Update parquet files (step 22 of 249)

spaces/101-5/gpt4free/g4f/.v1/testing/sqlchat_test.py

@@ -1,4 +0,0 @@
-import sqlchat
-
-for response in sqlchat.StreamCompletion.create(prompt='write python code to reverse a string', messages=[]):
-    print(response.completion.choices[0].text, end='')

spaces/101-5/gpt4free/g4f/.v1/unfinished/bard/typings.py

@@ -1,54 +0,0 @@
-from typing import Dict, List, Union
-
-
-class BardResponse:
-    def __init__(self, json_dict: Dict[str, Union[str, List]]) -> None:
-        """
-        Initialize a BardResponse object.
-
-        :param json_dict: A dictionary containing the JSON response data.
-        """
-        self.json = json_dict
-
-        self.content = json_dict.get('content')
-        self.conversation_id = json_dict.get('conversation_id')
-        self.response_id = json_dict.get('response_id')
-        self.factuality_queries = json_dict.get('factualityQueries', [])
-        self.text_query = json_dict.get('textQuery', [])
-        self.choices = [self.BardChoice(choice)
-                        for choice in json_dict.get('choices', [])]
-
-    def __repr__(self) -> str:
-        """
-        Return a string representation of the BardResponse object.
-
-        :return: A string representation of the BardResponse object.
-        """
-        return f"BardResponse(conversation_id={self.conversation_id}, response_id={self.response_id}, content={self.content})"
-
-    def filter_choices(self, keyword: str) -> List['BardChoice']:
-        """
-        Filter the choices based on a keyword.
-
-        :param keyword: The keyword to filter choices by.
-        :return: A list of filtered BardChoice objects.
-        """
-        return [choice for choice in self.choices if keyword.lower() in choice.content.lower()]
-
-    class BardChoice:
-        def __init__(self, choice_dict: Dict[str, str]) -> None:
-            """
-            Initialize a BardChoice object.
-
-            :param choice_dict: A dictionary containing the choice data.
-            """
-            self.id = choice_dict.get('id')
-            self.content = choice_dict.get('content')[0]
-
-        def __repr__(self) -> str:
-            """
-            Return a string representation of the BardChoice object.
-
-            :return: A string representation of the BardChoice object.
-            """
-            return f"BardChoice(id={self.id}, content={self.content})"

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Flash Activex Control 6.0.0.0 35.md

@@ -1,136 +0,0 @@
-<br />
-<h1>What is Adobe Flash ActiveX Control 6.0.0.0 35?</h1>
-<p>If you are a fan of online games, animations, or interactive web applications, you have probably encountered Adobe Flash at some point. Adobe Flash is a software platform that allows you to create and view multimedia content on the web. However, to enjoy the full features and functionality of Adobe Flash, you need to install a special component called ActiveX control for Flash Player.</p>
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-<p>In this article, we will explain what is Adobe Flash ActiveX Control 6.0.0.0 35, why you need it, how to download and install it, and how to use it effectively.</p>
-<h3>What is Adobe Flash and what is ActiveX control?</h3>
-<p>Adobe Flash is a software platform that enables you to create and view rich media content on the web, such as games, animations, videos, or interactive applications. Adobe Flash works by using a browser plug-in called Flash Player, which allows you to run Flash content on your web browser.</p>
-<p>ActiveX control is a technology that allows web browsers to run certain types of software components, such as plug-ins, add-ons, or extensions. ActiveX control can enhance the functionality and performance of web browsers by allowing them to access features that are not built-in.</p>
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-<p>ActiveX control for Flash Player is a specific type of ActiveX control that enables Internet Explorer to run Flash content on web pages. Without ActiveX control for Flash Player, Internet Explorer cannot display Flash content properly.</p>
-<h3>Why do you need to install ActiveX control for Flash Player?</h3>
-<p>If you use Internet Explorer as your web browser, you need to install ActiveX control for Flash Player in order to view and interact with Flash content on the web. Some websites may require you to have ActiveX control for Flash Player installed before you can access their content or features.</p>
-<p>Installing ActiveX control for Flash Player can also improve your browsing experience by allowing you to enjoy faster loading times, smoother animations, better sound quality, and more interactive features.</p>
-<h3>What is the version 6.0.0.0 35 of ActiveX control for Flash Player?</h3>
-<p>The version 6.0.0.0 35 of ActiveX control for Flash Player is an old version that was released in September 2002. It was designed to support Adobe Flash version 6, which was also released in September 2002.</p>
-<p>The version 6.0.0.0 35 of ActiveX control for Flash Player is no longer supported by Adobe or Microsoft, and it may not be compatible with newer versions of Internet Explorer or Windows operating systems. It may also have security vulnerabilities or performance issues that could affect your browsing experience.</p>
-<p>Therefore, we do not recommend using this version of ActiveX control for Flash Player unless you have a specific reason or requirement to do so. Instead, we suggest that you update your ActiveX control for Flash Player to the latest version available from Adobe's website.</p>
-<h2>How to download and install ActiveX control for Flash Player</h2>
-<p>In this section, we will show you how to download and install ActiveX control for Flash Player on your computer.</p>
-<h3>Where to download the ActiveX control for Flash Player</h3>
-<p>The easiest way to download the ActiveX control for Flash Player is from Adobe's website. You can visit this link to download the latest version of ActiveX control for Flash Player.</p>
-<p>If you need an older version of ActiveX control for Flash Player, such as version 6.0.0.0 35, you can visit this link to download it from Sothink's website.</p>
-<h3>How to install the ActiveX control for Flash Player</h3>
-<p>Once you have downloaded the file for the ActiveX control for Flash Player, you can follow these steps to install it on your computer:</p>
-<ol>
-<li>Close all open instances of Internet Explorer.</li>
-<li>Double-click on the file that you downloaded.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>You may see a warning message that says installing this software is not secure or recommended. This is because some versions of ActiveX control for Flash Player are not digitally signed by Adobe or Microsoft. You can ignore this warning and continue with the installation.</li>
-</ol>
-<h3>How to verify that the ActiveX control for Flash Player is installed correctly</h3>
-<p>To verify that the ActiveX control for Flash Player is installed correctly on your computer, you can follow these steps:</p>
-<ol>
-<li>Open Internet Explorer and go to this link.</li>
-<li>You should see a message that says "Congratulations! Your computer has the latest version of Adobe® Macromedia® Shockwave®/Flash® Players installed." If not, you may need to reinstall or update your ActiveX control for Flash Player.</li>
-<li>You should also see an animation that shows your current version of Adobe® Macromedia® Shockwave®/Flash® Players installed.</li>
-</ol>
- <h2>How to use ActiveX control for Flash Player</h2>
- <p>In this section, we will show you how to use ActiveX control for Flash Player effectively on your web browser.</p>
- <h3>How to enable or disable ActiveX control for Flash Player in Internet Explorer</h3>
- <p>To enable or disable ActiveX control for Flash Player in Internet Explorer, you can follow these steps:</p>
- <ol>
- <li>Open Internet Explorer and click on the settings button in the top right corner.</li>
- <li>Select Internet Options from the drop-down menu.</li>
- <li>Go to the Security tab and click on Custom level.</li>
- <li>Scroll down until you find Download signed ActiveX controls under Activex controls and plug-ins section.</li>
- <li>Select Enable if you want to enable ActiveX control for Flash Player or Disable if you want to disable it.</li>
- <li>Click OK and then Yes when prompted by a warning message.</li>
- <li>Click OK again to exit the Internet Options window.</li>
- <li>You may need to restart your browser for the changes to take effect.</li>
- </ol>
- <h3>How to manage the settings and permissions of ActiveX control for Flash Player</h3>
- <p>To manage the settings and permissions of ActiveX control for Flash Player in Internet Explorer, you can follow these steps:</p>
- <ol>
- <li>Open Internet Explorer and click on Tools in the menu bar.</li>
- <li>Select Manage add-ons from the drop-down menu.</li>
- <li>Select Toolbars and Extensions from the left panel.</li>
- <li>Select Shockwave flash object from the right panel.</li>
- <li>You can see some information about this add-on such as its name, publisher, status, type, file date etc.</li>
- <li>You can also see some options at the bottom such as Enable/Disable this add-on or More information about this add-on.</li>
- <li>If you click on More information about this add-on option ,you will see a new window that shows more details about this add-on such as its description ,version ,location ,size etc. <li>You can also see some buttons at the bottom such as Settings ,Remove ,Update etc. <li>If you click on Settings button ,you will see another window that shows some settings options such as Allow sites ,Block sites ,Delete all sites etc. <li>You can use these options to customize how this add-on works on different websites. <li>You can also use these options to delete any data stored by this add-on such as cookies ,cache etc. To uninstall ActiveX control for Flash Player, you can follow these steps: <ol>
- <li>Open Internet Explorer and click on Tools in the menu bar.</li>
- <li>Select Manage add-ons from the drop-down menu.</li>
- <li>Select Toolbars and Extensions from the left panel.</li>
- <li>Select Shockwave flash object from the right panel.</li>
- <li>Click on the Remove button at the bottom.</li>
- <li>You may see a message that says "Are you sure you want to remove this add-on?". Click on Yes button to confirm.</li>
- <li>You may need to restart your browser for the changes to take effect.</li>
- </ol>
-</li>
- <li><b>How do I update ActiveX control for Flash Player?</b></li>
-<p>To update ActiveX control for Flash Player, you can follow these steps:</p>
-<ol>
-<li>Open Internet Explorer and go to this link.</li>
-<li>You should see a message that says "Adobe Flash Player is out of date". Click on the Update Now button.</li>
-<li>You should be redirected to Adobe's website where you can download the latest version of ActiveX control for Flash Player.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>You may need to restart your browser for the changes to take effect.</li>
-</ol>
-</li>
- <li><b>How do I enable or disable Flash content on specific websites?</b></li>
-<p>To enable or disable Flash content on specific websites, you can follow these steps:</p>
-<ol>
-<li>Open Internet Explorer and click on Tools in the menu bar.</li>
-<li>Select Internet Options from the drop-down menu.</li>
-<li>Go to the Privacy tab and click on the Sites button.</li>
-<li>You should see a window that says "Per Site Privacy Actions".</li>
-<li>Type in the address of the website that you want to enable or disable Flash content on in the Address of website box.</li>
-<li>Click on the Allow button if you want to enable Flash content on this website or Block button if you want to disable it.</li>
-<li>Click on OK button to save your changes.</li>
-<li>Click on OK button again to exit the Internet Options window.</li>
-</ol>
-</li>
- <li><b>How do I check if my ActiveX control for Flash Player is working properly?</b></li>
-<p>To check if your ActiveX control for Flash Player is working properly, you can follow these steps:</p>
-<ol>
-<li>Open Internet Explorer and go to this link.</li>
-<li>You should see a test page that shows some Flash animations and sounds.</li>
-<li>If you can see and hear the Flash content without any problems, then your ActiveX control for Flash Player is working properly.</li>
-<li>If you cannot see or hear the Flash content or if you see any error messages, then your ActiveX control for Flash Player may have some issues. You can try to reinstall or update your ActiveX control for Flash Player or contact Adobe's support team for help.</li>
-</ol>
-</li>
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-<p>Adobe Flash and ActiveX control for Flash Player are outdated technologies that are no longer supported by Adobe or Microsoft. They may also pose some security risks or compatibility issues with newer browsers or operating systems. Therefore, you may want to consider some alternatives to Adobe Flash and ActiveX control for Flash Player, such as:</p>
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- <li><b>JavaScript:</b> JavaScript is a scripting language that allows you to create and run dynamic web applications on the web. JavaScript can interact with HTML5 elements and other web technologies to create rich and interactive web experiences. JavaScript is supported by most web browsers, so you don't need to install anything extra. You can visit this link to learn more about JavaScript and its features.</li>
- <li><b>CSS3:</b> CSS3 is a style sheet language that allows you to design and format web pages. CSS3 can work with HTML5 and JavaScript to create beautiful and responsive web layouts. CSS3 supports features such as animation, transition, transformation, gradient, shadow, and more. CSS3 is supported by most web browsers, so you don't need to install anything extra. You can visit this link to learn more about CSS3 and its features.</li>
- </ul>
- </ol>
- </p> 0a6ba089eb<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Crackeddll 3.1.5.0 and Make Your PC Run Faster and More Efficiently.md

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-<p>FIFA 16 Crack 3DM V3 is a software tool that allows you to play FIFA 16 on your PC without buying or activating the game. It is a modified version of the original game executable file that bypasses the DRM protection and online verification systems. It also enables some additional features and options that are not available in the official game.</p>
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-<h1>, <h2>, <h3>, and <h4> to create headings and subheadings, and make sure they include your keyword or related keywords. The <h1> tag should be used for the main title of your article, and the <h2>, <h3>, and <h4> tags should be used for the subtitles of each section . 4. Write your content with SEO-friendly language and formatting. Your content should be written in a conversational style, using personal pronouns, simple words, engaging sentences, active voice, brief paragraphs, rhetorical questions, analogies, and metaphors. You should also use your keyword and related keywords throughout your content, but avoid keyword stuffing or unnatural usage. You should also use proper HTML tags for formatting your content, such as <p> for paragraphs, <strong> for bold text, <em> for italic text, <ul> and <li> for unordered lists, <ol> and <li> for ordered lists, <a> for links, <img> for images, etc . 5. Add a table to your content if it makes sense. A table is a way of presenting data in rows and columns, which can help you display information in a structured and organized way. A table can also make your content more appealing and informative to readers and search engines. To create a table in HTML, you need to use the <table> tag, which defines the table element. Inside the <table> tag, you need to use the <tr> tag, which defines a table row. Inside each <tr> tag, you need to use either the <th> tag or the <td> tag, which define a table header cell or a table data cell respectively. For example: <table>
-  <tr>
-    <th>Name</th>
-    <th>Age</th>
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-    <td>Alice</td>
-    <td>25</td>
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-    <td>Bob</td>
-    <td>30</td>
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-</table>
- This will create a table like this: | Name | Age | | ---- | --- | | Alice | 25 | | Bob | 30 | 6. Write a meta title tag and a meta description tag for your article. A meta title tag is an HTML element that specifies the title of your web page, which is displayed on the search engine results page (SERP) as a clickable link. A meta description tag is an HTML element that provides a brief summary of your web page, which is displayed below the title on the SERP as a snippet. Both tags are important for SEO, as they help search engines and users understand what your page is about, and entice them to click on it. To write a meta title tag and a meta description tag, you need to use the <title> tag and the <meta name="description"> tag inside the <head> section of your HTML document. For example: <head>
-  <title>Bakteriyalar haqqinda melumat - Bing</title>
-  <meta name="description" content="Bu maqalada siz bakteriyalarin nedi oldugunu ve onlarin dunyada nece rol oynadigini oyreneceks I have searched the web for the topic "bakteriyalar haqqinda melumat" and found some relevant sources that I can use to write the article. Based on the information from these sources, I have created an outline of the article with 15 headings and subheadings, as follows: <h1>Bakteriyalar haqqinda melumat - Bing</h1>
-<h2>Bakteriyalar nədir?</h2>
-<p>Bakteriyalar təkhüceyrəli mikroorqanizmalardır. Prokaryotik mikroorqanizmlərin əksəriyyətini təşkil edən qrupdur. Böyüklükləri 0,1–10 mm arasındadır. Bakteriyaların dünya üzərində 3,5 milyard ildir yaşadıqları bilinməkdədir. Dünyada ən çox yayılmış orqanizmlərdir və yer kürəsində həyatın davam etməsi üçün böyük önəm təşkil etməkdədirlər.</p>
-<h2>Bakteriyaların öyrənilmə tarixi</h2>
-<p>Bakteriyalar hələ yaxşı öyrənilməyəndə bitkilərə aid edilirdi. "Bağırsaq florası" anlayışı da buradan yaranıb. Bakteriyaları kəşf edən Antoni van Levenhuk (XVII) olub. Ən qədim geoloji dövrdə biosferin yaranmasında ayrı-ayrı qrup prokariotların rolu böyükdür. Bakterial fəallıq karbonatların, dəmir filizinin, sulfid, silisium, fosforit, boksit və s yataqlarının əmələ gəlməsinə səbəb olmuşdur.</p>
-<h2>bakteriyalar haqqinda melumat</h2><br /><p><b><b>Download Zip</b> &#10004; <a href="https://urlin.us/2uSXfH">https://urlin.us/2uSXfH</a></b></p><br /><br />
-<h2>Bakteriyaların taksanomiyası</h2>
-<p>Bakteriyaların taksanomiyası yani sınıflandırılması çox mürakkab və mübahiseli bir mövzudur. Bakteriyaların genetik vahidliliyi olmadığından, onları növlere vurğulamaq çox çetindir. Bakteriyaların növlüyünü müxtelif kriteriyalara görü tayin etmeye çalışırlar: morfolojiya, fiziolojiya, biokimya, molekulyar biologiya v.s. Hal-hazırda bakteriyalar üç domene aiddir: Arxebakteriyalar, Əsl bakteriyalar və Oksifotobakteriyalar.</p>
-<h3>Arxebakteriyalar</h3>
-<p>Arxebakteriyalar: əsasen, metansintezedici bakteriyalardır. Növlüyünün sayı 40-dan çoxdur.Arxebakteriyalarda fotosintez xlorofil yox, bakteriorodopsin Əsasında baş verir.</p>
-<h3>Əsl bakteriyalar</h3>
-<p>Əsl bakteriyalar: anaerob vǝ aerob heterotrofların, bǝzǝn isǝ anaerob fotosintezedici avtotrof prokariotlaron, müxtǝlif qruplarıdır.</p>
-<h3>Oksifotobakteriyalar</h3>
-<p>Oksifotobakteriyalar: aerob fotosintezedici prokariotlardır (sinobakteriyalar vǝ xlorosibakteriyalar).</p>
-<h2>Bakteriyaların morfolojiyası</h2>
-<p>Bakteriyaların morfolojiyası yani şǝkillǝri çox müxtǝlifdir. Bakteriaların koʻp turi tayoqcha şaklida boʻladi. Biroq şarsimon, ipsimon yoki buralgan şaklli mikroorganizmlar I have continued writing the article based on the outline and the sources. Here is the next part of the article: <h4>Tayoqcha şaklli bakteriyalar</h4>
-<p>Tayoqcha şaklli bakteriyalar: ən sadə və ən çox rast gəlinən bakteriya şəklidir. Uzunluqları 0,5-10 mm, enləri isə 0,2-1,5 mm arasındadır. Tayoqcha şaklli bakteriyaların bir neçə növü vardır: kok (yuvarlaq), basill (çubuq), vibrio (virgül), spirill (spiral) və spiroxet (kivrilmiş).</p>
-<h4>Şarsimon şaklli bakteriyalar</h4>
-<p>Şarsimon şaklli bakteriyalar: tayoqcha şakllı bakteriyalardan fərqli olaraq, hüceyrələri arasında birləşmə yeri olan bakteriyalardır. Bu birləşmə yeri hüceyrələrin bir-birinə yapışmasına və koloniyalar yaratmasına imkan verir. Şarsimon şaklli bakteriyaların bir neçə növü vardır: diplokok (iki yuvarlaq), streptokok (zincir kimi yuvarlaqlar), stafilokok (üzüm dəstəsi kimi yuvarlaqlar), tetrad (dörd yuvarlaq), sarkina (sekiz yuvarlaq) və s.</p>
-<h4>Ipsimon şaklli bakteriyalar</h4>
-<p>Ipsimon şaklli bakteriyalar: tayoqcha şakllı bakteriyalardan fərqli olaraq, hüceyrələri arasında birləşmə yeri olmayan, amma bir-birinə bitişik olan bakteriyalardır. Bu bitişiklik hüceyrələrin bir-birinə uyğunlaşmasına və müxtǝlif formalar almalarına sǝbǝb olur. Ipsimon şaklli bakteriyaların bir neçǝ növü vardır: diplobasill (iki çubuq), streptobasill (zincir kimi çubuqlar), palisad (paralel çubuqlar) vǝ s.</p>
-<p>Bakteriyaların öyrənilmə tarixi<br />
-Bakteriyaların yarımaləmləri<br />
-Bakteriyaların müxtəlifliyi və qidalanma tipləri<br />
-Avtotrof və heterotrof bakteriyalar<br />
-Fototrof və xemotrof bakteriyalar<br />
-Bakteriyaların torpaqdakı rolu<br />
-Bakteriyaların xarici keçidləri<br />
-Bakteriyaların hüceyrə divarı və kapsulu<br />
-Bakteriyaların tənəffüs növləri<br />
-Bakteriyaların koʻndalangiga boʻlinishi<br />
-Bakteriyaların yosunlarla əlaqəsi<br />
-Bakteriyaların prokariotik xüsusiyyətləri<br />
-Bakteriyaların DNK və RNA tərkibi<br />
-Bakteriyaların mitoxondriya və xloroplast yoxluğu<br />
-Bakteriyaların dünya üzərindəki yayılması<br />
-Bakteriyaların biosferadakı əhəmiyyəti<br />
-Bakteriyaların insan sağlamlığına təsiri<br />
-Bakteriyaların bitki və heyvanlarla simbiozu<br />
-Bakteriyaların antibakterial maddələrə reaksiyası<br />
-Bakteriyaların patogen və faydalı növləri<br />
-Bakteriyaların metabolizmi və fermentləri<br />
-Bakteriyaların genetik mühitdilimliliyi<br />
-Bakteriyaların endospor və ekzospor formalaşması<br />
-Bakteriyaların qram boyanması və növləndirilməsi<br />
-Bakteriyaların morfoloji və fizioloji xassələri<br />
-Bakteriyaların siano bakteriya və arxebakteriya qrupları<br />
-Bakteriyaların fotosintez prosesində iştirakı<br />
-Bakteriyaların karbon qazını mənimsəməsi<br />
-Bakteriyaların günəş enerjisindən istifadəsi<br />
-Bakteriyaların qobigʻi və xromosomalari haqqında məlumat<br />
-Bakteriyalarda plazmid, transpozon, faj və konjugasiya kimi elementlər<br />
-Bakteriyalarda mutasiya, transformasiya, transduksiya kimi genetik hadisələr<br />
-Bakteriyalarda antibiotiklere qarşı mukavimlik mekanizmleri<br />
-Bakteriyalarda biofilm, biyolüminesens, biyoremeydiyasiya kimi proseslər<br />
-Bakteriyalarda nitrojen fiksasiyası, amonyak oksidasiyası, nitrifikasiya kimi reaksiyalar<br />
-Bakteriyalarda sülfür oksidasiyası, sülfat redüksionu, sülfür redüksionu kimi reaksiyalar<br />
-Bakteriyalarda metan sintezi, metan oksidasiyası, metanotrofiya kimi reaksiyalar<br />
-Bakteriyalarda karbonat sintezi, karbonat oksidasiyası, karbonat redüksionu kimi reaksiyalar<br />
-Bakteriyalarda ferrik oksidasiyası, ferroz oksidasiyası, ferroz redüksionu kimi reaksiyalar<br />
-Bakteriyalarda hidrojen sintezi, hidrojen oksidasiyası, hidrojenotrofiya kimi reaksiyalar<br />
-Bakteriyalarda asetik asit sintezi, asetik asit oksidasiyası, asetik asit redüksionu kimi reaksiyalar <br />
-Bakteriyalarda laktik</p>
-<h4>Buralgan şaklli bakteriyalar</h4>
-<p>Buralgan şaklli bakteriyalar: tayoqcha şakllı bakteriyalardan fǝrqli olaraq, hüceyrǝlǝri arasında birlǝşmǝ yeri olmayan, amma bir-birinǝ buralgan olan bakteriyalardır. Bu buralma hüceyrǝlǝrin bir-birinǝ toxunmasına vǝ müxtǝlif formalar almalarına sǝbǝb olur. Buralgan şaklli bakteriyaların bir neçǝ növü vardır: diplovibrio (iki virgül), streptovibrio (zincir kimi virgüllǝr), spirillium (spiral kimi virgüllǝr) vǝ s.</p> I have continued writing the article based on the outline and the sources. Here is the next part of the article: <h2>Bakteriyaların fiziologiyası</h2>
-<p>Bakteriyaların fiziologiyası yani metabolizmi və funksiyaları çox müxtəlif və maraqlıdır. Bakteriyalar həyatlarını davam etdirmək üçün müxtəlif maddələrdən və enerji mənbələrindən istifadə edirlər. Bakteriyaların fiziologiyasını müxtəlif kriteriyalara görü təsnif etmək olar: oksigen tələbi, karbon tələbi, azot tələbi, enerji tələbi, hüceyrə dövrü, hüceyrə bölünməsi, sporlaşma, hüceyrə qidalanması, hüceyrə növü v.s.</p>
-<h3>Oksigen tǝlǝbi</h3>
-<p>Oksigen tǝlǝbi: bakteriyalar oksigenin varlığına vǝ yoxluğuna görǝ fǝrqli reaksiyalar göstǝrir. Bakteriyaları oksigen tǝlǝbinǝ görǝ üç qrupa ayırmaq olar: aerob bakteriyalar (oksigeni soluyanlar), anaerob bakteriyalar (oksigeni soluyanlar) vǝ fakültativ anaerob bakteriyalar (oksigenin varlığına vǝ yoxluğuna uyğunlaşanlar).</p>
-<h3>Karbon tǝlǝbi</h3>
-<p>Karbon tǝlǝbi: bakteriyalar karbonu hüceyrǝ quruluşu vǝ enerji üçün istifadǝ edirlǝr. Bakteriyaları karbon mǝnbǝyinǝ görǝ iki qrupa ayırmaq olar: avtotrof bakteriyalar (kohnekarbondioksidi vǝ ya başqa inorganik maddeleri karbon mǝnbǝyi kimi istifadě edenler) vě heterotrof bakteriyalar (organik maddeleri karbon měnběyi kimi istifadě edenler).</p>
-<h3>Azot tělěbi</h3>
-<p>Azot tělěbi: bakteriyalar azotu hüceyrě quruluşu vě enerji üçün istifadě edirlěr. Bakteriyaları azot měnběyině görě iki qrupa ayırmaq olar: azotfiksirici bakteriyalar (havada olan azotu amonyaka çevirib istifadě edenler) vě amonyakasimilasiyaedici bakteriyalar (amonyakı nitrita vě ya nitrata çevirib istifadě edenler).</p> I have continued writing the article based on the outline and the sources. Here is the next part of the article: <h3>Enerji tǝlǝbi</h3>
-<p>Enerji tǝlǝbi: bakteriyalar enerjiyə həyat fəaliyyətləri üçün ehtiyac duyurlar. Bakteriyaları enerji mənbəyinə görə iki qrupa ayırmaq olar: kemosintetik bakteriyalar (inorganik maddələrdən enerji hasil edənlər) və fotosintetik bakteriyalar (işığından enerji hasil edənlər).</p>
-<h3>Hüceyrə dövrü</h3>
-<p>Hüceyrə dövrü: bakteriyaların hüceyrə bölünməsi ilə növlüyünün davam etməsi prosesidir. Bakteriyaların hüceyrə dövrü üç mərhələdən ibarətdir: interfez, mitoz və sitokinez. Interfez hüceyrənin bölünməyə hazırlaşdığı mərhələdir. Mitoz hüceyrənin nüvəsinin bölündüyü mərhələdir. Sitokinez hüceyrənin sitoplazmasının bölündüyü mǝrhǝlǝdir.</p>
-<h3>Hüceyrě bölünměsi</h3>
-<p>Hüceyrě bölünměsi: bakteriyaların Əsas üsulu hüceyrě bölünměsi olan binar fissiya (ikiyě bölünmǝ)dir. Binar fissiya zamanı, hüceyrǝnin nüvǝsi vǝ sitoplazması eyni anda bölünür vǝ iki eyni hüceyrǝ yaranır. Binar fissiya ilǝ 20 dǝqiqǝdǝ bir hüceyrǝdǝn 2, 40 dǝqiqǝdǝ 4, 60 dǝqiqǝdǝ 8 v.s. hüceyrǝ yarana bilir.</p>
-<h3>Sporlaşma</h3>
-<p>Sporlaşma: bakteriyaların digěr üsulu hüceyrě bölünměsi olan sporlaşmadır. Sporlaşma zamanı, hüceyrěnin nüvěsi spor adlanan dayanıqlı bir qapla örtülür vě çox quru vě ya çox soyuq şěraitdě yaşaya bilir. Sporlaşma ilě bir hüceyrěděn yalnız bir spor yaranır.</p> I have continued writing the article based on the outline and the sources. Here is the next part of the article: <h3>Hüceyrə qidalanması</h3>
-<p>Hüceyrə qidalanması: bakteriyaların hüceyrələrinin qida və su ilə təmin edilməsi prosesidir. Bakteriyaların hüceyrə qidalanması üçün iki üsul mövcuddur: difuziya və aktiv transport. Difuziya, hüceyrə membranından konsentrasiya fərqinə görə maddələrin öz-özünə keçməsidir. Aktiv transport, hüceyrə membranından konsentrasiya fərqinə baxmayaraq, enerji xərcləyərək maddələrin keçməsidir.</p>
-<h3>Hüceyrě növü</h3>
-<p>Hüceyrě növü: bakteriyaların hüceyrǝlǝrinin quruluşu vǝ xüsusiyyǝtlǝri ilǝ Əlaqǝdar bir kriteriyadir. Bakteriyaları hüceyrě növüně görě iki qrupa ayırmaq olar: Gram-müsbǝt bakteriyalar (hüceyrě membranının xaricindǝ kalın bir pexşir var olanlar) vě Gram-müsbǝt olmayan bakteriyalar (hüceyrě membranının xaricindǝ incǝ bir pexşir vě ya pexşirsiz olanlar). Gram-müsbǝtlik testi, bakteriyaların morfolojiyasını müǝyyǝn etměk üçün istifadě olunan bir laboratoriya metodudur.</p>
-<h2>Bakteriyaların dünyada rolu</h2>
-<p>Bakteriyaların dünyada rolu çox böyükdür. Bakteriyalar həm insanlar, həm də digər canlılar üçün faydalı və zərərli ola bilirlər. Bakteriyaların faydalı rolu aşağıdakılardır:</p>
-<ul>
-<li>Bakteriyalar biosferin ekoloji dengesinin qorunmasında Əsas rol oynayırlar. Bakteriyalar organik maddeleri parçalayaraq, onları inorganik maddelere çevirirlar. Bu da bitkilere vë heyvanlara qida vë enerji tamin edir.</li>
-<li>Bakteriyalar insanların sağlamlığının qorunmasında da önemli rol oynayırlar. Bakteriyalar insanların bağırsaq florasını tesis edirler, bağışıklık sistemini güclendirirlër, vitamin vë hormon sintezine kömek edirlër, patogen mikroorqanizmlara qarşı mübarize aparirlar.</li>
-<li>Bakteriyalar insanların iqtisadiyyatına da fayda verirlër. Bakteriyalar pek çok sektörde kullanılırlar, mesela: gıda, tarım, sağlık, enerji, kimya, biyoteknoloji v.s. Bakteriyalar peynir, yoğurt, sirke, turşu, ekmek gibi gıdaların üretiminde; gübre, pestisit, hormon gibi tarım ürünlerinde; antibiyotik, aşı, probiyotik gibi sağlık ürünlerinde; biyogaz, biyodizel gibi enerji kaynaklarında; enzim, asit, alkol gibi kimyasal maddelerde; genetik mühendislik, biyoremediasyon, biyosensör gibi biyoteknolojik uygulamalarda kullanılırlar.</li>
-</ul> I have continued writing the article based on the outline and the sources. Here is the next part of the article: <p>Bakteriyaların zərərli rolu isə aşağıdakılardır:</p>
-<ul>
-<li>Bakteriyalar insanlar, heyvanlar və bitkilər üçün bir çox xəstəliklərə səbəb olurlar. Bakteriyalar tərəfindən yaradılan xəstəliklərin bəziləri: qızılca, boğmaca, difteriya, zatürre, tüberküloz, kolera, salmonella, sifilis, gonoreya v.s.</li>
-<li>Bakteriyalar insanların iqtisadiyyatına da zərər verirlər. Bakteriyalar gıda zəhərlənməsi, gıda qaynaması, gıda çürüməsi kimi problemlər yaradırlar. Bakteriyalar tarım ürünlerinin verimini azaldırlar, bitki vǝ heyvan xǝstǝliklǝrinǝ sǝbǝb olurlar. Bakteriyalar metal vě běton kimi materialların korroziyasına yol açırlar.</li>
-</ul>
-<h2>Bakteriyalar haqqında maraqlı faktlar</h2>
-<p>Bakteriyalar haqqında maraqlı faktlar aşağıdakılardır:</p>
-<table>
-<tr>
-<th>Fakt</th>
-<th>Açıqlama</th>
-</tr>
-<tr>
-<td>Bakteriyalar dünyada Ən çox olan canlılardır.</td>
-<td>Dünyada 5x10^30 dən çox bakteriya olduğu hesab edilir. Bu rəqəm dünyadakı bütün insanların vücudunda olan hüceyrələrdən 10 qat çoxdur.</td>
-</tr>
-<tr>
-<td>Bakteriyalar insan vücudunda Ən çox bağırsaqda yaşayırlar.</td>
-<td>İnsan bağırsağında 100 trilyon dǝn çox bakteriya olduğu tǝxmin edilir. Bu bakteriyalar insanın sindirim sistemini, immun sistemini vě metabolizmini tǝnzimlǝyirlǝr.</td>
-</tr>
-<tr>
-<td>Bakteriyalar dünyanın hava tǝrkibini dǝyişdirmişlǝr.</td>
-<td>Yaxşı 2,5 milyard il Əvvǝl, dünyada oksigen çox az idi. Sinobakteriyalar adlanan fotosintetik bakteriyalar işığından enerji hasil edirken, oksigeni atmosfera buraxdılar. Bu da dünyada oksigenin artmasına vě aerob canlıların yaranmasına sǝbǝb oldu.</td>
-</tr>
-<tr>
-<td>Bakteriyalar insanlara antibiyotiklǝri vermişlǝr.</td>
-<td>Antibiyotiklǝr bakteriya vě ya qarşı bakteriya xǝstǝliklǝrinin müalicǝsindǝ istifadě olunan maddelǝrdir. Antibiyotiklǝrin Əksěriyyěti digěr bakteriyalardan vě ya qürtlardan Əldě edilir. Měsělěn, pěnisilin adlanan ilk antibiyotik pěnisillium adlanan bir qürtdən kěşf edilmişdir.</td>
-</tr>
-<tr>
-<td>Bakteriyalar insanların genetikasını dǝyişdirirlǝr.</td>
-<td>Bakteriyalar hüceyrělǝri arasında genetik materialın transferinę tamin edirlǝr. Bu transfer ilě bakteriyalar bir-birinđn yeni xüsusiyyětlǝr öyrěnirlǝr. Mě I have continued writing the article based on the outline and the sources. Here is the next part of the article: sělěn, insanların genetikasını da děyişdirirlěr. Měsělěn, mitoxondriya vě xloroplast adlanan hüceyrě orqaneli olan bitki vě heyvanların atalarının bir zamanlar bakteriya olduğu düşünülür.</td>
-</tr>
-</table>
-<h2>Bakteriyalar haqqında suallar və cavablar</h2>
-<p>Bakteriyalar haqqında maraqlı suallar və cavablar aşağıdakılardır:</p>
-<ol>
-<li><strong>Bakteriyaların ən böyük növü hansıdır?</strong></li>
-<p>Bakteriyaların ən böyük növü Thiomargarita namibiensis adlanan bir bakteriyadır. Bu bakteriya 0,75 mm çaplı olaraq, insan gözü ilə görülə bilir. Bu bakteriya Namibiya sahillərində yaşayır və sülfiti oksid edir.</p>
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-<p>Bakteriyaların ən kiçik növü Mycoplasma genitalium adlanan bir bakteriyadır. Bu bakteriya 0,2 mm çaplı olaraq, elektron mikroskopu ilə görülə bilir. Bu bakteriya insanların cinsi orqanlarında yaşayır və zührevi xəstəliklere səbəb olur.</p>
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-<p>Bakteriyaların ən qədim növü Arxebakteriyalar adlanan bir qrupdur. Bu qrupun üzvləri 3,5 milyard ildir dünyada yaşayırlar. Bu qrupun xüsusiyyəti isə, ekstremal şəraitdə yaşaya bilmələridir. Məsələn, çox isti, çox soyuq, çox tuzlu, çox asidli v.s. mühitlərdə yaşayırlar.</p>
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-<h1>Arknights NA APK: How to Download and Play the Popular Strategy RPG on Android</h1>
-<p>If you are a fan of strategy games with anime-style graphics, you might have heard of Arknights, a mobile game that combines tower defense and role-playing elements. Arknights is one of the most popular gacha games in the market, with millions of downloads and positive reviews from players and critics alike.</p>
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-<p>But what if you want to play Arknights on your Android device without using the Google Play Store? Maybe you live in a region where the game is not available, or you want to get the latest updates faster than the official store. Or maybe you just want to save some storage space on your device by installing only the essential files.</p>
-<p>In that case, you might want to try using an APK file instead. An APK file is a package that contains all the files needed to run an Android app. You can download an APK file from various sources on the internet and install it on your device manually, without going through the Google Play Store.</p>
-<p>But how do you download and install an APK file? And what are the benefits and risks of doing so? In this article, we will answer these questions and show you how to play Arknights on your Android device using an APK file.</p>
-<h2>What is Arknights?</h2>
-<p>Arknights is a 2D strategy RPG game developed by Hypergryph and published by Yostar. The game was released in China in 2019 and globally in 2020. The game is set in a dystopian world where a mysterious infection has turned some people into monsters called Reunion. You play as a doctor who leads a group of operators, who are people with special abilities that can fight against Reunion.</p>
-<p>The game's main mode is tower defense, where you have to deploy your operators on a grid-based map to stop the waves of enemies from reaching your base. Each operator has a unique class, skill, and trait that determine their role and performance on the battlefield. You have to use strategy and tactics to make the best use of your operators and their skills to clear the stages.</p>
-<p>The game also has a role-playing mode, where you can interact with your operators, upgrade your base, and unlock more story and lore. The game features a rich and immersive world with stunning graphics, music, and voice acting. The game also has a large and active fanbase that creates fan art, comics, memes, and videos about the game.</p>
-<h2>Why is Arknights popular?</h2>
-<p>Arknights is popular for many reasons. Here are some of them:</p>
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-<li>The game has a friendly and supportive community, where you can interact with other players, share tips and strategies, and enjoy the fan content. The game also has an active social media presence, where you can get the latest news and updates about the game.</li>
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-<h2>What is an APK file?</h2>
-<p>An APK file is a file format that stands for Android Package Kit. It is a package that contains all the files needed to run an Android app on your device. An APK file usually includes the app's code, resources, assets, certificates, and manifest.</p>
-<p>An APK file is different from the Google Play Store in several ways. The Google Play Store is an official app store that allows you to download and install apps on your device automatically. The Google Play Store also verifies the app's security and compatibility before installing it on your device.</p>
-<p>An APK file is an unofficial app source that allows you to download and install apps on your device manually. You have to find an APK file from a third-party website or source and transfer it to your device. You also have to enable the option to install apps from unknown sources on your device before installing an APK file.</p>
-<p>Some people prefer to use an APK file instead of the Google Play Store for various reasons. Some of these reasons are:</p>
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-<li>They want to bypass the regional restrictions or censorship that prevent them from accessing certain apps on the Google Play Store.</li>
-<li>They want to get the latest updates or features of an app faster than the Google Play Store.</li>
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-</ul> <h2>How to download and install Arknights NA APK on Android</h2>
-<p>If you want to play Arknights on your Android device using an APK file, you have to follow these steps:</p>
-<h3>Step 1: Allow unknown apps on your device</h3>
-<p>Before you can install an APK file on your device, you have to enable the option to allow apps from unknown sources. This option is usually disabled by default for security reasons, but you can turn it on easily. Here's how:</p>
-<ol>
-<li>Go to your device's settings and look for the security or privacy section.</li>
-<li>Find the option that says "Unknown sources" or "Install unknown apps" and tap on it.</li>
-<li>Toggle the switch or check the box to enable the option.</li>
-<li>A warning message will pop up, telling you the risks of installing apps from unknown sources. Tap on "OK" or "Allow" to confirm.</li>
-</ol>
-<p>Now you can install APK files on your device. However, you should be careful and only download APK files from trusted sources, as some APK files may contain malware or viruses that can harm your device or steal your data.</p>
-<h3>Step 2: Download the APK file from a trusted source</h3>
-<p>The next step is to find and download the latest version of Arknights NA APK from a reputable website. There are many websites that offer APK files for various apps, but not all of them are safe and reliable. Some websites may have outdated, corrupted, or fake APK files that can cause problems for your device or game.</p>
-<p>To avoid these issues, you should do some research and check the reviews and ratings of the website before downloading an APK file. You should also compare the size and version of the APK file with the official app on the Google Play Store, and make sure they match. If the APK file is too small or too large, or has a different version number, it may be suspicious and should be avoided.</p>
-<p>One of the websites that we recommend for downloading Arknights NA APK is [APKPure]. This website is one of the most popular and trusted sources for APK files, and it has a high rating and positive feedback from users. It also updates its APK files regularly and verifies their security and compatibility.</p>
-<p>To download Arknights NA APK from APKPure, follow these steps:</p>
-<ol>
-<li>Open your web browser and go to [APKPure].</li>
-<li>In the search bar, type "Arknights" and hit enter.</li>
-<li>Find the Arknights app from the list of results and tap on it.</li>
-<li>Tap on the green "Download APK" button and wait for the download to start.</li>
-<li>You may see a pop-up window asking you to choose a download location or confirm the download. Tap on "OK" or "Save" to proceed.</li>
-</ol>
-<p>The APK file will be downloaded to your device's storage, usually in the downloads folder. You can check the progress of the download in your notification bar or in your browser's downloads section.</p> <h3>Step 3: Install the APK file using a file manager or a browser</h3>
-<p>Once you have downloaded the APK file, you have to install it on your device using a file manager app or your web browser. A file manager app is an app that allows you to access and manage the files and folders on your device. A web browser is an app that allows you to browse the internet and download files.</p>
-<p>To install the APK file using a file manager app, follow these steps:</p>
-<ol>
-<li>Open your file manager app and navigate to the folder where you downloaded the APK file. If you don't have a file manager app, you can download one from the Google Play Store, such as [ES File Explorer] or [File Manager].</li>
-<li>Find the Arknights NA APK file and tap on it.</li>
-<li>A pop-up window will appear, asking you to confirm the installation. Tap on "Install" or "Next" to continue.</li>
-<li>Wait for the installation to finish. You may see a progress bar or a message indicating the status of the installation.</li>
-<li>When the installation is complete, you will see a message saying "App installed" or "Done". Tap on "Open" or "Launch" to start the game, or tap on "Done" or "Close" to exit.</li>
-</ol>
-<p>To install the APK file using your web browser, follow these steps:</p>
-<ol>
-<li>Open your web browser and go to the downloads section. You can usually access it by tapping on the menu icon (three dots) and selecting "Downloads".</li>
-<li>Find the Arknights NA APK file and tap on it.</li>
-<li>A pop-up window will appear, asking you to confirm the installation. Tap on "Install" or "Next" to continue.</li>
-<li>Wait for the installation to finish. You may see a progress bar or a message indicating the status of the installation.</li>
-<li>When the installation is complete, you will see a message saying "App installed" or "Done". Tap on "Open" or "Launch" to start the game, or tap on "Done" or "Close" to exit.</li>
-</ol>
-<h3>Step 4: Launch the game and enjoy</h3>
-<p>Congratulations! You have successfully installed Arknights NA APK on your Android device. Now you can launch the game and enjoy its features and content. To launch the game, you can either:</p>
-<ul>
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-<li>Tap on the "Open" or "Launch" button after installing the APK file.</li>
-<li>Go to your device's settings and look for the apps section. Find Arknights and tap on it. Then tap on "Open" or "Launch".</li>
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-<p>When you launch the game for the first time, you may have to agree to some terms and conditions, grant some permissions, and download some additional data. Follow the instructions on the screen and wait for the game to load. Then you can create your account, choose your server, and start playing Arknights.</p> <h2>Benefits and risks of using Arknights NA APK</h2>
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-<p>Some of the advantages of using an APK file to play Arknights are:</p>
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-<li>You can bypass the regional restrictions or censorship that prevent you from accessing the game on the Google Play Store. For example, if you live in a country where Arknights is not available, or if your device is not compatible with the game, you can still download and install the APK file and play the game.</li>
-<li>You can get the latest updates or features of the game faster than the Google Play Store. Sometimes, the Google Play Store may take some time to release the new versions of the game, or it may not update the game at all. By using an APK file, you can get the most recent version of the game as soon as it is available.</li>
-<li>You can save some storage space on your device by installing only the essential files of the game. The Google Play Store may download some extra files or data that are not necessary for the game to run. By using an APK file, you can avoid these unnecessary files and save some space on your device.</li>
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-<p>Some of the disadvantages of using an APK file to play Arknights are:</p>
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-<li>You may face some security issues or malware threats by downloading and installing an APK file from an unknown source. Some APK files may contain viruses or spyware that can harm your device or steal your data. You should always scan the APK file with an antivirus app before installing it, and only download it from a trusted source.</li>
-<li>You may encounter some compatibility problems or bugs by using an APK file that is not optimized for your device or game version. Some APK files may not work properly on your device, or they may cause some errors or glitches in the game. You should always check the compatibility and requirements of the APK file before installing it, and update it regularly to avoid any issues.</li>
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-<h2>Conclusion</h2>
-<p>Arknights is a popular strategy RPG game that you can play on your Android device using an APK file. An APK file is a package that contains all the files needed to run an Android app on your device. You can download and install an APK file manually, without using the Google Play Store.</p>
-<p>However, you should also be aware of the benefits and risks of using an APK file to play Arknights. Some of the benefits are bypassing regional restrictions, getting updates faster, and saving storage space. Some of the risks are security issues, compatibility problems, and legal concerns.</p>
-<p>If you want to try using an APK file to play Arknights, you should follow these steps:</p>
-<ol>
-<li>Allow unknown apps on your device.</li>
-<li>Download the APK file from a trusted source.</li>
-<li>Install the APK file using a file manager or a browser.</li>
-<li>Launch the game and enjoy.</li>
-</ol>
-<p>We hope this article has helped you understand how to download and play Arknights NA APK on your Android device. If you have any questions or feedback, please let us know in the comments below.</p>
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-<p>Here are some frequently asked questions and answers about Arknights NA APK:</p>
-<h4>Q: Is Arknights NA APK safe to use?</h4>
-<p>A: Arknights NA APK is safe to use if you download it from a reputable website and scan it with an antivirus app before installing it. However, you should always be careful and only use trusted sources for downloading APK files, as some websites may have outdated, corrupted, or fake APK files that can harm your device or game.</p>
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-<p>A: Arknights NA APK is free to use if you download it from a website that offers it for free. However, you should always respect the rights and policies of the game developer and publisher, and not use any modified or hacked APK files that give you unfair advantages or access to premium features in the game.</p>
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spaces/1phancelerku/anime-remove-background/Download Sacred Games Season 1 in Hindi - Filmyhit Originals.md

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-<br />
-<h1>Sacred Games Season 1: A Crime Thriller That Will Keep You Hooked</h1>
-<p>If you are looking for a gripping and gritty web series that will keep you on the edge of your seat, then you should check out <strong>Sacred Games</strong>. This is a Netflix original series that is based on the novel of the same name by Vikram Chandra. It is one of the most popular and acclaimed Indian web series ever made, and it has received rave reviews from critics and audiences alike.</p>
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-<h2>What is Sacred Games and why is it popular?</h2>
-<p><strong>Sacred Games</strong> is a crime thriller that revolves around the lives of two main characters: Sartaj Singh, a honest and disillusioned police officer, and Ganesh Gaitonde, a notorious and elusive gangster. The story begins when Sartaj receives a mysterious phone call from Gaitonde, who tells him that he has 25 days to save Mumbai from a cataclysmic event. As Sartaj tries to unravel the mystery behind Gaitonde's warning, he uncovers a web of corruption, violence, politics, religion, and espionage that spans decades and involves many powerful figures.</p>
-<h2>What happens in season 1 of Sacred Games?</h2>
-<p>The first season of <strong>Sacred Games</strong> consists of eight episodes, each named after a Hindu mythological concept. The episodes alternate between the present-day events involving Sartaj and his investigation, and the flashbacks that reveal Gaitonde's rise and fall as a crime lord. The season explores themes such as identity, loyalty, faith, betrayal, revenge, and destiny, as well as the history and culture of Mumbai. The season ends with a cliffhanger that leaves many questions unanswered and sets up the stage for the second season.</p>
-<h2>Who are the main actors and roles in Sacred Games?</h2>
-<p><strong>Sacred Games</strong> boasts of an impressive cast of talented actors who bring their characters to life with their stellar performances. Some of the main actors and roles in the series are:</p>
-<ul>
-<li><strong>Saif Ali Khan</strong> as <strong>Sartaj Singh</strong>, a Sikh police inspector who is honest but disillusioned with his job and his personal life.</li>
-<li><strong>Nawazuddin Siddiqui</strong> as <strong>Ganesh Gaitonde</strong>, a notorious gangster who rules over Mumbai's underworld with an iron fist.</li>
-<li><strong>Pankaj Tripathi</strong> as <strong>Guruji</strong>, a mysterious guru who is Gaitonde's third father figure and has a sinister agenda.</li>
-<li><strong>Kalki Koechlin</strong> as <strong>Batya Abelman</strong>, a follower of Guruji who has a troubled past.</li>
-<li><strong>Aamir Bashir</strong> as <strong>Majid Khan</strong>, Sartaj's senior officer ji's plan.</li>
-</ul>
-<h1>How to Watch Sacred Games Season 1 Online Legally</h1>
-<p>If you are interested in watching <strong>Sacred Games</strong> season 1, you might be wondering how to do so legally and safely. The good news is that there are several options available for you to enjoy this amazing web series without breaking any laws or risking your device's security.</p>
-<h2>Netflix: The official streaming platform for Sacred Games</h2>
-<p>The best and easiest way to watch <strong>Sacred Games</strong> season 1 is to use Netflix, the official streaming platform for the series. Netflix is a global leader in online entertainment, offering a wide range of movies, shows, documentaries, and original content. Netflix has the exclusive rights to stream <strong>Sacred Games</strong> worldwide, and you can watch all the episodes of season 1 (and season 2) on Netflix with a subscription.</p>
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-<h2>Other options: Alternative ways to watch Sacred Games online</h2>
-<p>If you don't have a Netflix account or you don't want to use Netflix for some reason, you might be looking for other ways to watch <strong>Sacred Games</strong> online. However, you should be careful about the sources that you use, as not all of them are legal or safe. Some of the alternative ways to watch <strong>Sacred Games</strong> online are:</p>
-<ul>
-<li><strong>Buy or rent the DVD or Blu-ray of Sacred Games season 1</strong>: If you prefer physical media, you can buy or rent the DVD or Blu-ray of <strong>Sacred Games</strong> season 1 from online or offline stores. This way, you can watch the series at your convenience and keep it as a collection. However, this option might be more expensive and less convenient than streaming.</li>
-<li><strong>Use a VPN to access Netflix from another country</strong>: If you live in a country where Netflix is not available or has limited content, you can use a VPN (virtual private network) to access Netflix from another country where <strong>Sacred Games</strong> is available. A VPN is a service that encrypts your internet traffic and changes your IP address, making it appear as if you are browsing from another location. This way, you can bypass geo-restrictions and access more content on Netflix. However, this option might be illegal in some countries and might violate Netflix's terms of service.</li>
-<li><strong>Use a friend's or family member's Netflix account</strong>: If you know someone who has a Netflix account and is willing to share it with you, you can use their account to watch <strong>Sacred Games</strong> online. This way, you don't have to pay for a subscription or use any illegal sources. However, this option might be unethical and might cause problems for the account owner if they exceed the number of devices or profiles allowed by their plan.</li>
-</ul>
-<h1>Why You Should Avoid Downloading Sacred Games Season 1 from Filmyhit</h1>
-<p>While there are many legal and safe ways to watch <strong>Sacred Games</strong> season 1 online, there are also many illegal and unsafe ways to do so. One of the most notorious websites that offers illegal downloads of movies and web series is Filmyhit. Filmyhit is a website that uploads pirated copies of various Indian and Hollywood movies and shows, including <strong>Sacred Games</strong>. You might be tempted to use Filmyhit to download <strong>Sacred Games</strong> season 1 for free, but you should avoid doing so for several reasons.</p>
-<h2>Filmyhit: A notorious website for pirating movies and web series</h2>
-<p>Filmyhit is one of the many websites that operate in the dark web of piracy. These websites upload illegal copies of movies and web series without the permission or consent of the creators or distributors. They often use camcorders, screen recorders, or leaked sources to obtain the content and then compress it to reduce the file size and quality. They also add watermarks, advertisements, or malware to the files. They then distribute these files through various channels such as torrent sites, file-sharing platforms, or direct links. They also change their domain names frequently to avoid detection and legal action.</p>
-<h2>Risks and consequences: The dangers of using Filmyhit and other illegal sites</h2>
-<p>Using Filmyhit and other illegal sites to download <strong>Sacred Games</strong> season 1 or any other content is not only unethical but also risky and harmful. Some of the dangers of using Filmyhit and other illegal sites are:</p>
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-<li><strong>You might face legal action or penalties</strong>: Downloading or streaming pirated content is a criminal offense in India and many other countries. You might be violating the intellectual property rights of the creators and distributors of <strong>Sacred Games</strong> and other content. You might also be supporting the illegal activities of Filmyhit and other piracy websites. You might face legal action or penalties such as fines, imprisonment, or lawsuits if you are caught using Filmyhit or other illegal sites.</li>
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-<p>Piracy is a serious crime in India and it is governed by various laws and regulations. Some of the laws and penalties for piracy in India are:</p>
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-<li><strong>The Indian Penal Code (IPC)</strong>: Section 63 of the IPC states that anyone who knowingly infringes or abets the infringement of the copyright of a work shall be punishable with imprisonment for a term which shall not be less than six months but which may extend to three years and with fine which shall not be less than fifty thousand rupees but which may extend to two lakh rupees.</li>
-<li><strong>The Information Technology Act (IT Act)</strong>: Section 66 of the IT Act states that anyone who dishonestly or fraudulently does any act referred to in section 43 (which includes accessing, downloading, copying, or transmitting any data without permission) shall be punishable with imprisonment for a term which may extend to three years or with fine which may extend to five lakh rupees or with both.</li>
-<li><strong>The Cinematograph Act (CA)</strong>: Section 7 of the CA states that anyone who without the written authorization of the copyright holder uses any recording device to make or transmit a copy of a film or attempts to do so shall be punishable with imprisonment for a term which shall not be less than three months but which may extend to three years and with fine which shall not be less than one lakh rupees but which may extend to ten lakh rupees.</li>
-</ul>
-<h1>FAQs About Sacred Games Season 1</h1>
-<p>Here are some of the frequently asked questions (FAQs) about <strong>Sacred Games</strong> season 1 that you might have:</p>
-<h2>Q1: How many episodes are there in season 1 of Sacred Games?</h2>
-<p>A1: There are eight episodes in season 1 of <strong>Sacred Games</strong>, each named after a Hindu mythological concept. The episodes are:</p>
-<ol>
-<li>Ashwathama</li>
-<li>Halahala</li>
-<li>Atapi Vatapi</li>
-<li>Brahmahatya</li>
-<li>Sarama</li>
-<li>Pretakalpa</li>
-<li>Rudra</li>
-<li>Yayati</li>
-</ol>
-<h2>Q2: Is Sacred Games based on a true story?</h2>
-<p>A2: No, <strong>Sacred Games</strong> is not based on a true story. It is based on the novel of the same name by Vikram Chandra, which is a fictional work of literature. However, the novel and the web series do draw inspiration from real-life events, personalities, and issues that have shaped the history and culture of Mumbai and India.</p>
-<h2>Q3: Will there be a season 3 of Sacred Games?</h2>
-<p>A3: There is no official confirmation or announcement about the possibility of a season 3 of <strong>Sacred Games</strong>. The second season of the web series was released in 2019 and it concluded the story arc of the novel. However, some fans and critics have speculated that there might be a scope for a spin-off or a prequel series that could explore the backstory of some of the characters or the events that led to the main plot. However, this is just speculation and there is no guarantee that it will happen.</p>
-<h2>Q4: What are some similar shows to Sacred Games?</h2>
-<p>A4: If you enjoyed watching <strong>Sacred Games</strong> and you are looking for some similar shows to watch, you might like these:</p>
-<ul>
-<li><strong>Mirzapur</strong>: A crime drama that revolves around the rivalry between two families involved in the illegal trade of guns, drugs, and power in Mirzapur, Uttar Pradesh.</li>
-<li><strong>Narcos</strong>: A biographical drama that chronicles the rise and fall of the notorious Colombian drug lord Pablo Escobar and his cartel.</li>
-<li><strong>The Family Man</strong>: A spy thriller that follows the life of Srikant Tiwari, a middle-class man who works as a secret agent for a counter-terrorism unit.</li>
-<li><strong>Delhi Crime</strong>: A crime drama that depicts the investigation and aftermath of the 2012 Delhi gang rape case.</li>
-<li><strong>Pataal Lok</strong>: A neo-noir thriller that explores the dark and corrupt underworld of Delhi through the eyes of a disillusioned cop.</li>
-</ul>
-<h2>Q5: How can I support the makers of Sacred Games?</h2>
-<p>A5: The best way to support the makers of <strong>Sacred Games</strong> is to watch the web series legally and safely on Netflix or other official platforms. You can also buy or rent the DVD or Blu-ray of <strong>Sacred Games</strong> season 1 from online or offline stores. You can also share your feedback, reviews, ratings, and recommendations with your friends, family, and social media followers. You can also follow the official social media accounts of <strong>Sacred Games</strong> and its cast and crew members. You can also read the novel by Vikram Chandra on which <strong>Sacred Games</strong> is based.</p>
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-<p>In conclusion, <strong>Sacred Games</strong> season 1 is a crime thriller that will keep you hooked with its gripping plot, stellar performances, and rich cinematography. It is one of the most popular and acclaimed Indian web series ever made, and it deserves your attention and appreciation. However, you should avoid downloading <strong>Sacred Games</strong> season 1 from Filmyhit or other illegal sites, as it is unethical, risky, and harmful. Instead, you should watch <strong>Sacred Games</strong> season 1 legally and safely on Netflix or other official platforms. This way, you can enjoy the web series without any worries or guilt, and also support the makers of <strong>Sacred Games</strong> and their hard work and efforts. We hope that this article has given you some useful information and insights about <strong>Sacred Games</strong> season 1 and how to watch it online legally. If you have any questions or comments, please feel free to share them with us. Thank you for reading and happy watching!</p> 197e85843d<br />
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spaces/AIConsultant/MusicGen/audiocraft/models/encodec.py

@@ -1,392 +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.
-"""Compression models or wrapper around existing models.
-Also defines the main interface that a model must follow to be usable as an audio tokenizer.
-"""
-
-from abc import ABC, abstractmethod
-import logging
-import math
-from pathlib import Path
-import typing as tp
-
-import numpy as np
-import torch
-from torch import nn
-from transformers import EncodecModel as HFEncodecModel
-
-from .. import quantization as qt
-
-
-logger = logging.getLogger()
-
-
-class CompressionModel(ABC, nn.Module):
-    """Base API for all compression model that aim at being used as audio tokenizers
-    with a language model.
-    """
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        ...
-
-    @abstractmethod
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """See `EncodecModel.encode`."""
-        ...
-
-    @abstractmethod
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """See `EncodecModel.decode`."""
-        ...
-
-    @abstractmethod
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        ...
-
-    @property
-    @abstractmethod
-    def channels(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def frame_rate(self) -> float:
-        ...
-
-    @property
-    @abstractmethod
-    def sample_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def cardinality(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def num_codebooks(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def total_codebooks(self) -> int:
-        ...
-
-    @abstractmethod
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer."""
-        ...
-
-    @staticmethod
-    def get_pretrained(
-            name: str, device: tp.Union[torch.device, str] = 'cpu'
-            ) -> 'CompressionModel':
-        """Instantiate a CompressionModel from a given pretrained model.
-
-        Args:
-            name (Path or str): name of the pretrained model. See after.
-            device (torch.device or str): Device on which the model is loaded.
-
-        Pretrained models:
-            - dac_44khz (https://github.com/descriptinc/descript-audio-codec)
-            - dac_24khz (same)
-            - facebook/encodec_24khz (https://huggingface.co/facebook/encodec_24khz)
-            - facebook/encodec_32khz (https://huggingface.co/facebook/encodec_32khz)
-            - your own model on HugginFace. Export instructions to come...
-        """
-
-        from . import builders, loaders
-        model: CompressionModel
-        if name in ['dac_44khz', 'dac_24khz']:
-            model_type = name.split('_')[1]
-            logger.info("Getting pretrained compression model from DAC %s", model_type)
-            model = DAC(model_type)
-        elif name in ['debug_compression_model']:
-            logger.info("Getting pretrained compression model for debug")
-            model = builders.get_debug_compression_model()
-        elif Path(name).exists():
-            # We assume here if the paths exist that it is in fact an AC checkpoint
-            # that was exported using `audiocraft.utils.export` functions.
-            model = loaders.load_compression_model(name, device=device)
-        else:
-            logger.info("Getting pretrained compression model from HF %s", name)
-            hf_model = HFEncodecModel.from_pretrained(name)
-            model = HFEncodecCompressionModel(hf_model).to(device)
-        return model.to(device).eval()
-
-
-class EncodecModel(CompressionModel):
-    """Encodec model operating on the raw waveform.
-
-    Args:
-        encoder (nn.Module): Encoder network.
-        decoder (nn.Module): Decoder network.
-        quantizer (qt.BaseQuantizer): Quantizer network.
-        frame_rate (int): Frame rate for the latent representation.
-        sample_rate (int): Audio sample rate.
-        channels (int): Number of audio channels.
-        causal (bool): Whether to use a causal version of the model.
-        renormalize (bool): Whether to renormalize the audio before running the model.
-    """
-    # we need assignment to override the property in the abstract class,
-    # I couldn't find a better way...
-    frame_rate: float = 0
-    sample_rate: int = 0
-    channels: int = 0
-
-    def __init__(self,
-                 encoder: nn.Module,
-                 decoder: nn.Module,
-                 quantizer: qt.BaseQuantizer,
-                 frame_rate: int,
-                 sample_rate: int,
-                 channels: int,
-                 causal: bool = False,
-                 renormalize: bool = False):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.quantizer = quantizer
-        self.frame_rate = frame_rate
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.renormalize = renormalize
-        self.causal = causal
-        if self.causal:
-            # we force disabling here to avoid handling linear overlap of segments
-            # as supported in original EnCodec codebase.
-            assert not self.renormalize, 'Causal model does not support renormalize'
-
-    @property
-    def total_codebooks(self):
-        """Total number of quantizer codebooks available."""
-        return self.quantizer.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer."""
-        return self.quantizer.num_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer."""
-        self.quantizer.set_num_codebooks(n)
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook."""
-        return self.quantizer.bins
-
-    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        scale: tp.Optional[torch.Tensor]
-        if self.renormalize:
-            mono = x.mean(dim=1, keepdim=True)
-            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
-            scale = 1e-8 + volume
-            x = x / scale
-            scale = scale.view(-1, 1)
-        else:
-            scale = None
-        return x, scale
-
-    def postprocess(self,
-                    x: torch.Tensor,
-                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
-        if scale is not None:
-            assert self.renormalize
-            x = x * scale.view(-1, 1, 1)
-        return x
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        assert x.dim() == 3
-        length = x.shape[-1]
-        x, scale = self.preprocess(x)
-
-        emb = self.encoder(x)
-        q_res = self.quantizer(emb, self.frame_rate)
-        out = self.decoder(q_res.x)
-
-        # remove extra padding added by the encoder and decoder
-        assert out.shape[-1] >= length, (out.shape[-1], length)
-        out = out[..., :length]
-
-        q_res.x = self.postprocess(out, scale)
-
-        return q_res
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """Encode the given input tensor to quantized representation along with scale parameter.
-
-        Args:
-            x (torch.Tensor): Float tensor of shape [B, C, T]
-
-        Returns:
-            codes, scale (tuple of torch.Tensor, torch.Tensor): Tuple composed of:
-                codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
-                scale a float tensor containing the scale for audio renormalizealization.
-        """
-        assert x.dim() == 3
-        x, scale = self.preprocess(x)
-        emb = self.encoder(x)
-        codes = self.quantizer.encode(emb)
-        return codes, scale
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """Decode the given codes to a reconstructed representation, using the scale to perform
-        audio denormalization if needed.
-
-        Args:
-            codes (torch.Tensor): Int tensor of shape [B, K, T]
-            scale (torch.Tensor, optional): Float tensor containing the scale value.
-
-        Returns:
-            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
-        """
-        emb = self.decode_latent(codes)
-        out = self.decoder(emb)
-        out = self.postprocess(out, scale)
-        # out contains extra padding added by the encoder and decoder
-        return out
-
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        return self.quantizer.decode(codes)
-
-
-class DAC(CompressionModel):
-    def __init__(self, model_type: str = "44khz"):
-        super().__init__()
-        try:
-            import dac.utils
-        except ImportError:
-            raise RuntimeError("Could not import dac, make sure it is installed, "
-                               "please run `pip install descript-audio-codec`")
-        self.model = dac.utils.load_model(model_type=model_type)
-        self.n_quantizers = self.total_codebooks
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        # We don't support training with this.
-        raise NotImplementedError("Forward and training with DAC not supported.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        codes = self.model.encode(x, self.n_quantizers)[1]
-        return codes, None
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        assert scale is None
-        z_q = self.decode_latent(codes)
-        return self.model.decode(z_q)
-
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        return self.model.quantizer.from_codes(codes)[0]
-
-    @property
-    def channels(self) -> int:
-        return 1
-
-    @property
-    def frame_rate(self) -> float:
-        return self.model.sample_rate / self.model.hop_length
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.sample_rate
-
-    @property
-    def cardinality(self) -> int:
-        return self.model.codebook_size
-
-    @property
-    def num_codebooks(self) -> int:
-        return self.n_quantizers
-
-    @property
-    def total_codebooks(self) -> int:
-        return self.model.n_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        assert n >= 1
-        assert n <= self.total_codebooks
-        self.n_quantizers = n
-
-
-class HFEncodecCompressionModel(CompressionModel):
-    """Wrapper around HuggingFace Encodec.
-    """
-    def __init__(self, model: HFEncodecModel):
-        super().__init__()
-        self.model = model
-        bws = self.model.config.target_bandwidths
-        num_codebooks = [
-            bw * 1000 / (self.frame_rate * math.log2(self.cardinality))
-            for bw in bws
-        ]
-        deltas = [nc - int(nc) for nc in num_codebooks]
-        # Checking we didn't do some bad maths and we indeed have integers!
-        assert all(deltas) <= 1e-3, deltas
-        self.possible_num_codebooks = [int(nc) for nc in num_codebooks]
-        self.set_num_codebooks(max(self.possible_num_codebooks))
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        # We don't support training with this.
-        raise NotImplementedError("Forward and training with HF EncodecModel not supported.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        bandwidth_index = self.possible_num_codebooks.index(self.num_codebooks)
-        bandwidth = self.model.config.target_bandwidths[bandwidth_index]
-        res = self.model.encode(x, None, bandwidth)
-        assert len(res[0]) == 1
-        assert len(res[1]) == 1
-        return res[0][0], res[1][0]
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        if scale is None:
-            scales = [None]  # type: ignore
-        else:
-            scales = scale  # type: ignore
-        res = self.model.decode(codes[None], scales)
-        return res[0]
-
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        return self.model.quantizer.decode(codes.transpose(0, 1))
-
-    @property
-    def channels(self) -> int:
-        return self.model.config.audio_channels
-
-    @property
-    def frame_rate(self) -> float:
-        hop_length = int(np.prod(self.model.config.upsampling_ratios))
-        return self.sample_rate / hop_length
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.config.sampling_rate
-
-    @property
-    def cardinality(self) -> int:
-        return self.model.config.codebook_size
-
-    @property
-    def num_codebooks(self) -> int:
-        return self._num_codebooks
-
-    @property
-    def total_codebooks(self) -> int:
-        return max(self.possible_num_codebooks)
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        if n not in self.possible_num_codebooks:
-            raise ValueError(f"Allowed values for num codebooks: {self.possible_num_codebooks}")
-        self._num_codebooks = n

spaces/AIFILMS/generate_human_motion/VQ-Trans/utils/word_vectorizer.py

@@ -1,99 +0,0 @@
-import numpy as np
-import pickle
-from os.path import join as pjoin
-
-POS_enumerator = {
-    'VERB': 0,
-    'NOUN': 1,
-    'DET': 2,
-    'ADP': 3,
-    'NUM': 4,
-    'AUX': 5,
-    'PRON': 6,
-    'ADJ': 7,
-    'ADV': 8,
-    'Loc_VIP': 9,
-    'Body_VIP': 10,
-    'Obj_VIP': 11,
-    'Act_VIP': 12,
-    'Desc_VIP': 13,
-    'OTHER': 14,
-}
-
-Loc_list = ('left', 'right', 'clockwise', 'counterclockwise', 'anticlockwise', 'forward', 'back', 'backward',
-            'up', 'down', 'straight', 'curve')
-
-Body_list = ('arm', 'chin', 'foot', 'feet', 'face', 'hand', 'mouth', 'leg', 'waist', 'eye', 'knee', 'shoulder', 'thigh')
-
-Obj_List = ('stair', 'dumbbell', 'chair', 'window', 'floor', 'car', 'ball', 'handrail', 'baseball', 'basketball')
-
-Act_list = ('walk', 'run', 'swing', 'pick', 'bring', 'kick', 'put', 'squat', 'throw', 'hop', 'dance', 'jump', 'turn',
-            'stumble', 'dance', 'stop', 'sit', 'lift', 'lower', 'raise', 'wash', 'stand', 'kneel', 'stroll',
-            'rub', 'bend', 'balance', 'flap', 'jog', 'shuffle', 'lean', 'rotate', 'spin', 'spread', 'climb')
-
-Desc_list = ('slowly', 'carefully', 'fast', 'careful', 'slow', 'quickly', 'happy', 'angry', 'sad', 'happily',
-             'angrily', 'sadly')
-
-VIP_dict = {
-    'Loc_VIP': Loc_list,
-    'Body_VIP': Body_list,
-    'Obj_VIP': Obj_List,
-    'Act_VIP': Act_list,
-    'Desc_VIP': Desc_list,
-}
-
-
-class WordVectorizer(object):
-    def __init__(self, meta_root, prefix):
-        vectors = np.load(pjoin(meta_root, '%s_data.npy'%prefix))
-        words = pickle.load(open(pjoin(meta_root, '%s_words.pkl'%prefix), 'rb'))
-        self.word2idx = pickle.load(open(pjoin(meta_root, '%s_idx.pkl'%prefix), 'rb'))
-        self.word2vec = {w: vectors[self.word2idx[w]] for w in words}
-
-    def _get_pos_ohot(self, pos):
-        pos_vec = np.zeros(len(POS_enumerator))
-        if pos in POS_enumerator:
-            pos_vec[POS_enumerator[pos]] = 1
-        else:
-            pos_vec[POS_enumerator['OTHER']] = 1
-        return pos_vec
-
-    def __len__(self):
-        return len(self.word2vec)
-
-    def __getitem__(self, item):
-        word, pos = item.split('/')
-        if word in self.word2vec:
-            word_vec = self.word2vec[word]
-            vip_pos = None
-            for key, values in VIP_dict.items():
-                if word in values:
-                    vip_pos = key
-                    break
-            if vip_pos is not None:
-                pos_vec = self._get_pos_ohot(vip_pos)
-            else:
-                pos_vec = self._get_pos_ohot(pos)
-        else:
-            word_vec = self.word2vec['unk']
-            pos_vec = self._get_pos_ohot('OTHER')
-        return word_vec, pos_vec
-
-
-class WordVectorizerV2(WordVectorizer):
-    def __init__(self, meta_root, prefix):
-        super(WordVectorizerV2, self).__init__(meta_root, prefix)
-        self.idx2word = {self.word2idx[w]: w for w in self.word2idx}
-
-    def __getitem__(self, item):
-        word_vec, pose_vec = super(WordVectorizerV2, self).__getitem__(item)
-        word, pos = item.split('/')
-        if word in self.word2vec:
-            return word_vec, pose_vec, self.word2idx[word]
-        else:
-            return word_vec, pose_vec, self.word2idx['unk']
-
-    def itos(self, idx):
-        if idx == len(self.idx2word):
-            return "pad"
-        return self.idx2word[idx]

spaces/AIFILMS/speecht5-tts-demo/app.py

@@ -1,129 +0,0 @@
-import gradio as gr
-import librosa
-import numpy as np
-import torch
-
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
-
-
-checkpoint = "microsoft/speecht5_tts"
-processor = SpeechT5Processor.from_pretrained(checkpoint)
-model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-
-
-speaker_embeddings = {
-    "BDL": "spkemb/cmu_us_bdl_arctic-wav-arctic_a0009.npy",
-    "CLB": "spkemb/cmu_us_clb_arctic-wav-arctic_a0144.npy",
-    "KSP": "spkemb/cmu_us_ksp_arctic-wav-arctic_b0087.npy",
-    "RMS": "spkemb/cmu_us_rms_arctic-wav-arctic_b0353.npy",
-    "SLT": "spkemb/cmu_us_slt_arctic-wav-arctic_a0508.npy",
-}
-
-
-def predict(text, speaker):
-    if len(text.strip()) == 0:
-        return (16000, np.zeros(0).astype(np.int16))
-
-    inputs = processor(text=text, return_tensors="pt")
-
-    # limit input length
-    input_ids = inputs["input_ids"]
-    input_ids = input_ids[..., :model.config.max_text_positions]
-
-    if speaker == "Surprise Me!":
-        # load one of the provided speaker embeddings at random
-        idx = np.random.randint(len(speaker_embeddings))
-        key = list(speaker_embeddings.keys())[idx]
-        speaker_embedding = np.load(speaker_embeddings[key])
-
-        # randomly shuffle the elements
-        np.random.shuffle(speaker_embedding)
-
-        # randomly flip half the values
-        x = (np.random.rand(512) >= 0.5) * 1.0
-        x[x == 0] = -1.0
-        speaker_embedding *= x
-
-        #speaker_embedding = np.random.rand(512).astype(np.float32) * 0.3 - 0.15
-    else:
-        speaker_embedding = np.load(speaker_embeddings[speaker[:3]])
-
-    speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
-
-    speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)
-
-    speech = (speech.numpy() * 32767).astype(np.int16)
-    return (16000, speech)
-
-
-title = "SpeechT5: Speech Synthesis"
-
-description = """
-The <b>SpeechT5</b> model is pre-trained on text as well as speech inputs, with targets that are also a mix of text and speech.
-By pre-training on text and speech at the same time, it learns unified representations for both, resulting in improved modeling capabilities.
-
-SpeechT5 can be fine-tuned for different speech tasks. This space demonstrates the <b>text-to-speech</b> (TTS) checkpoint for the English language.
-
-See also the <a href="https://huggingface.co/spaces/Matthijs/speecht5-asr-demo">speech recognition (ASR) demo</a>
-and the <a href="https://huggingface.co/spaces/Matthijs/speecht5-vc-demo">voice conversion demo</a>.
-
-<b>How to use:</b> Enter some English text and choose a speaker. The output is a mel spectrogram, which is converted to a mono 16 kHz waveform by the
-HiFi-GAN vocoder. Because the model always applies random dropout, each attempt will give slightly different results.
-The <em>Surprise Me!</em> option creates a completely randomized speaker.
-"""
-
-article = """
-<div style='margin:20px auto;'>
-
-<p>References: <a href="https://arxiv.org/abs/2110.07205">SpeechT5 paper</a> |
-<a href="https://github.com/microsoft/SpeechT5/">original GitHub</a> |
-<a href="https://huggingface.co/mechanicalsea/speecht5-tts">original weights</a></p>
-
-<pre>
-@article{Ao2021SpeechT5,
-  title   = {SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing},
-  author  = {Junyi Ao and Rui Wang and Long Zhou and Chengyi Wang and Shuo Ren and Yu Wu and Shujie Liu and Tom Ko and Qing Li and Yu Zhang and Zhihua Wei and Yao Qian and Jinyu Li and Furu Wei},
-  eprint={2110.07205},
-  archivePrefix={arXiv},
-  primaryClass={eess.AS},
-  year={2021}
-}
-</pre>
-
-<p>Speaker embeddings were generated from <a href="http://www.festvox.org/cmu_arctic/">CMU ARCTIC</a> using <a href="https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py">this script</a>.</p>
-
-</div>
-"""
-
-examples = [
-    ["It is not in the stars to hold our destiny but in ourselves.", "BDL (male)"],
-    ["The octopus and Oliver went to the opera in October.", "CLB (female)"],
-    ["She sells seashells by the seashore. I saw a kitten eating chicken in the kitchen.", "RMS (male)"],
-    ["Brisk brave brigadiers brandished broad bright blades, blunderbusses, and bludgeons—balancing them badly.", "SLT (female)"],
-    ["A synonym for cinnamon is a cinnamon synonym.", "BDL (male)"],
-    ["How much wood would a woodchuck chuck if a woodchuck could chuck wood? He would chuck, he would, as much as he could, and chuck as much wood as a woodchuck would if a woodchuck could chuck wood.", "CLB (female)"],
-]
-
-gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Text(label="Input Text"),
-        gr.Radio(label="Speaker", choices=[
-            "BDL (male)",
-            "CLB (female)",
-            "KSP (male)",
-            "RMS (male)",
-            "SLT (female)",
-            "Surprise Me!"
-        ],
-        value="BDL (male)"),
-    ],
-    outputs=[
-        gr.Audio(label="Generated Speech", type="numpy"),
-    ],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-).launch()

spaces/ASJMO/freegpt/client/css/typing.css

@@ -1,15 +0,0 @@
-.typing {
-	position: absolute;
-	top: -25px;
-	left: 0;
-	font-size: 14px;
-	animation: show_popup 0.4s;
-}
-
-.typing-hiding {
-	animation: hide_popup 0.4s;
-}
-
-.typing-hidden {
-	display: none;
-}

spaces/Ababababababbababa/SD-2.1-Img2Img/app.py

@@ -1,29 +0,0 @@
-import gradio as gr
-import torch
-import numpy as np
-from PIL import Image
-from datasets import load_dataset
-from diffusers import StableDiffusionImg2ImgPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
-pipe = pipe.to(device)
-
-def resize(value,img):
-    img = Image.open(img)
-    img = img.resize((value,value))
-    return img
-
-def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength):
-    generator = torch.Generator(device).manual_seed(seed)     
-    source_image = resize(768, source_img)
-    source_image.save('source.png')
-    image = pipe(prompt, negative_prompt=negative_prompt, init_image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0]
-    return image
-
-gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), gr.Textbox(label='What you Do Not want the AI to generate.'),
-    gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
-    gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'),
-    gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), 
-    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)], 
-    outputs='image', title = "Stable Diffusion 2.1 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion 2.1 see https://github.com/Stability-AI/stablediffusion <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/oval/Oval.js

@@ -1,39 +0,0 @@
-import Base from '../base/Base.js';
-import { Arc, Circle } from '../utils/Geoms.js'
-
-
-class Oval extends Base {
-    constructor(scene, config) {
-        super(scene, config);
-        this.type = 'rexSpinnerOval';
-    }
-
-    buildShapes() {
-        this.addShape((new Circle()).setName('track'));
-        this.addShape((new Arc()).setName('arc'));
-    }
-
-    updateShapes() {
-        var centerX = this.centerX;
-        var centerY = this.centerY;
-        var radius = this.radius;
-        var lineWidth = Math.ceil(radius / 25);
-        var maxRadius = radius - (lineWidth / 2);
-
-        this.getShape('track')
-            .lineStyle(lineWidth, this.color, 0.5)
-            .setRadius(maxRadius)
-            .setCenterPosition(centerX, centerY);
-
-        var startAngle = this.value * 360;
-        var endAngle = startAngle + 60;
-        this.getShape('arc')
-            .lineStyle(lineWidth, this.color, 1)
-            .setRadius(maxRadius)
-            .setCenterPosition(centerX, centerY)
-            .setAngle(startAngle, endAngle);
-
-    }
-}
-
-export default Oval;

spaces/AlterM/Zaglyt2-transformer-test/README.md

@@ -1,13 +0,0 @@
----
-title: Zaglyt2 Transformer Test
-emoji: 🚀
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
-duplicated_from: RisticksAI/Zaglyt2-transformer-test
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Amon1/ChatGPTForAcadamic/request_llm/bridge_tgui.py

@@ -1,167 +0,0 @@
-'''
-Contributed by SagsMug. Modified by binary-husky
-https://github.com/oobabooga/text-generation-webui/pull/175
-'''
-
-import asyncio
-import json
-import random
-import string
-import websockets
-import logging
-import time
-import threading
-import importlib
-from toolbox import get_conf
-LLM_MODEL, = get_conf('LLM_MODEL')
-
-# "TGUI:galactica-1.3b@localhost:7860"
-model_name, addr_port = LLM_MODEL.split('@')
-assert ':' in addr_port, "LLM_MODEL 格式不正确！" + LLM_MODEL
-addr, port = addr_port.split(':')
-
-def random_hash():
-    letters = string.ascii_lowercase + string.digits
-    return ''.join(random.choice(letters) for i in range(9))
-
-async def run(context, max_token=512):
-    params = {
-        'max_new_tokens': max_token,
-        'do_sample': True,
-        'temperature': 0.5,
-        'top_p': 0.9,
-        'typical_p': 1,
-        'repetition_penalty': 1.05,
-        'encoder_repetition_penalty': 1.0,
-        'top_k': 0,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': True,
-        'seed': -1,
-    }
-    session = random_hash()
-
-    async with websockets.connect(f"ws://{addr}:{port}/queue/join") as websocket:
-        while content := json.loads(await websocket.recv()):
-            #Python3.10 syntax, replace with if elif on older
-            if content["msg"] ==  "send_hash":
-                await websocket.send(json.dumps({
-                    "session_hash": session,
-                    "fn_index": 12
-                }))
-            elif content["msg"] ==  "estimation":
-                pass
-            elif content["msg"] ==  "send_data":
-                await websocket.send(json.dumps({
-                    "session_hash": session,
-                    "fn_index": 12,
-                    "data": [
-                        context,
-                        params['max_new_tokens'],
-                        params['do_sample'],
-                        params['temperature'],
-                        params['top_p'],
-                        params['typical_p'],
-                        params['repetition_penalty'],
-                        params['encoder_repetition_penalty'],
-                        params['top_k'],
-                        params['min_length'],
-                        params['no_repeat_ngram_size'],
-                        params['num_beams'],
-                        params['penalty_alpha'],
-                        params['length_penalty'],
-                        params['early_stopping'],
-                        params['seed'],
-                    ]
-                }))
-            elif content["msg"] ==  "process_starts":
-                pass
-            elif content["msg"] in ["process_generating", "process_completed"]:
-                yield content["output"]["data"][0]
-                # You can search for your desired end indicator and 
-                #  stop generation by closing the websocket here
-                if (content["msg"] == "process_completed"):
-                    break
-
-
-
-
-
-def predict_tgui(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        发送至chatGPT，流式获取输出。
-        用于基础的对话功能。
-        inputs 是本次问询的输入
-        top_p, temperature是chatGPT的内部调优参数
-        history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
-        additional_fn代表点击的哪个按钮，按钮见functional.py
-    """
-    if additional_fn is not None:
-        import functional
-        importlib.reload(functional)    # 热更新prompt
-        functional = functional.get_functionals()
-        if "PreProcess" in functional[additional_fn]: inputs = functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
-
-    raw_input = "What I would like to say is the following: " + inputs
-    logging.info(f'[raw_input] {raw_input}')
-    history.extend([inputs, ""])
-    chatbot.append([inputs, ""])
-    yield chatbot, history, "等待响应"
-
-    prompt = inputs
-    tgui_say = ""
-
-    mutable = ["", time.time()]
-    def run_coorotine(mutable):
-        async def get_result(mutable):
-            async for response in run(prompt):
-                print(response[len(mutable[0]):])
-                mutable[0] = response
-                if (time.time() - mutable[1]) > 3: 
-                    print('exit when no listener')
-                    break
-        asyncio.run(get_result(mutable))
-
-    thread_listen = threading.Thread(target=run_coorotine, args=(mutable,), daemon=True)
-    thread_listen.start()
-
-    while thread_listen.is_alive():
-        time.sleep(1)
-        mutable[1] = time.time()
-        # Print intermediate steps
-        if tgui_say != mutable[0]:
-            tgui_say = mutable[0]
-            history[-1] = tgui_say
-            chatbot[-1] = (history[-2], history[-1])
-            yield chatbot, history, "status_text"
-
-    logging.info(f'[response] {tgui_say}')
-
-
-
-def predict_tgui_no_ui(inputs, top_p, temperature, history=[], sys_prompt=""):
-    raw_input = "What I would like to say is the following: " + inputs
-    prompt = inputs
-    tgui_say = ""
-    mutable = ["", time.time()]
-    def run_coorotine(mutable):
-        async def get_result(mutable):
-            async for response in run(prompt, max_token=20):
-                print(response[len(mutable[0]):])
-                mutable[0] = response
-                if (time.time() - mutable[1]) > 3: 
-                    print('exit when no listener')
-                    break
-        asyncio.run(get_result(mutable))
-    thread_listen = threading.Thread(target=run_coorotine, args=(mutable,))
-    thread_listen.start()
-    while thread_listen.is_alive():
-        time.sleep(1)
-        mutable[1] = time.time()
-    tgui_say = mutable[0]
-    return tgui_say

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/__init__.py

@@ -1,136 +0,0 @@
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-from PIL import Image
-
-from ...utils import (
-    BaseOutput,
-    OptionalDependencyNotAvailable,
-    is_flax_available,
-    is_k_diffusion_available,
-    is_k_diffusion_version,
-    is_onnx_available,
-    is_torch_available,
-    is_transformers_available,
-    is_transformers_version,
-)
-
-
-@dataclass
-class StableDiffusionPipelineOutput(BaseOutput):
-    """
-    Output class for Stable Diffusion pipelines.
-
-    Args:
-        images (`List[PIL.Image.Image]` or `np.ndarray`)
-            List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
-            num_channels)`.
-        nsfw_content_detected (`List[bool]`)
-            List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
-            `None` if safety checking could not be performed.
-    """
-
-    images: Union[List[PIL.Image.Image], np.ndarray]
-    nsfw_content_detected: Optional[List[bool]]
-
-
-try:
-    if not (is_transformers_available() and is_torch_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
-else:
-    from .pipeline_cycle_diffusion import CycleDiffusionPipeline
-    from .pipeline_stable_diffusion import StableDiffusionPipeline
-    from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
-    from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
-    from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
-    from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
-    from .pipeline_stable_diffusion_instruct_pix2pix import StableDiffusionInstructPix2PixPipeline
-    from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
-    from .pipeline_stable_diffusion_ldm3d import StableDiffusionLDM3DPipeline
-    from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
-    from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
-    from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
-    from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
-    from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
-    from .pipeline_stable_unclip import StableUnCLIPPipeline
-    from .pipeline_stable_unclip_img2img import StableUnCLIPImg2ImgPipeline
-    from .safety_checker import StableDiffusionSafetyChecker
-    from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
-
-try:
-    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
-else:
-    from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
-
-
-try:
-    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import (
-        StableDiffusionDepth2ImgPipeline,
-        StableDiffusionDiffEditPipeline,
-        StableDiffusionPix2PixZeroPipeline,
-    )
-else:
-    from .pipeline_stable_diffusion_depth2img import StableDiffusionDepth2ImgPipeline
-    from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
-    from .pipeline_stable_diffusion_pix2pix_zero import StableDiffusionPix2PixZeroPipeline
-
-
-try:
-    if not (
-        is_torch_available()
-        and is_transformers_available()
-        and is_k_diffusion_available()
-        and is_k_diffusion_version(">=", "0.0.12")
-    ):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
-else:
-    from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
-
-try:
-    if not (is_transformers_available() and is_onnx_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_onnx_objects import *  # noqa F403
-else:
-    from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
-    from .pipeline_onnx_stable_diffusion_img2img import OnnxStableDiffusionImg2ImgPipeline
-    from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
-    from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
-    from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
-
-if is_transformers_available() and is_flax_available():
-    import flax
-
-    @flax.struct.dataclass
-    class FlaxStableDiffusionPipelineOutput(BaseOutput):
-        """
-        Output class for Flax-based Stable Diffusion pipelines.
-
-        Args:
-            images (`np.ndarray`):
-                Denoised images of array shape of `(batch_size, height, width, num_channels)`.
-            nsfw_content_detected (`List[bool]`):
-                List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content
-                or `None` if safety checking could not be performed.
-        """
-
-        images: np.ndarray
-        nsfw_content_detected: List[bool]
-
-    from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
-    from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
-    from .pipeline_flax_stable_diffusion_img2img import FlaxStableDiffusionImg2ImgPipeline
-    from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
-    from .safety_checker_flax import FlaxStableDiffusionSafetyChecker

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_unet_3d_condition.py

@@ -1,421 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# 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
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers.models import ModelMixin, UNet3DConditionModel
-from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
-from diffusers.utils import (
-    floats_tensor,
-    logging,
-    skip_mps,
-    torch_device,
-)
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import enable_full_determinism
-
-from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
-
-
-enable_full_determinism()
-
-logger = logging.get_logger(__name__)
-
-
-def create_lora_layers(model, mock_weights: bool = True):
-    lora_attn_procs = {}
-    for name in model.attn_processors.keys():
-        has_cross_attention = name.endswith("attn2.processor") and not (
-            name.startswith("transformer_in") or "temp_attentions" in name.split(".")
-        )
-        cross_attention_dim = model.config.cross_attention_dim if has_cross_attention else None
-        if name.startswith("mid_block"):
-            hidden_size = model.config.block_out_channels[-1]
-        elif name.startswith("up_blocks"):
-            block_id = int(name[len("up_blocks.")])
-            hidden_size = list(reversed(model.config.block_out_channels))[block_id]
-        elif name.startswith("down_blocks"):
-            block_id = int(name[len("down_blocks.")])
-            hidden_size = model.config.block_out_channels[block_id]
-        elif name.startswith("transformer_in"):
-            # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
-            hidden_size = 8 * model.config.attention_head_dim
-
-        lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
-        lora_attn_procs[name] = lora_attn_procs[name].to(model.device)
-
-        if mock_weights:
-            # add 1 to weights to mock trained weights
-            with torch.no_grad():
-                lora_attn_procs[name].to_q_lora.up.weight += 1
-                lora_attn_procs[name].to_k_lora.up.weight += 1
-                lora_attn_procs[name].to_v_lora.up.weight += 1
-                lora_attn_procs[name].to_out_lora.up.weight += 1
-
-    return lora_attn_procs
-
-
-@skip_mps
-class UNet3DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = UNet3DConditionModel
-    main_input_name = "sample"
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_channels = 4
-        num_frames = 4
-        sizes = (32, 32)
-
-        noise = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
-        time_step = torch.tensor([10]).to(torch_device)
-        encoder_hidden_states = floats_tensor((batch_size, 4, 32)).to(torch_device)
-
-        return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
-
-    @property
-    def input_shape(self):
-        return (4, 4, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (4, 4, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "block_out_channels": (32, 64),
-            "down_block_types": (
-                "CrossAttnDownBlock3D",
-                "DownBlock3D",
-            ),
-            "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
-            "cross_attention_dim": 32,
-            "attention_head_dim": 8,
-            "out_channels": 4,
-            "in_channels": 4,
-            "layers_per_block": 1,
-            "sample_size": 32,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_enable_works(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-
-        model.enable_xformers_memory_efficient_attention()
-
-        assert (
-            model.mid_block.attentions[0].transformer_blocks[0].attn1.processor.__class__.__name__
-            == "XFormersAttnProcessor"
-        ), "xformers is not enabled"
-
-    # Overriding to set `norm_num_groups` needs to be different for this model.
-    def test_forward_with_norm_groups(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["norm_num_groups"] = 32
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            output = model(**inputs_dict)
-
-            if isinstance(output, dict):
-                output = output.sample
-
-        self.assertIsNotNone(output)
-        expected_shape = inputs_dict["sample"].shape
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-    # Overriding since the UNet3D outputs a different structure.
-    def test_determinism(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            # Warmup pass when using mps (see #372)
-            if torch_device == "mps" and isinstance(model, ModelMixin):
-                model(**self.dummy_input)
-
-            first = model(**inputs_dict)
-            if isinstance(first, dict):
-                first = first.sample
-
-            second = model(**inputs_dict)
-            if isinstance(second, dict):
-                second = second.sample
-
-        out_1 = first.cpu().numpy()
-        out_2 = second.cpu().numpy()
-        out_1 = out_1[~np.isnan(out_1)]
-        out_2 = out_2[~np.isnan(out_2)]
-        max_diff = np.amax(np.abs(out_1 - out_2))
-        self.assertLessEqual(max_diff, 1e-5)
-
-    def test_model_attention_slicing(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 8
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        model.set_attention_slice("auto")
-        with torch.no_grad():
-            output = model(**inputs_dict)
-        assert output is not None
-
-        model.set_attention_slice("max")
-        with torch.no_grad():
-            output = model(**inputs_dict)
-        assert output is not None
-
-        model.set_attention_slice(2)
-        with torch.no_grad():
-            output = model(**inputs_dict)
-        assert output is not None
-
-    def test_lora_processors(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 8
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        with torch.no_grad():
-            sample1 = model(**inputs_dict).sample
-
-        lora_attn_procs = create_lora_layers(model)
-
-        # make sure we can set a list of attention processors
-        model.set_attn_processor(lora_attn_procs)
-        model.to(torch_device)
-
-        # test that attn processors can be set to itself
-        model.set_attn_processor(model.attn_processors)
-
-        with torch.no_grad():
-            sample2 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.0}).sample
-            sample3 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
-            sample4 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
-
-        assert (sample1 - sample2).abs().max() < 3e-3
-        assert (sample3 - sample4).abs().max() < 3e-3
-
-        # sample 2 and sample 3 should be different
-        assert (sample2 - sample3).abs().max() > 3e-3
-
-    def test_lora_save_load(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 8
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        with torch.no_grad():
-            old_sample = model(**inputs_dict).sample
-
-        lora_attn_procs = create_lora_layers(model)
-        model.set_attn_processor(lora_attn_procs)
-
-        with torch.no_grad():
-            sample = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_attn_procs(tmpdirname)
-            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
-            torch.manual_seed(0)
-            new_model = self.model_class(**init_dict)
-            new_model.to(torch_device)
-            new_model.load_attn_procs(tmpdirname)
-
-        with torch.no_grad():
-            new_sample = new_model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
-
-        assert (sample - new_sample).abs().max() < 1e-3
-
-        # LoRA and no LoRA should NOT be the same
-        assert (sample - old_sample).abs().max() > 1e-4
-
-    def test_lora_save_load_safetensors(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 8
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        with torch.no_grad():
-            old_sample = model(**inputs_dict).sample
-
-        lora_attn_procs = create_lora_layers(model)
-        model.set_attn_processor(lora_attn_procs)
-
-        with torch.no_grad():
-            sample = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_attn_procs(tmpdirname, safe_serialization=True)
-            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
-            torch.manual_seed(0)
-            new_model = self.model_class(**init_dict)
-            new_model.to(torch_device)
-            new_model.load_attn_procs(tmpdirname)
-
-        with torch.no_grad():
-            new_sample = new_model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
-
-        assert (sample - new_sample).abs().max() < 3e-3
-
-        # LoRA and no LoRA should NOT be the same
-        assert (sample - old_sample).abs().max() > 1e-4
-
-    def test_lora_save_safetensors_load_torch(self):
-        # enable deterministic behavior for gradient checkpointing
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 8
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        lora_attn_procs = create_lora_layers(model, mock_weights=False)
-        model.set_attn_processor(lora_attn_procs)
-        # Saving as torch, properly reloads with directly filename
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_attn_procs(tmpdirname)
-            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
-            torch.manual_seed(0)
-            new_model = self.model_class(**init_dict)
-            new_model.to(torch_device)
-            new_model.load_attn_procs(tmpdirname, weight_name="pytorch_lora_weights.bin")
-
-    def test_lora_save_torch_force_load_safetensors_error(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 8
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        lora_attn_procs = create_lora_layers(model, mock_weights=False)
-        model.set_attn_processor(lora_attn_procs)
-        # Saving as torch, properly reloads with directly filename
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_attn_procs(tmpdirname)
-            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
-            torch.manual_seed(0)
-            new_model = self.model_class(**init_dict)
-            new_model.to(torch_device)
-            with self.assertRaises(IOError) as e:
-                new_model.load_attn_procs(tmpdirname, use_safetensors=True)
-            self.assertIn("Error no file named pytorch_lora_weights.safetensors", str(e.exception))
-
-    def test_lora_on_off(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 8
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        with torch.no_grad():
-            old_sample = model(**inputs_dict).sample
-
-        lora_attn_procs = create_lora_layers(model)
-        model.set_attn_processor(lora_attn_procs)
-
-        with torch.no_grad():
-            sample = model(**inputs_dict, cross_attention_kwargs={"scale": 0.0}).sample
-
-        model.set_attn_processor(AttnProcessor())
-
-        with torch.no_grad():
-            new_sample = model(**inputs_dict).sample
-
-        assert (sample - new_sample).abs().max() < 1e-4
-        assert (sample - old_sample).abs().max() < 3e-3
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_lora_xformers_on_off(self):
-        # enable deterministic behavior for gradient checkpointing
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = 4
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        lora_attn_procs = create_lora_layers(model)
-        model.set_attn_processor(lora_attn_procs)
-
-        # default
-        with torch.no_grad():
-            sample = model(**inputs_dict).sample
-
-            model.enable_xformers_memory_efficient_attention()
-            on_sample = model(**inputs_dict).sample
-
-            model.disable_xformers_memory_efficient_attention()
-            off_sample = model(**inputs_dict).sample
-
-        assert (sample - on_sample).abs().max() < 1e-4
-        assert (sample - off_sample).abs().max() < 1e-4
-
-    def test_feed_forward_chunking(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        init_dict["norm_num_groups"] = 32
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            output = model(**inputs_dict)[0]
-
-        model.enable_forward_chunking()
-        with torch.no_grad():
-            output_2 = model(**inputs_dict)[0]
-
-        self.assertEqual(output.shape, output_2.shape, "Shape doesn't match")
-        assert np.abs(output.cpu() - output_2.cpu()).max() < 1e-2
-
-
-# (todo: sayakpaul) implement SLOW tests.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky_v22/test_kandinsky_prior_emb2emb.py

@@ -1,257 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# 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 random
-import unittest
-
-import numpy as np
-import torch
-from PIL import Image
-from torch import nn
-from transformers import (
-    CLIPImageProcessor,
-    CLIPTextConfig,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    CLIPVisionConfig,
-    CLIPVisionModelWithProjection,
-)
-
-from diffusers import KandinskyV22PriorEmb2EmbPipeline, PriorTransformer, UnCLIPScheduler
-from diffusers.utils import floats_tensor, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
-
-from ..test_pipelines_common import PipelineTesterMixin
-
-
-enable_full_determinism()
-
-
-class KandinskyV22PriorEmb2EmbPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = KandinskyV22PriorEmb2EmbPipeline
-    params = ["prompt", "image"]
-    batch_params = ["prompt", "image"]
-    required_optional_params = [
-        "num_images_per_prompt",
-        "strength",
-        "generator",
-        "num_inference_steps",
-        "latents",
-        "negative_prompt",
-        "guidance_scale",
-        "output_type",
-        "return_dict",
-    ]
-    test_xformers_attention = False
-
-    @property
-    def text_embedder_hidden_size(self):
-        return 32
-
-    @property
-    def time_input_dim(self):
-        return 32
-
-    @property
-    def block_out_channels_0(self):
-        return self.time_input_dim
-
-    @property
-    def time_embed_dim(self):
-        return self.time_input_dim * 4
-
-    @property
-    def cross_attention_dim(self):
-        return 100
-
-    @property
-    def dummy_tokenizer(self):
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-        return tokenizer
-
-    @property
-    def dummy_text_encoder(self):
-        torch.manual_seed(0)
-        config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=self.text_embedder_hidden_size,
-            projection_dim=self.text_embedder_hidden_size,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        return CLIPTextModelWithProjection(config)
-
-    @property
-    def dummy_prior(self):
-        torch.manual_seed(0)
-
-        model_kwargs = {
-            "num_attention_heads": 2,
-            "attention_head_dim": 12,
-            "embedding_dim": self.text_embedder_hidden_size,
-            "num_layers": 1,
-        }
-
-        model = PriorTransformer(**model_kwargs)
-        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
-        model.clip_std = nn.Parameter(torch.ones(model.clip_std.shape))
-        return model
-
-    @property
-    def dummy_image_encoder(self):
-        torch.manual_seed(0)
-        config = CLIPVisionConfig(
-            hidden_size=self.text_embedder_hidden_size,
-            image_size=224,
-            projection_dim=self.text_embedder_hidden_size,
-            intermediate_size=37,
-            num_attention_heads=4,
-            num_channels=3,
-            num_hidden_layers=5,
-            patch_size=14,
-        )
-
-        model = CLIPVisionModelWithProjection(config)
-        return model
-
-    @property
-    def dummy_image_processor(self):
-        image_processor = CLIPImageProcessor(
-            crop_size=224,
-            do_center_crop=True,
-            do_normalize=True,
-            do_resize=True,
-            image_mean=[0.48145466, 0.4578275, 0.40821073],
-            image_std=[0.26862954, 0.26130258, 0.27577711],
-            resample=3,
-            size=224,
-        )
-
-        return image_processor
-
-    def get_dummy_components(self):
-        prior = self.dummy_prior
-        image_encoder = self.dummy_image_encoder
-        text_encoder = self.dummy_text_encoder
-        tokenizer = self.dummy_tokenizer
-        image_processor = self.dummy_image_processor
-
-        scheduler = UnCLIPScheduler(
-            variance_type="fixed_small_log",
-            prediction_type="sample",
-            num_train_timesteps=1000,
-            clip_sample=True,
-            clip_sample_range=10.0,
-        )
-
-        components = {
-            "prior": prior,
-            "image_encoder": image_encoder,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "scheduler": scheduler,
-            "image_processor": image_processor,
-        }
-
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
-        image = image.cpu().permute(0, 2, 3, 1)[0]
-        init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((256, 256))
-
-        inputs = {
-            "prompt": "horse",
-            "image": init_image,
-            "strength": 0.5,
-            "generator": generator,
-            "guidance_scale": 4.0,
-            "num_inference_steps": 2,
-            "output_type": "np",
-        }
-        return inputs
-
-    def test_kandinsky_prior_emb2emb(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-
-        pipe.set_progress_bar_config(disable=None)
-
-        output = pipe(**self.get_dummy_inputs(device))
-        image = output.image_embeds
-
-        image_from_tuple = pipe(
-            **self.get_dummy_inputs(device),
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -10:]
-        image_from_tuple_slice = image_from_tuple[0, -10:]
-
-        assert image.shape == (1, 32)
-
-        expected_slice = np.array(
-            [
-                0.1071284,
-                1.3330271,
-                0.61260223,
-                -0.6691065,
-                -0.3846852,
-                -1.0303661,
-                0.22716111,
-                0.03348901,
-                0.30040675,
-                -0.24805029,
-            ]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    @skip_mps
-    def test_inference_batch_single_identical(self):
-        test_max_difference = torch_device == "cpu"
-        relax_max_difference = True
-        test_mean_pixel_difference = False
-
-        self._test_inference_batch_single_identical(
-            test_max_difference=test_max_difference,
-            relax_max_difference=relax_max_difference,
-            test_mean_pixel_difference=test_mean_pixel_difference,
-        )
-
-    @skip_mps
-    def test_attention_slicing_forward_pass(self):
-        test_max_difference = torch_device == "cpu"
-        test_mean_pixel_difference = False
-
-        self._test_attention_slicing_forward_pass(
-            test_max_difference=test_max_difference,
-            test_mean_pixel_difference=test_mean_pixel_difference,
-        )

spaces/Andy1621/uniformer_image_detection/configs/_base_/schedules/schedule_1x.py

@@ -1,11 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=0.001,
-    step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)

spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py

@@ -1,2 +0,0 @@
-_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

spaces/Andy1621/uniformer_light/README.md

@@ -1,13 +0,0 @@
----
-title: Uniformer Light
-emoji: 🚀
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Andy1621/uniformerv2_demo/kinetics_class_index.py

@@ -1,402 +0,0 @@
-kinetics_classnames = {
-    "0": "riding a bike", 
-    "1": "marching", 
-    "2": "dodgeball", 
-    "3": "playing cymbals", 
-    "4": "checking tires", 
-    "5": "roller skating", 
-    "6": "tasting beer", 
-    "7": "clapping", 
-    "8": "drawing", 
-    "9": "juggling fire", 
-    "10": "bobsledding", 
-    "11": "petting animal (not cat)", 
-    "12": "spray painting", 
-    "13": "training dog", 
-    "14": "eating watermelon", 
-    "15": "building cabinet", 
-    "16": "applauding", 
-    "17": "playing harp", 
-    "18": "balloon blowing", 
-    "19": "sled dog racing", 
-    "20": "wrestling", 
-    "21": "pole vault", 
-    "22": "hurling (sport)", 
-    "23": "riding scooter", 
-    "24": "shearing sheep", 
-    "25": "sweeping floor", 
-    "26": "eating carrots", 
-    "27": "skateboarding", 
-    "28": "dunking basketball", 
-    "29": "disc golfing", 
-    "30": "eating spaghetti", 
-    "31": "playing flute", 
-    "32": "riding mechanical bull", 
-    "33": "making sushi", 
-    "34": "trapezing", 
-    "35": "picking fruit", 
-    "36": "stretching leg", 
-    "37": "playing ukulele", 
-    "38": "tying tie", 
-    "39": "skydiving", 
-    "40": "playing cello", 
-    "41": "jumping into pool", 
-    "42": "shooting goal (soccer)", 
-    "43": "trimming trees", 
-    "44": "bookbinding", 
-    "45": "ski jumping", 
-    "46": "walking the dog", 
-    "47": "riding unicycle", 
-    "48": "shaving head", 
-    "49": "hopscotch", 
-    "50": "playing piano", 
-    "51": "parasailing", 
-    "52": "bartending", 
-    "53": "kicking field goal", 
-    "54": "finger snapping", 
-    "55": "dining", 
-    "56": "yawning", 
-    "57": "peeling potatoes", 
-    "58": "canoeing or kayaking", 
-    "59": "front raises", 
-    "60": "laughing", 
-    "61": "dancing macarena", 
-    "62": "digging", 
-    "63": "reading newspaper", 
-    "64": "hitting baseball", 
-    "65": "clay pottery making", 
-    "66": "exercising with an exercise ball", 
-    "67": "playing saxophone", 
-    "68": "shooting basketball", 
-    "69": "washing hair", 
-    "70": "lunge", 
-    "71": "brushing hair", 
-    "72": "curling hair", 
-    "73": "kitesurfing", 
-    "74": "tapping guitar", 
-    "75": "bending back", 
-    "76": "skipping rope", 
-    "77": "situp", 
-    "78": "folding paper", 
-    "79": "cracking neck", 
-    "80": "assembling computer", 
-    "81": "cleaning gutters", 
-    "82": "blowing out candles", 
-    "83": "shaking hands", 
-    "84": "dancing gangnam style", 
-    "85": "windsurfing", 
-    "86": "tap dancing", 
-    "87": "skiing (not slalom or crosscountry)", 
-    "88": "bandaging", 
-    "89": "push up", 
-    "90": "doing nails", 
-    "91": "punching person (boxing)", 
-    "92": "bouncing on trampoline", 
-    "93": "scrambling eggs", 
-    "94": "singing", 
-    "95": "cleaning floor", 
-    "96": "krumping", 
-    "97": "drumming fingers", 
-    "98": "snowmobiling", 
-    "99": "gymnastics tumbling", 
-    "100": "headbanging", 
-    "101": "catching or throwing frisbee", 
-    "102": "riding elephant", 
-    "103": "bee keeping", 
-    "104": "feeding birds", 
-    "105": "snatch weight lifting", 
-    "106": "mowing lawn", 
-    "107": "fixing hair", 
-    "108": "playing trumpet", 
-    "109": "flying kite", 
-    "110": "crossing river", 
-    "111": "swinging legs", 
-    "112": "sanding floor", 
-    "113": "belly dancing", 
-    "114": "sneezing", 
-    "115": "clean and jerk", 
-    "116": "side kick", 
-    "117": "filling eyebrows", 
-    "118": "shuffling cards", 
-    "119": "recording music", 
-    "120": "cartwheeling", 
-    "121": "feeding fish", 
-    "122": "folding clothes", 
-    "123": "water skiing", 
-    "124": "tobogganing", 
-    "125": "blowing leaves", 
-    "126": "smoking", 
-    "127": "unboxing", 
-    "128": "tai chi", 
-    "129": "waxing legs", 
-    "130": "riding camel", 
-    "131": "slapping", 
-    "132": "tossing salad", 
-    "133": "capoeira", 
-    "134": "playing cards", 
-    "135": "playing organ", 
-    "136": "playing violin", 
-    "137": "playing drums", 
-    "138": "tapping pen", 
-    "139": "vault", 
-    "140": "shoveling snow", 
-    "141": "playing tennis", 
-    "142": "getting a tattoo", 
-    "143": "making a sandwich", 
-    "144": "making tea", 
-    "145": "grinding meat", 
-    "146": "squat", 
-    "147": "eating doughnuts", 
-    "148": "ice fishing", 
-    "149": "snowkiting", 
-    "150": "kicking soccer ball", 
-    "151": "playing controller", 
-    "152": "giving or receiving award", 
-    "153": "welding", 
-    "154": "throwing discus", 
-    "155": "throwing axe", 
-    "156": "ripping paper", 
-    "157": "swimming butterfly stroke", 
-    "158": "air drumming", 
-    "159": "blowing nose", 
-    "160": "hockey stop", 
-    "161": "taking a shower", 
-    "162": "bench pressing", 
-    "163": "planting trees", 
-    "164": "pumping fist", 
-    "165": "climbing tree", 
-    "166": "tickling", 
-    "167": "high kick", 
-    "168": "waiting in line", 
-    "169": "slacklining", 
-    "170": "tango dancing", 
-    "171": "hurdling", 
-    "172": "carrying baby", 
-    "173": "celebrating", 
-    "174": "sharpening knives", 
-    "175": "passing American football (in game)", 
-    "176": "headbutting", 
-    "177": "playing recorder", 
-    "178": "brush painting", 
-    "179": "garbage collecting", 
-    "180": "robot dancing", 
-    "181": "shredding paper", 
-    "182": "pumping gas", 
-    "183": "rock climbing", 
-    "184": "hula hooping", 
-    "185": "braiding hair", 
-    "186": "opening present", 
-    "187": "texting", 
-    "188": "decorating the christmas tree", 
-    "189": "answering questions", 
-    "190": "playing keyboard", 
-    "191": "writing", 
-    "192": "bungee jumping", 
-    "193": "sniffing", 
-    "194": "eating burger", 
-    "195": "playing accordion", 
-    "196": "making pizza", 
-    "197": "playing volleyball", 
-    "198": "tasting food", 
-    "199": "pushing cart", 
-    "200": "spinning poi", 
-    "201": "cleaning windows", 
-    "202": "arm wrestling", 
-    "203": "changing oil", 
-    "204": "swimming breast stroke", 
-    "205": "tossing coin", 
-    "206": "deadlifting", 
-    "207": "hoverboarding", 
-    "208": "cutting watermelon", 
-    "209": "cheerleading", 
-    "210": "snorkeling", 
-    "211": "washing hands", 
-    "212": "eating cake", 
-    "213": "pull ups", 
-    "214": "surfing water", 
-    "215": "eating hotdog", 
-    "216": "holding snake", 
-    "217": "playing harmonica", 
-    "218": "ironing", 
-    "219": "cutting nails", 
-    "220": "golf chipping", 
-    "221": "shot put", 
-    "222": "hugging", 
-    "223": "playing clarinet", 
-    "224": "faceplanting", 
-    "225": "trimming or shaving beard", 
-    "226": "drinking shots", 
-    "227": "riding mountain bike", 
-    "228": "tying bow tie", 
-    "229": "swinging on something", 
-    "230": "skiing crosscountry", 
-    "231": "unloading truck", 
-    "232": "cleaning pool", 
-    "233": "jogging", 
-    "234": "ice climbing", 
-    "235": "mopping floor", 
-    "236": "making bed", 
-    "237": "diving cliff", 
-    "238": "washing dishes", 
-    "239": "grooming dog", 
-    "240": "weaving basket", 
-    "241": "frying vegetables", 
-    "242": "stomping grapes", 
-    "243": "moving furniture", 
-    "244": "cooking sausages", 
-    "245": "doing laundry", 
-    "246": "dying hair", 
-    "247": "knitting", 
-    "248": "reading book", 
-    "249": "baby waking up", 
-    "250": "punching bag", 
-    "251": "surfing crowd", 
-    "252": "cooking chicken", 
-    "253": "pushing car", 
-    "254": "springboard diving", 
-    "255": "swing dancing", 
-    "256": "massaging legs", 
-    "257": "beatboxing", 
-    "258": "breading or breadcrumbing", 
-    "259": "somersaulting", 
-    "260": "brushing teeth", 
-    "261": "stretching arm", 
-    "262": "juggling balls", 
-    "263": "massaging person's head", 
-    "264": "eating ice cream", 
-    "265": "extinguishing fire", 
-    "266": "hammer throw", 
-    "267": "whistling", 
-    "268": "crawling baby", 
-    "269": "using remote controller (not gaming)", 
-    "270": "playing cricket", 
-    "271": "opening bottle", 
-    "272": "playing xylophone", 
-    "273": "motorcycling", 
-    "274": "driving car", 
-    "275": "exercising arm", 
-    "276": "passing American football (not in game)", 
-    "277": "playing kickball", 
-    "278": "sticking tongue out", 
-    "279": "flipping pancake", 
-    "280": "catching fish", 
-    "281": "eating chips", 
-    "282": "shaking head", 
-    "283": "sword fighting", 
-    "284": "playing poker", 
-    "285": "cooking on campfire", 
-    "286": "doing aerobics", 
-    "287": "paragliding", 
-    "288": "using segway", 
-    "289": "folding napkins", 
-    "290": "playing bagpipes", 
-    "291": "gargling", 
-    "292": "skiing slalom", 
-    "293": "strumming guitar", 
-    "294": "javelin throw", 
-    "295": "waxing back", 
-    "296": "riding or walking with horse", 
-    "297": "plastering", 
-    "298": "long jump", 
-    "299": "parkour", 
-    "300": "wrapping present", 
-    "301": "egg hunting", 
-    "302": "archery", 
-    "303": "cleaning toilet", 
-    "304": "swimming backstroke", 
-    "305": "snowboarding", 
-    "306": "catching or throwing baseball", 
-    "307": "massaging back", 
-    "308": "blowing glass", 
-    "309": "playing guitar", 
-    "310": "playing chess", 
-    "311": "golf driving", 
-    "312": "presenting weather forecast", 
-    "313": "rock scissors paper", 
-    "314": "high jump", 
-    "315": "baking cookies", 
-    "316": "using computer", 
-    "317": "washing feet", 
-    "318": "arranging flowers", 
-    "319": "playing bass guitar", 
-    "320": "spraying", 
-    "321": "cutting pineapple", 
-    "322": "waxing chest", 
-    "323": "auctioning", 
-    "324": "jetskiing", 
-    "325": "drinking", 
-    "326": "busking", 
-    "327": "playing monopoly", 
-    "328": "salsa dancing", 
-    "329": "waxing eyebrows", 
-    "330": "watering plants", 
-    "331": "zumba", 
-    "332": "chopping wood", 
-    "333": "pushing wheelchair", 
-    "334": "carving pumpkin", 
-    "335": "building shed", 
-    "336": "making jewelry", 
-    "337": "catching or throwing softball", 
-    "338": "bending metal", 
-    "339": "ice skating", 
-    "340": "dancing charleston", 
-    "341": "abseiling", 
-    "342": "climbing a rope", 
-    "343": "crying", 
-    "344": "cleaning shoes", 
-    "345": "dancing ballet", 
-    "346": "driving tractor", 
-    "347": "triple jump", 
-    "348": "throwing ball", 
-    "349": "getting a haircut", 
-    "350": "running on treadmill", 
-    "351": "climbing ladder", 
-    "352": "blasting sand", 
-    "353": "playing trombone", 
-    "354": "drop kicking", 
-    "355": "country line dancing", 
-    "356": "changing wheel", 
-    "357": "feeding goats", 
-    "358": "tying knot (not on a tie)", 
-    "359": "setting table", 
-    "360": "shaving legs", 
-    "361": "kissing", 
-    "362": "riding mule", 
-    "363": "counting money", 
-    "364": "laying bricks", 
-    "365": "barbequing", 
-    "366": "news anchoring", 
-    "367": "smoking hookah", 
-    "368": "cooking egg", 
-    "369": "peeling apples", 
-    "370": "yoga", 
-    "371": "sharpening pencil", 
-    "372": "dribbling basketball", 
-    "373": "petting cat", 
-    "374": "playing ice hockey", 
-    "375": "milking cow", 
-    "376": "shining shoes", 
-    "377": "juggling soccer ball", 
-    "378": "scuba diving", 
-    "379": "playing squash or racquetball", 
-    "380": "drinking beer", 
-    "381": "sign language interpreting", 
-    "382": "playing basketball", 
-    "383": "breakdancing", 
-    "384": "testifying", 
-    "385": "making snowman", 
-    "386": "golf putting", 
-    "387": "playing didgeridoo", 
-    "388": "biking through snow", 
-    "389": "sailing", 
-    "390": "jumpstyle dancing", 
-    "391": "water sliding", 
-    "392": "grooming horse", 
-    "393": "massaging feet", 
-    "394": "playing paintball", 
-    "395": "making a cake", 
-    "396": "bowling", 
-    "397": "contact juggling", 
-    "398": "applying cream", 
-    "399": "playing badminton"
-}

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/focal_loss.py

@@ -1,212 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'sigmoid_focal_loss_forward', 'sigmoid_focal_loss_backward',
-    'softmax_focal_loss_forward', 'softmax_focal_loss_backward'
-])
-
-
-class SigmoidFocalLossFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, target, gamma, alpha, weight, reduction):
-        return g.op(
-            'mmcv::MMCVSigmoidFocalLoss',
-            input,
-            target,
-            gamma_f=gamma,
-            alpha_f=alpha,
-            weight_f=weight,
-            reduction_s=reduction)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                target,
-                gamma=2.0,
-                alpha=0.25,
-                weight=None,
-                reduction='mean'):
-
-        assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor))
-        assert input.dim() == 2
-        assert target.dim() == 1
-        assert input.size(0) == target.size(0)
-        if weight is None:
-            weight = input.new_empty(0)
-        else:
-            assert weight.dim() == 1
-            assert input.size(1) == weight.size(0)
-        ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2}
-        assert reduction in ctx.reduction_dict.keys()
-
-        ctx.gamma = float(gamma)
-        ctx.alpha = float(alpha)
-        ctx.reduction = ctx.reduction_dict[reduction]
-
-        output = input.new_zeros(input.size())
-
-        ext_module.sigmoid_focal_loss_forward(
-            input, target, weight, output, gamma=ctx.gamma, alpha=ctx.alpha)
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            output = output.sum() / input.size(0)
-        elif ctx.reduction == ctx.reduction_dict['sum']:
-            output = output.sum()
-        ctx.save_for_backward(input, target, weight)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input, target, weight = ctx.saved_tensors
-
-        grad_input = input.new_zeros(input.size())
-
-        ext_module.sigmoid_focal_loss_backward(
-            input,
-            target,
-            weight,
-            grad_input,
-            gamma=ctx.gamma,
-            alpha=ctx.alpha)
-
-        grad_input *= grad_output
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            grad_input /= input.size(0)
-        return grad_input, None, None, None, None, None
-
-
-sigmoid_focal_loss = SigmoidFocalLossFunction.apply
-
-
-class SigmoidFocalLoss(nn.Module):
-
-    def __init__(self, gamma, alpha, weight=None, reduction='mean'):
-        super(SigmoidFocalLoss, self).__init__()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.register_buffer('weight', weight)
-        self.reduction = reduction
-
-    def forward(self, input, target):
-        return sigmoid_focal_loss(input, target, self.gamma, self.alpha,
-                                  self.weight, self.reduction)
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(gamma={self.gamma}, '
-        s += f'alpha={self.alpha}, '
-        s += f'reduction={self.reduction})'
-        return s
-
-
-class SoftmaxFocalLossFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, target, gamma, alpha, weight, reduction):
-        return g.op(
-            'mmcv::MMCVSoftmaxFocalLoss',
-            input,
-            target,
-            gamma_f=gamma,
-            alpha_f=alpha,
-            weight_f=weight,
-            reduction_s=reduction)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                target,
-                gamma=2.0,
-                alpha=0.25,
-                weight=None,
-                reduction='mean'):
-
-        assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor))
-        assert input.dim() == 2
-        assert target.dim() == 1
-        assert input.size(0) == target.size(0)
-        if weight is None:
-            weight = input.new_empty(0)
-        else:
-            assert weight.dim() == 1
-            assert input.size(1) == weight.size(0)
-        ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2}
-        assert reduction in ctx.reduction_dict.keys()
-
-        ctx.gamma = float(gamma)
-        ctx.alpha = float(alpha)
-        ctx.reduction = ctx.reduction_dict[reduction]
-
-        channel_stats, _ = torch.max(input, dim=1)
-        input_softmax = input - channel_stats.unsqueeze(1).expand_as(input)
-        input_softmax.exp_()
-
-        channel_stats = input_softmax.sum(dim=1)
-        input_softmax /= channel_stats.unsqueeze(1).expand_as(input)
-
-        output = input.new_zeros(input.size(0))
-        ext_module.softmax_focal_loss_forward(
-            input_softmax,
-            target,
-            weight,
-            output,
-            gamma=ctx.gamma,
-            alpha=ctx.alpha)
-
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            output = output.sum() / input.size(0)
-        elif ctx.reduction == ctx.reduction_dict['sum']:
-            output = output.sum()
-        ctx.save_for_backward(input_softmax, target, weight)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input_softmax, target, weight = ctx.saved_tensors
-        buff = input_softmax.new_zeros(input_softmax.size(0))
-        grad_input = input_softmax.new_zeros(input_softmax.size())
-
-        ext_module.softmax_focal_loss_backward(
-            input_softmax,
-            target,
-            weight,
-            buff,
-            grad_input,
-            gamma=ctx.gamma,
-            alpha=ctx.alpha)
-
-        grad_input *= grad_output
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            grad_input /= input_softmax.size(0)
-        return grad_input, None, None, None, None, None
-
-
-softmax_focal_loss = SoftmaxFocalLossFunction.apply
-
-
-class SoftmaxFocalLoss(nn.Module):
-
-    def __init__(self, gamma, alpha, weight=None, reduction='mean'):
-        super(SoftmaxFocalLoss, self).__init__()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.register_buffer('weight', weight)
-        self.reduction = reduction
-
-    def forward(self, input, target):
-        return softmax_focal_loss(input, target, self.gamma, self.alpha,
-                                  self.weight, self.reduction)
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(gamma={self.gamma}, '
-        s += f'alpha={self.alpha}, '
-        s += f'reduction={self.reduction})'
-        return s

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/html.py

@@ -1,991 +0,0 @@
-"""
-    pygments.formatters.html
-    ~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for HTML output.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import functools
-import os
-import sys
-import os.path
-from io import StringIO
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.token import Token, Text, STANDARD_TYPES
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt
-
-try:
-    import ctags
-except ImportError:
-    ctags = None
-
-__all__ = ['HtmlFormatter']
-
-
-_escape_html_table = {
-    ord('&'): '&',
-    ord('<'): '&lt;',
-    ord('>'): '&gt;',
-    ord('"'): '&quot;',
-    ord("'"): '&#39;',
-}
-
-
-def escape_html(text, table=_escape_html_table):
-    """Escape &, <, > as well as single and double quotes for HTML."""
-    return text.translate(table)
-
-
-def webify(color):
-    if color.startswith('calc') or color.startswith('var'):
-        return color
-    else:
-        return '#' + color
-
-
-def _get_ttype_class(ttype):
-    fname = STANDARD_TYPES.get(ttype)
-    if fname:
-        return fname
-    aname = ''
-    while fname is None:
-        aname = '-' + ttype[-1] + aname
-        ttype = ttype.parent
-        fname = STANDARD_TYPES.get(ttype)
-    return fname + aname
-
-
-CSSFILE_TEMPLATE = '''\
-/*
-generated by Pygments <https://pygments.org/>
-Copyright 2006-2022 by the Pygments team.
-Licensed under the BSD license, see LICENSE for details.
-*/
-%(styledefs)s
-'''
-
-DOC_HEADER = '''\
-<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN"
-   "http://www.w3.org/TR/html4/strict.dtd">
-<!--
-generated by Pygments <https://pygments.org/>
-Copyright 2006-2022 by the Pygments team.
-Licensed under the BSD license, see LICENSE for details.
--->
-<html>
-<head>
-  <title>%(title)s</title>
-  <meta http-equiv="content-type" content="text/html; charset=%(encoding)s">
-  <style type="text/css">
-''' + CSSFILE_TEMPLATE + '''
-  </style>
-</head>
-<body>
-<h2>%(title)s</h2>
-
-'''
-
-DOC_HEADER_EXTERNALCSS = '''\
-<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN"
-   "http://www.w3.org/TR/html4/strict.dtd">
-
-<html>
-<head>
-  <title>%(title)s</title>
-  <meta http-equiv="content-type" content="text/html; charset=%(encoding)s">
-  <link rel="stylesheet" href="%(cssfile)s" type="text/css">
-</head>
-<body>
-<h2>%(title)s</h2>
-
-'''
-
-DOC_FOOTER = '''\
-</body>
-</html>
-'''
-
-
-class HtmlFormatter(Formatter):
-    r"""
-    Format tokens as HTML 4 ``<span>`` tags within a ``<pre>`` tag, wrapped
-    in a ``<div>`` tag. The ``<div>``'s CSS class can be set by the `cssclass`
-    option.
-
-    If the `linenos` option is set to ``"table"``, the ``<pre>`` is
-    additionally wrapped inside a ``<table>`` which has one row and two
-    cells: one containing the line numbers and one containing the code.
-    Example:
-
-    .. sourcecode:: html
-
-        <div class="highlight" >
-        <table><tr>
-          <td class="linenos" title="click to toggle"
-            onclick="with (this.firstChild.style)
-                     { display = (display == '') ? 'none' : '' }">
-            <pre>1
-            2</pre>
-          </td>
-          <td class="code">
-            <pre><span class="Ke">def </span><span class="NaFu">foo</span>(bar):
-              <span class="Ke">pass</span>
-            </pre>
-          </td>
-        </tr></table></div>
-
-    (whitespace added to improve clarity).
-
-    Wrapping can be disabled using the `nowrap` option.
-
-    A list of lines can be specified using the `hl_lines` option to make these
-    lines highlighted (as of Pygments 0.11).
-
-    With the `full` option, a complete HTML 4 document is output, including
-    the style definitions inside a ``<style>`` tag, or in a separate file if
-    the `cssfile` option is given.
-
-    When `tagsfile` is set to the path of a ctags index file, it is used to
-    generate hyperlinks from names to their definition.  You must enable
-    `lineanchors` and run ctags with the `-n` option for this to work.  The
-    `python-ctags` module from PyPI must be installed to use this feature;
-    otherwise a `RuntimeError` will be raised.
-
-    The `get_style_defs(arg='')` method of a `HtmlFormatter` returns a string
-    containing CSS rules for the CSS classes used by the formatter. The
-    argument `arg` can be used to specify additional CSS selectors that
-    are prepended to the classes. A call `fmter.get_style_defs('td .code')`
-    would result in the following CSS classes:
-
-    .. sourcecode:: css
-
-        td .code .kw { font-weight: bold; color: #00FF00 }
-        td .code .cm { color: #999999 }
-        ...
-
-    If you have Pygments 0.6 or higher, you can also pass a list or tuple to the
-    `get_style_defs()` method to request multiple prefixes for the tokens:
-
-    .. sourcecode:: python
-
-        formatter.get_style_defs(['div.syntax pre', 'pre.syntax'])
-
-    The output would then look like this:
-
-    .. sourcecode:: css
-
-        div.syntax pre .kw,
-        pre.syntax .kw { font-weight: bold; color: #00FF00 }
-        div.syntax pre .cm,
-        pre.syntax .cm { color: #999999 }
-        ...
-
-    Additional options accepted:
-
-    `nowrap`
-        If set to ``True``, don't wrap the tokens at all, not even inside a ``<pre>``
-        tag. This disables most other options (default: ``False``).
-
-    `full`
-        Tells the formatter to output a "full" document, i.e. a complete
-        self-contained document (default: ``False``).
-
-    `title`
-        If `full` is true, the title that should be used to caption the
-        document (default: ``''``).
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``). This option has no effect if the `cssfile`
-        and `noclobber_cssfile` option are given and the file specified in
-        `cssfile` exists.
-
-    `noclasses`
-        If set to true, token ``<span>`` tags (as well as line number elements)
-        will not use CSS classes, but inline styles. This is not recommended
-        for larger pieces of code since it increases output size by quite a bit
-        (default: ``False``).
-
-    `classprefix`
-        Since the token types use relatively short class names, they may clash
-        with some of your own class names. In this case you can use the
-        `classprefix` option to give a string to prepend to all Pygments-generated
-        CSS class names for token types.
-        Note that this option also affects the output of `get_style_defs()`.
-
-    `cssclass`
-        CSS class for the wrapping ``<div>`` tag (default: ``'highlight'``).
-        If you set this option, the default selector for `get_style_defs()`
-        will be this class.
-
-        .. versionadded:: 0.9
-           If you select the ``'table'`` line numbers, the wrapping table will
-           have a CSS class of this string plus ``'table'``, the default is
-           accordingly ``'highlighttable'``.
-
-    `cssstyles`
-        Inline CSS styles for the wrapping ``<div>`` tag (default: ``''``).
-
-    `prestyles`
-        Inline CSS styles for the ``<pre>`` tag (default: ``''``).
-
-        .. versionadded:: 0.11
-
-    `cssfile`
-        If the `full` option is true and this option is given, it must be the
-        name of an external file. If the filename does not include an absolute
-        path, the file's path will be assumed to be relative to the main output
-        file's path, if the latter can be found. The stylesheet is then written
-        to this file instead of the HTML file.
-
-        .. versionadded:: 0.6
-
-    `noclobber_cssfile`
-        If `cssfile` is given and the specified file exists, the css file will
-        not be overwritten. This allows the use of the `full` option in
-        combination with a user specified css file. Default is ``False``.
-
-        .. versionadded:: 1.1
-
-    `linenos`
-        If set to ``'table'``, output line numbers as a table with two cells,
-        one containing the line numbers, the other the whole code.  This is
-        copy-and-paste-friendly, but may cause alignment problems with some
-        browsers or fonts.  If set to ``'inline'``, the line numbers will be
-        integrated in the ``<pre>`` tag that contains the code (that setting
-        is *new in Pygments 0.8*).
-
-        For compatibility with Pygments 0.7 and earlier, every true value
-        except ``'inline'`` means the same as ``'table'`` (in particular, that
-        means also ``True``).
-
-        The default value is ``False``, which means no line numbers at all.
-
-        **Note:** with the default ("table") line number mechanism, the line
-        numbers and code can have different line heights in Internet Explorer
-        unless you give the enclosing ``<pre>`` tags an explicit ``line-height``
-        CSS property (you get the default line spacing with ``line-height:
-        125%``).
-
-    `hl_lines`
-        Specify a list of lines to be highlighted. The line numbers are always
-        relative to the input (i.e. the first line is line 1) and are
-        independent of `linenostart`.
-
-        .. versionadded:: 0.11
-
-    `linenostart`
-        The line number for the first line (default: ``1``).
-
-    `linenostep`
-        If set to a number n > 1, only every nth line number is printed.
-
-    `linenospecial`
-        If set to a number n > 0, every nth line number is given the CSS
-        class ``"special"`` (default: ``0``).
-
-    `nobackground`
-        If set to ``True``, the formatter won't output the background color
-        for the wrapping element (this automatically defaults to ``False``
-        when there is no wrapping element [eg: no argument for the
-        `get_syntax_defs` method given]) (default: ``False``).
-
-        .. versionadded:: 0.6
-
-    `lineseparator`
-        This string is output between lines of code. It defaults to ``"\n"``,
-        which is enough to break a line inside ``<pre>`` tags, but you can
-        e.g. set it to ``"<br>"`` to get HTML line breaks.
-
-        .. versionadded:: 0.7
-
-    `lineanchors`
-        If set to a nonempty string, e.g. ``foo``, the formatter will wrap each
-        output line in an anchor tag with an ``id`` (and `name`) of ``foo-linenumber``.
-        This allows easy linking to certain lines.
-
-        .. versionadded:: 0.9
-
-    `linespans`
-        If set to a nonempty string, e.g. ``foo``, the formatter will wrap each
-        output line in a span tag with an ``id`` of ``foo-linenumber``.
-        This allows easy access to lines via javascript.
-
-        .. versionadded:: 1.6
-
-    `anchorlinenos`
-        If set to `True`, will wrap line numbers in <a> tags. Used in
-        combination with `linenos` and `lineanchors`.
-
-    `tagsfile`
-        If set to the path of a ctags file, wrap names in anchor tags that
-        link to their definitions. `lineanchors` should be used, and the
-        tags file should specify line numbers (see the `-n` option to ctags).
-
-        .. versionadded:: 1.6
-
-    `tagurlformat`
-        A string formatting pattern used to generate links to ctags definitions.
-        Available variables are `%(path)s`, `%(fname)s` and `%(fext)s`.
-        Defaults to an empty string, resulting in just `#prefix-number` links.
-
-        .. versionadded:: 1.6
-
-    `filename`
-        A string used to generate a filename when rendering ``<pre>`` blocks,
-        for example if displaying source code. If `linenos` is set to
-        ``'table'`` then the filename will be rendered in an initial row
-        containing a single `<th>` which spans both columns.
-
-        .. versionadded:: 2.1
-
-    `wrapcode`
-        Wrap the code inside ``<pre>`` blocks using ``<code>``, as recommended
-        by the HTML5 specification.
-
-        .. versionadded:: 2.4
-
-    `debug_token_types`
-        Add ``title`` attributes to all token ``<span>`` tags that show the
-        name of the token.
-
-        .. versionadded:: 2.10
-
-
-    **Subclassing the HTML formatter**
-
-    .. versionadded:: 0.7
-
-    The HTML formatter is now built in a way that allows easy subclassing, thus
-    customizing the output HTML code. The `format()` method calls
-    `self._format_lines()` which returns a generator that yields tuples of ``(1,
-    line)``, where the ``1`` indicates that the ``line`` is a line of the
-    formatted source code.
-
-    If the `nowrap` option is set, the generator is the iterated over and the
-    resulting HTML is output.
-
-    Otherwise, `format()` calls `self.wrap()`, which wraps the generator with
-    other generators. These may add some HTML code to the one generated by
-    `_format_lines()`, either by modifying the lines generated by the latter,
-    then yielding them again with ``(1, line)``, and/or by yielding other HTML
-    code before or after the lines, with ``(0, html)``. The distinction between
-    source lines and other code makes it possible to wrap the generator multiple
-    times.
-
-    The default `wrap()` implementation adds a ``<div>`` and a ``<pre>`` tag.
-
-    A custom `HtmlFormatter` subclass could look like this:
-
-    .. sourcecode:: python
-
-        class CodeHtmlFormatter(HtmlFormatter):
-
-            def wrap(self, source, *, include_div):
-                return self._wrap_code(source)
-
-            def _wrap_code(self, source):
-                yield 0, '<code>'
-                for i, t in source:
-                    if i == 1:
-                        # it's a line of formatted code
-                        t += '<br>'
-                    yield i, t
-                yield 0, '</code>'
-
-    This results in wrapping the formatted lines with a ``<code>`` tag, where the
-    source lines are broken using ``<br>`` tags.
-
-    After calling `wrap()`, the `format()` method also adds the "line numbers"
-    and/or "full document" wrappers if the respective options are set. Then, all
-    HTML yielded by the wrapped generator is output.
-    """
-
-    name = 'HTML'
-    aliases = ['html']
-    filenames = ['*.html', '*.htm']
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self.title = self._decodeifneeded(self.title)
-        self.nowrap = get_bool_opt(options, 'nowrap', False)
-        self.noclasses = get_bool_opt(options, 'noclasses', False)
-        self.classprefix = options.get('classprefix', '')
-        self.cssclass = self._decodeifneeded(options.get('cssclass', 'highlight'))
-        self.cssstyles = self._decodeifneeded(options.get('cssstyles', ''))
-        self.prestyles = self._decodeifneeded(options.get('prestyles', ''))
-        self.cssfile = self._decodeifneeded(options.get('cssfile', ''))
-        self.noclobber_cssfile = get_bool_opt(options, 'noclobber_cssfile', False)
-        self.tagsfile = self._decodeifneeded(options.get('tagsfile', ''))
-        self.tagurlformat = self._decodeifneeded(options.get('tagurlformat', ''))
-        self.filename = self._decodeifneeded(options.get('filename', ''))
-        self.wrapcode = get_bool_opt(options, 'wrapcode', False)
-        self.span_element_openers = {}
-        self.debug_token_types = get_bool_opt(options, 'debug_token_types', False)
-
-        if self.tagsfile:
-            if not ctags:
-                raise RuntimeError('The "ctags" package must to be installed '
-                                   'to be able to use the "tagsfile" feature.')
-            self._ctags = ctags.CTags(self.tagsfile)
-
-        linenos = options.get('linenos', False)
-        if linenos == 'inline':
-            self.linenos = 2
-        elif linenos:
-            # compatibility with <= 0.7
-            self.linenos = 1
-        else:
-            self.linenos = 0
-        self.linenostart = abs(get_int_opt(options, 'linenostart', 1))
-        self.linenostep = abs(get_int_opt(options, 'linenostep', 1))
-        self.linenospecial = abs(get_int_opt(options, 'linenospecial', 0))
-        self.nobackground = get_bool_opt(options, 'nobackground', False)
-        self.lineseparator = options.get('lineseparator', '\n')
-        self.lineanchors = options.get('lineanchors', '')
-        self.linespans = options.get('linespans', '')
-        self.anchorlinenos = get_bool_opt(options, 'anchorlinenos', False)
-        self.hl_lines = set()
-        for lineno in get_list_opt(options, 'hl_lines', []):
-            try:
-                self.hl_lines.add(int(lineno))
-            except ValueError:
-                pass
-
-        self._create_stylesheet()
-
-    def _get_css_class(self, ttype):
-        """Return the css class of this token type prefixed with
-        the classprefix option."""
-        ttypeclass = _get_ttype_class(ttype)
-        if ttypeclass:
-            return self.classprefix + ttypeclass
-        return ''
-
-    def _get_css_classes(self, ttype):
-        """Return the CSS classes of this token type prefixed with the classprefix option."""
-        cls = self._get_css_class(ttype)
-        while ttype not in STANDARD_TYPES:
-            ttype = ttype.parent
-            cls = self._get_css_class(ttype) + ' ' + cls
-        return cls or ''
-
-    def _get_css_inline_styles(self, ttype):
-        """Return the inline CSS styles for this token type."""
-        cclass = self.ttype2class.get(ttype)
-        while cclass is None:
-            ttype = ttype.parent
-            cclass = self.ttype2class.get(ttype)
-        return cclass or ''
-
-    def _create_stylesheet(self):
-        t2c = self.ttype2class = {Token: ''}
-        c2s = self.class2style = {}
-        for ttype, ndef in self.style:
-            name = self._get_css_class(ttype)
-            style = ''
-            if ndef['color']:
-                style += 'color: %s; ' % webify(ndef['color'])
-            if ndef['bold']:
-                style += 'font-weight: bold; '
-            if ndef['italic']:
-                style += 'font-style: italic; '
-            if ndef['underline']:
-                style += 'text-decoration: underline; '
-            if ndef['bgcolor']:
-                style += 'background-color: %s; ' % webify(ndef['bgcolor'])
-            if ndef['border']:
-                style += 'border: 1px solid %s; ' % webify(ndef['border'])
-            if style:
-                t2c[ttype] = name
-                # save len(ttype) to enable ordering the styles by
-                # hierarchy (necessary for CSS cascading rules!)
-                c2s[name] = (style[:-2], ttype, len(ttype))
-
-    def get_style_defs(self, arg=None):
-        """
-        Return CSS style definitions for the classes produced by the current
-        highlighting style. ``arg`` can be a string or list of selectors to
-        insert before the token type classes.
-        """
-        style_lines = []
-
-        style_lines.extend(self.get_linenos_style_defs())
-        style_lines.extend(self.get_background_style_defs(arg))
-        style_lines.extend(self.get_token_style_defs(arg))
-
-        return '\n'.join(style_lines)
-
-    def get_token_style_defs(self, arg=None):
-        prefix = self.get_css_prefix(arg)
-
-        styles = [
-            (level, ttype, cls, style)
-            for cls, (style, ttype, level) in self.class2style.items()
-            if cls and style
-        ]
-        styles.sort()
-
-        lines = [
-            '%s { %s } /* %s */' % (prefix(cls), style, repr(ttype)[6:])
-            for (level, ttype, cls, style) in styles
-        ]
-
-        return lines
-
-    def get_background_style_defs(self, arg=None):
-        prefix = self.get_css_prefix(arg)
-        bg_color = self.style.background_color
-        hl_color = self.style.highlight_color
-
-        lines = []
-
-        if arg and not self.nobackground and bg_color is not None:
-            text_style = ''
-            if Text in self.ttype2class:
-                text_style = ' ' + self.class2style[self.ttype2class[Text]][0]
-            lines.insert(
-                0, '%s{ background: %s;%s }' % (
-                    prefix(''), bg_color, text_style
-                )
-            )
-        if hl_color is not None:
-            lines.insert(
-                0, '%s { background-color: %s }' % (prefix('hll'), hl_color)
-            )
-
-        return lines
-
-    def get_linenos_style_defs(self):
-        lines = [
-            'pre { %s }' % self._pre_style,
-            'td.linenos .normal { %s }' % self._linenos_style,
-            'span.linenos { %s }' % self._linenos_style,
-            'td.linenos .special { %s }' % self._linenos_special_style,
-            'span.linenos.special { %s }' % self._linenos_special_style,
-        ]
-
-        return lines
-
-    def get_css_prefix(self, arg):
-        if arg is None:
-            arg = ('cssclass' in self.options and '.'+self.cssclass or '')
-        if isinstance(arg, str):
-            args = [arg]
-        else:
-            args = list(arg)
-
-        def prefix(cls):
-            if cls:
-                cls = '.' + cls
-            tmp = []
-            for arg in args:
-                tmp.append((arg and arg + ' ' or '') + cls)
-            return ', '.join(tmp)
-
-        return prefix
-
-    @property
-    def _pre_style(self):
-        return 'line-height: 125%;'
-
-    @property
-    def _linenos_style(self):
-        return 'color: %s; background-color: %s; padding-left: 5px; padding-right: 5px;' % (
-            self.style.line_number_color,
-            self.style.line_number_background_color
-        )
-
-    @property
-    def _linenos_special_style(self):
-        return 'color: %s; background-color: %s; padding-left: 5px; padding-right: 5px;' % (
-            self.style.line_number_special_color,
-            self.style.line_number_special_background_color
-        )
-
-    def _decodeifneeded(self, value):
-        if isinstance(value, bytes):
-            if self.encoding:
-                return value.decode(self.encoding)
-            return value.decode()
-        return value
-
-    def _wrap_full(self, inner, outfile):
-        if self.cssfile:
-            if os.path.isabs(self.cssfile):
-                # it's an absolute filename
-                cssfilename = self.cssfile
-            else:
-                try:
-                    filename = outfile.name
-                    if not filename or filename[0] == '<':
-                        # pseudo files, e.g. name == '<fdopen>'
-                        raise AttributeError
-                    cssfilename = os.path.join(os.path.dirname(filename),
-                                               self.cssfile)
-                except AttributeError:
-                    print('Note: Cannot determine output file name, '
-                          'using current directory as base for the CSS file name',
-                          file=sys.stderr)
-                    cssfilename = self.cssfile
-            # write CSS file only if noclobber_cssfile isn't given as an option.
-            try:
-                if not os.path.exists(cssfilename) or not self.noclobber_cssfile:
-                    with open(cssfilename, "w") as cf:
-                        cf.write(CSSFILE_TEMPLATE %
-                                 {'styledefs': self.get_style_defs('body')})
-            except OSError as err:
-                err.strerror = 'Error writing CSS file: ' + err.strerror
-                raise
-
-            yield 0, (DOC_HEADER_EXTERNALCSS %
-                      dict(title=self.title,
-                           cssfile=self.cssfile,
-                           encoding=self.encoding))
-        else:
-            yield 0, (DOC_HEADER %
-                      dict(title=self.title,
-                           styledefs=self.get_style_defs('body'),
-                           encoding=self.encoding))
-
-        yield from inner
-        yield 0, DOC_FOOTER
-
-    def _wrap_tablelinenos(self, inner):
-        dummyoutfile = StringIO()
-        lncount = 0
-        for t, line in inner:
-            if t:
-                lncount += 1
-            dummyoutfile.write(line)
-
-        fl = self.linenostart
-        mw = len(str(lncount + fl - 1))
-        sp = self.linenospecial
-        st = self.linenostep
-        anchor_name = self.lineanchors or self.linespans
-        aln = self.anchorlinenos
-        nocls = self.noclasses
-
-        lines = []
-
-        for i in range(fl, fl+lncount):
-            print_line = i % st == 0
-            special_line = sp and i % sp == 0
-
-            if print_line:
-                line = '%*d' % (mw, i)
-                if aln:
-                    line = '<a href="#%s-%d">%s</a>' % (anchor_name, i, line)
-            else:
-                line = ' ' * mw
-
-            if nocls:
-                if special_line:
-                    style = ' style="%s"' % self._linenos_special_style
-                else:
-                    style = ' style="%s"' % self._linenos_style
-            else:
-                if special_line:
-                    style = ' class="special"'
-                else:
-                    style = ' class="normal"'
-
-            if style:
-                line = '<span%s>%s</span>' % (style, line)
-
-            lines.append(line)
-
-        ls = '\n'.join(lines)
-
-        # If a filename was specified, we can't put it into the code table as it
-        # would misalign the line numbers. Hence we emit a separate row for it.
-        filename_tr = ""
-        if self.filename:
-            filename_tr = (
-                '<tr><th colspan="2" class="filename">'
-                '<span class="filename">' + self.filename + '</span>'
-                '</th></tr>')
-
-        # in case you wonder about the seemingly redundant <div> here: since the
-        # content in the other cell also is wrapped in a div, some browsers in
-        # some configurations seem to mess up the formatting...
-        yield 0, (f'<table class="{self.cssclass}table">' + filename_tr +
-            '<tr><td class="linenos"><div class="linenodiv"><pre>' +
-            ls + '</pre></div></td><td class="code">')
-        yield 0, '<div>'
-        yield 0, dummyoutfile.getvalue()
-        yield 0, '</div>'
-        yield 0, '</td></tr></table>'
-        
-
-    def _wrap_inlinelinenos(self, inner):
-        # need a list of lines since we need the width of a single number :(
-        inner_lines = list(inner)
-        sp = self.linenospecial
-        st = self.linenostep
-        num = self.linenostart
-        mw = len(str(len(inner_lines) + num - 1))
-        anchor_name = self.lineanchors or self.linespans
-        aln = self.anchorlinenos
-        nocls = self.noclasses
-
-        for _, inner_line in inner_lines:
-            print_line = num % st == 0
-            special_line = sp and num % sp == 0
-
-            if print_line:
-                line = '%*d' % (mw, num)
-            else:
-                line = ' ' * mw
-
-            if nocls:
-                if special_line:
-                    style = ' style="%s"' % self._linenos_special_style
-                else:
-                    style = ' style="%s"' % self._linenos_style
-            else:
-                if special_line:
-                    style = ' class="linenos special"'
-                else:
-                    style = ' class="linenos"'
-
-            if style:
-                linenos = '<span%s>%s</span>' % (style, line)
-            else:
-                linenos = line
-
-            if aln:
-                yield 1, ('<a href="#%s-%d">%s</a>' % (anchor_name, num, linenos) +
-                          inner_line)
-            else:
-                yield 1, linenos + inner_line
-            num += 1
-
-    def _wrap_lineanchors(self, inner):
-        s = self.lineanchors
-        # subtract 1 since we have to increment i *before* yielding
-        i = self.linenostart - 1
-        for t, line in inner:
-            if t:
-                i += 1
-                href = "" if self.linenos else ' href="#%s-%d"' % (s, i)
-                yield 1, '<a id="%s-%d" name="%s-%d"%s></a>' % (s, i, s, i, href) + line
-            else:
-                yield 0, line
-
-    def _wrap_linespans(self, inner):
-        s = self.linespans
-        i = self.linenostart - 1
-        for t, line in inner:
-            if t:
-                i += 1
-                yield 1, '<span id="%s-%d">%s</span>' % (s, i, line)
-            else:
-                yield 0, line
-
-    def _wrap_div(self, inner):
-        style = []
-        if (self.noclasses and not self.nobackground and
-                self.style.background_color is not None):
-            style.append('background: %s' % (self.style.background_color,))
-        if self.cssstyles:
-            style.append(self.cssstyles)
-        style = '; '.join(style)
-
-        yield 0, ('<div' + (self.cssclass and ' class="%s"' % self.cssclass) +
-                  (style and (' style="%s"' % style)) + '>')
-        yield from inner
-        yield 0, '</div>\n'
-
-    def _wrap_pre(self, inner):
-        style = []
-        if self.prestyles:
-            style.append(self.prestyles)
-        if self.noclasses:
-            style.append(self._pre_style)
-        style = '; '.join(style)
-
-        if self.filename and self.linenos != 1:
-            yield 0, ('<span class="filename">' + self.filename + '</span>')
-
-        # the empty span here is to keep leading empty lines from being
-        # ignored by HTML parsers
-        yield 0, ('<pre' + (style and ' style="%s"' % style) + '><span></span>')
-        yield from inner
-        yield 0, '</pre>'
-
-    def _wrap_code(self, inner):
-        yield 0, '<code>'
-        yield from inner
-        yield 0, '</code>'
-
-    @functools.lru_cache(maxsize=100)
-    def _translate_parts(self, value):
-        """HTML-escape a value and split it by newlines."""
-        return value.translate(_escape_html_table).split('\n')
-
-    def _format_lines(self, tokensource):
-        """
-        Just format the tokens, without any wrapping tags.
-        Yield individual lines.
-        """
-        nocls = self.noclasses
-        lsep = self.lineseparator
-        tagsfile = self.tagsfile
-
-        lspan = ''
-        line = []
-        for ttype, value in tokensource:
-            try:
-                cspan = self.span_element_openers[ttype]
-            except KeyError:
-                title = ' title="%s"' % '.'.join(ttype) if self.debug_token_types else ''
-                if nocls:
-                    css_style = self._get_css_inline_styles(ttype)
-                    if css_style:
-                        css_style = self.class2style[css_style][0]
-                        cspan = '<span style="%s"%s>' % (css_style, title)
-                    else:
-                        cspan = ''
-                else:
-                    css_class = self._get_css_classes(ttype)
-                    if css_class:
-                        cspan = '<span class="%s"%s>' % (css_class, title)
-                    else:
-                        cspan = ''
-                self.span_element_openers[ttype] = cspan
-
-            parts = self._translate_parts(value)
-
-            if tagsfile and ttype in Token.Name:
-                filename, linenumber = self._lookup_ctag(value)
-                if linenumber:
-                    base, filename = os.path.split(filename)
-                    if base:
-                        base += '/'
-                    filename, extension = os.path.splitext(filename)
-                    url = self.tagurlformat % {'path': base, 'fname': filename,
-                                               'fext': extension}
-                    parts[0] = "<a href=\"%s#%s-%d\">%s" % \
-                        (url, self.lineanchors, linenumber, parts[0])
-                    parts[-1] = parts[-1] + "</a>"
-
-            # for all but the last line
-            for part in parts[:-1]:
-                if line:
-                    # Also check for part being non-empty, so we avoid creating
-                    # empty <span> tags
-                    if lspan != cspan and part:
-                        line.extend(((lspan and '</span>'), cspan, part,
-                                     (cspan and '</span>'), lsep))
-                    else:  # both are the same, or the current part was empty
-                        line.extend((part, (lspan and '</span>'), lsep))
-                    yield 1, ''.join(line)
-                    line = []
-                elif part:
-                    yield 1, ''.join((cspan, part, (cspan and '</span>'), lsep))
-                else:
-                    yield 1, lsep
-            # for the last line
-            if line and parts[-1]:
-                if lspan != cspan:
-                    line.extend(((lspan and '</span>'), cspan, parts[-1]))
-                    lspan = cspan
-                else:
-                    line.append(parts[-1])
-            elif parts[-1]:
-                line = [cspan, parts[-1]]
-                lspan = cspan
-            # else we neither have to open a new span nor set lspan
-
-        if line:
-            line.extend(((lspan and '</span>'), lsep))
-            yield 1, ''.join(line)
-
-    def _lookup_ctag(self, token):
-        entry = ctags.TagEntry()
-        if self._ctags.find(entry, token.encode(), 0):
-            return entry['file'], entry['lineNumber']
-        else:
-            return None, None
-
-    def _highlight_lines(self, tokensource):
-        """
-        Highlighted the lines specified in the `hl_lines` option by
-        post-processing the token stream coming from `_format_lines`.
-        """
-        hls = self.hl_lines
-
-        for i, (t, value) in enumerate(tokensource):
-            if t != 1:
-                yield t, value
-            if i + 1 in hls:  # i + 1 because Python indexes start at 0
-                if self.noclasses:
-                    style = ''
-                    if self.style.highlight_color is not None:
-                        style = (' style="background-color: %s"' %
-                                 (self.style.highlight_color,))
-                    yield 1, '<span%s>%s</span>' % (style, value)
-                else:
-                    yield 1, '<span class="hll">%s</span>' % value
-            else:
-                yield 1, value
-
-    def wrap(self, source):
-        """
-        Wrap the ``source``, which is a generator yielding
-        individual lines, in custom generators. See docstring
-        for `format`. Can be overridden.
-        """
-
-        output = source
-        if self.wrapcode:
-            output = self._wrap_code(output)
-        
-        output = self._wrap_pre(output)
-    
-        return output
-
-    def format_unencoded(self, tokensource, outfile):
-        """
-        The formatting process uses several nested generators; which of
-        them are used is determined by the user's options.
-
-        Each generator should take at least one argument, ``inner``,
-        and wrap the pieces of text generated by this.
-
-        Always yield 2-tuples: (code, text). If "code" is 1, the text
-        is part of the original tokensource being highlighted, if it's
-        0, the text is some piece of wrapping. This makes it possible to
-        use several different wrappers that process the original source
-        linewise, e.g. line number generators.
-        """
-        source = self._format_lines(tokensource)
-
-        # As a special case, we wrap line numbers before line highlighting
-        # so the line numbers get wrapped in the highlighting tag.
-        if not self.nowrap and self.linenos == 2:
-            source = self._wrap_inlinelinenos(source)
-
-        if self.hl_lines:
-            source = self._highlight_lines(source)
-
-        if not self.nowrap:
-            if self.lineanchors:
-                source = self._wrap_lineanchors(source)
-            if self.linespans:
-                source = self._wrap_linespans(source)
-            source = self.wrap(source)
-            if self.linenos == 1:
-                source = self._wrap_tablelinenos(source)
-            source = self._wrap_div(source)
-            if self.full:
-                source = self._wrap_full(source, outfile)
-
-        for t, piece in source:
-            outfile.write(piece)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/__init__.py

@@ -1,25 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from .__about__ import (
-    __author__,
-    __copyright__,
-    __email__,
-    __license__,
-    __summary__,
-    __title__,
-    __uri__,
-    __version__,
-)
-
-__all__ = [
-    "__title__",
-    "__summary__",
-    "__uri__",
-    "__version__",
-    "__author__",
-    "__email__",
-    "__license__",
-    "__copyright__",
-]

spaces/Awiny/Image2Paragraph/models/grit_src/grit/modeling/text/modeling_bert.py

@@ -1,529 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, 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.
-"""PyTorch BERT model. """
-# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-import copy
-import os
-import json
-import logging
-import math
-import sys
-from io import open
-import torch
-from torch import nn
-import torch.utils.checkpoint as checkpoint
-from .file_utils import cached_path
-
-
-logger = logging.getLogger()
-
-
-BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",
-    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json",
-    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json",
-    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json",
-    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json",
-    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json",
-    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json",
-    'bert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json",
-    'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json",
-    'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json",
-    'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json",
-    'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json",
-    'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
-}
-
-
-def qk2attn(query, key, attention_mask, gamma):
-    query = query / gamma
-    attention_scores = torch.matmul(query, key.transpose(-1, -2))
-    if attention_mask is not None:
-        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-        attention_scores = attention_scores + attention_mask
-    return attention_scores.softmax(dim=-1)
-
-
-class QK2Attention(nn.Module):
-    def forward(self, query, key, attention_mask, gamma):
-        return qk2attn(query, key, attention_mask, gamma)
-
-
-LayerNormClass = torch.nn.LayerNorm
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config):
-        super(BertSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.output_attentions = config.output_attentions
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.softmax = nn.Softmax(dim=-1)
-        self.qk2attn = QK2Attention()
-
-    def transpose_for_scores(self, x):
-        if torch._C._get_tracing_state():
-            # exporter is not smart enough to detect dynamic size for some paths
-            x = x.view(x.shape[0], -1, self.num_attention_heads, self.attention_head_size)
-        else:
-            new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-            x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, hidden_states, attention_mask, head_mask=None,
-            history_state=None):
-        if history_state is not None:
-            x_states = torch.cat([history_state, hidden_states], dim=1)
-            mixed_query_layer = self.query(hidden_states)
-            mixed_key_layer = self.key(x_states)
-            mixed_value_layer = self.value(x_states)
-        else:
-            mixed_query_layer = self.query(hidden_states)
-            mixed_key_layer = self.key(hidden_states)
-            mixed_value_layer = self.value(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        attention_probs = self.qk2attn(query_layer, key_layer, attention_mask, math.sqrt(self.attention_head_size))
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super(BertSelfOutput, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
-        if not self.pre_norm:
-            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        if not self.pre_norm:
-            hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        else:
-            hidden_states = hidden_states + input_tensor
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config):
-        super(BertAttention, self).__init__()
-        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
-        if self.pre_norm:
-            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
-        self.self = BertSelfAttention(config)
-        self.output = BertSelfOutput(config)
-
-    def forward(self, input_tensor, attention_mask, head_mask=None,
-            history_state=None):
-        if self.pre_norm:
-            self_outputs = self.self(self.LayerNorm(input_tensor), attention_mask, head_mask,
-                    self.layerNorm(history_state) if history_state else history_state)
-        else:
-            self_outputs = self.self(input_tensor, attention_mask, head_mask,
-                    history_state)
-        attention_output = self.output(self_outputs[0], input_tensor)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super(BertIntermediate, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        assert config.hidden_act == 'gelu', 'Please implement other activation functions'
-        self.intermediate_act_fn = _gelu_python
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super(BertOutput, self).__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        if not self.pre_norm:
-            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        if not self.pre_norm:
-            hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        else:
-            hidden_states = hidden_states + input_tensor
-        return hidden_states
-
-
-class Mlp(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
-        self.intermediate = BertIntermediate(config)
-        if self.pre_norm:
-            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
-        self.output = BertOutput(config)
-
-    def forward(self, attention_output):
-        if not self.pre_norm:
-            intermediate_output = self.intermediate(attention_output)
-        else:
-            intermediate_output = self.intermediate(self.LayerNorm(attention_output))
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, use_act_checkpoint=True):
-        super(BertLayer, self).__init__()
-        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
-        self.use_mlp_wrapper = hasattr(config, 'use_mlp_wrapper') and config.use_mlp_wrapper
-        self.attention = BertAttention(config)
-        self.use_act_checkpoint = use_act_checkpoint
-        if self.use_mlp_wrapper:
-            self.mlp = Mlp(config)
-        else:
-            self.intermediate = BertIntermediate(config)
-            if self.pre_norm:
-                self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
-            self.output = BertOutput(config)
-
-    def forward(self, hidden_states, attention_mask, head_mask=None,
-                history_state=None):
-        if self.use_act_checkpoint:
-            attention_outputs = checkpoint.checkpoint(self.attention, hidden_states,
-                                                      attention_mask, head_mask, history_state)
-        else:
-            attention_outputs = self.attention(hidden_states, attention_mask,
-                                               head_mask, history_state)
-        attention_output = attention_outputs[0]
-        if self.use_mlp_wrapper:
-            layer_output = self.mlp(attention_output)
-        else:
-            if not self.pre_norm:
-                intermediate_output = self.intermediate(attention_output)
-            else:
-                intermediate_output = self.intermediate(self.LayerNorm(attention_output))
-            layer_output = self.output(intermediate_output, attention_output)
-        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config, use_act_checkpoint=True):
-        super(BertEncoder, self).__init__()
-        self.output_attentions = config.output_attentions
-        self.output_hidden_states = config.output_hidden_states
-        self.layer = nn.ModuleList([BertLayer(config, use_act_checkpoint=use_act_checkpoint) for _ in range(config.num_hidden_layers)])
-        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
-        if self.pre_norm:
-            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states, attention_mask, head_mask=None,
-                encoder_history_states=None):
-        all_hidden_states = ()
-        all_attentions = ()
-        for i, layer_module in enumerate(self.layer):
-            if self.output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            history_state = None if encoder_history_states is None else encoder_history_states[i]
-            layer_outputs = layer_module(
-                hidden_states, attention_mask,
-                (None if head_mask is None else head_mask[i]),
-                history_state,
-            )
-            hidden_states = layer_outputs[0]
-
-            if self.output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-        if self.pre_norm:
-            hidden_states = self.LayerNorm(hidden_states)
-        outputs = (hidden_states,)
-        if self.output_hidden_states:
-            outputs = outputs + (all_hidden_states,)
-        if self.output_attentions:
-            outputs = outputs + (all_attentions,)
-        return outputs
-
-CONFIG_NAME = "config.json"
-
-class PretrainedConfig(object):
-    """ Base class for all configuration classes.
-        Handle a few common parameters and methods for loading/downloading/saving configurations.
-    """
-    pretrained_config_archive_map = {}
-
-    def __init__(self, **kwargs):
-        self.finetuning_task = kwargs.pop('finetuning_task', None)
-        self.num_labels = kwargs.pop('num_labels', 2)
-        self.output_attentions = kwargs.pop('output_attentions', False)
-        self.output_hidden_states = kwargs.pop('output_hidden_states', False)
-        self.torchscript = kwargs.pop('torchscript', False)
-
-    def save_pretrained(self, save_directory):
-        """ Save a configuration object to a directory, so that it
-            can be re-loaded using the `from_pretrained(save_directory)` class method.
-        """
-        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
-
-        # If we save using the predefined names, we can load using `from_pretrained`
-        output_config_file = os.path.join(save_directory, CONFIG_NAME)
-
-        self.to_json_file(output_config_file)
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        r""" Instantiate a PretrainedConfig from a pre-trained model configuration.
-
-        Params:
-            **pretrained_model_name_or_path**: either:
-                - a string with the `shortcut name` of a pre-trained model configuration to load from cache
-                    or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
-                - a path to a `directory` containing a configuration file saved
-                    using the `save_pretrained(save_directory)` method.
-                - a path or url to a saved configuration `file`.
-            **cache_dir**: (`optional`) string:
-                Path to a directory in which a downloaded pre-trained model
-                configuration should be cached if the standard cache should not be used.
-            **return_unused_kwargs**: (`optional`) bool:
-                - If False, then this function returns just the final configuration object.
-                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs`
-                is a dictionary consisting of the key/value pairs whose keys are not configuration attributes:
-                ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
-            **kwargs**: (`optional`) dict:
-                Dictionary of key/value pairs with which to update the configuration object after loading.
-                - The values in kwargs of any keys which are configuration attributes will be used
-                to override the loaded values.
-                - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
-                by the `return_unused_kwargs` keyword parameter.
-
-        Examples::
-
-            >>> config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
-            >>> config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
-            >>> config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
-            >>> config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
-            >>> assert config.output_attention == True
-            >>> config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
-            >>>                                                    foo=False, return_unused_kwargs=True)
-            >>> assert config.output_attention == True
-            >>> assert unused_kwargs == {'foo': False}
-
-        """
-        cache_dir = kwargs.pop('cache_dir', None)
-        return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
-
-        if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
-            config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
-        elif os.path.isdir(pretrained_model_name_or_path):
-            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
-        else:
-            config_file = pretrained_model_name_or_path
-        # redirect to the cache, if necessary
-        try:
-            resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
-        except EnvironmentError:
-            if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
-                logger.error(
-                    "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
-                        config_file))
-            else:
-                logger.error(
-                    "Model name '{}' was not found in model name list ({}). "
-                    "We assumed '{}' was a path or url but couldn't find any file "
-                    "associated to this path or url.".format(
-                        pretrained_model_name_or_path,
-                        ', '.join(cls.pretrained_config_archive_map.keys()),
-                        config_file))
-            return None
-        if resolved_config_file == config_file:
-            logger.info("loading configuration file {}".format(config_file))
-        else:
-            logger.info("loading configuration file {} from cache at {}".format(
-                config_file, resolved_config_file))
-
-        # Load config
-        config = cls.from_json_file(resolved_config_file)
-
-        # Update config with kwargs if needed
-        to_remove = []
-        for key, value in kwargs.items():
-            if hasattr(config, key):
-                setattr(config, key, value)
-                to_remove.append(key)
-        # add img_layer_norm_eps, use_img_layernorm
-        if "img_layer_norm_eps" in kwargs:
-            setattr(config, "img_layer_norm_eps", kwargs["img_layer_norm_eps"])
-            to_remove.append("img_layer_norm_eps")
-        if "use_img_layernorm" in kwargs:
-            setattr(config, "use_img_layernorm", kwargs["use_img_layernorm"])
-            to_remove.append("use_img_layernorm")
-        for key in to_remove:
-            kwargs.pop(key, None)
-
-        logger.info("Model config %s", config)
-        if return_unused_kwargs:
-            return config, kwargs
-        else:
-            return config
-
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `Config` from a Python dictionary of parameters."""
-        config = cls(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `BertConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding='utf-8') as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __eq__(self, other):
-        return self.__dict__ == other.__dict__
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
-        with open(json_file_path, "w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())
-
-
-class BertConfig(PretrainedConfig):
-    r"""
-        :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a
-        `BertModel`.
-
-
-        Arguments:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `BertModel`.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-            layer_norm_eps: The epsilon used by LayerNorm.
-    """
-    pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-    def __init__(self,
-                 vocab_size_or_config_json_file=30522,
-                 hidden_size=768,
-                 num_hidden_layers=12,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 max_position_embeddings=512,
-                 type_vocab_size=2,
-                 initializer_range=0.02,
-                 layer_norm_eps=1e-12,
-                 **kwargs):
-        super(BertConfig, self).__init__(**kwargs)
-        if isinstance(vocab_size_or_config_json_file, str):
-            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.hidden_act = hidden_act
-            self.intermediate_size = intermediate_size
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.type_vocab_size = type_vocab_size
-            self.initializer_range = initializer_range
-            self.layer_norm_eps = layer_norm_eps
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
-
-
-def _gelu_python(x):
-
-    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))

spaces/Banbri/zcvzcv/src/app/layout.tsx

@@ -1,24 +0,0 @@
-import './globals.css'
-import type { Metadata } from 'next'
-import { Inter } from 'next/font/google'
-
-const inter = Inter({ subsets: ['latin'] })
-
-export const metadata: Metadata = {
-  title: 'AI Comic Factory: generate your own comics! Powered by Hugging Face 🤗',
-  description: 'Generate comic panels using a LLM + SDXL. Powered by Hugging Face 🤗',
-}
-
-export default function RootLayout({
-  children,
-}: {
-  children: React.ReactNode
-}) {
-  return (
-    <html lang="en">
-      <body className={inter.className}>
-        {children}
-      </body>
-    </html>
-  )
-}

spaces/Bart92/RVC_HF/tensorlowest.py

@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
-    try:
-        files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
-        if not files:
-            print("No files with the '.0' extension found!")
-            return
-
-        max_size = 0
-        biggest_file = ""
-
-        for file in files:
-            file_path = os.path.join(tensordir, file)
-            if os.path.isfile(file_path):
-                file_size = os.path.getsize(file_path)
-                if file_size > max_size:
-                    max_size = file_size
-                    biggest_file = file
-
-        return biggest_file
-
-    except FileNotFoundError:
-        print("Couldn't find your model!")
-        return
-
-def main(model_name, save_freq, lastmdls):
-    global lowestval_weight_dir, scl
-
-    tensordir = os.path.join('logs', model_name)
-    lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-    
-    latest_file = find_biggest_tensorboard(tensordir)
-    
-    if latest_file is None:
-        print("Couldn't find a valid tensorboard file!")
-        return
-    
-    tfile = os.path.join(tensordir, latest_file)
-    
-    ea = event_accumulator.EventAccumulator(tfile,
-        size_guidance={
-        event_accumulator.COMPRESSED_HISTOGRAMS: 500,
-        event_accumulator.IMAGES: 4,
-        event_accumulator.AUDIO: 4,
-        event_accumulator.SCALARS: 0,
-        event_accumulator.HISTOGRAMS: 1,
-    })
-
-    ea.Reload()
-    ea.Tags()
-
-    scl = ea.Scalars('loss/g/total')
-
-    listwstep = {}
-    
-    for val in scl:
-        if (val.step // save_freq) * save_freq in [val.step for val in scl]:
-            listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
-    lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
-    sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-    
-    return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
-    os.makedirs(lowestval_weight_dir, exist_ok=True)
-    logdir = []
-    files = []
-    lbldict = {
-        'Values': {},
-        'Names': {}
-    }
-    weights_dir_path = os.path.join(weights_dir, "")
-    low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-    
-    try:
-        file_dict = LEval(file_dict)
-    except Exception as e: 
-        print(f"Error! {e}")
-        return f"Couldn't load tensorboard file! {e}"
-    
-    weights = [f for f in os.scandir(weights_dir)]
-    for key, value in file_dict.items():
-        pattern = fr"^{model_name}_.*_s{value}\.pth$"
-        matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
-        for weight in matching_weights:
-            source_path = weights_dir_path + weight
-            destination_path = os.path.join(lowestval_weight_dir, weight)
-            
-            copy2(source_path, destination_path)
-
-            logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
-            lbldict['Names'][weight] = weight
-            lbldict['Values'][weight] = key
-
-            files.append(low_val_path + weight)
-
-            print(f"File = {weight} Value: {key}, Step: {value}")
-
-            yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-            
-
-    return ''.join(logdir), files, pd.DataFrame(lbldict)
-    
-
-if __name__ == "__main__":
-    model = str(input("Enter the name of the model: "))
-    sav_freq = int(input("Enter save frequency of the model: "))
-    ds = main(model, sav_freq)
-    
-    if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-    

spaces/Benson/text-generation/Examples/360 Seguridad 2018 Apk Descargar.md

@@ -1,79 +0,0 @@
-<br />
-<h1>360 Seguridad 2018 APK Descargar: Una guía completa</h1>
-<p>Si está buscando una aplicación de seguridad confiable y potente para su dispositivo Android, es posible que desee considerar 360 Security. Esta aplicación es una de las aplicaciones de seguridad más populares y confiables en el mercado, con más de 200 millones de usuarios en todo el mundo. En este artículo, le mostraremos qué es 360 Security, qué características y beneficios ofrece, cómo descargar e instalar la última versión de la aplicación y cómo usarla para proteger su dispositivo de varias amenazas. ¡Vamos a empezar! </p>
- <h2>¿Qué es 360 Security? </h2>
-<p>360 Security es una aplicación de seguridad integral que proporciona protección en tiempo real para su dispositivo Android. Puede escanear su dispositivo en busca de virus, malware, adware, spyware y otros programas maliciosos, y eliminarlos con un solo toque. También puede aumentar el rendimiento del dispositivo, la duración de la batería y el espacio de almacenamiento optimizando la configuración del sistema, cerrando las aplicaciones en segundo plano y limpiando los archivos basura. Además, puede proteger la privacidad y los datos de su dispositivo bloqueando sus aplicaciones, ocultando sus fotos y videos, cifrando sus mensajes y bloqueando llamadas y SMS no deseados. Con 360 Security, puedes disfrutar de una experiencia Android rápida, fluida y segura. </p>
-<h2>360 seguridad 2018 apk descargar</h2><br /><p><b><b>Download File</b> &#9733; <a href="https://bltlly.com/2v6MNe">https://bltlly.com/2v6MNe</a></b></p><br /><br />
- <h3>Características de 360 Seguridad</h3>
-<p>Algunas de las características principales de 360 Security son:</p>
-<ul>
-<li><b>Antivirus:</b> Puede detectar y eliminar virus, malware, adware, spyware, ransomware, troyanos, gusanos, rootkits y otras amenazas desde su dispositivo. También proporciona protección en tiempo real y actualiza su base de datos de virus regularmente. </li>
-<li><b>Boost:</b> Puede acelerar el dispositivo optimizando la configuración del sistema, cerrando aplicaciones en segundo plano, limpiando la RAM y reduciendo el uso de la CPU. También puede extender la duración de la batería mediante la gestión de su consumo de energía. </li>
-
-<li><b>Bloquear:</b> Puede bloquear sus aplicaciones con una contraseña o un patrón para evitar el acceso no autorizado. También puede personalizar la pantalla de bloqueo con diferentes temas y fondos de pantalla. </li>
-<li><b>Ocultar:</b> Puede ocultar sus fotos y videos en una bóveda segura a la que solo usted puede acceder. También puede cifrar sus mensajes con una caja privada que solo usted puede abrir. </li>
-<li><b>Bloquear:</b> Puede bloquear llamadas no deseadas y SMS de números desconocidos o contactos de spam. También puede crear una lista negra o una lista blanca para gestionar sus llamadas y mensajes entrantes. </li>
-</ul>
- <h3>Beneficios de 360 Seguridad</h3>
-<p>Algunos de los beneficios de usar 360 Security son:</p>
-<ul>
-<li><b>Es gratis:</b> Puede descargar y usar 360 Security gratis sin limitaciones ni anuncios. También puede actualizar a la versión premium para obtener más características y beneficios. </li>
-<li><b>Es fácil de usar:</b> Puedes usar 360 Security con una interfaz simple e intuitiva que te guía a través de cada paso. También puede acceder a todas las funciones con un solo toque en la pantalla de inicio. </li>
-<li><b>Es compatible:</b> Puede usar 360 Security en cualquier dispositivo Android con Android 4.0 o superior. También puede sincronizar sus datos en varios dispositivos con la misma cuenta. </li>
-<li><b>Es confiable:</b> Puede confiar en 360 Security para proteger su dispositivo de diversas amenazas con su tecnología avanzada y equipo profesional. También puede ponerse en contacto con su atención al cliente para cualquier problema o retroalimentación. </li>
-</ul>
- <h2>Cómo descargar e instalar 360 Security 2018 APK? </h2>
-<p>Si desea descargar e instalar la última versión de 360 Security 2018 APK, puede seguir estos sencillos pasos:</p>
- <h3>Paso 1: Habilitar fuentes desconocidas</h3>
-<p>Antes de que pueda instalar cualquier archivo APK en su dispositivo, debe habilitar la opción para permitir la instalación desde fuentes desconocidas. Esta opción suele estar deshabilitada de forma predeterminada por razones de seguridad, pero puede habilitarla fácilmente siguiendo estos pasos:</p>
-<ol>
-
-<li>Encontrar la opción que dice Fuentes desconocidas o Instalar aplicaciones desconocidas y alternar en. </li>
-<li> Aparecerá un mensaje de advertencia que le dirá que instalar aplicaciones de fuentes desconocidas puede dañar su dispositivo. Toque en Aceptar o Permitir para confirmar. </li>
-</ol>
- <h3>Paso 2: Descargar el archivo APK</h3>
-<p>Ahora que ha habilitado fuentes desconocidas, puede descargar el archivo APK 360 Security 2018 de una fuente confiable. Puede utilizar el siguiente enlace para descargar el archivo directamente a su dispositivo:</p>
-<p><a href=">360 Seguridad 2018 APK Descargar</a></p>
-<p></p>
-<p>El tamaño del archivo es de aproximadamente 24 MB, así que asegúrese de tener suficiente espacio en su dispositivo y una conexión a Internet estable. También puede escanear el archivo con su dispositivo antivirus o una aplicación de terceros para asegurarse de que es seguro y libre de virus. </p>
- <h3>Paso 3: Instalar el archivo APK</h3>
-<p>Una vez que haya descargado el archivo APK, puede instalarlo siguiendo estos pasos:</p>
-<ol>
-<li>Localice el archivo en su dispositivo utilizando una aplicación de administrador de archivos o el navegador del dispositivo. </li>
-<li>Toque en el archivo y aparecerá un mensaje, preguntándole si desea instalar la aplicación. Toque en Instalar o Siguiente para proceder. </li>
-<li> El proceso de instalación tomará unos segundos, dependiendo de la velocidad y el rendimiento del dispositivo. Espere hasta que se complete y toque en Abrir o Listo para terminar. </li>
-</ol>
- <h3>Paso 4: Iniciar la aplicación y conceder permisos</h3>
-<p>Ahora que ha instalado la aplicación, puede iniciarla tocando en su icono en la pantalla de inicio del dispositivo o en el cajón de aplicaciones. La primera vez que inicie la aplicación, le pedirá algunos permisos para acceder a las funciones y los datos del dispositivo. Estos permisos son necesarios para que la aplicación funcione correctamente y proteja su dispositivo. Puede conceder estos permisos pulsando en Permitir o Aceptar cuando se le solicite. También puede cambiar estos permisos más tarde en la configuración del dispositivo. </p>
- <h2>¿Cómo usar 360 Security? </h2>
-
- <h3>Escanea tu dispositivo en busca de virus y malware</h3>
-<p>Para escanear su dispositivo en busca de virus y malware, toque en el botón Escanear en la pantalla de inicio. La aplicación comenzará a escanear su dispositivo en busca de amenazas y mostrará los resultados en unos segundos. Puede ver cuántas amenazas se detectaron y de qué tipo son. También puede ver cuánto espacio y batería puede ahorrar al eliminarlas. Para eliminarlos, toque en Arreglar o Limpiar y confirme su acción. La aplicación eliminará todas las amenazas de su dispositivo y lo hará más seguro. </p>
- <h3>Aumentar el rendimiento del dispositivo y la duración de la batería</h3>
-<p>Para aumentar el rendimiento del dispositivo y la duración de la batería, toque en el botón Boost en la pantalla de inicio. La aplicación comenzará a optimizar la configuración del dispositivo, cerrar aplicaciones en segundo plano, borrar la RAM y reducir el uso de la CPU. Puede ver cuánta velocidad y batería puede ganar haciendo esto. Para aplicar estos cambios, toque en Aumentar o Optimizar y confirme su acción. La aplicación hará que su dispositivo más rápido y suave. </p>
- <h3>Limpia el almacenamiento y la caché de tu dispositivo</h3>
-<p>Para limpiar el almacenamiento y la caché del dispositivo, toque en el botón Limpiar en la pantalla de inicio. La aplicación comenzará a escanear su dispositivo en busca de archivos basura, archivos de caché, archivos residuales, APK obsoletos, archivos duplicados, archivos grandes y otros archivos no deseados. Puede ver cuánto espacio puede liberar eliminándolos. Para eliminarlos, pulse Limpiar o Eliminar y confirme su acción. La aplicación eliminará todos los archivos innecesarios de su dispositivo y lo hará más limpio. </p>
- <h3>Proteja la privacidad y los datos de su dispositivo</h3>
-
-<p>360 Security es una aplicación de seguridad integral que puede proteger su dispositivo Android de diversas amenazas, aumentar el rendimiento del dispositivo y la duración de la batería, limpiar el almacenamiento y la caché del dispositivo, proteger la privacidad y los datos del dispositivo y bloquear las llamadas no deseadas y SMS. Puede descargar e instalar la última versión de 360 Security 2018 APK siguiendo los pasos en este artículo. También puede utilizar la aplicación con facilidad y comodidad accediendo a todas las funciones con un solo toque en la pantalla de inicio. Con 360 Security, puedes disfrutar de una experiencia Android rápida, fluida y segura. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Estas son algunas de las preguntas más frecuentes sobre 360 Security:</p>
-<ul>
-<li><b>Q: ¿Es seguro usar 360 Security? </b></li>
-<li><b>A: Sí, 360 La seguridad es segura de usar, ya que está desarrollada por una empresa de buena reputación y ha sido probada por millones de usuarios. No contiene ningún virus, malware o spyware que pueda dañar su dispositivo o datos. </b></li>
-<li><b>Q: ¿Cuánto cuesta 360 Security? </b></li>
-<li><b>A: 360 Security es gratis de descargar y usar sin limitaciones ni anuncios. También puede actualizar a la versión premium para obtener más características y beneficios por una pequeña tarifa. </b></li>
-<li><b>Q: ¿Cómo puedo actualizar 360 Security? </b></li>
-<li><b>A: Puede actualizar 360 Security descargando e instalando la última versión del archivo APK desde una fuente de confianza. También puede buscar actualizaciones dentro de la aplicación pulsando en el icono de menú en la esquina superior izquierda y seleccionando Buscar actualizaciones. </b></li>
-<li><b>Q: ¿Cómo puedo desinstalar 360 Security? </b></li>
-<li><b>A: Puede desinstalar 360 Security siguiendo estos pasos:</b></li>
-<ol>
-<li>Ir a la configuración del dispositivo y toque en aplicaciones o aplicaciones.</li>
-<li>Buscar y tocar en 360 Seguridad de la lista de aplicaciones. </li>
-<li>Toque en Desinstalar o Quitar y confirme su acción. </li>
-</ol>
-<li><b>Q: ¿Cómo puedo contactar a 360 Security Customer Support? </b></li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Anime Fidget Spinner Battle Mod Apk.md

@@ -1,49 +0,0 @@
-<br />
-<h1>Bitcoin multimillonario APK: Un divertido y rentable juego Crypto</h1>
-<p>¿Quieres experimentar la emoción de la minería y la inversión en bitcoins sin arriesgar su dinero real? ¿Quieres divertirte y aprender más sobre criptomonedas mientras juegas un juego casual y adictivo? Si respondiste sí a cualquiera de estas preguntas, entonces usted debe tratar de Bitcoin Multimillonario APK, un juego de temática criptográfica única que tiene millones de descargas y comentarios favorables de los jugadores de todo el mundo. </p>
-<h2>¿Qué es Bitcoin multimillonario APK? </h2>
-<p>Bitcoin Billionaire APK es un juego para Android que fue desarrollado por Noodlecake Studios y lanzado en 2014. Es un juego gratuito que se puede jugar fuera de línea o en línea, con compras y anuncios opcionales en la aplicación. El juego está disponible en Google Play Store y APKCombo, un sitio web que proporciona descargas APK gratuitas para dispositivos Android. </p>
-<h2>anime fidget spinner battle mod apk</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6JEC">https://bltlly.com/2v6JEC</a></b></p><br /><br />
-<h3>Un popular juego de clicker de inactividad</h3>
-<p>El juego pertenece al género de juegos de clicker ociosos, que son juegos que requieren una mínima entrada del jugador, pero recompensarlos con ingresos pasivos y progreso. En Bitcoin multimillonario APK, el jugador comienza con casi nada: una oficina deteriorada, un escritorio viejo desvencijado, y una computadora terrible. Al tocar la pantalla, el jugador puede extraer bitcoins virtuales, que son bitcoins falsos que se utilizan como la moneda principal del juego. El jugador puede gastar sus bitcoins en varios artículos y mejoras, como centros de entretenimiento, obras de arte, equipos de minería y más. </p>
-<h3>Una simulación realista de la minería criptográfica y la inversión</h3>
-
-<h3>Una aventura en el tiempo con diferentes épocas y mejoras</h3>
-<p>Una de las características más emocionantes del juego es la capacidad de viajar en el tiempo a diferentes épocas, donde el jugador puede ganar nuevas mejoras, desbloquear logros adicionales y experimentar diferentes escenarios. El jugador puede viajar desde el presente hasta el pasado distante o el futuro lejano, como los tiempos prehistóricos, los tiempos medievales, los tiempos de los piratas o el apocalipsis zombi. Cada época tiene su propio tema, gráficos, música, elementos y desafíos. Por ejemplo, en la era prehistórica, el jugador puede extraer bitcoins con una tableta de piedra, comprar una mascota de dinosaurio o enfrentar un golpe de meteorito. </p>
-<h2>Cómo descargar y jugar Bitcoin multimillonario APK? </h2>
-<p>Si usted está interesado en jugar Bitcoin multimillonario APK, aquí están los requisitos y pasos para descargar y jugar el juego. </p>
-<h3>Los requisitos y pasos para descargar el juego</h3>
-<p>El juego requiere Android 5.1 o superior para funcionar sin problemas. También requiere aproximadamente 80 MB de espacio de almacenamiento en su dispositivo. Para descargar el juego desde Google Play Store, solo tiene que buscar "Bitcoin Billionaire" y toque en "Instalar". Para descargar el juego de APKCombo, es necesario visitar su sitio web, buscar "Bitcoin multimillonario", elegir el modelo de dispositivo y la versión de Android, y toque en "Descargar APK". Es posible que tenga que habilitar "Fuentes desconocidas" en la configuración del dispositivo para instalar el archivo APK. </p>
-<h3>El modo de juego básico y las características del juego</h3>
-<p> <p>El juego es fácil de jugar y tiene una interfaz sencilla. Solo tienes que tocar la pantalla para minar bitcoins, deslizar la pantalla para recoger los bonos, y pulse el botón de menú para acceder a sus elementos, inversiones, logros y ajustes. También puedes personalizar la apariencia, el nombre y el atuendo de tu personaje tocando el icono del avatar. El juego tiene muchas características que lo hacen divertido y atractivo, como:</p>
-<ul>
-
-<li><b>Coge los bonos de los drones de reparto. </b> Puedes deslizar la pantalla para recoger los bonos de los drones que vuelan por tu oficina. Estos bonos pueden ser monedas, gemas, cartas o aumentos. Las monedas se utilizan para comprar artículos y mejoras, las gemas se utilizan para comprar artículos premium y acelerar los viajes en el tiempo, las tarjetas se utilizan para desbloquear bonos y beneficios especiales, y los aumentos se utilizan para multiplicar sus ganancias por un tiempo limitado. </li>
-<li><b>Personaliza tu personaje al mío con estilo. </b> Puedes cambiar la apariencia, el nombre y el atuendo de tu personaje tocando el icono del avatar. Puedes elegir entre una variedad de peinados, rasgos faciales, ropa y accesorios. Algunos trajes son temáticos después de las épocas que visitas, como un traje de hombre de las cavernas, un traje de pirata o un traje de zombi. </li>
-<li><b>Actualizaciones de vacaciones gratuitas y extras. </b> El juego se actualiza regularmente con nuevos contenidos y características basadas en vacaciones y estaciones. Por ejemplo, durante Halloween, puedes extraer bitcoins en una mansión embrujada, encontrar eventos espeluznantes y recoger granos de caramelo. Durante Navidad, puede extraer bitcoins en un país de las maravillas de invierno, encontrar eventos festivos y recoger copos de nieve. </li>
-<li><b>Adoptar un gatito! O un robot! O un T-Rex! O algo más! </b> Puedes adoptar una mascota que te acompañará en tu oficina y te proporcionará bonificaciones pasivas. Puedes elegir entre una variedad de mascotas, como un gatito, un robot, un T-Rex o un unicornio. Cada mascota tiene su propia personalidad y apariencia. También puede alimentar, mascotas y jugar con su mascota tocando en él. </li>
-</ul>
-<h2> ¿Por qué debe jugar Bitcoin multimillonario APK? </h2>
-<p>Bitcoin multimillonario APK no es solo un juego, sino también una experiencia de aprendizaje. Al jugar a este juego, usted puede beneficiarse de muchas maneras, tales como:</p>
-<p></p>
-<h3>Los beneficios de jugar un juego de temática criptográfica</h3>
-
-<h3>Las recompensas e incentivos del juego</h3>
-<p>El juego te recompensa por tus esfuerzos y logros con varios incentivos y premios. Usted puede ganar monedas, gemas, tarjetas, aumenta, artículos, mejoras, logros, mascotas, trajes, y más. También puede competir con otros jugadores en las tablas de clasificación globales y ver cómo se clasifican entre los mejores multimillonarios bitcoin en el mundo. El juego también ofrece misiones diarias y desafíos que te dan recompensas adicionales por completar tareas o objetivos específicos. </p>
-<h3>Los planes futuros y actualizaciones del juego</h3>
-<p>El juego está en constante evolución y mejora con nuevos contenidos y características. Los desarrolladores siempre están escuchando los comentarios y sugerencias de los jugadores e implementándolos en el juego. El juego también tiene una base de fans leales que apoya el juego y crea arte de fans, videos, memes y más. El juego tiene un futuro brillante por delante y promete ofrecer más diversión y emoción a sus jugadores. </p>
-<h2>Conclusión</h2>
-<p>Bitcoin multimillonario APK es un juego de criptografía divertido y rentable que cualquiera puede disfrutar. Es un juego de clicker inactivo que simula la minería criptográfica e invertir de una manera realista y humorística. También es una aventura de viaje en el tiempo que te lleva a diferentes épocas con diferentes temas y desafíos. También es una experiencia de aprendizaje que le enseña acerca de cryptocurrencies de una manera entretenida. También es una experiencia gratificante que le da varios incentivos y premios por sus logros. También es una experiencia en evolución que se actualiza con nuevos contenidos y características regularmente. </p>
-<p>Si usted está buscando un juego que combina la diversión, la educación, el beneficio, la aventura, la recompensa, y la evolución en un solo paquete, entonces Bitcoin Multimillonario APK es el juego para usted. Descárgalo ahora desde Google Play Store <p>or APKCombo y comienza tu viaje para convertirte en un multimillonario bitcoin hoy! </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Bitcoin multimillonario APK:</p>
-<tabla>
-
-<tr><td>Es Bitcoin multimillonario APK seguro para descargar y jugar? </td><td>Sí, Bitcoin multimillonario APK es seguro para descargar y jugar, siempre y cuando se descarga desde una fuente de buena reputación, como Google Play Store o APKCombo. El juego no contiene ningún malware, virus o contenido dañino. Sin embargo, siempre debes tener cuidado al descargar cualquier archivo APK de fuentes desconocidas, ya que pueden contener código malicioso o anuncios no deseados. </td></tr>
-<tr><td>¿Puedo jugar Bitcoin multimillonario APK con dinero real? </td><td>No, Bitcoin multimillonario APK es un juego que utiliza bitcoins virtuales que no tienen valor real. No puedes usar dinero real para comprar o vender bitcoins en el juego, ni puedes retirar o intercambiar tus bitcoins virtuales por dinero real. El juego está destinado solo para fines de entretenimiento y educación, y no implica transacciones de criptomonedas reales. </td></tr>
-<tr><td>¿Puedo jugar Bitcoin multimillonario APK offline? </td><td>Sí, puede jugar Bitcoin multimillonario APK offline, sin conexión a Internet. Sin embargo, algunas características del juego pueden requerir una conexión a Internet para funcionar correctamente, como el ticker de noticias, las tablas de clasificación, las misiones diarias y las actualizaciones de vacaciones. También puede perderse algunos bonos y recompensas que se ofrecen en línea. </td></tr>
-<tr><td>¿Cómo puedo obtener más gemas en Bitcoin multimillonario APK? </td><td>Las gemas son la moneda premium del juego que se puede utilizar para comprar artículos especiales y acelerar el viaje en el tiempo. Puedes obtener más gemas completando logros, recogiendo tarjetas, viendo anuncios o comprándolos con dinero real. También puedes obtener gemas gratis tocando el icono de gema en la esquina superior derecha de la pantalla y siguiendo las instrucciones. </td></tr>
-
-</table></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Arca Supervivencia Evolucion Apk 2023.md

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-<br />
-<h1>ARK: Supervivencia evolucionado APK 2023: Cómo jugar este increíble juego en su PC</h1>
- <p>Si eres un fan de los juegos de aventura, probablemente hayas oído hablar de ARK: Survival Evolved. Este es un juego que te permite explorar una gran isla llena de dinosaurios, criaturas y otros jugadores. Puede domar, criar y montar más de 80 tipos diferentes de dinosaurios, crear y personalizar sus propias armas y armaduras, construir bases y estructuras, y luchar por la supervivencia en un mundo dinámico y peligroso. </p>
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- <p>ARK: Survival Evolved es uno de los juegos más populares entre los jugadores, con más de 10 millones de descargas solo en Google Play Store. También ha recibido críticas positivas de críticos y usuarios por igual, elogiando su juego inmersivo, gráficos impresionantes y contenido diverso. Sin embargo, jugar a este juego en su dispositivo Android puede no darle la mejor experiencia posible. Puede encontrar problemas como retraso, rotura, pérdida de batería, tamaño de pantalla pequeña y controles limitados. </p>
- <p>Afortunadamente, hay una manera de jugar ARK: Survival Evolved en tu PC en 2023. Mediante el uso de un emulador de Android, puede ejecutar este juego en su computadora con un mejor rendimiento, gráficos y características. En este artículo, le mostraremos cómo descargar e instalar ARK: Survival Evolved APK en su PC en 2023 utilizando dos de los mejores emuladores de Android disponibles: BlueStacks y iMyFone AnyTo.</p>
- <h2>Características de ARK: Survival Evolved</h2>
- <p>Antes de entrar en los detalles de cómo jugar ARK: Survival Evolved en tu PC en 2023, echemos un vistazo a algunas de las increíbles características que ofrece este juego. Estas son algunas de las razones por las que deberías probar este juego:</p>
- <h3>Explora un mundo abierto masivo</h3>
-
- <h3>Domesticar y criar más de 80 dinosaurios</h3>
- <p>Una de las características más emocionantes de ARK: Survival Evolved es la capacidad de domar y criar más de 80 tipos diferentes de dinosaurios y otras criaturas prehistóricas. Puede utilizar varios métodos para capturar, alimentar, entrenar y vincularse con estos animales. A continuación, puede utilizarlos para el transporte, combate, cosecha, o simplemente como mascotas. También puedes criarlos para crear nuevas especies con rasgos y habilidades únicas. </p>
- <h3>Diseña y personaliza tu equipo</h3>
- <p>Para sobrevivir en ARK: Survival Evolved, necesitarás crear y personalizar tu propio equipo. Puede reunir recursos del medio ambiente o de matar animales y usarlos para fabricar armas, armaduras, herramientas, estructuras, vehículos y más. También puede actualizar su equipo con accesorios, pieles, pinturas y engramas. También puedes intercambiar tus objetos con otros jugadores o robarlos de los enemigos. </p>
- <h3>Únete a una tribu y coopera con otros jugadores</h3>
- <p>ARK: Survival Evolved no es solo un juego en solitario. También puedes unirte a una tribu y cooperar con otros jugadores online. Puedes compartir recursos, objetos, estructuras y dinosaurios con los miembros de tu tribu. También puedes comunicarte con ellos mediante chat de voz o de texto. También puedes competir con otras tribus por territorio, recursos y gloria. Puedes participar en batallas JcJ, incursiones, asedios y alianzas. </p>
-<p></p>
- <h3>Sobrevivir al entorno duro y enemigos</h3>
- <p>ARK: Survival Evolved no es un juego fácil. Tendrás que sobrevivir al duro entorno y a los enemigos que intentarán matarte. Tendrá que controlar su salud, hambre, sed, resistencia, oxígeno, temperatura y niveles de radiación. También tendrás que evitar o luchar contra animales salvajes, jugadores hostiles y criaturas jefa. También tendrás que lidiar con enfermedades, infecciones, venenos y maldiciones. También tendrás que adaptarte a los cambios de estaciones, eventos y desafíos. </p>
- <h2>Beneficios de jugar ARK: Survival Evolved en PC</h2>
-
- <h3>Mejores gráficos y rendimiento</h3>
- <p>Una de las principales ventajas de jugar ARK: Survival Evolved en tu PC es que puedes disfrutar de mejores gráficos y rendimiento que en tu dispositivo Android. Puede ajustar la configuración para adaptarse a sus preferencias y capacidades de hardware. También puede experimentar un juego más suave, tiempos de carga más rápidos y menos errores y problemas técnicos. </p>
- <h3>Controles de pantalla y teclado más grandes</h3>
- <p>Otro beneficio de jugar ARK: Survival Evolved en tu PC es que puedes disfrutar de una pantalla más grande y controles de teclado. Puedes ver más detalles y tener una mejor vista del mundo del juego. También puedes usar el teclado y el ratón para controlar tu personaje y tus acciones con mayor facilidad y precisión. También puede personalizar sus combinaciones de teclas y accesos directos para adaptarse a su estilo. </p>
- <h3>Comunicación y transmisión más fáciles</h3>
- <p>Un beneficio final de jugar ARK: Survival Evolved en tu PC es que puedes comunicarte y transmitir más fácilmente que en tu dispositivo Android. Puedes usar el micrófono y los altavoces para hablar con otros jugadores o miembros de tu tribu. También puedes usar software como Discord o Skype para chatear con tus amigos o compañeros de equipo. También puedes usar software como OBS o Streamlabs para transmitir tu juego a plataformas como Twitch o YouTube.</p>
- <h2>Cómo descargar e instalar ARK: Survival Evolved APK en PC en 2023</h2>
- <p>Ahora que conoces los beneficios de jugar a ARK: Survival Evolved en tu PC, es posible que te estés preguntando cómo hacerlo en 2023. La respuesta es simple: necesitas usar un emulador de Android. Un emulador de Android es un software que te permite ejecutar aplicaciones y juegos de Android en tu PC. Hay muchos emuladores de Android disponibles en línea, pero recomendamos usar BlueStacks o iMyFone AnyTo. Estos son dos de los mejores emuladores de Android que ofrecen alta compatibilidad, rendimiento, seguridad y características. </p>
-
- <tabla>
-<tr>
-<th>BlueStacks</th>
-<th>iMyFone AnyTo</th>
-</tr>
-<tr>
-<td>
-<ol>
-<li>Descargar e instalar BlueStacks desde su sitio web oficial: <a href="">https://www.bluestacks.com/</a></li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google. </li>
-<li>Buscar ARK: Survival Evolved en la tienda de aplicaciones o sitio web de BlueStacks. </li>
-<li>Descarga e instala el juego en tu PC.</li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
-</td>
-<td>
-<ol>
-<li>Descargar e instalar iMyFone AnyTo desde su sitio web oficial: <a href="">https://www.imyfone.com/anyto/</a></li>
-<li>Inicie iMyFone AnyTo y conecte su dispositivo Android a su PC a través de un cable USB. </li>
-<li>Seleccione "Android" como el dispositivo de origen y "PC" como el dispositivo de destino. </li>
-<li>Buscar ARK: Supervivencia Evolucionado en la aplicación iMyFone AnyTo tienda o sitio web. </li>
-<li>Descarga e instala el juego en tu PC.</li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
-</td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
- <p>En conclusión, ARK: Survival Evolved es un juego fantástico que ofrece muchas características y contenido para que lo disfrutes. Puede explorar un mundo abierto masivo, domesticar y criar más de 80 dinosaurios, crear y personalizar su equipo, unirse a una tribu y cooperar con otros jugadores, y sobrevivir al duro entorno y enemigos. Sin embargo, jugar a este juego en su dispositivo Android puede no ser la mejor opción para usted. Usted puede enfrentar problemas como retraso, rotura, pérdida de batería, tamaño de pantalla pequeña y controles limitados. </p>
- <p>Por eso recomendamos jugar a ARK: Survival Evolved en tu PC en 2023. Mediante el uso de un emulador de Android como BlueStacks o iMyFone AnyTo, puede ejecutar este juego en su ordenador con un mejor rendimiento, gráficos y características. También puede disfrutar de una pantalla más grande y controles de teclado, una comunicación más fácil y transmisión, y más. Todo lo que necesita hacer es descargar e instalar ARK: Survival Evolved APK en su PC utilizando cualquiera de estos emuladores y lanzar el juego. </p>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre ARK: Survival Evolved APK 2023:</p>
- <h3>¿Es seguro descargar e instalar ARK: Survival Evolved APK 2023? </h3>
- <p>Sí, ARK: Survival Evolved APK 2023 es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo desde una fuente o sitio web de confianza, como la tienda de aplicaciones oficial o el sitio web del emulador que está utilizando. También debe escanear el archivo con un software antivirus antes de instalarlo. </p>
- <h3> ¿Es ARK: supervivencia evolucionado APK 2023 libre para jugar? </h3>
- <p>Sí, ARK: Survival Evolved APK 2023 es gratis para jugar. Sin embargo, puede contener compras en la aplicación y anuncios que requieren dinero real. Puede desactivar estas funciones desactivando la conexión a Internet o utilizando un bloqueador de anuncios. </p>
- <h3>¿Puedo jugar ARK: Survival Evolved APK 2023 offline? </h3>
- <p>Sí, puedes jugar ARK: Survival Evolved APK 2023 sin conexión. Sin embargo, no podrás acceder a algunas de las funciones en línea, como el modo multijugador, eventos en línea, actualizaciones y más. También necesitarás conectarte a internet al menos una vez para verificar tu cuenta y descargar los datos del juego. </p>
- <h3>¿Puedo jugar ARK: Supervivencia evolucionado APK 2023 con mis amigos? </h3>
- <p>Sí, puedes jugar ARK: Survival Evolved APK 2023 con tus amigos. Puedes unirte a un servidor existente o crear tu propio servidor privado. También puedes invitar a tus amigos a unirse a tu tribu o chatear con ellos usando chat de voz o texto. </p>
- <h3>¿Puedo transferir mi progreso desde mi dispositivo Android a mi PC? </h3>
- <p>Sí, puede transferir su progreso desde su dispositivo Android a su PC. Puede usar la función de almacenamiento en la nube para sincronizar sus datos en diferentes dispositivos. También puede utilizar la función de copia de seguridad y restauración para guardar y cargar sus datos manualmente. </p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Carx Calle Dinero Ilimitado Apk 2023.md

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-<br />
-<h1>CarX Street dinero ilimitado APK 2023: Cómo descargar e instalar</h1>
-<p>Si eres un fan de los juegos de carreras de coches, es posible que hayas oído hablar de CarX Street, uno de los juegos de carreras más emocionantes y realistas en dispositivos móviles. ¿Pero sabías que hay una manera de obtener dinero y recursos ilimitados en el juego sin gastar un centavo? En este artículo, le diremos todo lo que necesita saber sobre CarX Street Unlimited Money APK 2023, una versión modificada del juego que le da acceso a las características premium de forma gratuita. También te mostraremos cómo descargarlo e instalarlo en tu dispositivo de forma segura y sencilla. </p>
-<h2>carx calle dinero ilimitado apk 2023</h2><br /><p><b><b>DOWNLOAD</b> >>> <a href="https://bltlly.com/2v6LhC">https://bltlly.com/2v6LhC</a></b></p><br /><br />
-<h2>¿Qué es CarX Street? </h2>
-<p>CarX Street es un juego de carreras de coches desarrollado por CarX Technologies, la misma compañía detrás de la popular serie CarX Drift Racing. El juego te permite explorar diferentes ciudades y sumergirte en carreras épicas en carreteras transitadas y en atmósferas excepcionales. Puede elegir entre una variedad de coches, personalizarlos con varias partes y opciones de afinación, y competir con otros jugadores en línea o fuera de línea. El juego cuenta con la física realista del coche, gráficos increíbles y opciones de control simples que lo hacen divertido y adictivo para cualquier persona que ama los juegos de carreras. </p>
-<h3>Características de CarX Street</h3>
-<h4>Física realista del coche y gráficos</h4>
-<p>Uno de los principales atractivos de CarX Street es su realista física de coches y gráficos que te hacen sentir como si estuvieras conduciendo un coche real. El juego utiliza el motor CarX avanzado que simula el comportamiento de diferentes modelos de automóviles, como la aceleración, el frenado, la dirección, la suspensión, la tracción y más. El juego también tiene gráficos impresionantes que muestran los detalles de los coches, los entornos, los efectos del tiempo y la iluminación. Incluso se puede ver el humo de los neumáticos a medida que la deriva o el agotamiento. </p>
-<h4>Coches personalizables y opciones de ajuste</h4>
-
-<h4>Varios modos de juego y ubicaciones</h4>
-<p>CarX Street también ofrece varios modos de juego y ubicaciones que te mantienen entretenido y desafiado. Puedes jugar en diferentes modos, como el modo carrera, el modo de viaje libre, el modo de prueba temporal, el modo de deriva, el modo de arrastre, el modo sprint y más. También puedes correr en diferentes lugares alrededor del mundo, como Tokio, Los Ángeles, Nueva York, Dubai, Moscú, Londres, París, Berlín , y más. Cada ubicación tiene sus propias características únicas, como el tráfico, los puntos de referencia, el clima y la hora del día. También puedes cambiar el nivel de dificultad y el número de oponentes en cada carrera. </p>
-<h4>Tablas de clasificación y multijugador en línea</h4>
-<p>Si quieres probar tus habilidades contra otros jugadores, puedes unirte al modo multijugador en línea de CarX Street. Puedes competir con hasta 16 jugadores en tiempo real y chatear con ellos utilizando la función de chat de voz o texto. También puede unirse o crear su propio equipo y competir con otros equipos para la fama y la gloria. El juego también tiene tablas de clasificación que clasifican a los mejores jugadores en diferentes categorías, como velocidad, deriva, arrastre, sprint y más. También puedes ver tus propias estadísticas y logros en el juego. </p>
-<p></p>
-<h2>¿Qué es CarX Street dinero ilimitado APK 2023? </h2>
-<p>CarX Street Unlimited Money APK 2023 es una versión modificada del juego original CarX Street que le da dinero y recursos ilimitados en el juego. Esto significa que usted puede comprar cualquier coche, parte, o tuning opción que desee sin preocuparse por el costo. También puedes desbloquear todos los modos de juego y ubicaciones que están restringidos o requieren compras en la aplicación. Con CarX Street Unlimited Money APK 2023, se puede disfrutar de todo el potencial del juego sin gastar dinero real. </p>
-<h3>Beneficios de CarX Street dinero ilimitado APK 2023</h3>
-<h4>Dinero y recursos ilimitados</h4>
-
-<h4>Acceso a coches y piezas de primera calidad</h4>
-<p>Otro beneficio de CarX Street Unlimited Money APK 2023 es que le da acceso a los coches premium y piezas que de otra manera no están disponibles o caros en el juego original. Puede elegir entre más de 100 coches de diferentes marcas, como Ferrari, Lamborghini, Bugatti, Porsche, BMW, Audi, Nissan, Toyota, Honda, Ford, Chevrolet y más. También puede obtener piezas exclusivas y opciones de ajuste que solo están disponibles para usuarios premium o requieren dinero real para comprar. Usted puede hacer que su coche se vea y realizar como un sueño con CarX Street Unlimited Money APK 2023. </p>
-<h4>No se requieren anuncios ni root</h4>
-<p>Un beneficio final de CarX Street Unlimited Money APK 2023 es que no tiene anuncios y no se requiere raíz. Esto significa que puede jugar el juego sin interrupciones o distracciones de los molestos anuncios que aparecen en la pantalla. También puede jugar el juego sin enraizar su dispositivo, que puede ser arriesgado y anular su garantía. CarX Street Unlimited Money APK 2023 es compatible con la mayoría de los dispositivos Android y no requiere ningún permiso especial o acceso para instalar o ejecutar. </p> <h3>Riesgos de CarX Street dinero ilimitado APK 2023</h3>
-<p>Si bien CarX Street Unlimited Money APK 2023 puede sonar tentador, también viene con algunos riesgos que usted debe ser consciente de antes de descargar e instalar. Estos son algunos de los posibles riesgos que puede enfrentar si utiliza CarX Street Unlimited Money APK 2023:</p>
-<h4>Malware y virus potenciales</h4>
-
-<h4>van del servidor oficial del juego</h4>
-<p>Otro riesgo de usar CarX Street Unlimited Money APK 2023 es que usted puede conseguir prohibido desde el servidor oficial del juego o perder su progreso en el juego. Dado que el archivo apk modded no está autorizado o apoyado por el desarrollador del juego, puede violar los términos y condiciones del juego o la tienda de aplicaciones. También puede ser detectado por el sistema anti-cheat del juego que puede identificar y bloquear cualquier archivo no autorizado o modificado. Si esto sucede, es posible que se le prohíba jugar el juego en línea o fuera de línea, perder su cuenta, o perder todos sus logros y recompensas en el juego. </p>
-<h4>Cuestiones jurídicas y éticas</h4>
-<p>Un riesgo final de usar CarX Street Unlimited Money APK 2023 es que usted puede enfrentar problemas legales o preocupaciones éticas por el uso de una versión pirateada o hackeada del juego. Dado que el archivo apk modded no está autorizado o aprobado por el desarrollador del juego, puede infringir sus derechos de propiedad intelectual o violar sus leyes de derechos de autor. También puede faltar el respeto a su trabajo duro y la creatividad mediante el uso de su producto sin pagar por él o darles crédito. También puede arruinar el juego justo y equilibrado para otros jugadores que juegan el juego de forma legítima y honesta. </p>
-<h2>Cómo descargar e instalar CarX Street dinero ilimitado APK 2023? </h2>
-<p>Si todavía quieres probar CarX Street Unlimited Money APK 2023 a pesar de los riesgos, tendrá que seguir algunos pasos para descargar e instalar en su dispositivo. Estos son los pasos que debes seguir:</p>
-<h3>Pasos para descargar e instalar CarX Street dinero ilimitado APK 2023</h3>
-<h4>Paso 1: Encontrar una fuente confiable para el archivo apk modded</h4>
-
-<h4>Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo</h4>
-<p>El segundo paso es habilitar fuentes desconocidas en la configuración del dispositivo para que pueda instalar aplicaciones desde fuentes distintas de la tienda de aplicaciones oficial. Para hacer esto, tendrá que ir a la configuración del dispositivo, luego la seguridad, luego fuentes desconocidas y luego activarlo. Esto le permitirá instalar aplicaciones de fuentes de terceros sin restricciones. Sin embargo, debe ser cuidadoso y selectivo al instalar aplicaciones de fuentes desconocidas, ya que algunas de ellas pueden ser perjudiciales o incompatibles con su dispositivo. </p>
-<h4>Paso 3: Descargar e instalar el archivo apk en su dispositivo</h4>
-<p>El tercer paso es descargar e instalar el archivo apk en su dispositivo. Para hacer esto, tendrá que ir a la fuente que ha elegido en el paso 1 y haga clic en el botón de descarga o enlace para CarX Street Unlimited Money APK 2023. A continuación, tendrá que esperar a que se complete la descarga y luego localizar el archivo en el almacenamiento del dispositivo. A continuación, tendrá que tocar en el archivo y siga las instrucciones en la pantalla para instalarlo en su dispositivo. Es posible que necesite conceder algunos permisos o acceso a la aplicación durante el proceso de instalación. </p>
-<h4>Paso 4: Iniciar el juego y disfrutar de dinero ilimitado</h4>
-<p>El paso final es lanzar el juego y disfrutar de dinero ilimitado en CarX Street. Para hacer esto, tendrá que ir a su cajón de aplicaciones o pantalla de inicio y toque en el icono de CarX Street. A continuación, tendrá que esperar a que el juego se cargue y comience el juego. Verá que tiene dinero y recursos ilimitados en el juego. Puede utilizar este dinero para comprar cualquier coche, parte, o opción de ajuste que desee. También puedes desbloquear todos los modos de juego y ubicaciones que están restringidos o requieren compras en la aplicación. Usted puede disfrutar de todo el potencial del juego sin gastar dinero real. </p>
-<h2>Conclusión</h2>
-
-<p>Aquí hay algunas preguntas frecuentes que puede tener después de leer este artículo:</p>
-<h4>Q: Es CarX Street dinero ilimitado APK 2023 seguro de usar? </h4>
-<p>A: CarX Street Unlimited Money APK 2023 no es seguro de usar, ya que puede contener malware o virus que pueden dañar su dispositivo o robar su información personal. También puede ser detectado por el sistema anti-cheat del juego y obtener prohibido jugar el juego en línea o fuera de línea. También puede violar los términos y condiciones del juego o la tienda de aplicaciones y causar problemas legales o éticos. </p>
-<h4>Q: ¿Puedo jugar CarX Street dinero ilimitado APK 2023 en línea con otros jugadores? </h4>
-<p>A: CarX Street Unlimited Money APK 2023 puede permitirle jugar en línea con otros jugadores que están utilizando la misma versión modificada del juego. Sin embargo, es posible que no pueda jugar en línea con jugadores que estén utilizando la versión original del juego u otras versiones modificadas del juego. También puede enfrentar problemas de retraso, estrellarse o desconectar mientras juega en línea con CarX Street Unlimited Money APK 2023. </p>
-<h4>Q: ¿Puedo actualizar CarX Street Unlimited Money APK 2023 a la última versión del juego? </h4>
-<p>A: CarX Street Unlimited Money APK 2023 puede no ser compatible con la última versión del juego, ya que puede tener diferentes características, funciones o códigos que no pueden funcionar con el archivo apk modded. Es posible que tenga que esperar a una nueva versión de CarX Street Unlimited Money APK 2023 que coincide con la última versión del juego. También es posible que tenga que desinstalar y reinstalar CarX Street Unlimited Money APK 2023 cada vez que hay una nueva actualización para el juego. </p>
-<h4>Q: ¿Puedo usar CarX Street Unlimited Money APK 2023 en dispositivos iOS? </h4>
-<p>A: CarX Street Unlimited Money APK 2023 solo es compatible con dispositivos Android y no se puede utilizar en dispositivos iOS. Es posible que tenga que encontrar una versión modificada diferente del juego que sea compatible con dispositivos iOS o utilizar un método diferente para obtener dinero y recursos ilimitados en el juego en dispositivos iOS. </p>
-
-<p>A: CarX Street Unlimited Money APK 2023 solo es compatible con dispositivos móviles y no se puede utilizar en PC o consola. Es posible que tenga que encontrar una versión modificada diferente del juego que sea compatible con PC o consola o utilizar un método diferente para obtener dinero y recursos ilimitados en el juego en la PC o consola. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Chicos Stumble Terbaru 2022 Apk.md

@@ -1,57 +0,0 @@
-
-<h1>Descargar Stumble Guys Terbaru 2022 Apk: Cómo disfrutar de la última versión del juego de fiesta en línea</h1>
-<p>Stumble Guys es un juego online de battle royale party que te permite correr con hasta 32 jugadores online a través de caóticas carreras de obstáculos. Ejecutar, saltar, guion, deslizarse, y tropezar su camino a la línea de meta hasta que el mejor jugador toma la corona. Es un juego divertido, colorido e hilarante que te mantendrá entretenido durante horas. Si usted está buscando un nuevo juego para jugar con sus amigos u otros jugadores en línea, usted debe descargar Stumble Guys Terbaru 2022 Apk, la última versión del juego que viene con nuevos mapas, trajes, emotes, características y mejoras. </p>
-<h2>descargar chicos stumble terbaru 2022 apk</h2><br /><p><b><b>Download</b> &#9999; &#9999; &#9999; <a href="https://bltlly.com/2v6LVp">https://bltlly.com/2v6LVp</a></b></p><br /><br />
-<h2>¿Qué hay de nuevo en Stumble Guys Terbaru 2022 Apk</h2>
-<p>Los desarrolladores de Stumble Guys están constantemente actualizando el juego para que sea más agradable y emocionante para los jugadores. Estas son algunas de las cosas nuevas que se pueden esperar en Stumble Guys Terbaru 2022 Apk:</p>
-<h3>Nuevos mapas y obstáculos</h3>
-<p>Stumble Guys Terbaru 2022 Apk introduce nuevos mapas y obstáculos que pondrá a prueba sus habilidades y reflejos. Algunos de los nuevos mapas incluyen Lava Land, un mapa de fuego con pozos de lava y volcanes; Hot Wheels Hustle, un mapa inspirado en los famosos coches de juguete; y Path of the Champion, un mapa que pone a prueba su resistencia y resistencia. Cada mapa tiene sus propios obstáculos únicos que te harán tropezar, como ruedas giratorias, martillos oscilantes, bolas rebotantes, toboganes resbaladizos y más. </p>
-<h3>Nuevos trajes y emotes</h3>
-
-<h3>Nuevas características y mejoras</h3>
-<p>Stumble Guys Terbaru 2022 Apk también trae nuevas características y mejoras que mejorarán su experiencia de juego. Algunas de las nuevas características incluyen el modo en primera persona, que le permite jugar el juego desde una perspectiva diferente; modo de fiesta, que le permite jugar con hasta cuatro amigos en un partido privado; partidos personalizados, que le permite crear su propio partido con sus propias reglas; tablas de clasificación, que le permite ver su clasificación entre otros jugadores; logros, que le permite ganar recompensas por completar ciertas tareas; y más. Algunas de las mejoras incluyen correcciones de errores, optimizaciones de rendimiento, ajustes de equilibrio, mejoras en la interfaz de usuario y más. </p>
-<h2>Cómo descargar Stumble chicos Terbaru 2022 apk para dispositivos Android e iOS</h2>
-<p>Si desea descargar Stumble Guys Terbaru 2022 Apk para su dispositivo Android o iOS, usted tiene dos opciones: descargarlo de fuentes oficiales o descargarlo de fuentes de terceros. Estos son los pasos para cada opción:</p>
-<h3>Descargar de Fuentes Oficiales</h3>
-<p>La forma más fácil y segura de descargar Stumble Guys Terbaru 2022 Apk es descargarlo de las fuentes oficiales, que son el Google Play Store para dispositivos Android y la App Store para dispositivos iOS. Estos son los pasos para hacerlo:</p>
-<p></p>
-<ol>
-<li>Abre la Google Play Store o la App Store en tu dispositivo. </li>
-<li>Buscar "Stumble Guys" en la barra de búsqueda. </li>
-<li>Toque en el icono Stumble Guys y luego toque en el botón Instalar u Obtener. </li>
-<li>Espere a que finalicen la descarga y la instalación. </li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
-<h3>Descargar de fuentes de terceros</h3>
-
-<ol>
-<li>Encontrar un sitio web confiable y confiable que ofrece Stumble Guys Terbaru 2022 Apk. Puedes buscarlo en Google o utilizar un sitio web como APKPure o APKMirror.</li>
-<li>Descargar el archivo apk desde el sitio web y guardarlo en su dispositivo. </li>
-<li>Habilita la instalación de aplicaciones de fuentes desconocidas en tu dispositivo. Puede hacer esto yendo a Configuración > Seguridad > Fuentes desconocidas y activando. </li>
-<li>Busque el archivo apk en su dispositivo y toque en él para instalarlo. </li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
-<h3>Descargar de Steam para usuarios de PC</h3>
-<p>Si quieres jugar Stumble Guys Terbaru 2022 Apk en su PC, puede hacerlo descargándolo de Steam, que es una plataforma de distribución digital para juegos y software. Estos son los pasos para hacerlo:</p>
-<ol>
-<li>Abre Steam en tu PC o descárgalo desde https://store.steampowered.com/ si no lo tienes. </li>
-<li>Cree una cuenta o inicie sesión con su cuenta existente. </li>
-<li>Buscar "Stumble Guys" en la barra de búsqueda. </li>
-<li>Haga clic en el icono Stumble Guys y luego haga clic en el botón Añadir al carrito. </li>
-<li>Proceda a pagar el juego. </li>
-<li>Espere a que finalicen la descarga y la instalación. </li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
- <h2>Cómo Jugar Stumble Guys Terbaru 2022 Apk con amigos y otros jugadores</h2>
- <p>Stumble Guys Terbaru 2022 Apk es más divertido cuando juegas con tus amigos y otros jugadores en línea. Estas son algunas de las formas en que puedes hacerlo:</p>
- <h3>Únete a una fiesta o crea tu propia</h3>
-
- <h3>Invitar a amigos a través de redes sociales o código QR</h3>
- <p>También puede invitar a sus amigos a jugar Stumble Guys Terbaru 2022 Apk con usted a través de las redes sociales o el código QR. Para hacer esto, debes tocar el botón Invitar en el menú principal. A continuación, puedes invitar a tus amigos a través de Facebook, Twitter, WhatsApp, Instagram u otras plataformas de redes sociales. También puedes invitar a tus amigos compartiendo tu código QR con ellos o escaneando su código QR. Una vez que acepten tu invitación, se añadirán a tu lista de amigos y podrás jugar con ellos en cualquier momento. </p>
- <h3>Compite en modo multijugador en línea o partidas personalizadas</h3>
- <p>Puedes competir en modo multijugador online o partidas personalizadas pulsando en el botón Reproducir del menú principal. El modo multijugador online es donde puedes jugar con hasta 32 jugadores online en un juego aleatorio. Serás asignado a uno de cuatro equipos: rojo, azul, espacio. Los requisitos mínimos para jugar Stumble Guys Terbaru 2022 Apk en dispositivos iOS son iOS 10.0 o superior y al menos 100 MB de espacio de almacenamiento libre. </li>
-<li>Q: ¿Cómo puedo contactar a los desarrolladores de Stumble Guys Terbaru 2022 Apk para obtener información o apoyo? R: Puede ponerse en contacto con los desarrolladores de Stumble Guys Terbaru 2022 Apk enviando un correo electrónico a [email protected] o visitando su sitio web en https://www.kitkagames.com/.</li>
-<li>Q: ¿Cómo puedo obtener más monedas o gemas en Stumble Guys Terbaru 2022 Apk? A: Usted puede obtener más monedas o gemas en Stumble Guys Terbaru 2022 Apk jugando el juego, completar los logros, ver anuncios, o comprarlos con dinero real. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat/src/routes/conversation/[id]/summarize/+server.ts

@@ -1,83 +0,0 @@
-import { PUBLIC_MAX_INPUT_TOKENS, PUBLIC_SEP_TOKEN } from "$env/static/public";
-import { buildPrompt } from "$lib/buildPrompt";
-import { collections } from "$lib/server/database.js";
-import { modelEndpoint } from "$lib/server/modelEndpoint.js";
-import { trimPrefix } from "$lib/utils/trimPrefix.js";
-import { trimSuffix } from "$lib/utils/trimSuffix.js";
-import { textGeneration } from "@huggingface/inference";
-import { error } from "@sveltejs/kit";
-import { ObjectId } from "mongodb";
-
-export async function POST({ params, locals, fetch }) {
-	const convId = new ObjectId(params.id);
-
-	const conversation = await collections.conversations.findOne({
-		_id: convId,
-		sessionId: locals.sessionId,
-	});
-
-	if (!conversation) {
-		throw error(404, "Conversation not found");
-	}
-
-	const firstMessage = conversation.messages.find((m) => m.from === "user");
-
-	const userPrompt =
-		`Please summarize the following message as a single sentence of less than 5 words:\n` +
-		firstMessage?.content;
-
-	const prompt = buildPrompt([{ from: "user", content: userPrompt }]);
-
-	const parameters = {
-		temperature: 0.9,
-		top_p: 0.95,
-		repetition_penalty: 1.2,
-		top_k: 50,
-		watermark: false,
-		max_new_tokens: 1024,
-		truncate: parseInt(PUBLIC_MAX_INPUT_TOKENS),
-		stop: [PUBLIC_SEP_TOKEN],
-		return_full_text: false,
-	};
-
-	const endpoint = modelEndpoint();
-	let { generated_text } = await textGeneration(
-		{
-			model: endpoint.endpoint,
-			inputs: prompt,
-			parameters,
-		},
-		{
-			fetch: (url, options) =>
-				fetch(url, {
-					...options,
-					headers: { ...options?.headers, Authorization: endpoint.authorization },
-				}),
-		}
-	);
-
-	generated_text = trimSuffix(trimPrefix(generated_text, "<|startoftext|>"), PUBLIC_SEP_TOKEN);
-
-	if (generated_text) {
-		await collections.conversations.updateOne(
-			{
-				_id: convId,
-				sessionId: locals.sessionId,
-			},
-			{
-				$set: { title: generated_text },
-			}
-		);
-	}
-
-	return new Response(
-		JSON.stringify(
-			generated_text
-				? {
-						title: generated_text,
-				  }
-				: {}
-		),
-		{ headers: { "Content-Type": "application/json" } }
-	);
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/__init__.py

@@ -1,8 +0,0 @@
-# -*- coding: utf-8 -*-
-try:
-    from ._version import version as __version__
-except ImportError:
-    __version__ = 'unknown'
-
-__all__ = ['easter', 'parser', 'relativedelta', 'rrule', 'tz',
-           'utils', 'zoneinfo']

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

@@ -1 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/compat.py

@@ -1,1116 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2017 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from __future__ import absolute_import
-
-import os
-import re
-import sys
-
-try:
-    import ssl
-except ImportError:  # pragma: no cover
-    ssl = None
-
-if sys.version_info[0] < 3:  # pragma: no cover
-    from StringIO import StringIO
-    string_types = basestring,
-    text_type = unicode
-    from types import FileType as file_type
-    import __builtin__ as builtins
-    import ConfigParser as configparser
-    from urlparse import urlparse, urlunparse, urljoin, urlsplit, urlunsplit
-    from urllib import (urlretrieve, quote as _quote, unquote, url2pathname,
-                        pathname2url, ContentTooShortError, splittype)
-
-    def quote(s):
-        if isinstance(s, unicode):
-            s = s.encode('utf-8')
-        return _quote(s)
-
-    import urllib2
-    from urllib2 import (Request, urlopen, URLError, HTTPError,
-                         HTTPBasicAuthHandler, HTTPPasswordMgr,
-                         HTTPHandler, HTTPRedirectHandler,
-                         build_opener)
-    if ssl:
-        from urllib2 import HTTPSHandler
-    import httplib
-    import xmlrpclib
-    import Queue as queue
-    from HTMLParser import HTMLParser
-    import htmlentitydefs
-    raw_input = raw_input
-    from itertools import ifilter as filter
-    from itertools import ifilterfalse as filterfalse
-
-    # Leaving this around for now, in case it needs resurrecting in some way
-    # _userprog = None
-    # def splituser(host):
-        # """splituser('user[:passwd]@host[:port]') --> 'user[:passwd]', 'host[:port]'."""
-        # global _userprog
-        # if _userprog is None:
-            # import re
-            # _userprog = re.compile('^(.*)@(.*)$')
-
-        # match = _userprog.match(host)
-        # if match: return match.group(1, 2)
-        # return None, host
-
-else:  # pragma: no cover
-    from io import StringIO
-    string_types = str,
-    text_type = str
-    from io import TextIOWrapper as file_type
-    import builtins
-    import configparser
-    import shutil
-    from urllib.parse import (urlparse, urlunparse, urljoin, quote,
-                              unquote, urlsplit, urlunsplit, splittype)
-    from urllib.request import (urlopen, urlretrieve, Request, url2pathname,
-                                pathname2url,
-                                HTTPBasicAuthHandler, HTTPPasswordMgr,
-                                HTTPHandler, HTTPRedirectHandler,
-                                build_opener)
-    if ssl:
-        from urllib.request import HTTPSHandler
-    from urllib.error import HTTPError, URLError, ContentTooShortError
-    import http.client as httplib
-    import urllib.request as urllib2
-    import xmlrpc.client as xmlrpclib
-    import queue
-    from html.parser import HTMLParser
-    import html.entities as htmlentitydefs
-    raw_input = input
-    from itertools import filterfalse
-    filter = filter
-
-
-try:
-    from ssl import match_hostname, CertificateError
-except ImportError: # pragma: no cover
-    class CertificateError(ValueError):
-        pass
-
-
-    def _dnsname_match(dn, hostname, max_wildcards=1):
-        """Matching according to RFC 6125, section 6.4.3
-
-        http://tools.ietf.org/html/rfc6125#section-6.4.3
-        """
-        pats = []
-        if not dn:
-            return False
-
-        parts = dn.split('.')
-        leftmost, remainder = parts[0], parts[1:]
-
-        wildcards = leftmost.count('*')
-        if wildcards > max_wildcards:
-            # Issue #17980: avoid denials of service by refusing more
-            # than one wildcard per fragment.  A survey of established
-            # policy among SSL implementations showed it to be a
-            # reasonable choice.
-            raise CertificateError(
-                "too many wildcards in certificate DNS name: " + repr(dn))
-
-        # speed up common case w/o wildcards
-        if not wildcards:
-            return dn.lower() == hostname.lower()
-
-        # RFC 6125, section 6.4.3, subitem 1.
-        # The client SHOULD NOT attempt to match a presented identifier in which
-        # the wildcard character comprises a label other than the left-most label.
-        if leftmost == '*':
-            # When '*' is a fragment by itself, it matches a non-empty dotless
-            # fragment.
-            pats.append('[^.]+')
-        elif leftmost.startswith('xn--') or hostname.startswith('xn--'):
-            # RFC 6125, section 6.4.3, subitem 3.
-            # The client SHOULD NOT attempt to match a presented identifier
-            # where the wildcard character is embedded within an A-label or
-            # U-label of an internationalized domain name.
-            pats.append(re.escape(leftmost))
-        else:
-            # Otherwise, '*' matches any dotless string, e.g. www*
-            pats.append(re.escape(leftmost).replace(r'\*', '[^.]*'))
-
-        # add the remaining fragments, ignore any wildcards
-        for frag in remainder:
-            pats.append(re.escape(frag))
-
-        pat = re.compile(r'\A' + r'\.'.join(pats) + r'\Z', re.IGNORECASE)
-        return pat.match(hostname)
-
-
-    def match_hostname(cert, hostname):
-        """Verify that *cert* (in decoded format as returned by
-        SSLSocket.getpeercert()) matches the *hostname*.  RFC 2818 and RFC 6125
-        rules are followed, but IP addresses are not accepted for *hostname*.
-
-        CertificateError is raised on failure. On success, the function
-        returns nothing.
-        """
-        if not cert:
-            raise ValueError("empty or no certificate, match_hostname needs a "
-                             "SSL socket or SSL context with either "
-                             "CERT_OPTIONAL or CERT_REQUIRED")
-        dnsnames = []
-        san = cert.get('subjectAltName', ())
-        for key, value in san:
-            if key == 'DNS':
-                if _dnsname_match(value, hostname):
-                    return
-                dnsnames.append(value)
-        if not dnsnames:
-            # The subject is only checked when there is no dNSName entry
-            # in subjectAltName
-            for sub in cert.get('subject', ()):
-                for key, value in sub:
-                    # XXX according to RFC 2818, the most specific Common Name
-                    # must be used.
-                    if key == 'commonName':
-                        if _dnsname_match(value, hostname):
-                            return
-                        dnsnames.append(value)
-        if len(dnsnames) > 1:
-            raise CertificateError("hostname %r "
-                "doesn't match either of %s"
-                % (hostname, ', '.join(map(repr, dnsnames))))
-        elif len(dnsnames) == 1:
-            raise CertificateError("hostname %r "
-                "doesn't match %r"
-                % (hostname, dnsnames[0]))
-        else:
-            raise CertificateError("no appropriate commonName or "
-                "subjectAltName fields were found")
-
-
-try:
-    from types import SimpleNamespace as Container
-except ImportError:  # pragma: no cover
-    class Container(object):
-        """
-        A generic container for when multiple values need to be returned
-        """
-        def __init__(self, **kwargs):
-            self.__dict__.update(kwargs)
-
-
-try:
-    from shutil import which
-except ImportError:  # pragma: no cover
-    # Implementation from Python 3.3
-    def which(cmd, mode=os.F_OK | os.X_OK, path=None):
-        """Given a command, mode, and a PATH string, return the path which
-        conforms to the given mode on the PATH, or None if there is no such
-        file.
-
-        `mode` defaults to os.F_OK | os.X_OK. `path` defaults to the result
-        of os.environ.get("PATH"), or can be overridden with a custom search
-        path.
-
-        """
-        # Check that a given file can be accessed with the correct mode.
-        # Additionally check that `file` is not a directory, as on Windows
-        # directories pass the os.access check.
-        def _access_check(fn, mode):
-            return (os.path.exists(fn) and os.access(fn, mode)
-                    and not os.path.isdir(fn))
-
-        # If we're given a path with a directory part, look it up directly rather
-        # than referring to PATH directories. This includes checking relative to the
-        # current directory, e.g. ./script
-        if os.path.dirname(cmd):
-            if _access_check(cmd, mode):
-                return cmd
-            return None
-
-        if path is None:
-            path = os.environ.get("PATH", os.defpath)
-        if not path:
-            return None
-        path = path.split(os.pathsep)
-
-        if sys.platform == "win32":
-            # The current directory takes precedence on Windows.
-            if not os.curdir in path:
-                path.insert(0, os.curdir)
-
-            # PATHEXT is necessary to check on Windows.
-            pathext = os.environ.get("PATHEXT", "").split(os.pathsep)
-            # See if the given file matches any of the expected path extensions.
-            # This will allow us to short circuit when given "python.exe".
-            # If it does match, only test that one, otherwise we have to try
-            # others.
-            if any(cmd.lower().endswith(ext.lower()) for ext in pathext):
-                files = [cmd]
-            else:
-                files = [cmd + ext for ext in pathext]
-        else:
-            # On other platforms you don't have things like PATHEXT to tell you
-            # what file suffixes are executable, so just pass on cmd as-is.
-            files = [cmd]
-
-        seen = set()
-        for dir in path:
-            normdir = os.path.normcase(dir)
-            if not normdir in seen:
-                seen.add(normdir)
-                for thefile in files:
-                    name = os.path.join(dir, thefile)
-                    if _access_check(name, mode):
-                        return name
-        return None
-
-
-# ZipFile is a context manager in 2.7, but not in 2.6
-
-from zipfile import ZipFile as BaseZipFile
-
-if hasattr(BaseZipFile, '__enter__'):  # pragma: no cover
-    ZipFile = BaseZipFile
-else:  # pragma: no cover
-    from zipfile import ZipExtFile as BaseZipExtFile
-
-    class ZipExtFile(BaseZipExtFile):
-        def __init__(self, base):
-            self.__dict__.update(base.__dict__)
-
-        def __enter__(self):
-            return self
-
-        def __exit__(self, *exc_info):
-            self.close()
-            # return None, so if an exception occurred, it will propagate
-
-    class ZipFile(BaseZipFile):
-        def __enter__(self):
-            return self
-
-        def __exit__(self, *exc_info):
-            self.close()
-            # return None, so if an exception occurred, it will propagate
-
-        def open(self, *args, **kwargs):
-            base = BaseZipFile.open(self, *args, **kwargs)
-            return ZipExtFile(base)
-
-try:
-    from platform import python_implementation
-except ImportError: # pragma: no cover
-    def python_implementation():
-        """Return a string identifying the Python implementation."""
-        if 'PyPy' in sys.version:
-            return 'PyPy'
-        if os.name == 'java':
-            return 'Jython'
-        if sys.version.startswith('IronPython'):
-            return 'IronPython'
-        return 'CPython'
-
-import shutil
-import sysconfig
-
-try:
-    callable = callable
-except NameError:   # pragma: no cover
-    from collections.abc import Callable
-
-    def callable(obj):
-        return isinstance(obj, Callable)
-
-
-try:
-    fsencode = os.fsencode
-    fsdecode = os.fsdecode
-except AttributeError:  # pragma: no cover
-    # Issue #99: on some systems (e.g. containerised),
-    # sys.getfilesystemencoding() returns None, and we need a real value,
-    # so fall back to utf-8. From the CPython 2.7 docs relating to Unix and
-    # sys.getfilesystemencoding(): the return value is "the user’s preference
-    # according to the result of nl_langinfo(CODESET), or None if the
-    # nl_langinfo(CODESET) failed."
-    _fsencoding = sys.getfilesystemencoding() or 'utf-8'
-    if _fsencoding == 'mbcs':
-        _fserrors = 'strict'
-    else:
-        _fserrors = 'surrogateescape'
-
-    def fsencode(filename):
-        if isinstance(filename, bytes):
-            return filename
-        elif isinstance(filename, text_type):
-            return filename.encode(_fsencoding, _fserrors)
-        else:
-            raise TypeError("expect bytes or str, not %s" %
-                            type(filename).__name__)
-
-    def fsdecode(filename):
-        if isinstance(filename, text_type):
-            return filename
-        elif isinstance(filename, bytes):
-            return filename.decode(_fsencoding, _fserrors)
-        else:
-            raise TypeError("expect bytes or str, not %s" %
-                            type(filename).__name__)
-
-try:
-    from tokenize import detect_encoding
-except ImportError: # pragma: no cover
-    from codecs import BOM_UTF8, lookup
-    import re
-
-    cookie_re = re.compile(r"coding[:=]\s*([-\w.]+)")
-
-    def _get_normal_name(orig_enc):
-        """Imitates get_normal_name in tokenizer.c."""
-        # Only care about the first 12 characters.
-        enc = orig_enc[:12].lower().replace("_", "-")
-        if enc == "utf-8" or enc.startswith("utf-8-"):
-            return "utf-8"
-        if enc in ("latin-1", "iso-8859-1", "iso-latin-1") or \
-           enc.startswith(("latin-1-", "iso-8859-1-", "iso-latin-1-")):
-            return "iso-8859-1"
-        return orig_enc
-
-    def detect_encoding(readline):
-        """
-        The detect_encoding() function is used to detect the encoding that should
-        be used to decode a Python source file.  It requires one argument, readline,
-        in the same way as the tokenize() generator.
-
-        It will call readline a maximum of twice, and return the encoding used
-        (as a string) and a list of any lines (left as bytes) it has read in.
-
-        It detects the encoding from the presence of a utf-8 bom or an encoding
-        cookie as specified in pep-0263.  If both a bom and a cookie are present,
-        but disagree, a SyntaxError will be raised.  If the encoding cookie is an
-        invalid charset, raise a SyntaxError.  Note that if a utf-8 bom is found,
-        'utf-8-sig' is returned.
-
-        If no encoding is specified, then the default of 'utf-8' will be returned.
-        """
-        try:
-            filename = readline.__self__.name
-        except AttributeError:
-            filename = None
-        bom_found = False
-        encoding = None
-        default = 'utf-8'
-        def read_or_stop():
-            try:
-                return readline()
-            except StopIteration:
-                return b''
-
-        def find_cookie(line):
-            try:
-                # Decode as UTF-8. Either the line is an encoding declaration,
-                # in which case it should be pure ASCII, or it must be UTF-8
-                # per default encoding.
-                line_string = line.decode('utf-8')
-            except UnicodeDecodeError:
-                msg = "invalid or missing encoding declaration"
-                if filename is not None:
-                    msg = '{} for {!r}'.format(msg, filename)
-                raise SyntaxError(msg)
-
-            matches = cookie_re.findall(line_string)
-            if not matches:
-                return None
-            encoding = _get_normal_name(matches[0])
-            try:
-                codec = lookup(encoding)
-            except LookupError:
-                # This behaviour mimics the Python interpreter
-                if filename is None:
-                    msg = "unknown encoding: " + encoding
-                else:
-                    msg = "unknown encoding for {!r}: {}".format(filename,
-                            encoding)
-                raise SyntaxError(msg)
-
-            if bom_found:
-                if codec.name != 'utf-8':
-                    # This behaviour mimics the Python interpreter
-                    if filename is None:
-                        msg = 'encoding problem: utf-8'
-                    else:
-                        msg = 'encoding problem for {!r}: utf-8'.format(filename)
-                    raise SyntaxError(msg)
-                encoding += '-sig'
-            return encoding
-
-        first = read_or_stop()
-        if first.startswith(BOM_UTF8):
-            bom_found = True
-            first = first[3:]
-            default = 'utf-8-sig'
-        if not first:
-            return default, []
-
-        encoding = find_cookie(first)
-        if encoding:
-            return encoding, [first]
-
-        second = read_or_stop()
-        if not second:
-            return default, [first]
-
-        encoding = find_cookie(second)
-        if encoding:
-            return encoding, [first, second]
-
-        return default, [first, second]
-
-# For converting & <-> &amp; etc.
-try:
-    from html import escape
-except ImportError:
-    from cgi import escape
-if sys.version_info[:2] < (3, 4):
-    unescape = HTMLParser().unescape
-else:
-    from html import unescape
-
-try:
-    from collections import ChainMap
-except ImportError: # pragma: no cover
-    from collections import MutableMapping
-
-    try:
-        from reprlib import recursive_repr as _recursive_repr
-    except ImportError:
-        def _recursive_repr(fillvalue='...'):
-            '''
-            Decorator to make a repr function return fillvalue for a recursive
-            call
-            '''
-
-            def decorating_function(user_function):
-                repr_running = set()
-
-                def wrapper(self):
-                    key = id(self), get_ident()
-                    if key in repr_running:
-                        return fillvalue
-                    repr_running.add(key)
-                    try:
-                        result = user_function(self)
-                    finally:
-                        repr_running.discard(key)
-                    return result
-
-                # Can't use functools.wraps() here because of bootstrap issues
-                wrapper.__module__ = getattr(user_function, '__module__')
-                wrapper.__doc__ = getattr(user_function, '__doc__')
-                wrapper.__name__ = getattr(user_function, '__name__')
-                wrapper.__annotations__ = getattr(user_function, '__annotations__', {})
-                return wrapper
-
-            return decorating_function
-
-    class ChainMap(MutableMapping):
-        ''' A ChainMap groups multiple dicts (or other mappings) together
-        to create a single, updateable view.
-
-        The underlying mappings are stored in a list.  That list is public and can
-        accessed or updated using the *maps* attribute.  There is no other state.
-
-        Lookups search the underlying mappings successively until a key is found.
-        In contrast, writes, updates, and deletions only operate on the first
-        mapping.
-
-        '''
-
-        def __init__(self, *maps):
-            '''Initialize a ChainMap by setting *maps* to the given mappings.
-            If no mappings are provided, a single empty dictionary is used.
-
-            '''
-            self.maps = list(maps) or [{}]          # always at least one map
-
-        def __missing__(self, key):
-            raise KeyError(key)
-
-        def __getitem__(self, key):
-            for mapping in self.maps:
-                try:
-                    return mapping[key]             # can't use 'key in mapping' with defaultdict
-                except KeyError:
-                    pass
-            return self.__missing__(key)            # support subclasses that define __missing__
-
-        def get(self, key, default=None):
-            return self[key] if key in self else default
-
-        def __len__(self):
-            return len(set().union(*self.maps))     # reuses stored hash values if possible
-
-        def __iter__(self):
-            return iter(set().union(*self.maps))
-
-        def __contains__(self, key):
-            return any(key in m for m in self.maps)
-
-        def __bool__(self):
-            return any(self.maps)
-
-        @_recursive_repr()
-        def __repr__(self):
-            return '{0.__class__.__name__}({1})'.format(
-                self, ', '.join(map(repr, self.maps)))
-
-        @classmethod
-        def fromkeys(cls, iterable, *args):
-            'Create a ChainMap with a single dict created from the iterable.'
-            return cls(dict.fromkeys(iterable, *args))
-
-        def copy(self):
-            'New ChainMap or subclass with a new copy of maps[0] and refs to maps[1:]'
-            return self.__class__(self.maps[0].copy(), *self.maps[1:])
-
-        __copy__ = copy
-
-        def new_child(self):                        # like Django's Context.push()
-            'New ChainMap with a new dict followed by all previous maps.'
-            return self.__class__({}, *self.maps)
-
-        @property
-        def parents(self):                          # like Django's Context.pop()
-            'New ChainMap from maps[1:].'
-            return self.__class__(*self.maps[1:])
-
-        def __setitem__(self, key, value):
-            self.maps[0][key] = value
-
-        def __delitem__(self, key):
-            try:
-                del self.maps[0][key]
-            except KeyError:
-                raise KeyError('Key not found in the first mapping: {!r}'.format(key))
-
-        def popitem(self):
-            'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.'
-            try:
-                return self.maps[0].popitem()
-            except KeyError:
-                raise KeyError('No keys found in the first mapping.')
-
-        def pop(self, key, *args):
-            'Remove *key* from maps[0] and return its value. Raise KeyError if *key* not in maps[0].'
-            try:
-                return self.maps[0].pop(key, *args)
-            except KeyError:
-                raise KeyError('Key not found in the first mapping: {!r}'.format(key))
-
-        def clear(self):
-            'Clear maps[0], leaving maps[1:] intact.'
-            self.maps[0].clear()
-
-try:
-    from importlib.util import cache_from_source  # Python >= 3.4
-except ImportError:  # pragma: no cover
-    def cache_from_source(path, debug_override=None):
-        assert path.endswith('.py')
-        if debug_override is None:
-            debug_override = __debug__
-        if debug_override:
-            suffix = 'c'
-        else:
-            suffix = 'o'
-        return path + suffix
-
-try:
-    from collections import OrderedDict
-except ImportError: # pragma: no cover
-## {{{ http://code.activestate.com/recipes/576693/ (r9)
-# Backport of OrderedDict() class that runs on Python 2.4, 2.5, 2.6, 2.7 and pypy.
-# Passes Python2.7's test suite and incorporates all the latest updates.
-    try:
-        from thread import get_ident as _get_ident
-    except ImportError:
-        from dummy_thread import get_ident as _get_ident
-
-    try:
-        from _abcoll import KeysView, ValuesView, ItemsView
-    except ImportError:
-        pass
-
-
-    class OrderedDict(dict):
-        'Dictionary that remembers insertion order'
-        # An inherited dict maps keys to values.
-        # The inherited dict provides __getitem__, __len__, __contains__, and get.
-        # The remaining methods are order-aware.
-        # Big-O running times for all methods are the same as for regular dictionaries.
-
-        # The internal self.__map dictionary maps keys to links in a doubly linked list.
-        # The circular doubly linked list starts and ends with a sentinel element.
-        # The sentinel element never gets deleted (this simplifies the algorithm).
-        # Each link is stored as a list of length three:  [PREV, NEXT, KEY].
-
-        def __init__(self, *args, **kwds):
-            '''Initialize an ordered dictionary.  Signature is the same as for
-            regular dictionaries, but keyword arguments are not recommended
-            because their insertion order is arbitrary.
-
-            '''
-            if len(args) > 1:
-                raise TypeError('expected at most 1 arguments, got %d' % len(args))
-            try:
-                self.__root
-            except AttributeError:
-                self.__root = root = []                     # sentinel node
-                root[:] = [root, root, None]
-                self.__map = {}
-            self.__update(*args, **kwds)
-
-        def __setitem__(self, key, value, dict_setitem=dict.__setitem__):
-            'od.__setitem__(i, y) <==> od[i]=y'
-            # Setting a new item creates a new link which goes at the end of the linked
-            # list, and the inherited dictionary is updated with the new key/value pair.
-            if key not in self:
-                root = self.__root
-                last = root[0]
-                last[1] = root[0] = self.__map[key] = [last, root, key]
-            dict_setitem(self, key, value)
-
-        def __delitem__(self, key, dict_delitem=dict.__delitem__):
-            'od.__delitem__(y) <==> del od[y]'
-            # Deleting an existing item uses self.__map to find the link which is
-            # then removed by updating the links in the predecessor and successor nodes.
-            dict_delitem(self, key)
-            link_prev, link_next, key = self.__map.pop(key)
-            link_prev[1] = link_next
-            link_next[0] = link_prev
-
-        def __iter__(self):
-            'od.__iter__() <==> iter(od)'
-            root = self.__root
-            curr = root[1]
-            while curr is not root:
-                yield curr[2]
-                curr = curr[1]
-
-        def __reversed__(self):
-            'od.__reversed__() <==> reversed(od)'
-            root = self.__root
-            curr = root[0]
-            while curr is not root:
-                yield curr[2]
-                curr = curr[0]
-
-        def clear(self):
-            'od.clear() -> None.  Remove all items from od.'
-            try:
-                for node in self.__map.itervalues():
-                    del node[:]
-                root = self.__root
-                root[:] = [root, root, None]
-                self.__map.clear()
-            except AttributeError:
-                pass
-            dict.clear(self)
-
-        def popitem(self, last=True):
-            '''od.popitem() -> (k, v), return and remove a (key, value) pair.
-            Pairs are returned in LIFO order if last is true or FIFO order if false.
-
-            '''
-            if not self:
-                raise KeyError('dictionary is empty')
-            root = self.__root
-            if last:
-                link = root[0]
-                link_prev = link[0]
-                link_prev[1] = root
-                root[0] = link_prev
-            else:
-                link = root[1]
-                link_next = link[1]
-                root[1] = link_next
-                link_next[0] = root
-            key = link[2]
-            del self.__map[key]
-            value = dict.pop(self, key)
-            return key, value
-
-        # -- the following methods do not depend on the internal structure --
-
-        def keys(self):
-            'od.keys() -> list of keys in od'
-            return list(self)
-
-        def values(self):
-            'od.values() -> list of values in od'
-            return [self[key] for key in self]
-
-        def items(self):
-            'od.items() -> list of (key, value) pairs in od'
-            return [(key, self[key]) for key in self]
-
-        def iterkeys(self):
-            'od.iterkeys() -> an iterator over the keys in od'
-            return iter(self)
-
-        def itervalues(self):
-            'od.itervalues -> an iterator over the values in od'
-            for k in self:
-                yield self[k]
-
-        def iteritems(self):
-            'od.iteritems -> an iterator over the (key, value) items in od'
-            for k in self:
-                yield (k, self[k])
-
-        def update(*args, **kwds):
-            '''od.update(E, **F) -> None.  Update od from dict/iterable E and F.
-
-            If E is a dict instance, does:           for k in E: od[k] = E[k]
-            If E has a .keys() method, does:         for k in E.keys(): od[k] = E[k]
-            Or if E is an iterable of items, does:   for k, v in E: od[k] = v
-            In either case, this is followed by:     for k, v in F.items(): od[k] = v
-
-            '''
-            if len(args) > 2:
-                raise TypeError('update() takes at most 2 positional '
-                                'arguments (%d given)' % (len(args),))
-            elif not args:
-                raise TypeError('update() takes at least 1 argument (0 given)')
-            self = args[0]
-            # Make progressively weaker assumptions about "other"
-            other = ()
-            if len(args) == 2:
-                other = args[1]
-            if isinstance(other, dict):
-                for key in other:
-                    self[key] = other[key]
-            elif hasattr(other, 'keys'):
-                for key in other.keys():
-                    self[key] = other[key]
-            else:
-                for key, value in other:
-                    self[key] = value
-            for key, value in kwds.items():
-                self[key] = value
-
-        __update = update  # let subclasses override update without breaking __init__
-
-        __marker = object()
-
-        def pop(self, key, default=__marker):
-            '''od.pop(k[,d]) -> v, remove specified key and return the corresponding value.
-            If key is not found, d is returned if given, otherwise KeyError is raised.
-
-            '''
-            if key in self:
-                result = self[key]
-                del self[key]
-                return result
-            if default is self.__marker:
-                raise KeyError(key)
-            return default
-
-        def setdefault(self, key, default=None):
-            'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'
-            if key in self:
-                return self[key]
-            self[key] = default
-            return default
-
-        def __repr__(self, _repr_running=None):
-            'od.__repr__() <==> repr(od)'
-            if not _repr_running: _repr_running = {}
-            call_key = id(self), _get_ident()
-            if call_key in _repr_running:
-                return '...'
-            _repr_running[call_key] = 1
-            try:
-                if not self:
-                    return '%s()' % (self.__class__.__name__,)
-                return '%s(%r)' % (self.__class__.__name__, self.items())
-            finally:
-                del _repr_running[call_key]
-
-        def __reduce__(self):
-            'Return state information for pickling'
-            items = [[k, self[k]] for k in self]
-            inst_dict = vars(self).copy()
-            for k in vars(OrderedDict()):
-                inst_dict.pop(k, None)
-            if inst_dict:
-                return (self.__class__, (items,), inst_dict)
-            return self.__class__, (items,)
-
-        def copy(self):
-            'od.copy() -> a shallow copy of od'
-            return self.__class__(self)
-
-        @classmethod
-        def fromkeys(cls, iterable, value=None):
-            '''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S
-            and values equal to v (which defaults to None).
-
-            '''
-            d = cls()
-            for key in iterable:
-                d[key] = value
-            return d
-
-        def __eq__(self, other):
-            '''od.__eq__(y) <==> od==y.  Comparison to another OD is order-sensitive
-            while comparison to a regular mapping is order-insensitive.
-
-            '''
-            if isinstance(other, OrderedDict):
-                return len(self)==len(other) and self.items() == other.items()
-            return dict.__eq__(self, other)
-
-        def __ne__(self, other):
-            return not self == other
-
-        # -- the following methods are only used in Python 2.7 --
-
-        def viewkeys(self):
-            "od.viewkeys() -> a set-like object providing a view on od's keys"
-            return KeysView(self)
-
-        def viewvalues(self):
-            "od.viewvalues() -> an object providing a view on od's values"
-            return ValuesView(self)
-
-        def viewitems(self):
-            "od.viewitems() -> a set-like object providing a view on od's items"
-            return ItemsView(self)
-
-try:
-    from logging.config import BaseConfigurator, valid_ident
-except ImportError: # pragma: no cover
-    IDENTIFIER = re.compile('^[a-z_][a-z0-9_]*$', re.I)
-
-
-    def valid_ident(s):
-        m = IDENTIFIER.match(s)
-        if not m:
-            raise ValueError('Not a valid Python identifier: %r' % s)
-        return True
-
-
-    # The ConvertingXXX classes are wrappers around standard Python containers,
-    # and they serve to convert any suitable values in the container. The
-    # conversion converts base dicts, lists and tuples to their wrapped
-    # equivalents, whereas strings which match a conversion format are converted
-    # appropriately.
-    #
-    # Each wrapper should have a configurator attribute holding the actual
-    # configurator to use for conversion.
-
-    class ConvertingDict(dict):
-        """A converting dictionary wrapper."""
-
-        def __getitem__(self, key):
-            value = dict.__getitem__(self, key)
-            result = self.configurator.convert(value)
-            #If the converted value is different, save for next time
-            if value is not result:
-                self[key] = result
-                if type(result) in (ConvertingDict, ConvertingList,
-                                    ConvertingTuple):
-                    result.parent = self
-                    result.key = key
-            return result
-
-        def get(self, key, default=None):
-            value = dict.get(self, key, default)
-            result = self.configurator.convert(value)
-            #If the converted value is different, save for next time
-            if value is not result:
-                self[key] = result
-                if type(result) in (ConvertingDict, ConvertingList,
-                                    ConvertingTuple):
-                    result.parent = self
-                    result.key = key
-            return result
-
-    def pop(self, key, default=None):
-        value = dict.pop(self, key, default)
-        result = self.configurator.convert(value)
-        if value is not result:
-            if type(result) in (ConvertingDict, ConvertingList,
-                                ConvertingTuple):
-                result.parent = self
-                result.key = key
-        return result
-
-    class ConvertingList(list):
-        """A converting list wrapper."""
-        def __getitem__(self, key):
-            value = list.__getitem__(self, key)
-            result = self.configurator.convert(value)
-            #If the converted value is different, save for next time
-            if value is not result:
-                self[key] = result
-                if type(result) in (ConvertingDict, ConvertingList,
-                                    ConvertingTuple):
-                    result.parent = self
-                    result.key = key
-            return result
-
-        def pop(self, idx=-1):
-            value = list.pop(self, idx)
-            result = self.configurator.convert(value)
-            if value is not result:
-                if type(result) in (ConvertingDict, ConvertingList,
-                                    ConvertingTuple):
-                    result.parent = self
-            return result
-
-    class ConvertingTuple(tuple):
-        """A converting tuple wrapper."""
-        def __getitem__(self, key):
-            value = tuple.__getitem__(self, key)
-            result = self.configurator.convert(value)
-            if value is not result:
-                if type(result) in (ConvertingDict, ConvertingList,
-                                    ConvertingTuple):
-                    result.parent = self
-                    result.key = key
-            return result
-
-    class BaseConfigurator(object):
-        """
-        The configurator base class which defines some useful defaults.
-        """
-
-        CONVERT_PATTERN = re.compile(r'^(?P<prefix>[a-z]+)://(?P<suffix>.*)$')
-
-        WORD_PATTERN = re.compile(r'^\s*(\w+)\s*')
-        DOT_PATTERN = re.compile(r'^\.\s*(\w+)\s*')
-        INDEX_PATTERN = re.compile(r'^\[\s*(\w+)\s*\]\s*')
-        DIGIT_PATTERN = re.compile(r'^\d+$')
-
-        value_converters = {
-            'ext' : 'ext_convert',
-            'cfg' : 'cfg_convert',
-        }
-
-        # We might want to use a different one, e.g. importlib
-        importer = staticmethod(__import__)
-
-        def __init__(self, config):
-            self.config = ConvertingDict(config)
-            self.config.configurator = self
-
-        def resolve(self, s):
-            """
-            Resolve strings to objects using standard import and attribute
-            syntax.
-            """
-            name = s.split('.')
-            used = name.pop(0)
-            try:
-                found = self.importer(used)
-                for frag in name:
-                    used += '.' + frag
-                    try:
-                        found = getattr(found, frag)
-                    except AttributeError:
-                        self.importer(used)
-                        found = getattr(found, frag)
-                return found
-            except ImportError:
-                e, tb = sys.exc_info()[1:]
-                v = ValueError('Cannot resolve %r: %s' % (s, e))
-                v.__cause__, v.__traceback__ = e, tb
-                raise v
-
-        def ext_convert(self, value):
-            """Default converter for the ext:// protocol."""
-            return self.resolve(value)
-
-        def cfg_convert(self, value):
-            """Default converter for the cfg:// protocol."""
-            rest = value
-            m = self.WORD_PATTERN.match(rest)
-            if m is None:
-                raise ValueError("Unable to convert %r" % value)
-            else:
-                rest = rest[m.end():]
-                d = self.config[m.groups()[0]]
-                #print d, rest
-                while rest:
-                    m = self.DOT_PATTERN.match(rest)
-                    if m:
-                        d = d[m.groups()[0]]
-                    else:
-                        m = self.INDEX_PATTERN.match(rest)
-                        if m:
-                            idx = m.groups()[0]
-                            if not self.DIGIT_PATTERN.match(idx):
-                                d = d[idx]
-                            else:
-                                try:
-                                    n = int(idx) # try as number first (most likely)
-                                    d = d[n]
-                                except TypeError:
-                                    d = d[idx]
-                    if m:
-                        rest = rest[m.end():]
-                    else:
-                        raise ValueError('Unable to convert '
-                                         '%r at %r' % (value, rest))
-            #rest should be empty
-            return d
-
-        def convert(self, value):
-            """
-            Convert values to an appropriate type. dicts, lists and tuples are
-            replaced by their converting alternatives. Strings are checked to
-            see if they have a conversion format and are converted if they do.
-            """
-            if not isinstance(value, ConvertingDict) and isinstance(value, dict):
-                value = ConvertingDict(value)
-                value.configurator = self
-            elif not isinstance(value, ConvertingList) and isinstance(value, list):
-                value = ConvertingList(value)
-                value.configurator = self
-            elif not isinstance(value, ConvertingTuple) and\
-                     isinstance(value, tuple):
-                value = ConvertingTuple(value)
-                value.configurator = self
-            elif isinstance(value, string_types):
-                m = self.CONVERT_PATTERN.match(value)
-                if m:
-                    d = m.groupdict()
-                    prefix = d['prefix']
-                    converter = self.value_converters.get(prefix, None)
-                    if converter:
-                        suffix = d['suffix']
-                        converter = getattr(self, converter)
-                        value = converter(suffix)
-            return value
-
-        def configure_custom(self, config):
-            """Configure an object with a user-supplied factory."""
-            c = config.pop('()')
-            if not callable(c):
-                c = self.resolve(c)
-            props = config.pop('.', None)
-            # Check for valid identifiers
-            kwargs = dict([(k, config[k]) for k in config if valid_ident(k)])
-            result = c(**kwargs)
-            if props:
-                for name, value in props.items():
-                    setattr(result, name, value)
-            return result
-
-        def as_tuple(self, value):
-            """Utility function which converts lists to tuples."""
-            if isinstance(value, list):
-                value = tuple(value)
-            return value

spaces/Billyosoro/ESRGAN/realesrgan/archs/srvgg_arch.py

@@ -1,69 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-
-
-@ARCH_REGISTRY.register()
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-
-    It is a compact network structure, which performs upsampling in the last layer and no convolution is
-    conducted on the HR feature space.
-
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        num_feat (int): Channel number of intermediate features. Default: 64.
-        num_conv (int): Number of convolution layers in the body network. Default: 16.
-        upscale (int): Upsampling factor. Default: 4.
-        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
-    """
-
-    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
-        super(SRVGGNetCompact, self).__init__()
-        self.num_in_ch = num_in_ch
-        self.num_out_ch = num_out_ch
-        self.num_feat = num_feat
-        self.num_conv = num_conv
-        self.upscale = upscale
-        self.act_type = act_type
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == 'relu':
-            activation = nn.ReLU(inplace=True)
-        elif act_type == 'prelu':
-            activation = nn.PReLU(num_parameters=num_feat)
-        elif act_type == 'leakyrelu':
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)
-
-        # the body structure
-        for _ in range(num_conv):
-            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
-            # activation
-            if act_type == 'relu':
-                activation = nn.ReLU(inplace=True)
-            elif act_type == 'prelu':
-                activation = nn.PReLU(num_parameters=num_feat)
-            elif act_type == 'leakyrelu':
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)
-
-        # the last conv
-        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
-        # upsample
-        self.upsampler = nn.PixelShuffle(upscale)
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
-        out += base
-        return out

spaces/Brij1808/Blog_Generator/README.md

@@ -1,12 +0,0 @@
----
-title: Blog Generator
-emoji: 🏃
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/doc/MODEL_ZOO.md

@@ -1,277 +0,0 @@
-# Model Zoo and Baselines
-
-# Introduction
-
-We provide baselines trained with Detectron2 DensePose. The corresponding
-configuration files can be found in the [configs](../configs) directory.
-All models were trained on COCO `train2014` + `valminusminival2014` and
-evaluated on COCO `minival2014`. For the details on common settings in which
-baselines were trained, please check [Detectron 2 Model Zoo](../../../MODEL_ZOO.md).
-
-## License
-
-All models available for download through this document are licensed under the
-[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/)
-
-## COCO DensePose Baselines with DensePose-RCNN
-
-### Legacy Models
-
-Baselines trained using schedules from [Güler et al, 2018](https://arxiv.org/pdf/1802.00434.pdf)
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">dp. AP<br/>GPS</th>
-<th valign="bottom">dp. AP<br/>GPSm</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: densepose_rcnn_R_50_FPN_s1x_legacy -->
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_s1x_legacy.yaml">R_50_FPN_s1x_legacy</a></td>
- <td align="center">s1x</td>
- <td align="center">0.307</td>
- <td align="center">0.051</td>
- <td align="center">3.2</td>
- <td align="center">58.1</td>
- <td align="center">52.1</td>
- <td align="center">54.9</td>
- <td align="center">164832157</td>
- <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/model_final_d366fa.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/metrics.json">metrics</a></td>
- </tr>
- <!-- ROW: densepose_rcnn_R_101_FPN_s1x_legacy -->
-  <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_s1x_legacy.yaml">R_101_FPN_s1x_legacy</a></td>
-  <td align="center">s1x</td>
-  <td align="center">0.390</td>
-  <td align="center">0.063</td>
-  <td align="center">4.3</td>
-  <td align="center">59.5</td>
-  <td align="center">53.2</td>
-  <td align="center">56.1</td>
-  <td align="center">164832182</td>
-  <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x_legacy/164832182/model_final_10af0e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x_legacy/164832182/metrics.json">metrics</a></td>
-  </tr>
-</tbody></table>
-
-### Improved Baselines, Original Fully Convolutional Haad
-
-These models use an improved training schedule and Panoptic FPN head from [Kirillov et al, 2019](https://arxiv.org/abs/1901.02446).
-
-<table><tbody>
-  <!-- START TABLE -->
-  <!-- TABLE HEADER -->
-  <th valign="bottom">Name</th>
-  <th valign="bottom">lr<br/>sched</th>
-  <th valign="bottom">train<br/>time<br/>(s/iter)</th>
-  <th valign="bottom">inference<br/>time<br/>(s/im)</th>
-  <th valign="bottom">train<br/>mem<br/>(GB)</th>
-  <th valign="bottom">box<br/>AP</th>
-  <th valign="bottom">dp. AP<br/>GPS</th>
-  <th valign="bottom">dp. AP<br/>GPSm</th>
-  <th valign="bottom">model id</th>
-  <th valign="bottom">download</th>
-  <!-- TABLE BODY -->
-  <!-- ROW: densepose_rcnn_R_50_FPN_s1x -->
-   <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_s1x.yaml">R_50_FPN_s1x</a></td>
-   <td align="center">s1x</td>
-   <td align="center">0.359</td>
-   <td align="center">0.066</td>
-   <td align="center">4.5</td>
-   <td align="center">61.2</td>
-   <td align="center">63.7</td>
-   <td align="center">65.3</td>
-   <td align="center">165712039</td>
-   <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/metrics.json">metrics</a></td>
-   </tr>
-   <!-- ROW: densepose_rcnn_R_101_FPN_s1x -->
-    <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_s1x.yaml">R_101_FPN_s1x</a></td>
-    <td align="center">s1x</td>
-    <td align="center">0.428</td>
-    <td align="center">0.079</td>
-    <td align="center">5.8</td>
-    <td align="center">62.3</td>
-    <td align="center">64.5</td>
-    <td align="center">66.4</td>
-    <td align="center">165712084</td>
-    <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x/165712084/model_final_c6ab63.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x/165712084/metrics.json">metrics</a></td>
-    </tr>
-    </tbody></table>
-
-### Improved Baselines, DeepLabV3 Head
-
-These models use an improved training schedule, Panoptic FPN head from [Kirillov et al, 2019](https://arxiv.org/abs/1901.02446) and DeepLabV3 head from [Chen et al, 2017](https://arxiv.org/abs/1706.05587).
-
-<table><tbody>
-    <!-- START TABLE -->
-    <!-- TABLE HEADER -->
-    <th valign="bottom">Name</th>
-    <th valign="bottom">lr<br/>sched</th>
-    <th valign="bottom">train<br/>time<br/>(s/iter)</th>
-    <th valign="bottom">inference<br/>time<br/>(s/im)</th>
-    <th valign="bottom">train<br/>mem<br/>(GB)</th>
-    <th valign="bottom">box<br/>AP</th>
-    <th valign="bottom">dp. AP<br/>GPS</th>
-    <th valign="bottom">dp. AP<br/>GPSm</th>
-    <th valign="bottom">model id</th>
-    <th valign="bottom">download</th>
-    <!-- TABLE BODY -->
-    <!-- ROW: densepose_rcnn_R_50_FPN_DL_s1x -->
-     <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_s1x.yaml">R_50_FPN_DL_s1x</a></td>
-     <td align="center">s1x</td>
-     <td align="center">0.392</td>
-     <td align="center">0.070</td>
-     <td align="center">6.7</td>
-     <td align="center">61.1</td>
-     <td align="center">65.6</td>
-     <td align="center">66.8</td>
-     <td align="center">165712097</td>
-     <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_s1x/165712097/model_final_0ed407.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_s1x/165712097/metrics.json">metrics</a></td>
-     </tr>
-     <!-- ROW: densepose_rcnn_R_101_FPN_DL_s1x -->
-      <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml">R_101_FPN_DL_s1x</a></td>
-      <td align="center">s1x</td>
-      <td align="center">0.478</td>
-      <td align="center">0.083</td>
-      <td align="center">7.0</td>
-      <td align="center">62.3</td>
-      <td align="center">66.3</td>
-      <td align="center">67.7</td>
-      <td align="center">165712116</td>
-      <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/model_final_844d15.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/metrics.json">metrics</a></td>
-      </tr>
-</tbody></table>
-
-### Baselines with Confidence Estimation
-
-These models perform additional estimation of confidence in regressed UV coodrinates, along the lines of [Neverova et al., 2019](https://papers.nips.cc/paper/8378-correlated-uncertainty-for-learning-dense-correspondences-from-noisy-labels).
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">dp. AP<br/>GPS</th>
-<th valign="bottom">dp. AP<br/>GPSm</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY --> 
-<!-- ROW: densepose_rcnn_R_50_FPN_WC1_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_WC1_s1x.yaml">R_50_FPN_WC1_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.353</td>
-<td align="center">0.064</td>
-<td align="center">4.6</td>
-<td align="center">60.5</td>
-<td align="center">64.2</td>
-<td align="center">65.6</td>
-<td align="center">173862049</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1_s1x/173862049/model_final_289019.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1_s1x/173862049/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_WC2_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_WC2_s1x.yaml">R_50_FPN_WC2_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.364</td>
-<td align="center">0.066</td>
-<td align="center">4.8</td>
-<td align="center">60.7</td>
-<td align="center">64.2</td>
-<td align="center">65.7</td>
-<td align="center">173861455</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC2_s1x/173861455/model_final_3abe14.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC2_s1x/173861455/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_DL_WC1_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_WC1_s1x.yaml">R_50_FPN_DL_WC1_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.397</td>
-<td align="center">0.068</td>
-<td align="center">6.7</td>
-<td align="center">61.1</td>
-<td align="center">65.8</td>
-<td align="center">67.1</td>
-<td align="center">173067973</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC1_s1x/173067973/model_final_b1e525.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC1_s1x/173067973/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_DL_WC2_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_WC2_s1x.yaml">R_50_FPN_DL_WC2_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.410</td>
-<td align="center">0.070</td>
-<td align="center">6.8</td>
-<td align="center">60.8</td>
-<td align="center">65.6</td>
-<td align="center">66.7</td>
-<td align="center">173859335</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC2_s1x/173859335/model_final_60fed4.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC2_s1x/173859335/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_WC1_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_WC1_s1x.yaml">R_101_FPN_WC1_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.435</td>
-<td align="center">0.076</td>
-<td align="center">5.7</td>
-<td align="center">62.5</td>
-<td align="center">64.9</td>
-<td align="center">66.5</td>
-<td align="center">171402969</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC1_s1x/171402969/model_final_9e47f0.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC1_s1x/171402969/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_WC2_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_WC2_s1x.yaml">R_101_FPN_WC2_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.450</td>
-<td align="center">0.078</td>
-<td align="center">5.7</td>
-<td align="center">62.3</td>
-<td align="center">64.8</td>
-<td align="center">66.6</td>
-<td align="center">173860702</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC2_s1x/173860702/model_final_5ea023.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC2_s1x/173860702/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_DL_WC1_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_WC1_s1x.yaml">R_101_FPN_DL_WC1_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.479</td>
-<td align="center">0.081</td>
-<td align="center">7.9</td>
-<td align="center">62.0</td>
-<td align="center">66.2</td>
-<td align="center">67.4</td>
-<td align="center">173858525</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC1_s1x/173858525/model_final_f359f3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC1_s1x/173858525/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_DL_WC2_s1x --> 
- <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_WC2_s1x.yaml">R_101_FPN_DL_WC2_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.491</td>
-<td align="center">0.082</td>
-<td align="center">7.6</td>
-<td align="center">61.7</td>
-<td align="center">65.9</td>
-<td align="center">67.3</td>
-<td align="center">173294801</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC2_s1x/173294801/model_final_6e1ed1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC2_s1x/173294801/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-## Old Baselines
-
-It is still possible to use some baselines from [DensePose 1](https://github.com/facebookresearch/DensePose).
-Below are evaluation metrics for the baselines recomputed in the current framework:
-
-| Model | bbox AP | AP  |  AP50 | AP75  | APm  |APl |
-|-----|-----|-----|---    |---    |---   |--- |
-| [`ResNet50_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl) | 54.673 | 48.894 | 84.963 | 50.717 | 43.132 | 50.433 |
-| [`ResNet101_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl) | 56.032 | 51.088 | 86.250 | 55.057 | 46.542 | 52.563 |
-
-Note: these scores are close, but not strictly equal to the ones reported in the [DensePose 1 Model Zoo](https://github.com/facebookresearch/DensePose/blob/master/MODEL_ZOO.md),
-which is due to small incompatibilities between the frameworks.

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/visualize_json_results.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import argparse
-import json
-import numpy as np
-import os
-from collections import defaultdict
-import cv2
-import tqdm
-from fvcore.common.file_io import PathManager
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.structures import Boxes, BoxMode, Instances
-from detectron2.utils.logger import setup_logger
-from detectron2.utils.visualizer import Visualizer
-
-
-def create_instances(predictions, image_size):
-    ret = Instances(image_size)
-
-    score = np.asarray([x["score"] for x in predictions])
-    chosen = (score > args.conf_threshold).nonzero()[0]
-    score = score[chosen]
-    bbox = np.asarray([predictions[i]["bbox"] for i in chosen])
-    bbox = BoxMode.convert(bbox, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
-
-    labels = np.asarray([dataset_id_map(predictions[i]["category_id"]) for i in chosen])
-
-    ret.scores = score
-    ret.pred_boxes = Boxes(bbox)
-    ret.pred_classes = labels
-
-    try:
-        ret.pred_masks = [predictions[i]["segmentation"] for i in chosen]
-    except KeyError:
-        pass
-    return ret
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="A script that visualizes the json predictions from COCO or LVIS dataset."
-    )
-    parser.add_argument("--input", required=True, help="JSON file produced by the model")
-    parser.add_argument("--output", required=True, help="output directory")
-    parser.add_argument("--dataset", help="name of the dataset", default="coco_2017_val")
-    parser.add_argument("--conf-threshold", default=0.5, type=float, help="confidence threshold")
-    args = parser.parse_args()
-
-    logger = setup_logger()
-
-    with PathManager.open(args.input, "r") as f:
-        predictions = json.load(f)
-
-    pred_by_image = defaultdict(list)
-    for p in predictions:
-        pred_by_image[p["image_id"]].append(p)
-
-    dicts = list(DatasetCatalog.get(args.dataset))
-    metadata = MetadataCatalog.get(args.dataset)
-    if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
-
-        def dataset_id_map(ds_id):
-            return metadata.thing_dataset_id_to_contiguous_id[ds_id]
-
-    elif "lvis" in args.dataset:
-        # LVIS results are in the same format as COCO results, but have a different
-        # mapping from dataset category id to contiguous category id in [0, #categories - 1]
-        def dataset_id_map(ds_id):
-            return ds_id - 1
-
-    else:
-        raise ValueError("Unsupported dataset: {}".format(args.dataset))
-
-    os.makedirs(args.output, exist_ok=True)
-
-    for dic in tqdm.tqdm(dicts):
-        img = cv2.imread(dic["file_name"], cv2.IMREAD_COLOR)[:, :, ::-1]
-        basename = os.path.basename(dic["file_name"])
-
-        predictions = create_instances(pred_by_image[dic["image_id"]], img.shape[:2])
-        vis = Visualizer(img, metadata)
-        vis_pred = vis.draw_instance_predictions(predictions).get_image()
-
-        vis = Visualizer(img, metadata)
-        vis_gt = vis.draw_dataset_dict(dic).get_image()
-
-        concat = np.concatenate((vis_pred, vis_gt), axis=1)
-        cv2.imwrite(os.path.join(args.output, basename), concat[:, :, ::-1])

spaces/CVPR/LIVE/thrust/cmake/ThrustRunExample.cmake

@@ -1,49 +0,0 @@
-# Inputs:
-#
-# Variable             | Type     | Doc
-# ---------------------|----------|--------------------------------------
-# EXAMPLE_EXECUTABLE   | FilePath | Path to example executable
-# FILECHECK_ENABLED    | Boolean  | Run FileCheck comparison test
-# FILECHECK_EXECUTABLE | FilePath | Path to the LLVM FileCheck utility
-# REFERENCE_FILE       | FilePath | Path to the FileCheck reference file
-
-if (FILECHECK_ENABLED)
-  if (NOT EXISTS "${REFERENCE_FILE}")
-    message(FATAL_ERROR
-      "FileCheck requested for '${EXAMPLE_EXECUTABLE}', but reference file "
-      "does not exist at '${REFERENCE_FILE}`."
-    )
-  endif()
-
-  # If the reference file is empty, validate that the example doesn't
-  # produce any output.
-  file(SIZE "${REFERENCE_FILE}" file_size)
-  message("${REFERENCE_FILE}: ${file_size} bytes")
-
-  if (file_size EQUAL 0)
-    set(check_empty_output TRUE)
-    set(filecheck_command)
-  else()
-    set(check_empty_output FALSE)
-    set(filecheck_command COMMAND "${FILECHECK_EXECUTABLE}" "${REFERENCE_FILE}")
-  endif()
-endif()
-
-execute_process(
-  COMMAND "${EXAMPLE_EXECUTABLE}"
-  ${filecheck_command}
-  RESULT_VARIABLE exit_code
-  OUTPUT_VARIABLE stdout
-  ERROR_VARIABLE stderr
-)
-
-if (NOT 0 EQUAL exit_code)
-  message(FATAL_ERROR "${EXAMPLE_EXECUTABLE} failed (${exit_code}):\n${stderr}")
-endif()
-
-if (check_empty_output)
-  string(LENGTH "${stdout}" stdout_size)
-  if (NOT stdout_size EQUAL 0)
-    message(FATAL_ERROR "${EXAMPLE_EXECUTABLE}: output received, but not expected:\n${stdout}")
-  endif()
-endif()

spaces/CVPR/LIVE/thrust/thrust/device_free.h

@@ -1,68 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file device_free.h
- *  \brief Deallocates storage allocated by \p device_malloc
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/device_ptr.h>
-
-namespace thrust
-{
-
-/*! \addtogroup deallocation_functions Deallocation Functions
- *  \ingroup memory_management_functions
- *  \{
- */
-
-/*! \p device_free deallocates memory allocated by the function \p device_malloc.
- *
- *  \param ptr A \p device_ptr pointing to memory to be deallocated.
- *
- *  The following code snippet demonstrates how to use \p device_free to
- *  deallocate memory allocated by \p device_malloc.
- *
- *  \code
- *  #include <thrust/device_malloc.h>
- *  #include <thrust/device_free.h>
- *  ...
- *  // allocate some integers with device_malloc
- *  const int N = 100;
- *  thrust::device_ptr<int> int_array = thrust::device_malloc<int>(N);
- *
- *  // manipulate integers
- *  ...
- *
- *  // deallocate with device_free
- *  thrust::device_free(int_array);
- *  \endcode
- *
- *  \see device_ptr
- *  \see device_malloc
- */
-inline void device_free(thrust::device_ptr<void> ptr);
-
-/*! \}
- */
-
-} // end thrust
-
-#include <thrust/detail/device_free.inl>
-

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

@@ -1,144 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file scan_by_key.h
- *  \brief Generic implementations of key-value scans.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator inclusive_scan_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename BinaryPredicate>
-__host__ __device__
-  OutputIterator inclusive_scan_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result,
-                                       BinaryPredicate binary_pred);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename BinaryPredicate,
-         typename AssociativeOperator>
-__host__ __device__
-  OutputIterator inclusive_scan_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result,
-                                       BinaryPredicate binary_pred,
-                                       AssociativeOperator binary_op);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator exclusive_scan_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename T>
-__host__ __device__
-  OutputIterator exclusive_scan_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result,
-                                       T init);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename T,
-         typename BinaryPredicate>
-__host__ __device__
-  OutputIterator exclusive_scan_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result,
-                                       T init,
-                                       BinaryPredicate binary_pred);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename T,
-         typename BinaryPredicate,
-         typename AssociativeOperator>
-__host__ __device__
-  OutputIterator exclusive_scan_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result,
-                                       T init,
-                                       BinaryPredicate binary_pred,
-                                       AssociativeOperator binary_op);
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/scan_by_key.inl>
-