        # We turn on grouping mode when the professor launches a group discussion
-        if len(environment.last_messages) == 1 and environment.last_messages[
-            0
-        ].content.startswith("[GroupDiscuss]"):
-            environment.rule_params["is_grouped"] = True
-            # We randomly group the students
-            environment.rule_params["groups"] = self.group_students(environment)
-            # Update the receiver for each agent
-            self.update_receiver(environment)
-        else:
-            # If now in grouping mode, then we check if the group discussion is over
-            if environment.rule_params.get("is_grouped", False):
-                self.current_turn += 1
-                if self.current_turn >= self.num_discussion_turn:
-                    self.reset()
-                    environment.rule_params["is_grouped"] = False
-                    environment.rule_params["is_grouped_ended"] = True
-                    self.update_receiver(environment, reset=True)
-    def group_students(self, environment: BaseEnvironment) -> List[List[int]]:
-        if isinstance(self.grouping, str):
-            student_index = list(range(1, len(environment.agents)))
-            result = []
-            if self.grouping == "random":
-                random.shuffle(student_index)
-                for i in range(0, len(student_index), self.student_per_group):
-                    result.append(student_index[i : i + self.student_per_group])
-            elif self.grouping == "sequential":
-                for i in range(0, len(student_index), self.student_per_group):
-                    result.append(student_index[i : i + self.student_per_group])
-            else:
-                raise ValueError(f"Unsupported grouping method {self.grouping}")
-            return result
-        else:
-            # If the grouping information is provided, then we use it directly
-            return self.grouping
-    def update_receiver(self, environment: BaseEnvironment, reset=False):
-        if reset:
-            for agent in environment.agents:
-                agent.set_receiver(set({"all"}))
-        else:
-            groups = environment.rule_params["groups"]
-            for group in groups:
-                group_name = set({environment.agents[i].name for i in group})
-                for agent_id in group:
-                    environment.agents[agent_id].set_receiver(group_name)
-    def reset(self):
-        self.current_turn = 0

spaces/Aloento/9Nine-PITS/text/frontend/zh_normalization/text_normlization.py DELETED Viewed

@@ -1,116 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-from typing import List
-from .char_convert import tranditional_to_simplified
-from .chronology import RE_DATE
-from .chronology import RE_DATE2
-from .chronology import RE_TIME
-from .chronology import RE_TIME_RANGE
-from .chronology import replace_date
-from .chronology import replace_date2
-from .chronology import replace_time
-from .constants import F2H_ASCII_LETTERS
-from .constants import F2H_DIGITS
-from .constants import F2H_SPACE
-from .num import RE_DECIMAL_NUM
-from .num import RE_DEFAULT_NUM
-from .num import RE_FRAC
-from .num import RE_INTEGER
-from .num import RE_NUMBER
-from .num import RE_PERCENTAGE
-from .num import RE_POSITIVE_QUANTIFIERS
-from .num import RE_RANGE
-from .num import replace_default_num
-from .num import replace_frac
-from .num import replace_negative_num
-from .num import replace_number
-from .num import replace_percentage
-from .num import replace_positive_quantifier
-from .num import replace_range
-from .phonecode import RE_MOBILE_PHONE
-from .phonecode import RE_NATIONAL_UNIFORM_NUMBER
-from .phonecode import RE_TELEPHONE
-from .phonecode import replace_mobile
-from .phonecode import replace_phone
-from .quantifier import RE_TEMPERATURE
-from .quantifier import replace_temperature
-class TextNormalizer():
-  def __init__(self):
-    self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!….][”’]?)')
-  def _split(self, text: str, lang="zh") -> List[str]:
-    """Split long text into sentences with sentence-splitting punctuations.
-    Args:
-        text (str): The input text.
-    Returns:
-        List[str]: Sentences.
-    """
-    # Only for pure Chinese here
-    if lang == "zh":
-      text = text.replace(" ", "")
-      # 过滤掉特殊字符
-      text = re.sub(r'[《》【】<=>{}()（）&@“”^_|\\]', '', text)
-    text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
-    text = text.strip()
-    sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
-    return sentences
-  def _post_replace(self, sentence: str) -> str:
-    sentence = sentence.replace('/', '每')
-    sentence = sentence.replace('~', '至')
-    return sentence
-  def normalize_sentence(self, sentence: str) -> str:
-    # basic character conversions
-    sentence = tranditional_to_simplified(sentence)
-    sentence = sentence.translate(F2H_ASCII_LETTERS).translate(
-      F2H_DIGITS).translate(F2H_SPACE)
-    # number related NSW verbalization
-    sentence = RE_DATE.sub(replace_date, sentence)
-    sentence = RE_DATE2.sub(replace_date2, sentence)
-    # range first
-    sentence = RE_TIME_RANGE.sub(replace_time, sentence)
-    sentence = RE_TIME.sub(replace_time, sentence)
-    sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
-    sentence = RE_FRAC.sub(replace_frac, sentence)
-    sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
-    sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)
-    sentence = RE_TELEPHONE.sub(replace_phone, sentence)
-    sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence)
-    sentence = RE_RANGE.sub(replace_range, sentence)
-    sentence = RE_INTEGER.sub(replace_negative_num, sentence)
-    sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
-    sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier,
-                                           sentence)
-    sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
-    sentence = RE_NUMBER.sub(replace_number, sentence)
-    sentence = self._post_replace(sentence)
-    return sentence
-  def normalize(self, text: str) -> List[str]:
-    sentences = self._split(text)
-    sentences = [self.normalize_sentence(sent) for sent in sentences]
-    return sentences

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r2060.py DELETED Viewed

@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-config = edict()
-config.loss = "arcface"
-config.network = "r2060"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 64
-config.lr = 0.1  # batch size is 512
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]

spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/mapper/datasets/__init__.py DELETED Viewed

File without changes

spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/op_edit/__init__.py DELETED Viewed

@@ -1,4 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d

spaces/Amrrs/DragGan-Inversion/stylegan_human/training/training_loop.py DELETED Viewed

@@ -1,499 +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.
-"""Main training loop."""
-import os
-import time
-import copy
-import json
-import pickle
-import psutil
-import PIL.Image
-import numpy as np
-import torch
-import dnnlib
-from torch_utils import misc
-from torch_utils import training_stats
-from torch_utils.ops import conv2d_gradfix
-from torch_utils.ops import grid_sample_gradfix
-import legacy
-from metrics import metric_main
-# ----------------------------------------------------------------------------
-def setup_snapshot_image_grid(training_set, random_seed=0):
-    rnd = np.random.RandomState(random_seed)
-    gw = np.clip(7680 // training_set.image_shape[2], 7, 32)
-    gh = np.clip(4320 // training_set.image_shape[1], 4, 32)
-    # No labels => show random subset of training samples.
-    if not training_set.has_labels:
-        all_indices = list(range(len(training_set)))
-        rnd.shuffle(all_indices)
-        grid_indices = [all_indices[i %
-                                    len(all_indices)] for i in range(gw * gh)]
-    else:
-        # Group training samples by label.
-        label_groups = dict()  # label => [idx, ...]
-        for idx in range(len(training_set)):
-            label = tuple(training_set.get_details(idx).raw_label.flat[::-1])
-            if label not in label_groups:
-                label_groups[label] = []
-            label_groups[label].append(idx)
-        # Reorder.
-        label_order = sorted(label_groups.keys())
-        for label in label_order:
-            rnd.shuffle(label_groups[label])
-        # Organize into grid.
-        grid_indices = []
-        for y in range(gh):
-            label = label_order[y % len(label_order)]
-            indices = label_groups[label]
-            grid_indices += [indices[x % len(indices)] for x in range(gw)]
-            label_groups[label] = [
-                indices[(i + gw) % len(indices)] for i in range(len(indices))]
-    # Load data.
-    images, labels = zip(*[training_set[i] for i in grid_indices])
-    return (gw, gh), np.stack(images), np.stack(labels)
-# ----------------------------------------------------------------------------
-def save_image_grid(img, fname, drange, grid_size):
-    lo, hi = drange
-    img = np.asarray(img, dtype=np.float32)
-    img = (img - lo) * (255 / (hi - lo))
-    img = np.rint(img).clip(0, 255).astype(np.uint8)
-    gw, gh = grid_size
-    _N, C, H, W = img.shape
-    img = img.reshape([gh, gw, C, H, W])
-    img = img.transpose(0, 3, 1, 4, 2)
-    img = img.reshape([gh * H, gw * W, C])
-    assert C in [1, 3]
-    if C == 1:
-        PIL.Image.fromarray(img[:, :, 0], 'L').save(fname)
-    if C == 3:
-        PIL.Image.fromarray(img, 'RGB').save(fname)
-# ----------------------------------------------------------------------------
-def training_loop(
-    run_dir='.',      # Output directory.
-    training_set_kwargs={},       # Options for training set.
-    data_loader_kwargs={},       # Options for torch.utils.data.DataLoader.
-    G_kwargs={},       # Options for generator network.
-    D_kwargs={},       # Options for discriminator network.
-    G_opt_kwargs={},       # Options for generator optimizer.
-    D_opt_kwargs={},       # Options for discriminator optimizer.
-    # Options for augmentation pipeline. None = disable.
-    augment_kwargs=None,
-    loss_kwargs={},       # Options for loss function.
-    metrics=[],       # Metrics to evaluate during training.
-    random_seed=0,        # Global random seed.
-    num_gpus=1,        # Number of GPUs participating in the training.
-    rank=0,        # Rank of the current process in [0, num_gpus[.
-    # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus.
-    batch_size=4,
-    batch_gpu=4,        # Number of samples processed at a time by one GPU.
-    # Half-life of the exponential moving average (EMA) of generator weights.
-    ema_kimg=10,
-    ema_rampup=0.05,     # EMA ramp-up coefficient. None = no rampup.
-    # How often to perform regularization for G? None = disable lazy regularization.
-    G_reg_interval=None,
-    # How often to perform regularization for D? None = disable lazy regularization.
-    D_reg_interval=16,
-    augment_p=0,        # Initial value of augmentation probability.
-    ada_target=None,     # ADA target value. None = fixed p.
-    ada_interval=4,        # How often to perform ADA adjustment?
-    # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit.
-    ada_kimg=500,
-    # Total length of the training, measured in thousands of real images.
-    total_kimg=25000,
-    kimg_per_tick=4,        # Progress snapshot interval.
-    # How often to save image snapshots? None = disable.
-    image_snapshot_ticks=50,
-    # How often to save network snapshots? None = disable.
-    network_snapshot_ticks=50,
-    resume_pkl=None,     # Network pickle to resume training from.
-    resume_kimg=0,        # First kimg to report when resuming training.
-    cudnn_benchmark=True,     # Enable torch.backends.cudnn.benchmark?
-    # Callback function for determining whether to abort training. Must return consistent results across ranks.
-    abort_fn=None,
-    # Callback function for updating training progress. Called for all ranks.
-    progress_fn=None,
-):
-    # Initialize.
-    start_time = time.time()
-    device = torch.device('cuda', rank)
-    np.random.seed(random_seed * num_gpus + rank)
-    torch.manual_seed(random_seed * num_gpus + rank)
-    # Improves training speed.
-    torch.backends.cudnn.benchmark = cudnn_benchmark
-    # Improves numerical accuracy.
-    torch.backends.cuda.matmul.allow_tf32 = False
-    # Improves numerical accuracy.
-    torch.backends.cudnn.allow_tf32 = False
-    # Improves training speed.
-    conv2d_gradfix.enabled = True
-    # Avoids errors with the augmentation pipe.
-    grid_sample_gradfix.enabled = True
-    # Load training set.
-    if rank == 0:
-        print('Loading training set...')
-    training_set = dnnlib.util.construct_class_by_name(
-        **training_set_kwargs)  # subclass of training.dataset.Dataset
-    training_set_sampler = misc.InfiniteSampler(
-        dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed)
-    training_set_iterator = iter(torch.utils.data.DataLoader(
-        dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs))
-    if rank == 0:
-        print()
-        print('Num images: ', len(training_set))
-        print('Image shape:', training_set.image_shape)
-        print('Label shape:', training_set.label_shape)
-        print()
-    # Construct networks.
-    if rank == 0:
-        print('Constructing networks...')
-    common_kwargs = dict(c_dim=training_set.label_dim,
-                         img_resolution=training_set.resolution, img_channels=training_set.num_channels)
-    G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train(
-    ).requires_grad_(False).to(device)  # subclass of torch.nn.Module
-    D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train(
-    ).requires_grad_(False).to(device)  # subclass of torch.nn.Module
-    G_ema = copy.deepcopy(G).eval()
-    # Resume from existing pickle.
-    if (resume_pkl is not None) and (rank == 0):
-        print(f'Resuming from "{resume_pkl}"')
-        with dnnlib.util.open_url(resume_pkl) as f:
-            resume_data = legacy.load_network_pkl(f)
-        for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]:
-            misc.copy_params_and_buffers(
-                resume_data[name], module, require_all=False)
-    # Print network summary tables.
-    if rank == 0:
-        z = torch.empty([batch_gpu, G.z_dim], device=device)
-        c = torch.empty([batch_gpu, G.c_dim], device=device)
-        img = misc.print_module_summary(G, [z, c])
-        misc.print_module_summary(D, [img, c])
-    # Setup augmentation.
-    if rank == 0:
-        print('Setting up augmentation...')
-    augment_pipe = None
-    ada_stats = None
-    if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None):
-        augment_pipe = dnnlib.util.construct_class_by_name(
-            **augment_kwargs).train().requires_grad_(False).to(device)  # subclass of torch.nn.Module
-        augment_pipe.p.copy_(torch.as_tensor(augment_p))
-        if ada_target is not None:
-            ada_stats = training_stats.Collector(regex='Loss/signs/real')
-    # Distribute across GPUs.
-    if rank == 0:
-        print(f'Distributing across {num_gpus} GPUs...')
-    for module in [G, D, G_ema, augment_pipe]:
-        if module is not None and num_gpus > 1:
-            for param in misc.params_and_buffers(module):
-                torch.distributed.broadcast(param, src=0)
-    # Setup training phases.
-    if rank == 0:
-        print('Setting up training phases...')
-    loss = dnnlib.util.construct_class_by_name(
-        device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs)  # subclass of training.loss.Loss
-    phases = []
-    for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]:
-        if reg_interval is None:
-            opt = dnnlib.util.construct_class_by_name(
-                params=module.parameters(), **opt_kwargs)  # subclass of torch.optim.Optimizer
-            phases += [dnnlib.EasyDict(name=name+'both',
-                                       module=module, opt=opt, interval=1)]
-        else:  # Lazy regularization.
-            mb_ratio = reg_interval / (reg_interval + 1)
-            opt_kwargs = dnnlib.EasyDict(opt_kwargs)
-            opt_kwargs.lr = opt_kwargs.lr * mb_ratio
-            opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas]
-            opt = dnnlib.util.construct_class_by_name(
-                module.parameters(), **opt_kwargs)  # subclass of torch.optim.Optimizer
-            phases += [dnnlib.EasyDict(name=name+'main',
-                                       module=module, opt=opt, interval=1)]
-            phases += [dnnlib.EasyDict(name=name+'reg',
-                                       module=module, opt=opt, interval=reg_interval)]
-    for phase in phases:
-        phase.start_event = None
-        phase.end_event = None
-        if rank == 0:
-            phase.start_event = torch.cuda.Event(enable_timing=True)
-            phase.end_event = torch.cuda.Event(enable_timing=True)
-    # Export sample images.
-    grid_size = None
-    grid_z = None
-    grid_c = None
-    if rank == 0:
-        print('Exporting sample images...')
-        grid_size, images, labels = setup_snapshot_image_grid(
-            training_set=training_set)
-        save_image_grid(images, os.path.join(run_dir, 'reals.png'),
-                        drange=[0, 255], grid_size=grid_size)
-        grid_z = torch.randn([labels.shape[0], G.z_dim],
-                             device=device).split(batch_gpu)
-        grid_c = torch.from_numpy(labels).to(device).split(batch_gpu)
-        images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu()
-                           for z, c in zip(grid_z, grid_c)]).numpy()
-        save_image_grid(images, os.path.join(
-            run_dir, 'fakes_init.png'), drange=[-1, 1], grid_size=grid_size)
-    # Initialize logs.
-    if rank == 0:
-        print('Initializing logs...')
-    stats_collector = training_stats.Collector(regex='.*')
-    stats_metrics = dict()
-    stats_jsonl = None
-    stats_tfevents = None
-    if rank == 0:
-        stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt')
-        try:
-            import torch.utils.tensorboard as tensorboard
-            stats_tfevents = tensorboard.SummaryWriter(run_dir)
-        except ImportError as err:
-            print('Skipping tfevents export:', err)
-    # Train.
-    if rank == 0:
-        print(f'Training for {total_kimg} kimg...')
-        print()
-    cur_nimg = resume_kimg * 1000
-    cur_tick = 0
-    tick_start_nimg = cur_nimg
-    tick_start_time = time.time()
-    maintenance_time = tick_start_time - start_time
-    batch_idx = 0
-    if progress_fn is not None:
-        progress_fn(0, total_kimg)
-    while True:
-        # Fetch training data.
-        with torch.autograd.profiler.record_function('data_fetch'):
-            phase_real_img, phase_real_c = next(training_set_iterator)
-            phase_real_img = (phase_real_img.to(device).to(
-                torch.float32) / 127.5 - 1).split(batch_gpu)
-            phase_real_c = phase_real_c.to(device).split(batch_gpu)
-            all_gen_z = torch.randn(
-                [len(phases) * batch_size, G.z_dim], device=device)
-            all_gen_z = [phase_gen_z.split(
-                batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)]
-            all_gen_c = [training_set.get_label(np.random.randint(
-                len(training_set))) for _ in range(len(phases) * batch_size)]
-            all_gen_c = torch.from_numpy(
-                np.stack(all_gen_c)).pin_memory().to(device)
-            all_gen_c = [phase_gen_c.split(
-                batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)]
-        # Execute training phases.
-        for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c):
-            if batch_idx % phase.interval != 0:
-                continue
-            if phase.start_event is not None:
-                phase.start_event.record(torch.cuda.current_stream(device))
-            # Accumulate gradients.
-            phase.opt.zero_grad(set_to_none=True)
-            phase.module.requires_grad_(True)
-            for real_img, real_c, gen_z, gen_c in zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c):
-                loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c,
-                                          gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg)
-            phase.module.requires_grad_(False)
-            # Update weights.
-            with torch.autograd.profiler.record_function(phase.name + '_opt'):
-                params = [param for param in phase.module.parameters()
-                          if param.grad is not None]
-                if len(params) > 0:
-                    flat = torch.cat([param.grad.flatten()
-                                     for param in params])
-                    if num_gpus > 1:
-                        torch.distributed.all_reduce(flat)
-                        flat /= num_gpus
-                    misc.nan_to_num(flat, nan=0, posinf=1e5,
-                                    neginf=-1e5, out=flat)
-                    grads = flat.split([param.numel() for param in params])
-                    for param, grad in zip(params, grads):
-                        param.grad = grad.reshape(param.shape)
-                phase.opt.step()
-            # Phase done.
-            if phase.end_event is not None:
-                phase.end_event.record(torch.cuda.current_stream(device))
-        # Update G_ema.
-        with torch.autograd.profiler.record_function('Gema'):
-            ema_nimg = ema_kimg * 1000
-            if ema_rampup is not None:
-                ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)
-            ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8))
-            for p_ema, p in zip(G_ema.parameters(), G.parameters()):
-                p_ema.copy_(p.lerp(p_ema, ema_beta))
-            for b_ema, b in zip(G_ema.buffers(), G.buffers()):
-                b_ema.copy_(b)
-        # Update state.
-        cur_nimg += batch_size
-        batch_idx += 1
-        # Execute ADA heuristic.
-        if (ada_stats is not None) and (batch_idx % ada_interval == 0):
-            ada_stats.update()
-            adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * \
-                (batch_size * ada_interval) / (ada_kimg * 1000)
-            augment_pipe.p.copy_(
-                (augment_pipe.p + adjust).max(misc.constant(0, device=device)))
-        # Perform maintenance tasks once per tick.
-        done = (cur_nimg >= total_kimg * 1000)
-        if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000):
-            continue
-        # Print status line, accumulating the same information in training_stats.
-        tick_end_time = time.time()
-        fields = []
-        fields += [
-            f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"]
-        fields += [
-            f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"]
-        fields += [
-            f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"]
-        fields += [
-            f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"]
-        fields += [
-            f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"]
-        fields += [
-            f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"]
-        fields += [
-            f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"]
-        fields += [
-            f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"]
-        fields += [
-            f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"]
-        torch.cuda.reset_peak_memory_stats()
-        fields += [
-            f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"]
-        training_stats.report0('Timing/total_hours',
-                               (tick_end_time - start_time) / (60 * 60))
-        training_stats.report0('Timing/total_days',
-                               (tick_end_time - start_time) / (24 * 60 * 60))
-        if rank == 0:
-            print(' '.join(fields))
-        # Check for abort.
-        if (not done) and (abort_fn is not None) and abort_fn():
-            done = True
-            if rank == 0:
-                print()
-                print('Aborting...')
-        # Save image snapshot.
-        if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0):
-            images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu()
-                               for z, c in zip(grid_z, grid_c)]).numpy()
-            save_image_grid(images, os.path.join(
-                run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1, 1], grid_size=grid_size)
-        # Save network snapshot.
-        snapshot_pkl = None
-        snapshot_data = None
-        if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0):
-            snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe,
-                                 training_set_kwargs=dict(training_set_kwargs))
-            for key, value in snapshot_data.items():
-                if isinstance(value, torch.nn.Module):
-                    value = copy.deepcopy(value).eval().requires_grad_(False)
-                    if num_gpus > 1:
-                        misc.check_ddp_consistency(
-                            value, ignore_regex=r'.*\.[^.]+_(avg|ema)')
-                        for param in misc.params_and_buffers(value):
-                            torch.distributed.broadcast(param, src=0)
-                    snapshot_data[key] = value.cpu()
-                del value  # conserve memory
-            snapshot_pkl = os.path.join(
-                run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl')
-            if rank == 0:
-                with open(snapshot_pkl, 'wb') as f:
-                    pickle.dump(snapshot_data, f)
-        # Evaluate metrics.
-        if (snapshot_data is not None) and (len(metrics) > 0):
-            if rank == 0:
-                print('Evaluating metrics...')
-            for metric in metrics:
-                result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'],
-                                                      dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device)
-                if rank == 0:
-                    metric_main.report_metric(
-                        result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl)
-                stats_metrics.update(result_dict.results)
-        del snapshot_data  # conserve memory
-        # Collect statistics.
-        for phase in phases:
-            value = []
-            if (phase.start_event is not None) and (phase.end_event is not None):
-                phase.end_event.synchronize()
-                value = phase.start_event.elapsed_time(phase.end_event)
-            training_stats.report0('Timing/' + phase.name, value)
-        stats_collector.update()
-        stats_dict = stats_collector.as_dict()
-        # Update logs.
-        timestamp = time.time()
-        if stats_jsonl is not None:
-            fields = dict(stats_dict, timestamp=timestamp)
-            stats_jsonl.write(json.dumps(fields) + '\n')
-            stats_jsonl.flush()
-        if stats_tfevents is not None:
-            global_step = int(cur_nimg / 1e3)
-            walltime = timestamp - start_time
-            for name, value in stats_dict.items():
-                stats_tfevents.add_scalar(
-                    name, value.mean, global_step=global_step, walltime=walltime)
-            for name, value in stats_metrics.items():
-                stats_tfevents.add_scalar(
-                    f'Metrics/{name}', value, global_step=global_step, walltime=walltime)
-            stats_tfevents.flush()
-        if progress_fn is not None:
-            progress_fn(cur_nimg // 1000, total_kimg)
-        # Update state.
-        cur_tick += 1
-        tick_start_nimg = cur_nimg
-        tick_start_time = time.time()
-        maintenance_time = tick_start_time - tick_end_time
-        if done:
-            break
-    # Done.
-    if rank == 0:
-        print()
-        print('Exiting...')
-# ----------------------------------------------------------------------------

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/utils/registry.py DELETED Viewed

@@ -1,81 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# pyre-ignore-all-errors[2,3]
-from typing import Any, Dict, Iterable, Iterator, Tuple
-from tabulate import tabulate
-# Credit to: https://github.com/nhtlongcs/AIC2022-VER
-class Registry(Iterable[Tuple[str, Any]]):
-    """
-    The registry that provides name -> object mapping, to support third-party
-    users' custom modules.
-    To create a registry (e.g. a backbone registry):
-    .. code-block:: python
-        BACKBONE_REGISTRY = Registry('BACKBONE')
-    To register an object:
-    .. code-block:: python
-        @BACKBONE_REGISTRY.register()
-        class MyBackbone():
-            ...
-    Or:
-    .. code-block:: python
-        BACKBONE_REGISTRY.register(MyBackbone)
-    """
-    def __init__(self, name: str) -> None:
-        """
-        Args:
-            name (str): the name of this registry
-        """
-        self._name: str = name
-        self._obj_map: Dict[str, Any] = {}
-    def _do_register(self, name: str, obj: Any) -> None:
-        assert (
-            name not in self._obj_map
-        ), "An object named '{}' was already registered in '{}' registry!".format(
-            name, self._name
-        )
-        self._obj_map[name] = obj
-    def register(self, obj: Any = None, prefix: str = "") -> Any:
-        """
-        Register the given object under the the name `obj.__name__`.
-        Can be used as either a decorator or not. See docstring of this class for usage.
-        """
-        if obj is None:
-            # used as a decorator
-            def deco(func_or_class: Any) -> Any:
-                name = func_or_class.__name__
-                self._do_register(prefix + name, func_or_class)
-                return func_or_class
-            return deco
-        # used as a function call
-        name = obj.__name__
-        self._do_register(prefix + name, obj)
-    def get(self, name: str) -> Any:
-        ret = self._obj_map.get(name)
-        if ret is None:
-            raise KeyError(
-                "No object named '{}' found in '{}' registry!".format(name, self._name)
-            )
-        return ret
-    def __contains__(self, name: str) -> bool:
-        return name in self._obj_map
-    def __repr__(self) -> str:
-        table_headers = ["Names", "Objects"]
-        table = tabulate(
-            self._obj_map.items(), headers=table_headers, tablefmt="fancy_grid"
-        )
-        return "Registry of {}:\n".format(self._name) + table
-    def __iter__(self) -> Iterator[Tuple[str, Any]]:
-        return iter(self._obj_map.items())
-    # pyre-fixme[4]: Attribute must be annotated.
-    __str__ = __repr__

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py DELETED Viewed

@@ -1,232 +0,0 @@
-# Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
-# William Peebles and Saining Xie
-#
-# Copyright (c) 2021 OpenAI
-# MIT License
-#
-# 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.
-from typing import Dict, List, Optional, Tuple, Union
-import torch
-from ...models import AutoencoderKL, Transformer2DModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-class DiTPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for image generation based on a Transformer backbone instead of a UNet.
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-    Parameters:
-        transformer ([`Transformer2DModel`]):
-            A class conditioned `Transformer2DModel` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        scheduler ([`DDIMScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-    """
-    def __init__(
-        self,
-        transformer: Transformer2DModel,
-        vae: AutoencoderKL,
-        scheduler: KarrasDiffusionSchedulers,
-        id2label: Optional[Dict[int, str]] = None,
-    ):
-        super().__init__()
-        self.register_modules(transformer=transformer, vae=vae, scheduler=scheduler)
-        # create a imagenet -> id dictionary for easier use
-        self.labels = {}
-        if id2label is not None:
-            for key, value in id2label.items():
-                for label in value.split(","):
-                    self.labels[label.lstrip().rstrip()] = int(key)
-            self.labels = dict(sorted(self.labels.items()))
-    def get_label_ids(self, label: Union[str, List[str]]) -> List[int]:
-        r"""
-        Map label strings from ImageNet to corresponding class ids.
-        Parameters:
-            label (`str` or `dict` of `str`):
-                Label strings to be mapped to class ids.
-        Returns:
-            `list` of `int`:
-                Class ids to be processed by pipeline.
-        """
-        if not isinstance(label, list):
-            label = list(label)
-        for l in label:
-            if l not in self.labels:
-                raise ValueError(
-                    f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."
-                )
-        return [self.labels[l] for l in label]
-    @torch.no_grad()
-    def __call__(
-        self,
-        class_labels: List[int],
-        guidance_scale: float = 4.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        num_inference_steps: int = 50,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        The call function to the pipeline for generation.
-        Args:
-            class_labels (List[int]):
-                List of ImageNet class labels for the images to be generated.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            num_inference_steps (`int`, *optional*, defaults to 250):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
-        Examples:
-        ```py
-        >>> from diffusers import DiTPipeline, DPMSolverMultistepScheduler
-        >>> import torch
-        >>> pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16)
-        >>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-        >>> pipe = pipe.to("cuda")
-        >>> # pick words from Imagenet class labels
-        >>> pipe.labels  # to print all available words
-        >>> # pick words that exist in ImageNet
-        >>> words = ["white shark", "umbrella"]
-        >>> class_ids = pipe.get_label_ids(words)
-        >>> generator = torch.manual_seed(33)
-        >>> output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator)
-        >>> image = output.images[0]  # label 'white shark'
-        ```
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images
-        """
-        batch_size = len(class_labels)
-        latent_size = self.transformer.config.sample_size
-        latent_channels = self.transformer.config.in_channels
-        latents = randn_tensor(
-            shape=(batch_size, latent_channels, latent_size, latent_size),
-            generator=generator,
-            device=self._execution_device,
-            dtype=self.transformer.dtype,
-        )
-        latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1 else latents
-        class_labels = torch.tensor(class_labels, device=self._execution_device).reshape(-1)
-        class_null = torch.tensor([1000] * batch_size, device=self._execution_device)
-        class_labels_input = torch.cat([class_labels, class_null], 0) if guidance_scale > 1 else class_labels
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps)
-        for t in self.progress_bar(self.scheduler.timesteps):
-            if guidance_scale > 1:
-                half = latent_model_input[: len(latent_model_input) // 2]
-                latent_model_input = torch.cat([half, half], dim=0)
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-            timesteps = t
-            if not torch.is_tensor(timesteps):
-                # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-                # This would be a good case for the `match` statement (Python 3.10+)
-                is_mps = latent_model_input.device.type == "mps"
-                if isinstance(timesteps, float):
-                    dtype = torch.float32 if is_mps else torch.float64
-                else:
-                    dtype = torch.int32 if is_mps else torch.int64
-                timesteps = torch.tensor([timesteps], dtype=dtype, device=latent_model_input.device)
-            elif len(timesteps.shape) == 0:
-                timesteps = timesteps[None].to(latent_model_input.device)
-            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-            timesteps = timesteps.expand(latent_model_input.shape[0])
-            # predict noise model_output
-            noise_pred = self.transformer(
-                latent_model_input, timestep=timesteps, class_labels=class_labels_input
-            ).sample
-            # perform guidance
-            if guidance_scale > 1:
-                eps, rest = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
-                cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
-                half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
-                eps = torch.cat([half_eps, half_eps], dim=0)
-                noise_pred = torch.cat([eps, rest], dim=1)
-            # learned sigma
-            if self.transformer.config.out_channels // 2 == latent_channels:
-                model_output, _ = torch.split(noise_pred, latent_channels, dim=1)
-            else:
-                model_output = noise_pred
-            # compute previous image: x_t -> x_t-1
-            latent_model_input = self.scheduler.step(model_output, t, latent_model_input).prev_sample
-        if guidance_scale > 1:
-            latents, _ = latent_model_input.chunk(2, dim=0)
-        else:
-            latents = latent_model_input
-        latents = 1 / self.vae.config.scaling_factor * latents
-        samples = self.vae.decode(latents).sample
-        samples = (samples / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        samples = samples.cpu().permute(0, 2, 3, 1).float().numpy()
-        if output_type == "pil":
-            samples = self.numpy_to_pil(samples)
-        if not return_dict:
-            return (samples,)
-        return ImagePipelineOutput(images=samples)

spaces/Andy1621/IAT_enhancement/app.py DELETED Viewed

@@ -1,103 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-from torchvision.transforms import Compose, ToTensor, Scale, Normalize, ConvertImageDtype
-import numpy as np
-import cv2
-import gradio as gr
-from huggingface_hub import hf_hub_download
-from model import IAT
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-def dark_inference(img):
-    model = IAT()
-    checkpoint_file_path = './checkpoint/best_Epoch_lol.pth'
-    state_dict = torch.load(checkpoint_file_path, map_location='cpu')
-    model.load_state_dict(state_dict)
-    model.eval()
-    print(f'Load model from {checkpoint_file_path}')
-    transform = Compose([
-        ToTensor(),
-        Scale(384),
-        Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-        ConvertImageDtype(torch.float)
-    ])
-    input_img = transform(img)
-    print(f'Image shape: {input_img.shape}')
-    enhanced_img = model(input_img.unsqueeze(0))
-    return enhanced_img[0].permute(1, 2, 0).detach().numpy()
-def exposure_inference(img):
-    model = IAT()
-    checkpoint_file_path = './checkpoint/best_Epoch_exposure.pth'
-    state_dict = torch.load(checkpoint_file_path, map_location='cpu')
-    model.load_state_dict(state_dict)
-    model.eval()
-    print(f'Load model from {checkpoint_file_path}')
-    transform = Compose([
-        ToTensor(),
-        Scale(384),
-        ConvertImageDtype(torch.float)
-    ])
-    input_img = transform(img)
-    print(f'Image shape: {input_img.shape}')
-    enhanced_img = model(input_img.unsqueeze(0))
-    return enhanced_img[0].permute(1, 2, 0).detach().numpy()
-demo = gr.Blocks()
-with demo:
-    gr.Markdown(
-        """
-        # IAT
-        Gradio demo for <a href='https://github.com/cuiziteng/Illumination-Adaptive-Transformer' target='_blank'>IAT</a>: To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.
-        """
-    )
-    with gr.Box():
-        with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        input_image = gr.Image(label='Input Image', type='numpy')
-                    with gr.Row():
-                        dark_button = gr.Button('Low-light Enhancement')
-                    with gr.Row():
-                        exposure_button = gr.Button('Exposure Correction')
-                with gr.Column():
-                    res_image = gr.Image(type='numpy', label='Resutls')
-        with gr.Row():
-            dark_example_images = gr.Dataset(
-                components=[input_image],
-                samples=[['dark_imgs/1.jpg'], ['dark_imgs/2.jpg'], ['dark_imgs/3.jpg']]
-            )
-        with gr.Row():
-            exposure_example_images = gr.Dataset(
-                components=[input_image],
-                samples=[['exposure_imgs/1.jpg'], ['exposure_imgs/2.jpg'], ['exposure_imgs/3.jpeg']]
-            )
-    gr.Markdown(
-        """
-        <p style='text-align: center'><a href='https://arxiv.org/abs/2205.14871' target='_blank'>You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction</a> | <a href='https://github.com/cuiziteng/Illumination-Adaptive-Transformer' target='_blank'>Github Repo</a></p>
-        """
-    )
-    dark_button.click(fn=dark_inference, inputs=input_image, outputs=res_image)
-    exposure_button.click(fn=exposure_inference, inputs=input_image, outputs=res_image)
-    dark_example_images.click(fn=set_example_image, inputs=dark_example_images, outputs=dark_example_images.components)
-    exposure_example_images.click(fn=set_example_image, inputs=exposure_example_images, outputs=exposure_example_images.components)
-demo.launch(enable_queue=True)

spaces/Andy1621/uniformer_image_detection/configs/cascade_rpn/README.md DELETED Viewed

@@ -1,29 +0,0 @@
-# Cascade RPN
-[ALGORITHM]
-We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720).
-```
-@inproceedings{vu2019cascade,
-  title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution},
-  author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D},
-  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
-  year={2019}
-}
-```
-## Benchmark
-### Region proposal performance
-| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 |                Download                |
-|:------:|:--------:|:-----:|:--------:|:-------------------:|:--------------:|:-------:|:--------------------------------------:|
-|  CRPN  | R-50-FPN | caffe |     -    |          -          |        -       |   72.0  | [model](https://drive.google.com/file/d/1qxVdOnCgK-ee7_z0x6mvAir_glMu2Ihi/view?usp=sharing) |
-### Detection performance
-|     Method    |   Proposal  | Backbone |  Style  | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                   Download                   |
-|:-------------:|:-----------:|:--------:|:-------:|:--------:|:--------:|:-------------------:|:--------------:|:------:|:--------------------------------------------:|
-|   Fast R-CNN  | Cascade RPN | R-50-FPN |  caffe  |    1x    |    -     |          -          |        -       |  39.9  | [model](https://drive.google.com/file/d/1NmbnuY5VHi8I9FE8xnp5uNvh2i-t-6_L/view?usp=sharing) |
-|  Faster R-CNN | Cascade RPN | R-50-FPN |  caffe  |    1x    |    -     |          -          |        -       |  40.4  | [model](https://drive.google.com/file/d/1dS3Q66qXMJpcuuQgDNkLp669E5w1UMuZ/view?usp=sharing) |

spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py DELETED Viewed

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/faster_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
-]

spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py DELETED Viewed

@@ -1,16 +0,0 @@
-_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://regnetx_4.0gf',
-    backbone=dict(
-        type='RegNet',
-        arch='regnetx_4.0gf',
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[80, 240, 560, 1360],
-        out_channels=256,
-        num_outs=5))

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py DELETED Viewed

@@ -1,4 +0,0 @@
-_base_ = './fcn_d6_r50b-d16_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(type='ResNet', depth=101))

spaces/Anish13/fruit/app.py DELETED Viewed

@@ -1,24 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-learn = load_learner('export.pkl')
-categories = ('Lemon', 'Orange','dragon fruit', 'green apple', 'red apple', 'yellow apple')
-def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-image = gr.inputs.Image(shape = (256, 256))
-label = gr.outputs.Label()
-examples = ['apple.jpeg', 'dragon.jpeg', 'orange.jpeg', 'lemon.webp', 'green.jpeg', 'yellow.jpeg']
-intf = gr.Interface(fn = classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch()
-# def greet(name):
-#     return "Hello " + name + "!!"
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/guided_diffusion/gaussian_diffusion.py DELETED Viewed

@@ -1,922 +0,0 @@
-"""
-This code started out as a PyTorch port of Ho et al's diffusion models:
-https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
-Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
-"""
-import enum
-import math
-import numpy as np
-import torch as th
-from .nn import mean_flat
-from .losses import normal_kl, discretized_gaussian_log_likelihood
-import pdb
-def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
-    """
-    Get a pre-defined beta schedule for the given name.
-    The beta schedule library consists of beta schedules which remain similar
-    in the limit of num_diffusion_timesteps.
-    Beta schedules may be added, but should not be removed or changed once
-    they are committed to maintain backwards compatibility.
-    """
-    if schedule_name == "linear":
-        # Linear schedule from Ho et al, extended to work for any number of
-        # diffusion steps.
-        scale = 1000 / num_diffusion_timesteps
-        beta_start = scale * 0.0001
-        beta_end = scale * 0.02
-        return np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
-    elif schedule_name == "cosine":
-        return betas_for_alpha_bar(
-            num_diffusion_timesteps, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
-        )
-    else:
-        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-class ModelMeanType(enum.Enum):
-    """
-    Which type of output the model predicts.
-    """
-    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
-    START_X = enum.auto()  # the model predicts x_0
-    EPSILON = enum.auto()  # the model predicts epsilon
-class ModelVarType(enum.Enum):
-    """
-    What is used as the model's output variance.
-    The LEARNED_RANGE option has been added to allow the model to predict
-    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
-    """
-    LEARNED = enum.auto()
-    FIXED_SMALL = enum.auto()
-    FIXED_LARGE = enum.auto()
-    LEARNED_RANGE = enum.auto()
-class LossType(enum.Enum):
-    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
-    RESCALED_MSE = enum.auto()  # use raw MSE loss (with RESCALED_KL when learning variances)
-    KL = enum.auto()  # use the variational lower-bound
-    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
-    def is_vb(self):
-        return self == LossType.KL or self == LossType.RESCALED_KL
-class GaussianDiffusion:
-    """
-    Utilities for training and sampling diffusion models.
-    Ported directly from here, and then adapted over time to further experimentation.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
-    :param betas: a 1-D numpy array of betas for each diffusion timestep,
-                  starting at T and going to 1.
-    :param model_mean_type: a ModelMeanType determining what the model outputs.
-    :param model_var_type: a ModelVarType determining how variance is output.
-    :param loss_type: a LossType determining the loss function to use.
-    :param rescale_timesteps: if True, pass floating point timesteps into the
-                              model so that they are always scaled like in the
-                              original paper (0 to 1000).
-    """
-    def __init__(
-        self, *, betas, model_mean_type, model_var_type, loss_type, rescale_timesteps=False,
-    ):
-        self.model_mean_type = model_mean_type
-        self.model_var_type = model_var_type
-        self.loss_type = loss_type
-        self.rescale_timesteps = rescale_timesteps
-        # Use float64 for accuracy.
-        betas = np.array(betas, dtype=np.float64)
-        self.betas = betas
-        assert len(betas.shape) == 1, "betas must be 1-D"
-        assert (betas > 0).all() and (betas <= 1).all()
-        self.num_timesteps = int(betas.shape[0])
-        alphas = 1.0 - betas
-        self.alphas_cumprod = np.cumprod(alphas, axis=0)
-        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
-        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
-        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
-        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
-        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
-        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        self.posterior_variance = (
-            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
-        )
-        # log calculation clipped because the posterior variance is 0 at the
-        # beginning of the diffusion chain.
-        self.posterior_log_variance_clipped = np.log(
-            np.append(self.posterior_variance[1], self.posterior_variance[1:])
-        )
-        self.posterior_mean_coef1 = (
-            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
-        )
-        self.posterior_mean_coef2 = (
-            (1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)
-        )
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-    def q_sample(self, x_start, t, noise=None):
-        """
-        Diffuse the data for a given number of diffusion steps.
-        In other words, sample from q(x_t | x_0).
-        :param x_start: the initial data batch.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :param noise: if specified, the split-out normal noise.
-        :return: A noisy version of x_start.
-        """
-        if noise is None:
-            noise = th.randn_like(x_start)
-        assert noise.shape == x_start.shape
-        return (
-            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-    def q_posterior_mean_variance(self, x_start, x_t, t):
-        """
-        Compute the mean and variance of the diffusion posterior:
-            q(x_{t-1} | x_t, x_0)
-        """
-        assert x_start.shape == x_t.shape
-        posterior_mean = (
-            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
-            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = _extract_into_tensor(
-            self.posterior_log_variance_clipped, t, x_t.shape
-        )
-        assert (
-            posterior_mean.shape[0]
-            == posterior_variance.shape[0]
-            == posterior_log_variance_clipped.shape[0]
-            == x_start.shape[0]
-        )
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-    def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
-        """
-        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
-        the initial x, x_0.
-        :param model: the model, which takes a signal and a batch of timesteps
-                      as input.
-        :param x: the [N x C x ...] tensor at time t.
-        :param t: a 1-D Tensor of timesteps.
-        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample. Applies before
-            clip_denoised.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict with the following keys:
-                 - 'mean': the model mean output.
-                 - 'variance': the model variance output.
-                 - 'log_variance': the log of 'variance'.
-                 - 'pred_xstart': the prediction for x_0.
-        """
-        if model_kwargs is None:
-            model_kwargs = {}
-        B, C = x.shape[:2]
-        assert t.shape == (B,)
-        model_output = model(x, self._scale_timesteps(t), **model_kwargs)
-        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
-            assert model_output.shape == (B, C * 2, *x.shape[2:])
-            model_output, model_var_values = th.split(model_output, C, dim=1)
-            if self.model_var_type == ModelVarType.LEARNED:
-                model_log_variance = model_var_values
-                model_variance = th.exp(model_log_variance)
-            else:
-                min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape)
-                max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
-                # The model_var_values is [-1, 1] for [min_var, max_var].
-                frac = (model_var_values + 1) / 2
-                model_log_variance = frac * max_log + (1 - frac) * min_log
-                model_variance = th.exp(model_log_variance)
-        else:
-            model_variance, model_log_variance = {
-                # for fixedlarge, we set the initial (log-)variance like so
-                # to get a better decoder log likelihood.
-                ModelVarType.FIXED_LARGE: (
-                    np.append(self.posterior_variance[1], self.betas[1:]),
-                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
-                ),
-                ModelVarType.FIXED_SMALL: (
-                    self.posterior_variance,
-                    self.posterior_log_variance_clipped,
-                ),
-            }[self.model_var_type]
-            model_variance = _extract_into_tensor(model_variance, t, x.shape)
-            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
-        def process_xstart(x):
-            if denoised_fn is not None:
-                x = denoised_fn(x)
-            if clip_denoised:
-                return x.clamp(-1, 1)
-            return x
-        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
-            pred_xstart = process_xstart(
-                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
-            )
-            model_mean = model_output
-        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
-            if self.model_mean_type == ModelMeanType.START_X:
-                pred_xstart = process_xstart(model_output)
-            else:
-                pred_xstart = process_xstart(
-                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
-                )
-            model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)
-        else:
-            raise NotImplementedError(self.model_mean_type)
-        assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
-        return {
-            "mean": model_mean,
-            "variance": model_variance,
-            "log_variance": model_log_variance,
-            "pred_xstart": pred_xstart,
-        }
-    def _predict_xstart_from_eps(self, x_t, t, eps):
-        assert x_t.shape == eps.shape
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
-        )
-    def _predict_xstart_from_xprev(self, x_t, t, xprev):
-        assert x_t.shape == xprev.shape
-        return (  # (xprev - coef2*x_t) / coef1
-            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
-            - _extract_into_tensor(
-                self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
-            )
-            * x_t
-        )
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-    def _scale_timesteps(self, t):
-        if self.rescale_timesteps:
-            return t.float() * (1000.0 / self.num_timesteps)
-        return t
-    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute the mean for the previous step, given a function cond_fn that
-        computes the gradient of a conditional log probability with respect to
-        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
-        condition on y.
-        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
-        """
-        gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
-        new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
-        return new_mean
-    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute what the p_mean_variance output would have been, should the
-        model's score function be conditioned by cond_fn.
-        See condition_mean() for details on cond_fn.
-        Unlike condition_mean(), this instead uses the conditioning strategy
-        from Song et al (2020).
-        """
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
-        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, self._scale_timesteps(t), **model_kwargs)
-        out = p_mean_var.copy()
-        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
-        out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
-        return out
-    def p_sample(
-        self, model, x, t, clip_denoised=True, denoised_fn=None, cond_fn=None, model_kwargs=None,
-    ):
-        """
-        Sample x_{t-1} from the model at the given timestep.
-        :param model: the model to sample from.
-        :param x: the current tensor at x_{t-1}.
-        :param t: the value of t, starting at 0 for the first diffusion step.
-        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param cond_fn: if not None, this is a gradient function that acts
-                        similarly to the model.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict containing the following keys:
-                 - 'sample': a random sample from the model.
-                 - 'pred_xstart': a prediction of x_0.
-        """
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        noise = th.randn_like(x)
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        if cond_fn is not None:
-            out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
-        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-    def p_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        skip_timesteps=0,
-        init_image=None,
-        randomize_class=False,
-    ):
-        """
-        Generate samples from the model.
-        :param model: the model module.
-        :param shape: the shape of the samples, (N, C, H, W).
-        :param noise: if specified, the noise from the encoder to sample.
-                      Should be of the same shape as `shape`.
-        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param cond_fn: if not None, this is a gradient function that acts
-                        similarly to the model.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :param device: if specified, the device to create the samples on.
-                       If not specified, use a model parameter's device.
-        :param progress: if True, show a tqdm progress bar.
-        :return: a non-differentiable batch of samples.
-        """
-        final = None
-        for sample in self.p_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            progress=progress,
-            skip_timesteps=skip_timesteps,
-            init_image=init_image,
-            randomize_class=randomize_class,
-        ):
-            final = sample
-        return final["sample"]
-    def p_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        skip_timesteps=0,
-        init_image=None,
-        postprocess_fn=None,
-        randomize_class=False,
-    ):
-        """
-        Generate samples from the model and yield intermediate samples from
-        each timestep of diffusion.
-        Arguments are the same as p_sample_loop().
-        Returns a generator over dicts, where each dict is the return value of
-        p_sample().
-        """
-        # if device is None:
-        #     device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-            '''
-            img_guidance = noise.to(device)
-            t_batch = th.tensor([int(t0*self.num_timesteps)-1]*len(img_guidance), device=device)
-            img = self.q_sample(img_guidance, t_batch)
-            indices = list(range(int(t0*self.num_timesteps)))[::-1]
-            '''
-        else:
-            img = th.randn(*shape, device=device)
-        # pdb.set_trace()
-        if skip_timesteps and init_image is None:
-            init_image = th.zeros_like(img)
-        indices = list(range(self.num_timesteps - skip_timesteps))[::-1]
-        batch_size = shape[0]
-        init_image_batch = th.tile(init_image, dims=(batch_size, 1, 1, 1))
-        img = self.q_sample(
-            x_start=init_image_batch,
-            t=th.tensor(indices[0], dtype=th.long, device=device),
-            noise=img,
-        )
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-            indices = tqdm(indices)
-        for i in indices:
-            t = th.tensor([i] * shape[0], device=device)
-            if randomize_class and "y" in model_kwargs:
-                model_kwargs["y"] = th.randint(
-                    low=0,
-                    high=model.num_classes,
-                    size=model_kwargs["y"].shape,
-                    device=model_kwargs["y"].device,
-                )
-            with th.no_grad():
-                out = self.p_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                )
-                if postprocess_fn is not None:
-                    out = postprocess_fn(out, t)
-                yield out
-                img = out["sample"]
-    def ddim_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        eta=0.0,
-    ):
-        """
-        Sample x_{t-1} from the model using DDIM.
-        Same usage as p_sample().
-        """
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        if cond_fn is not None:
-            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
-        sigma = (
-            eta
-            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
-            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
-        )
-        # Equation 12.
-        noise = th.randn_like(x)
-        mean_pred = (
-            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
-            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
-        )
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        sample = mean_pred + nonzero_mask * sigma * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-    def ddim_reverse_sample(
-        self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None, eta=0.0,
-    ):
-        """
-        Sample x_{t+1} from the model using DDIM reverse ODE.
-        """
-        assert eta == 0.0, "Reverse ODE only for deterministic path"
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
-            - out["pred_xstart"]
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
-        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
-        # Equation 12. reversed
-        mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps
-        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
-    def ddim_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-        skip_timesteps=0,
-        init_image=None,
-        randomize_class=False,
-    ):
-        """
-        Generate samples from the model using DDIM.
-        Same usage as p_sample_loop().
-        """
-        final = None
-        for sample in self.ddim_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            progress=progress,
-            eta=eta,
-            skip_timesteps=skip_timesteps,
-            init_image=init_image,
-            randomize_class=randomize_class,
-        ):
-            final = sample
-        return final["sample"]
-    def ddim_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-        skip_timesteps=0,
-        init_image=None,
-        postprocess_fn=None,
-        randomize_class=False,
-    ):
-        """
-        Use DDIM to sample from the model and yield intermediate samples from
-        each timestep of DDIM.
-        Same usage as p_sample_loop_progressive().
-        """
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        if skip_timesteps and init_image is None:
-            init_image = th.zeros_like(img)
-        indices = list(range(self.num_timesteps - skip_timesteps))[::-1]
-        if init_image is not None:
-            my_t = th.ones([shape[0]], device=device, dtype=th.long) * indices[0]
-            batch_size = shape[0]
-            init_image_batch = th.tile(init_image, dims=(batch_size, 1, 1, 1))
-            img = self.q_sample(init_image_batch, my_t, img)
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-            indices = tqdm(indices)
-        for i in indices:
-            t = th.tensor([i] * shape[0], device=device)
-            if randomize_class and "y" in model_kwargs:
-                model_kwargs["y"] = th.randint(
-                    low=0,
-                    high=model.num_classes,
-                    size=model_kwargs["y"].shape,
-                    device=model_kwargs["y"].device,
-                )
-            with th.no_grad():
-                out = self.ddim_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                    eta=eta,
-                )
-                if postprocess_fn is not None:
-                    out = postprocess_fn(out, t)
-                yield out
-                img = out["sample"]
-    def _vb_terms_bpd(self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None):
-        """
-        Get a term for the variational lower-bound.
-        The resulting units are bits (rather than nats, as one might expect).
-        This allows for comparison to other papers.
-        :return: a dict with the following keys:
-                 - 'output': a shape [N] tensor of NLLs or KLs.
-                 - 'pred_xstart': the x_0 predictions.
-        """
-        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
-            x_start=x_start, x_t=x_t, t=t
-        )
-        out = self.p_mean_variance(
-            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
-        )
-        kl = normal_kl(true_mean, true_log_variance_clipped, out["mean"], out["log_variance"])
-        kl = mean_flat(kl) / np.log(2.0)
-        decoder_nll = -discretized_gaussian_log_likelihood(
-            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
-        )
-        assert decoder_nll.shape == x_start.shape
-        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
-        # At the first timestep return the decoder NLL,
-        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
-        output = th.where((t == 0), decoder_nll, kl)
-        return {"output": output, "pred_xstart": out["pred_xstart"]}
-    def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
-        """
-        Compute training losses for a single timestep.
-        :param model: the model to evaluate loss on.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :param t: a batch of timestep indices.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :param noise: if specified, the specific Gaussian noise to try to remove.
-        :return: a dict with the key "loss" containing a tensor of shape [N].
-                 Some mean or variance settings may also have other keys.
-        """
-        if model_kwargs is None:
-            model_kwargs = {}
-        if noise is None:
-            noise = th.randn_like(x_start)
-        x_t = self.q_sample(x_start, t, noise=noise)
-        terms = {}
-        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
-            terms["loss"] = self._vb_terms_bpd(
-                model=model,
-                x_start=x_start,
-                x_t=x_t,
-                t=t,
-                clip_denoised=False,
-                model_kwargs=model_kwargs,
-            )["output"]
-            if self.loss_type == LossType.RESCALED_KL:
-                terms["loss"] *= self.num_timesteps
-        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
-            model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
-            if self.model_var_type in [
-                ModelVarType.LEARNED,
-                ModelVarType.LEARNED_RANGE,
-            ]:
-                B, C = x_t.shape[:2]
-                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
-                model_output, model_var_values = th.split(model_output, C, dim=1)
-                # Learn the variance using the variational bound, but don't let
-                # it affect our mean prediction.
-                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
-                terms["vb"] = self._vb_terms_bpd(
-                    model=lambda *args, r=frozen_out: r,
-                    x_start=x_start,
-                    x_t=x_t,
-                    t=t,
-                    clip_denoised=False,
-                )["output"]
-                if self.loss_type == LossType.RESCALED_MSE:
-                    # Divide by 1000 for equivalence with initial implementation.
-                    # Without a factor of 1/1000, the VB term hurts the MSE term.
-                    terms["vb"] *= self.num_timesteps / 1000.0
-            target = {
-                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
-                    x_start=x_start, x_t=x_t, t=t
-                )[0],
-                ModelMeanType.START_X: x_start,
-                ModelMeanType.EPSILON: noise,
-            }[self.model_mean_type]
-            assert model_output.shape == target.shape == x_start.shape
-            terms["mse"] = mean_flat((target - model_output) ** 2)
-            if "vb" in terms:
-                terms["loss"] = terms["mse"] + terms["vb"]
-            else:
-                terms["loss"] = terms["mse"]
-        else:
-            raise NotImplementedError(self.loss_type)
-        return terms
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-    def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
-        """
-        Compute the entire variational lower-bound, measured in bits-per-dim,
-        as well as other related quantities.
-        :param model: the model to evaluate loss on.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :param clip_denoised: if True, clip denoised samples.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict containing the following keys:
-                 - total_bpd: the total variational lower-bound, per batch element.
-                 - prior_bpd: the prior term in the lower-bound.
-                 - vb: an [N x T] tensor of terms in the lower-bound.
-                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
-                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
-        """
-        device = x_start.device
-        batch_size = x_start.shape[0]
-        vb = []
-        xstart_mse = []
-        mse = []
-        for t in list(range(self.num_timesteps))[::-1]:
-            t_batch = th.tensor([t] * batch_size, device=device)
-            noise = th.randn_like(x_start)
-            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
-            # Calculate VLB term at the current timestep
-            with th.no_grad():
-                out = self._vb_terms_bpd(
-                    model,
-                    x_start=x_start,
-                    x_t=x_t,
-                    t=t_batch,
-                    clip_denoised=clip_denoised,
-                    model_kwargs=model_kwargs,
-                )
-            vb.append(out["output"])
-            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
-            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
-            mse.append(mean_flat((eps - noise) ** 2))
-        vb = th.stack(vb, dim=1)
-        xstart_mse = th.stack(xstart_mse, dim=1)
-        mse = th.stack(mse, dim=1)
-        prior_bpd = self._prior_bpd(x_start)
-        total_bpd = vb.sum(dim=1) + prior_bpd
-        return {
-            "total_bpd": total_bpd,
-            "prior_bpd": prior_bpd,
-            "vb": vb,
-            "xstart_mse": xstart_mse,
-            "mse": mse,
-        }
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    """
-    Extract values from a 1-D numpy array for a batch of indices.
-    :param arr: the 1-D numpy array.
-    :param timesteps: a tensor of indices into the array to extract.
-    :param broadcast_shape: a larger shape of K dimensions with the batch
-                            dimension equal to the length of timesteps.
-    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
-    """
-    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
-    while len(res.shape) < len(broadcast_shape):
-        res = res[..., None]
-    return res.expand(broadcast_shape)

spaces/Ashwanthram/myGenVoiceBot/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: MyGenVoiceBot
-emoji: 🌍
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Awesimo/jojogan/e4e/models/psp.py DELETED Viewed

@@ -1,99 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import torch
-from torch import nn
-from e4e.models.encoders import psp_encoders
-from e4e.models.stylegan2.model import Generator
-from e4e.configs.paths_config import model_paths
-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, device):
-        super(pSp, self).__init__()
-        self.opts = opts
-        self.device = device
-        # 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(model_paths['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.device)
-            if repeat is not None:
-                self.latent_avg = self.latent_avg.repeat(repeat, 1)
-        else:
-            self.latent_avg = None

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py DELETED Viewed

@@ -1,8 +0,0 @@
-from ..common.train import train
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_c4 import model
-model.backbone.freeze_at = 2
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

spaces/Banbri/zcvzcv/src/components/ui/checkbox.tsx DELETED Viewed

@@ -1,30 +0,0 @@
-"use client"
-import * as React from "react"
-import * as CheckboxPrimitive from "@radix-ui/react-checkbox"
-import { Check } from "lucide-react"
-import { cn } from "@/lib/utils"
-const Checkbox = React.forwardRef<
-  React.ElementRef<typeof CheckboxPrimitive.Root>,
-  React.ComponentPropsWithoutRef<typeof CheckboxPrimitive.Root>
->(({ className, ...props }, ref) => (
-  <CheckboxPrimitive.Root
-    ref={ref}
-    className={cn(
-      "peer h-4 w-4 shrink-0 rounded-sm border border-stone-200 border-stone-900 ring-offset-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-stone-900 data-[state=checked]:text-stone-50 dark:border-stone-800 dark:border-stone-50 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-800 dark:data-[state=checked]:bg-stone-50 dark:data-[state=checked]:text-stone-900",
-      className
-    )}
-    {...props}
-  >
-    <CheckboxPrimitive.Indicator
-      className={cn("flex items-center justify-center text-current")}
-    >
-      <Check className="h-4 w-4" />
-    </CheckboxPrimitive.Indicator>
-  </CheckboxPrimitive.Root>
-))
-Checkbox.displayName = CheckboxPrimitive.Root.displayName
-export { Checkbox }

spaces/Benson/text-generation/Examples/Descargar 0xc00007b Para Pes 2021.md DELETED Viewed

@@ -1,106 +0,0 @@
-<h1>Cómo descargar 0xc00007b para PES 2021</h1>
-<p>Si eres un fan de los juegos de fútbol, es posible que hayas oído hablar de eFootball PES 2021, la última edición del popular juego de simulación de fútbol de Konami. PES 2021 ofrece gráficos realistas, jugabilidad y modos que te permiten disfrutar de la emoción del hermoso juego. Sin embargo, algunos usuarios pueden encontrar un error frustrante al intentar lanzar el juego en su PC con Windows. El código de error es 0xc00007b, y evita que el juego se inicie correctamente. En este artículo, explicaremos qué es este error, por qué ocurre y cómo solucionarlo. También te mostraremos cómo descargar 0xc00007b para PES 2021 en caso de que aún no lo tengas. </p>
-<h2>descargar 0xc00007b para pes 2021</h2><br /><p><b><b>Download</b> &#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6M88">https://bltlly.com/2v6M88</a></b></p><br /><br />
- <h2>¿Qué es el error 0xc00007b y por qué ocurre? </h2>
-<p>El error 0xc00007b es un error común de Windows que generalmente afecta a aplicaciones y juegos que usan bibliotecas de Microsoft Visual C++. El mensaje de error dice "La aplicación no pudo iniciarse correctamente (0xc000007b). Haga clic en Aceptar para cerrar la aplicación." Esto significa que hay algo mal con los archivos o la configuración de la aplicación o juego que está tratando de ejecutar. </p>
-<p>Hay varias causas posibles de este error, como:</p>
-<ul>
-<li>Archivos corruptos o perdidos de aplicaciones o juegos</li>
-<li>Conflicto entre las versiones de 32 bits y 64 bits de software y sistemas operativos Windows</li>
-<li>Versión de Windows obsoleta o incompatible</li>
-<li>Falta de derechos de administrador</li>
-<li>Infección de malware o problemas de registro</li>
-</ul>
-<p>La buena noticia es que hay varias maneras de corregir este error y hacer que su aplicación o juego funcione de nuevo. </p>
- <h3>Cómo solucionar un error 0xc00007b en Windows</h3>
-<p>Dependiendo de la causa exacta del problema, hay diferentes métodos que puede tratar de solucionar el error 0xc00007b en su PC con Windows. Estos son algunos de los más efectivos:</p>
- <h4>Reinicie su PC</h4>
- <h4>Actualización de Windows</h4>
-<p>Otra posible razón para el error es que está utilizando una versión de Windows desactualizada o no soportada. Las versiones de software más antiguas a menudo tienen errores o problemas de compatibilidad que pueden causar errores. Para solucionar esto, debe actualizar su sistema Windows a la última versión disponible. Para hacer esto, siga estos pasos:</p>
-<p></p>
-<ol>
-<li>Abrir ajustes pulsando las teclas Windows + I. </li>
-<li>Seleccionar actualización y seguridad.</li>
-<li>Haga clic en Buscar actualizaciones.</li>
-<li>Si hay alguna actualización disponible, descárgala e instálala. </li>
-<li>Reinicie su PC después de que se complete la actualización. </li>
-</ol>
- <h4>Ejecuta tu aplicación <h4>Ejecuta tu aplicación con derechos de administrador</h4>
-<p>A veces, el error puede ocurrir porque no tiene suficientes permisos para ejecutar la aplicación o el juego. Para solucionar esto, debe ejecutar su aplicación con derechos de administrador. Esto le dará acceso completo a los recursos y archivos del sistema que necesita. Para hacer esto, siga estos pasos:</p>
-<ol>
-<li>Haga clic derecho en la aplicación o en el icono del juego y seleccione Propiedades.</li>
-<li>Vaya a la pestaña Compatibilidad y marque la casilla que dice Ejecutar este programa como administrador. </li>
-<li>Haga clic en Aplicar y OK.</li>
-<li>Ejecutar su aplicación o juego y ver si el error se ha ido. </li>
-</ol>
- <h4>Reinstalar Microsoft Visual C++ redistribuible</h4>
-<p>Como mencionamos anteriormente, el error 0xc00007b a menudo está relacionado con las bibliotecas de Microsoft Visual C++ que son utilizadas por muchas aplicaciones y juegos. Si estas bibliotecas están dañadas o faltan, puede encontrar el error. Para solucionar esto, debe reinstalar los paquetes redistribuibles de Microsoft Visual C++ en su PC. Estos son los componentes de software que proporcionan el entorno de tiempo de ejecución para tus aplicaciones y juegos. Para ello, sigue estos pasos:</p>
-<ol>
-<li>Vaya al sitio web oficial de Microsoft y descargue las últimas versiones de los paquetes redistribuibles de Microsoft Visual C++ para su versión y arquitectura de Windows (32 bits o 64 bits). Puedes encontrarlos aquí: </li>
-<li>Instale los paquetes descargados siguiendo las instrucciones en la pantalla. </li>
-<li>Reinicie su PC después de que se complete la instalación. </li>
-</ol>
- <h4>Desinstalar y reinstalar PES 2021</h4>
-<p>Si ninguno de los métodos anteriores funciona, es posible que necesite desinstalar y reinstalar PES 2021 en su PC. Esto asegurará que usted tiene una instalación fresca y limpia del juego, sin ningún archivo corrupto o faltante que puede causar el error. Para hacer esto, siga estos pasos:</p>
-<ol>
-<li>Ir al Panel de control > Programas > Programas y características y seleccionar PES 2021 de la lista. </li>
-<li>Haga clic en Desinstalar y siga las instrucciones en la pantalla. </li>
-<li>Elimina cualquier archivo o carpeta sobrante relacionada con PES 2021 desde tu PC. Puedes usar una herramienta como CCleaner para ayudarte con esta tarea. </li>
-<li>Descargar PES 2021 de nuevo desde el sitio web oficial o su plataforma preferida (Steam, Origin, etc.). </li>
-<li>Instalar PES 2021 siguiendo las instrucciones en la pantalla. </li>
-<li>Ejecutar PES 2021 y ver si el error se ha ido. </li>
-</ol>
- <h4>Arreglar archivos corruptos de Windows</h4>
-<p>El último método que puede intentar para solucionar el error 0xc00007b es arreglar cualquier archivo dañado o dañado en su sistema Windows. Estos archivos pueden interferir con el correcto funcionamiento de sus aplicaciones y juegos, y causar errores. Para solucionarlos, debe usar una herramienta integrada llamada System File Checker (SFC). Esta herramienta escaneará su sistema en busca de errores y los reparará automáticamente. Para hacer esto, siga estos pasos:</p>
-<ol>
-<li>Abra el símbolo del sistema como administrador presionando las teclas Windows + X y seleccionando el símbolo del sistema (Admin). </li>
-<li>Escriba sfc /scannow y presione Enter.</li>
-<li>Espere a que el escaneo se complete. Puede tomar algún tiempo dependiendo de su sistema. </li>
-<li>Si se encuentran errores, se corregirán automáticamente. </li>
-<li>Reinicie su PC después de realizar el escaneo. </li>
-</ol>
- <h2>Cómo descargar 0xc00007b para PES 2021</h2>
- <h3>Visite el sitio web oficial de PES 2021</h3>
-<p>El primer paso es visitar el sitio web oficial de PES 2021, que es . Aquí encontrarás toda la información sobre el juego, como sus características, modos, equipos, jugadores, etc. También encontrarás enlaces para descargar PES 2021 para diferentes plataformas, como PC, PlayStation, Xbox, etc.</p>
- <h3>Elija su plataforma y edición</h3>
-<p>El siguiente paso es elegir su plataforma y edición preferida de PES 2021. El juego <h3>Elija su plataforma y edición</h3>
-<p>El siguiente paso es elegir su plataforma y edición preferida de PES 2021. El juego está disponible para PC, PlayStation 4, PlayStation 5, Xbox One y Xbox Series X/S. También puede elegir entre la edición estándar y la edición de actualización de temporada. La edición estándar incluye el juego completo y algunos artículos de bonificación, como el modo UEFA Euro 2020, la Iconic Moment Series y las monedas myClub. La edición de actualización de temporada incluye el mismo contenido que la edición estándar, pero con listas actualizadas, kits y estadios para la temporada 2020/2021. Los precios de las ediciones varían dependiendo de su plataforma y región. </p>
- <h3>Descargar e instalar el juego</h3>
-<p>El paso final es descargar e instalar PES 2021 en su PC o consola. Para hacer esto, necesita tener suficiente espacio de almacenamiento y una conexión a Internet estable. El tamaño de descarga de PES 2021 es de unos 40 GB para PC y 50 GB para consolas. El proceso de instalación puede tardar algún tiempo dependiendo de su sistema. Para descargar e instalar PES 2021, siga estos pasos:</p>
-<ol>
-<li>Vaya al sitio web oficial de PES 2021 y haga clic en el enlace de descarga de su plataforma. </li>
-<li>Siga las instrucciones en la pantalla para completar el proceso de compra y pago. </li>
-<li>Espera a que el juego se descargue en tu PC o consola. </li>
-<li> Inicie el juego y siga las instrucciones en la pantalla para completar el proceso de instalación. </li>
-<li>Disfruta jugando PES 2021! </li>
-</ol>
- <h2>Conclusión</h2>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre PES 2021 y el error 0xc00007b:</p>
- <h4>Q: ¿Cuáles son los requisitos mínimos del sistema para PES 2021 en PC? </h4>
-<p>A: Según el sitio web oficial de PES 2021, estos son los requisitos mínimos del sistema para PES 2021 en PC:</p>
-<ul>
-<li>OS: Windows 8.1/10 - 64bit</li>
-<li>CPU: Intel Core i5-3470 / AMD FX 4350</li>
-<li>RAM: 8 GB</li>
-<li>GPU: NVIDIA GTX 670 / AMD Radeon HD 7870</li>
-<li>DirectX: Versión 11</li>
-<li>Almacenamiento: 40 GB de espacio disponible</li>
-<li>Resolución: 1280 x 720</li>
-</ul>
- <h4>Q: ¿Cómo puedo jugar PES 2021 online con otros jugadores? </h4>
-<p>A: Para jugar PES 2021 online con otros jugadores, necesitas tener una conexión a Internet y una cuenta de Konami ID. Puedes crear una cuenta de Konami ID gratis visitando . Una vez que tenga una cuenta, puede acceder a varios modos en línea en PES 2021, como el modo eFootball, el modo myClub, el modo Matchday, el modo cooperativo en línea, etc.</p>
- <h4>Q: ¿Cómo puedo actualizar PES 2021 para obtener las últimas características y contenido? </h4>
-<p>A: Para actualizar PES 2021 para obtener las últimas características y contenido, necesita tener una conexión a Internet y suficiente espacio de almacenamiento en su PC o consola. Puede comprobar las actualizaciones manualmente en Configuración > Sistema > Actualizaciones en PES 2021. Alternativamente, puede habilitar las actualizaciones automáticas yendo a Configuración > Sistema > Actualizaciones automáticas en PES <h4>Q: ¿Cómo puedo actualizar PES 2021 para obtener las últimas características y contenido? </h4>
- <h4>Q: ¿Cómo puedo personalizar mi experiencia PES 2021? </h4>
-<p>A: PES 2021 ofrece muchas opciones para personalizar tu experiencia de juego según tus preferencias y estilo. Puede cambiar varios ajustes, como el ángulo de la cámara, el nivel de dificultad, la velocidad del juego, los efectos de sonido, etc. También puede editar sus jugadores, equipos, kits, logotipos, etc. utilizando el modo de edición. También puedes descargar e instalar varios mods y parches de la comunidad PES que añaden más características y contenido al juego. </p>
- <h4>Q: ¿Cómo puedo contactar al equipo de soporte de PES 2021 si tengo algún problema o pregunta? </h4>
-<p>A: Si tiene algún problema o pregunta con respecto a PES 2021, puede ponerse en contacto con el equipo de soporte de PES 2021 visitando . Aquí encontrarás una sección de preguntas frecuentes que responde a algunas de las preguntas más frecuentes sobre el juego. También puede enviar una solicitud de soporte llenando un formulario con sus detalles y problemas. El equipo de soporte le responderá lo antes posible. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/models/target_python.py DELETED Viewed

@@ -1,110 +0,0 @@
-import sys
-from typing import List, Optional, Tuple
-from pip._vendor.packaging.tags import Tag
-from pip._internal.utils.compatibility_tags import get_supported, version_info_to_nodot
-from pip._internal.utils.misc import normalize_version_info
-class TargetPython:
-    """
-    Encapsulates the properties of a Python interpreter one is targeting
-    for a package install, download, etc.
-    """
-    __slots__ = [
-        "_given_py_version_info",
-        "abis",
-        "implementation",
-        "platforms",
-        "py_version",
-        "py_version_info",
-        "_valid_tags",
-    ]
-    def __init__(
-        self,
-        platforms: Optional[List[str]] = None,
-        py_version_info: Optional[Tuple[int, ...]] = None,
-        abis: Optional[List[str]] = None,
-        implementation: Optional[str] = None,
-    ) -> None:
-        """
-        :param platforms: A list of strings or None. If None, searches for
-            packages that are supported by the current system. Otherwise, will
-            find packages that can be built on the platforms passed in. These
-            packages will only be downloaded for distribution: they will
-            not be built locally.
-        :param py_version_info: An optional tuple of ints representing the
-            Python version information to use (e.g. `sys.version_info[:3]`).
-            This can have length 1, 2, or 3 when provided.
-        :param abis: A list of strings or None. This is passed to
-            compatibility_tags.py's get_supported() function as is.
-        :param implementation: A string or None. This is passed to
-            compatibility_tags.py's get_supported() function as is.
-        """
-        # Store the given py_version_info for when we call get_supported().
-        self._given_py_version_info = py_version_info
-        if py_version_info is None:
-            py_version_info = sys.version_info[:3]
-        else:
-            py_version_info = normalize_version_info(py_version_info)
-        py_version = ".".join(map(str, py_version_info[:2]))
-        self.abis = abis
-        self.implementation = implementation
-        self.platforms = platforms
-        self.py_version = py_version
-        self.py_version_info = py_version_info
-        # This is used to cache the return value of get_tags().
-        self._valid_tags: Optional[List[Tag]] = None
-    def format_given(self) -> str:
-        """
-        Format the given, non-None attributes for display.
-        """
-        display_version = None
-        if self._given_py_version_info is not None:
-            display_version = ".".join(
-                str(part) for part in self._given_py_version_info
-            )
-        key_values = [
-            ("platforms", self.platforms),
-            ("version_info", display_version),
-            ("abis", self.abis),
-            ("implementation", self.implementation),
-        ]
-        return " ".join(
-            f"{key}={value!r}" for key, value in key_values if value is not None
-        )
-    def get_tags(self) -> List[Tag]:
-        """
-        Return the supported PEP 425 tags to check wheel candidates against.
-        The tags are returned in order of preference (most preferred first).
-        """
-        if self._valid_tags is None:
-            # Pass versions=None if no py_version_info was given since
-            # versions=None uses special default logic.
-            py_version_info = self._given_py_version_info
-            if py_version_info is None:
-                version = None
-            else:
-                version = version_info_to_nodot(py_version_info)
-            tags = get_supported(
-                version=version,
-                platforms=self.platforms,
-                abis=self.abis,
-                impl=self.implementation,
-            )
-            self._valid_tags = tags
-        return self._valid_tags

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/codingstatemachinedict.py DELETED Viewed

@@ -1,19 +0,0 @@
-from typing import TYPE_CHECKING, Tuple
-if TYPE_CHECKING:
-    # TypedDict was introduced in Python 3.8.
-    #
-    # TODO: Remove the else block and TYPE_CHECKING check when dropping support
-    # for Python 3.7.
-    from typing import TypedDict
-    class CodingStateMachineDict(TypedDict, total=False):
-        class_table: Tuple[int, ...]
-        class_factor: int
-        state_table: Tuple[int, ...]
-        char_len_table: Tuple[int, ...]
-        name: str
-        language: str  # Optional key
-else:
-    CodingStateMachineDict = dict

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/colorama/tests/isatty_test.py DELETED Viewed

@@ -1,57 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import sys
-from unittest import TestCase, main
-from ..ansitowin32 import StreamWrapper, AnsiToWin32
-from .utils import pycharm, replace_by, replace_original_by, StreamTTY, StreamNonTTY
-def is_a_tty(stream):
-    return StreamWrapper(stream, None).isatty()
-class IsattyTest(TestCase):
-    def test_TTY(self):
-        tty = StreamTTY()
-        self.assertTrue(is_a_tty(tty))
-        with pycharm():
-            self.assertTrue(is_a_tty(tty))
-    def test_nonTTY(self):
-        non_tty = StreamNonTTY()
-        self.assertFalse(is_a_tty(non_tty))
-        with pycharm():
-            self.assertFalse(is_a_tty(non_tty))
-    def test_withPycharm(self):
-        with pycharm():
-            self.assertTrue(is_a_tty(sys.stderr))
-            self.assertTrue(is_a_tty(sys.stdout))
-    def test_withPycharmTTYOverride(self):
-        tty = StreamTTY()
-        with pycharm(), replace_by(tty):
-            self.assertTrue(is_a_tty(tty))
-    def test_withPycharmNonTTYOverride(self):
-        non_tty = StreamNonTTY()
-        with pycharm(), replace_by(non_tty):
-            self.assertFalse(is_a_tty(non_tty))
-    def test_withPycharmNoneOverride(self):
-        with pycharm():
-            with replace_by(None), replace_original_by(None):
-                self.assertFalse(is_a_tty(None))
-                self.assertFalse(is_a_tty(StreamNonTTY()))
-                self.assertTrue(is_a_tty(StreamTTY()))
-    def test_withPycharmStreamWrapped(self):
-        with pycharm():
-            self.assertTrue(AnsiToWin32(StreamTTY()).stream.isatty())
-            self.assertFalse(AnsiToWin32(StreamNonTTY()).stream.isatty())
-            self.assertTrue(AnsiToWin32(sys.stdout).stream.isatty())
-            self.assertTrue(AnsiToWin32(sys.stderr).stream.isatty())
-if __name__ == '__main__':
-    main()

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/bdist_dumb.py DELETED Viewed

@@ -1,144 +0,0 @@
-"""distutils.command.bdist_dumb
-Implements the Distutils 'bdist_dumb' command (create a "dumb" built
-distribution -- i.e., just an archive to be unpacked under $prefix or
-$exec_prefix)."""
-import os
-from distutils.core import Command
-from distutils.util import get_platform
-from distutils.dir_util import remove_tree, ensure_relative
-from distutils.errors import DistutilsPlatformError
-from distutils.sysconfig import get_python_version
-from distutils import log
-class bdist_dumb(Command):
-    description = "create a \"dumb\" built distribution"
-    user_options = [
-        ('bdist-dir=', 'd', "temporary directory for creating the distribution"),
-        (
-            'plat-name=',
-            'p',
-            "platform name to embed in generated filenames "
-            "(default: %s)" % get_platform(),
-        ),
-        (
-            'format=',
-            'f',
-            "archive format to create (tar, gztar, bztar, xztar, " "ztar, zip)",
-        ),
-        (
-            'keep-temp',
-            'k',
-            "keep the pseudo-installation tree around after "
-            + "creating the distribution archive",
-        ),
-        ('dist-dir=', 'd', "directory to put final built distributions in"),
-        ('skip-build', None, "skip rebuilding everything (for testing/debugging)"),
-        (
-            'relative',
-            None,
-            "build the archive using relative paths " "(default: false)",
-        ),
-        (
-            'owner=',
-            'u',
-            "Owner name used when creating a tar file" " [default: current user]",
-        ),
-        (
-            'group=',
-            'g',
-            "Group name used when creating a tar file" " [default: current group]",
-        ),
-    ]
-    boolean_options = ['keep-temp', 'skip-build', 'relative']
-    default_format = {'posix': 'gztar', 'nt': 'zip'}
-    def initialize_options(self):
-        self.bdist_dir = None
-        self.plat_name = None
-        self.format = None
-        self.keep_temp = 0
-        self.dist_dir = None
-        self.skip_build = None
-        self.relative = 0
-        self.owner = None
-        self.group = None
-    def finalize_options(self):
-        if self.bdist_dir is None:
-            bdist_base = self.get_finalized_command('bdist').bdist_base
-            self.bdist_dir = os.path.join(bdist_base, 'dumb')
-        if self.format is None:
-            try:
-                self.format = self.default_format[os.name]
-            except KeyError:
-                raise DistutilsPlatformError(
-                    "don't know how to create dumb built distributions "
-                    "on platform %s" % os.name
-                )
-        self.set_undefined_options(
-            'bdist',
-            ('dist_dir', 'dist_dir'),
-            ('plat_name', 'plat_name'),
-            ('skip_build', 'skip_build'),
-        )
-    def run(self):
-        if not self.skip_build:
-            self.run_command('build')
-        install = self.reinitialize_command('install', reinit_subcommands=1)
-        install.root = self.bdist_dir
-        install.skip_build = self.skip_build
-        install.warn_dir = 0
-        log.info("installing to %s", self.bdist_dir)
-        self.run_command('install')
-        # And make an archive relative to the root of the
-        # pseudo-installation tree.
-        archive_basename = "{}.{}".format(
-            self.distribution.get_fullname(), self.plat_name
-        )
-        pseudoinstall_root = os.path.join(self.dist_dir, archive_basename)
-        if not self.relative:
-            archive_root = self.bdist_dir
-        else:
-            if self.distribution.has_ext_modules() and (
-                install.install_base != install.install_platbase
-            ):
-                raise DistutilsPlatformError(
-                    "can't make a dumb built distribution where "
-                    "base and platbase are different (%s, %s)"
-                    % (repr(install.install_base), repr(install.install_platbase))
-                )
-            else:
-                archive_root = os.path.join(
-                    self.bdist_dir, ensure_relative(install.install_base)
-                )
-        # Make the archive
-        filename = self.make_archive(
-            pseudoinstall_root,
-            self.format,
-            root_dir=archive_root,
-            owner=self.owner,
-            group=self.group,
-        )
-        if self.distribution.has_ext_modules():
-            pyversion = get_python_version()
-        else:
-            pyversion = 'any'
-        self.distribution.dist_files.append(('bdist_dumb', pyversion, filename))
-        if not self.keep_temp:
-            remove_tree(self.bdist_dir, dry_run=self.dry_run)