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<h1>Sri Lalitha Sahasranamam Lyrics In Tamil Pdf Downloadl</h1>
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<p>If you are a devotee of Goddess Lalitha, the Divine Mother, you might be interested in downloading Sri Lalitha Sahasranamam lyrics in Tamil pdf. Sri Lalitha Sahasranamam is a sacred Hindu text that contains the thousand names of Goddess Lalitha, who is also known as Lalita Devi, Tripura Sundari, Shodashi, Rajarajeshwari, and many other names. In this article, we will tell you what is Sri Lalitha Sahasranamam, how to download it in Tamil pdf format, and how to chant it for maximum benefits.</p>
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<h2>What is Sri Lalitha Sahasranamam?</h2>
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<p>Sri Lalitha Sahasranamam is a part of the Brahmanda Purana, one of the 18 major Puranas in Hinduism. It is a hymn that praises Goddess Lalitha as the supreme power and creator of the universe. It describes her various attributes, qualities, forms, manifestations, and deeds. It also reveals her secret names that can grant various boons and blessings to her devotees.</p>
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<h2>Sri Lalitha Sahasranamam Lyrics In Tamil Pdf Downloadl</h2><br /><p><b><b>Download Zip</b> ➡ <a href="https://byltly.com/2uKyK6">https://byltly.com/2uKyK6</a></b></p><br /><br />
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<h3>The origin and meaning of Sri Lalitha Sahasranamam</h3>
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<p>According to the legend, Sri Lalitha Sahasranamam was revealed by Lord Hayagriva, an incarnation of Lord Vishnu, to Sage Agastya, one of the seven great sages in Hinduism. Lord Hayagriva told Sage Agastya the story of how Goddess Lalitha incarnated as the daughter of Himalaya, the king of mountains, and married Lord Shiva, the destroyer of evil. He also narrated how she fought and killed a powerful demon named Bhandasura, who was created from the ashes of Kamadeva, the god of love. He then taught him the thousand names of Goddess Lalitha that can please her and invoke her grace.</p>
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<p>The meaning of Sri Lalitha Sahasranamam is "the thousand names of Sri Lalitha". The word "Sri" means auspiciousness, wealth, beauty, grace, and respect. The word "Lalitha" means playful, charming, delightful, graceful, and lovely. The word "Sahasranama" means thousand names. Each name of Goddess Lalitha has a deep meaning and significance that reflects her various aspects and powers. Some of her names are:</p>
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<ul>
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<li>Srimata: The mother of all</li>
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<li>Sri Maharajni: The great queen</li>
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<li>Srimat Simhasaneswari: The one who sits on the lion throne</li>
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<li>Chidagni Kunda Sambhuta: The one who emerged from the fire of consciousness</li>
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<li>Deva Karya Samudyata: The one who is ready for the divine work</li>
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<li>Udyad Bhanu Sahasrabha: The one who shines like a thousand rising suns</li>
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<li>Raga Swarupa Pasa Dhyaa: The one who holds the rope of attachment</li>
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<li>Krodha Kara Ankusa Jwala: The one who holds the goad of anger</li>
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<li>Mano Rupa Eksu Kodanda: The one who holds the bow of mind</li>
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<li>Pancha Tanmatra Sayaka: The one who holds the arrows of five senses</li>
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<li>Nija Runa Prabha Pura Majjat Brahmhanda Mandala: The one whose red radiance pervades the entire universe</li>
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<li>Campaka Asoka Punnaga Saugandhika Lasat Kacha: The one whose hair is fragrant with flowers like campaka, asoka, punnaga, and saugandhika</li>
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<li>Kuru Vinda Mani Sreni Kanat Kotira Mandita: The one whose forehead is adorned with a row of rubies</li>
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<li>Astami Chandra Vibhraja Dalikasthala Sobhita: The one whose crescent moon on her forehead beautifies her face</li>
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<li>Mukha Chandra Kalankabha Mriganabhi Viseshaka: The one whose nose stud resembles a spot on the moon or a deer's eye</li>
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</ul>
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<p>And so on...</p>
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<h3>The benefits and significance of Sri Lalitha Sahasranamam</h3>
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<p>Sri Lalitha Sahasranamam is not just a hymn but a powerful mantra that can bestow various benefits to those who recite it with devotion and faith. Some of the benefits are:</p>
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<p>Sri Lalitha Sahasranamam Tamil Script Pdf Free Download<br />
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Lalitha Sahasranamam Lyrics in Tamil with Meaning Pdf<br />
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Lalitha Sahasranamam Lyrics in Tamil and English Pdf<br />
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Lalitha Sahasranamam in Tamil by Sivananda Vijayalakshmi Pdf Download<br />
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Sri Lalitha Sahasranamam Tamil Script with Commentary Pdf<br />
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Lalitha Sahasranamam in Tamil by Nitya Santhoshini Pdf Download<br />
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Sri Lalitha Sahasranamam Tamil Script with Explanation Pdf<br />
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Lalitha Sahasranamam in Tamil by Priya Sisters Pdf Download<br />
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Sri Lalitha Sahasranamam Tamil Script with Benefits Pdf<br />
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Lalitha Sahasranamam in Tamil by Sowmya Narayanan Pdf Download</p>
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<ul>
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<li>It grants peace, happiness, prosperity, health, wealth, fame, success, and protection to the devotees.</li>
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<li>It removes all kinds of obstacles, troubles, fears, sins, diseases, curses, and enemies from the devotees.</li>
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<li>It fulfills all kinds of desires, wishes, aspirations, and goals of the devotees.</li>
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<li>It awakens the latent spiritual power and wisdom in the devotees.</li>
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<li>It elevates the mind and soul of the devotees to higher levels of consciousness and bliss.</li>
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<li>It connects the devotees with Goddess Lalitha and makes them eligible for her grace and blessings.</li>
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</ul>
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<p>The significance of Sri Lalitha Sahasranamam is that it reveals the true nature and glory of Goddess Lalitha as the supreme reality and source of everything. It also teaches us how to worship her with love and devotion. It also helps us to understand ourselves better as we are reflections of her divine attributes. It also guides us to attain liberation from the cycle of birth and death by merging with her supreme self.</p>
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<h2>How to download Sri Lalitha Sahasranamam lyrics in Tamil pdf?</h2>
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<p>If you want to download Sri Lalitha Sahasranamam lyrics in Tamil pdf format for your convenience and ease of reading, you can follow these steps:</p>
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<h3>The sources and steps to download Sri Lalitha Sahasranama lyrics in Tamil pdf</h3>
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<ul>
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<li>Go to Google.com or any other search engine.</li>
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<li>Type "Sri Lalitha Sahasranama lyrics in Tamil pdf" or any similar keywords in the search box.</li>
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<li>You will get many results that offer free downloads or online reading of Sri Lalitha Sahasranama lyrics in Tamil pdf format.</li>
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<li>Select any reliable and authentic source that suits your preference.</li>
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<li>Click on the link or button that says "Download" or "Read Online".</li>
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<li>You will be directed to a page where you can either save or open the file on your device.</li>
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<li>You can also print or share the file with others if you wish.</li>
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</ul>
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<h3>The tips and precautions to download Sri Lalitha Sahasranama lyrics in Tamil pdf</h3>
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<ul>
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<li>Make sure you have a good internet connection and enough storage space on your device before downloading.</li>
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<li>Make sure you have a pdf reader or viewer installed on your device before opening.</li>
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<li>Make sure you download from a trusted and verified source that does not contain any malware or viruses.</li>
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<li>Make sure you respect the copyright laws and do not distribute or sell the file without permission.</li>
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<li>Make sure you recite or read Sri Lalitha Sahasranama lyrics in Tamil pdf with reverence and devotion.</li>
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</ul>
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<h2>How to chant Sri Lalitha Sahasranamam?</h2>
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<p>Chanting Sri Lalitha Sahasranamam is a simple and effective way to worship Goddess Lalitha and receive her grace and blessings. However, there are some guidelines and rules that one should follow to chant it properly and correctly. Here are some of them:</p>
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<h3>The best time and place to chant Sri Lalitha Sahasranamam</h3>
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<ul>
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<li>The best time to chant Sri Lalitha Sahasranamam is in the morning or evening, preferably during the Brahma Muhurta (the auspicious time before sunrise) or the Sandhya (the twilight time).</li>
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<li>The best place to chant Sri Lalitha Sahasranamam is in a clean, quiet, and sacred place, preferably in front of an idol or picture of Goddess Lalitha or in a temple dedicated to her.</li>
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<li>One should take a bath, wear clean clothes, and apply sandalwood paste or kumkum on the forehead before chanting.</li>
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<li>One should sit on a mat or a cloth facing east or north, with a straight spine and a calm mind.</li>
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<li>One should light a lamp or a candle and offer some flowers, fruits, or sweets to Goddess Lalitha before chanting.</li>
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</ul>
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<h3>The procedure and rules to chant Sri Lalitha Sahasranamam</h3>
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<ul>
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<li>One should chant Sri Lalitha Sahasranamam with devotion, concentration, and understanding.</li>
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<li>One should chant Sri Lalitha Sahasranamam with a clear and loud voice, pronouncing each word and syllable correctly and distinctly.</li>
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<li>One should chant Sri Lalitha Sahasranamam without any interruptions, distractions, or mistakes.</li>
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<li>One should chant Sri Lalitha Sahasranamam with a rosary or a mala made of rudraksha, crystal, lotus seed, or any other sacred material.</li>
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<li>One should chant Sri Lalitha Sahasranamam 108 times or any multiple of 9 times.</li>
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<li>One should chant Sri Lalitha Sahasranamam after invoking Goddess Lalitha with her dhyana (meditation), panchapuja (five-fold worship), and mula mantra (root mantra).</li>
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<li>One should chant Sri Lalitha Sahasranamam by following the order of the names as given in the text.</li>
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<li>One should chant Sri Lalitha Sahasranamam by offering a flower or a leaf to Goddess Lalitha after each name.</li>
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</ul>
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<h3>The effects and experiences of chanting Sri Lalitha Sahasranamam</h3>
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<ul>
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<li>Chanting Sri Lalitha Sahasranamam can have various positive effects and experiences on the physical, mental, emotional, and spiritual levels of the chanter.</li>
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<li>Chanting Sri Lalitha Sahasranamam can purify the body, mind, and soul of the chanter from all impurities and negativities.</li>
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<li>Chanting Sri Lalitha Sahasranamam can enhance the health, vitality, beauty, intelligence, creativity, and memory of the chanter.</li>
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<li>Chanting Sri Lalitha Sahasranamam can attract the love, affection, respect, admiration, and support of others towards the chanter.</li>
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<li>Chanting Sri Lalitha Sahasranamam can increase the wealth, prosperity, abundance, success, and happiness of the chanter.</li>
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<li>Chanting Sri Lalitha Sahasranamam can protect the chanter from all kinds of dangers, enemies, evils, and misfortunes.</li>
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<li>Chanting Sri Lalitha Sahasranamam can fulfill the desires, wishes, aspirations, and goals of the chanter.</li>
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<li>Chanting Sri Lalitha Sahasranamam can awaken the latent spiritual power and wisdom in the chanter.</li>
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<li>Chanting Sri Lalitha Sahasranamam can connect the chanter with Goddess Lalitha and make him or her eligible for her grace and blessings.</li>
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<li>Chanting Sri Lalitha Sahasranamam can elevate the mind and soul of the chanter to higher levels of consciousness and bliss.</li>
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</ul>
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<h2>Conclusion</h2>
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you can follow the steps and tips given in this article. You can also chant Sri Lalitha Sahasranama with devotion and faith to receive her grace and blessings. We hope you enjoyed reading this article and learned something new and useful. Thank you for your time and attention.</p>
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<h3>FAQs</h3>
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<p>Here are some frequently asked questions about Sri Lalitha Sahasranama and their answers.</p>
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<ol>
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<li>What is the meaning of Lalitha?</li>
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<p>Lalitha means playful, charming, delightful, graceful, and lovely. It is one of the names of Goddess Lalitha, who is also known as Lalita Devi, Tripura Sundari, Shodashi, Rajarajeshwari, and many other names.</p>
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<li>Who wrote Sri Lalitha Sahasranama?</li>
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<p>Sri Lalitha Sahasranama was revealed by Lord Hayagriva, an incarnation of Lord Vishnu, to Sage Agastya, one of the seven great sages in Hinduism. It is a part of the Brahmanda Purana, one of the 18 major Puranas in Hinduism.</p>
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<li>How many times should one chant Sri Lalitha Sahasranama?</li>
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<p>One should chant Sri Lalitha Sahasranama 108 times or any multiple of 9 times. One can also chant it as many times as one wishes or as per one's convenience and availability of time.</p>
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<li>What are the benefits of chanting Sri Lalitha Sahasranama?</li>
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<p>Chanting Sri Lalitha Sahasranama can bestow various benefits to the chanter such as peace, happiness, prosperity, health, wealth, fame, success, protection, fulfillment of desires, spiritual awakening, and liberation.</p>
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<li>What are the rules to chant Sri Lalitha Sahasranama?</li>
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<p>Some of the rules to chant Sri Lalitha Sahasranama are to chant it with devotion, concentration, and understanding; to chant it with a clear and loud voice; to chant it without any interruptions or mistakes; to chant it with a rosary or a mala; to chant it after invoking Goddess Lalitha with her dhyana, panchapuja, and mula mantra; to chant it by following the order of the names; and to chant it by offering a flower or a leaf to Goddess Lalitha after each name.</p>
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</ol>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Criminal Case Save the World! Mod APK - The Ultimate Adventure Game for Crime Lovers.md
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<h1>Criminal Case World Mod Apk: A Guide for Crime Solvers</h1>
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<p>If you are a fan of detective stories and hidden object games, you might have heard of Criminal Case, one of the most popular and addictive games on Facebook. But did you know that there is a mod apk version of the game that gives you unlimited energy and hints, as well as access to all the cases and features? In this article, we will tell you everything you need to know about Criminal Case World Mod Apk, including what it is, how to download and install it, why you should play it, and some tips and tricks to help you solve crimes faster and easier.</p>
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<h2>What is Criminal Case World Mod Apk?</h2>
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<h3>A brief introduction to the game and its features</h3>
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<p>Criminal Case is a hidden object game that puts you in the role of a detective who investigates murder cases in different locations around the world. You have to search for clues in crime scenes, examine evidence in the lab, interrogate suspects and witnesses, and bring the killer to justice. Along the way, you will also meet various characters, such as your partner, your boss, your forensic team, and other police officers.</p>
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<p>Criminal Case World Mod Apk is a modified version of the game that gives you some advantages over the original one. For example, you will have unlimited energy and hints, which means you can play as long as you want without waiting for them to refill. You will also be able to unlock all the cases and features in the game, such as new locations, new outfits, new pets, new trophies, and more. You will also be able to play with your friends who are also using the mod apk version.</p>
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<h3>How to download and install the mod apk</h3>
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<p>To download and install Criminal Case World Mod Apk, you will need an Android device that meets the minimum requirements of the game. You will also need to enable unknown sources in your device settings, so that you can install apps from outside the Google Play Store. Here are the steps to follow:</p>
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<ol>
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<li>Go to [this link](^1^) and download the mod apk file.</li>
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<li>Locate the file in your device storage and tap on it to start the installation process.</li>
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<li>Follow the instructions on the screen and wait for the installation to finish.</li>
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<li>Launch the game from your app drawer or home screen.</li>
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<li>Enjoy playing Criminal Case World Mod Apk with unlimited energy and hints.</li>
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</ol>
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<h2>Why Play Criminal Case World Mod Apk?</h2>
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<h3>The benefits of playing with unlimited energy and hints</h3>
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<p>One of the main reasons why you should play Criminal Case World Mod Apk is that you will never run out of energy or hints while playing. Energy is used to enter crime scenes and mini-games, while hints are used to highlight objects or areas that are relevant to the investigation. In the original game, both energy and hints are limited and take time to regenerate. This can be frustrating if you want to play more or if you are stuck on a difficult scene. With the mod apk version, you can play without any interruptions or limitations. You can also use hints more freely to help you find clues faster and easier.</p>
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<h3>The challenges and rewards of solving murder cases</h3>
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<p>Another reason why you should play Criminal Case World Mod Apk is that you will experience the thrill and satisfaction of solving murder cases. Each case has a unique story, a different setting, and a diverse cast of characters. You will have to use your observation skills, your logic, and your intuition to find the evidence, analyze it, and deduce the killer. You will also have to face some twists and turns along the way, such as false leads, red herrings, and unexpected revelations. Solving cases will not only test your intelligence, but also your morality and your empathy.</p>
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<p>As you solve cases, you will also earn rewards, such as stars, coins, cash, and experience points. Stars are used to unlock new scenes and mini-games, as well as to perform certain actions, such as examining evidence or interrogating suspects. Coins are used to buy items in the shop, such as clothes, accessories, pets, and boosters. Cash is used to buy premium items, such as energy refills, hints, or special outfits. Experience points are used to level up and unlock new features and cases.</p>
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| 64 |
-
<h3>The fun and excitement of playing with friends</h3>
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| 65 |
-
<p>A third reason why you should play Criminal Case World Mod Apk is that you will have more fun and excitement by playing with your friends. You can connect your game account to your Facebook account and invite your friends who are also using the mod apk version to join you. You can then team up with them to solve cases together, or compete with them to see who can score higher or rank higher in the leaderboards. You can also chat with them, send them gifts, ask them for help, or help them in return.</p>
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| 66 |
-
<p>Playing with friends will not only make the game more enjoyable, but also more social and interactive. You can share your opinions, your theories, your strategies, and your emotions with your friends. You can also learn from them, challenge them, support them, and congratulate them. Playing with friends will also motivate you to play more and improve your skills.</p>
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| 67 |
-
<h2>Tips and Tricks for Criminal Case World Mod Apk</h2>
|
| 68 |
-
<h3>How to rank up and earn stars faster</h3>
|
| 69 |
-
<p>If you want to rank up and earn stars faster in Criminal Case World Mod Apk, here are some tips and tricks that you can follow:</p>
|
| 70 |
-
<ul>
|
| 71 |
-
<li>Play the scenes that have higher scores or higher star ratings. These scenes will give you more points and more stars per energy spent.</li>
|
| 72 |
-
<li>Play the scenes that have fewer objects or smaller areas. These scenes will be easier to complete and will take less time.</li>
|
| 73 |
-
<li>Play the scenes that have bonus items or lucky cards. These items will give you extra points or extra hints when you find them.</li>
|
| 74 |
-
<li>Use boosters and power-ups before or during the scenes. Boosters are items that enhance your performance in the scenes, such as increasing your score multiplier or slowing down the timer. Power-ups are items that help you find objects or clues in the scenes, such as revealing their locations or magnifying them.</li>
|
| 75 |
-
<li>Repeat the scenes that you have already completed. This will help you memorize the objects and their locations, as well as improve your speed and accuracy.</li>
|
| 76 |
-
</ul>
|
| 77 |
-
<h3>How to use boosters and power-ups effectively</h3>
|
| 78 |
-
<p>Boosters and power-ups are very useful items that can help you solve cases faster and easier in Criminal Case World Mod Apk. However, they are also limited and costly, so you need to use them wisely. Here are some tips on how to use boosters and power-ups effectively:</p>
|
| 79 |
-
<ul>
|
| 80 |
-
<li>Use boosters before entering a scene or a mini-game. Boosters last for a certain amount of time or until you exit the scene or the mini-game. Therefore, it is better to use them before starting rather than during or after.</li>
|
| 81 |
-
<li>Use power-ups only when necessary or when they have a significant impact. Power-ups are consumed immediately after using them. Therefore, it is better to use them only when you are stuck or when they can make a big difference in your score or your progress.</li>
|
| 82 |
-
<li>Choose the right booster or power-up for the right scene or mini-game. Different boosters and power-ups have different effects and purposes. Therefore, it is better to use the ones that match the type of scene or mini-game that you are playing.</li>
|
| 83 |
-
<li>Save some boosters and power-ups for later cases or harder scenes or mini-games. Boosters and power-ups become more scarce and more expensive as you progress in the game. Therefore, it is better to save some of them for later cases or harder scenes or mini-games, where they can be more helpful and valuable.</li>
|
| 84 |
-
</ul>
|
| 85 |
-
<h3>How to find clues and evidence easily</h3>
|
| 86 |
-
<p>Clues and evidence are essential items that can help you solve cases and identify the killer in Criminal Case World Mod Apk. However, they are not always easy to find or recognize in the scenes or the mini-games. Here are some tips on how to find clues and evidence easily:</p>
|
| 87 |
-
<ul>
|
| 88 |
-
<li>Look for objects or areas that are highlighted, circled, or marked in some way. These are usually clues or evidence that are relevant to the investigation.</li>
|
| 89 |
-
<li>Look for objects or areas that are out of place, unusual, or suspicious. These are also likely to be clues or evidence that can provide some information or insight.</li>
|
| 90 |
-
<li>Look for objects or areas that are related to the victim, the suspects, the witnesses, or the motive. These can also be clues or evidence that can link them to the crime or the killer.</li>
|
| 91 |
-
<li>Use hints if you are stuck or unsure. Hints will show you the location or the name of an object or an area that is a clue or evidence.</li>
|
| 92 |
-
<li>Pay attention to the comments and suggestions of your partner, your boss, your forensic team, and other police officers. They will often give you hints or directions on where to look for clues or evidence.</li>
|
| 93 |
-
</ul>
|
| 94 |
-
<h2>Conclusion</h2>
|
| 95 |
-
<p>Criminal Case World Mod Apk is a great game for anyone who loves detective stories and hidden object games. It offers unlimited energy and hints, as well as access to all the cases and features in the game. It also allows you to play with your friends who are also using the mod apk version. It is a fun and exciting way to test your intelligence, your morality, and your empathy as you solve murder cases around the world. If you want to download and install Criminal Case World Mod Apk, just follow the steps that we have provided in this article. And if you want to rank up and earn stars faster, use boosters and power-ups effectively, and find clues and evidence easily, just follow the tips and tricks that we have shared with you. We hope that this article has been helpful and informative for you. Now, what are you waiting for? Grab your magnifying glass and your badge, and start solving crimes with Criminal Case World Mod Apk!</p>
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| 96 |
-
<h2>FAQs</h2>
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| 97 |
-
<h3>Q1: Is Criminal Case World Mod Apk safe to use?</h3>
|
| 98 |
-
<p>A1: Yes, Criminal Case World Mod Apk is safe to use as long as you download it from a trusted source and scan it with an antivirus program before installing it. However, you should be aware that using mod apk versions of games may violate their terms of service and may result in your account being banned or suspended by the developers. Therefore, use it at your own risk and discretion.</p>
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| 99 |
-
<h3>Q2: How can I update Criminal Case World Mod Apk?</h3>
|
| 100 |
-
<p>A2: To update Criminal Case World Mod Apk, you will need to download the latest version of the mod apk file from [this link] and install it over the existing one. You do not need to uninstall the previous version first. However, you should back up your game data before updating, in case something goes wrong during the process.</p>
|
| 101 |
-
<h3>Q3: How can I get more friends to play with?</h3>
|
| 102 |
-
<p>A3: To get more friends to play with in Criminal Case World Mod Apk, you can invite your existing Facebook friends who are also using the mod apk version to join you. You can also join online communities and groups of Criminal Case players who are looking for new friends and partners. You can also add random players who appear in your game as potential friends.</p>
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| 103 |
-
<h3>Q4: How can I report a bug or a problem with the game?</h3>
|
| 104 |
-
<p>A4: To report a bug or a problem with Criminal Case World Mod Apk, you can contact the developers or the support team through their official website [here]. You can also leave a comment or a review on [this page] where you downloaded the mod apk file. Please provide as much detail as possible about the issue that you encountered, such as when it happened, what you were doing, what device you were using, what error message you received, etc.</p>
|
| 105 |
-
<h3>Q5: How can I contact the developers or the support team?</h3>
|
| 106 |
-
<p>A5: To contact the developers or the support team of Criminal Case World Mod Apk, you can use one of the following methods:</p>
|
| 107 |
-
<ul>
|
| 108 |
-
<li>Email: [email protected]</li>
|
| 109 |
-
<li>Facebook: https://www.facebook.com/CriminalCaseGame/</li>
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| 110 |
-
<li>Twitter: https://twitter.com/CriminalCase_PS</li>
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| 111 |
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<li>Website: https://www.prettysimplegames.com/</li>
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</ul>
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| 113 |
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<p>We hope that this article has answered all your questions about Criminal Case World Mod Apk. If you have any other questions, feel free to contact us through any of the methods above. Thank you for reading and happy crime solving!</p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download !NEW! 1o5 Version Please Open Via Salary.md
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<h1>What is 1o5 version and why you should download it</h1>
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<p>Have you ever wondered how you can pay your salary and suppliers online without hassle or fees? If so, you might want to check out <strong>1o5 version</strong>, a new way to make payments with your phone using Google Pay or WhatsApp.</p>
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<p>1o5 version is a solution that integrates directly with your Sage software, allowing you to send and receive money instantly, securely, and conveniently. You can also earn rewards, discover offers, and understand your spending with Google Pay. Or you can enjoy private messaging, voice and video calls, and group chats with WhatsApp.</p>
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<p>In this article, we will show you how to download 1o5 version on your device, how to open it via salary*, and what benefits you can get from using it. We will also answer some frequently asked questions about this innovative payment method.</p>
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<p>Downloading 1o5 version is easy and free. All you need is a smartphone or tablet that supports Google Pay or WhatsApp. Here are the steps to follow:</p>
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<ol>
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<li>Go to the Google Play Store or the App Store and search for "Google Pay: Save and Pay" or "WhatsApp Messenger". </li>
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<li>Tap on the app icon and then tap on "Install".</li>
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<li>Open the app and follow the instructions to set up your account and link your bank card.</li>
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<p>Opening 1o5 version via salary* is simple and fast. All you need is a Sage account that supports salary and supplier payments. Here are the steps to follow:</p>
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<ol>
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<li>Log in to your Sage account and go to the "Salary and Supplier Payments" section.</li>
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<li>Choose the option to pay your staff or suppliers with Google Pay or WhatsApp.</li>
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<li>Enter the amount, the recipient's phone number, and a reference.</li>
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<p>That's it! You have successfully opened 1o5 version via salary* and made a payment with your phone. You will receive a confirmation message and a receipt for your transaction.</p>
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<p></p>
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<p>You may have some questions about 1o5 version via salary*. Here are some of the most common ones:</p>
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<h3>What is salary*?</h3>
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<p>Salary* is a service that allows you to pay your salary and suppliers online with Sage. You can choose from various payment methods, such as bank transfer, debit card, credit card, PayPal, Google Pay, or WhatsApp. You can also access real-time reports, analytics, and insights on your payments.</p>
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<h3>Is 1o5 version safe to use?</h3>
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<p>Yes, 1o5 version is safe to use. Google Pay and WhatsApp use advanced encryption and security features to protect your personal and financial information. They also comply with the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR). Sage also uses secure servers and encryption to safeguard your data.</p>
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<h3>How can I track my payments with 1o5 version?</h3>
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<p>You can track your payments with 1o5 version by logging in to your Sage account and going to the "Salary and Supplier Payments" section. There you can see the status, date, amount, recipient, and reference of each payment. You can also download or print receipts for your records.</p>
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<h3>What if I have a problem with my payment?</h3>
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<p>If you have a problem with your payment, you can contact the customer support team of Google Pay or WhatsApp, depending on which app you used. They will help you resolve the issue as soon as possible. You can also contact Sage support if you need assistance with your Sage account or software.</p>
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<h3>How can I get more information about 1o5 version?</h3>
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<p>If you want to get more information about 1o5 version, you can visit the official websites of Google Pay or WhatsApp, or read their FAQs . You can also visit the Sage website or read their blog for more tips and insights on how to use salary* effectively.</p>
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<h2>Conclusion</h2>
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<p>In conclusion, 1o5 version is a new way to pay your salary and suppliers online with your phone using Google Pay or WhatsApp. It is easy to download, simple to open via salary*, and beneficial for your business. It can help you save money and time, improve your cash flow, and streamline your operations.</p>
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<p>Why not give it a try today? Download 1o5 version on your device and open it via salary*. You will be amazed by how convenient and rewarding it is to make payments with your phone.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/CarX Street v0.8.6 Mod Apk The Ultimate Street Racing Game with Unlimited Cash.md
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<h1>CarX Street Mod APK 0.8.6 Download: A Guide for Android Users</h1>
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<p>If you are a fan of racing games, you might have heard of CarX Street, a dynamic open world game that lets you become a street racer the way you want. In this game, you can customize your car, challenge other racers, and explore the city of Sunset City. But what if you want to enjoy the game with more features and unlimited resources? That's where CarX Street Mod APK 0.8.6 comes in handy.</p>
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<p>In this article, we will tell you what CarX Street is, how to download and install CarX Street Mod APK 0.8.6 on your Android device, why you should play it, and some tips and tricks to help you become a legend of the streets.</p>
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<p>CarX Street is a racing game developed by CarX Technologies, the creators of CarX Drift Racing and CarX Highway Racing. It was released in February 2022 for Android and iOS devices, and has received positive reviews from players and critics alike.</p>
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<p>CarX Street is different from other racing games because it gives you more freedom and control over your car and your racing style. You can choose from over 50 cars, each with its own characteristics and customization options. You can also tune your car's performance, appearance, and sound to suit your preferences.</p>
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<p>But CarX Street is not just about racing. It's also about exploring the vast and vibrant city of Sunset City, where you can find various events, challenges, and secrets. You can also interact with other racers, join clubs, or create your own club and invite your friends.</p>
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<h3>Features of CarX Street</h3>
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<p>Some of the features that make CarX Street an amazing game are:</p>
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<ul>
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<li>Realistic physics and graphics that create an immersive racing experience.</li>
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<li>A dynamic open world that changes according to the time of day, weather, and traffic.</li>
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<li>A variety of game modes, such as sprint, circuit, drift, drag, time attack, and more.</li>
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<li>A rich story mode that follows your journey as a street racer in Sunset City.</li>
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<li>A multiplayer mode that allows you to compete with other players online or offline.</li>
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<li>A social system that lets you chat with other racers, join clubs, or create your own club.</li>
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<li>A garage system that lets you store and manage your cars.</li>
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<li>A workshop system that lets you customize and upgrade your cars.</li>
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</ul>
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<h3>How to download and install CarX Street Mod APK 0.8.6 on Android?</h3>
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<p>If you want to play CarX Street with more features and unlimited resources, you can download and install CarX Street Mod APK 0.8.6 on your Android device. Here are the steps to do so:</p>
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<ol>
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<li>Download the CarX Street Mod APK 0.8.6 file from a trusted source, such as [PlayMods](^1^).</li>
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<li>Go to your device's settings and enable the installation of apps from unknown sources.</li>
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<li>Locate the downloaded file in your device's storage and tap on it to install it.</li>
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<li>Wait for the installation process to finish and launch the game.</li>
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<li>Enjoy playing CarX Street Mod APK 0.8.6 with unlimited money, gold, diamonds, fuel, and more.</li>
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</ol>
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<h2>Why should you play CarX Street Mod APK 0.8.6?</h2>
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<p>CarX Street Mod APK 0.8.6 is not just a regular racing game. It's a game that offers you more fun, <p>CarX Street Mod APK 0.8.6 is not just a regular racing game. It's a game that offers you more fun, excitement, and customization than ever before. Here are some of the benefits of playing CarX Street Mod APK 0.8.6:</p>
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<h3>Benefits of playing CarX Street Mod APK 0.8.6</h3>
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<ul>
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<li>You can access all the cars, parts, and upgrades without spending any real money.</li>
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<p>If you want to become a legend of the streets, you need to master the skills and strategies of racing in CarX Street Mod APK 0.8.6. Here are some tips and tricks to help you out:</p>
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<ul>
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<li>Choose the right car for each game mode and event. Different cars have different strengths and weaknesses, such as speed, acceleration, handling, drift, and durability.</li>
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<li>Tune your car's performance according to the track and the weather conditions. You can adjust your car's engine, transmission, suspension, brakes, tires, and more.</li>
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<p>Yes, CarX Street Mod APK 0.8.6 is safe to download and install as long as you get it from a trusted source like [PlayMods]. However, <p>Yes, CarX Street Mod APK 0.8.6 is safe to download and install as long as you get it from a trusted source like [PlayMods]. However, you should be aware that modded apps may not be compatible with the official version of the game or the latest updates. You should also backup your data before installing the modded app, in case something goes wrong.</p>
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<p>To play CarX Street Mod APK 0.8.6, you need an Android device that has at least 4 GB of RAM, 2 GB of free storage space, and Android 5.0 or higher. You also need a stable internet connection to play online or offline.</p>
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spaces/1phancelerku/anime-remove-background/Enjoy PUBG MOBILE on PC Mac with BlueStacks Emulator.md
DELETED
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<br />
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<p>If you are a fan of <strong>PUBG Mobile</strong>, the popular battle royale game for mobile devices, you might be wondering how you can play it on your PC. After all, playing on a bigger screen with better graphics and controls can enhance your gaming experience and give you an edge over your opponents. Fortunately, there is a way to do that: by using a <strong>PUBG Mobile emulator</strong>.</p>
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<p>A PUBG Mobile emulator is a software application that allows you to run PUBG Mobile on your PC. It simulates the Android environment and lets you access the Google Play Store and download the game. However, not all emulators are created equal. Some are faster, smoother, and more compatible than others. So, how do you choose the best PUBG Mobile emulator for your PC? And how do you download and install it? In this article, we will answer these questions and more. We will also review some of the best emulators available in the market and compare their features and performance.</p>
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<h2>What is PUBG Mobile Emulator?</h2>
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<p>A <strong>PUBG Mobile emulator</strong> is a program that allows you to play PUBG Mobile on your PC. It works by creating a virtual Android device on your computer, where you can install and run apps from the Google Play Store. An emulator acts as a bridge between your PC and your mobile game, enabling you to enjoy the best of both worlds.</p>
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<p>There are many reasons why you might want to use a PUBG Mobile emulator. For instance, you might have a low-end or old smartphone that cannot run the game smoothly or at all. Or, you might prefer playing on a larger screen with higher resolution and frame rate. Or, you might want to use a keyboard and mouse instead of touch controls for more accuracy and responsiveness. Whatever your reason, a PUBG Mobile emulator can help you achieve it.</p>
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<p>Using a PUBG Mobile emulator has many benefits and advantages over playing on your smartphone. Here are some of them:</p>
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<ul>
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<li><strong>Better graphics and performance</strong>: Playing on a PC allows you to enjoy higher graphics settings, resolution, frame rate, and sound quality than on a mobile device. You can also adjust these settings according to your preference and system specifications.</li>
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<li><strong>Bigger screen</strong>: Playing on a PC gives you a wider field of view and more details than playing on a small screen. You can also see more enemies, items, vehicles, and map features.</li>
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<li><strong>Better controls</strong>: Playing on a PC lets you use a keyboard and mouse instead of touch controls, which can be more precise, comfortable, and customizable. You can also use a gamepad or other peripherals if you prefer. You can also map the keys and buttons to your liking and create macros and shortcuts for faster actions.</li>
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<li><strong>More features and options</strong>: Playing on a PC gives you access to more features and options than playing on a mobile device. For example, you can record and stream your gameplay, chat with your friends, use cheats and mods, and customize your game settings.</li>
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<li><strong>Free and legal</strong>: Playing on a PC does not cost you anything, as long as you have a compatible emulator and a Google account. You can download and install PUBG Mobile for free from the Google Play Store. Moreover, using an emulator is not illegal or against the game's terms of service, as long as you do not use any hacks or unfair advantages.</li>
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</ul>
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<p>As you can see, using a PUBG Mobile emulator can enhance your gaming experience and make it more fun and enjoyable. However, not all emulators are the same. Some are better than others in terms of compatibility, performance, stability, and features. Therefore, you need to choose the best PUBG Mobile emulator for your PC.</p>
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<h2>How to Choose the Best PUBG Mobile Emulator?</h2>
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<p>There are many factors and criteria that you need to consider when choosing the best PUBG Mobile emulator for your PC. Here are some of them:</p>
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<ul>
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<li><strong>Compatibility</strong>: The emulator should be compatible with your PC's operating system, hardware, and software. It should also be compatible with the latest version of PUBG Mobile and support all its modes and features.</li>
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<li><strong>Performance</strong>: The emulator should run PUBG Mobile smoothly and without lag, stuttering, or crashing. It should also have low CPU and memory usage and support high graphics settings and frame rate.</li>
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<li><strong>Stability</strong>: The emulator should be stable and reliable, without any bugs, errors, or glitches. It should also have regular updates and fixes to ensure its functionality and security.</li>
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<li><strong>Features</strong>: The emulator should have features that enhance your gameplay and convenience, such as keyboard and mouse support, gamepad support, key mapping, macros, screen recording, streaming, chat, cheats, mods, etc.</li>
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<li><strong>User-friendliness</strong>: The emulator should be easy to download, install, set up, and use. It should also have a simple and intuitive interface and design that makes it easy to navigate and customize.</li>
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<li><strong>Reputation</strong>: The emulator should have a good reputation among users and reviewers. It should also have positive feedback, ratings, reviews, and testimonials from its users.</li>
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</ul>
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<p>Based on these criteria, we have selected the best PUBG Mobile emulator for your PC: <strong>GameLoop</strong>.</p>
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<h2>The Best PUBG Mobile Emulator: GameLoop</h2>
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<p><strong>GameLoop</strong> is the official emulator for PUBG Mobile developed by Tencent Games, the same company that created the game. It is designed specifically for PUBG Mobile and optimized for its performance and features. It is also one of the most popular and widely used emulators for PUBG Mobile in the world.</p>
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<p>GameLoop has many advantages over other emulators for PUBG Mobile. Here are some of them:</p>
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| 80 |
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<h3>How to Download and Install GameLoop Emulator?</h3>
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| 81 |
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<p>Downloading and installing GameLoop emulator is very easy and straightforward. Here are the steps that you need to follow:</p>
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<ol>
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<li>Go to the official website of GameLoop at <a href="">https://gameloop.fun/</a>.</li>
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<li>Click on the "Download" button on the homepage to download the installer file.</li>
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<li>Run the installer file and follow the instructions on the screen to install GameLoop on your PC.</li>
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<li>Launch GameLoop from your desktop or start menu.</li>
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<li>On the Game Center tab, search for PUBG Mobile or browse through the categories to find it.</li>
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<li>Click on the "Install" button to download and install PUBG Mobile on GameLoop.</li>
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<li>Once the installation is complete, click on the "Play" button to launch PUBG Mobile on GameLoop.</li>
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</ol>
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<h3>How to Play PUBG Mobile on GameLoop Emulator?</h3>
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<p>Playing PUBG Mobile on GameLoop emulator is very similar to playing it on your smartphone. However, there are some tips and tricks that you can use to optimize your gameplay and make it more enjoyable. Here are some of them:</p>
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<ul>
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<li><strong>Adjust your graphics settings</strong>: You can adjust your graphics settings according to your PC's specifications and your preference. You can choose from low, medium, high , ultra, or extreme. You can also enable or disable anti-aliasing, shadows, and other effects. Higher graphics settings will make the game look more realistic and detailed, but they will also consume more resources and affect your performance. Lower graphics settings will make the game run faster and smoother, but they will also reduce the quality and clarity of the game.</li>
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<li><strong>Use keyboard and mouse controls</strong>: You can use your keyboard and mouse to control your character, aim, shoot, move, and perform other actions in the game. You can also customize your key mapping and sensitivity settings to suit your preference and style. Keyboard and mouse controls will give you more accuracy and responsiveness than touch controls, especially in combat situations.</li>
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<li><strong>Enable smart mode</strong>: You can enable smart mode in GameLoop to automatically adjust your graphics settings and frame rate according to your network speed and PC performance. This will help you avoid lag, stuttering, or freezing issues that might affect your gameplay.</li>
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<li><strong>Use turbo mode</strong>: You can use turbo mode in GameLoop to boost your game performance and speed up your loading time. Turbo mode will allocate more CPU and memory resources to the game and reduce the background processes that might interfere with the game.</li>
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<li><strong>Use game booster</strong>: You can use game booster in GameLoop to optimize your PC settings and improve your game performance. Game booster will scan your PC and detect any issues that might affect your game, such as outdated drivers, junk files, malware, etc. It will then fix these issues and enhance your PC's performance.</li>
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</ul>
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<h3>What are the Features of GameLoop Emulator?</h3>
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<p>GameLoop emulator has many features that make it one of the best emulators for PUBG Mobile. Here are some of them:</p>
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<ul>
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<li><strong>Official support</strong>: GameLoop is the official emulator for PUBG Mobile, which means that it has full support from Tencent Games and PUBG Corporation. It also means that it is compatible with all the updates, patches, events, and features of PUBG Mobile.</li>
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<li><strong>High compatibility</strong>: GameLoop is compatible with most Windows PC systems, from Windows 7 to Windows 10. It also supports both 32-bit and 64-bit versions of Windows. It can run PUBG Mobile on low-end or old PCs as well as high-end or new PCs.</li>
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<li><strong>High performance</strong>: GameLoop is optimized for PUBG Mobile and delivers high performance and stability. It can run PUBG Mobile at 60 FPS or higher on most PCs. It also has low CPU and memory usage and supports high graphics settings.</li>
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<li><strong>High security</strong>: GameLoop is secure and safe to use. It does not contain any viruses, malware, spyware, or adware. It also does not use any hacks or cheats that might get you banned from PUBG Mobile.</li>
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<li><strong>Multiple languages</strong>: GameLoop supports multiple languages, including English, Chinese, Korean, Spanish, Portuguese, Arabic, Turkish, etc. You can choose your preferred language from the settings menu.</li>
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<li><strong>Multiple games</strong>: GameLoop is not only for PUBG Mobile. It also supports other popular mobile games, such as Call of Duty: Mobile, Free Fire, Clash of Clans, Among Us, etc. You can download and play these games from the Game Center tab.</li>
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<li><strong>Multiple features</strong>: GameLoop has many features that enhance your gameplay and convenience, such as keyboard and mouse support, gamepad support , key mapping, macros, screen recording, streaming, chat, cheats, mods, etc.</li>
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</ul>
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| 111 |
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<p>As you can see, GameLoop is a powerful and versatile emulator that can provide you with the best PUBG Mobile experience on your PC. However, if you want to try other emulators, there are some alternatives that you can consider.</p>
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| 112 |
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<h2>Other PUBG Mobile Emulators to Consider</h2>
|
| 113 |
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<p>GameLoop is not the only emulator that can run PUBG Mobile on your PC. There are other emulators that have their own strengths and weaknesses. Here are some of them:</p>
|
| 114 |
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<h3>BlueStacks Emulator</h3>
|
| 115 |
-
<p><strong>BlueStacks</strong> is one of the oldest and most popular emulators for Android games and apps. It has a large user base and a wide range of games and apps that it supports. It also has many features and options that make it user-friendly and customizable.</p>
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| 116 |
-
<p>However, BlueStacks is not very optimized for PUBG Mobile. It can run the game, but not as smoothly or as fast as GameLoop. It also has higher CPU and memory usage and lower graphics quality. Moreover, BlueStacks is not officially supported by Tencent Games or PUBG Corporation, which means that it might have compatibility or security issues in the future.</p>
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| 117 |
-
<h3>Tencent Gaming Buddy (AKA Gameloop) Emulator</h3>
|
| 118 |
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<p><strong>Tencent Gaming Buddy</strong> is the predecessor of GameLoop. It is the original emulator for PUBG Mobile developed by Tencent Games. It is still available for download and use, but it is no longer updated or maintained by Tencent Games.</p>
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| 119 |
-
<p>Tencent Gaming Buddy is similar to GameLoop in many aspects, such as compatibility, performance, stability, and features. However, it is not as advanced or as refined as GameLoop. It also does not support the latest version of PUBG Mobile or its new modes and features. Therefore, it is recommended to use GameLoop instead of Tencent Gaming Buddy for PUBG Mobile.</p>
|
| 120 |
-
<h3>Comparison Table of PUBG Mobile Emulators</h3>
|
| 121 |
-
<p>To help you compare and choose the best PUBG Mobile emulator for your PC, here is a table that summarizes and compares the main features and performance of GameLoop, BlueStacks, and Tencent Gaming Buddy:</p>
|
| 122 |
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<table>
|
| 123 |
-
<tr>
|
| 124 |
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<th>Emulator</th>
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| 125 |
-
<th>Compatibility</th>
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| 126 |
-
<th>Performance</th>
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| 127 |
-
<th>Stability</th>
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| 128 |
-
<th>Features</th>
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| 129 |
-
<th>User-friendliness</th>
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| 130 |
-
<th>Reputation</th>
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| 131 |
-
</tr>
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| 132 |
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<tr>
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| 133 |
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<td>GameLoop</td>
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| 134 |
-
<td>High</td>
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| 135 |
-
<td>High</td>
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| 136 |
-
<td>High</td>
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| 137 |
-
<td>High</td>
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<td>High</td>
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-
<td>High</td>
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</tr>
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| 141 |
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<tr>
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| 142 |
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<td>BlueStacks</td>
|
| 143 |
-
<td>Medium</td>
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| 144 |
-
<td>Medium</td>
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| 145 |
-
<td>Medium</td>
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| 146 |
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<td>Medium</td>
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<td>Medium</td>
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<td>High</td>
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</tr>
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<tr>
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| 151 |
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<td>Tencent Gaming Buddy (AKA Gameloop)</td>
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| 152 |
-
<td>Medium</td>
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| 153 |
-
<td>Medium</td>
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<td>Medium</td>
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<td>Medium</td>
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<td>Medium</td >td>Medium</td>
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</tr>
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</table>
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| 159 |
-
<h2>Conclusion</h2>
|
| 160 |
-
<p>PUBG Mobile is one of the most popular and exciting mobile games in the world. It offers a thrilling and immersive battle royale experience that you can enjoy with your friends or solo. However, playing on a mobile device might not be the best way to experience PUBG Mobile. You might face issues such as low graphics quality, small screen size, poor controls, battery drain, overheating, etc.</p>
|
| 161 |
-
<p>That is why using a PUBG Mobile emulator can be a great solution. A PUBG Mobile emulator allows you to play PUBG Mobile on your PC, which can improve your gameplay and convenience. You can enjoy better graphics and performance, bigger screen, better controls, more features and options, and more.</p>
|
| 162 |
-
<p>However, not all PUBG Mobile emulators are the same. Some are better than others in terms of compatibility, performance, stability, features, user-friendliness, and reputation. Therefore, you need to choose the best PUBG Mobile emulator for your PC.</p>
|
| 163 |
-
<p>In this article, we have reviewed and compared some of the best PUBG Mobile emulators available in the market. We have also provided a step-by-step guide on how to download and install GameLoop emulator, which is the official and best emulator for PUBG Mobile. We have also given some tips and tricks on how to play PUBG Mobile on GameLoop emulator and optimize your gameplay.</p>
|
| 164 |
-
<p>We hope that this article has helped you learn how to download PUBG Mobile emulator for PC and enjoy PUBG Mobile on a bigger and better platform. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!</p>
|
| 165 |
-
<h2>FAQs</h2>
|
| 166 |
-
<p>Here are some frequently asked questions and answers about PUBG Mobile emulator for PC:</p>
|
| 167 |
-
<ul>
|
| 168 |
-
<li><strong>Q: Is using a PUBG Mobile emulator illegal or cheating?</strong></li>
|
| 169 |
-
<li><strong>A: No, using a PUBG Mobile emulator is not illegal or cheating. It is allowed and supported by Tencent Games and PUBG Corporation, as long as you do not use any hacks or unfair advantages that might ruin the game for other players.</strong></li>
|
| 170 |
-
<li><strong>Q: Can I play with my friends who are using mobile devices?</strong></li>
|
| 171 |
-
<li><strong>A: Yes, you can play with your friends who are using mobile devices. However, you will only be matched with other players who are using emulators or cross-platform mode. This is to ensure fair and balanced gameplay for everyone.</strong></li>
|
| 172 |
-
<li><strong>Q: Which PUBG Mobile emulator is the best for low-end or old PCs?</strong></li>
|
| 173 |
-
<li><strong>A: GameLoop is the best PUBG Mobile emulator for low-end or old PCs. It can run PUBG Mobile smoothly and without lag on most PCs. It also has low CPU and memory usage and supports high graphics settings.</strong></li>
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| 174 |
-
<li><strong>Q: How can I update PUBG Mobile on my emulator?</strong></li >li>
|
| 175 |
-
<strong>A: You can update PUBG Mobile on your emulator by following these steps:</strong>
|
| 176 |
-
<ol>
|
| 177 |
-
<li>Launch GameLoop from your desktop or start menu.</li>
|
| 178 |
-
<li>On the Game Center tab, find PUBG Mobile and click on the "Update" button if available.</li>
|
| 179 |
-
<li>Wait for the update to download and install.</li>
|
| 180 |
-
<li>Click on the "Play" button to launch PUBG Mobile on GameLoop.</li>
|
| 181 |
-
</ol>
|
| 182 |
-
</li>
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| 183 |
-
<li><strong>Q: How can I fix PUBG Mobile emulator errors or issues?</strong></li>
|
| 184 |
-
<li><strong>A: If you encounter any errors or issues while using PUBG Mobile emulator, you can try these solutions:</strong>
|
| 185 |
-
<ul>
|
| 186 |
-
<li>Restart your PC and your emulator.</li>
|
| 187 |
-
<li>Update your PC drivers and software.</li>
|
| 188 |
-
<li>Update your emulator and PUBG Mobile to the latest version.</li>
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| 189 |
-
<li>Clear your emulator cache and data.</li>
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| 190 |
-
<li>Change your emulator settings and compatibility mode.</li>
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| 191 |
-
<li>Contact the emulator support team or visit their official website or forum for help.</li>
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| 192 |
-
</ul>
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| 193 |
-
</li>
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| 194 |
-
</ul>
|
| 195 |
-
<h2>References</h2>
|
| 196 |
-
<p>Here are some sources and links that we used in this article:</p>
|
| 197 |
-
<ul>
|
| 198 |
-
<li><a href="">https://gameloop.fun/</a>: The official website of GameLoop emulator.</li>
|
| 199 |
-
<li><a href="">https://www.bluestacks.com/</a>: The official website of BlueStacks emulator.</li>
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| 200 |
-
<li><a href="">https://www.pubgmobile.com/en-US/home.shtml</a>: The official website of PUBG Mobile.</li>
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| 201 |
-
<li><a href="">https://www.youtube.com/watch?v=3w0yqAdJ1iY</a>: A video tutorial on how to download and install GameLoop emulator.</li>
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| 202 |
-
<li><a href="">https://www.androidauthority.com/best-pubg-mobile-emulators-1017527/</a>: An article that reviews and compares different PUBG Mobile emulators.</li>
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| 203 |
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</ul></p> 197e85843d<br />
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|
spaces/AI-Dashboards/ScrabbleSolverWordThesaurus/backupapp.py
DELETED
|
@@ -1,35 +0,0 @@
|
|
| 1 |
-
import streamlit as st
|
| 2 |
-
import itertools
|
| 3 |
-
from nltk.corpus import wordnet
|
| 4 |
-
|
| 5 |
-
def get_synonyms(word):
|
| 6 |
-
synonyms = set()
|
| 7 |
-
for syn in wordnet.synsets(word):
|
| 8 |
-
for lemma in syn.lemmas():
|
| 9 |
-
synonyms.add(lemma.name())
|
| 10 |
-
return list(synonyms)
|
| 11 |
-
|
| 12 |
-
def generate_words(letters, length=None):
|
| 13 |
-
permutations = set()
|
| 14 |
-
for i in range(1, len(letters) + 1):
|
| 15 |
-
for p in itertools.permutations(letters, i):
|
| 16 |
-
word = "".join(p)
|
| 17 |
-
if length is None or len(word) == length:
|
| 18 |
-
permutations.add(word)
|
| 19 |
-
return permutations
|
| 20 |
-
|
| 21 |
-
st.title("Scrabble Helper")
|
| 22 |
-
|
| 23 |
-
letters = st.text_input("Enter the letters you have:")
|
| 24 |
-
word_length = st.number_input("Enter the word length (optional):", min_value=0, value=0, step=1)
|
| 25 |
-
|
| 26 |
-
if letters:
|
| 27 |
-
st.header("Generated Words")
|
| 28 |
-
words = generate_words(letters, length=word_length if word_length > 0 else None)
|
| 29 |
-
st.write(words)
|
| 30 |
-
|
| 31 |
-
st.header("Thesaurus Lookup")
|
| 32 |
-
selected_word = st.selectbox("Select a word to look up synonyms:", [""] + sorted(words))
|
| 33 |
-
if selected_word:
|
| 34 |
-
synonyms = get_synonyms(selected_word)
|
| 35 |
-
st.write(synonyms)
|
|
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|
spaces/AP123/ai-avatars/train_dreambooth.py
DELETED
|
@@ -1,881 +0,0 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
import itertools
|
| 3 |
-
import math
|
| 4 |
-
import os
|
| 5 |
-
from pathlib import Path
|
| 6 |
-
from typing import Optional
|
| 7 |
-
import subprocess
|
| 8 |
-
import sys
|
| 9 |
-
import gc
|
| 10 |
-
import random
|
| 11 |
-
|
| 12 |
-
import torch
|
| 13 |
-
import torch.nn.functional as F
|
| 14 |
-
import torch.utils.checkpoint
|
| 15 |
-
from torch.utils.data import Dataset
|
| 16 |
-
|
| 17 |
-
from accelerate import Accelerator
|
| 18 |
-
from accelerate.logging import get_logger
|
| 19 |
-
from accelerate.utils import set_seed
|
| 20 |
-
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
|
| 21 |
-
from diffusers.optimization import get_scheduler
|
| 22 |
-
from huggingface_hub import HfFolder, Repository, whoami
|
| 23 |
-
from PIL import Image
|
| 24 |
-
from torchvision import transforms
|
| 25 |
-
from tqdm.auto import tqdm
|
| 26 |
-
from transformers import CLIPTextModel, CLIPTokenizer
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
logger = get_logger(__name__)
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
def parse_args():
|
| 33 |
-
parser = argparse.ArgumentParser(description="Simple example of a training script.")
|
| 34 |
-
parser.add_argument(
|
| 35 |
-
"--pretrained_model_name_or_path",
|
| 36 |
-
type=str,
|
| 37 |
-
default=None,
|
| 38 |
-
#required=True,
|
| 39 |
-
help="Path to pretrained model or model identifier from huggingface.co/models.",
|
| 40 |
-
)
|
| 41 |
-
parser.add_argument(
|
| 42 |
-
"--tokenizer_name",
|
| 43 |
-
type=str,
|
| 44 |
-
default=None,
|
| 45 |
-
help="Pretrained tokenizer name or path if not the same as model_name",
|
| 46 |
-
)
|
| 47 |
-
parser.add_argument(
|
| 48 |
-
"--instance_data_dir",
|
| 49 |
-
type=str,
|
| 50 |
-
default=None,
|
| 51 |
-
#required=True,
|
| 52 |
-
help="A folder containing the training data of instance images.",
|
| 53 |
-
)
|
| 54 |
-
parser.add_argument(
|
| 55 |
-
"--class_data_dir",
|
| 56 |
-
type=str,
|
| 57 |
-
default=None,
|
| 58 |
-
#required=False,
|
| 59 |
-
help="A folder containing the training data of class images.",
|
| 60 |
-
)
|
| 61 |
-
parser.add_argument(
|
| 62 |
-
"--instance_prompt",
|
| 63 |
-
type=str,
|
| 64 |
-
default=None,
|
| 65 |
-
help="The prompt with identifier specifying the instance",
|
| 66 |
-
)
|
| 67 |
-
parser.add_argument(
|
| 68 |
-
"--class_prompt",
|
| 69 |
-
type=str,
|
| 70 |
-
default="",
|
| 71 |
-
help="The prompt to specify images in the same class as provided instance images.",
|
| 72 |
-
)
|
| 73 |
-
parser.add_argument(
|
| 74 |
-
"--with_prior_preservation",
|
| 75 |
-
default=False,
|
| 76 |
-
action="store_true",
|
| 77 |
-
help="Flag to add prior preservation loss.",
|
| 78 |
-
)
|
| 79 |
-
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
|
| 80 |
-
parser.add_argument(
|
| 81 |
-
"--num_class_images",
|
| 82 |
-
type=int,
|
| 83 |
-
default=100,
|
| 84 |
-
help=(
|
| 85 |
-
"Minimal class images for prior preservation loss. If not have enough images, additional images will be"
|
| 86 |
-
" sampled with class_prompt."
|
| 87 |
-
),
|
| 88 |
-
)
|
| 89 |
-
parser.add_argument(
|
| 90 |
-
"--output_dir",
|
| 91 |
-
type=str,
|
| 92 |
-
default="",
|
| 93 |
-
help="The output directory where the model predictions and checkpoints will be written.",
|
| 94 |
-
)
|
| 95 |
-
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
|
| 96 |
-
parser.add_argument(
|
| 97 |
-
"--resolution",
|
| 98 |
-
type=int,
|
| 99 |
-
default=512,
|
| 100 |
-
help=(
|
| 101 |
-
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
|
| 102 |
-
" resolution"
|
| 103 |
-
),
|
| 104 |
-
)
|
| 105 |
-
parser.add_argument(
|
| 106 |
-
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
|
| 107 |
-
)
|
| 108 |
-
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
|
| 109 |
-
parser.add_argument(
|
| 110 |
-
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
|
| 111 |
-
)
|
| 112 |
-
parser.add_argument(
|
| 113 |
-
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
|
| 114 |
-
)
|
| 115 |
-
parser.add_argument("--num_train_epochs", type=int, default=1)
|
| 116 |
-
parser.add_argument(
|
| 117 |
-
"--max_train_steps",
|
| 118 |
-
type=int,
|
| 119 |
-
default=None,
|
| 120 |
-
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
|
| 121 |
-
)
|
| 122 |
-
parser.add_argument(
|
| 123 |
-
"--gradient_accumulation_steps",
|
| 124 |
-
type=int,
|
| 125 |
-
default=1,
|
| 126 |
-
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
| 127 |
-
)
|
| 128 |
-
parser.add_argument(
|
| 129 |
-
"--gradient_checkpointing",
|
| 130 |
-
action="store_true",
|
| 131 |
-
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
|
| 132 |
-
)
|
| 133 |
-
parser.add_argument(
|
| 134 |
-
"--learning_rate",
|
| 135 |
-
type=float,
|
| 136 |
-
default=5e-6,
|
| 137 |
-
help="Initial learning rate (after the potential warmup period) to use.",
|
| 138 |
-
)
|
| 139 |
-
parser.add_argument(
|
| 140 |
-
"--scale_lr",
|
| 141 |
-
action="store_true",
|
| 142 |
-
default=False,
|
| 143 |
-
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
|
| 144 |
-
)
|
| 145 |
-
parser.add_argument(
|
| 146 |
-
"--lr_scheduler",
|
| 147 |
-
type=str,
|
| 148 |
-
default="constant",
|
| 149 |
-
help=(
|
| 150 |
-
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
|
| 151 |
-
' "constant", "constant_with_warmup"]'
|
| 152 |
-
),
|
| 153 |
-
)
|
| 154 |
-
parser.add_argument(
|
| 155 |
-
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
|
| 156 |
-
)
|
| 157 |
-
parser.add_argument(
|
| 158 |
-
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
|
| 159 |
-
)
|
| 160 |
-
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
|
| 161 |
-
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
|
| 162 |
-
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
|
| 163 |
-
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
|
| 164 |
-
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
| 165 |
-
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
|
| 166 |
-
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
|
| 167 |
-
parser.add_argument(
|
| 168 |
-
"--hub_model_id",
|
| 169 |
-
type=str,
|
| 170 |
-
default=None,
|
| 171 |
-
help="The name of the repository to keep in sync with the local `output_dir`.",
|
| 172 |
-
)
|
| 173 |
-
parser.add_argument(
|
| 174 |
-
"--logging_dir",
|
| 175 |
-
type=str,
|
| 176 |
-
default="logs",
|
| 177 |
-
help=(
|
| 178 |
-
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
|
| 179 |
-
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
|
| 180 |
-
),
|
| 181 |
-
)
|
| 182 |
-
parser.add_argument(
|
| 183 |
-
"--mixed_precision",
|
| 184 |
-
type=str,
|
| 185 |
-
default="no",
|
| 186 |
-
choices=["no", "fp16", "bf16"],
|
| 187 |
-
help=(
|
| 188 |
-
"Whether to use mixed precision. Choose"
|
| 189 |
-
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
|
| 190 |
-
"and an Nvidia Ampere GPU."
|
| 191 |
-
),
|
| 192 |
-
)
|
| 193 |
-
|
| 194 |
-
parser.add_argument(
|
| 195 |
-
"--save_n_steps",
|
| 196 |
-
type=int,
|
| 197 |
-
default=1,
|
| 198 |
-
help=("Save the model every n global_steps"),
|
| 199 |
-
)
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
parser.add_argument(
|
| 203 |
-
"--save_starting_step",
|
| 204 |
-
type=int,
|
| 205 |
-
default=1,
|
| 206 |
-
help=("The step from which it starts saving intermediary checkpoints"),
|
| 207 |
-
)
|
| 208 |
-
|
| 209 |
-
parser.add_argument(
|
| 210 |
-
"--stop_text_encoder_training",
|
| 211 |
-
type=int,
|
| 212 |
-
default=1000000,
|
| 213 |
-
help=("The step at which the text_encoder is no longer trained"),
|
| 214 |
-
)
|
| 215 |
-
|
| 216 |
-
|
| 217 |
-
parser.add_argument(
|
| 218 |
-
"--image_captions_filename",
|
| 219 |
-
action="store_true",
|
| 220 |
-
help="Get captions from filename",
|
| 221 |
-
)
|
| 222 |
-
|
| 223 |
-
|
| 224 |
-
parser.add_argument(
|
| 225 |
-
"--dump_only_text_encoder",
|
| 226 |
-
action="store_true",
|
| 227 |
-
default=False,
|
| 228 |
-
help="Dump only text encoder",
|
| 229 |
-
)
|
| 230 |
-
|
| 231 |
-
parser.add_argument(
|
| 232 |
-
"--train_only_unet",
|
| 233 |
-
action="store_true",
|
| 234 |
-
default=False,
|
| 235 |
-
help="Train only the unet",
|
| 236 |
-
)
|
| 237 |
-
|
| 238 |
-
parser.add_argument(
|
| 239 |
-
"--cache_latents",
|
| 240 |
-
action="store_true",
|
| 241 |
-
default=False,
|
| 242 |
-
help="Train only the unet",
|
| 243 |
-
)
|
| 244 |
-
|
| 245 |
-
parser.add_argument(
|
| 246 |
-
"--Session_dir",
|
| 247 |
-
type=str,
|
| 248 |
-
default="",
|
| 249 |
-
help="Current session directory",
|
| 250 |
-
)
|
| 251 |
-
|
| 252 |
-
|
| 253 |
-
|
| 254 |
-
|
| 255 |
-
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
| 256 |
-
|
| 257 |
-
args = parser.parse_args()
|
| 258 |
-
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
| 259 |
-
if env_local_rank != -1 and env_local_rank != args.local_rank:
|
| 260 |
-
args.local_rank = env_local_rank
|
| 261 |
-
|
| 262 |
-
#if args.instance_data_dir is None:
|
| 263 |
-
# raise ValueError("You must specify a train data directory.")
|
| 264 |
-
|
| 265 |
-
#if args.with_prior_preservation:
|
| 266 |
-
# if args.class_data_dir is None:
|
| 267 |
-
# raise ValueError("You must specify a data directory for class images.")
|
| 268 |
-
# if args.class_prompt is None:
|
| 269 |
-
# raise ValueError("You must specify prompt for class images.")
|
| 270 |
-
|
| 271 |
-
return args
|
| 272 |
-
|
| 273 |
-
|
| 274 |
-
class DreamBoothDataset(Dataset):
|
| 275 |
-
"""
|
| 276 |
-
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
|
| 277 |
-
It pre-processes the images and the tokenizes prompts.
|
| 278 |
-
"""
|
| 279 |
-
|
| 280 |
-
def __init__(
|
| 281 |
-
self,
|
| 282 |
-
instance_data_root,
|
| 283 |
-
instance_prompt,
|
| 284 |
-
tokenizer,
|
| 285 |
-
args,
|
| 286 |
-
class_data_root=None,
|
| 287 |
-
class_prompt=None,
|
| 288 |
-
size=512,
|
| 289 |
-
center_crop=False,
|
| 290 |
-
):
|
| 291 |
-
self.size = size
|
| 292 |
-
self.center_crop = center_crop
|
| 293 |
-
self.tokenizer = tokenizer
|
| 294 |
-
self.image_captions_filename = None
|
| 295 |
-
|
| 296 |
-
self.instance_data_root = Path(instance_data_root)
|
| 297 |
-
if not self.instance_data_root.exists():
|
| 298 |
-
raise ValueError("Instance images root doesn't exists.")
|
| 299 |
-
|
| 300 |
-
self.instance_images_path = list(Path(instance_data_root).iterdir())
|
| 301 |
-
self.num_instance_images = len(self.instance_images_path)
|
| 302 |
-
self.instance_prompt = instance_prompt
|
| 303 |
-
self._length = self.num_instance_images
|
| 304 |
-
|
| 305 |
-
if args.image_captions_filename:
|
| 306 |
-
self.image_captions_filename = True
|
| 307 |
-
|
| 308 |
-
if class_data_root is not None:
|
| 309 |
-
self.class_data_root = Path(class_data_root)
|
| 310 |
-
self.class_data_root.mkdir(parents=True, exist_ok=True)
|
| 311 |
-
self.class_images_path = list(self.class_data_root.iterdir())
|
| 312 |
-
random.shuffle(self.class_images_path)
|
| 313 |
-
self.num_class_images = len(self.class_images_path)
|
| 314 |
-
self._length = max(self.num_class_images, self.num_instance_images)
|
| 315 |
-
self.class_prompt = class_prompt
|
| 316 |
-
else:
|
| 317 |
-
self.class_data_root = None
|
| 318 |
-
|
| 319 |
-
self.image_transforms = transforms.Compose(
|
| 320 |
-
[
|
| 321 |
-
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
|
| 322 |
-
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
|
| 323 |
-
transforms.ToTensor(),
|
| 324 |
-
transforms.Normalize([0.5], [0.5]),
|
| 325 |
-
]
|
| 326 |
-
)
|
| 327 |
-
|
| 328 |
-
def __len__(self):
|
| 329 |
-
return self._length
|
| 330 |
-
|
| 331 |
-
def __getitem__(self, index):
|
| 332 |
-
example = {}
|
| 333 |
-
path = self.instance_images_path[index % self.num_instance_images]
|
| 334 |
-
instance_image = Image.open(path)
|
| 335 |
-
if not instance_image.mode == "RGB":
|
| 336 |
-
instance_image = instance_image.convert("RGB")
|
| 337 |
-
|
| 338 |
-
instance_prompt = self.instance_prompt
|
| 339 |
-
|
| 340 |
-
if self.image_captions_filename:
|
| 341 |
-
filename = Path(path).stem
|
| 342 |
-
pt=''.join([i for i in filename if not i.isdigit()])
|
| 343 |
-
pt=pt.replace("_"," ")
|
| 344 |
-
pt=pt.replace("(","")
|
| 345 |
-
pt=pt.replace(")","")
|
| 346 |
-
pt=pt.replace("-","")
|
| 347 |
-
instance_prompt = pt
|
| 348 |
-
sys.stdout.write(" [0;32m" +instance_prompt+" [0m")
|
| 349 |
-
sys.stdout.flush()
|
| 350 |
-
|
| 351 |
-
|
| 352 |
-
example["instance_images"] = self.image_transforms(instance_image)
|
| 353 |
-
example["instance_prompt_ids"] = self.tokenizer(
|
| 354 |
-
instance_prompt,
|
| 355 |
-
padding="do_not_pad",
|
| 356 |
-
truncation=True,
|
| 357 |
-
max_length=self.tokenizer.model_max_length,
|
| 358 |
-
).input_ids
|
| 359 |
-
|
| 360 |
-
if self.class_data_root:
|
| 361 |
-
class_image = Image.open(self.class_images_path[index % self.num_class_images])
|
| 362 |
-
if not class_image.mode == "RGB":
|
| 363 |
-
class_image = class_image.convert("RGB")
|
| 364 |
-
example["class_images"] = self.image_transforms(class_image)
|
| 365 |
-
example["class_prompt_ids"] = self.tokenizer(
|
| 366 |
-
self.class_prompt,
|
| 367 |
-
padding="do_not_pad",
|
| 368 |
-
truncation=True,
|
| 369 |
-
max_length=self.tokenizer.model_max_length,
|
| 370 |
-
).input_ids
|
| 371 |
-
|
| 372 |
-
return example
|
| 373 |
-
|
| 374 |
-
|
| 375 |
-
|
| 376 |
-
class PromptDataset(Dataset):
|
| 377 |
-
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
|
| 378 |
-
|
| 379 |
-
def __init__(self, prompt, num_samples):
|
| 380 |
-
self.prompt = prompt
|
| 381 |
-
self.num_samples = num_samples
|
| 382 |
-
|
| 383 |
-
def __len__(self):
|
| 384 |
-
return self.num_samples
|
| 385 |
-
|
| 386 |
-
def __getitem__(self, index):
|
| 387 |
-
example = {}
|
| 388 |
-
example["prompt"] = self.prompt
|
| 389 |
-
example["index"] = index
|
| 390 |
-
return example
|
| 391 |
-
|
| 392 |
-
class LatentsDataset(Dataset):
|
| 393 |
-
def __init__(self, latents_cache, text_encoder_cache):
|
| 394 |
-
self.latents_cache = latents_cache
|
| 395 |
-
self.text_encoder_cache = text_encoder_cache
|
| 396 |
-
|
| 397 |
-
def __len__(self):
|
| 398 |
-
return len(self.latents_cache)
|
| 399 |
-
|
| 400 |
-
def __getitem__(self, index):
|
| 401 |
-
return self.latents_cache[index], self.text_encoder_cache[index]
|
| 402 |
-
|
| 403 |
-
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
|
| 404 |
-
if token is None:
|
| 405 |
-
token = HfFolder.get_token()
|
| 406 |
-
if organization is None:
|
| 407 |
-
username = whoami(token)["name"]
|
| 408 |
-
return f"{username}/{model_id}"
|
| 409 |
-
else:
|
| 410 |
-
return f"{organization}/{model_id}"
|
| 411 |
-
|
| 412 |
-
def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict:
|
| 413 |
-
"""
|
| 414 |
-
Starts from base starting dict and then adds the remaining key values from updater replacing the values from
|
| 415 |
-
the first starting/base dict with the second updater dict.
|
| 416 |
-
|
| 417 |
-
For later: how does d = {**d1, **d2} replace collision?
|
| 418 |
-
|
| 419 |
-
:param starting_dict:
|
| 420 |
-
:param updater_dict:
|
| 421 |
-
:return:
|
| 422 |
-
"""
|
| 423 |
-
new_dict: dict = starting_dict.copy() # start with keys and values of starting_dict
|
| 424 |
-
new_dict.update(updater_dict) # modifies starting_dict with keys and values of updater_dict
|
| 425 |
-
return new_dict
|
| 426 |
-
|
| 427 |
-
def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace:
|
| 428 |
-
"""
|
| 429 |
-
|
| 430 |
-
ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x
|
| 431 |
-
:param args1:
|
| 432 |
-
:param args2:
|
| 433 |
-
:return:
|
| 434 |
-
"""
|
| 435 |
-
# - the merged args
|
| 436 |
-
# The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}.
|
| 437 |
-
merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2))
|
| 438 |
-
args = argparse.Namespace(**merged_key_values_for_namespace)
|
| 439 |
-
return args
|
| 440 |
-
|
| 441 |
-
def run_training(args_imported):
|
| 442 |
-
args_default = parse_args()
|
| 443 |
-
args = merge_args(args_default, args_imported)
|
| 444 |
-
print(args)
|
| 445 |
-
logging_dir = Path(args.output_dir, args.logging_dir)
|
| 446 |
-
i=args.save_starting_step
|
| 447 |
-
accelerator = Accelerator(
|
| 448 |
-
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
| 449 |
-
mixed_precision=args.mixed_precision,
|
| 450 |
-
log_with="tensorboard",
|
| 451 |
-
logging_dir=logging_dir,
|
| 452 |
-
)
|
| 453 |
-
|
| 454 |
-
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
|
| 455 |
-
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
|
| 456 |
-
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
|
| 457 |
-
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
|
| 458 |
-
raise ValueError(
|
| 459 |
-
"Gradient accumulation is not supported when training the text encoder in distributed training. "
|
| 460 |
-
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
|
| 461 |
-
)
|
| 462 |
-
|
| 463 |
-
if args.seed is not None:
|
| 464 |
-
set_seed(args.seed)
|
| 465 |
-
|
| 466 |
-
if args.with_prior_preservation:
|
| 467 |
-
class_images_dir = Path(args.class_data_dir)
|
| 468 |
-
if not class_images_dir.exists():
|
| 469 |
-
class_images_dir.mkdir(parents=True)
|
| 470 |
-
cur_class_images = len(list(class_images_dir.iterdir()))
|
| 471 |
-
|
| 472 |
-
if cur_class_images < args.num_class_images:
|
| 473 |
-
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
|
| 474 |
-
pipeline = StableDiffusionPipeline.from_pretrained(
|
| 475 |
-
args.pretrained_model_name_or_path, torch_dtype=torch_dtype
|
| 476 |
-
)
|
| 477 |
-
pipeline.set_progress_bar_config(disable=True)
|
| 478 |
-
|
| 479 |
-
num_new_images = args.num_class_images - cur_class_images
|
| 480 |
-
logger.info(f"Number of class images to sample: {num_new_images}.")
|
| 481 |
-
|
| 482 |
-
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
|
| 483 |
-
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
|
| 484 |
-
|
| 485 |
-
sample_dataloader = accelerator.prepare(sample_dataloader)
|
| 486 |
-
pipeline.to(accelerator.device)
|
| 487 |
-
|
| 488 |
-
for example in tqdm(
|
| 489 |
-
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
|
| 490 |
-
):
|
| 491 |
-
with torch.autocast("cuda"):
|
| 492 |
-
images = pipeline(example["prompt"]).images
|
| 493 |
-
|
| 494 |
-
for i, image in enumerate(images):
|
| 495 |
-
image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")
|
| 496 |
-
|
| 497 |
-
del pipeline
|
| 498 |
-
if torch.cuda.is_available():
|
| 499 |
-
torch.cuda.empty_cache()
|
| 500 |
-
|
| 501 |
-
# Handle the repository creation
|
| 502 |
-
if accelerator.is_main_process:
|
| 503 |
-
if args.push_to_hub:
|
| 504 |
-
if args.hub_model_id is None:
|
| 505 |
-
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
|
| 506 |
-
else:
|
| 507 |
-
repo_name = args.hub_model_id
|
| 508 |
-
repo = Repository(args.output_dir, clone_from=repo_name)
|
| 509 |
-
|
| 510 |
-
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
|
| 511 |
-
if "step_*" not in gitignore:
|
| 512 |
-
gitignore.write("step_*\n")
|
| 513 |
-
if "epoch_*" not in gitignore:
|
| 514 |
-
gitignore.write("epoch_*\n")
|
| 515 |
-
elif args.output_dir is not None:
|
| 516 |
-
os.makedirs(args.output_dir, exist_ok=True)
|
| 517 |
-
|
| 518 |
-
# Load the tokenizer
|
| 519 |
-
if args.tokenizer_name:
|
| 520 |
-
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
|
| 521 |
-
elif args.pretrained_model_name_or_path:
|
| 522 |
-
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
|
| 523 |
-
|
| 524 |
-
# Load models and create wrapper for stable diffusion
|
| 525 |
-
if args.train_only_unet:
|
| 526 |
-
if os.path.exists(str(args.output_dir+"/text_encoder_trained")):
|
| 527 |
-
text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
|
| 528 |
-
elif os.path.exists(str(args.output_dir+"/text_encoder")):
|
| 529 |
-
text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder")
|
| 530 |
-
else:
|
| 531 |
-
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
|
| 532 |
-
else:
|
| 533 |
-
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
|
| 534 |
-
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
|
| 535 |
-
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
|
| 536 |
-
|
| 537 |
-
vae.requires_grad_(False)
|
| 538 |
-
if not args.train_text_encoder:
|
| 539 |
-
text_encoder.requires_grad_(False)
|
| 540 |
-
|
| 541 |
-
if args.gradient_checkpointing:
|
| 542 |
-
unet.enable_gradient_checkpointing()
|
| 543 |
-
if args.train_text_encoder:
|
| 544 |
-
text_encoder.gradient_checkpointing_enable()
|
| 545 |
-
|
| 546 |
-
if args.scale_lr:
|
| 547 |
-
args.learning_rate = (
|
| 548 |
-
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
| 549 |
-
)
|
| 550 |
-
|
| 551 |
-
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
|
| 552 |
-
if args.use_8bit_adam:
|
| 553 |
-
try:
|
| 554 |
-
import bitsandbytes as bnb
|
| 555 |
-
except ImportError:
|
| 556 |
-
raise ImportError(
|
| 557 |
-
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
|
| 558 |
-
)
|
| 559 |
-
|
| 560 |
-
optimizer_class = bnb.optim.AdamW8bit
|
| 561 |
-
else:
|
| 562 |
-
optimizer_class = torch.optim.AdamW
|
| 563 |
-
|
| 564 |
-
params_to_optimize = (
|
| 565 |
-
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
|
| 566 |
-
)
|
| 567 |
-
optimizer = optimizer_class(
|
| 568 |
-
params_to_optimize,
|
| 569 |
-
lr=args.learning_rate,
|
| 570 |
-
betas=(args.adam_beta1, args.adam_beta2),
|
| 571 |
-
weight_decay=args.adam_weight_decay,
|
| 572 |
-
eps=args.adam_epsilon,
|
| 573 |
-
)
|
| 574 |
-
|
| 575 |
-
noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
|
| 576 |
-
|
| 577 |
-
train_dataset = DreamBoothDataset(
|
| 578 |
-
instance_data_root=args.instance_data_dir,
|
| 579 |
-
instance_prompt=args.instance_prompt,
|
| 580 |
-
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
|
| 581 |
-
class_prompt=args.class_prompt,
|
| 582 |
-
tokenizer=tokenizer,
|
| 583 |
-
size=args.resolution,
|
| 584 |
-
center_crop=args.center_crop,
|
| 585 |
-
args=args,
|
| 586 |
-
)
|
| 587 |
-
|
| 588 |
-
def collate_fn(examples):
|
| 589 |
-
input_ids = [example["instance_prompt_ids"] for example in examples]
|
| 590 |
-
pixel_values = [example["instance_images"] for example in examples]
|
| 591 |
-
|
| 592 |
-
# Concat class and instance examples for prior preservation.
|
| 593 |
-
# We do this to avoid doing two forward passes.
|
| 594 |
-
if args.with_prior_preservation:
|
| 595 |
-
input_ids += [example["class_prompt_ids"] for example in examples]
|
| 596 |
-
pixel_values += [example["class_images"] for example in examples]
|
| 597 |
-
|
| 598 |
-
pixel_values = torch.stack(pixel_values)
|
| 599 |
-
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
| 600 |
-
|
| 601 |
-
input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
|
| 602 |
-
|
| 603 |
-
batch = {
|
| 604 |
-
"input_ids": input_ids,
|
| 605 |
-
"pixel_values": pixel_values,
|
| 606 |
-
}
|
| 607 |
-
return batch
|
| 608 |
-
|
| 609 |
-
train_dataloader = torch.utils.data.DataLoader(
|
| 610 |
-
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
|
| 611 |
-
)
|
| 612 |
-
|
| 613 |
-
# Scheduler and math around the number of training steps.
|
| 614 |
-
overrode_max_train_steps = False
|
| 615 |
-
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
| 616 |
-
if args.max_train_steps is None:
|
| 617 |
-
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
| 618 |
-
overrode_max_train_steps = True
|
| 619 |
-
|
| 620 |
-
lr_scheduler = get_scheduler(
|
| 621 |
-
args.lr_scheduler,
|
| 622 |
-
optimizer=optimizer,
|
| 623 |
-
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
|
| 624 |
-
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
| 625 |
-
)
|
| 626 |
-
|
| 627 |
-
if args.train_text_encoder:
|
| 628 |
-
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
| 629 |
-
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
|
| 630 |
-
)
|
| 631 |
-
else:
|
| 632 |
-
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
| 633 |
-
unet, optimizer, train_dataloader, lr_scheduler
|
| 634 |
-
)
|
| 635 |
-
|
| 636 |
-
weight_dtype = torch.float32
|
| 637 |
-
if args.mixed_precision == "fp16":
|
| 638 |
-
weight_dtype = torch.float16
|
| 639 |
-
elif args.mixed_precision == "bf16":
|
| 640 |
-
weight_dtype = torch.bfloat16
|
| 641 |
-
|
| 642 |
-
# Move text_encode and vae to gpu.
|
| 643 |
-
# For mixed precision training we cast the text_encoder and vae weights to half-precision
|
| 644 |
-
# as these models are only used for inference, keeping weights in full precision is not required.
|
| 645 |
-
vae.to(accelerator.device, dtype=weight_dtype)
|
| 646 |
-
if not args.train_text_encoder:
|
| 647 |
-
text_encoder.to(accelerator.device, dtype=weight_dtype)
|
| 648 |
-
|
| 649 |
-
|
| 650 |
-
if args.cache_latents:
|
| 651 |
-
latents_cache = []
|
| 652 |
-
text_encoder_cache = []
|
| 653 |
-
for batch in tqdm(train_dataloader, desc="Caching latents"):
|
| 654 |
-
with torch.no_grad():
|
| 655 |
-
batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype)
|
| 656 |
-
batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True)
|
| 657 |
-
latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
|
| 658 |
-
if args.train_text_encoder:
|
| 659 |
-
text_encoder_cache.append(batch["input_ids"])
|
| 660 |
-
else:
|
| 661 |
-
text_encoder_cache.append(text_encoder(batch["input_ids"])[0])
|
| 662 |
-
train_dataset = LatentsDataset(latents_cache, text_encoder_cache)
|
| 663 |
-
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True)
|
| 664 |
-
|
| 665 |
-
del vae
|
| 666 |
-
#if not args.train_text_encoder:
|
| 667 |
-
# del text_encoder
|
| 668 |
-
if torch.cuda.is_available():
|
| 669 |
-
torch.cuda.empty_cache()
|
| 670 |
-
|
| 671 |
-
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
| 672 |
-
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
| 673 |
-
if overrode_max_train_steps:
|
| 674 |
-
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
| 675 |
-
# Afterwards we recalculate our number of training epochs
|
| 676 |
-
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
| 677 |
-
|
| 678 |
-
# We need to initialize the trackers we use, and also store our configuration.
|
| 679 |
-
# The trackers initializes automatically on the main process.
|
| 680 |
-
if accelerator.is_main_process:
|
| 681 |
-
accelerator.init_trackers("dreambooth", config=vars(args))
|
| 682 |
-
|
| 683 |
-
def bar(prg):
|
| 684 |
-
br='|'+'█' * prg + ' ' * (25-prg)+'|'
|
| 685 |
-
return br
|
| 686 |
-
|
| 687 |
-
# Train!
|
| 688 |
-
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
| 689 |
-
|
| 690 |
-
logger.info("***** Running training *****")
|
| 691 |
-
logger.info(f" Num examples = {len(train_dataset)}")
|
| 692 |
-
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
|
| 693 |
-
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
| 694 |
-
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
|
| 695 |
-
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
| 696 |
-
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
| 697 |
-
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
| 698 |
-
# Only show the progress bar once on each machine.
|
| 699 |
-
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
|
| 700 |
-
global_step = 0
|
| 701 |
-
|
| 702 |
-
for epoch in range(args.num_train_epochs):
|
| 703 |
-
unet.train()
|
| 704 |
-
if args.train_text_encoder:
|
| 705 |
-
text_encoder.train()
|
| 706 |
-
for step, batch in enumerate(train_dataloader):
|
| 707 |
-
with accelerator.accumulate(unet):
|
| 708 |
-
# Convert images to latent space
|
| 709 |
-
with torch.no_grad():
|
| 710 |
-
if args.cache_latents:
|
| 711 |
-
latents_dist = batch[0][0]
|
| 712 |
-
else:
|
| 713 |
-
latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist
|
| 714 |
-
latents = latents_dist.sample() * 0.18215
|
| 715 |
-
|
| 716 |
-
# Sample noise that we'll add to the latents
|
| 717 |
-
noise = torch.randn_like(latents)
|
| 718 |
-
bsz = latents.shape[0]
|
| 719 |
-
# Sample a random timestep for each image
|
| 720 |
-
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
|
| 721 |
-
timesteps = timesteps.long()
|
| 722 |
-
|
| 723 |
-
# Add noise to the latents according to the noise magnitude at each timestep
|
| 724 |
-
# (this is the forward diffusion process)
|
| 725 |
-
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
| 726 |
-
|
| 727 |
-
# Get the text embedding for conditioning
|
| 728 |
-
if(args.cache_latents):
|
| 729 |
-
if args.train_text_encoder:
|
| 730 |
-
encoder_hidden_states = text_encoder(batch[0][1])[0]
|
| 731 |
-
else:
|
| 732 |
-
encoder_hidden_states = batch[0][1]
|
| 733 |
-
else:
|
| 734 |
-
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
|
| 735 |
-
|
| 736 |
-
# Predict the noise residual
|
| 737 |
-
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
| 738 |
-
|
| 739 |
-
# Get the target for loss depending on the prediction type
|
| 740 |
-
if noise_scheduler.config.prediction_type == "epsilon":
|
| 741 |
-
target = noise
|
| 742 |
-
elif noise_scheduler.config.prediction_type == "v_prediction":
|
| 743 |
-
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
| 744 |
-
else:
|
| 745 |
-
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
| 746 |
-
|
| 747 |
-
if args.with_prior_preservation:
|
| 748 |
-
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
|
| 749 |
-
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
|
| 750 |
-
target, target_prior = torch.chunk(target, 2, dim=0)
|
| 751 |
-
|
| 752 |
-
# Compute instance loss
|
| 753 |
-
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
|
| 754 |
-
|
| 755 |
-
# Compute prior loss
|
| 756 |
-
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
|
| 757 |
-
|
| 758 |
-
# Add the prior loss to the instance loss.
|
| 759 |
-
loss = loss + args.prior_loss_weight * prior_loss
|
| 760 |
-
else:
|
| 761 |
-
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
| 762 |
-
|
| 763 |
-
accelerator.backward(loss)
|
| 764 |
-
if accelerator.sync_gradients:
|
| 765 |
-
params_to_clip = (
|
| 766 |
-
itertools.chain(unet.parameters(), text_encoder.parameters())
|
| 767 |
-
if args.train_text_encoder
|
| 768 |
-
else unet.parameters()
|
| 769 |
-
)
|
| 770 |
-
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
| 771 |
-
optimizer.step()
|
| 772 |
-
lr_scheduler.step()
|
| 773 |
-
optimizer.zero_grad()
|
| 774 |
-
|
| 775 |
-
# Checks if the accelerator has performed an optimization step behind the scenes
|
| 776 |
-
if accelerator.sync_gradients:
|
| 777 |
-
progress_bar.update(1)
|
| 778 |
-
global_step += 1
|
| 779 |
-
|
| 780 |
-
fll=round((global_step*100)/args.max_train_steps)
|
| 781 |
-
fll=round(fll/4)
|
| 782 |
-
pr=bar(fll)
|
| 783 |
-
|
| 784 |
-
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
| 785 |
-
progress_bar.set_postfix(**logs)
|
| 786 |
-
progress_bar.set_description_str("Progress:"+pr)
|
| 787 |
-
accelerator.log(logs, step=global_step)
|
| 788 |
-
|
| 789 |
-
if global_step >= args.max_train_steps:
|
| 790 |
-
break
|
| 791 |
-
|
| 792 |
-
if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30:
|
| 793 |
-
if accelerator.is_main_process:
|
| 794 |
-
print(" [0;32m" +" Freezing the text_encoder ..."+" [0m")
|
| 795 |
-
frz_dir=args.output_dir + "/text_encoder_frozen"
|
| 796 |
-
if os.path.exists(frz_dir):
|
| 797 |
-
subprocess.call('rm -r '+ frz_dir, shell=True)
|
| 798 |
-
os.mkdir(frz_dir)
|
| 799 |
-
pipeline = StableDiffusionPipeline.from_pretrained(
|
| 800 |
-
args.pretrained_model_name_or_path,
|
| 801 |
-
unet=accelerator.unwrap_model(unet),
|
| 802 |
-
text_encoder=accelerator.unwrap_model(text_encoder),
|
| 803 |
-
)
|
| 804 |
-
pipeline.text_encoder.save_pretrained(frz_dir)
|
| 805 |
-
|
| 806 |
-
if args.save_n_steps >= 200:
|
| 807 |
-
if global_step < args.max_train_steps and global_step+1==i:
|
| 808 |
-
ckpt_name = "_step_" + str(global_step+1)
|
| 809 |
-
save_dir = Path(args.output_dir+ckpt_name)
|
| 810 |
-
save_dir=str(save_dir)
|
| 811 |
-
save_dir=save_dir.replace(" ", "_")
|
| 812 |
-
if not os.path.exists(save_dir):
|
| 813 |
-
os.mkdir(save_dir)
|
| 814 |
-
inst=save_dir[16:]
|
| 815 |
-
inst=inst.replace(" ", "_")
|
| 816 |
-
print(" [1;32mSAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt")
|
| 817 |
-
# Create the pipeline using the trained modules and save it.
|
| 818 |
-
if accelerator.is_main_process:
|
| 819 |
-
pipeline = StableDiffusionPipeline.from_pretrained(
|
| 820 |
-
args.pretrained_model_name_or_path,
|
| 821 |
-
unet=accelerator.unwrap_model(unet),
|
| 822 |
-
text_encoder=accelerator.unwrap_model(text_encoder),
|
| 823 |
-
)
|
| 824 |
-
pipeline.save_pretrained(save_dir)
|
| 825 |
-
frz_dir=args.output_dir + "/text_encoder_frozen"
|
| 826 |
-
if args.train_text_encoder and os.path.exists(frz_dir):
|
| 827 |
-
subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True)
|
| 828 |
-
subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True)
|
| 829 |
-
chkpth=args.Session_dir+"/"+inst+".ckpt"
|
| 830 |
-
subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True)
|
| 831 |
-
subprocess.call('rm -r '+ save_dir, shell=True)
|
| 832 |
-
i=i+args.save_n_steps
|
| 833 |
-
|
| 834 |
-
accelerator.wait_for_everyone()
|
| 835 |
-
|
| 836 |
-
# Create the pipeline using using the trained modules and save it.
|
| 837 |
-
if accelerator.is_main_process:
|
| 838 |
-
if args.dump_only_text_encoder:
|
| 839 |
-
txt_dir=args.output_dir + "/text_encoder_trained"
|
| 840 |
-
if not os.path.exists(txt_dir):
|
| 841 |
-
os.mkdir(txt_dir)
|
| 842 |
-
pipeline = StableDiffusionPipeline.from_pretrained(
|
| 843 |
-
args.pretrained_model_name_or_path,
|
| 844 |
-
unet=accelerator.unwrap_model(unet),
|
| 845 |
-
text_encoder=accelerator.unwrap_model(text_encoder),
|
| 846 |
-
)
|
| 847 |
-
pipeline.text_encoder.save_pretrained(txt_dir)
|
| 848 |
-
|
| 849 |
-
elif args.train_only_unet:
|
| 850 |
-
pipeline = StableDiffusionPipeline.from_pretrained(
|
| 851 |
-
args.pretrained_model_name_or_path,
|
| 852 |
-
unet=accelerator.unwrap_model(unet),
|
| 853 |
-
text_encoder=accelerator.unwrap_model(text_encoder),
|
| 854 |
-
)
|
| 855 |
-
pipeline.save_pretrained(args.output_dir)
|
| 856 |
-
txt_dir=args.output_dir + "/text_encoder_trained"
|
| 857 |
-
subprocess.call('rm -r '+txt_dir, shell=True)
|
| 858 |
-
|
| 859 |
-
else:
|
| 860 |
-
pipeline = StableDiffusionPipeline.from_pretrained(
|
| 861 |
-
args.pretrained_model_name_or_path,
|
| 862 |
-
unet=accelerator.unwrap_model(unet),
|
| 863 |
-
text_encoder=accelerator.unwrap_model(text_encoder),
|
| 864 |
-
)
|
| 865 |
-
frz_dir=args.output_dir + "/text_encoder_frozen"
|
| 866 |
-
pipeline.save_pretrained(args.output_dir)
|
| 867 |
-
if args.train_text_encoder and os.path.exists(frz_dir):
|
| 868 |
-
subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True)
|
| 869 |
-
subprocess.call('rm -r '+ frz_dir, shell=True)
|
| 870 |
-
|
| 871 |
-
if args.push_to_hub:
|
| 872 |
-
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
|
| 873 |
-
|
| 874 |
-
accelerator.end_training()
|
| 875 |
-
del pipeline
|
| 876 |
-
torch.cuda.empty_cache()
|
| 877 |
-
gc.collect()
|
| 878 |
-
if __name__ == "__main__":
|
| 879 |
-
pass
|
| 880 |
-
#main()
|
| 881 |
-
|
|
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|
spaces/Abdullah-Habib/Rabbit_or_Hare/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Rabbit Or Hare
|
| 3 |
-
emoji: 📊
|
| 4 |
-
colorFrom: pink
|
| 5 |
-
colorTo: blue
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.35.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: apache-2.0
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
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|
spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/reflection.py
DELETED
|
@@ -1,227 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
"""
|
| 4 |
-
An agent based upon Observation-Planning-Reflection architecture.
|
| 5 |
-
"""
|
| 6 |
-
|
| 7 |
-
from logging import getLogger
|
| 8 |
-
|
| 9 |
-
from abc import abstractmethod
|
| 10 |
-
from typing import List, Set, Union, NamedTuple, TYPE_CHECKING
|
| 11 |
-
|
| 12 |
-
from pydantic import BaseModel, Field, validator
|
| 13 |
-
|
| 14 |
-
from agentverse.llms import BaseLLM
|
| 15 |
-
from agentverse.memory import BaseMemory, ChatHistoryMemory
|
| 16 |
-
from agentverse.message import Message
|
| 17 |
-
from agentverse.output_parser import OutputParser
|
| 18 |
-
|
| 19 |
-
from agentverse.message import Message
|
| 20 |
-
from agentverse.agents.base import BaseAgent
|
| 21 |
-
|
| 22 |
-
from datetime import datetime as dt
|
| 23 |
-
import datetime
|
| 24 |
-
|
| 25 |
-
#from . import agent_registry
|
| 26 |
-
from string import Template
|
| 27 |
-
|
| 28 |
-
from agentverse.agents import agent_registry
|
| 29 |
-
from agentverse.agents.base import BaseAgent
|
| 30 |
-
|
| 31 |
-
logger = getLogger(__file__)
|
| 32 |
-
|
| 33 |
-
if TYPE_CHECKING:
|
| 34 |
-
from agentverse.environments.base import BaseEnvironment
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
@agent_registry.register("reflection")
|
| 38 |
-
class ReflectionAgent(BaseAgent):
|
| 39 |
-
async_mode: bool = (True,)
|
| 40 |
-
current_time: str = (None,)
|
| 41 |
-
environment: BaseEnvironment = None
|
| 42 |
-
step_cnt: int = 0
|
| 43 |
-
|
| 44 |
-
manipulated_memory: str = Field(
|
| 45 |
-
default="", description="one fragment used in prompt construction"
|
| 46 |
-
)
|
| 47 |
-
|
| 48 |
-
@validator("current_time")
|
| 49 |
-
def convert_str_to_dt(cls, current_time):
|
| 50 |
-
if not isinstance(current_time, str):
|
| 51 |
-
raise ValueError("current_time should be str")
|
| 52 |
-
return dt.strptime(current_time, "%Y-%m-%d %H:%M:%S")
|
| 53 |
-
|
| 54 |
-
def step(self, current_time: dt, env_description: str = "") -> Message:
|
| 55 |
-
"""
|
| 56 |
-
Call this method at each time frame
|
| 57 |
-
"""
|
| 58 |
-
self.current_time = current_time
|
| 59 |
-
|
| 60 |
-
self.manipulated_memory = self.memory_manipulator.manipulate_memory()
|
| 61 |
-
|
| 62 |
-
prompt = self._fill_prompt_template(env_description)
|
| 63 |
-
|
| 64 |
-
parsed_response, reaction, target = None, None, None
|
| 65 |
-
for i in range(self.max_retry):
|
| 66 |
-
try:
|
| 67 |
-
response = self.llm.agenerate_response(prompt)
|
| 68 |
-
parsed_response = self.output_parser.parse(response)
|
| 69 |
-
|
| 70 |
-
if "say(" in parsed_response.return_values["output"]:
|
| 71 |
-
reaction, target = eval(
|
| 72 |
-
"self._" + parsed_response.return_values["output"].strip()
|
| 73 |
-
)
|
| 74 |
-
elif "act(" in parsed_response.return_values["output"]:
|
| 75 |
-
reaction, target = eval(
|
| 76 |
-
"self._" + parsed_response.return_values["output"].strip()
|
| 77 |
-
)
|
| 78 |
-
elif "do_nothing(" in parsed_response.return_values["output"]:
|
| 79 |
-
reaction, target = None, None
|
| 80 |
-
else:
|
| 81 |
-
raise Exception(
|
| 82 |
-
f"no valid parsed_response detected, "
|
| 83 |
-
f"cur response {parsed_response.return_values['output']}"
|
| 84 |
-
)
|
| 85 |
-
break
|
| 86 |
-
|
| 87 |
-
except Exception as e:
|
| 88 |
-
logger.error(e)
|
| 89 |
-
logger.warn("Retrying...")
|
| 90 |
-
continue
|
| 91 |
-
|
| 92 |
-
if parsed_response is None:
|
| 93 |
-
logger.error(f"{self.name} failed to generate valid response.")
|
| 94 |
-
|
| 95 |
-
if reaction is None:
|
| 96 |
-
reaction = "Keep doing last action ..."
|
| 97 |
-
|
| 98 |
-
message = Message(
|
| 99 |
-
content="" if reaction is None else reaction,
|
| 100 |
-
sender=self.name,
|
| 101 |
-
receiver=self.get_receiver()
|
| 102 |
-
if target is None
|
| 103 |
-
else self.get_valid_receiver(target),
|
| 104 |
-
)
|
| 105 |
-
|
| 106 |
-
self.step_cnt += 1
|
| 107 |
-
|
| 108 |
-
return message
|
| 109 |
-
|
| 110 |
-
async def astep(self, current_time: dt, env_description: str = "") -> Message:
|
| 111 |
-
"""Asynchronous version of step"""
|
| 112 |
-
# use environment's time to update agent's time
|
| 113 |
-
self.current_time = current_time
|
| 114 |
-
# Before the agent step, we check current status,
|
| 115 |
-
# TODO add this func after
|
| 116 |
-
# self.check_status_passive()
|
| 117 |
-
|
| 118 |
-
self.manipulated_memory = self.memory_manipulator.manipulate_memory()
|
| 119 |
-
|
| 120 |
-
prompt = self._fill_prompt_template(env_description)
|
| 121 |
-
|
| 122 |
-
parsed_response, reaction, target = None, None, None
|
| 123 |
-
for i in range(self.max_retry):
|
| 124 |
-
try:
|
| 125 |
-
response = await self.llm.agenerate_response(prompt)
|
| 126 |
-
parsed_response = self.output_parser.parse(response)
|
| 127 |
-
|
| 128 |
-
if "say(" in parsed_response.return_values["output"]:
|
| 129 |
-
reaction, target = eval(
|
| 130 |
-
"self._" + parsed_response.return_values["output"].strip()
|
| 131 |
-
)
|
| 132 |
-
elif "act(" in parsed_response.return_values["output"]:
|
| 133 |
-
reaction, target = eval(
|
| 134 |
-
"self._" + parsed_response.return_values["output"].strip()
|
| 135 |
-
)
|
| 136 |
-
elif "do_nothing(" in parsed_response.return_values["output"]:
|
| 137 |
-
reaction, target = None, None
|
| 138 |
-
else:
|
| 139 |
-
raise Exception(
|
| 140 |
-
f"no valid parsed_response detected, "
|
| 141 |
-
f"cur response {parsed_response.return_values['output']}"
|
| 142 |
-
)
|
| 143 |
-
|
| 144 |
-
break
|
| 145 |
-
|
| 146 |
-
except Exception as e:
|
| 147 |
-
logger.error(e)
|
| 148 |
-
logger.warn("Retrying...")
|
| 149 |
-
continue
|
| 150 |
-
|
| 151 |
-
if parsed_response is None:
|
| 152 |
-
logger.error(f"{self.name} failed to generate valid response.")
|
| 153 |
-
|
| 154 |
-
if reaction is None:
|
| 155 |
-
reaction = "Keep doing last action ..."
|
| 156 |
-
|
| 157 |
-
message = Message(
|
| 158 |
-
content="" if reaction is None else reaction,
|
| 159 |
-
sender=self.name,
|
| 160 |
-
receiver=self.get_receiver()
|
| 161 |
-
if target is None
|
| 162 |
-
else self.get_valid_receiver(target),
|
| 163 |
-
)
|
| 164 |
-
|
| 165 |
-
self.step_cnt += 1
|
| 166 |
-
|
| 167 |
-
return message
|
| 168 |
-
|
| 169 |
-
def _act(self, description=None, target=None):
|
| 170 |
-
if description is None:
|
| 171 |
-
return ""
|
| 172 |
-
if target is None:
|
| 173 |
-
reaction_content = f"{self.name} performs action: '{description}'."
|
| 174 |
-
else:
|
| 175 |
-
reaction_content = (
|
| 176 |
-
f"{self.name} performs action to {target}: '{description}'."
|
| 177 |
-
)
|
| 178 |
-
# self.environment.broadcast_observations(self, target, reaction_content)
|
| 179 |
-
return reaction_content, target
|
| 180 |
-
|
| 181 |
-
def _say(self, description, target=None):
|
| 182 |
-
if description is None:
|
| 183 |
-
return ""
|
| 184 |
-
if target is None:
|
| 185 |
-
reaction_content = f"{self.name} says: '{description}'."
|
| 186 |
-
else:
|
| 187 |
-
reaction_content = f"{self.name} says to {target}: '{description}'."
|
| 188 |
-
# self.environment.broadcast_observations(self, target, reaction_content)
|
| 189 |
-
return reaction_content, target
|
| 190 |
-
|
| 191 |
-
def get_valid_receiver(self, target: str) -> set():
|
| 192 |
-
all_agents_name = []
|
| 193 |
-
for agent in self.environment.agents:
|
| 194 |
-
all_agents_name.append(agent.name)
|
| 195 |
-
|
| 196 |
-
if not (target in all_agents_name):
|
| 197 |
-
return {"all"}
|
| 198 |
-
else:
|
| 199 |
-
return {target}
|
| 200 |
-
|
| 201 |
-
def _fill_prompt_template(self, env_description: str = "") -> str:
|
| 202 |
-
"""Fill the placeholders in the prompt template
|
| 203 |
-
|
| 204 |
-
In the conversation agent, three placeholders are supported:
|
| 205 |
-
- ${agent_name}: the name of the agent
|
| 206 |
-
- ${env_description}: the description of the environment
|
| 207 |
-
- ${role_description}: the description of the role of the agent
|
| 208 |
-
- ${chat_history}: the chat history of the agent
|
| 209 |
-
"""
|
| 210 |
-
input_arguments = {
|
| 211 |
-
"agent_name": self.name,
|
| 212 |
-
"role_description": self.role_description,
|
| 213 |
-
"chat_history": self.memory.to_string(add_sender_prefix=True),
|
| 214 |
-
"current_time": self.current_time,
|
| 215 |
-
"env_description": env_description,
|
| 216 |
-
}
|
| 217 |
-
return Template(self.prompt_template).safe_substitute(input_arguments)
|
| 218 |
-
|
| 219 |
-
def add_message_to_memory(self, messages: List[Message]) -> None:
|
| 220 |
-
self.memory.add_message(messages)
|
| 221 |
-
|
| 222 |
-
def reset(self, environment: BaseEnvironment) -> None:
|
| 223 |
-
"""Reset the agent"""
|
| 224 |
-
self.environment = environment
|
| 225 |
-
self.memory.reset()
|
| 226 |
-
self.memory_manipulator.agent = self
|
| 227 |
-
self.memory_manipulator.memory = self.memory
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/rings/Rings.js
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
import Base from '../base/Base.js';
|
| 2 |
-
import { Circle } from '../utils/Geoms.js'
|
| 3 |
-
import Yoyo from '../utils/Yoyo.js';
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class Rings extends Base {
|
| 7 |
-
constructor(scene, config) {
|
| 8 |
-
super(scene, config);
|
| 9 |
-
this.type = 'rexSpinnerRings';
|
| 10 |
-
}
|
| 11 |
-
|
| 12 |
-
buildShapes() {
|
| 13 |
-
for (var i = 0; i < 2; i++) {
|
| 14 |
-
this.addShape(new Circle());
|
| 15 |
-
}
|
| 16 |
-
}
|
| 17 |
-
|
| 18 |
-
updateShapes() {
|
| 19 |
-
var centerX = this.centerX;
|
| 20 |
-
var centerY = this.centerY;
|
| 21 |
-
var radius = this.radius;
|
| 22 |
-
var lineWidth = Math.ceil(radius / 25);
|
| 23 |
-
var maxRingRadius = radius - lineWidth;
|
| 24 |
-
|
| 25 |
-
var shapes = this.getShapes();
|
| 26 |
-
for (var i = 0, cnt = shapes.length; i < cnt; i++) {
|
| 27 |
-
var ring = shapes[i];
|
| 28 |
-
var t = (this.value + (i / cnt)) % 1;
|
| 29 |
-
var alpha = Yoyo(t);
|
| 30 |
-
ring
|
| 31 |
-
.lineStyle(lineWidth, this.color, alpha)
|
| 32 |
-
.setRadius(t * maxRingRadius)
|
| 33 |
-
.setCenterPosition(centerX, centerY)
|
| 34 |
-
}
|
| 35 |
-
}
|
| 36 |
-
}
|
| 37 |
-
|
| 38 |
-
export default Rings;
|
|
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|
spaces/Aloento/9Nine-PITS/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: 9Nine PITS
|
| 3 |
-
emoji: 🚀
|
| 4 |
-
colorFrom: red
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.23.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: agpl-3.0
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
|
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|
|
|
spaces/Ameaou/academic-chatgpt3.1/crazy_functions/批量总结PDF文档.py
DELETED
|
@@ -1,166 +0,0 @@
|
|
| 1 |
-
from toolbox import update_ui
|
| 2 |
-
from toolbox import CatchException, report_execption, write_results_to_file
|
| 3 |
-
import re
|
| 4 |
-
import unicodedata
|
| 5 |
-
fast_debug = False
|
| 6 |
-
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
| 7 |
-
|
| 8 |
-
def is_paragraph_break(match):
|
| 9 |
-
"""
|
| 10 |
-
根据给定的匹配结果来判断换行符是否表示段落分隔。
|
| 11 |
-
如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
|
| 12 |
-
也可以根据之前的内容长度来判断段落是否已经足够长。
|
| 13 |
-
"""
|
| 14 |
-
prev_char, next_char = match.groups()
|
| 15 |
-
|
| 16 |
-
# 句子结束标志
|
| 17 |
-
sentence_endings = ".!?"
|
| 18 |
-
|
| 19 |
-
# 设定一个最小段落长度阈值
|
| 20 |
-
min_paragraph_length = 140
|
| 21 |
-
|
| 22 |
-
if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
|
| 23 |
-
return "\n\n"
|
| 24 |
-
else:
|
| 25 |
-
return " "
|
| 26 |
-
|
| 27 |
-
def normalize_text(text):
|
| 28 |
-
"""
|
| 29 |
-
通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
|
| 30 |
-
例如，将连字 "fi" 转换为 "f" 和 "i"。
|
| 31 |
-
"""
|
| 32 |
-
# 对文本进行归一化处理，分解连字
|
| 33 |
-
normalized_text = unicodedata.normalize("NFKD", text)
|
| 34 |
-
|
| 35 |
-
# 替换其他特殊字符
|
| 36 |
-
cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
|
| 37 |
-
|
| 38 |
-
return cleaned_text
|
| 39 |
-
|
| 40 |
-
def clean_text(raw_text):
|
| 41 |
-
"""
|
| 42 |
-
对从 PDF 提取出的原始文本进行清洗和格式化处理。
|
| 43 |
-
1. 对原始文本进行归一化处理。
|
| 44 |
-
2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
|
| 45 |
-
3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
|
| 46 |
-
"""
|
| 47 |
-
# 对文本进行归一化处理
|
| 48 |
-
normalized_text = normalize_text(raw_text)
|
| 49 |
-
|
| 50 |
-
# 替换跨行的连词
|
| 51 |
-
text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
|
| 52 |
-
|
| 53 |
-
# 根据前后相邻字符的特点，找到原文本中的换行符
|
| 54 |
-
newlines = re.compile(r'(\S)\n(\S)')
|
| 55 |
-
|
| 56 |
-
# 根据 heuristic 规则，用空格或段落分隔符替换原换行符
|
| 57 |
-
final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
|
| 58 |
-
|
| 59 |
-
return final_text.strip()
|
| 60 |
-
|
| 61 |
-
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
| 62 |
-
import time, glob, os, fitz
|
| 63 |
-
print('begin analysis on:', file_manifest)
|
| 64 |
-
for index, fp in enumerate(file_manifest):
|
| 65 |
-
with fitz.open(fp) as doc:
|
| 66 |
-
file_content = ""
|
| 67 |
-
for page in doc:
|
| 68 |
-
file_content += page.get_text()
|
| 69 |
-
file_content = clean_text(file_content)
|
| 70 |
-
print(file_content)
|
| 71 |
-
|
| 72 |
-
prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
|
| 73 |
-
i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
|
| 74 |
-
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
| 75 |
-
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
| 76 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 77 |
-
|
| 78 |
-
if not fast_debug:
|
| 79 |
-
msg = '正常'
|
| 80 |
-
# ** gpt request **
|
| 81 |
-
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
| 82 |
-
inputs=i_say,
|
| 83 |
-
inputs_show_user=i_say_show_user,
|
| 84 |
-
llm_kwargs=llm_kwargs,
|
| 85 |
-
chatbot=chatbot,
|
| 86 |
-
history=[],
|
| 87 |
-
sys_prompt="总结文章。"
|
| 88 |
-
) # 带超时倒计时
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
chatbot[-1] = (i_say_show_user, gpt_say)
|
| 92 |
-
history.append(i_say_show_user); history.append(gpt_say)
|
| 93 |
-
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
| 94 |
-
if not fast_debug: time.sleep(2)
|
| 95 |
-
|
| 96 |
-
all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
|
| 97 |
-
i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
|
| 98 |
-
chatbot.append((i_say, "[Local Message] waiting gpt response."))
|
| 99 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 100 |
-
|
| 101 |
-
if not fast_debug:
|
| 102 |
-
msg = '正常'
|
| 103 |
-
# ** gpt request **
|
| 104 |
-
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
| 105 |
-
inputs=i_say,
|
| 106 |
-
inputs_show_user=i_say,
|
| 107 |
-
llm_kwargs=llm_kwargs,
|
| 108 |
-
chatbot=chatbot,
|
| 109 |
-
history=history,
|
| 110 |
-
sys_prompt="总结文章。"
|
| 111 |
-
) # 带超时倒计时
|
| 112 |
-
|
| 113 |
-
chatbot[-1] = (i_say, gpt_say)
|
| 114 |
-
history.append(i_say); history.append(gpt_say)
|
| 115 |
-
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界���
|
| 116 |
-
res = write_results_to_file(history)
|
| 117 |
-
chatbot.append(("完成了吗？", res))
|
| 118 |
-
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
@CatchException
|
| 122 |
-
def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
| 123 |
-
import glob, os
|
| 124 |
-
|
| 125 |
-
# 基本信息：功能、贡献者
|
| 126 |
-
chatbot.append([
|
| 127 |
-
"函数插件功能？",
|
| 128 |
-
"批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
|
| 129 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 130 |
-
|
| 131 |
-
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
| 132 |
-
try:
|
| 133 |
-
import fitz
|
| 134 |
-
except:
|
| 135 |
-
report_execption(chatbot, history,
|
| 136 |
-
a = f"解析项目: {txt}",
|
| 137 |
-
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
|
| 138 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 139 |
-
return
|
| 140 |
-
|
| 141 |
-
# 清空历史，以免输入溢出
|
| 142 |
-
history = []
|
| 143 |
-
|
| 144 |
-
# 检测输入参数，如没有给定输入参数，直接退出
|
| 145 |
-
if os.path.exists(txt):
|
| 146 |
-
project_folder = txt
|
| 147 |
-
else:
|
| 148 |
-
if txt == "": txt = '空空如也的输入栏'
|
| 149 |
-
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
| 150 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 151 |
-
return
|
| 152 |
-
|
| 153 |
-
# 搜索需要处理的文件清单
|
| 154 |
-
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
|
| 155 |
-
# [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
|
| 156 |
-
# [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
|
| 157 |
-
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
|
| 158 |
-
|
| 159 |
-
# 如果没找到任何文件
|
| 160 |
-
if len(file_manifest) == 0:
|
| 161 |
-
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
|
| 162 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 163 |
-
return
|
| 164 |
-
|
| 165 |
-
# 开始正式执行任务
|
| 166 |
-
yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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|
spaces/Amon1/ChatGPTForAcadamic/crazy_functions/读文章写摘要.py
DELETED
|
@@ -1,70 +0,0 @@
|
|
| 1 |
-
from predict import predict_no_ui
|
| 2 |
-
from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
|
| 3 |
-
fast_debug = False
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
def 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
|
| 7 |
-
import time, glob, os
|
| 8 |
-
print('begin analysis on:', file_manifest)
|
| 9 |
-
for index, fp in enumerate(file_manifest):
|
| 10 |
-
with open(fp, 'r', encoding='utf-8') as f:
|
| 11 |
-
file_content = f.read()
|
| 12 |
-
|
| 13 |
-
prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
|
| 14 |
-
i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
|
| 15 |
-
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
| 16 |
-
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
| 17 |
-
print('[1] yield chatbot, history')
|
| 18 |
-
yield chatbot, history, '正常'
|
| 19 |
-
|
| 20 |
-
if not fast_debug:
|
| 21 |
-
msg = '正常'
|
| 22 |
-
# ** gpt request **
|
| 23 |
-
gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[]) # 带超时倒计时
|
| 24 |
-
|
| 25 |
-
print('[2] end gpt req')
|
| 26 |
-
chatbot[-1] = (i_say_show_user, gpt_say)
|
| 27 |
-
history.append(i_say_show_user); history.append(gpt_say)
|
| 28 |
-
print('[3] yield chatbot, history')
|
| 29 |
-
yield chatbot, history, msg
|
| 30 |
-
print('[4] next')
|
| 31 |
-
if not fast_debug: time.sleep(2)
|
| 32 |
-
|
| 33 |
-
all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
|
| 34 |
-
i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
|
| 35 |
-
chatbot.append((i_say, "[Local Message] waiting gpt response."))
|
| 36 |
-
yield chatbot, history, '正常'
|
| 37 |
-
|
| 38 |
-
if not fast_debug:
|
| 39 |
-
msg = '正常'
|
| 40 |
-
# ** gpt request **
|
| 41 |
-
gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history) # 带超时倒计时
|
| 42 |
-
|
| 43 |
-
chatbot[-1] = (i_say, gpt_say)
|
| 44 |
-
history.append(i_say); history.append(gpt_say)
|
| 45 |
-
yield chatbot, history, msg
|
| 46 |
-
res = write_results_to_file(history)
|
| 47 |
-
chatbot.append(("完成了吗？", res))
|
| 48 |
-
yield chatbot, history, msg
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
@CatchException
|
| 53 |
-
def 读文章写摘要(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
|
| 54 |
-
history = [] # 清空历史，以免输入溢出
|
| 55 |
-
import glob, os
|
| 56 |
-
if os.path.exists(txt):
|
| 57 |
-
project_folder = txt
|
| 58 |
-
else:
|
| 59 |
-
if txt == "": txt = '空空如也的输入栏'
|
| 60 |
-
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
| 61 |
-
yield chatbot, history, '正常'
|
| 62 |
-
return
|
| 63 |
-
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
|
| 64 |
-
# [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
|
| 65 |
-
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
|
| 66 |
-
if len(file_manifest) == 0:
|
| 67 |
-
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
| 68 |
-
yield chatbot, history, '正常'
|
| 69 |
-
return
|
| 70 |
-
yield from 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
|
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stochastic_karras_ve.md
DELETED
|
@@ -1,33 +0,0 @@
|
|
| 1 |
-
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
|
| 3 |
-
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
| 4 |
-
the License. You may obtain a copy of the License at
|
| 5 |
-
|
| 6 |
-
http://www.apache.org/licenses/LICENSE-2.0
|
| 7 |
-
|
| 8 |
-
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
| 9 |
-
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
| 10 |
-
specific language governing permissions and limitations under the License.
|
| 11 |
-
-->
|
| 12 |
-
|
| 13 |
-
# Stochastic Karras VE
|
| 14 |
-
|
| 15 |
-
[Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) is by Tero Karras, Miika Aittala, Timo Aila and Samuli Laine. This pipeline implements the stochastic sampling tailored to variance expanding (VE) models.
|
| 16 |
-
|
| 17 |
-
The abstract from the paper:
|
| 18 |
-
|
| 19 |
-
*We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us identify several changes to both the sampling and training processes, as well as preconditioning of the score networks. Together, our improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a class-conditional setting and 1.97 in an unconditional setting, with much faster sampling (35 network evaluations per image) than prior designs. To further demonstrate their modular nature, we show that our design changes dramatically improve both the efficiency and quality obtainable with pre-trained score networks from previous work, including improving the FID of an existing ImageNet-64 model from 2.07 to near-SOTA 1.55.*
|
| 20 |
-
|
| 21 |
-
<Tip>
|
| 22 |
-
|
| 23 |
-
Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
|
| 24 |
-
|
| 25 |
-
</Tip>
|
| 26 |
-
|
| 27 |
-
## KarrasVePipeline
|
| 28 |
-
[[autodoc]] KarrasVePipeline
|
| 29 |
-
- all
|
| 30 |
-
- __call__
|
| 31 |
-
|
| 32 |
-
## ImagePipelineOutput
|
| 33 |
-
[[autodoc]] pipelines.ImagePipelineOutput
|
|
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/open_vino.md
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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-
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
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the License. You may obtain a copy of the License at
|
| 5 |
-
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| 6 |
-
http://www.apache.org/licenses/LICENSE-2.0
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| 7 |
-
|
| 8 |
-
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
| 9 |
-
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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| 10 |
-
specific language governing permissions and limitations under the License.
|
| 11 |
-
-->
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| 12 |
-
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| 13 |
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| 14 |
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# How to use OpenVINO for inference
|
| 15 |
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| 16 |
-
🤗 [Optimum](https://github.com/huggingface/optimum-intel) provides Stable Diffusion pipelines compatible with OpenVINO. You can now easily perform inference with OpenVINO Runtime on a variety of Intel processors ([see](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) the full list of supported devices).
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| 17 |
-
|
| 18 |
-
## Installation
|
| 19 |
-
|
| 20 |
-
Install 🤗 Optimum Intel with the following command:
|
| 21 |
-
|
| 22 |
-
```
|
| 23 |
-
pip install --upgrade-strategy eager optimum["openvino"]
|
| 24 |
-
```
|
| 25 |
-
|
| 26 |
-
The `--upgrade-strategy eager` option is needed to ensure [`optimum-intel`](https://github.com/huggingface/optimum-intel) is upgraded to its latest version.
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
## Stable Diffusion
|
| 30 |
-
|
| 31 |
-
### Inference
|
| 32 |
-
|
| 33 |
-
To load an OpenVINO model and run inference with OpenVINO Runtime, you need to replace `StableDiffusionPipeline` with `OVStableDiffusionPipeline`. In case you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, you can set `export=True`.
|
| 34 |
-
|
| 35 |
-
```python
|
| 36 |
-
from optimum.intel import OVStableDiffusionPipeline
|
| 37 |
-
|
| 38 |
-
model_id = "runwayml/stable-diffusion-v1-5"
|
| 39 |
-
pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
|
| 40 |
-
prompt = "sailing ship in storm by Rembrandt"
|
| 41 |
-
image = pipeline(prompt).images[0]
|
| 42 |
-
|
| 43 |
-
# Don't forget to save the exported model
|
| 44 |
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pipeline.save_pretrained("openvino-sd-v1-5")
|
| 45 |
-
```
|
| 46 |
-
|
| 47 |
-
To further speed up inference, the model can be statically reshaped :
|
| 48 |
-
|
| 49 |
-
```python
|
| 50 |
-
# Define the shapes related to the inputs and desired outputs
|
| 51 |
-
batch_size, num_images, height, width = 1, 1, 512, 512
|
| 52 |
-
|
| 53 |
-
# Statically reshape the model
|
| 54 |
-
pipeline.reshape(batch_size, height, width, num_images)
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| 55 |
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# Compile the model before inference
|
| 56 |
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pipeline.compile()
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| 57 |
-
|
| 58 |
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image = pipeline(
|
| 59 |
-
prompt,
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| 60 |
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height=height,
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width=width,
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num_images_per_prompt=num_images,
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| 63 |
-
).images[0]
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| 64 |
-
```
|
| 65 |
-
|
| 66 |
-
In case you want to change any parameters such as the outputs height or width, you’ll need to statically reshape your model once again.
|
| 67 |
-
|
| 68 |
-
<div class="flex justify-center">
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| 69 |
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<img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/openvino/stable_diffusion_v1_5_sail_boat_rembrandt.png">
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| 70 |
-
</div>
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| 71 |
-
|
| 72 |
-
|
| 73 |
-
### Supported tasks
|
| 74 |
-
|
| 75 |
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| Task | Loading Class |
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| 76 |
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|--------------------------------------|--------------------------------------|
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| 77 |
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| `text-to-image` | `OVStableDiffusionPipeline` |
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| 78 |
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| `image-to-image` | `OVStableDiffusionImg2ImgPipeline` |
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| 79 |
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| `inpaint` | `OVStableDiffusionInpaintPipeline` |
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| 80 |
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| 81 |
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You can find more examples in the optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion).
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| 82 |
-
|
| 83 |
-
|
| 84 |
-
## Stable Diffusion XL
|
| 85 |
-
|
| 86 |
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### Inference
|
| 87 |
-
|
| 88 |
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```python
|
| 89 |
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from optimum.intel import OVStableDiffusionXLPipeline
|
| 90 |
-
|
| 91 |
-
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
|
| 92 |
-
pipeline = OVStableDiffusionXLPipeline.from_pretrained(model_id, export=True)
|
| 93 |
-
prompt = "sailing ship in storm by Rembrandt"
|
| 94 |
-
image = pipeline(prompt).images[0]
|
| 95 |
-
```
|
| 96 |
-
|
| 97 |
-
To further speed up inference, the model can be statically reshaped as showed above.
|
| 98 |
-
You can find more examples in the optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion-xl).
|
| 99 |
-
|
| 100 |
-
### Supported tasks
|
| 101 |
-
|
| 102 |
-
| Task | Loading Class |
|
| 103 |
-
|--------------------------------------|--------------------------------------|
|
| 104 |
-
| `text-to-image` | `OVStableDiffusionXLPipeline` |
|
| 105 |
-
| `image-to-image` | `OVStableDiffusionXLImg2ImgPipeline` |
|
| 106 |
-
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| 107 |
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| 108 |
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky_v22/test_kandinsky_inpaint.py
DELETED
|
@@ -1,295 +0,0 @@
|
|
| 1 |
-
# coding=utf-8
|
| 2 |
-
# Copyright 2023 HuggingFace Inc.
|
| 3 |
-
#
|
| 4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
# you may not use this file except in compliance with the License.
|
| 6 |
-
# You may obtain a copy of the License at
|
| 7 |
-
#
|
| 8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
#
|
| 10 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
# See the License for the specific language governing permissions and
|
| 14 |
-
# limitations under the License.
|
| 15 |
-
|
| 16 |
-
import gc
|
| 17 |
-
import random
|
| 18 |
-
import unittest
|
| 19 |
-
|
| 20 |
-
import numpy as np
|
| 21 |
-
import torch
|
| 22 |
-
from PIL import Image
|
| 23 |
-
|
| 24 |
-
from diffusers import (
|
| 25 |
-
DDIMScheduler,
|
| 26 |
-
KandinskyV22InpaintPipeline,
|
| 27 |
-
KandinskyV22PriorPipeline,
|
| 28 |
-
UNet2DConditionModel,
|
| 29 |
-
VQModel,
|
| 30 |
-
)
|
| 31 |
-
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
|
| 32 |
-
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
|
| 33 |
-
|
| 34 |
-
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
enable_full_determinism()
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
class Dummies:
|
| 41 |
-
@property
|
| 42 |
-
def text_embedder_hidden_size(self):
|
| 43 |
-
return 32
|
| 44 |
-
|
| 45 |
-
@property
|
| 46 |
-
def time_input_dim(self):
|
| 47 |
-
return 32
|
| 48 |
-
|
| 49 |
-
@property
|
| 50 |
-
def block_out_channels_0(self):
|
| 51 |
-
return self.time_input_dim
|
| 52 |
-
|
| 53 |
-
@property
|
| 54 |
-
def time_embed_dim(self):
|
| 55 |
-
return self.time_input_dim * 4
|
| 56 |
-
|
| 57 |
-
@property
|
| 58 |
-
def cross_attention_dim(self):
|
| 59 |
-
return 32
|
| 60 |
-
|
| 61 |
-
@property
|
| 62 |
-
def dummy_unet(self):
|
| 63 |
-
torch.manual_seed(0)
|
| 64 |
-
|
| 65 |
-
model_kwargs = {
|
| 66 |
-
"in_channels": 9,
|
| 67 |
-
# Out channels is double in channels because predicts mean and variance
|
| 68 |
-
"out_channels": 8,
|
| 69 |
-
"addition_embed_type": "image",
|
| 70 |
-
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
|
| 71 |
-
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
|
| 72 |
-
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
|
| 73 |
-
"block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
|
| 74 |
-
"layers_per_block": 1,
|
| 75 |
-
"encoder_hid_dim": self.text_embedder_hidden_size,
|
| 76 |
-
"encoder_hid_dim_type": "image_proj",
|
| 77 |
-
"cross_attention_dim": self.cross_attention_dim,
|
| 78 |
-
"attention_head_dim": 4,
|
| 79 |
-
"resnet_time_scale_shift": "scale_shift",
|
| 80 |
-
"class_embed_type": None,
|
| 81 |
-
}
|
| 82 |
-
|
| 83 |
-
model = UNet2DConditionModel(**model_kwargs)
|
| 84 |
-
return model
|
| 85 |
-
|
| 86 |
-
@property
|
| 87 |
-
def dummy_movq_kwargs(self):
|
| 88 |
-
return {
|
| 89 |
-
"block_out_channels": [32, 64],
|
| 90 |
-
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
|
| 91 |
-
"in_channels": 3,
|
| 92 |
-
"latent_channels": 4,
|
| 93 |
-
"layers_per_block": 1,
|
| 94 |
-
"norm_num_groups": 8,
|
| 95 |
-
"norm_type": "spatial",
|
| 96 |
-
"num_vq_embeddings": 12,
|
| 97 |
-
"out_channels": 3,
|
| 98 |
-
"up_block_types": [
|
| 99 |
-
"AttnUpDecoderBlock2D",
|
| 100 |
-
"UpDecoderBlock2D",
|
| 101 |
-
],
|
| 102 |
-
"vq_embed_dim": 4,
|
| 103 |
-
}
|
| 104 |
-
|
| 105 |
-
@property
|
| 106 |
-
def dummy_movq(self):
|
| 107 |
-
torch.manual_seed(0)
|
| 108 |
-
model = VQModel(**self.dummy_movq_kwargs)
|
| 109 |
-
return model
|
| 110 |
-
|
| 111 |
-
def get_dummy_components(self):
|
| 112 |
-
unet = self.dummy_unet
|
| 113 |
-
movq = self.dummy_movq
|
| 114 |
-
|
| 115 |
-
scheduler = DDIMScheduler(
|
| 116 |
-
num_train_timesteps=1000,
|
| 117 |
-
beta_schedule="linear",
|
| 118 |
-
beta_start=0.00085,
|
| 119 |
-
beta_end=0.012,
|
| 120 |
-
clip_sample=False,
|
| 121 |
-
set_alpha_to_one=False,
|
| 122 |
-
steps_offset=1,
|
| 123 |
-
prediction_type="epsilon",
|
| 124 |
-
thresholding=False,
|
| 125 |
-
)
|
| 126 |
-
|
| 127 |
-
components = {
|
| 128 |
-
"unet": unet,
|
| 129 |
-
"scheduler": scheduler,
|
| 130 |
-
"movq": movq,
|
| 131 |
-
}
|
| 132 |
-
|
| 133 |
-
return components
|
| 134 |
-
|
| 135 |
-
def get_dummy_inputs(self, device, seed=0):
|
| 136 |
-
image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)
|
| 137 |
-
negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(
|
| 138 |
-
device
|
| 139 |
-
)
|
| 140 |
-
# create init_image
|
| 141 |
-
image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
|
| 142 |
-
image = image.cpu().permute(0, 2, 3, 1)[0]
|
| 143 |
-
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((256, 256))
|
| 144 |
-
# create mask
|
| 145 |
-
mask = np.zeros((64, 64), dtype=np.float32)
|
| 146 |
-
mask[:32, :32] = 1
|
| 147 |
-
|
| 148 |
-
if str(device).startswith("mps"):
|
| 149 |
-
generator = torch.manual_seed(seed)
|
| 150 |
-
else:
|
| 151 |
-
generator = torch.Generator(device=device).manual_seed(seed)
|
| 152 |
-
inputs = {
|
| 153 |
-
"image": init_image,
|
| 154 |
-
"mask_image": mask,
|
| 155 |
-
"image_embeds": image_embeds,
|
| 156 |
-
"negative_image_embeds": negative_image_embeds,
|
| 157 |
-
"generator": generator,
|
| 158 |
-
"height": 64,
|
| 159 |
-
"width": 64,
|
| 160 |
-
"num_inference_steps": 2,
|
| 161 |
-
"guidance_scale": 4.0,
|
| 162 |
-
"output_type": "np",
|
| 163 |
-
}
|
| 164 |
-
return inputs
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
class KandinskyV22InpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
|
| 168 |
-
pipeline_class = KandinskyV22InpaintPipeline
|
| 169 |
-
params = ["image_embeds", "negative_image_embeds", "image", "mask_image"]
|
| 170 |
-
batch_params = [
|
| 171 |
-
"image_embeds",
|
| 172 |
-
"negative_image_embeds",
|
| 173 |
-
"image",
|
| 174 |
-
"mask_image",
|
| 175 |
-
]
|
| 176 |
-
required_optional_params = [
|
| 177 |
-
"generator",
|
| 178 |
-
"height",
|
| 179 |
-
"width",
|
| 180 |
-
"latents",
|
| 181 |
-
"guidance_scale",
|
| 182 |
-
"num_inference_steps",
|
| 183 |
-
"return_dict",
|
| 184 |
-
"guidance_scale",
|
| 185 |
-
"num_images_per_prompt",
|
| 186 |
-
"output_type",
|
| 187 |
-
"return_dict",
|
| 188 |
-
]
|
| 189 |
-
test_xformers_attention = False
|
| 190 |
-
|
| 191 |
-
def get_dummy_components(self):
|
| 192 |
-
dummies = Dummies()
|
| 193 |
-
return dummies.get_dummy_components()
|
| 194 |
-
|
| 195 |
-
def get_dummy_inputs(self, device, seed=0):
|
| 196 |
-
dummies = Dummies()
|
| 197 |
-
return dummies.get_dummy_inputs(device=device, seed=seed)
|
| 198 |
-
|
| 199 |
-
def test_kandinsky_inpaint(self):
|
| 200 |
-
device = "cpu"
|
| 201 |
-
|
| 202 |
-
components = self.get_dummy_components()
|
| 203 |
-
|
| 204 |
-
pipe = self.pipeline_class(**components)
|
| 205 |
-
pipe = pipe.to(device)
|
| 206 |
-
|
| 207 |
-
pipe.set_progress_bar_config(disable=None)
|
| 208 |
-
|
| 209 |
-
output = pipe(**self.get_dummy_inputs(device))
|
| 210 |
-
image = output.images
|
| 211 |
-
|
| 212 |
-
image_from_tuple = pipe(
|
| 213 |
-
**self.get_dummy_inputs(device),
|
| 214 |
-
return_dict=False,
|
| 215 |
-
)[0]
|
| 216 |
-
|
| 217 |
-
image_slice = image[0, -3:, -3:, -1]
|
| 218 |
-
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
| 219 |
-
|
| 220 |
-
assert image.shape == (1, 64, 64, 3)
|
| 221 |
-
|
| 222 |
-
expected_slice = np.array(
|
| 223 |
-
[0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]
|
| 224 |
-
)
|
| 225 |
-
|
| 226 |
-
assert (
|
| 227 |
-
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
| 228 |
-
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
|
| 229 |
-
assert (
|
| 230 |
-
np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
| 231 |
-
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
|
| 232 |
-
|
| 233 |
-
def test_inference_batch_single_identical(self):
|
| 234 |
-
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
|
| 235 |
-
|
| 236 |
-
|
| 237 |
-
@slow
|
| 238 |
-
@require_torch_gpu
|
| 239 |
-
class KandinskyV22InpaintPipelineIntegrationTests(unittest.TestCase):
|
| 240 |
-
def tearDown(self):
|
| 241 |
-
# clean up the VRAM after each test
|
| 242 |
-
super().tearDown()
|
| 243 |
-
gc.collect()
|
| 244 |
-
torch.cuda.empty_cache()
|
| 245 |
-
|
| 246 |
-
def test_kandinsky_inpaint(self):
|
| 247 |
-
expected_image = load_numpy(
|
| 248 |
-
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
| 249 |
-
"/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"
|
| 250 |
-
)
|
| 251 |
-
|
| 252 |
-
init_image = load_image(
|
| 253 |
-
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
|
| 254 |
-
)
|
| 255 |
-
mask = np.zeros((768, 768), dtype=np.float32)
|
| 256 |
-
mask[:250, 250:-250] = 1
|
| 257 |
-
|
| 258 |
-
prompt = "a hat"
|
| 259 |
-
|
| 260 |
-
pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
|
| 261 |
-
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
|
| 262 |
-
)
|
| 263 |
-
pipe_prior.to(torch_device)
|
| 264 |
-
|
| 265 |
-
pipeline = KandinskyV22InpaintPipeline.from_pretrained(
|
| 266 |
-
"kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16
|
| 267 |
-
)
|
| 268 |
-
pipeline = pipeline.to(torch_device)
|
| 269 |
-
pipeline.set_progress_bar_config(disable=None)
|
| 270 |
-
|
| 271 |
-
generator = torch.Generator(device="cpu").manual_seed(0)
|
| 272 |
-
image_emb, zero_image_emb = pipe_prior(
|
| 273 |
-
prompt,
|
| 274 |
-
generator=generator,
|
| 275 |
-
num_inference_steps=5,
|
| 276 |
-
negative_prompt="",
|
| 277 |
-
).to_tuple()
|
| 278 |
-
|
| 279 |
-
output = pipeline(
|
| 280 |
-
image=init_image,
|
| 281 |
-
mask_image=mask,
|
| 282 |
-
image_embeds=image_emb,
|
| 283 |
-
negative_image_embeds=zero_image_emb,
|
| 284 |
-
generator=generator,
|
| 285 |
-
num_inference_steps=100,
|
| 286 |
-
height=768,
|
| 287 |
-
width=768,
|
| 288 |
-
output_type="np",
|
| 289 |
-
)
|
| 290 |
-
|
| 291 |
-
image = output.images[0]
|
| 292 |
-
|
| 293 |
-
assert image.shape == (768, 768, 3)
|
| 294 |
-
|
| 295 |
-
assert_mean_pixel_difference(image, expected_image)
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py
DELETED
|
@@ -1,385 +0,0 @@
|
|
| 1 |
-
# coding=utf-8
|
| 2 |
-
# Copyright 2023 HuggingFace Inc.
|
| 3 |
-
#
|
| 4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
# you may not use this file except in compliance with the License.
|
| 6 |
-
# You may obtain a copy of the License at
|
| 7 |
-
#
|
| 8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
#
|
| 10 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
# See the License for the specific language governing permissions and
|
| 14 |
-
# limitations under the License.
|
| 15 |
-
|
| 16 |
-
import gc
|
| 17 |
-
import random
|
| 18 |
-
import unittest
|
| 19 |
-
|
| 20 |
-
import numpy as np
|
| 21 |
-
import torch
|
| 22 |
-
from PIL import Image
|
| 23 |
-
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
|
| 24 |
-
|
| 25 |
-
from diffusers import (
|
| 26 |
-
AutoencoderKL,
|
| 27 |
-
DDIMScheduler,
|
| 28 |
-
EulerAncestralDiscreteScheduler,
|
| 29 |
-
LMSDiscreteScheduler,
|
| 30 |
-
PNDMScheduler,
|
| 31 |
-
StableDiffusionInstructPix2PixPipeline,
|
| 32 |
-
UNet2DConditionModel,
|
| 33 |
-
)
|
| 34 |
-
from diffusers.image_processor import VaeImageProcessor
|
| 35 |
-
from diffusers.utils import floats_tensor, load_image, slow, torch_device
|
| 36 |
-
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
|
| 37 |
-
|
| 38 |
-
from ..pipeline_params import (
|
| 39 |
-
IMAGE_TO_IMAGE_IMAGE_PARAMS,
|
| 40 |
-
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
|
| 41 |
-
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
|
| 42 |
-
)
|
| 43 |
-
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
enable_full_determinism()
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
class StableDiffusionInstructPix2PixPipelineFastTests(
|
| 50 |
-
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
|
| 51 |
-
):
|
| 52 |
-
pipeline_class = StableDiffusionInstructPix2PixPipeline
|
| 53 |
-
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
|
| 54 |
-
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
|
| 55 |
-
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
|
| 56 |
-
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
|
| 57 |
-
|
| 58 |
-
def get_dummy_components(self):
|
| 59 |
-
torch.manual_seed(0)
|
| 60 |
-
unet = UNet2DConditionModel(
|
| 61 |
-
block_out_channels=(32, 64),
|
| 62 |
-
layers_per_block=2,
|
| 63 |
-
sample_size=32,
|
| 64 |
-
in_channels=8,
|
| 65 |
-
out_channels=4,
|
| 66 |
-
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
|
| 67 |
-
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
|
| 68 |
-
cross_attention_dim=32,
|
| 69 |
-
)
|
| 70 |
-
scheduler = PNDMScheduler(skip_prk_steps=True)
|
| 71 |
-
torch.manual_seed(0)
|
| 72 |
-
vae = AutoencoderKL(
|
| 73 |
-
block_out_channels=[32, 64],
|
| 74 |
-
in_channels=3,
|
| 75 |
-
out_channels=3,
|
| 76 |
-
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
|
| 77 |
-
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
|
| 78 |
-
latent_channels=4,
|
| 79 |
-
)
|
| 80 |
-
torch.manual_seed(0)
|
| 81 |
-
text_encoder_config = CLIPTextConfig(
|
| 82 |
-
bos_token_id=0,
|
| 83 |
-
eos_token_id=2,
|
| 84 |
-
hidden_size=32,
|
| 85 |
-
intermediate_size=37,
|
| 86 |
-
layer_norm_eps=1e-05,
|
| 87 |
-
num_attention_heads=4,
|
| 88 |
-
num_hidden_layers=5,
|
| 89 |
-
pad_token_id=1,
|
| 90 |
-
vocab_size=1000,
|
| 91 |
-
)
|
| 92 |
-
text_encoder = CLIPTextModel(text_encoder_config)
|
| 93 |
-
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
| 94 |
-
|
| 95 |
-
components = {
|
| 96 |
-
"unet": unet,
|
| 97 |
-
"scheduler": scheduler,
|
| 98 |
-
"vae": vae,
|
| 99 |
-
"text_encoder": text_encoder,
|
| 100 |
-
"tokenizer": tokenizer,
|
| 101 |
-
"safety_checker": None,
|
| 102 |
-
"feature_extractor": None,
|
| 103 |
-
}
|
| 104 |
-
return components
|
| 105 |
-
|
| 106 |
-
def get_dummy_inputs(self, device, seed=0):
|
| 107 |
-
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
|
| 108 |
-
image = image.cpu().permute(0, 2, 3, 1)[0]
|
| 109 |
-
image = Image.fromarray(np.uint8(image)).convert("RGB")
|
| 110 |
-
if str(device).startswith("mps"):
|
| 111 |
-
generator = torch.manual_seed(seed)
|
| 112 |
-
else:
|
| 113 |
-
generator = torch.Generator(device=device).manual_seed(seed)
|
| 114 |
-
inputs = {
|
| 115 |
-
"prompt": "A painting of a squirrel eating a burger",
|
| 116 |
-
"image": image,
|
| 117 |
-
"generator": generator,
|
| 118 |
-
"num_inference_steps": 2,
|
| 119 |
-
"guidance_scale": 6.0,
|
| 120 |
-
"image_guidance_scale": 1,
|
| 121 |
-
"output_type": "numpy",
|
| 122 |
-
}
|
| 123 |
-
return inputs
|
| 124 |
-
|
| 125 |
-
def test_stable_diffusion_pix2pix_default_case(self):
|
| 126 |
-
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
| 127 |
-
components = self.get_dummy_components()
|
| 128 |
-
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
| 129 |
-
sd_pipe = sd_pipe.to(device)
|
| 130 |
-
sd_pipe.set_progress_bar_config(disable=None)
|
| 131 |
-
|
| 132 |
-
inputs = self.get_dummy_inputs(device)
|
| 133 |
-
image = sd_pipe(**inputs).images
|
| 134 |
-
image_slice = image[0, -3:, -3:, -1]
|
| 135 |
-
assert image.shape == (1, 32, 32, 3)
|
| 136 |
-
expected_slice = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815])
|
| 137 |
-
|
| 138 |
-
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
| 139 |
-
|
| 140 |
-
def test_stable_diffusion_pix2pix_negative_prompt(self):
|
| 141 |
-
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
| 142 |
-
components = self.get_dummy_components()
|
| 143 |
-
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
| 144 |
-
sd_pipe = sd_pipe.to(device)
|
| 145 |
-
sd_pipe.set_progress_bar_config(disable=None)
|
| 146 |
-
|
| 147 |
-
inputs = self.get_dummy_inputs(device)
|
| 148 |
-
negative_prompt = "french fries"
|
| 149 |
-
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
|
| 150 |
-
image = output.images
|
| 151 |
-
image_slice = image[0, -3:, -3:, -1]
|
| 152 |
-
|
| 153 |
-
assert image.shape == (1, 32, 32, 3)
|
| 154 |
-
expected_slice = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831])
|
| 155 |
-
|
| 156 |
-
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
| 157 |
-
|
| 158 |
-
def test_stable_diffusion_pix2pix_multiple_init_images(self):
|
| 159 |
-
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
| 160 |
-
components = self.get_dummy_components()
|
| 161 |
-
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
| 162 |
-
sd_pipe = sd_pipe.to(device)
|
| 163 |
-
sd_pipe.set_progress_bar_config(disable=None)
|
| 164 |
-
|
| 165 |
-
inputs = self.get_dummy_inputs(device)
|
| 166 |
-
inputs["prompt"] = [inputs["prompt"]] * 2
|
| 167 |
-
|
| 168 |
-
image = np.array(inputs["image"]).astype(np.float32) / 255.0
|
| 169 |
-
image = torch.from_numpy(image).unsqueeze(0).to(device)
|
| 170 |
-
image = image / 2 + 0.5
|
| 171 |
-
image = image.permute(0, 3, 1, 2)
|
| 172 |
-
inputs["image"] = image.repeat(2, 1, 1, 1)
|
| 173 |
-
|
| 174 |
-
image = sd_pipe(**inputs).images
|
| 175 |
-
image_slice = image[-1, -3:, -3:, -1]
|
| 176 |
-
|
| 177 |
-
assert image.shape == (2, 32, 32, 3)
|
| 178 |
-
expected_slice = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579])
|
| 179 |
-
|
| 180 |
-
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
| 181 |
-
|
| 182 |
-
def test_stable_diffusion_pix2pix_euler(self):
|
| 183 |
-
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
| 184 |
-
components = self.get_dummy_components()
|
| 185 |
-
components["scheduler"] = EulerAncestralDiscreteScheduler(
|
| 186 |
-
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
|
| 187 |
-
)
|
| 188 |
-
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
| 189 |
-
sd_pipe = sd_pipe.to(device)
|
| 190 |
-
sd_pipe.set_progress_bar_config(disable=None)
|
| 191 |
-
|
| 192 |
-
inputs = self.get_dummy_inputs(device)
|
| 193 |
-
image = sd_pipe(**inputs).images
|
| 194 |
-
image_slice = image[0, -3:, -3:, -1]
|
| 195 |
-
|
| 196 |
-
slice = [round(x, 4) for x in image_slice.flatten().tolist()]
|
| 197 |
-
print(",".join([str(x) for x in slice]))
|
| 198 |
-
|
| 199 |
-
assert image.shape == (1, 32, 32, 3)
|
| 200 |
-
expected_slice = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986])
|
| 201 |
-
|
| 202 |
-
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
| 203 |
-
|
| 204 |
-
def test_inference_batch_single_identical(self):
|
| 205 |
-
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
|
| 206 |
-
|
| 207 |
-
# Overwrite the default test_latents_inputs because pix2pix encode the image differently
|
| 208 |
-
def test_latents_input(self):
|
| 209 |
-
components = self.get_dummy_components()
|
| 210 |
-
pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
| 211 |
-
pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
|
| 212 |
-
pipe = pipe.to(torch_device)
|
| 213 |
-
pipe.set_progress_bar_config(disable=None)
|
| 214 |
-
|
| 215 |
-
out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
|
| 216 |
-
|
| 217 |
-
vae = components["vae"]
|
| 218 |
-
inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt")
|
| 219 |
-
|
| 220 |
-
for image_param in self.image_latents_params:
|
| 221 |
-
if image_param in inputs.keys():
|
| 222 |
-
inputs[image_param] = vae.encode(inputs[image_param]).latent_dist.mode()
|
| 223 |
-
|
| 224 |
-
out_latents_inputs = pipe(**inputs)[0]
|
| 225 |
-
|
| 226 |
-
max_diff = np.abs(out - out_latents_inputs).max()
|
| 227 |
-
self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image")
|
| 228 |
-
|
| 229 |
-
|
| 230 |
-
@slow
|
| 231 |
-
@require_torch_gpu
|
| 232 |
-
class StableDiffusionInstructPix2PixPipelineSlowTests(unittest.TestCase):
|
| 233 |
-
def tearDown(self):
|
| 234 |
-
super().tearDown()
|
| 235 |
-
gc.collect()
|
| 236 |
-
torch.cuda.empty_cache()
|
| 237 |
-
|
| 238 |
-
def get_inputs(self, seed=0):
|
| 239 |
-
generator = torch.manual_seed(seed)
|
| 240 |
-
image = load_image(
|
| 241 |
-
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"
|
| 242 |
-
)
|
| 243 |
-
inputs = {
|
| 244 |
-
"prompt": "turn him into a cyborg",
|
| 245 |
-
"image": image,
|
| 246 |
-
"generator": generator,
|
| 247 |
-
"num_inference_steps": 3,
|
| 248 |
-
"guidance_scale": 7.5,
|
| 249 |
-
"image_guidance_scale": 1.0,
|
| 250 |
-
"output_type": "numpy",
|
| 251 |
-
}
|
| 252 |
-
return inputs
|
| 253 |
-
|
| 254 |
-
def test_stable_diffusion_pix2pix_default(self):
|
| 255 |
-
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
| 256 |
-
"timbrooks/instruct-pix2pix", safety_checker=None
|
| 257 |
-
)
|
| 258 |
-
pipe.to(torch_device)
|
| 259 |
-
pipe.set_progress_bar_config(disable=None)
|
| 260 |
-
pipe.enable_attention_slicing()
|
| 261 |
-
|
| 262 |
-
inputs = self.get_inputs()
|
| 263 |
-
image = pipe(**inputs).images
|
| 264 |
-
image_slice = image[0, -3:, -3:, -1].flatten()
|
| 265 |
-
|
| 266 |
-
assert image.shape == (1, 512, 512, 3)
|
| 267 |
-
expected_slice = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555])
|
| 268 |
-
|
| 269 |
-
assert np.abs(expected_slice - image_slice).max() < 1e-3
|
| 270 |
-
|
| 271 |
-
def test_stable_diffusion_pix2pix_k_lms(self):
|
| 272 |
-
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
| 273 |
-
"timbrooks/instruct-pix2pix", safety_checker=None
|
| 274 |
-
)
|
| 275 |
-
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
|
| 276 |
-
pipe.to(torch_device)
|
| 277 |
-
pipe.set_progress_bar_config(disable=None)
|
| 278 |
-
pipe.enable_attention_slicing()
|
| 279 |
-
|
| 280 |
-
inputs = self.get_inputs()
|
| 281 |
-
image = pipe(**inputs).images
|
| 282 |
-
image_slice = image[0, -3:, -3:, -1].flatten()
|
| 283 |
-
|
| 284 |
-
assert image.shape == (1, 512, 512, 3)
|
| 285 |
-
expected_slice = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301])
|
| 286 |
-
|
| 287 |
-
assert np.abs(expected_slice - image_slice).max() < 1e-3
|
| 288 |
-
|
| 289 |
-
def test_stable_diffusion_pix2pix_ddim(self):
|
| 290 |
-
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
| 291 |
-
"timbrooks/instruct-pix2pix", safety_checker=None
|
| 292 |
-
)
|
| 293 |
-
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
|
| 294 |
-
pipe.to(torch_device)
|
| 295 |
-
pipe.set_progress_bar_config(disable=None)
|
| 296 |
-
pipe.enable_attention_slicing()
|
| 297 |
-
|
| 298 |
-
inputs = self.get_inputs()
|
| 299 |
-
image = pipe(**inputs).images
|
| 300 |
-
image_slice = image[0, -3:, -3:, -1].flatten()
|
| 301 |
-
|
| 302 |
-
assert image.shape == (1, 512, 512, 3)
|
| 303 |
-
expected_slice = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753])
|
| 304 |
-
|
| 305 |
-
assert np.abs(expected_slice - image_slice).max() < 1e-3
|
| 306 |
-
|
| 307 |
-
def test_stable_diffusion_pix2pix_intermediate_state(self):
|
| 308 |
-
number_of_steps = 0
|
| 309 |
-
|
| 310 |
-
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
|
| 311 |
-
callback_fn.has_been_called = True
|
| 312 |
-
nonlocal number_of_steps
|
| 313 |
-
number_of_steps += 1
|
| 314 |
-
if step == 1:
|
| 315 |
-
latents = latents.detach().cpu().numpy()
|
| 316 |
-
assert latents.shape == (1, 4, 64, 64)
|
| 317 |
-
latents_slice = latents[0, -3:, -3:, -1]
|
| 318 |
-
expected_slice = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983])
|
| 319 |
-
|
| 320 |
-
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
|
| 321 |
-
elif step == 2:
|
| 322 |
-
latents = latents.detach().cpu().numpy()
|
| 323 |
-
assert latents.shape == (1, 4, 64, 64)
|
| 324 |
-
latents_slice = latents[0, -3:, -3:, -1]
|
| 325 |
-
expected_slice = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115])
|
| 326 |
-
|
| 327 |
-
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
|
| 328 |
-
|
| 329 |
-
callback_fn.has_been_called = False
|
| 330 |
-
|
| 331 |
-
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
| 332 |
-
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16
|
| 333 |
-
)
|
| 334 |
-
pipe = pipe.to(torch_device)
|
| 335 |
-
pipe.set_progress_bar_config(disable=None)
|
| 336 |
-
pipe.enable_attention_slicing()
|
| 337 |
-
|
| 338 |
-
inputs = self.get_inputs()
|
| 339 |
-
pipe(**inputs, callback=callback_fn, callback_steps=1)
|
| 340 |
-
assert callback_fn.has_been_called
|
| 341 |
-
assert number_of_steps == 3
|
| 342 |
-
|
| 343 |
-
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
|
| 344 |
-
torch.cuda.empty_cache()
|
| 345 |
-
torch.cuda.reset_max_memory_allocated()
|
| 346 |
-
torch.cuda.reset_peak_memory_stats()
|
| 347 |
-
|
| 348 |
-
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
| 349 |
-
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16
|
| 350 |
-
)
|
| 351 |
-
pipe = pipe.to(torch_device)
|
| 352 |
-
pipe.set_progress_bar_config(disable=None)
|
| 353 |
-
pipe.enable_attention_slicing(1)
|
| 354 |
-
pipe.enable_sequential_cpu_offload()
|
| 355 |
-
|
| 356 |
-
inputs = self.get_inputs()
|
| 357 |
-
_ = pipe(**inputs)
|
| 358 |
-
|
| 359 |
-
mem_bytes = torch.cuda.max_memory_allocated()
|
| 360 |
-
# make sure that less than 2.2 GB is allocated
|
| 361 |
-
assert mem_bytes < 2.2 * 10**9
|
| 362 |
-
|
| 363 |
-
def test_stable_diffusion_pix2pix_pipeline_multiple_of_8(self):
|
| 364 |
-
inputs = self.get_inputs()
|
| 365 |
-
# resize to resolution that is divisible by 8 but not 16 or 32
|
| 366 |
-
inputs["image"] = inputs["image"].resize((504, 504))
|
| 367 |
-
|
| 368 |
-
model_id = "timbrooks/instruct-pix2pix"
|
| 369 |
-
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
| 370 |
-
model_id,
|
| 371 |
-
safety_checker=None,
|
| 372 |
-
)
|
| 373 |
-
pipe.to(torch_device)
|
| 374 |
-
pipe.set_progress_bar_config(disable=None)
|
| 375 |
-
pipe.enable_attention_slicing()
|
| 376 |
-
|
| 377 |
-
output = pipe(**inputs)
|
| 378 |
-
image = output.images[0]
|
| 379 |
-
|
| 380 |
-
image_slice = image[255:258, 383:386, -1]
|
| 381 |
-
|
| 382 |
-
assert image.shape == (504, 504, 3)
|
| 383 |
-
expected_slice = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590])
|
| 384 |
-
|
| 385 |
-
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
|
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spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/shared_heads/res_layer.py
DELETED
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@@ -1,77 +0,0 @@
|
|
| 1 |
-
import torch.nn as nn
|
| 2 |
-
from mmcv.cnn import constant_init, kaiming_init
|
| 3 |
-
from mmcv.runner import auto_fp16, load_checkpoint
|
| 4 |
-
|
| 5 |
-
from mmdet.models.backbones import ResNet
|
| 6 |
-
from mmdet.models.builder import SHARED_HEADS
|
| 7 |
-
from mmdet.models.utils import ResLayer as _ResLayer
|
| 8 |
-
from mmdet.utils import get_root_logger
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
@SHARED_HEADS.register_module()
|
| 12 |
-
class ResLayer(nn.Module):
|
| 13 |
-
|
| 14 |
-
def __init__(self,
|
| 15 |
-
depth,
|
| 16 |
-
stage=3,
|
| 17 |
-
stride=2,
|
| 18 |
-
dilation=1,
|
| 19 |
-
style='pytorch',
|
| 20 |
-
norm_cfg=dict(type='BN', requires_grad=True),
|
| 21 |
-
norm_eval=True,
|
| 22 |
-
with_cp=False,
|
| 23 |
-
dcn=None):
|
| 24 |
-
super(ResLayer, self).__init__()
|
| 25 |
-
self.norm_eval = norm_eval
|
| 26 |
-
self.norm_cfg = norm_cfg
|
| 27 |
-
self.stage = stage
|
| 28 |
-
self.fp16_enabled = False
|
| 29 |
-
block, stage_blocks = ResNet.arch_settings[depth]
|
| 30 |
-
stage_block = stage_blocks[stage]
|
| 31 |
-
planes = 64 * 2**stage
|
| 32 |
-
inplanes = 64 * 2**(stage - 1) * block.expansion
|
| 33 |
-
|
| 34 |
-
res_layer = _ResLayer(
|
| 35 |
-
block,
|
| 36 |
-
inplanes,
|
| 37 |
-
planes,
|
| 38 |
-
stage_block,
|
| 39 |
-
stride=stride,
|
| 40 |
-
dilation=dilation,
|
| 41 |
-
style=style,
|
| 42 |
-
with_cp=with_cp,
|
| 43 |
-
norm_cfg=self.norm_cfg,
|
| 44 |
-
dcn=dcn)
|
| 45 |
-
self.add_module(f'layer{stage + 1}', res_layer)
|
| 46 |
-
|
| 47 |
-
def init_weights(self, pretrained=None):
|
| 48 |
-
"""Initialize the weights in the module.
|
| 49 |
-
|
| 50 |
-
Args:
|
| 51 |
-
pretrained (str, optional): Path to pre-trained weights.
|
| 52 |
-
Defaults to None.
|
| 53 |
-
"""
|
| 54 |
-
if isinstance(pretrained, str):
|
| 55 |
-
logger = get_root_logger()
|
| 56 |
-
load_checkpoint(self, pretrained, strict=False, logger=logger)
|
| 57 |
-
elif pretrained is None:
|
| 58 |
-
for m in self.modules():
|
| 59 |
-
if isinstance(m, nn.Conv2d):
|
| 60 |
-
kaiming_init(m)
|
| 61 |
-
elif isinstance(m, nn.BatchNorm2d):
|
| 62 |
-
constant_init(m, 1)
|
| 63 |
-
else:
|
| 64 |
-
raise TypeError('pretrained must be a str or None')
|
| 65 |
-
|
| 66 |
-
@auto_fp16()
|
| 67 |
-
def forward(self, x):
|
| 68 |
-
res_layer = getattr(self, f'layer{self.stage + 1}')
|
| 69 |
-
out = res_layer(x)
|
| 70 |
-
return out
|
| 71 |
-
|
| 72 |
-
def train(self, mode=True):
|
| 73 |
-
super(ResLayer, self).train(mode)
|
| 74 |
-
if self.norm_eval:
|
| 75 |
-
for m in self.modules():
|
| 76 |
-
if isinstance(m, nn.BatchNorm2d):
|
| 77 |
-
m.eval()
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spaces/Andy1621/uniformer_image_detection/mmdet/utils/profiling.py
DELETED
|
@@ -1,39 +0,0 @@
|
|
| 1 |
-
import contextlib
|
| 2 |
-
import sys
|
| 3 |
-
import time
|
| 4 |
-
|
| 5 |
-
import torch
|
| 6 |
-
|
| 7 |
-
if sys.version_info >= (3, 7):
|
| 8 |
-
|
| 9 |
-
@contextlib.contextmanager
|
| 10 |
-
def profile_time(trace_name,
|
| 11 |
-
name,
|
| 12 |
-
enabled=True,
|
| 13 |
-
stream=None,
|
| 14 |
-
end_stream=None):
|
| 15 |
-
"""Print time spent by CPU and GPU.
|
| 16 |
-
|
| 17 |
-
Useful as a temporary context manager to find sweet spots of code
|
| 18 |
-
suitable for async implementation.
|
| 19 |
-
"""
|
| 20 |
-
if (not enabled) or not torch.cuda.is_available():
|
| 21 |
-
yield
|
| 22 |
-
return
|
| 23 |
-
stream = stream if stream else torch.cuda.current_stream()
|
| 24 |
-
end_stream = end_stream if end_stream else stream
|
| 25 |
-
start = torch.cuda.Event(enable_timing=True)
|
| 26 |
-
end = torch.cuda.Event(enable_timing=True)
|
| 27 |
-
stream.record_event(start)
|
| 28 |
-
try:
|
| 29 |
-
cpu_start = time.monotonic()
|
| 30 |
-
yield
|
| 31 |
-
finally:
|
| 32 |
-
cpu_end = time.monotonic()
|
| 33 |
-
end_stream.record_event(end)
|
| 34 |
-
end.synchronize()
|
| 35 |
-
cpu_time = (cpu_end - cpu_start) * 1000
|
| 36 |
-
gpu_time = start.elapsed_time(end)
|
| 37 |
-
msg = f'{trace_name} {name} cpu_time {cpu_time:.2f} ms '
|
| 38 |
-
msg += f'gpu_time {gpu_time:.2f} ms stream {stream}'
|
| 39 |
-
print(msg, end_stream)
|
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spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/testing.py
DELETED
|
@@ -1,140 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Open-MMLab.
|
| 2 |
-
import sys
|
| 3 |
-
from collections.abc import Iterable
|
| 4 |
-
from runpy import run_path
|
| 5 |
-
from shlex import split
|
| 6 |
-
from typing import Any, Dict, List
|
| 7 |
-
from unittest.mock import patch
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
def check_python_script(cmd):
|
| 11 |
-
"""Run the python cmd script with `__main__`. The difference between
|
| 12 |
-
`os.system` is that, this function exectues code in the current process, so
|
| 13 |
-
that it can be tracked by coverage tools. Currently it supports two forms:
|
| 14 |
-
|
| 15 |
-
- ./tests/data/scripts/hello.py zz
|
| 16 |
-
- python tests/data/scripts/hello.py zz
|
| 17 |
-
"""
|
| 18 |
-
args = split(cmd)
|
| 19 |
-
if args[0] == 'python':
|
| 20 |
-
args = args[1:]
|
| 21 |
-
with patch.object(sys, 'argv', args):
|
| 22 |
-
run_path(args[0], run_name='__main__')
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
def _any(judge_result):
|
| 26 |
-
"""Since built-in ``any`` works only when the element of iterable is not
|
| 27 |
-
iterable, implement the function."""
|
| 28 |
-
if not isinstance(judge_result, Iterable):
|
| 29 |
-
return judge_result
|
| 30 |
-
|
| 31 |
-
try:
|
| 32 |
-
for element in judge_result:
|
| 33 |
-
if _any(element):
|
| 34 |
-
return True
|
| 35 |
-
except TypeError:
|
| 36 |
-
# Maybe encounter the case: torch.tensor(True) | torch.tensor(False)
|
| 37 |
-
if judge_result:
|
| 38 |
-
return True
|
| 39 |
-
return False
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
def assert_dict_contains_subset(dict_obj: Dict[Any, Any],
|
| 43 |
-
expected_subset: Dict[Any, Any]) -> bool:
|
| 44 |
-
"""Check if the dict_obj contains the expected_subset.
|
| 45 |
-
|
| 46 |
-
Args:
|
| 47 |
-
dict_obj (Dict[Any, Any]): Dict object to be checked.
|
| 48 |
-
expected_subset (Dict[Any, Any]): Subset expected to be contained in
|
| 49 |
-
dict_obj.
|
| 50 |
-
|
| 51 |
-
Returns:
|
| 52 |
-
bool: Whether the dict_obj contains the expected_subset.
|
| 53 |
-
"""
|
| 54 |
-
|
| 55 |
-
for key, value in expected_subset.items():
|
| 56 |
-
if key not in dict_obj.keys() or _any(dict_obj[key] != value):
|
| 57 |
-
return False
|
| 58 |
-
return True
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
def assert_attrs_equal(obj: Any, expected_attrs: Dict[str, Any]) -> bool:
|
| 62 |
-
"""Check if attribute of class object is correct.
|
| 63 |
-
|
| 64 |
-
Args:
|
| 65 |
-
obj (object): Class object to be checked.
|
| 66 |
-
expected_attrs (Dict[str, Any]): Dict of the expected attrs.
|
| 67 |
-
|
| 68 |
-
Returns:
|
| 69 |
-
bool: Whether the attribute of class object is correct.
|
| 70 |
-
"""
|
| 71 |
-
for attr, value in expected_attrs.items():
|
| 72 |
-
if not hasattr(obj, attr) or _any(getattr(obj, attr) != value):
|
| 73 |
-
return False
|
| 74 |
-
return True
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
def assert_dict_has_keys(obj: Dict[str, Any],
|
| 78 |
-
expected_keys: List[str]) -> bool:
|
| 79 |
-
"""Check if the obj has all the expected_keys.
|
| 80 |
-
|
| 81 |
-
Args:
|
| 82 |
-
obj (Dict[str, Any]): Object to be checked.
|
| 83 |
-
expected_keys (List[str]): Keys expected to contained in the keys of
|
| 84 |
-
the obj.
|
| 85 |
-
|
| 86 |
-
Returns:
|
| 87 |
-
bool: Whether the obj has the expected keys.
|
| 88 |
-
"""
|
| 89 |
-
return set(expected_keys).issubset(set(obj.keys()))
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
def assert_keys_equal(result_keys: List[str], target_keys: List[str]) -> bool:
|
| 93 |
-
"""Check if target_keys is equal to result_keys.
|
| 94 |
-
|
| 95 |
-
Args:
|
| 96 |
-
result_keys (List[str]): Result keys to be checked.
|
| 97 |
-
target_keys (List[str]): Target keys to be checked.
|
| 98 |
-
|
| 99 |
-
Returns:
|
| 100 |
-
bool: Whether target_keys is equal to result_keys.
|
| 101 |
-
"""
|
| 102 |
-
return set(result_keys) == set(target_keys)
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
def assert_is_norm_layer(module) -> bool:
|
| 106 |
-
"""Check if the module is a norm layer.
|
| 107 |
-
|
| 108 |
-
Args:
|
| 109 |
-
module (nn.Module): The module to be checked.
|
| 110 |
-
|
| 111 |
-
Returns:
|
| 112 |
-
bool: Whether the module is a norm layer.
|
| 113 |
-
"""
|
| 114 |
-
from .parrots_wrapper import _BatchNorm, _InstanceNorm
|
| 115 |
-
from torch.nn import GroupNorm, LayerNorm
|
| 116 |
-
norm_layer_candidates = (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)
|
| 117 |
-
return isinstance(module, norm_layer_candidates)
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
def assert_params_all_zeros(module) -> bool:
|
| 121 |
-
"""Check if the parameters of the module is all zeros.
|
| 122 |
-
|
| 123 |
-
Args:
|
| 124 |
-
module (nn.Module): The module to be checked.
|
| 125 |
-
|
| 126 |
-
Returns:
|
| 127 |
-
bool: Whether the parameters of the module is all zeros.
|
| 128 |
-
"""
|
| 129 |
-
weight_data = module.weight.data
|
| 130 |
-
is_weight_zero = weight_data.allclose(
|
| 131 |
-
weight_data.new_zeros(weight_data.size()))
|
| 132 |
-
|
| 133 |
-
if hasattr(module, 'bias') and module.bias is not None:
|
| 134 |
-
bias_data = module.bias.data
|
| 135 |
-
is_bias_zero = bias_data.allclose(
|
| 136 |
-
bias_data.new_zeros(bias_data.size()))
|
| 137 |
-
else:
|
| 138 |
-
is_bias_zero = True
|
| 139 |
-
|
| 140 |
-
return is_weight_zero and is_bias_zero
|
|
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spaces/Anthony7906/MengHuiMXD_GPT/modules/shared.py
DELETED
|
@@ -1,55 +0,0 @@
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|
| 1 |
-
from modules.presets import COMPLETION_URL, BALANCE_API_URL, USAGE_API_URL, API_HOST
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| 2 |
-
import os
|
| 3 |
-
import queue
|
| 4 |
-
|
| 5 |
-
class State:
|
| 6 |
-
interrupted = False
|
| 7 |
-
multi_api_key = False
|
| 8 |
-
completion_url = COMPLETION_URL
|
| 9 |
-
balance_api_url = BALANCE_API_URL
|
| 10 |
-
usage_api_url = USAGE_API_URL
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| 11 |
-
|
| 12 |
-
def interrupt(self):
|
| 13 |
-
self.interrupted = True
|
| 14 |
-
|
| 15 |
-
def recover(self):
|
| 16 |
-
self.interrupted = False
|
| 17 |
-
|
| 18 |
-
def set_api_host(self, api_host):
|
| 19 |
-
self.completion_url = f"https://{api_host}/v1/chat/completions"
|
| 20 |
-
self.balance_api_url = f"https://{api_host}/dashboard/billing/credit_grants"
|
| 21 |
-
self.usage_api_url = f"https://{api_host}/dashboard/billing/usage"
|
| 22 |
-
os.environ["OPENAI_API_BASE"] = f"https://{api_host}/v1"
|
| 23 |
-
|
| 24 |
-
def reset_api_host(self):
|
| 25 |
-
self.completion_url = COMPLETION_URL
|
| 26 |
-
self.balance_api_url = BALANCE_API_URL
|
| 27 |
-
self.usage_api_url = USAGE_API_URL
|
| 28 |
-
os.environ["OPENAI_API_BASE"] = f"https://{API_HOST}/v1"
|
| 29 |
-
return API_HOST
|
| 30 |
-
|
| 31 |
-
def reset_all(self):
|
| 32 |
-
self.interrupted = False
|
| 33 |
-
self.completion_url = COMPLETION_URL
|
| 34 |
-
|
| 35 |
-
def set_api_key_queue(self, api_key_list):
|
| 36 |
-
self.multi_api_key = True
|
| 37 |
-
self.api_key_queue = queue.Queue()
|
| 38 |
-
for api_key in api_key_list:
|
| 39 |
-
self.api_key_queue.put(api_key)
|
| 40 |
-
|
| 41 |
-
def switching_api_key(self, func):
|
| 42 |
-
if not hasattr(self, "api_key_queue"):
|
| 43 |
-
return func
|
| 44 |
-
|
| 45 |
-
def wrapped(*args, **kwargs):
|
| 46 |
-
api_key = self.api_key_queue.get()
|
| 47 |
-
args[0].api_key = api_key
|
| 48 |
-
ret = func(*args, **kwargs)
|
| 49 |
-
self.api_key_queue.put(api_key)
|
| 50 |
-
return ret
|
| 51 |
-
|
| 52 |
-
return wrapped
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
state = State()
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spaces/Apk/anything-v3.0/utils.py
DELETED
|
@@ -1,6 +0,0 @@
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|
| 1 |
-
def is_google_colab():
|
| 2 |
-
try:
|
| 3 |
-
import google.colab
|
| 4 |
-
return True
|
| 5 |
-
except:
|
| 6 |
-
return False
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/req/req_set.py
DELETED
|
@@ -1,82 +0,0 @@
|
|
| 1 |
-
import logging
|
| 2 |
-
from collections import OrderedDict
|
| 3 |
-
from typing import Dict, List
|
| 4 |
-
|
| 5 |
-
from pip._vendor.packaging.utils import canonicalize_name
|
| 6 |
-
|
| 7 |
-
from pip._internal.req.req_install import InstallRequirement
|
| 8 |
-
|
| 9 |
-
logger = logging.getLogger(__name__)
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class RequirementSet:
|
| 13 |
-
def __init__(self, check_supported_wheels: bool = True) -> None:
|
| 14 |
-
"""Create a RequirementSet."""
|
| 15 |
-
|
| 16 |
-
self.requirements: Dict[str, InstallRequirement] = OrderedDict()
|
| 17 |
-
self.check_supported_wheels = check_supported_wheels
|
| 18 |
-
|
| 19 |
-
self.unnamed_requirements: List[InstallRequirement] = []
|
| 20 |
-
|
| 21 |
-
def __str__(self) -> str:
|
| 22 |
-
requirements = sorted(
|
| 23 |
-
(req for req in self.requirements.values() if not req.comes_from),
|
| 24 |
-
key=lambda req: canonicalize_name(req.name or ""),
|
| 25 |
-
)
|
| 26 |
-
return " ".join(str(req.req) for req in requirements)
|
| 27 |
-
|
| 28 |
-
def __repr__(self) -> str:
|
| 29 |
-
requirements = sorted(
|
| 30 |
-
self.requirements.values(),
|
| 31 |
-
key=lambda req: canonicalize_name(req.name or ""),
|
| 32 |
-
)
|
| 33 |
-
|
| 34 |
-
format_string = "<{classname} object; {count} requirement(s): {reqs}>"
|
| 35 |
-
return format_string.format(
|
| 36 |
-
classname=self.__class__.__name__,
|
| 37 |
-
count=len(requirements),
|
| 38 |
-
reqs=", ".join(str(req.req) for req in requirements),
|
| 39 |
-
)
|
| 40 |
-
|
| 41 |
-
def add_unnamed_requirement(self, install_req: InstallRequirement) -> None:
|
| 42 |
-
assert not install_req.name
|
| 43 |
-
self.unnamed_requirements.append(install_req)
|
| 44 |
-
|
| 45 |
-
def add_named_requirement(self, install_req: InstallRequirement) -> None:
|
| 46 |
-
assert install_req.name
|
| 47 |
-
|
| 48 |
-
project_name = canonicalize_name(install_req.name)
|
| 49 |
-
self.requirements[project_name] = install_req
|
| 50 |
-
|
| 51 |
-
def has_requirement(self, name: str) -> bool:
|
| 52 |
-
project_name = canonicalize_name(name)
|
| 53 |
-
|
| 54 |
-
return (
|
| 55 |
-
project_name in self.requirements
|
| 56 |
-
and not self.requirements[project_name].constraint
|
| 57 |
-
)
|
| 58 |
-
|
| 59 |
-
def get_requirement(self, name: str) -> InstallRequirement:
|
| 60 |
-
project_name = canonicalize_name(name)
|
| 61 |
-
|
| 62 |
-
if project_name in self.requirements:
|
| 63 |
-
return self.requirements[project_name]
|
| 64 |
-
|
| 65 |
-
raise KeyError(f"No project with the name {name!r}")
|
| 66 |
-
|
| 67 |
-
@property
|
| 68 |
-
def all_requirements(self) -> List[InstallRequirement]:
|
| 69 |
-
return self.unnamed_requirements + list(self.requirements.values())
|
| 70 |
-
|
| 71 |
-
@property
|
| 72 |
-
def requirements_to_install(self) -> List[InstallRequirement]:
|
| 73 |
-
"""Return the list of requirements that need to be installed.
|
| 74 |
-
|
| 75 |
-
TODO remove this property together with the legacy resolver, since the new
|
| 76 |
-
resolver only returns requirements that need to be installed.
|
| 77 |
-
"""
|
| 78 |
-
return [
|
| 79 |
-
install_req
|
| 80 |
-
for install_req in self.all_requirements
|
| 81 |
-
if not install_req.constraint and not install_req.satisfied_by
|
| 82 |
-
]
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/markers.py
DELETED
|
@@ -1,152 +0,0 @@
|
|
| 1 |
-
# -*- coding: utf-8 -*-
|
| 2 |
-
#
|
| 3 |
-
# Copyright (C) 2012-2017 Vinay Sajip.
|
| 4 |
-
# Licensed to the Python Software Foundation under a contributor agreement.
|
| 5 |
-
# See LICENSE.txt and CONTRIBUTORS.txt.
|
| 6 |
-
#
|
| 7 |
-
"""
|
| 8 |
-
Parser for the environment markers micro-language defined in PEP 508.
|
| 9 |
-
"""
|
| 10 |
-
|
| 11 |
-
# Note: In PEP 345, the micro-language was Python compatible, so the ast
|
| 12 |
-
# module could be used to parse it. However, PEP 508 introduced operators such
|
| 13 |
-
# as ~= and === which aren't in Python, necessitating a different approach.
|
| 14 |
-
|
| 15 |
-
import os
|
| 16 |
-
import re
|
| 17 |
-
import sys
|
| 18 |
-
import platform
|
| 19 |
-
|
| 20 |
-
from .compat import string_types
|
| 21 |
-
from .util import in_venv, parse_marker
|
| 22 |
-
from .version import NormalizedVersion as NV
|
| 23 |
-
|
| 24 |
-
__all__ = ['interpret']
|
| 25 |
-
|
| 26 |
-
_VERSION_PATTERN = re.compile(r'((\d+(\.\d+)*\w*)|\'(\d+(\.\d+)*\w*)\'|\"(\d+(\.\d+)*\w*)\")')
|
| 27 |
-
|
| 28 |
-
def _is_literal(o):
|
| 29 |
-
if not isinstance(o, string_types) or not o:
|
| 30 |
-
return False
|
| 31 |
-
return o[0] in '\'"'
|
| 32 |
-
|
| 33 |
-
def _get_versions(s):
|
| 34 |
-
result = []
|
| 35 |
-
for m in _VERSION_PATTERN.finditer(s):
|
| 36 |
-
result.append(NV(m.groups()[0]))
|
| 37 |
-
return set(result)
|
| 38 |
-
|
| 39 |
-
class Evaluator(object):
|
| 40 |
-
"""
|
| 41 |
-
This class is used to evaluate marker expessions.
|
| 42 |
-
"""
|
| 43 |
-
|
| 44 |
-
operations = {
|
| 45 |
-
'==': lambda x, y: x == y,
|
| 46 |
-
'===': lambda x, y: x == y,
|
| 47 |
-
'~=': lambda x, y: x == y or x > y,
|
| 48 |
-
'!=': lambda x, y: x != y,
|
| 49 |
-
'<': lambda x, y: x < y,
|
| 50 |
-
'<=': lambda x, y: x == y or x < y,
|
| 51 |
-
'>': lambda x, y: x > y,
|
| 52 |
-
'>=': lambda x, y: x == y or x > y,
|
| 53 |
-
'and': lambda x, y: x and y,
|
| 54 |
-
'or': lambda x, y: x or y,
|
| 55 |
-
'in': lambda x, y: x in y,
|
| 56 |
-
'not in': lambda x, y: x not in y,
|
| 57 |
-
}
|
| 58 |
-
|
| 59 |
-
def evaluate(self, expr, context):
|
| 60 |
-
"""
|
| 61 |
-
Evaluate a marker expression returned by the :func:`parse_requirement`
|
| 62 |
-
function in the specified context.
|
| 63 |
-
"""
|
| 64 |
-
if isinstance(expr, string_types):
|
| 65 |
-
if expr[0] in '\'"':
|
| 66 |
-
result = expr[1:-1]
|
| 67 |
-
else:
|
| 68 |
-
if expr not in context:
|
| 69 |
-
raise SyntaxError('unknown variable: %s' % expr)
|
| 70 |
-
result = context[expr]
|
| 71 |
-
else:
|
| 72 |
-
assert isinstance(expr, dict)
|
| 73 |
-
op = expr['op']
|
| 74 |
-
if op not in self.operations:
|
| 75 |
-
raise NotImplementedError('op not implemented: %s' % op)
|
| 76 |
-
elhs = expr['lhs']
|
| 77 |
-
erhs = expr['rhs']
|
| 78 |
-
if _is_literal(expr['lhs']) and _is_literal(expr['rhs']):
|
| 79 |
-
raise SyntaxError('invalid comparison: %s %s %s' % (elhs, op, erhs))
|
| 80 |
-
|
| 81 |
-
lhs = self.evaluate(elhs, context)
|
| 82 |
-
rhs = self.evaluate(erhs, context)
|
| 83 |
-
if ((elhs == 'python_version' or erhs == 'python_version') and
|
| 84 |
-
op in ('<', '<=', '>', '>=', '===', '==', '!=', '~=')):
|
| 85 |
-
lhs = NV(lhs)
|
| 86 |
-
rhs = NV(rhs)
|
| 87 |
-
elif elhs == 'python_version' and op in ('in', 'not in'):
|
| 88 |
-
lhs = NV(lhs)
|
| 89 |
-
rhs = _get_versions(rhs)
|
| 90 |
-
result = self.operations[op](lhs, rhs)
|
| 91 |
-
return result
|
| 92 |
-
|
| 93 |
-
_DIGITS = re.compile(r'\d+\.\d+')
|
| 94 |
-
|
| 95 |
-
def default_context():
|
| 96 |
-
def format_full_version(info):
|
| 97 |
-
version = '%s.%s.%s' % (info.major, info.minor, info.micro)
|
| 98 |
-
kind = info.releaselevel
|
| 99 |
-
if kind != 'final':
|
| 100 |
-
version += kind[0] + str(info.serial)
|
| 101 |
-
return version
|
| 102 |
-
|
| 103 |
-
if hasattr(sys, 'implementation'):
|
| 104 |
-
implementation_version = format_full_version(sys.implementation.version)
|
| 105 |
-
implementation_name = sys.implementation.name
|
| 106 |
-
else:
|
| 107 |
-
implementation_version = '0'
|
| 108 |
-
implementation_name = ''
|
| 109 |
-
|
| 110 |
-
ppv = platform.python_version()
|
| 111 |
-
m = _DIGITS.match(ppv)
|
| 112 |
-
pv = m.group(0)
|
| 113 |
-
result = {
|
| 114 |
-
'implementation_name': implementation_name,
|
| 115 |
-
'implementation_version': implementation_version,
|
| 116 |
-
'os_name': os.name,
|
| 117 |
-
'platform_machine': platform.machine(),
|
| 118 |
-
'platform_python_implementation': platform.python_implementation(),
|
| 119 |
-
'platform_release': platform.release(),
|
| 120 |
-
'platform_system': platform.system(),
|
| 121 |
-
'platform_version': platform.version(),
|
| 122 |
-
'platform_in_venv': str(in_venv()),
|
| 123 |
-
'python_full_version': ppv,
|
| 124 |
-
'python_version': pv,
|
| 125 |
-
'sys_platform': sys.platform,
|
| 126 |
-
}
|
| 127 |
-
return result
|
| 128 |
-
|
| 129 |
-
DEFAULT_CONTEXT = default_context()
|
| 130 |
-
del default_context
|
| 131 |
-
|
| 132 |
-
evaluator = Evaluator()
|
| 133 |
-
|
| 134 |
-
def interpret(marker, execution_context=None):
|
| 135 |
-
"""
|
| 136 |
-
Interpret a marker and return a result depending on environment.
|
| 137 |
-
|
| 138 |
-
:param marker: The marker to interpret.
|
| 139 |
-
:type marker: str
|
| 140 |
-
:param execution_context: The context used for name lookup.
|
| 141 |
-
:type execution_context: mapping
|
| 142 |
-
"""
|
| 143 |
-
try:
|
| 144 |
-
expr, rest = parse_marker(marker)
|
| 145 |
-
except Exception as e:
|
| 146 |
-
raise SyntaxError('Unable to interpret marker syntax: %s: %s' % (marker, e))
|
| 147 |
-
if rest and rest[0] != '#':
|
| 148 |
-
raise SyntaxError('unexpected trailing data in marker: %s: %s' % (marker, rest))
|
| 149 |
-
context = dict(DEFAULT_CONTEXT)
|
| 150 |
-
if execution_context:
|
| 151 |
-
context.update(execution_context)
|
| 152 |
-
return evaluator.evaluate(expr, context)
|
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/abc.py
DELETED
|
@@ -1,137 +0,0 @@
|
|
| 1 |
-
import abc
|
| 2 |
-
from typing import BinaryIO, Iterable, Text
|
| 3 |
-
|
| 4 |
-
from ._compat import runtime_checkable, Protocol
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
class ResourceReader(metaclass=abc.ABCMeta):
|
| 8 |
-
"""Abstract base class for loaders to provide resource reading support."""
|
| 9 |
-
|
| 10 |
-
@abc.abstractmethod
|
| 11 |
-
def open_resource(self, resource: Text) -> BinaryIO:
|
| 12 |
-
"""Return an opened, file-like object for binary reading.
|
| 13 |
-
|
| 14 |
-
The 'resource' argument is expected to represent only a file name.
|
| 15 |
-
If the resource cannot be found, FileNotFoundError is raised.
|
| 16 |
-
"""
|
| 17 |
-
# This deliberately raises FileNotFoundError instead of
|
| 18 |
-
# NotImplementedError so that if this method is accidentally called,
|
| 19 |
-
# it'll still do the right thing.
|
| 20 |
-
raise FileNotFoundError
|
| 21 |
-
|
| 22 |
-
@abc.abstractmethod
|
| 23 |
-
def resource_path(self, resource: Text) -> Text:
|
| 24 |
-
"""Return the file system path to the specified resource.
|
| 25 |
-
|
| 26 |
-
The 'resource' argument is expected to represent only a file name.
|
| 27 |
-
If the resource does not exist on the file system, raise
|
| 28 |
-
FileNotFoundError.
|
| 29 |
-
"""
|
| 30 |
-
# This deliberately raises FileNotFoundError instead of
|
| 31 |
-
# NotImplementedError so that if this method is accidentally called,
|
| 32 |
-
# it'll still do the right thing.
|
| 33 |
-
raise FileNotFoundError
|
| 34 |
-
|
| 35 |
-
@abc.abstractmethod
|
| 36 |
-
def is_resource(self, path: Text) -> bool:
|
| 37 |
-
"""Return True if the named 'path' is a resource.
|
| 38 |
-
|
| 39 |
-
Files are resources, directories are not.
|
| 40 |
-
"""
|
| 41 |
-
raise FileNotFoundError
|
| 42 |
-
|
| 43 |
-
@abc.abstractmethod
|
| 44 |
-
def contents(self) -> Iterable[str]:
|
| 45 |
-
"""Return an iterable of entries in `package`."""
|
| 46 |
-
raise FileNotFoundError
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
@runtime_checkable
|
| 50 |
-
class Traversable(Protocol):
|
| 51 |
-
"""
|
| 52 |
-
An object with a subset of pathlib.Path methods suitable for
|
| 53 |
-
traversing directories and opening files.
|
| 54 |
-
"""
|
| 55 |
-
|
| 56 |
-
@abc.abstractmethod
|
| 57 |
-
def iterdir(self):
|
| 58 |
-
"""
|
| 59 |
-
Yield Traversable objects in self
|
| 60 |
-
"""
|
| 61 |
-
|
| 62 |
-
def read_bytes(self):
|
| 63 |
-
"""
|
| 64 |
-
Read contents of self as bytes
|
| 65 |
-
"""
|
| 66 |
-
with self.open('rb') as strm:
|
| 67 |
-
return strm.read()
|
| 68 |
-
|
| 69 |
-
def read_text(self, encoding=None):
|
| 70 |
-
"""
|
| 71 |
-
Read contents of self as text
|
| 72 |
-
"""
|
| 73 |
-
with self.open(encoding=encoding) as strm:
|
| 74 |
-
return strm.read()
|
| 75 |
-
|
| 76 |
-
@abc.abstractmethod
|
| 77 |
-
def is_dir(self) -> bool:
|
| 78 |
-
"""
|
| 79 |
-
Return True if self is a directory
|
| 80 |
-
"""
|
| 81 |
-
|
| 82 |
-
@abc.abstractmethod
|
| 83 |
-
def is_file(self) -> bool:
|
| 84 |
-
"""
|
| 85 |
-
Return True if self is a file
|
| 86 |
-
"""
|
| 87 |
-
|
| 88 |
-
@abc.abstractmethod
|
| 89 |
-
def joinpath(self, child):
|
| 90 |
-
"""
|
| 91 |
-
Return Traversable child in self
|
| 92 |
-
"""
|
| 93 |
-
|
| 94 |
-
def __truediv__(self, child):
|
| 95 |
-
"""
|
| 96 |
-
Return Traversable child in self
|
| 97 |
-
"""
|
| 98 |
-
return self.joinpath(child)
|
| 99 |
-
|
| 100 |
-
@abc.abstractmethod
|
| 101 |
-
def open(self, mode='r', *args, **kwargs):
|
| 102 |
-
"""
|
| 103 |
-
mode may be 'r' or 'rb' to open as text or binary. Return a handle
|
| 104 |
-
suitable for reading (same as pathlib.Path.open).
|
| 105 |
-
|
| 106 |
-
When opening as text, accepts encoding parameters such as those
|
| 107 |
-
accepted by io.TextIOWrapper.
|
| 108 |
-
"""
|
| 109 |
-
|
| 110 |
-
@abc.abstractproperty
|
| 111 |
-
def name(self) -> str:
|
| 112 |
-
"""
|
| 113 |
-
The base name of this object without any parent references.
|
| 114 |
-
"""
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
class TraversableResources(ResourceReader):
|
| 118 |
-
"""
|
| 119 |
-
The required interface for providing traversable
|
| 120 |
-
resources.
|
| 121 |
-
"""
|
| 122 |
-
|
| 123 |
-
@abc.abstractmethod
|
| 124 |
-
def files(self):
|
| 125 |
-
"""Return a Traversable object for the loaded package."""
|
| 126 |
-
|
| 127 |
-
def open_resource(self, resource):
|
| 128 |
-
return self.files().joinpath(resource).open('rb')
|
| 129 |
-
|
| 130 |
-
def resource_path(self, resource):
|
| 131 |
-
raise FileNotFoundError(resource)
|
| 132 |
-
|
| 133 |
-
def is_resource(self, path):
|
| 134 |
-
return self.files().joinpath(path).is_file()
|
| 135 |
-
|
| 136 |
-
def contents(self):
|
| 137 |
-
return (item.name for item in self.files().iterdir())
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/actions.py
DELETED
|
@@ -1,207 +0,0 @@
|
|
| 1 |
-
# actions.py
|
| 2 |
-
|
| 3 |
-
from .exceptions import ParseException
|
| 4 |
-
from .util import col
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
class OnlyOnce:
|
| 8 |
-
"""
|
| 9 |
-
Wrapper for parse actions, to ensure they are only called once.
|
| 10 |
-
"""
|
| 11 |
-
|
| 12 |
-
def __init__(self, method_call):
|
| 13 |
-
from .core import _trim_arity
|
| 14 |
-
|
| 15 |
-
self.callable = _trim_arity(method_call)
|
| 16 |
-
self.called = False
|
| 17 |
-
|
| 18 |
-
def __call__(self, s, l, t):
|
| 19 |
-
if not self.called:
|
| 20 |
-
results = self.callable(s, l, t)
|
| 21 |
-
self.called = True
|
| 22 |
-
return results
|
| 23 |
-
raise ParseException(s, l, "OnlyOnce obj called multiple times w/out reset")
|
| 24 |
-
|
| 25 |
-
def reset(self):
|
| 26 |
-
"""
|
| 27 |
-
Allow the associated parse action to be called once more.
|
| 28 |
-
"""
|
| 29 |
-
|
| 30 |
-
self.called = False
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
def match_only_at_col(n):
|
| 34 |
-
"""
|
| 35 |
-
Helper method for defining parse actions that require matching at
|
| 36 |
-
a specific column in the input text.
|
| 37 |
-
"""
|
| 38 |
-
|
| 39 |
-
def verify_col(strg, locn, toks):
|
| 40 |
-
if col(locn, strg) != n:
|
| 41 |
-
raise ParseException(strg, locn, "matched token not at column {}".format(n))
|
| 42 |
-
|
| 43 |
-
return verify_col
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
def replace_with(repl_str):
|
| 47 |
-
"""
|
| 48 |
-
Helper method for common parse actions that simply return
|
| 49 |
-
a literal value. Especially useful when used with
|
| 50 |
-
:class:`transform_string<ParserElement.transform_string>` ().
|
| 51 |
-
|
| 52 |
-
Example::
|
| 53 |
-
|
| 54 |
-
num = Word(nums).set_parse_action(lambda toks: int(toks[0]))
|
| 55 |
-
na = one_of("N/A NA").set_parse_action(replace_with(math.nan))
|
| 56 |
-
term = na | num
|
| 57 |
-
|
| 58 |
-
term[1, ...].parse_string("324 234 N/A 234") # -> [324, 234, nan, 234]
|
| 59 |
-
"""
|
| 60 |
-
return lambda s, l, t: [repl_str]
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
def remove_quotes(s, l, t):
|
| 64 |
-
"""
|
| 65 |
-
Helper parse action for removing quotation marks from parsed
|
| 66 |
-
quoted strings.
|
| 67 |
-
|
| 68 |
-
Example::
|
| 69 |
-
|
| 70 |
-
# by default, quotation marks are included in parsed results
|
| 71 |
-
quoted_string.parse_string("'Now is the Winter of our Discontent'") # -> ["'Now is the Winter of our Discontent'"]
|
| 72 |
-
|
| 73 |
-
# use remove_quotes to strip quotation marks from parsed results
|
| 74 |
-
quoted_string.set_parse_action(remove_quotes)
|
| 75 |
-
quoted_string.parse_string("'Now is the Winter of our Discontent'") # -> ["Now is the Winter of our Discontent"]
|
| 76 |
-
"""
|
| 77 |
-
return t[0][1:-1]
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
def with_attribute(*args, **attr_dict):
|
| 81 |
-
"""
|
| 82 |
-
Helper to create a validating parse action to be used with start
|
| 83 |
-
tags created with :class:`make_xml_tags` or
|
| 84 |
-
:class:`make_html_tags`. Use ``with_attribute`` to qualify
|
| 85 |
-
a starting tag with a required attribute value, to avoid false
|
| 86 |
-
matches on common tags such as ``<TD>`` or ``<DIV>``.
|
| 87 |
-
|
| 88 |
-
Call ``with_attribute`` with a series of attribute names and
|
| 89 |
-
values. Specify the list of filter attributes names and values as:
|
| 90 |
-
|
| 91 |
-
- keyword arguments, as in ``(align="right")``, or
|
| 92 |
-
- as an explicit dict with ``**`` operator, when an attribute
|
| 93 |
-
name is also a Python reserved word, as in ``**{"class":"Customer", "align":"right"}``
|
| 94 |
-
- a list of name-value tuples, as in ``(("ns1:class", "Customer"), ("ns2:align", "right"))``
|
| 95 |
-
|
| 96 |
-
For attribute names with a namespace prefix, you must use the second
|
| 97 |
-
form. Attribute names are matched insensitive to upper/lower case.
|
| 98 |
-
|
| 99 |
-
If just testing for ``class`` (with or without a namespace), use
|
| 100 |
-
:class:`with_class`.
|
| 101 |
-
|
| 102 |
-
To verify that the attribute exists, but without specifying a value,
|
| 103 |
-
pass ``with_attribute.ANY_VALUE`` as the value.
|
| 104 |
-
|
| 105 |
-
Example::
|
| 106 |
-
|
| 107 |
-
html = '''
|
| 108 |
-
<div>
|
| 109 |
-
Some text
|
| 110 |
-
<div type="grid">1 4 0 1 0</div>
|
| 111 |
-
<div type="graph">1,3 2,3 1,1</div>
|
| 112 |
-
<div>this has no type</div>
|
| 113 |
-
</div>
|
| 114 |
-
|
| 115 |
-
'''
|
| 116 |
-
div,div_end = make_html_tags("div")
|
| 117 |
-
|
| 118 |
-
# only match div tag having a type attribute with value "grid"
|
| 119 |
-
div_grid = div().set_parse_action(with_attribute(type="grid"))
|
| 120 |
-
grid_expr = div_grid + SkipTo(div | div_end)("body")
|
| 121 |
-
for grid_header in grid_expr.search_string(html):
|
| 122 |
-
print(grid_header.body)
|
| 123 |
-
|
| 124 |
-
# construct a match with any div tag having a type attribute, regardless of the value
|
| 125 |
-
div_any_type = div().set_parse_action(with_attribute(type=with_attribute.ANY_VALUE))
|
| 126 |
-
div_expr = div_any_type + SkipTo(div | div_end)("body")
|
| 127 |
-
for div_header in div_expr.search_string(html):
|
| 128 |
-
print(div_header.body)
|
| 129 |
-
|
| 130 |
-
prints::
|
| 131 |
-
|
| 132 |
-
1 4 0 1 0
|
| 133 |
-
|
| 134 |
-
1 4 0 1 0
|
| 135 |
-
1,3 2,3 1,1
|
| 136 |
-
"""
|
| 137 |
-
if args:
|
| 138 |
-
attrs = args[:]
|
| 139 |
-
else:
|
| 140 |
-
attrs = attr_dict.items()
|
| 141 |
-
attrs = [(k, v) for k, v in attrs]
|
| 142 |
-
|
| 143 |
-
def pa(s, l, tokens):
|
| 144 |
-
for attrName, attrValue in attrs:
|
| 145 |
-
if attrName not in tokens:
|
| 146 |
-
raise ParseException(s, l, "no matching attribute " + attrName)
|
| 147 |
-
if attrValue != with_attribute.ANY_VALUE and tokens[attrName] != attrValue:
|
| 148 |
-
raise ParseException(
|
| 149 |
-
s,
|
| 150 |
-
l,
|
| 151 |
-
"attribute {!r} has value {!r}, must be {!r}".format(
|
| 152 |
-
attrName, tokens[attrName], attrValue
|
| 153 |
-
),
|
| 154 |
-
)
|
| 155 |
-
|
| 156 |
-
return pa
|
| 157 |
-
|
| 158 |
-
|
| 159 |
-
with_attribute.ANY_VALUE = object()
|
| 160 |
-
|
| 161 |
-
|
| 162 |
-
def with_class(classname, namespace=""):
|
| 163 |
-
"""
|
| 164 |
-
Simplified version of :class:`with_attribute` when
|
| 165 |
-
matching on a div class - made difficult because ``class`` is
|
| 166 |
-
a reserved word in Python.
|
| 167 |
-
|
| 168 |
-
Example::
|
| 169 |
-
|
| 170 |
-
html = '''
|
| 171 |
-
<div>
|
| 172 |
-
Some text
|
| 173 |
-
<div class="grid">1 4 0 1 0</div>
|
| 174 |
-
<div class="graph">1,3 2,3 1,1</div>
|
| 175 |
-
<div>this <div> has no class</div>
|
| 176 |
-
</div>
|
| 177 |
-
|
| 178 |
-
'''
|
| 179 |
-
div,div_end = make_html_tags("div")
|
| 180 |
-
div_grid = div().set_parse_action(with_class("grid"))
|
| 181 |
-
|
| 182 |
-
grid_expr = div_grid + SkipTo(div | div_end)("body")
|
| 183 |
-
for grid_header in grid_expr.search_string(html):
|
| 184 |
-
print(grid_header.body)
|
| 185 |
-
|
| 186 |
-
div_any_type = div().set_parse_action(with_class(withAttribute.ANY_VALUE))
|
| 187 |
-
div_expr = div_any_type + SkipTo(div | div_end)("body")
|
| 188 |
-
for div_header in div_expr.search_string(html):
|
| 189 |
-
print(div_header.body)
|
| 190 |
-
|
| 191 |
-
prints::
|
| 192 |
-
|
| 193 |
-
1 4 0 1 0
|
| 194 |
-
|
| 195 |
-
1 4 0 1 0
|
| 196 |
-
1,3 2,3 1,1
|
| 197 |
-
"""
|
| 198 |
-
classattr = "{}:class".format(namespace) if namespace else "class"
|
| 199 |
-
return with_attribute(**{classattr: classname})
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
# pre-PEP8 compatibility symbols
|
| 203 |
-
replaceWith = replace_with
|
| 204 |
-
removeQuotes = remove_quotes
|
| 205 |
-
withAttribute = with_attribute
|
| 206 |
-
withClass = with_class
|
| 207 |
-
matchOnlyAtCol = match_only_at_col
|
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|
spaces/Awesimo/jojogan/e4e/models/encoders/psp_encoders.py
DELETED
|
@@ -1,200 +0,0 @@
|
|
| 1 |
-
from enum import Enum
|
| 2 |
-
import math
|
| 3 |
-
import numpy as np
|
| 4 |
-
import torch
|
| 5 |
-
from torch import nn
|
| 6 |
-
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
|
| 7 |
-
|
| 8 |
-
from e4e.models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE, _upsample_add
|
| 9 |
-
from e4e.models.stylegan2.model import EqualLinear
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class ProgressiveStage(Enum):
|
| 13 |
-
WTraining = 0
|
| 14 |
-
Delta1Training = 1
|
| 15 |
-
Delta2Training = 2
|
| 16 |
-
Delta3Training = 3
|
| 17 |
-
Delta4Training = 4
|
| 18 |
-
Delta5Training = 5
|
| 19 |
-
Delta6Training = 6
|
| 20 |
-
Delta7Training = 7
|
| 21 |
-
Delta8Training = 8
|
| 22 |
-
Delta9Training = 9
|
| 23 |
-
Delta10Training = 10
|
| 24 |
-
Delta11Training = 11
|
| 25 |
-
Delta12Training = 12
|
| 26 |
-
Delta13Training = 13
|
| 27 |
-
Delta14Training = 14
|
| 28 |
-
Delta15Training = 15
|
| 29 |
-
Delta16Training = 16
|
| 30 |
-
Delta17Training = 17
|
| 31 |
-
Inference = 18
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
class GradualStyleBlock(Module):
|
| 35 |
-
def __init__(self, in_c, out_c, spatial):
|
| 36 |
-
super(GradualStyleBlock, self).__init__()
|
| 37 |
-
self.out_c = out_c
|
| 38 |
-
self.spatial = spatial
|
| 39 |
-
num_pools = int(np.log2(spatial))
|
| 40 |
-
modules = []
|
| 41 |
-
modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
|
| 42 |
-
nn.LeakyReLU()]
|
| 43 |
-
for i in range(num_pools - 1):
|
| 44 |
-
modules += [
|
| 45 |
-
Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
|
| 46 |
-
nn.LeakyReLU()
|
| 47 |
-
]
|
| 48 |
-
self.convs = nn.Sequential(*modules)
|
| 49 |
-
self.linear = EqualLinear(out_c, out_c, lr_mul=1)
|
| 50 |
-
|
| 51 |
-
def forward(self, x):
|
| 52 |
-
x = self.convs(x)
|
| 53 |
-
x = x.view(-1, self.out_c)
|
| 54 |
-
x = self.linear(x)
|
| 55 |
-
return x
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
class GradualStyleEncoder(Module):
|
| 59 |
-
def __init__(self, num_layers, mode='ir', opts=None):
|
| 60 |
-
super(GradualStyleEncoder, self).__init__()
|
| 61 |
-
assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
|
| 62 |
-
assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
|
| 63 |
-
blocks = get_blocks(num_layers)
|
| 64 |
-
if mode == 'ir':
|
| 65 |
-
unit_module = bottleneck_IR
|
| 66 |
-
elif mode == 'ir_se':
|
| 67 |
-
unit_module = bottleneck_IR_SE
|
| 68 |
-
self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
|
| 69 |
-
BatchNorm2d(64),
|
| 70 |
-
PReLU(64))
|
| 71 |
-
modules = []
|
| 72 |
-
for block in blocks:
|
| 73 |
-
for bottleneck in block:
|
| 74 |
-
modules.append(unit_module(bottleneck.in_channel,
|
| 75 |
-
bottleneck.depth,
|
| 76 |
-
bottleneck.stride))
|
| 77 |
-
self.body = Sequential(*modules)
|
| 78 |
-
|
| 79 |
-
self.styles = nn.ModuleList()
|
| 80 |
-
log_size = int(math.log(opts.stylegan_size, 2))
|
| 81 |
-
self.style_count = 2 * log_size - 2
|
| 82 |
-
self.coarse_ind = 3
|
| 83 |
-
self.middle_ind = 7
|
| 84 |
-
for i in range(self.style_count):
|
| 85 |
-
if i < self.coarse_ind:
|
| 86 |
-
style = GradualStyleBlock(512, 512, 16)
|
| 87 |
-
elif i < self.middle_ind:
|
| 88 |
-
style = GradualStyleBlock(512, 512, 32)
|
| 89 |
-
else:
|
| 90 |
-
style = GradualStyleBlock(512, 512, 64)
|
| 91 |
-
self.styles.append(style)
|
| 92 |
-
self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
|
| 93 |
-
self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
|
| 94 |
-
|
| 95 |
-
def forward(self, x):
|
| 96 |
-
x = self.input_layer(x)
|
| 97 |
-
|
| 98 |
-
latents = []
|
| 99 |
-
modulelist = list(self.body._modules.values())
|
| 100 |
-
for i, l in enumerate(modulelist):
|
| 101 |
-
x = l(x)
|
| 102 |
-
if i == 6:
|
| 103 |
-
c1 = x
|
| 104 |
-
elif i == 20:
|
| 105 |
-
c2 = x
|
| 106 |
-
elif i == 23:
|
| 107 |
-
c3 = x
|
| 108 |
-
|
| 109 |
-
for j in range(self.coarse_ind):
|
| 110 |
-
latents.append(self.styles[j](c3))
|
| 111 |
-
|
| 112 |
-
p2 = _upsample_add(c3, self.latlayer1(c2))
|
| 113 |
-
for j in range(self.coarse_ind, self.middle_ind):
|
| 114 |
-
latents.append(self.styles[j](p2))
|
| 115 |
-
|
| 116 |
-
p1 = _upsample_add(p2, self.latlayer2(c1))
|
| 117 |
-
for j in range(self.middle_ind, self.style_count):
|
| 118 |
-
latents.append(self.styles[j](p1))
|
| 119 |
-
|
| 120 |
-
out = torch.stack(latents, dim=1)
|
| 121 |
-
return out
|
| 122 |
-
|
| 123 |
-
|
| 124 |
-
class Encoder4Editing(Module):
|
| 125 |
-
def __init__(self, num_layers, mode='ir', opts=None):
|
| 126 |
-
super(Encoder4Editing, self).__init__()
|
| 127 |
-
assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
|
| 128 |
-
assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
|
| 129 |
-
blocks = get_blocks(num_layers)
|
| 130 |
-
if mode == 'ir':
|
| 131 |
-
unit_module = bottleneck_IR
|
| 132 |
-
elif mode == 'ir_se':
|
| 133 |
-
unit_module = bottleneck_IR_SE
|
| 134 |
-
self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
|
| 135 |
-
BatchNorm2d(64),
|
| 136 |
-
PReLU(64))
|
| 137 |
-
modules = []
|
| 138 |
-
for block in blocks:
|
| 139 |
-
for bottleneck in block:
|
| 140 |
-
modules.append(unit_module(bottleneck.in_channel,
|
| 141 |
-
bottleneck.depth,
|
| 142 |
-
bottleneck.stride))
|
| 143 |
-
self.body = Sequential(*modules)
|
| 144 |
-
|
| 145 |
-
self.styles = nn.ModuleList()
|
| 146 |
-
log_size = int(math.log(opts.stylegan_size, 2))
|
| 147 |
-
self.style_count = 2 * log_size - 2
|
| 148 |
-
self.coarse_ind = 3
|
| 149 |
-
self.middle_ind = 7
|
| 150 |
-
|
| 151 |
-
for i in range(self.style_count):
|
| 152 |
-
if i < self.coarse_ind:
|
| 153 |
-
style = GradualStyleBlock(512, 512, 16)
|
| 154 |
-
elif i < self.middle_ind:
|
| 155 |
-
style = GradualStyleBlock(512, 512, 32)
|
| 156 |
-
else:
|
| 157 |
-
style = GradualStyleBlock(512, 512, 64)
|
| 158 |
-
self.styles.append(style)
|
| 159 |
-
|
| 160 |
-
self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
|
| 161 |
-
self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
|
| 162 |
-
|
| 163 |
-
self.progressive_stage = ProgressiveStage.Inference
|
| 164 |
-
|
| 165 |
-
def get_deltas_starting_dimensions(self):
|
| 166 |
-
''' Get a list of the initial dimension of every delta from which it is applied '''
|
| 167 |
-
return list(range(self.style_count)) # Each dimension has a delta applied to it
|
| 168 |
-
|
| 169 |
-
def set_progressive_stage(self, new_stage: ProgressiveStage):
|
| 170 |
-
self.progressive_stage = new_stage
|
| 171 |
-
print('Changed progressive stage to: ', new_stage)
|
| 172 |
-
|
| 173 |
-
def forward(self, x):
|
| 174 |
-
x = self.input_layer(x)
|
| 175 |
-
|
| 176 |
-
modulelist = list(self.body._modules.values())
|
| 177 |
-
for i, l in enumerate(modulelist):
|
| 178 |
-
x = l(x)
|
| 179 |
-
if i == 6:
|
| 180 |
-
c1 = x
|
| 181 |
-
elif i == 20:
|
| 182 |
-
c2 = x
|
| 183 |
-
elif i == 23:
|
| 184 |
-
c3 = x
|
| 185 |
-
|
| 186 |
-
# Infer main W and duplicate it
|
| 187 |
-
w0 = self.styles[0](c3)
|
| 188 |
-
w = w0.repeat(self.style_count, 1, 1).permute(1, 0, 2)
|
| 189 |
-
stage = self.progressive_stage.value
|
| 190 |
-
features = c3
|
| 191 |
-
for i in range(1, min(stage + 1, self.style_count)): # Infer additional deltas
|
| 192 |
-
if i == self.coarse_ind:
|
| 193 |
-
p2 = _upsample_add(c3, self.latlayer1(c2)) # FPN's middle features
|
| 194 |
-
features = p2
|
| 195 |
-
elif i == self.middle_ind:
|
| 196 |
-
p1 = _upsample_add(p2, self.latlayer2(c1)) # FPN's fine features
|
| 197 |
-
features = p1
|
| 198 |
-
delta_i = self.styles[i](features)
|
| 199 |
-
w[:, i] += delta_i
|
| 200 |
-
return w
|
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|
spaces/BAAI/AltDiffusion/js/index.js
DELETED
|
@@ -1,186 +0,0 @@
|
|
| 1 |
-
window.SD = (() => {
|
| 2 |
-
/*
|
| 3 |
-
* Painterro is made a field of the SD global object
|
| 4 |
-
* To provide convinience when using w() method in css_and_js.py
|
| 5 |
-
*/
|
| 6 |
-
class PainterroClass {
|
| 7 |
-
static isOpen = false;
|
| 8 |
-
static async init ({ x, toId }) {
|
| 9 |
-
console.log(x)
|
| 10 |
-
|
| 11 |
-
const originalImage = x[2] === 'Mask' ? x[1]?.image : x[0];
|
| 12 |
-
|
| 13 |
-
if (window.Painterro === undefined) {
|
| 14 |
-
try {
|
| 15 |
-
await this.load();
|
| 16 |
-
} catch (e) {
|
| 17 |
-
SDClass.error(e);
|
| 18 |
-
|
| 19 |
-
return this.fallback(originalImage);
|
| 20 |
-
}
|
| 21 |
-
}
|
| 22 |
-
|
| 23 |
-
if (this.isOpen) {
|
| 24 |
-
return this.fallback(originalImage);
|
| 25 |
-
}
|
| 26 |
-
this.isOpen = true;
|
| 27 |
-
|
| 28 |
-
let resolveResult;
|
| 29 |
-
const paintClient = Painterro({
|
| 30 |
-
hiddenTools: ['arrow'],
|
| 31 |
-
onHide: () => {
|
| 32 |
-
resolveResult?.(null);
|
| 33 |
-
},
|
| 34 |
-
saveHandler: (image, done) => {
|
| 35 |
-
const data = image.asDataURL();
|
| 36 |
-
|
| 37 |
-
// ensures stable performance even
|
| 38 |
-
// when the editor is in interactive mode
|
| 39 |
-
SD.clearImageInput(SD.el.get(`#${toId}`));
|
| 40 |
-
|
| 41 |
-
resolveResult(data);
|
| 42 |
-
|
| 43 |
-
done(true);
|
| 44 |
-
paintClient.hide();
|
| 45 |
-
},
|
| 46 |
-
});
|
| 47 |
-
|
| 48 |
-
const result = await new Promise((resolve) => {
|
| 49 |
-
resolveResult = resolve;
|
| 50 |
-
paintClient.show(originalImage);
|
| 51 |
-
});
|
| 52 |
-
this.isOpen = false;
|
| 53 |
-
|
| 54 |
-
return result ? this.success(result) : this.fallback(originalImage);
|
| 55 |
-
}
|
| 56 |
-
static success (result) { return [result, { image: result, mask: result }] };
|
| 57 |
-
static fallback (image) { return [image, { image: image, mask: image }] };
|
| 58 |
-
static load () {
|
| 59 |
-
return new Promise((resolve, reject) => {
|
| 60 |
-
const scriptId = '__painterro-script';
|
| 61 |
-
if (document.getElementById(scriptId)) {
|
| 62 |
-
reject(new Error('Tried to load painterro script, but script tag already exists.'));
|
| 63 |
-
return;
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
const styleId = '__painterro-css-override';
|
| 67 |
-
if (!document.getElementById(styleId)) {
|
| 68 |
-
/* Ensure Painterro window is always on top */
|
| 69 |
-
const style = document.createElement('style');
|
| 70 |
-
style.id = styleId;
|
| 71 |
-
style.setAttribute('type', 'text/css');
|
| 72 |
-
style.appendChild(document.createTextNode(`
|
| 73 |
-
.ptro-holder-wrapper {
|
| 74 |
-
z-index: 100;
|
| 75 |
-
}
|
| 76 |
-
`));
|
| 77 |
-
document.head.appendChild(style);
|
| 78 |
-
}
|
| 79 |
-
|
| 80 |
-
const script = document.createElement('script');
|
| 81 |
-
script.id = scriptId;
|
| 82 |
-
script.src = 'https://unpkg.com/[email protected]/build/painterro.min.js';
|
| 83 |
-
script.onload = () => resolve(true);
|
| 84 |
-
script.onerror = (e) => {
|
| 85 |
-
// remove self on error to enable reattempting load
|
| 86 |
-
document.head.removeChild(script);
|
| 87 |
-
reject(e);
|
| 88 |
-
};
|
| 89 |
-
document.head.appendChild(script);
|
| 90 |
-
});
|
| 91 |
-
}
|
| 92 |
-
}
|
| 93 |
-
|
| 94 |
-
/*
|
| 95 |
-
* Turns out caching elements doesn't actually work in gradio
|
| 96 |
-
* As elements in tabs might get recreated
|
| 97 |
-
*/
|
| 98 |
-
class ElementCache {
|
| 99 |
-
#el;
|
| 100 |
-
constructor () {
|
| 101 |
-
this.root = document.querySelector('gradio-app').shadowRoot;
|
| 102 |
-
}
|
| 103 |
-
get (selector) {
|
| 104 |
-
return this.root.querySelector(selector);
|
| 105 |
-
}
|
| 106 |
-
}
|
| 107 |
-
|
| 108 |
-
/*
|
| 109 |
-
* The main helper class to incapsulate functions
|
| 110 |
-
* that change gradio ui functionality
|
| 111 |
-
*/
|
| 112 |
-
class SDClass {
|
| 113 |
-
el = new ElementCache();
|
| 114 |
-
Painterro = PainterroClass;
|
| 115 |
-
moveImageFromGallery ({ x, fromId, toId }) {
|
| 116 |
-
x = x[0];
|
| 117 |
-
if (!Array.isArray(x) || x.length === 0) return;
|
| 118 |
-
|
| 119 |
-
this.clearImageInput(this.el.get(`#${toId}`));
|
| 120 |
-
|
| 121 |
-
const i = this.#getGallerySelectedIndex(this.el.get(`#${fromId}`));
|
| 122 |
-
|
| 123 |
-
return [x[i].replace('data:;','data:image/png;')];
|
| 124 |
-
}
|
| 125 |
-
async copyImageFromGalleryToClipboard ({ x, fromId }) {
|
| 126 |
-
x = x[0];
|
| 127 |
-
if (!Array.isArray(x) || x.length === 0) return;
|
| 128 |
-
|
| 129 |
-
const i = this.#getGallerySelectedIndex(this.el.get(`#${fromId}`));
|
| 130 |
-
|
| 131 |
-
const data = x[i];
|
| 132 |
-
const blob = await (await fetch(data.replace('data:;','data:image/png;'))).blob();
|
| 133 |
-
const item = new ClipboardItem({'image/png': blob});
|
| 134 |
-
|
| 135 |
-
await this.copyToClipboard([item]);
|
| 136 |
-
}
|
| 137 |
-
clickFirstVisibleButton({ rowId }) {
|
| 138 |
-
const generateButtons = this.el.get(`#${rowId}`).querySelectorAll('.gr-button-primary');
|
| 139 |
-
|
| 140 |
-
if (!generateButtons) return;
|
| 141 |
-
|
| 142 |
-
for (let i = 0, arr = [...generateButtons]; i < arr.length; i++) {
|
| 143 |
-
const cs = window.getComputedStyle(arr[i]);
|
| 144 |
-
|
| 145 |
-
if (cs.display !== 'none' && cs.visibility !== 'hidden') {
|
| 146 |
-
console.log(arr[i]);
|
| 147 |
-
|
| 148 |
-
arr[i].click();
|
| 149 |
-
break;
|
| 150 |
-
}
|
| 151 |
-
}
|
| 152 |
-
}
|
| 153 |
-
async gradioInputToClipboard ({ x }) { return this.copyToClipboard(x[0]); }
|
| 154 |
-
async copyToClipboard (value) {
|
| 155 |
-
if (!value || typeof value === 'boolean') return;
|
| 156 |
-
try {
|
| 157 |
-
if (Array.isArray(value) &&
|
| 158 |
-
value.length &&
|
| 159 |
-
value[0] instanceof ClipboardItem) {
|
| 160 |
-
await navigator.clipboard.write(value);
|
| 161 |
-
} else {
|
| 162 |
-
await navigator.clipboard.writeText(value);
|
| 163 |
-
}
|
| 164 |
-
} catch (e) {
|
| 165 |
-
SDClass.error(e);
|
| 166 |
-
}
|
| 167 |
-
}
|
| 168 |
-
static error (e) {
|
| 169 |
-
console.error(e);
|
| 170 |
-
if (typeof e === 'string') {
|
| 171 |
-
alert(e);
|
| 172 |
-
} else if(typeof e === 'object' && Object.hasOwn(e, 'message')) {
|
| 173 |
-
alert(e.message);
|
| 174 |
-
}
|
| 175 |
-
}
|
| 176 |
-
clearImageInput (imageEditor) {
|
| 177 |
-
imageEditor?.querySelector('.modify-upload button:last-child')?.click();
|
| 178 |
-
}
|
| 179 |
-
#getGallerySelectedIndex (gallery) {
|
| 180 |
-
const selected = gallery.querySelector(`.\\!ring-2`);
|
| 181 |
-
return selected ? [...selected.parentNode.children].indexOf(selected) : 0;
|
| 182 |
-
}
|
| 183 |
-
}
|
| 184 |
-
|
| 185 |
-
return new SDClass();
|
| 186 |
-
})();
|
|
|
|
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|
spaces/Benson/text-generation/Examples/Cmo Descargar Y Jugar Entre Nosotros En El PC.md
DELETED
|
@@ -1,101 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Cómo descargar Brawl Stars para iPhone</h1>
|
| 3 |
-
<p>Si estás buscando un juego de ritmo rápido, lleno de acción y lleno de diversión para jugar en tu iPhone, definitivamente deberías ver Brawl Stars. Brawl Stars es un juego multijugador de arena de batalla en línea (MOBA) desarrollado por Supercell, los creadores de Clash of Clans y Clash Royale. En este juego, puedes elegir entre docenas de personajes únicos llamados Brawlers, cada uno con sus propias habilidades, armas y personalidades. Puedes hacer equipo con tus amigos o jugar solo en varios modos de juego, como Gem Grab, Showdown, Brawl Ball, Bounty, Heist y más. También puedes desbloquear nuevos skins, gadgets, poderes estelares y pines para personalizar tus Brawlers y mostrar tu estilo. </p>
|
| 4 |
-
<h2>cómo descargar y jugar entre nosotros en el PC</h2><br /><p><b><b>Download File</b> … <a href="https://bltlly.com/2v6ILh">https://bltlly.com/2v6ILh</a></b></p><br /><br />
|
| 5 |
-
<p>Brawl Stars es uno de los juegos más populares para dispositivos móviles en este momento, con más de 100 millones de descargas solo en Google Play. Pero ¿qué pasa si quieres jugar en tu iPhone? No te preocupes, tenemos todo cubierto. En este artículo, te mostraremos cómo descargar Brawl Stars para iPhone en solo unos sencillos pasos. También te daremos algunos consejos y trucos para jugar Brawl Stars en el iPhone y responder a algunas preguntas frecuentes sobre el juego. Así que sin más preámbulos, ¡empecemos! </p>
|
| 6 |
-
<h2>¿Qué es Brawl Stars? </h2>
|
| 7 |
-
<p>Brawl Stars es un juego MOBA 3v3 que combina elementos de tiro, lucha, estrategia y trabajo en equipo. El juego cuenta con varios modos que requieren diferentes objetivos y habilidades. Por ejemplo, en el modo Gem Grab, tienes que recoger y guardar 10 gemas para ganar; en el modo Showdown, tienes que sobrevivir el mayor tiempo posible en un battle royale; en el modo Brawl Ball, tienes que anotar dos goles antes que el otro equipo; y así sucesivamente. </p>
|
| 8 |
-
|
| 9 |
-
<p>Brawl Stars es un juego que es fácil de aprender pero difícil de dominar. Tienes que usar tus habilidades, estrategia y trabajo en equipo para ganar partidos y subir de rango. También puede unirse o crear un club para chatear con otros jugadores, compartir consejos y jugar juntos. Brawl Stars es un juego que se actualiza constantemente con nuevos contenidos, como nuevos luchadores, skins, mapas, eventos y características. También puedes participar en desafíos especiales y torneos para ganar recompensas y fama. </p>
|
| 10 |
-
<h2>¿Por qué jugar Brawl estrellas en el iPhone? </h2>
|
| 11 |
-
<p>Brawl Stars es un juego diseñado para dispositivos móviles, y jugarlo en iPhone tiene muchas ventajas. Estas son algunas de las razones por las que deberías jugar Brawl Stars en iPhone:</p>
|
| 12 |
-
<p></p>
|
| 13 |
-
<ul>
|
| 14 |
-
<li><strong>Compatibilidad</strong>: Brawl Stars es compatible con la mayoría de modelos de iPhone, desde iPhone 6S y versiones posteriores. No necesitas un dispositivo de alta gama para disfrutar del juego, ya que funciona sin problemas y de manera eficiente en la mayoría de los iPhones. </li>
|
| 15 |
-
<li><strong>Rendimiento</strong>: Brawl Stars tiene una alta velocidad de fotogramas y baja latencia en el iPhone, lo que significa que puedes jugar el juego sin retrasos ni tartamudeos. También puede ajustar la calidad gráfica y el modo de ahorro de batería para optimizar el rendimiento de acuerdo con su preferencia. </li>
|
| 16 |
-
<li><strong>Gráficos</strong>: Brawl Stars tiene gráficos coloridos y vibrantes que se ven muy bien en la pantalla de retina del iPhone. El juego tiene un estilo caricaturesco y encantador que atrae a jugadores de todas las edades. También puede apreciar los detalles y animaciones de los luchadores, pieles, mapas y efectos en la pantalla del iPhone. </li>
|
| 17 |
-
<li><strong>Controles</strong>: Brawl Stars tiene controles simples e intuitivos que son fáciles de usar en la pantalla táctil del iPhone. Puede mover su brawler con el joystick izquierdo y apuntar y disparar con el joystick derecho. También puedes tocar para usar tu súper habilidad, deslizar el dedo para usar tu gadget y pellizcar para acercar o alejar. También puede personalizar los controles para adaptarse a su estilo de juego y comodidad. </li>
|
| 18 |
-
|
| 19 |
-
</ul>
|
| 20 |
-
<h2>¿Cómo obtener estrellas de pelea en el iPhone? </h2>
|
| 21 |
-
<p>Ahora que sabes por qué deberías jugar Brawl Stars en el iPhone, vamos a ver cómo puedes conseguirlo en tu dispositivo. El proceso es muy simple y sencillo, y solo toma unos minutos. Estos son los pasos que debes seguir:</p>
|
| 22 |
-
<h3>Paso 1: Abra la aplicación App Store</h3>
|
| 23 |
-
<p>Lo primero que tienes que hacer es abrir la aplicación App Store en tu iPhone. Puedes encontrarla en tu pantalla de inicio o en tu biblioteca de aplicaciones. La aplicación App Store tiene un icono azul con una letra blanca A dentro. </p>
|
| 24 |
-
<p><img src="https://i.imgur.com/0wYQlXf.png" alt="Icono de la App Store" width="100" height="100"></p>
|
| 25 |
-
<h3>Paso 2: Buscar estrellas de pelea</h3>
|
| 26 |
-
<p>Una vez que abra la aplicación App Store, debe buscar Brawl Stars en la pestaña de búsqueda. Puede encontrar la pestaña de búsqueda en la esquina inferior derecha de la pantalla. Tiene un icono de lupa. </p>
|
| 27 |
-
<p><img src="https://i.imgur.com/4nqZm8R.png" alt="Icono de la pestaña de búsqueda" width="100" height="100"></p>
|
| 28 |
-
<p>Toque en la pestaña de búsqueda y escriba "Brawl Stars" en la barra de búsqueda. Verá una lista de resultados que coinciden con su consulta. Busca el que dice "Brawl Stars" de Supercell y tiene un icono rojo con tres estrellas dentro. </p>
|
| 29 |
-
<p><img src="https://i.imgur.com/9uGy0Wk.png" alt="Resultado de Brawl Stars" width="300" height="150"></p>
|
| 30 |
-
<h3>Paso 3: Toque Obtener o el precio</h3>
|
| 31 |
-
<p>Cuando encuentre Brawl Stars en los resultados, toque en él para abrir su página en el App Store. Verás información sobre el juego, como su descripción, capturas de pantalla, valoraciones, reseñas y más. </p>
|
| 32 |
-
<p>Para descargar Brawl Stars, necesitas tocar el botón Obtener o el precio si no es gratis en tu región. El botón Obtener o el precio se encuentra en la esquina superior derecha de la pantalla, junto al icono y nombre del juego. </p>
|
| 33 |
-
<p><img src="https://i.imgur.com/6WQ3w0F.png" alt="Obtener botón" width="300" height="150"></p>
|
| 34 |
-
<h3>Paso 4: Confirmar la descarga</h3>
|
| 35 |
-
|
| 36 |
-
<p><img src="https://i.imgur.com/9Zy8JlU.png" alt="Confirmar descarga" width="300" height="150"></p>
|
| 37 |
-
<p>Introduzca su contraseña o utilice su huella digital o su cara para confirmar la descarga. Verá un mensaje de confirmación que dice "Descargar..." o "Comprar". </p>
|
| 38 |
-
<h3>Paso 5: Espera a que termine la descarga</h3>
|
| 39 |
-
<p>Ahora solo tienes que esperar a que termine la descarga. Puedes comprobar el progreso de la descarga mirando el círculo alrededor del icono del juego. El círculo se llenará a medida que avance la descarga. También puede ver el estado de descarga en la pestaña Actualizaciones de la aplicación App Store. </p>
|
| 40 |
-
<p><img src="https://i.imgur.com/1ZqXa1G.png" alt="Download progress" width="300" height="150"></p>
|
| 41 |
-
<p>Brawl Stars es de unos 300 MB de tamaño, por lo que puede tardar unos minutos en descargarse dependiendo de su velocidad y conexión a Internet. Asegúrate de tener suficiente espacio de almacenamiento en tu iPhone y una conexión Wi-Fi o datos móviles estable. </p>
|
| 42 |
-
<h3>Paso 6: Estrellas de pelea abierta y disfrutar</h3>
|
| 43 |
-
<p>Enhorabuena, ¡has descargado con éxito Brawl Stars para iPhone! Ahora puedes abrir el juego y empezar a jugar. Puedes encontrar Brawl Stars en tu pantalla de inicio o en tu biblioteca de aplicaciones. El icono del juego es rojo con tres estrellas dentro. </p>
|
| 44 |
-
<p><img src="https://i.imgur.com/0wYQlXf.png" alt="Brawl Stars icon" width="100" height="></p>
|
| 45 |
-
<p>Toque en el icono para abrir Brawl Stars. Verá una pantalla de carga con el logotipo del juego y algunos consejos. Espere a que el juego se cargue y luego siga las instrucciones en la pantalla para configurar su cuenta, elegir su nombre y completar el tutorial. También recibirá un luchador gratis como regalo de bienvenida. </p>
|
| 46 |
-
<p><img src="https://i.imgur.com/2LJQv4r.png" alt="Brawl Stars loading screen" width="300" height="150"></p>
|
| 47 |
-
|
| 48 |
-
<p><img src="https://i.imgur.com/7kqgYx9.png" alt="Brawl Stars main menú" width="300" height="150"></p>
|
| 49 |
-
<p>¡Ahora estás listo para pelear! ¡Diviértete y disfruta de Brawl Stars en tu iPhone! </p>
|
| 50 |
-
<h2>Consejos y trucos para jugar Brawl estrellas en el iPhone</h2>
|
| 51 |
-
<p>Brawl Stars es un juego que requiere habilidad, estrategia y trabajo en equipo para ganar. Estos son algunos consejos y trucos que pueden ayudarte a mejorar tu experiencia de juego y convertirte en un mejor luchador:</p>
|
| 52 |
-
<ul>
|
| 53 |
-
<li><strong>Ajusta tus ajustes</strong>: Puedes personalizar tus ajustes para adaptarlos a tu estilo de juego y comodidad. Por ejemplo, puede cambiar el tamaño y la posición de los joysticks, activar o desactivar el objetivo automático, cambiar entre el modo vertical o horizontal y elegir entre tocar o deslizar para disparar. También puede activar o desactivar la vibración, los efectos de sonido, la música, el chat de voz y las notificaciones. </li>
|
| 54 |
-
<li><strong>Únete a un club</strong>: Un club es un grupo de jugadores que pueden chatear, jugar juntos y compartir consejos. Unirte a un club puede ayudarte a hacer amigos, aprender de otros jugadores y divertirte más. Puede unirse a un club existente o crear su propio club con sus amigos. También puede participar en eventos del club y guerras para ganar recompensas y fama. </li>
|
| 55 |
-
<li><strong>Usa gadgets y poderes estelares</strong>: Los gadgets y poderes estelares son habilidades especiales que pueden mejorar el rendimiento de tus luchadores. Los gadgets se activan deslizando sobre la pantalla y tienen un número limitado de usos por partido. Los poderes estelares son habilidades pasivas que siempre están activas una vez desbloqueadas. Puedes desbloquear gadgets y poderes estelares abriendo cajas o alcanzando ciertos hitos de trofeos. También puedes comprar gadgets y poderes estrella con monedas en la tienda. Los gadgets y los poderes de las estrellas pueden darte una ventaja en la batalla, pero tienes que usarlos sabiamente y estratégicamente. </li>
|
| 56 |
-
|
| 57 |
-
<li><strong>Aprende de los pros</strong>: Si quieres mejorar tus habilidades y conocimientos, puedes ver vídeos y secuencias de reproductores profesionales y creadores de contenido. Puedes aprender de sus consejos, trucos, estrategias y errores. También puedes interactuar con ellos y hacer preguntas en el chat o comentarios. Puedes encontrar muchos videos y transmisiones de Brawl Stars en YouTube, Twitch, Reddit y otras plataformas. </li>
|
| 58 |
-
</ul>
|
| 59 |
-
<h2>Preguntas frecuentes sobre Brawl Stars en iPhone</h2>
|
| 60 |
-
<p>Aquí están algunas de las preguntas y respuestas más comunes sobre Brawl Stars en iPhone:</p>
|
| 61 |
-
<h4>¿Cómo actualizo Brawl Stars en el iPhone? </h4>
|
| 62 |
-
<p>Para actualizar Brawl Stars en el iPhone, debe abrir la aplicación App Store e ir a la pestaña Actualizaciones. Verá una lista de aplicaciones que tienen actualizaciones disponibles. Busque Brawl Stars y toque en el botón Actualizar junto a él. También puede habilitar las actualizaciones automáticas en la configuración de la aplicación App Store. </p>
|
| 63 |
-
<h4>¿Cómo puedo restaurar mis compras en Brawl Stars en iPhone? </h4>
|
| 64 |
-
<p>Si has comprado gemas u otros artículos en Brawl Stars con dinero real y los has perdido debido a un cambio de dispositivo o un problema de juego, puedes restaurar tus compras siguiendo estos pasos:</p>
|
| 65 |
-
<ol>
|
| 66 |
-
<li>Abrir Brawl Stars e ir al icono de configuración en la esquina superior derecha de la pantalla. </li>
|
| 67 |
-
<li>Toque en Ayuda y Soporte.</li>
|
| 68 |
-
<li>Toque en Contáctenos.</li>
|
| 69 |
-
<li>Escriba un mensaje explicando su situación y proporcione su etiqueta de jugador, número de recibo, fecha de compra y cantidad de compra. </li>
|
| 70 |
-
<li> Enviar el mensaje y esperar una respuesta del equipo de soporte. </li>
|
| 71 |
-
</ol>
|
| 72 |
-
<h4>¿Cómo puedo contactar al equipo de soporte de Brawl Stars en el iPhone? </h4>
|
| 73 |
-
<p>Si tienes algún problema, preguntas o comentarios sobre Brawl Stars en el iPhone, puedes ponerte en contacto con el equipo de soporte siguiendo estos pasos:</p>
|
| 74 |
-
<ol>
|
| 75 |
-
<li>Abrir Brawl Stars e ir al icono de configuración en la esquina superior derecha de la pantalla. </li>
|
| 76 |
-
<li>Toque en Ayuda y Soporte.</li>
|
| 77 |
-
<li>Toque en Contáctenos.</li>
|
| 78 |
-
|
| 79 |
-
<li> Enviar el mensaje y esperar una respuesta del equipo de soporte. </li>
|
| 80 |
-
</ol>
|
| 81 |
-
<h4>¿Cómo puedo jugar con mis amigos en Brawl Stars en iPhone? </h4>
|
| 82 |
-
<p>Si quieres jugar con tus amigos en Brawl Stars en iPhone, tienes dos opciones:</p>
|
| 83 |
-
<ul>
|
| 84 |
-
<li><strong>Crear o unirse a una habitación amigable</strong>: Una habitación amigable es una habitación privada donde puedes invitar a tus amigos o miembros del club a jugar juntos. Puedes crear o unirte a una sala amigable tocando el botón de juego amigable en la esquina inferior izquierda del menú principal. Puedes elegir el modo de juego y el mapa que quieras, y también puedes habilitar modificadores y bots. Puedes invitar a tus amigos o miembros del club tocando el botón de invitación en la esquina inferior derecha de la pantalla de la habitación. También puede compartir un código de habitación con sus amigos o miembros del club pulsando en el botón compartir en la esquina superior derecha de la pantalla de la habitación amigable. </li>
|
| 85 |
-
<li><strong>Crear o unirse a un código de equipo</strong>: Un código de equipo es un código que se puede utilizar para unirse a un equipo con otros jugadores que quieren jugar juntos. Puede crear o unirse a un código de equipo pulsando en el botón de reproducción en la esquina inferior derecha del menú principal. Verás una lista de modos de juego que están disponibles para emparejar. Elige uno y pulsa sobre él. Verá una pantalla donde puede seleccionar su luchador y ver a sus compañeros de equipo. En la esquina superior izquierda de esta pantalla, verá un botón que dice "Crear/ Unirse al código de equipo". Toque en él para crear o unirse a un código de equipo. Puedes compartir tu código de equipo con tus amigos o miembros del club pulsando en el botón de compartir al lado. También puede introducir un código de equipo que alguien más ha compartido con usted pulsando en el botón entrar junto a él. </li>
|
| 86 |
-
</ul>
|
| 87 |
-
<h4>¿Cómo puedo canjear códigos en Brawl Stars en iPhone? </h4>
|
| 88 |
-
|
| 89 |
-
<ol>
|
| 90 |
-
<li>Abre Brawl Stars y ve al icono de la tienda en la esquina superior izquierda del menú principal. </li>
|
| 91 |
-
<li>Desplácese hacia abajo hasta la parte inferior de la pantalla de la tienda y busque un botón que diga "Canjear código". Toque en él para abrir una ventana emergente. </li>
|
| 92 |
-
<li> Introduzca el código que ha recibido en el cuadro de texto y toque en el botón confirmar. </li>
|
| 93 |
-
<li> Verá un mensaje que dice "Código redimido" y las recompensas que ha recibido. Toque en el botón de reclamación para recoger sus recompensas. </li>
|
| 94 |
-
</ol>
|
| 95 |
-
<p>Tenga en cuenta que los códigos distinguen entre mayúsculas y minúsculas y tienen una fecha de vencimiento. Solo puede usar un código por cuenta. Si introduce un código inválido o caducado, verá un mensaje de error que dice "Código inválido" o "Código caducado". </p>
|
| 96 |
-
<h2>Conclusión</h2>
|
| 97 |
-
<p>Brawl Stars es un juego divertido y emocionante que puedes jugar en tu iPhone. Puedes descargarlo desde la App Store en unos sencillos pasos y disfrutar de sus características, modos, personajes y jugabilidad. También puedes mejorar tus habilidades, unirte a un club, participar en eventos y canjear códigos para obtener más recompensas y diversión. Brawl Stars es un juego que se actualiza constantemente con nuevos contenidos y mejoras, por lo que nunca te aburrirás de él. </p>
|
| 98 |
-
<p>¿Qué estás esperando? Descargar Brawl Stars para iPhone hoy y unirse a los millones de jugadores que están luchando su camino a la gloria! </p>
|
| 99 |
-
<h2></h2></p> 64aa2da5cf<br />
|
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<br />
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| 101 |
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distro/__main__.py
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|
| 1 |
-
from .distro import main
|
| 2 |
-
|
| 3 |
-
if __name__ == "__main__":
|
| 4 |
-
main()
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/pangomarkup.py
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@@ -1,83 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
pygments.formatters.pangomarkup
|
| 3 |
-
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
| 4 |
-
|
| 5 |
-
Formatter for Pango markup output.
|
| 6 |
-
|
| 7 |
-
:copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
|
| 8 |
-
:license: BSD, see LICENSE for details.
|
| 9 |
-
"""
|
| 10 |
-
|
| 11 |
-
from pip._vendor.pygments.formatter import Formatter
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
__all__ = ['PangoMarkupFormatter']
|
| 15 |
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|
| 16 |
-
|
| 17 |
-
_escape_table = {
|
| 18 |
-
ord('&'): '&',
|
| 19 |
-
ord('<'): '<',
|
| 20 |
-
}
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
def escape_special_chars(text, table=_escape_table):
|
| 24 |
-
"""Escape & and < for Pango Markup."""
|
| 25 |
-
return text.translate(table)
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
class PangoMarkupFormatter(Formatter):
|
| 29 |
-
"""
|
| 30 |
-
Format tokens as Pango Markup code. It can then be rendered to an SVG.
|
| 31 |
-
|
| 32 |
-
.. versionadded:: 2.9
|
| 33 |
-
"""
|
| 34 |
-
|
| 35 |
-
name = 'Pango Markup'
|
| 36 |
-
aliases = ['pango', 'pangomarkup']
|
| 37 |
-
filenames = []
|
| 38 |
-
|
| 39 |
-
def __init__(self, **options):
|
| 40 |
-
Formatter.__init__(self, **options)
|
| 41 |
-
|
| 42 |
-
self.styles = {}
|
| 43 |
-
|
| 44 |
-
for token, style in self.style:
|
| 45 |
-
start = ''
|
| 46 |
-
end = ''
|
| 47 |
-
if style['color']:
|
| 48 |
-
start += '<span fgcolor="#%s">' % style['color']
|
| 49 |
-
end = '</span>' + end
|
| 50 |
-
if style['bold']:
|
| 51 |
-
start += '<b>'
|
| 52 |
-
end = '</b>' + end
|
| 53 |
-
if style['italic']:
|
| 54 |
-
start += '<i>'
|
| 55 |
-
end = '</i>' + end
|
| 56 |
-
if style['underline']:
|
| 57 |
-
start += '<u>'
|
| 58 |
-
end = '</u>' + end
|
| 59 |
-
self.styles[token] = (start, end)
|
| 60 |
-
|
| 61 |
-
def format_unencoded(self, tokensource, outfile):
|
| 62 |
-
lastval = ''
|
| 63 |
-
lasttype = None
|
| 64 |
-
|
| 65 |
-
outfile.write('<tt>')
|
| 66 |
-
|
| 67 |
-
for ttype, value in tokensource:
|
| 68 |
-
while ttype not in self.styles:
|
| 69 |
-
ttype = ttype.parent
|
| 70 |
-
if ttype == lasttype:
|
| 71 |
-
lastval += escape_special_chars(value)
|
| 72 |
-
else:
|
| 73 |
-
if lastval:
|
| 74 |
-
stylebegin, styleend = self.styles[lasttype]
|
| 75 |
-
outfile.write(stylebegin + lastval + styleend)
|
| 76 |
-
lastval = escape_special_chars(value)
|
| 77 |
-
lasttype = ttype
|
| 78 |
-
|
| 79 |
-
if lastval:
|
| 80 |
-
stylebegin, styleend = self.styles[lasttype]
|
| 81 |
-
outfile.write(stylebegin + lastval + styleend)
|
| 82 |
-
|
| 83 |
-
outfile.write('</tt>')
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/style.py
DELETED
|
@@ -1,796 +0,0 @@
|
|
| 1 |
-
import sys
|
| 2 |
-
from functools import lru_cache
|
| 3 |
-
from marshal import dumps, loads
|
| 4 |
-
from random import randint
|
| 5 |
-
from typing import Any, Dict, Iterable, List, Optional, Type, Union, cast
|
| 6 |
-
|
| 7 |
-
from . import errors
|
| 8 |
-
from .color import Color, ColorParseError, ColorSystem, blend_rgb
|
| 9 |
-
from .repr import Result, rich_repr
|
| 10 |
-
from .terminal_theme import DEFAULT_TERMINAL_THEME, TerminalTheme
|
| 11 |
-
|
| 12 |
-
# Style instances and style definitions are often interchangeable
|
| 13 |
-
StyleType = Union[str, "Style"]
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class _Bit:
|
| 17 |
-
"""A descriptor to get/set a style attribute bit."""
|
| 18 |
-
|
| 19 |
-
__slots__ = ["bit"]
|
| 20 |
-
|
| 21 |
-
def __init__(self, bit_no: int) -> None:
|
| 22 |
-
self.bit = 1 << bit_no
|
| 23 |
-
|
| 24 |
-
def __get__(self, obj: "Style", objtype: Type["Style"]) -> Optional[bool]:
|
| 25 |
-
if obj._set_attributes & self.bit:
|
| 26 |
-
return obj._attributes & self.bit != 0
|
| 27 |
-
return None
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
@rich_repr
|
| 31 |
-
class Style:
|
| 32 |
-
"""A terminal style.
|
| 33 |
-
|
| 34 |
-
A terminal style consists of a color (`color`), a background color (`bgcolor`), and a number of attributes, such
|
| 35 |
-
as bold, italic etc. The attributes have 3 states: they can either be on
|
| 36 |
-
(``True``), off (``False``), or not set (``None``).
|
| 37 |
-
|
| 38 |
-
Args:
|
| 39 |
-
color (Union[Color, str], optional): Color of terminal text. Defaults to None.
|
| 40 |
-
bgcolor (Union[Color, str], optional): Color of terminal background. Defaults to None.
|
| 41 |
-
bold (bool, optional): Enable bold text. Defaults to None.
|
| 42 |
-
dim (bool, optional): Enable dim text. Defaults to None.
|
| 43 |
-
italic (bool, optional): Enable italic text. Defaults to None.
|
| 44 |
-
underline (bool, optional): Enable underlined text. Defaults to None.
|
| 45 |
-
blink (bool, optional): Enabled blinking text. Defaults to None.
|
| 46 |
-
blink2 (bool, optional): Enable fast blinking text. Defaults to None.
|
| 47 |
-
reverse (bool, optional): Enabled reverse text. Defaults to None.
|
| 48 |
-
conceal (bool, optional): Enable concealed text. Defaults to None.
|
| 49 |
-
strike (bool, optional): Enable strikethrough text. Defaults to None.
|
| 50 |
-
underline2 (bool, optional): Enable doubly underlined text. Defaults to None.
|
| 51 |
-
frame (bool, optional): Enable framed text. Defaults to None.
|
| 52 |
-
encircle (bool, optional): Enable encircled text. Defaults to None.
|
| 53 |
-
overline (bool, optional): Enable overlined text. Defaults to None.
|
| 54 |
-
link (str, link): Link URL. Defaults to None.
|
| 55 |
-
|
| 56 |
-
"""
|
| 57 |
-
|
| 58 |
-
_color: Optional[Color]
|
| 59 |
-
_bgcolor: Optional[Color]
|
| 60 |
-
_attributes: int
|
| 61 |
-
_set_attributes: int
|
| 62 |
-
_hash: Optional[int]
|
| 63 |
-
_null: bool
|
| 64 |
-
_meta: Optional[bytes]
|
| 65 |
-
|
| 66 |
-
__slots__ = [
|
| 67 |
-
"_color",
|
| 68 |
-
"_bgcolor",
|
| 69 |
-
"_attributes",
|
| 70 |
-
"_set_attributes",
|
| 71 |
-
"_link",
|
| 72 |
-
"_link_id",
|
| 73 |
-
"_ansi",
|
| 74 |
-
"_style_definition",
|
| 75 |
-
"_hash",
|
| 76 |
-
"_null",
|
| 77 |
-
"_meta",
|
| 78 |
-
]
|
| 79 |
-
|
| 80 |
-
# maps bits on to SGR parameter
|
| 81 |
-
_style_map = {
|
| 82 |
-
0: "1",
|
| 83 |
-
1: "2",
|
| 84 |
-
2: "3",
|
| 85 |
-
3: "4",
|
| 86 |
-
4: "5",
|
| 87 |
-
5: "6",
|
| 88 |
-
6: "7",
|
| 89 |
-
7: "8",
|
| 90 |
-
8: "9",
|
| 91 |
-
9: "21",
|
| 92 |
-
10: "51",
|
| 93 |
-
11: "52",
|
| 94 |
-
12: "53",
|
| 95 |
-
}
|
| 96 |
-
|
| 97 |
-
STYLE_ATTRIBUTES = {
|
| 98 |
-
"dim": "dim",
|
| 99 |
-
"d": "dim",
|
| 100 |
-
"bold": "bold",
|
| 101 |
-
"b": "bold",
|
| 102 |
-
"italic": "italic",
|
| 103 |
-
"i": "italic",
|
| 104 |
-
"underline": "underline",
|
| 105 |
-
"u": "underline",
|
| 106 |
-
"blink": "blink",
|
| 107 |
-
"blink2": "blink2",
|
| 108 |
-
"reverse": "reverse",
|
| 109 |
-
"r": "reverse",
|
| 110 |
-
"conceal": "conceal",
|
| 111 |
-
"c": "conceal",
|
| 112 |
-
"strike": "strike",
|
| 113 |
-
"s": "strike",
|
| 114 |
-
"underline2": "underline2",
|
| 115 |
-
"uu": "underline2",
|
| 116 |
-
"frame": "frame",
|
| 117 |
-
"encircle": "encircle",
|
| 118 |
-
"overline": "overline",
|
| 119 |
-
"o": "overline",
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
def __init__(
|
| 123 |
-
self,
|
| 124 |
-
*,
|
| 125 |
-
color: Optional[Union[Color, str]] = None,
|
| 126 |
-
bgcolor: Optional[Union[Color, str]] = None,
|
| 127 |
-
bold: Optional[bool] = None,
|
| 128 |
-
dim: Optional[bool] = None,
|
| 129 |
-
italic: Optional[bool] = None,
|
| 130 |
-
underline: Optional[bool] = None,
|
| 131 |
-
blink: Optional[bool] = None,
|
| 132 |
-
blink2: Optional[bool] = None,
|
| 133 |
-
reverse: Optional[bool] = None,
|
| 134 |
-
conceal: Optional[bool] = None,
|
| 135 |
-
strike: Optional[bool] = None,
|
| 136 |
-
underline2: Optional[bool] = None,
|
| 137 |
-
frame: Optional[bool] = None,
|
| 138 |
-
encircle: Optional[bool] = None,
|
| 139 |
-
overline: Optional[bool] = None,
|
| 140 |
-
link: Optional[str] = None,
|
| 141 |
-
meta: Optional[Dict[str, Any]] = None,
|
| 142 |
-
):
|
| 143 |
-
self._ansi: Optional[str] = None
|
| 144 |
-
self._style_definition: Optional[str] = None
|
| 145 |
-
|
| 146 |
-
def _make_color(color: Union[Color, str]) -> Color:
|
| 147 |
-
return color if isinstance(color, Color) else Color.parse(color)
|
| 148 |
-
|
| 149 |
-
self._color = None if color is None else _make_color(color)
|
| 150 |
-
self._bgcolor = None if bgcolor is None else _make_color(bgcolor)
|
| 151 |
-
self._set_attributes = sum(
|
| 152 |
-
(
|
| 153 |
-
bold is not None,
|
| 154 |
-
dim is not None and 2,
|
| 155 |
-
italic is not None and 4,
|
| 156 |
-
underline is not None and 8,
|
| 157 |
-
blink is not None and 16,
|
| 158 |
-
blink2 is not None and 32,
|
| 159 |
-
reverse is not None and 64,
|
| 160 |
-
conceal is not None and 128,
|
| 161 |
-
strike is not None and 256,
|
| 162 |
-
underline2 is not None and 512,
|
| 163 |
-
frame is not None and 1024,
|
| 164 |
-
encircle is not None and 2048,
|
| 165 |
-
overline is not None and 4096,
|
| 166 |
-
)
|
| 167 |
-
)
|
| 168 |
-
self._attributes = (
|
| 169 |
-
sum(
|
| 170 |
-
(
|
| 171 |
-
bold and 1 or 0,
|
| 172 |
-
dim and 2 or 0,
|
| 173 |
-
italic and 4 or 0,
|
| 174 |
-
underline and 8 or 0,
|
| 175 |
-
blink and 16 or 0,
|
| 176 |
-
blink2 and 32 or 0,
|
| 177 |
-
reverse and 64 or 0,
|
| 178 |
-
conceal and 128 or 0,
|
| 179 |
-
strike and 256 or 0,
|
| 180 |
-
underline2 and 512 or 0,
|
| 181 |
-
frame and 1024 or 0,
|
| 182 |
-
encircle and 2048 or 0,
|
| 183 |
-
overline and 4096 or 0,
|
| 184 |
-
)
|
| 185 |
-
)
|
| 186 |
-
if self._set_attributes
|
| 187 |
-
else 0
|
| 188 |
-
)
|
| 189 |
-
|
| 190 |
-
self._link = link
|
| 191 |
-
self._meta = None if meta is None else dumps(meta)
|
| 192 |
-
self._link_id = (
|
| 193 |
-
f"{randint(0, 999999)}{hash(self._meta)}" if (link or meta) else ""
|
| 194 |
-
)
|
| 195 |
-
self._hash: Optional[int] = None
|
| 196 |
-
self._null = not (self._set_attributes or color or bgcolor or link or meta)
|
| 197 |
-
|
| 198 |
-
@classmethod
|
| 199 |
-
def null(cls) -> "Style":
|
| 200 |
-
"""Create an 'null' style, equivalent to Style(), but more performant."""
|
| 201 |
-
return NULL_STYLE
|
| 202 |
-
|
| 203 |
-
@classmethod
|
| 204 |
-
def from_color(
|
| 205 |
-
cls, color: Optional[Color] = None, bgcolor: Optional[Color] = None
|
| 206 |
-
) -> "Style":
|
| 207 |
-
"""Create a new style with colors and no attributes.
|
| 208 |
-
|
| 209 |
-
Returns:
|
| 210 |
-
color (Optional[Color]): A (foreground) color, or None for no color. Defaults to None.
|
| 211 |
-
bgcolor (Optional[Color]): A (background) color, or None for no color. Defaults to None.
|
| 212 |
-
"""
|
| 213 |
-
style: Style = cls.__new__(Style)
|
| 214 |
-
style._ansi = None
|
| 215 |
-
style._style_definition = None
|
| 216 |
-
style._color = color
|
| 217 |
-
style._bgcolor = bgcolor
|
| 218 |
-
style._set_attributes = 0
|
| 219 |
-
style._attributes = 0
|
| 220 |
-
style._link = None
|
| 221 |
-
style._link_id = ""
|
| 222 |
-
style._meta = None
|
| 223 |
-
style._null = not (color or bgcolor)
|
| 224 |
-
style._hash = None
|
| 225 |
-
return style
|
| 226 |
-
|
| 227 |
-
@classmethod
|
| 228 |
-
def from_meta(cls, meta: Optional[Dict[str, Any]]) -> "Style":
|
| 229 |
-
"""Create a new style with meta data.
|
| 230 |
-
|
| 231 |
-
Returns:
|
| 232 |
-
meta (Optional[Dict[str, Any]]): A dictionary of meta data. Defaults to None.
|
| 233 |
-
"""
|
| 234 |
-
style: Style = cls.__new__(Style)
|
| 235 |
-
style._ansi = None
|
| 236 |
-
style._style_definition = None
|
| 237 |
-
style._color = None
|
| 238 |
-
style._bgcolor = None
|
| 239 |
-
style._set_attributes = 0
|
| 240 |
-
style._attributes = 0
|
| 241 |
-
style._link = None
|
| 242 |
-
style._meta = dumps(meta)
|
| 243 |
-
style._link_id = f"{randint(0, 999999)}{hash(style._meta)}"
|
| 244 |
-
style._hash = None
|
| 245 |
-
style._null = not (meta)
|
| 246 |
-
return style
|
| 247 |
-
|
| 248 |
-
@classmethod
|
| 249 |
-
def on(cls, meta: Optional[Dict[str, Any]] = None, **handlers: Any) -> "Style":
|
| 250 |
-
"""Create a blank style with meta information.
|
| 251 |
-
|
| 252 |
-
Example:
|
| 253 |
-
style = Style.on(click=self.on_click)
|
| 254 |
-
|
| 255 |
-
Args:
|
| 256 |
-
meta (Optional[Dict[str, Any]], optional): An optional dict of meta information.
|
| 257 |
-
**handlers (Any): Keyword arguments are translated in to handlers.
|
| 258 |
-
|
| 259 |
-
Returns:
|
| 260 |
-
Style: A Style with meta information attached.
|
| 261 |
-
"""
|
| 262 |
-
meta = {} if meta is None else meta
|
| 263 |
-
meta.update({f"@{key}": value for key, value in handlers.items()})
|
| 264 |
-
return cls.from_meta(meta)
|
| 265 |
-
|
| 266 |
-
bold = _Bit(0)
|
| 267 |
-
dim = _Bit(1)
|
| 268 |
-
italic = _Bit(2)
|
| 269 |
-
underline = _Bit(3)
|
| 270 |
-
blink = _Bit(4)
|
| 271 |
-
blink2 = _Bit(5)
|
| 272 |
-
reverse = _Bit(6)
|
| 273 |
-
conceal = _Bit(7)
|
| 274 |
-
strike = _Bit(8)
|
| 275 |
-
underline2 = _Bit(9)
|
| 276 |
-
frame = _Bit(10)
|
| 277 |
-
encircle = _Bit(11)
|
| 278 |
-
overline = _Bit(12)
|
| 279 |
-
|
| 280 |
-
@property
|
| 281 |
-
def link_id(self) -> str:
|
| 282 |
-
"""Get a link id, used in ansi code for links."""
|
| 283 |
-
return self._link_id
|
| 284 |
-
|
| 285 |
-
def __str__(self) -> str:
|
| 286 |
-
"""Re-generate style definition from attributes."""
|
| 287 |
-
if self._style_definition is None:
|
| 288 |
-
attributes: List[str] = []
|
| 289 |
-
append = attributes.append
|
| 290 |
-
bits = self._set_attributes
|
| 291 |
-
if bits & 0b0000000001111:
|
| 292 |
-
if bits & 1:
|
| 293 |
-
append("bold" if self.bold else "not bold")
|
| 294 |
-
if bits & (1 << 1):
|
| 295 |
-
append("dim" if self.dim else "not dim")
|
| 296 |
-
if bits & (1 << 2):
|
| 297 |
-
append("italic" if self.italic else "not italic")
|
| 298 |
-
if bits & (1 << 3):
|
| 299 |
-
append("underline" if self.underline else "not underline")
|
| 300 |
-
if bits & 0b0000111110000:
|
| 301 |
-
if bits & (1 << 4):
|
| 302 |
-
append("blink" if self.blink else "not blink")
|
| 303 |
-
if bits & (1 << 5):
|
| 304 |
-
append("blink2" if self.blink2 else "not blink2")
|
| 305 |
-
if bits & (1 << 6):
|
| 306 |
-
append("reverse" if self.reverse else "not reverse")
|
| 307 |
-
if bits & (1 << 7):
|
| 308 |
-
append("conceal" if self.conceal else "not conceal")
|
| 309 |
-
if bits & (1 << 8):
|
| 310 |
-
append("strike" if self.strike else "not strike")
|
| 311 |
-
if bits & 0b1111000000000:
|
| 312 |
-
if bits & (1 << 9):
|
| 313 |
-
append("underline2" if self.underline2 else "not underline2")
|
| 314 |
-
if bits & (1 << 10):
|
| 315 |
-
append("frame" if self.frame else "not frame")
|
| 316 |
-
if bits & (1 << 11):
|
| 317 |
-
append("encircle" if self.encircle else "not encircle")
|
| 318 |
-
if bits & (1 << 12):
|
| 319 |
-
append("overline" if self.overline else "not overline")
|
| 320 |
-
if self._color is not None:
|
| 321 |
-
append(self._color.name)
|
| 322 |
-
if self._bgcolor is not None:
|
| 323 |
-
append("on")
|
| 324 |
-
append(self._bgcolor.name)
|
| 325 |
-
if self._link:
|
| 326 |
-
append("link")
|
| 327 |
-
append(self._link)
|
| 328 |
-
self._style_definition = " ".join(attributes) or "none"
|
| 329 |
-
return self._style_definition
|
| 330 |
-
|
| 331 |
-
def __bool__(self) -> bool:
|
| 332 |
-
"""A Style is false if it has no attributes, colors, or links."""
|
| 333 |
-
return not self._null
|
| 334 |
-
|
| 335 |
-
def _make_ansi_codes(self, color_system: ColorSystem) -> str:
|
| 336 |
-
"""Generate ANSI codes for this style.
|
| 337 |
-
|
| 338 |
-
Args:
|
| 339 |
-
color_system (ColorSystem): Color system.
|
| 340 |
-
|
| 341 |
-
Returns:
|
| 342 |
-
str: String containing codes.
|
| 343 |
-
"""
|
| 344 |
-
|
| 345 |
-
if self._ansi is None:
|
| 346 |
-
sgr: List[str] = []
|
| 347 |
-
append = sgr.append
|
| 348 |
-
_style_map = self._style_map
|
| 349 |
-
attributes = self._attributes & self._set_attributes
|
| 350 |
-
if attributes:
|
| 351 |
-
if attributes & 1:
|
| 352 |
-
append(_style_map[0])
|
| 353 |
-
if attributes & 2:
|
| 354 |
-
append(_style_map[1])
|
| 355 |
-
if attributes & 4:
|
| 356 |
-
append(_style_map[2])
|
| 357 |
-
if attributes & 8:
|
| 358 |
-
append(_style_map[3])
|
| 359 |
-
if attributes & 0b0000111110000:
|
| 360 |
-
for bit in range(4, 9):
|
| 361 |
-
if attributes & (1 << bit):
|
| 362 |
-
append(_style_map[bit])
|
| 363 |
-
if attributes & 0b1111000000000:
|
| 364 |
-
for bit in range(9, 13):
|
| 365 |
-
if attributes & (1 << bit):
|
| 366 |
-
append(_style_map[bit])
|
| 367 |
-
if self._color is not None:
|
| 368 |
-
sgr.extend(self._color.downgrade(color_system).get_ansi_codes())
|
| 369 |
-
if self._bgcolor is not None:
|
| 370 |
-
sgr.extend(
|
| 371 |
-
self._bgcolor.downgrade(color_system).get_ansi_codes(
|
| 372 |
-
foreground=False
|
| 373 |
-
)
|
| 374 |
-
)
|
| 375 |
-
self._ansi = ";".join(sgr)
|
| 376 |
-
return self._ansi
|
| 377 |
-
|
| 378 |
-
@classmethod
|
| 379 |
-
@lru_cache(maxsize=1024)
|
| 380 |
-
def normalize(cls, style: str) -> str:
|
| 381 |
-
"""Normalize a style definition so that styles with the same effect have the same string
|
| 382 |
-
representation.
|
| 383 |
-
|
| 384 |
-
Args:
|
| 385 |
-
style (str): A style definition.
|
| 386 |
-
|
| 387 |
-
Returns:
|
| 388 |
-
str: Normal form of style definition.
|
| 389 |
-
"""
|
| 390 |
-
try:
|
| 391 |
-
return str(cls.parse(style))
|
| 392 |
-
except errors.StyleSyntaxError:
|
| 393 |
-
return style.strip().lower()
|
| 394 |
-
|
| 395 |
-
@classmethod
|
| 396 |
-
def pick_first(cls, *values: Optional[StyleType]) -> StyleType:
|
| 397 |
-
"""Pick first non-None style."""
|
| 398 |
-
for value in values:
|
| 399 |
-
if value is not None:
|
| 400 |
-
return value
|
| 401 |
-
raise ValueError("expected at least one non-None style")
|
| 402 |
-
|
| 403 |
-
def __rich_repr__(self) -> Result:
|
| 404 |
-
yield "color", self.color, None
|
| 405 |
-
yield "bgcolor", self.bgcolor, None
|
| 406 |
-
yield "bold", self.bold, None,
|
| 407 |
-
yield "dim", self.dim, None,
|
| 408 |
-
yield "italic", self.italic, None
|
| 409 |
-
yield "underline", self.underline, None,
|
| 410 |
-
yield "blink", self.blink, None
|
| 411 |
-
yield "blink2", self.blink2, None
|
| 412 |
-
yield "reverse", self.reverse, None
|
| 413 |
-
yield "conceal", self.conceal, None
|
| 414 |
-
yield "strike", self.strike, None
|
| 415 |
-
yield "underline2", self.underline2, None
|
| 416 |
-
yield "frame", self.frame, None
|
| 417 |
-
yield "encircle", self.encircle, None
|
| 418 |
-
yield "link", self.link, None
|
| 419 |
-
if self._meta:
|
| 420 |
-
yield "meta", self.meta
|
| 421 |
-
|
| 422 |
-
def __eq__(self, other: Any) -> bool:
|
| 423 |
-
if not isinstance(other, Style):
|
| 424 |
-
return NotImplemented
|
| 425 |
-
return self.__hash__() == other.__hash__()
|
| 426 |
-
|
| 427 |
-
def __ne__(self, other: Any) -> bool:
|
| 428 |
-
if not isinstance(other, Style):
|
| 429 |
-
return NotImplemented
|
| 430 |
-
return self.__hash__() != other.__hash__()
|
| 431 |
-
|
| 432 |
-
def __hash__(self) -> int:
|
| 433 |
-
if self._hash is not None:
|
| 434 |
-
return self._hash
|
| 435 |
-
self._hash = hash(
|
| 436 |
-
(
|
| 437 |
-
self._color,
|
| 438 |
-
self._bgcolor,
|
| 439 |
-
self._attributes,
|
| 440 |
-
self._set_attributes,
|
| 441 |
-
self._link,
|
| 442 |
-
self._meta,
|
| 443 |
-
)
|
| 444 |
-
)
|
| 445 |
-
return self._hash
|
| 446 |
-
|
| 447 |
-
@property
|
| 448 |
-
def color(self) -> Optional[Color]:
|
| 449 |
-
"""The foreground color or None if it is not set."""
|
| 450 |
-
return self._color
|
| 451 |
-
|
| 452 |
-
@property
|
| 453 |
-
def bgcolor(self) -> Optional[Color]:
|
| 454 |
-
"""The background color or None if it is not set."""
|
| 455 |
-
return self._bgcolor
|
| 456 |
-
|
| 457 |
-
@property
|
| 458 |
-
def link(self) -> Optional[str]:
|
| 459 |
-
"""Link text, if set."""
|
| 460 |
-
return self._link
|
| 461 |
-
|
| 462 |
-
@property
|
| 463 |
-
def transparent_background(self) -> bool:
|
| 464 |
-
"""Check if the style specified a transparent background."""
|
| 465 |
-
return self.bgcolor is None or self.bgcolor.is_default
|
| 466 |
-
|
| 467 |
-
@property
|
| 468 |
-
def background_style(self) -> "Style":
|
| 469 |
-
"""A Style with background only."""
|
| 470 |
-
return Style(bgcolor=self.bgcolor)
|
| 471 |
-
|
| 472 |
-
@property
|
| 473 |
-
def meta(self) -> Dict[str, Any]:
|
| 474 |
-
"""Get meta information (can not be changed after construction)."""
|
| 475 |
-
return {} if self._meta is None else cast(Dict[str, Any], loads(self._meta))
|
| 476 |
-
|
| 477 |
-
@property
|
| 478 |
-
def without_color(self) -> "Style":
|
| 479 |
-
"""Get a copy of the style with color removed."""
|
| 480 |
-
if self._null:
|
| 481 |
-
return NULL_STYLE
|
| 482 |
-
style: Style = self.__new__(Style)
|
| 483 |
-
style._ansi = None
|
| 484 |
-
style._style_definition = None
|
| 485 |
-
style._color = None
|
| 486 |
-
style._bgcolor = None
|
| 487 |
-
style._attributes = self._attributes
|
| 488 |
-
style._set_attributes = self._set_attributes
|
| 489 |
-
style._link = self._link
|
| 490 |
-
style._link_id = f"{randint(0, 999999)}" if self._link else ""
|
| 491 |
-
style._null = False
|
| 492 |
-
style._meta = None
|
| 493 |
-
style._hash = None
|
| 494 |
-
return style
|
| 495 |
-
|
| 496 |
-
@classmethod
|
| 497 |
-
@lru_cache(maxsize=4096)
|
| 498 |
-
def parse(cls, style_definition: str) -> "Style":
|
| 499 |
-
"""Parse a style definition.
|
| 500 |
-
|
| 501 |
-
Args:
|
| 502 |
-
style_definition (str): A string containing a style.
|
| 503 |
-
|
| 504 |
-
Raises:
|
| 505 |
-
errors.StyleSyntaxError: If the style definition syntax is invalid.
|
| 506 |
-
|
| 507 |
-
Returns:
|
| 508 |
-
`Style`: A Style instance.
|
| 509 |
-
"""
|
| 510 |
-
if style_definition.strip() == "none" or not style_definition:
|
| 511 |
-
return cls.null()
|
| 512 |
-
|
| 513 |
-
STYLE_ATTRIBUTES = cls.STYLE_ATTRIBUTES
|
| 514 |
-
color: Optional[str] = None
|
| 515 |
-
bgcolor: Optional[str] = None
|
| 516 |
-
attributes: Dict[str, Optional[Any]] = {}
|
| 517 |
-
link: Optional[str] = None
|
| 518 |
-
|
| 519 |
-
words = iter(style_definition.split())
|
| 520 |
-
for original_word in words:
|
| 521 |
-
word = original_word.lower()
|
| 522 |
-
if word == "on":
|
| 523 |
-
word = next(words, "")
|
| 524 |
-
if not word:
|
| 525 |
-
raise errors.StyleSyntaxError("color expected after 'on'")
|
| 526 |
-
try:
|
| 527 |
-
Color.parse(word) is None
|
| 528 |
-
except ColorParseError as error:
|
| 529 |
-
raise errors.StyleSyntaxError(
|
| 530 |
-
f"unable to parse {word!r} as background color; {error}"
|
| 531 |
-
) from None
|
| 532 |
-
bgcolor = word
|
| 533 |
-
|
| 534 |
-
elif word == "not":
|
| 535 |
-
word = next(words, "")
|
| 536 |
-
attribute = STYLE_ATTRIBUTES.get(word)
|
| 537 |
-
if attribute is None:
|
| 538 |
-
raise errors.StyleSyntaxError(
|
| 539 |
-
f"expected style attribute after 'not', found {word!r}"
|
| 540 |
-
)
|
| 541 |
-
attributes[attribute] = False
|
| 542 |
-
|
| 543 |
-
elif word == "link":
|
| 544 |
-
word = next(words, "")
|
| 545 |
-
if not word:
|
| 546 |
-
raise errors.StyleSyntaxError("URL expected after 'link'")
|
| 547 |
-
link = word
|
| 548 |
-
|
| 549 |
-
elif word in STYLE_ATTRIBUTES:
|
| 550 |
-
attributes[STYLE_ATTRIBUTES[word]] = True
|
| 551 |
-
|
| 552 |
-
else:
|
| 553 |
-
try:
|
| 554 |
-
Color.parse(word)
|
| 555 |
-
except ColorParseError as error:
|
| 556 |
-
raise errors.StyleSyntaxError(
|
| 557 |
-
f"unable to parse {word!r} as color; {error}"
|
| 558 |
-
) from None
|
| 559 |
-
color = word
|
| 560 |
-
style = Style(color=color, bgcolor=bgcolor, link=link, **attributes)
|
| 561 |
-
return style
|
| 562 |
-
|
| 563 |
-
@lru_cache(maxsize=1024)
|
| 564 |
-
def get_html_style(self, theme: Optional[TerminalTheme] = None) -> str:
|
| 565 |
-
"""Get a CSS style rule."""
|
| 566 |
-
theme = theme or DEFAULT_TERMINAL_THEME
|
| 567 |
-
css: List[str] = []
|
| 568 |
-
append = css.append
|
| 569 |
-
|
| 570 |
-
color = self.color
|
| 571 |
-
bgcolor = self.bgcolor
|
| 572 |
-
if self.reverse:
|
| 573 |
-
color, bgcolor = bgcolor, color
|
| 574 |
-
if self.dim:
|
| 575 |
-
foreground_color = (
|
| 576 |
-
theme.foreground_color if color is None else color.get_truecolor(theme)
|
| 577 |
-
)
|
| 578 |
-
color = Color.from_triplet(
|
| 579 |
-
blend_rgb(foreground_color, theme.background_color, 0.5)
|
| 580 |
-
)
|
| 581 |
-
if color is not None:
|
| 582 |
-
theme_color = color.get_truecolor(theme)
|
| 583 |
-
append(f"color: {theme_color.hex}")
|
| 584 |
-
append(f"text-decoration-color: {theme_color.hex}")
|
| 585 |
-
if bgcolor is not None:
|
| 586 |
-
theme_color = bgcolor.get_truecolor(theme, foreground=False)
|
| 587 |
-
append(f"background-color: {theme_color.hex}")
|
| 588 |
-
if self.bold:
|
| 589 |
-
append("font-weight: bold")
|
| 590 |
-
if self.italic:
|
| 591 |
-
append("font-style: italic")
|
| 592 |
-
if self.underline:
|
| 593 |
-
append("text-decoration: underline")
|
| 594 |
-
if self.strike:
|
| 595 |
-
append("text-decoration: line-through")
|
| 596 |
-
if self.overline:
|
| 597 |
-
append("text-decoration: overline")
|
| 598 |
-
return "; ".join(css)
|
| 599 |
-
|
| 600 |
-
@classmethod
|
| 601 |
-
def combine(cls, styles: Iterable["Style"]) -> "Style":
|
| 602 |
-
"""Combine styles and get result.
|
| 603 |
-
|
| 604 |
-
Args:
|
| 605 |
-
styles (Iterable[Style]): Styles to combine.
|
| 606 |
-
|
| 607 |
-
Returns:
|
| 608 |
-
Style: A new style instance.
|
| 609 |
-
"""
|
| 610 |
-
iter_styles = iter(styles)
|
| 611 |
-
return sum(iter_styles, next(iter_styles))
|
| 612 |
-
|
| 613 |
-
@classmethod
|
| 614 |
-
def chain(cls, *styles: "Style") -> "Style":
|
| 615 |
-
"""Combine styles from positional argument in to a single style.
|
| 616 |
-
|
| 617 |
-
Args:
|
| 618 |
-
*styles (Iterable[Style]): Styles to combine.
|
| 619 |
-
|
| 620 |
-
Returns:
|
| 621 |
-
Style: A new style instance.
|
| 622 |
-
"""
|
| 623 |
-
iter_styles = iter(styles)
|
| 624 |
-
return sum(iter_styles, next(iter_styles))
|
| 625 |
-
|
| 626 |
-
def copy(self) -> "Style":
|
| 627 |
-
"""Get a copy of this style.
|
| 628 |
-
|
| 629 |
-
Returns:
|
| 630 |
-
Style: A new Style instance with identical attributes.
|
| 631 |
-
"""
|
| 632 |
-
if self._null:
|
| 633 |
-
return NULL_STYLE
|
| 634 |
-
style: Style = self.__new__(Style)
|
| 635 |
-
style._ansi = self._ansi
|
| 636 |
-
style._style_definition = self._style_definition
|
| 637 |
-
style._color = self._color
|
| 638 |
-
style._bgcolor = self._bgcolor
|
| 639 |
-
style._attributes = self._attributes
|
| 640 |
-
style._set_attributes = self._set_attributes
|
| 641 |
-
style._link = self._link
|
| 642 |
-
style._link_id = f"{randint(0, 999999)}" if self._link else ""
|
| 643 |
-
style._hash = self._hash
|
| 644 |
-
style._null = False
|
| 645 |
-
style._meta = self._meta
|
| 646 |
-
return style
|
| 647 |
-
|
| 648 |
-
@lru_cache(maxsize=128)
|
| 649 |
-
def clear_meta_and_links(self) -> "Style":
|
| 650 |
-
"""Get a copy of this style with link and meta information removed.
|
| 651 |
-
|
| 652 |
-
Returns:
|
| 653 |
-
Style: New style object.
|
| 654 |
-
"""
|
| 655 |
-
if self._null:
|
| 656 |
-
return NULL_STYLE
|
| 657 |
-
style: Style = self.__new__(Style)
|
| 658 |
-
style._ansi = self._ansi
|
| 659 |
-
style._style_definition = self._style_definition
|
| 660 |
-
style._color = self._color
|
| 661 |
-
style._bgcolor = self._bgcolor
|
| 662 |
-
style._attributes = self._attributes
|
| 663 |
-
style._set_attributes = self._set_attributes
|
| 664 |
-
style._link = None
|
| 665 |
-
style._link_id = ""
|
| 666 |
-
style._hash = self._hash
|
| 667 |
-
style._null = False
|
| 668 |
-
style._meta = None
|
| 669 |
-
return style
|
| 670 |
-
|
| 671 |
-
def update_link(self, link: Optional[str] = None) -> "Style":
|
| 672 |
-
"""Get a copy with a different value for link.
|
| 673 |
-
|
| 674 |
-
Args:
|
| 675 |
-
link (str, optional): New value for link. Defaults to None.
|
| 676 |
-
|
| 677 |
-
Returns:
|
| 678 |
-
Style: A new Style instance.
|
| 679 |
-
"""
|
| 680 |
-
style: Style = self.__new__(Style)
|
| 681 |
-
style._ansi = self._ansi
|
| 682 |
-
style._style_definition = self._style_definition
|
| 683 |
-
style._color = self._color
|
| 684 |
-
style._bgcolor = self._bgcolor
|
| 685 |
-
style._attributes = self._attributes
|
| 686 |
-
style._set_attributes = self._set_attributes
|
| 687 |
-
style._link = link
|
| 688 |
-
style._link_id = f"{randint(0, 999999)}" if link else ""
|
| 689 |
-
style._hash = None
|
| 690 |
-
style._null = False
|
| 691 |
-
style._meta = self._meta
|
| 692 |
-
return style
|
| 693 |
-
|
| 694 |
-
def render(
|
| 695 |
-
self,
|
| 696 |
-
text: str = "",
|
| 697 |
-
*,
|
| 698 |
-
color_system: Optional[ColorSystem] = ColorSystem.TRUECOLOR,
|
| 699 |
-
legacy_windows: bool = False,
|
| 700 |
-
) -> str:
|
| 701 |
-
"""Render the ANSI codes for the style.
|
| 702 |
-
|
| 703 |
-
Args:
|
| 704 |
-
text (str, optional): A string to style. Defaults to "".
|
| 705 |
-
color_system (Optional[ColorSystem], optional): Color system to render to. Defaults to ColorSystem.TRUECOLOR.
|
| 706 |
-
|
| 707 |
-
Returns:
|
| 708 |
-
str: A string containing ANSI style codes.
|
| 709 |
-
"""
|
| 710 |
-
if not text or color_system is None:
|
| 711 |
-
return text
|
| 712 |
-
attrs = self._ansi or self._make_ansi_codes(color_system)
|
| 713 |
-
rendered = f"\x1b[{attrs}m{text}\x1b[0m" if attrs else text
|
| 714 |
-
if self._link and not legacy_windows:
|
| 715 |
-
rendered = (
|
| 716 |
-
f"\x1b]8;id={self._link_id};{self._link}\x1b\\{rendered}\x1b]8;;\x1b\\"
|
| 717 |
-
)
|
| 718 |
-
return rendered
|
| 719 |
-
|
| 720 |
-
def test(self, text: Optional[str] = None) -> None:
|
| 721 |
-
"""Write text with style directly to terminal.
|
| 722 |
-
|
| 723 |
-
This method is for testing purposes only.
|
| 724 |
-
|
| 725 |
-
Args:
|
| 726 |
-
text (Optional[str], optional): Text to style or None for style name.
|
| 727 |
-
|
| 728 |
-
"""
|
| 729 |
-
text = text or str(self)
|
| 730 |
-
sys.stdout.write(f"{self.render(text)}\n")
|
| 731 |
-
|
| 732 |
-
@lru_cache(maxsize=1024)
|
| 733 |
-
def _add(self, style: Optional["Style"]) -> "Style":
|
| 734 |
-
if style is None or style._null:
|
| 735 |
-
return self
|
| 736 |
-
if self._null:
|
| 737 |
-
return style
|
| 738 |
-
new_style: Style = self.__new__(Style)
|
| 739 |
-
new_style._ansi = None
|
| 740 |
-
new_style._style_definition = None
|
| 741 |
-
new_style._color = style._color or self._color
|
| 742 |
-
new_style._bgcolor = style._bgcolor or self._bgcolor
|
| 743 |
-
new_style._attributes = (self._attributes & ~style._set_attributes) | (
|
| 744 |
-
style._attributes & style._set_attributes
|
| 745 |
-
)
|
| 746 |
-
new_style._set_attributes = self._set_attributes | style._set_attributes
|
| 747 |
-
new_style._link = style._link or self._link
|
| 748 |
-
new_style._link_id = style._link_id or self._link_id
|
| 749 |
-
new_style._null = style._null
|
| 750 |
-
if self._meta and style._meta:
|
| 751 |
-
new_style._meta = dumps({**self.meta, **style.meta})
|
| 752 |
-
else:
|
| 753 |
-
new_style._meta = self._meta or style._meta
|
| 754 |
-
new_style._hash = None
|
| 755 |
-
return new_style
|
| 756 |
-
|
| 757 |
-
def __add__(self, style: Optional["Style"]) -> "Style":
|
| 758 |
-
combined_style = self._add(style)
|
| 759 |
-
return combined_style.copy() if combined_style.link else combined_style
|
| 760 |
-
|
| 761 |
-
|
| 762 |
-
NULL_STYLE = Style()
|
| 763 |
-
|
| 764 |
-
|
| 765 |
-
class StyleStack:
|
| 766 |
-
"""A stack of styles."""
|
| 767 |
-
|
| 768 |
-
__slots__ = ["_stack"]
|
| 769 |
-
|
| 770 |
-
def __init__(self, default_style: "Style") -> None:
|
| 771 |
-
self._stack: List[Style] = [default_style]
|
| 772 |
-
|
| 773 |
-
def __repr__(self) -> str:
|
| 774 |
-
return f"<stylestack {self._stack!r}>"
|
| 775 |
-
|
| 776 |
-
@property
|
| 777 |
-
def current(self) -> Style:
|
| 778 |
-
"""Get the Style at the top of the stack."""
|
| 779 |
-
return self._stack[-1]
|
| 780 |
-
|
| 781 |
-
def push(self, style: Style) -> None:
|
| 782 |
-
"""Push a new style on to the stack.
|
| 783 |
-
|
| 784 |
-
Args:
|
| 785 |
-
style (Style): New style to combine with current style.
|
| 786 |
-
"""
|
| 787 |
-
self._stack.append(self._stack[-1] + style)
|
| 788 |
-
|
| 789 |
-
def pop(self) -> Style:
|
| 790 |
-
"""Pop last style and discard.
|
| 791 |
-
|
| 792 |
-
Returns:
|
| 793 |
-
Style: New current style (also available as stack.current)
|
| 794 |
-
"""
|
| 795 |
-
self._stack.pop()
|
| 796 |
-
return self._stack[-1]
|
|
|
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|
spaces/BraydenMoore/MARCI-NFL-Betting/Source/Test/xgboost_ML.py
DELETED
|
@@ -1,59 +0,0 @@
|
|
| 1 |
-
import xgboost as xgb
|
| 2 |
-
import pandas as pd
|
| 3 |
-
import pickle as pkl
|
| 4 |
-
import numpy as np
|
| 5 |
-
import os
|
| 6 |
-
|
| 7 |
-
model = 'xgboost_ML_no_odds_71.4%'
|
| 8 |
-
|
| 9 |
-
current_directory = os.path.dirname(os.path.abspath(__file__))
|
| 10 |
-
parent_directory = os.path.dirname(current_directory)
|
| 11 |
-
data_directory = os.path.join(parent_directory, 'Data')
|
| 12 |
-
model_directory = os.path.join(parent_directory, 'Models')
|
| 13 |
-
pickle_directory = os.path.join(parent_directory, 'Pickles')
|
| 14 |
-
|
| 15 |
-
file_path = os.path.join(model_directory, f'{model}.json')
|
| 16 |
-
xgb_ml = xgb.Booster()
|
| 17 |
-
xgb_ml.load_model(file_path)
|
| 18 |
-
|
| 19 |
-
file_path = os.path.join(pickle_directory, 'test_games_ML_no_odds.pkl')
|
| 20 |
-
with open(file_path,'rb') as f:
|
| 21 |
-
test_games = pkl.load(f).tolist()
|
| 22 |
-
|
| 23 |
-
file_path = os.path.join(data_directory, 'gbg_and_odds.csv')
|
| 24 |
-
gbg_and_odds = pd.read_csv(file_path)
|
| 25 |
-
test_data = gbg_and_odds.loc[gbg_and_odds['game_id'].isin(test_games)]
|
| 26 |
-
test_data_matrix = xgb.DMatrix(test_data.drop(columns=['game_id','Over','Home-Team-Win','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings','Away Odds','Home Odds']).astype(float).values)
|
| 27 |
-
|
| 28 |
-
predicted_probas = xgb_ml.predict(test_data_matrix)
|
| 29 |
-
predictions = np.argmax(predicted_probas, axis=1)
|
| 30 |
-
test_data['predicted_proba'] = [i[1] for i in predicted_probas]
|
| 31 |
-
test_data['prediction'] = (test_data['predicted_proba']>0.5).astype(int)
|
| 32 |
-
test_data['correct'] = test_data['Home-Team-Win']==test_data['prediction']
|
| 33 |
-
|
| 34 |
-
bets = test_data.loc[(test_data['predicted_proba']>0.6) | (test_data['predicted_proba']<0.4)]
|
| 35 |
-
bets['winnings'] = [h if p==1 else a for h,a,p in bets[['Home Winnings','Away Winnings','prediction']].values]
|
| 36 |
-
|
| 37 |
-
import matplotlib.pyplot as plt
|
| 38 |
-
fig = plt.figure(facecolor='black')
|
| 39 |
-
ax = fig.add_subplot(1, 1, 1, facecolor='black')
|
| 40 |
-
|
| 41 |
-
# Plot data with line color as RGB(0, 128, 0)
|
| 42 |
-
ax.plot(bets['winnings'].cumsum().values*100, linewidth=3, color=(0/255, 128/255, 0/255))
|
| 43 |
-
|
| 44 |
-
# Set title and labels
|
| 45 |
-
ax.set_title('MARCI 3.0 - MoneyLine w/ 60% Confidence Threshold', color='white')
|
| 46 |
-
ax.set_xlabel('Games Bet On', color='white')
|
| 47 |
-
ax.set_ylabel('Return (%)', color='white')
|
| 48 |
-
|
| 49 |
-
# Change tick colors to white
|
| 50 |
-
ax.tick_params(axis='x', colors='white')
|
| 51 |
-
ax.tick_params(axis='y', colors='white')
|
| 52 |
-
|
| 53 |
-
# Change axis edge colors
|
| 54 |
-
ax.spines['bottom'].set_color('white')
|
| 55 |
-
ax.spines['top'].set_color('white')
|
| 56 |
-
ax.spines['left'].set_color('white')
|
| 57 |
-
ax.spines['right'].set_color('white')
|
| 58 |
-
|
| 59 |
-
plt.savefig(f'{model}_dark.png', facecolor='black')
|
|
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spaces/BrianL/CoE197-Fil-DialectTranslator/app.py
DELETED
|
@@ -1,36 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from transformers import pipeline
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
def trnslt(TagalogText,Language):
|
| 6 |
-
txt_inp = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-tl-en")
|
| 7 |
-
if Language=="Cebuano":
|
| 8 |
-
ceb1 = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-ceb")
|
| 9 |
-
out_ceb = gr.Series(txt_inp,ceb1)
|
| 10 |
-
return out_ceb(TagalogText)
|
| 11 |
-
elif Language=="Ilocano":
|
| 12 |
-
ilo1 = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-ilo")
|
| 13 |
-
out_ilo = gr.Series(txt_inp,ilo1)
|
| 14 |
-
return out_ilo(TagalogText)
|
| 15 |
-
elif Language=="Hiligaynon":
|
| 16 |
-
hil1 = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-hil")
|
| 17 |
-
out_hil = gr.Series(txt_inp,hil1)
|
| 18 |
-
return out_hil(TagalogText)
|
| 19 |
-
|
| 20 |
-
iface = gr.Interface(
|
| 21 |
-
fn=trnslt,
|
| 22 |
-
inputs=[gr.inputs.Textbox(label="Input Tagalog Text"),
|
| 23 |
-
gr.inputs.Radio(["Cebuano","Ilocano","Hiligaynon"],label="Translate to",optional=False)],
|
| 24 |
-
outputs='text',
|
| 25 |
-
examples=[["Magandang Umaga","Cebuano"],["Magandang gabi","Ilocano"],["Masarap ang Adobo","Hiligaynon"],
|
| 26 |
-
["Kumusta Ka Na","Cebuano"],["Bumibili si Juan ng manok","Ilocano"],["Magandang umaga","Hiligaynon"]],
|
| 27 |
-
live=True,
|
| 28 |
-
theme="dark-seafoam",
|
| 29 |
-
title="Basic Filipino Dialect Translator",
|
| 30 |
-
description=" This application uses Helsinki-NLP models to translate Tagalog texts to 3 other dialects of the Filipino language",
|
| 31 |
-
css=".footer{display:none !important}",
|
| 32 |
-
)
|
| 33 |
-
|
| 34 |
-
iface.launch()
|
| 35 |
-
|
| 36 |
-
|
|
|
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spaces/CVPR/LIVE/thrust/thrust/binary_search.h
DELETED
|
@@ -1,1902 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
/*! \file binary_search.h
|
| 19 |
-
* \brief Search for values in sorted ranges.
|
| 20 |
-
*/
|
| 21 |
-
|
| 22 |
-
#pragma once
|
| 23 |
-
|
| 24 |
-
#include <thrust/detail/config.h>
|
| 25 |
-
#include <thrust/detail/execution_policy.h>
|
| 26 |
-
#include <thrust/pair.h>
|
| 27 |
-
|
| 28 |
-
namespace thrust
|
| 29 |
-
{
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
/*! \addtogroup algorithms
|
| 33 |
-
*/
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
/*! \addtogroup searching
|
| 37 |
-
* \ingroup algorithms
|
| 38 |
-
* \{
|
| 39 |
-
*/
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
/*! \addtogroup binary_search Binary Search
|
| 43 |
-
* \ingroup searching
|
| 44 |
-
* \{
|
| 45 |
-
*/
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
//////////////////////
|
| 49 |
-
// Scalar Functions //
|
| 50 |
-
//////////////////////
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
/*! \p lower_bound is a version of binary search: it attempts to find
|
| 54 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 55 |
-
* Specifically, it returns the first position where value could be
|
| 56 |
-
* inserted without violating the ordering. This version of
|
| 57 |
-
* \p lower_bound uses <tt>operator<</tt> for comparison and returns
|
| 58 |
-
* the furthermost iterator \c i in <tt>[first, last)</tt> such that,
|
| 59 |
-
* for every iterator \c j in <tt>[first, i)</tt>, <tt>*j < value</tt>.
|
| 60 |
-
*
|
| 61 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 62 |
-
*
|
| 63 |
-
* \param exec The execution policy to use for parallelization.
|
| 64 |
-
* \param first The beginning of the ordered sequence.
|
| 65 |
-
* \param last The end of the ordered sequence.
|
| 66 |
-
* \param value The value to be searched.
|
| 67 |
-
* \return The furthermost iterator \c i, such that <tt>*i < value</tt>.
|
| 68 |
-
*
|
| 69 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 70 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 71 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 72 |
-
*
|
| 73 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 74 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
|
| 75 |
-
*
|
| 76 |
-
* \code
|
| 77 |
-
* #include <thrust/binary_search.h>
|
| 78 |
-
* #include <thrust/device_vector.h>
|
| 79 |
-
* #include <thrust/execution_policy.h>
|
| 80 |
-
* ...
|
| 81 |
-
* thrust::device_vector<int> input(5);
|
| 82 |
-
*
|
| 83 |
-
* input[0] = 0;
|
| 84 |
-
* input[1] = 2;
|
| 85 |
-
* input[2] = 5;
|
| 86 |
-
* input[3] = 7;
|
| 87 |
-
* input[4] = 8;
|
| 88 |
-
*
|
| 89 |
-
* thrust::lower_bound(thrust::device, input.begin(), input.end(), 0); // returns input.begin()
|
| 90 |
-
* thrust::lower_bound(thrust::device, input.begin(), input.end(), 1); // returns input.begin() + 1
|
| 91 |
-
* thrust::lower_bound(thrust::device, input.begin(), input.end(), 2); // returns input.begin() + 1
|
| 92 |
-
* thrust::lower_bound(thrust::device, input.begin(), input.end(), 3); // returns input.begin() + 2
|
| 93 |
-
* thrust::lower_bound(thrust::device, input.begin(), input.end(), 8); // returns input.begin() + 4
|
| 94 |
-
* thrust::lower_bound(thrust::device, input.begin(), input.end(), 9); // returns input.end()
|
| 95 |
-
* \endcode
|
| 96 |
-
*
|
| 97 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 98 |
-
* \see \p upper_bound
|
| 99 |
-
* \see \p equal_range
|
| 100 |
-
* \see \p binary_search
|
| 101 |
-
*/
|
| 102 |
-
template<typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
|
| 103 |
-
__host__ __device__
|
| 104 |
-
ForwardIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 105 |
-
ForwardIterator first,
|
| 106 |
-
ForwardIterator last,
|
| 107 |
-
const LessThanComparable &value);
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
/*! \p lower_bound is a version of binary search: it attempts to find
|
| 111 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 112 |
-
* Specifically, it returns the first position where value could be
|
| 113 |
-
* inserted without violating the ordering. This version of
|
| 114 |
-
* \p lower_bound uses <tt>operator<</tt> for comparison and returns
|
| 115 |
-
* the furthermost iterator \c i in <tt>[first, last)</tt> such that,
|
| 116 |
-
* for every iterator \c j in <tt>[first, i)</tt>, <tt>*j < value</tt>.
|
| 117 |
-
*
|
| 118 |
-
* \param first The beginning of the ordered sequence.
|
| 119 |
-
* \param last The end of the ordered sequence.
|
| 120 |
-
* \param value The value to be searched.
|
| 121 |
-
* \return The furthermost iterator \c i, such that <tt>*i < value</tt>.
|
| 122 |
-
*
|
| 123 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 124 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 125 |
-
*
|
| 126 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 127 |
-
* to search for values in a ordered range.
|
| 128 |
-
*
|
| 129 |
-
* \code
|
| 130 |
-
* #include <thrust/binary_search.h>
|
| 131 |
-
* #include <thrust/device_vector.h>
|
| 132 |
-
* ...
|
| 133 |
-
* thrust::device_vector<int> input(5);
|
| 134 |
-
*
|
| 135 |
-
* input[0] = 0;
|
| 136 |
-
* input[1] = 2;
|
| 137 |
-
* input[2] = 5;
|
| 138 |
-
* input[3] = 7;
|
| 139 |
-
* input[4] = 8;
|
| 140 |
-
*
|
| 141 |
-
* thrust::lower_bound(input.begin(), input.end(), 0); // returns input.begin()
|
| 142 |
-
* thrust::lower_bound(input.begin(), input.end(), 1); // returns input.begin() + 1
|
| 143 |
-
* thrust::lower_bound(input.begin(), input.end(), 2); // returns input.begin() + 1
|
| 144 |
-
* thrust::lower_bound(input.begin(), input.end(), 3); // returns input.begin() + 2
|
| 145 |
-
* thrust::lower_bound(input.begin(), input.end(), 8); // returns input.begin() + 4
|
| 146 |
-
* thrust::lower_bound(input.begin(), input.end(), 9); // returns input.end()
|
| 147 |
-
* \endcode
|
| 148 |
-
*
|
| 149 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 150 |
-
* \see \p upper_bound
|
| 151 |
-
* \see \p equal_range
|
| 152 |
-
* \see \p binary_search
|
| 153 |
-
*/
|
| 154 |
-
template <class ForwardIterator, class LessThanComparable>
|
| 155 |
-
ForwardIterator lower_bound(ForwardIterator first,
|
| 156 |
-
ForwardIterator last,
|
| 157 |
-
const LessThanComparable& value);
|
| 158 |
-
|
| 159 |
-
|
| 160 |
-
/*! \p lower_bound is a version of binary search: it attempts to find
|
| 161 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 162 |
-
* Specifically, it returns the first position where value could be
|
| 163 |
-
* inserted without violating the ordering. This version of
|
| 164 |
-
* \p lower_bound uses function object \c comp for comparison
|
| 165 |
-
* and returns the furthermost iterator \c i in <tt>[first, last)</tt>
|
| 166 |
-
* such that, for every iterator \c j in <tt>[first, i)</tt>,
|
| 167 |
-
* <tt>comp(*j, value)</tt> is \c true.
|
| 168 |
-
*
|
| 169 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 170 |
-
*
|
| 171 |
-
* \param exec The execution policy to use for parallelization.
|
| 172 |
-
* \param first The beginning of the ordered sequence.
|
| 173 |
-
* \param last The end of the ordered sequence.
|
| 174 |
-
* \param value The value to be searched.
|
| 175 |
-
* \param comp The comparison operator.
|
| 176 |
-
* \return The furthermost iterator \c i, such that <tt>comp(*i, value)</tt> is \c true.
|
| 177 |
-
*
|
| 178 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 179 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 180 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 181 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 182 |
-
*
|
| 183 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 184 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
|
| 185 |
-
*
|
| 186 |
-
* \code
|
| 187 |
-
* #include <thrust/binary_search.h>
|
| 188 |
-
* #include <thrust/device_vector.h>
|
| 189 |
-
* #include <thrust/functional.h>
|
| 190 |
-
* #include <thrust/execution_policy.h>
|
| 191 |
-
* ...
|
| 192 |
-
* thrust::device_vector<int> input(5);
|
| 193 |
-
*
|
| 194 |
-
* input[0] = 0;
|
| 195 |
-
* input[1] = 2;
|
| 196 |
-
* input[2] = 5;
|
| 197 |
-
* input[3] = 7;
|
| 198 |
-
* input[4] = 8;
|
| 199 |
-
*
|
| 200 |
-
* thrust::lower_bound(input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin()
|
| 201 |
-
* thrust::lower_bound(input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
|
| 202 |
-
* thrust::lower_bound(input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 1
|
| 203 |
-
* thrust::lower_bound(input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
|
| 204 |
-
* thrust::lower_bound(input.begin(), input.end(), 8, thrust::less<int>()); // returns input.begin() + 4
|
| 205 |
-
* thrust::lower_bound(input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
|
| 206 |
-
* \endcode
|
| 207 |
-
*
|
| 208 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 209 |
-
* \see \p upper_bound
|
| 210 |
-
* \see \p equal_range
|
| 211 |
-
* \see \p binary_search
|
| 212 |
-
*/
|
| 213 |
-
template<typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
|
| 214 |
-
__host__ __device__
|
| 215 |
-
ForwardIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 216 |
-
ForwardIterator first,
|
| 217 |
-
ForwardIterator last,
|
| 218 |
-
const T &value,
|
| 219 |
-
StrictWeakOrdering comp);
|
| 220 |
-
|
| 221 |
-
|
| 222 |
-
/*! \p lower_bound is a version of binary search: it attempts to find
|
| 223 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 224 |
-
* Specifically, it returns the first position where value could be
|
| 225 |
-
* inserted without violating the ordering. This version of
|
| 226 |
-
* \p lower_bound uses function object \c comp for comparison
|
| 227 |
-
* and returns the furthermost iterator \c i in <tt>[first, last)</tt>
|
| 228 |
-
* such that, for every iterator \c j in <tt>[first, i)</tt>,
|
| 229 |
-
* <tt>comp(*j, value)</tt> is \c true.
|
| 230 |
-
*
|
| 231 |
-
* \param first The beginning of the ordered sequence.
|
| 232 |
-
* \param last The end of the ordered sequence.
|
| 233 |
-
* \param value The value to be searched.
|
| 234 |
-
* \param comp The comparison operator.
|
| 235 |
-
* \return The furthermost iterator \c i, such that <tt>comp(*i, value)</tt> is \c true.
|
| 236 |
-
*
|
| 237 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 238 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 239 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 240 |
-
*
|
| 241 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 242 |
-
* to search for values in a ordered range.
|
| 243 |
-
*
|
| 244 |
-
* \code
|
| 245 |
-
* #include <thrust/binary_search.h>
|
| 246 |
-
* #include <thrust/device_vector.h>
|
| 247 |
-
* #include <thrust/functional.h>
|
| 248 |
-
* ...
|
| 249 |
-
* thrust::device_vector<int> input(5);
|
| 250 |
-
*
|
| 251 |
-
* input[0] = 0;
|
| 252 |
-
* input[1] = 2;
|
| 253 |
-
* input[2] = 5;
|
| 254 |
-
* input[3] = 7;
|
| 255 |
-
* input[4] = 8;
|
| 256 |
-
*
|
| 257 |
-
* thrust::lower_bound(input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin()
|
| 258 |
-
* thrust::lower_bound(input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
|
| 259 |
-
* thrust::lower_bound(input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 1
|
| 260 |
-
* thrust::lower_bound(input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
|
| 261 |
-
* thrust::lower_bound(input.begin(), input.end(), 8, thrust::less<int>()); // returns input.begin() + 4
|
| 262 |
-
* thrust::lower_bound(input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
|
| 263 |
-
* \endcode
|
| 264 |
-
*
|
| 265 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 266 |
-
* \see \p upper_bound
|
| 267 |
-
* \see \p equal_range
|
| 268 |
-
* \see \p binary_search
|
| 269 |
-
*/
|
| 270 |
-
template <class ForwardIterator, class T, class StrictWeakOrdering>
|
| 271 |
-
ForwardIterator lower_bound(ForwardIterator first,
|
| 272 |
-
ForwardIterator last,
|
| 273 |
-
const T& value,
|
| 274 |
-
StrictWeakOrdering comp);
|
| 275 |
-
|
| 276 |
-
|
| 277 |
-
/*! \p upper_bound is a version of binary search: it attempts to find
|
| 278 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 279 |
-
* Specifically, it returns the last position where value could be
|
| 280 |
-
* inserted without violating the ordering. This version of
|
| 281 |
-
* \p upper_bound uses <tt>operator<</tt> for comparison and returns
|
| 282 |
-
* the furthermost iterator \c i in <tt>[first, last)</tt> such that,
|
| 283 |
-
* for every iterator \c j in <tt>[first, i)</tt>, <tt>value < *j</tt>
|
| 284 |
-
* is \c false.
|
| 285 |
-
*
|
| 286 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 287 |
-
*
|
| 288 |
-
* \param exec The execution policy to use for parallelization.
|
| 289 |
-
* \param first The beginning of the ordered sequence.
|
| 290 |
-
* \param last The end of the ordered sequence.
|
| 291 |
-
* \param value The value to be searched.
|
| 292 |
-
* \return The furthermost iterator \c i, such that <tt>value < *i</tt> is \c false.
|
| 293 |
-
*
|
| 294 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 295 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 296 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 297 |
-
*
|
| 298 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 299 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelism:
|
| 300 |
-
*
|
| 301 |
-
* \code
|
| 302 |
-
* #include <thrust/binary_search.h>
|
| 303 |
-
* #include <thrust/device_vector.h>
|
| 304 |
-
* #include <thrust/execution_policy.h>
|
| 305 |
-
* ...
|
| 306 |
-
* thrust::device_vector<int> input(5);
|
| 307 |
-
*
|
| 308 |
-
* input[0] = 0;
|
| 309 |
-
* input[1] = 2;
|
| 310 |
-
* input[2] = 5;
|
| 311 |
-
* input[3] = 7;
|
| 312 |
-
* input[4] = 8;
|
| 313 |
-
*
|
| 314 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 0); // returns input.begin() + 1
|
| 315 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 1); // returns input.begin() + 1
|
| 316 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 2); // returns input.begin() + 2
|
| 317 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 3); // returns input.begin() + 2
|
| 318 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 8); // returns input.end()
|
| 319 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 9); // returns input.end()
|
| 320 |
-
* \endcode
|
| 321 |
-
*
|
| 322 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 323 |
-
* \see \p lower_bound
|
| 324 |
-
* \see \p equal_range
|
| 325 |
-
* \see \p binary_search
|
| 326 |
-
*/
|
| 327 |
-
template<typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
|
| 328 |
-
__host__ __device__
|
| 329 |
-
ForwardIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 330 |
-
ForwardIterator first,
|
| 331 |
-
ForwardIterator last,
|
| 332 |
-
const LessThanComparable &value);
|
| 333 |
-
|
| 334 |
-
|
| 335 |
-
/*! \p upper_bound is a version of binary search: it attempts to find
|
| 336 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 337 |
-
* Specifically, it returns the last position where value could be
|
| 338 |
-
* inserted without violating the ordering. This version of
|
| 339 |
-
* \p upper_bound uses <tt>operator<</tt> for comparison and returns
|
| 340 |
-
* the furthermost iterator \c i in <tt>[first, last)</tt> such that,
|
| 341 |
-
* for every iterator \c j in <tt>[first, i)</tt>, <tt>value < *j</tt>
|
| 342 |
-
* is \c false.
|
| 343 |
-
*
|
| 344 |
-
* \param first The beginning of the ordered sequence.
|
| 345 |
-
* \param last The end of the ordered sequence.
|
| 346 |
-
* \param value The value to be searched.
|
| 347 |
-
* \return The furthermost iterator \c i, such that <tt>value < *i</tt> is \c false.
|
| 348 |
-
*
|
| 349 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 350 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 351 |
-
*
|
| 352 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 353 |
-
* to search for values in a ordered range.
|
| 354 |
-
*
|
| 355 |
-
* \code
|
| 356 |
-
* #include <thrust/binary_search.h>
|
| 357 |
-
* #include <thrust/device_vector.h>
|
| 358 |
-
* ...
|
| 359 |
-
* thrust::device_vector<int> input(5);
|
| 360 |
-
*
|
| 361 |
-
* input[0] = 0;
|
| 362 |
-
* input[1] = 2;
|
| 363 |
-
* input[2] = 5;
|
| 364 |
-
* input[3] = 7;
|
| 365 |
-
* input[4] = 8;
|
| 366 |
-
*
|
| 367 |
-
* thrust::upper_bound(input.begin(), input.end(), 0); // returns input.begin() + 1
|
| 368 |
-
* thrust::upper_bound(input.begin(), input.end(), 1); // returns input.begin() + 1
|
| 369 |
-
* thrust::upper_bound(input.begin(), input.end(), 2); // returns input.begin() + 2
|
| 370 |
-
* thrust::upper_bound(input.begin(), input.end(), 3); // returns input.begin() + 2
|
| 371 |
-
* thrust::upper_bound(input.begin(), input.end(), 8); // returns input.end()
|
| 372 |
-
* thrust::upper_bound(input.begin(), input.end(), 9); // returns input.end()
|
| 373 |
-
* \endcode
|
| 374 |
-
*
|
| 375 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 376 |
-
* \see \p lower_bound
|
| 377 |
-
* \see \p equal_range
|
| 378 |
-
* \see \p binary_search
|
| 379 |
-
*/
|
| 380 |
-
template <class ForwardIterator, class LessThanComparable>
|
| 381 |
-
ForwardIterator upper_bound(ForwardIterator first,
|
| 382 |
-
ForwardIterator last,
|
| 383 |
-
const LessThanComparable& value);
|
| 384 |
-
|
| 385 |
-
|
| 386 |
-
/*! \p upper_bound is a version of binary search: it attempts to find
|
| 387 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 388 |
-
* Specifically, it returns the last position where value could be
|
| 389 |
-
* inserted without violating the ordering. This version of
|
| 390 |
-
* \p upper_bound uses function object \c comp for comparison and returns
|
| 391 |
-
* the furthermost iterator \c i in <tt>[first, last)</tt> such that,
|
| 392 |
-
* for every iterator \c j in <tt>[first, i)</tt>, <tt>comp(value, *j)</tt>
|
| 393 |
-
* is \c false.
|
| 394 |
-
*
|
| 395 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 396 |
-
*
|
| 397 |
-
* \param exec The execution policy to use for parallelization.
|
| 398 |
-
* \param first The beginning of the ordered sequence.
|
| 399 |
-
* \param last The end of the ordered sequence.
|
| 400 |
-
* \param value The value to be searched.
|
| 401 |
-
* \param comp The comparison operator.
|
| 402 |
-
* \return The furthermost iterator \c i, such that <tt>comp(value, *i)</tt> is \c false.
|
| 403 |
-
*
|
| 404 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 405 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 406 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 407 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 408 |
-
*
|
| 409 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 410 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
|
| 411 |
-
*
|
| 412 |
-
* \code
|
| 413 |
-
* #include <thrust/binary_search.h>
|
| 414 |
-
* #include <thrust/device_vector.h>
|
| 415 |
-
* #include <thrust/functional.h>
|
| 416 |
-
* #include <thrust/execution_policy.h>
|
| 417 |
-
* ...
|
| 418 |
-
* thrust::device_vector<int> input(5);
|
| 419 |
-
*
|
| 420 |
-
* input[0] = 0;
|
| 421 |
-
* input[1] = 2;
|
| 422 |
-
* input[2] = 5;
|
| 423 |
-
* input[3] = 7;
|
| 424 |
-
* input[4] = 8;
|
| 425 |
-
*
|
| 426 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin() + 1
|
| 427 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
|
| 428 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 2
|
| 429 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
|
| 430 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 8, thrust::less<int>()); // returns input.end()
|
| 431 |
-
* thrust::upper_bound(thrust::device, input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
|
| 432 |
-
* \endcode
|
| 433 |
-
*
|
| 434 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 435 |
-
* \see \p lower_bound
|
| 436 |
-
* \see \p equal_range
|
| 437 |
-
* \see \p binary_search
|
| 438 |
-
*/
|
| 439 |
-
template<typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
|
| 440 |
-
__host__ __device__
|
| 441 |
-
ForwardIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 442 |
-
ForwardIterator first,
|
| 443 |
-
ForwardIterator last,
|
| 444 |
-
const T &value,
|
| 445 |
-
StrictWeakOrdering comp);
|
| 446 |
-
|
| 447 |
-
/*! \p upper_bound is a version of binary search: it attempts to find
|
| 448 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 449 |
-
* Specifically, it returns the last position where value could be
|
| 450 |
-
* inserted without violating the ordering. This version of
|
| 451 |
-
* \p upper_bound uses function object \c comp for comparison and returns
|
| 452 |
-
* the furthermost iterator \c i in <tt>[first, last)</tt> such that,
|
| 453 |
-
* for every iterator \c j in <tt>[first, i)</tt>, <tt>comp(value, *j)</tt>
|
| 454 |
-
* is \c false.
|
| 455 |
-
*
|
| 456 |
-
* \param first The beginning of the ordered sequence.
|
| 457 |
-
* \param last The end of the ordered sequence.
|
| 458 |
-
* \param value The value to be searched.
|
| 459 |
-
* \param comp The comparison operator.
|
| 460 |
-
* \return The furthermost iterator \c i, such that <tt>comp(value, *i)</tt> is \c false.
|
| 461 |
-
*
|
| 462 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 463 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 464 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 465 |
-
*
|
| 466 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 467 |
-
* to search for values in a ordered range.
|
| 468 |
-
*
|
| 469 |
-
* \code
|
| 470 |
-
* #include <thrust/binary_search.h>
|
| 471 |
-
* #include <thrust/device_vector.h>
|
| 472 |
-
* #include <thrust/functional.h>
|
| 473 |
-
* ...
|
| 474 |
-
* thrust::device_vector<int> input(5);
|
| 475 |
-
*
|
| 476 |
-
* input[0] = 0;
|
| 477 |
-
* input[1] = 2;
|
| 478 |
-
* input[2] = 5;
|
| 479 |
-
* input[3] = 7;
|
| 480 |
-
* input[4] = 8;
|
| 481 |
-
*
|
| 482 |
-
* thrust::upper_bound(input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin() + 1
|
| 483 |
-
* thrust::upper_bound(input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
|
| 484 |
-
* thrust::upper_bound(input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 2
|
| 485 |
-
* thrust::upper_bound(input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
|
| 486 |
-
* thrust::upper_bound(input.begin(), input.end(), 8, thrust::less<int>()); // returns input.end()
|
| 487 |
-
* thrust::upper_bound(input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
|
| 488 |
-
* \endcode
|
| 489 |
-
*
|
| 490 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 491 |
-
* \see \p lower_bound
|
| 492 |
-
* \see \p equal_range
|
| 493 |
-
* \see \p binary_search
|
| 494 |
-
*/
|
| 495 |
-
template <class ForwardIterator, class T, class StrictWeakOrdering>
|
| 496 |
-
ForwardIterator upper_bound(ForwardIterator first,
|
| 497 |
-
ForwardIterator last,
|
| 498 |
-
const T& value,
|
| 499 |
-
StrictWeakOrdering comp);
|
| 500 |
-
|
| 501 |
-
|
| 502 |
-
/*! \p binary_search is a version of binary search: it attempts to find
|
| 503 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 504 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 505 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 506 |
-
* exists. Specifically, this version returns \c true if and only if
|
| 507 |
-
* there exists an iterator \c i in <tt>[first, last)</tt> such that
|
| 508 |
-
* <tt>*i < value</tt> and <tt>value < *i</tt> are both \c false.
|
| 509 |
-
*
|
| 510 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 511 |
-
*
|
| 512 |
-
* \param exec The execution policy to use for parallelization.
|
| 513 |
-
* \param first The beginning of the ordered sequence.
|
| 514 |
-
* \param last The end of the ordered sequence.
|
| 515 |
-
* \param value The value to be searched.
|
| 516 |
-
* \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
|
| 517 |
-
*
|
| 518 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 519 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 520 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 521 |
-
*
|
| 522 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 523 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
|
| 524 |
-
*
|
| 525 |
-
* \code
|
| 526 |
-
* #include <thrust/binary_search.h>
|
| 527 |
-
* #include <thrust/device_vector.h>
|
| 528 |
-
* #include <thrust/execution_policy.h>
|
| 529 |
-
* ...
|
| 530 |
-
* thrust::device_vector<int> input(5);
|
| 531 |
-
*
|
| 532 |
-
* input[0] = 0;
|
| 533 |
-
* input[1] = 2;
|
| 534 |
-
* input[2] = 5;
|
| 535 |
-
* input[3] = 7;
|
| 536 |
-
* input[4] = 8;
|
| 537 |
-
*
|
| 538 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 0); // returns true
|
| 539 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 1); // returns false
|
| 540 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 2); // returns true
|
| 541 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 3); // returns false
|
| 542 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 8); // returns true
|
| 543 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 9); // returns false
|
| 544 |
-
* \endcode
|
| 545 |
-
*
|
| 546 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 547 |
-
* \see \p lower_bound
|
| 548 |
-
* \see \p upper_bound
|
| 549 |
-
* \see \p equal_range
|
| 550 |
-
*/
|
| 551 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
|
| 552 |
-
__host__ __device__
|
| 553 |
-
bool binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 554 |
-
ForwardIterator first,
|
| 555 |
-
ForwardIterator last,
|
| 556 |
-
const LessThanComparable& value);
|
| 557 |
-
|
| 558 |
-
|
| 559 |
-
/*! \p binary_search is a version of binary search: it attempts to find
|
| 560 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 561 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 562 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 563 |
-
* exists. Specifically, this version returns \c true if and only if
|
| 564 |
-
* there exists an iterator \c i in <tt>[first, last)</tt> such that
|
| 565 |
-
* <tt>*i < value</tt> and <tt>value < *i</tt> are both \c false.
|
| 566 |
-
*
|
| 567 |
-
* \param first The beginning of the ordered sequence.
|
| 568 |
-
* \param last The end of the ordered sequence.
|
| 569 |
-
* \param value The value to be searched.
|
| 570 |
-
* \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
|
| 571 |
-
*
|
| 572 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 573 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 574 |
-
*
|
| 575 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 576 |
-
* to search for values in a ordered range.
|
| 577 |
-
*
|
| 578 |
-
* \code
|
| 579 |
-
* #include <thrust/binary_search.h>
|
| 580 |
-
* #include <thrust/device_vector.h>
|
| 581 |
-
* ...
|
| 582 |
-
* thrust::device_vector<int> input(5);
|
| 583 |
-
*
|
| 584 |
-
* input[0] = 0;
|
| 585 |
-
* input[1] = 2;
|
| 586 |
-
* input[2] = 5;
|
| 587 |
-
* input[3] = 7;
|
| 588 |
-
* input[4] = 8;
|
| 589 |
-
*
|
| 590 |
-
* thrust::binary_search(input.begin(), input.end(), 0); // returns true
|
| 591 |
-
* thrust::binary_search(input.begin(), input.end(), 1); // returns false
|
| 592 |
-
* thrust::binary_search(input.begin(), input.end(), 2); // returns true
|
| 593 |
-
* thrust::binary_search(input.begin(), input.end(), 3); // returns false
|
| 594 |
-
* thrust::binary_search(input.begin(), input.end(), 8); // returns true
|
| 595 |
-
* thrust::binary_search(input.begin(), input.end(), 9); // returns false
|
| 596 |
-
* \endcode
|
| 597 |
-
*
|
| 598 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 599 |
-
* \see \p lower_bound
|
| 600 |
-
* \see \p upper_bound
|
| 601 |
-
* \see \p equal_range
|
| 602 |
-
*/
|
| 603 |
-
template <class ForwardIterator, class LessThanComparable>
|
| 604 |
-
bool binary_search(ForwardIterator first,
|
| 605 |
-
ForwardIterator last,
|
| 606 |
-
const LessThanComparable& value);
|
| 607 |
-
|
| 608 |
-
|
| 609 |
-
/*! \p binary_search is a version of binary search: it attempts to find
|
| 610 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 611 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 612 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 613 |
-
* exists. Specifically, this version returns \c true if and only if
|
| 614 |
-
* there exists an iterator \c i in <tt>[first, last)</tt> such that
|
| 615 |
-
* <tt>comp(*i, value)</tt> and <tt>comp(value, *i)</tt> are both \c false.
|
| 616 |
-
*
|
| 617 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 618 |
-
*
|
| 619 |
-
* \param exec The execution policy to use for parallelization.
|
| 620 |
-
* \param first The beginning of the ordered sequence.
|
| 621 |
-
* \param last The end of the ordered sequence.
|
| 622 |
-
* \param value The value to be searched.
|
| 623 |
-
* \param comp The comparison operator.
|
| 624 |
-
* \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
|
| 625 |
-
*
|
| 626 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 627 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 628 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 629 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 630 |
-
*
|
| 631 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 632 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
|
| 633 |
-
*
|
| 634 |
-
* \code
|
| 635 |
-
* #include <thrust/binary_search.h>
|
| 636 |
-
* #include <thrust/device_vector.h>
|
| 637 |
-
* #include <thrust/functional.h>
|
| 638 |
-
* #include <thrust/execution_policy.h>
|
| 639 |
-
* ...
|
| 640 |
-
* thrust::device_vector<int> input(5);
|
| 641 |
-
*
|
| 642 |
-
* input[0] = 0;
|
| 643 |
-
* input[1] = 2;
|
| 644 |
-
* input[2] = 5;
|
| 645 |
-
* input[3] = 7;
|
| 646 |
-
* input[4] = 8;
|
| 647 |
-
*
|
| 648 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 0, thrust::less<int>()); // returns true
|
| 649 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 1, thrust::less<int>()); // returns false
|
| 650 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 2, thrust::less<int>()); // returns true
|
| 651 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 3, thrust::less<int>()); // returns false
|
| 652 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 8, thrust::less<int>()); // returns true
|
| 653 |
-
* thrust::binary_search(thrust::device, input.begin(), input.end(), 9, thrust::less<int>()); // returns false
|
| 654 |
-
* \endcode
|
| 655 |
-
*
|
| 656 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 657 |
-
* \see \p lower_bound
|
| 658 |
-
* \see \p upper_bound
|
| 659 |
-
* \see \p equal_range
|
| 660 |
-
*/
|
| 661 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
|
| 662 |
-
__host__ __device__
|
| 663 |
-
bool binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 664 |
-
ForwardIterator first,
|
| 665 |
-
ForwardIterator last,
|
| 666 |
-
const T& value,
|
| 667 |
-
StrictWeakOrdering comp);
|
| 668 |
-
|
| 669 |
-
|
| 670 |
-
/*! \p binary_search is a version of binary search: it attempts to find
|
| 671 |
-
* the element value in an ordered range <tt>[first, last)</tt>.
|
| 672 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 673 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 674 |
-
* exists. Specifically, this version returns \c true if and only if
|
| 675 |
-
* there exists an iterator \c i in <tt>[first, last)</tt> such that
|
| 676 |
-
* <tt>comp(*i, value)</tt> and <tt>comp(value, *i)</tt> are both \c false.
|
| 677 |
-
*
|
| 678 |
-
* \param first The beginning of the ordered sequence.
|
| 679 |
-
* \param last The end of the ordered sequence.
|
| 680 |
-
* \param value The value to be searched.
|
| 681 |
-
* \param comp The comparison operator.
|
| 682 |
-
* \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
|
| 683 |
-
*
|
| 684 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 685 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 686 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 687 |
-
*
|
| 688 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 689 |
-
* to search for values in a ordered range.
|
| 690 |
-
*
|
| 691 |
-
* \code
|
| 692 |
-
* #include <thrust/binary_search.h>
|
| 693 |
-
* #include <thrust/device_vector.h>
|
| 694 |
-
* #include <thrust/functional.h>
|
| 695 |
-
* ...
|
| 696 |
-
* thrust::device_vector<int> input(5);
|
| 697 |
-
*
|
| 698 |
-
* input[0] = 0;
|
| 699 |
-
* input[1] = 2;
|
| 700 |
-
* input[2] = 5;
|
| 701 |
-
* input[3] = 7;
|
| 702 |
-
* input[4] = 8;
|
| 703 |
-
*
|
| 704 |
-
* thrust::binary_search(input.begin(), input.end(), 0, thrust::less<int>()); // returns true
|
| 705 |
-
* thrust::binary_search(input.begin(), input.end(), 1, thrust::less<int>()); // returns false
|
| 706 |
-
* thrust::binary_search(input.begin(), input.end(), 2, thrust::less<int>()); // returns true
|
| 707 |
-
* thrust::binary_search(input.begin(), input.end(), 3, thrust::less<int>()); // returns false
|
| 708 |
-
* thrust::binary_search(input.begin(), input.end(), 8, thrust::less<int>()); // returns true
|
| 709 |
-
* thrust::binary_search(input.begin(), input.end(), 9, thrust::less<int>()); // returns false
|
| 710 |
-
* \endcode
|
| 711 |
-
*
|
| 712 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 713 |
-
* \see \p lower_bound
|
| 714 |
-
* \see \p upper_bound
|
| 715 |
-
* \see \p equal_range
|
| 716 |
-
*/
|
| 717 |
-
template <class ForwardIterator, class T, class StrictWeakOrdering>
|
| 718 |
-
bool binary_search(ForwardIterator first,
|
| 719 |
-
ForwardIterator last,
|
| 720 |
-
const T& value,
|
| 721 |
-
StrictWeakOrdering comp);
|
| 722 |
-
|
| 723 |
-
|
| 724 |
-
/*! \p equal_range is a version of binary search: it attempts to find
|
| 725 |
-
* the element value in an ordered range <tt>[first, last)</tt>. The
|
| 726 |
-
* value returned by \p equal_range is essentially a combination of
|
| 727 |
-
* the values returned by \p lower_bound and \p upper_bound: it returns
|
| 728 |
-
* a \p pair of iterators \c i and \c j such that \c i is the first
|
| 729 |
-
* position where value could be inserted without violating the
|
| 730 |
-
* ordering and \c j is the last position where value could be inserted
|
| 731 |
-
* without violating the ordering. It follows that every element in the
|
| 732 |
-
* range <tt>[i, j)</tt> is equivalent to value, and that
|
| 733 |
-
* <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
|
| 734 |
-
* has this property.
|
| 735 |
-
*
|
| 736 |
-
* This version of \p equal_range returns a \p pair of iterators
|
| 737 |
-
* <tt>[i, j)</tt>, where \c i is the furthermost iterator in
|
| 738 |
-
* <tt>[first, last)</tt> such that, for every iterator \c k in
|
| 739 |
-
* <tt>[first, i)</tt>, <tt>*k < value</tt>. \c j is the furthermost
|
| 740 |
-
* iterator in <tt>[first, last)</tt> such that, for every iterator
|
| 741 |
-
* \c k in <tt>[first, j)</tt>, <tt>value < *k</tt> is \c false.
|
| 742 |
-
* For every iterator \c k in <tt>[i, j)</tt>, neither
|
| 743 |
-
* <tt>value < *k</tt> nor <tt>*k < value</tt> is \c true.
|
| 744 |
-
*
|
| 745 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 746 |
-
*
|
| 747 |
-
* \param exec The execution policy to use for parallelization.
|
| 748 |
-
* \param first The beginning of the ordered sequence.
|
| 749 |
-
* \param last The end of the ordered sequence.
|
| 750 |
-
* \param value The value to be searched.
|
| 751 |
-
* \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
|
| 752 |
-
*
|
| 753 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 754 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 755 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 756 |
-
*
|
| 757 |
-
* The following code snippet demonstrates how to use \p equal_range
|
| 758 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
|
| 759 |
-
*
|
| 760 |
-
* \code
|
| 761 |
-
* #include <thrust/binary_search.h>
|
| 762 |
-
* #include <thrust/device_vector.h>
|
| 763 |
-
* #include <thrust/execution_policy.h>
|
| 764 |
-
* ...
|
| 765 |
-
* thrust::device_vector<int> input(5);
|
| 766 |
-
*
|
| 767 |
-
* input[0] = 0;
|
| 768 |
-
* input[1] = 2;
|
| 769 |
-
* input[2] = 5;
|
| 770 |
-
* input[3] = 7;
|
| 771 |
-
* input[4] = 8;
|
| 772 |
-
*
|
| 773 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 0); // returns [input.begin(), input.begin() + 1)
|
| 774 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 1); // returns [input.begin() + 1, input.begin() + 1)
|
| 775 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 2); // returns [input.begin() + 1, input.begin() + 2)
|
| 776 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 3); // returns [input.begin() + 2, input.begin() + 2)
|
| 777 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 8); // returns [input.begin() + 4, input.end)
|
| 778 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 9); // returns [input.end(), input.end)
|
| 779 |
-
* \endcode
|
| 780 |
-
*
|
| 781 |
-
* \see http://www.sgi.com/tech/stl/equal_range.html
|
| 782 |
-
* \see \p lower_bound
|
| 783 |
-
* \see \p upper_bound
|
| 784 |
-
* \see \p binary_search
|
| 785 |
-
*/
|
| 786 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
|
| 787 |
-
__host__ __device__
|
| 788 |
-
thrust::pair<ForwardIterator, ForwardIterator>
|
| 789 |
-
equal_range(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 790 |
-
ForwardIterator first,
|
| 791 |
-
ForwardIterator last,
|
| 792 |
-
const LessThanComparable& value);
|
| 793 |
-
|
| 794 |
-
|
| 795 |
-
/*! \p equal_range is a version of binary search: it attempts to find
|
| 796 |
-
* the element value in an ordered range <tt>[first, last)</tt>. The
|
| 797 |
-
* value returned by \p equal_range is essentially a combination of
|
| 798 |
-
* the values returned by \p lower_bound and \p upper_bound: it returns
|
| 799 |
-
* a \p pair of iterators \c i and \c j such that \c i is the first
|
| 800 |
-
* position where value could be inserted without violating the
|
| 801 |
-
* ordering and \c j is the last position where value could be inserted
|
| 802 |
-
* without violating the ordering. It follows that every element in the
|
| 803 |
-
* range <tt>[i, j)</tt> is equivalent to value, and that
|
| 804 |
-
* <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
|
| 805 |
-
* has this property.
|
| 806 |
-
*
|
| 807 |
-
* This version of \p equal_range returns a \p pair of iterators
|
| 808 |
-
* <tt>[i, j)</tt>, where \c i is the furthermost iterator in
|
| 809 |
-
* <tt>[first, last)</tt> such that, for every iterator \c k in
|
| 810 |
-
* <tt>[first, i)</tt>, <tt>*k < value</tt>. \c j is the furthermost
|
| 811 |
-
* iterator in <tt>[first, last)</tt> such that, for every iterator
|
| 812 |
-
* \c k in <tt>[first, j)</tt>, <tt>value < *k</tt> is \c false.
|
| 813 |
-
* For every iterator \c k in <tt>[i, j)</tt>, neither
|
| 814 |
-
* <tt>value < *k</tt> nor <tt>*k < value</tt> is \c true.
|
| 815 |
-
*
|
| 816 |
-
* \param first The beginning of the ordered sequence.
|
| 817 |
-
* \param last The end of the ordered sequence.
|
| 818 |
-
* \param value The value to be searched.
|
| 819 |
-
* \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
|
| 820 |
-
*
|
| 821 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 822 |
-
* \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 823 |
-
*
|
| 824 |
-
* The following code snippet demonstrates how to use \p equal_range
|
| 825 |
-
* to search for values in a ordered range.
|
| 826 |
-
*
|
| 827 |
-
* \code
|
| 828 |
-
* #include <thrust/binary_search.h>
|
| 829 |
-
* #include <thrust/device_vector.h>
|
| 830 |
-
* ...
|
| 831 |
-
* thrust::device_vector<int> input(5);
|
| 832 |
-
*
|
| 833 |
-
* input[0] = 0;
|
| 834 |
-
* input[1] = 2;
|
| 835 |
-
* input[2] = 5;
|
| 836 |
-
* input[3] = 7;
|
| 837 |
-
* input[4] = 8;
|
| 838 |
-
*
|
| 839 |
-
* thrust::equal_range(input.begin(), input.end(), 0); // returns [input.begin(), input.begin() + 1)
|
| 840 |
-
* thrust::equal_range(input.begin(), input.end(), 1); // returns [input.begin() + 1, input.begin() + 1)
|
| 841 |
-
* thrust::equal_range(input.begin(), input.end(), 2); // returns [input.begin() + 1, input.begin() + 2)
|
| 842 |
-
* thrust::equal_range(input.begin(), input.end(), 3); // returns [input.begin() + 2, input.begin() + 2)
|
| 843 |
-
* thrust::equal_range(input.begin(), input.end(), 8); // returns [input.begin() + 4, input.end)
|
| 844 |
-
* thrust::equal_range(input.begin(), input.end(), 9); // returns [input.end(), input.end)
|
| 845 |
-
* \endcode
|
| 846 |
-
*
|
| 847 |
-
* \see http://www.sgi.com/tech/stl/equal_range.html
|
| 848 |
-
* \see \p lower_bound
|
| 849 |
-
* \see \p upper_bound
|
| 850 |
-
* \see \p binary_search
|
| 851 |
-
*/
|
| 852 |
-
template <class ForwardIterator, class LessThanComparable>
|
| 853 |
-
thrust::pair<ForwardIterator, ForwardIterator>
|
| 854 |
-
equal_range(ForwardIterator first,
|
| 855 |
-
ForwardIterator last,
|
| 856 |
-
const LessThanComparable& value);
|
| 857 |
-
|
| 858 |
-
|
| 859 |
-
/*! \p equal_range is a version of binary search: it attempts to find
|
| 860 |
-
* the element value in an ordered range <tt>[first, last)</tt>. The
|
| 861 |
-
* value returned by \p equal_range is essentially a combination of
|
| 862 |
-
* the values returned by \p lower_bound and \p upper_bound: it returns
|
| 863 |
-
* a \p pair of iterators \c i and \c j such that \c i is the first
|
| 864 |
-
* position where value could be inserted without violating the
|
| 865 |
-
* ordering and \c j is the last position where value could be inserted
|
| 866 |
-
* without violating the ordering. It follows that every element in the
|
| 867 |
-
* range <tt>[i, j)</tt> is equivalent to value, and that
|
| 868 |
-
* <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
|
| 869 |
-
* has this property.
|
| 870 |
-
*
|
| 871 |
-
* This version of \p equal_range returns a \p pair of iterators
|
| 872 |
-
* <tt>[i, j)</tt>. \c i is the furthermost iterator in
|
| 873 |
-
* <tt>[first, last)</tt> such that, for every iterator \c k in
|
| 874 |
-
* <tt>[first, i)</tt>, <tt>comp(*k, value)</tt> is \c true.
|
| 875 |
-
* \c j is the furthermost iterator in <tt>[first, last)</tt> such
|
| 876 |
-
* that, for every iterator \c k in <tt>[first, last)</tt>,
|
| 877 |
-
* <tt>comp(value, *k)</tt> is \c false. For every iterator \c k
|
| 878 |
-
* in <tt>[i, j)</tt>, neither <tt>comp(value, *k)</tt> nor
|
| 879 |
-
* <tt>comp(*k, value)</tt> is \c true.
|
| 880 |
-
*
|
| 881 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 882 |
-
*
|
| 883 |
-
* \param exec The execution policy to use for parallelization.
|
| 884 |
-
* \param first The beginning of the ordered sequence.
|
| 885 |
-
* \param last The end of the ordered sequence.
|
| 886 |
-
* \param value The value to be searched.
|
| 887 |
-
* \param comp The comparison operator.
|
| 888 |
-
* \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
|
| 889 |
-
*
|
| 890 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 891 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 892 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 893 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 894 |
-
*
|
| 895 |
-
* The following code snippet demonstrates how to use \p equal_range
|
| 896 |
-
* to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
|
| 897 |
-
*
|
| 898 |
-
* \code
|
| 899 |
-
* #include <thrust/binary_search.h>
|
| 900 |
-
* #include <thrust/device_vector.h>
|
| 901 |
-
* #include <thrust/functional.h>
|
| 902 |
-
* #include <thrust/execution_policy.h>
|
| 903 |
-
* ...
|
| 904 |
-
* thrust::device_vector<int> input(5);
|
| 905 |
-
*
|
| 906 |
-
* input[0] = 0;
|
| 907 |
-
* input[1] = 2;
|
| 908 |
-
* input[2] = 5;
|
| 909 |
-
* input[3] = 7;
|
| 910 |
-
* input[4] = 8;
|
| 911 |
-
*
|
| 912 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 0, thrust::less<int>()); // returns [input.begin(), input.begin() + 1)
|
| 913 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 1, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 1)
|
| 914 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 2, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 2)
|
| 915 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 3, thrust::less<int>()); // returns [input.begin() + 2, input.begin() + 2)
|
| 916 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 8, thrust::less<int>()); // returns [input.begin() + 4, input.end)
|
| 917 |
-
* thrust::equal_range(thrust::device, input.begin(), input.end(), 9, thrust::less<int>()); // returns [input.end(), input.end)
|
| 918 |
-
* \endcode
|
| 919 |
-
*
|
| 920 |
-
* \see http://www.sgi.com/tech/stl/equal_range.html
|
| 921 |
-
* \see \p lower_bound
|
| 922 |
-
* \see \p upper_bound
|
| 923 |
-
* \see \p binary_search
|
| 924 |
-
*/
|
| 925 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
|
| 926 |
-
__host__ __device__
|
| 927 |
-
thrust::pair<ForwardIterator, ForwardIterator>
|
| 928 |
-
equal_range(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 929 |
-
ForwardIterator first,
|
| 930 |
-
ForwardIterator last,
|
| 931 |
-
const T& value,
|
| 932 |
-
StrictWeakOrdering comp);
|
| 933 |
-
|
| 934 |
-
|
| 935 |
-
/*! \p equal_range is a version of binary search: it attempts to find
|
| 936 |
-
* the element value in an ordered range <tt>[first, last)</tt>. The
|
| 937 |
-
* value returned by \p equal_range is essentially a combination of
|
| 938 |
-
* the values returned by \p lower_bound and \p upper_bound: it returns
|
| 939 |
-
* a \p pair of iterators \c i and \c j such that \c i is the first
|
| 940 |
-
* position where value could be inserted without violating the
|
| 941 |
-
* ordering and \c j is the last position where value could be inserted
|
| 942 |
-
* without violating the ordering. It follows that every element in the
|
| 943 |
-
* range <tt>[i, j)</tt> is equivalent to value, and that
|
| 944 |
-
* <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
|
| 945 |
-
* has this property.
|
| 946 |
-
*
|
| 947 |
-
* This version of \p equal_range returns a \p pair of iterators
|
| 948 |
-
* <tt>[i, j)</tt>. \c i is the furthermost iterator in
|
| 949 |
-
* <tt>[first, last)</tt> such that, for every iterator \c k in
|
| 950 |
-
* <tt>[first, i)</tt>, <tt>comp(*k, value)</tt> is \c true.
|
| 951 |
-
* \c j is the furthermost iterator in <tt>[first, last)</tt> such
|
| 952 |
-
* that, for every iterator \c k in <tt>[first, last)</tt>,
|
| 953 |
-
* <tt>comp(value, *k)</tt> is \c false. For every iterator \c k
|
| 954 |
-
* in <tt>[i, j)</tt>, neither <tt>comp(value, *k)</tt> nor
|
| 955 |
-
* <tt>comp(*k, value)</tt> is \c true.
|
| 956 |
-
*
|
| 957 |
-
* \param first The beginning of the ordered sequence.
|
| 958 |
-
* \param last The end of the ordered sequence.
|
| 959 |
-
* \param value The value to be searched.
|
| 960 |
-
* \param comp The comparison operator.
|
| 961 |
-
* \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
|
| 962 |
-
*
|
| 963 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 964 |
-
* \tparam T is comparable to \p ForwardIterator's \c value_type.
|
| 965 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 966 |
-
*
|
| 967 |
-
* The following code snippet demonstrates how to use \p equal_range
|
| 968 |
-
* to search for values in a ordered range.
|
| 969 |
-
*
|
| 970 |
-
* \code
|
| 971 |
-
* #include <thrust/binary_search.h>
|
| 972 |
-
* #include <thrust/device_vector.h>
|
| 973 |
-
* #include <thrust/functional.h>
|
| 974 |
-
* ...
|
| 975 |
-
* thrust::device_vector<int> input(5);
|
| 976 |
-
*
|
| 977 |
-
* input[0] = 0;
|
| 978 |
-
* input[1] = 2;
|
| 979 |
-
* input[2] = 5;
|
| 980 |
-
* input[3] = 7;
|
| 981 |
-
* input[4] = 8;
|
| 982 |
-
*
|
| 983 |
-
* thrust::equal_range(input.begin(), input.end(), 0, thrust::less<int>()); // returns [input.begin(), input.begin() + 1)
|
| 984 |
-
* thrust::equal_range(input.begin(), input.end(), 1, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 1)
|
| 985 |
-
* thrust::equal_range(input.begin(), input.end(), 2, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 2)
|
| 986 |
-
* thrust::equal_range(input.begin(), input.end(), 3, thrust::less<int>()); // returns [input.begin() + 2, input.begin() + 2)
|
| 987 |
-
* thrust::equal_range(input.begin(), input.end(), 8, thrust::less<int>()); // returns [input.begin() + 4, input.end)
|
| 988 |
-
* thrust::equal_range(input.begin(), input.end(), 9, thrust::less<int>()); // returns [input.end(), input.end)
|
| 989 |
-
* \endcode
|
| 990 |
-
*
|
| 991 |
-
* \see http://www.sgi.com/tech/stl/equal_range.html
|
| 992 |
-
* \see \p lower_bound
|
| 993 |
-
* \see \p upper_bound
|
| 994 |
-
* \see \p binary_search
|
| 995 |
-
*/
|
| 996 |
-
template <class ForwardIterator, class T, class StrictWeakOrdering>
|
| 997 |
-
thrust::pair<ForwardIterator, ForwardIterator>
|
| 998 |
-
equal_range(ForwardIterator first,
|
| 999 |
-
ForwardIterator last,
|
| 1000 |
-
const T& value,
|
| 1001 |
-
StrictWeakOrdering comp);
|
| 1002 |
-
|
| 1003 |
-
|
| 1004 |
-
/*! \addtogroup vectorized_binary_search Vectorized Searches
|
| 1005 |
-
* \ingroup binary_search
|
| 1006 |
-
* \{
|
| 1007 |
-
*/
|
| 1008 |
-
|
| 1009 |
-
|
| 1010 |
-
//////////////////////
|
| 1011 |
-
// Vector Functions //
|
| 1012 |
-
//////////////////////
|
| 1013 |
-
|
| 1014 |
-
|
| 1015 |
-
/*! \p lower_bound is a vectorized version of binary search: for each
|
| 1016 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1017 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1018 |
-
* Specifically, it returns the index of first position where value could
|
| 1019 |
-
* be inserted without violating the ordering.
|
| 1020 |
-
*
|
| 1021 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 1022 |
-
*
|
| 1023 |
-
* \param exec The execution policy to use for parallelization.
|
| 1024 |
-
* \param first The beginning of the ordered sequence.
|
| 1025 |
-
* \param last The end of the ordered sequence.
|
| 1026 |
-
* \param values_first The beginning of the search values sequence.
|
| 1027 |
-
* \param values_last The end of the search values sequence.
|
| 1028 |
-
* \param result The beginning of the output sequence.
|
| 1029 |
-
*
|
| 1030 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 1031 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1032 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1033 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1034 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1035 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1036 |
-
*
|
| 1037 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1038 |
-
*
|
| 1039 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 1040 |
-
* to search for multiple values in a ordered range using the \p thrust::device execution policy for
|
| 1041 |
-
* parallelization:
|
| 1042 |
-
*
|
| 1043 |
-
* \code
|
| 1044 |
-
* #include <thrust/binary_search.h>
|
| 1045 |
-
* #include <thrust/device_vector.h>
|
| 1046 |
-
* #include <thrust/execution_policy.h>
|
| 1047 |
-
* ...
|
| 1048 |
-
* thrust::device_vector<int> input(5);
|
| 1049 |
-
*
|
| 1050 |
-
* input[0] = 0;
|
| 1051 |
-
* input[1] = 2;
|
| 1052 |
-
* input[2] = 5;
|
| 1053 |
-
* input[3] = 7;
|
| 1054 |
-
* input[4] = 8;
|
| 1055 |
-
*
|
| 1056 |
-
* thrust::device_vector<int> values(6);
|
| 1057 |
-
* values[0] = 0;
|
| 1058 |
-
* values[1] = 1;
|
| 1059 |
-
* values[2] = 2;
|
| 1060 |
-
* values[3] = 3;
|
| 1061 |
-
* values[4] = 8;
|
| 1062 |
-
* values[5] = 9;
|
| 1063 |
-
*
|
| 1064 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1065 |
-
*
|
| 1066 |
-
* thrust::lower_bound(thrust::device,
|
| 1067 |
-
* input.begin(), input.end(),
|
| 1068 |
-
* values.begin(), values.end(),
|
| 1069 |
-
* output.begin());
|
| 1070 |
-
*
|
| 1071 |
-
* // output is now [0, 1, 1, 2, 4, 5]
|
| 1072 |
-
* \endcode
|
| 1073 |
-
*
|
| 1074 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 1075 |
-
* \see \p upper_bound
|
| 1076 |
-
* \see \p equal_range
|
| 1077 |
-
* \see \p binary_search
|
| 1078 |
-
*/
|
| 1079 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator>
|
| 1080 |
-
__host__ __device__
|
| 1081 |
-
OutputIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 1082 |
-
ForwardIterator first,
|
| 1083 |
-
ForwardIterator last,
|
| 1084 |
-
InputIterator values_first,
|
| 1085 |
-
InputIterator values_last,
|
| 1086 |
-
OutputIterator result);
|
| 1087 |
-
|
| 1088 |
-
|
| 1089 |
-
/*! \p lower_bound is a vectorized version of binary search: for each
|
| 1090 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1091 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1092 |
-
* Specifically, it returns the index of first position where value could
|
| 1093 |
-
* be inserted without violating the ordering.
|
| 1094 |
-
*
|
| 1095 |
-
* \param first The beginning of the ordered sequence.
|
| 1096 |
-
* \param last The end of the ordered sequence.
|
| 1097 |
-
* \param values_first The beginning of the search values sequence.
|
| 1098 |
-
* \param values_last The end of the search values sequence.
|
| 1099 |
-
* \param result The beginning of the output sequence.
|
| 1100 |
-
*
|
| 1101 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1102 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1103 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1104 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1105 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1106 |
-
*
|
| 1107 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1108 |
-
*
|
| 1109 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 1110 |
-
* to search for multiple values in a ordered range.
|
| 1111 |
-
*
|
| 1112 |
-
* \code
|
| 1113 |
-
* #include <thrust/binary_search.h>
|
| 1114 |
-
* #include <thrust/device_vector.h>
|
| 1115 |
-
* ...
|
| 1116 |
-
* thrust::device_vector<int> input(5);
|
| 1117 |
-
*
|
| 1118 |
-
* input[0] = 0;
|
| 1119 |
-
* input[1] = 2;
|
| 1120 |
-
* input[2] = 5;
|
| 1121 |
-
* input[3] = 7;
|
| 1122 |
-
* input[4] = 8;
|
| 1123 |
-
*
|
| 1124 |
-
* thrust::device_vector<int> values(6);
|
| 1125 |
-
* values[0] = 0;
|
| 1126 |
-
* values[1] = 1;
|
| 1127 |
-
* values[2] = 2;
|
| 1128 |
-
* values[3] = 3;
|
| 1129 |
-
* values[4] = 8;
|
| 1130 |
-
* values[5] = 9;
|
| 1131 |
-
*
|
| 1132 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1133 |
-
*
|
| 1134 |
-
* thrust::lower_bound(input.begin(), input.end(),
|
| 1135 |
-
* values.begin(), values.end(),
|
| 1136 |
-
* output.begin());
|
| 1137 |
-
*
|
| 1138 |
-
* // output is now [0, 1, 1, 2, 4, 5]
|
| 1139 |
-
* \endcode
|
| 1140 |
-
*
|
| 1141 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 1142 |
-
* \see \p upper_bound
|
| 1143 |
-
* \see \p equal_range
|
| 1144 |
-
* \see \p binary_search
|
| 1145 |
-
*/
|
| 1146 |
-
template <class ForwardIterator, class InputIterator, class OutputIterator>
|
| 1147 |
-
OutputIterator lower_bound(ForwardIterator first,
|
| 1148 |
-
ForwardIterator last,
|
| 1149 |
-
InputIterator values_first,
|
| 1150 |
-
InputIterator values_last,
|
| 1151 |
-
OutputIterator result);
|
| 1152 |
-
|
| 1153 |
-
|
| 1154 |
-
/*! \p lower_bound is a vectorized version of binary search: for each
|
| 1155 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1156 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1157 |
-
* Specifically, it returns the index of first position where value could
|
| 1158 |
-
* be inserted without violating the ordering. This version of
|
| 1159 |
-
* \p lower_bound uses function object \c comp for comparison.
|
| 1160 |
-
*
|
| 1161 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 1162 |
-
*
|
| 1163 |
-
* \param exec The execution policy to use for parallelization.
|
| 1164 |
-
* \param first The beginning of the ordered sequence.
|
| 1165 |
-
* \param last The end of the ordered sequence.
|
| 1166 |
-
* \param values_first The beginning of the search values sequence.
|
| 1167 |
-
* \param values_last The end of the search values sequence.
|
| 1168 |
-
* \param result The beginning of the output sequence.
|
| 1169 |
-
* \param comp The comparison operator.
|
| 1170 |
-
*
|
| 1171 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 1172 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1173 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1174 |
-
* and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
|
| 1175 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1176 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1177 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 1178 |
-
*
|
| 1179 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1180 |
-
*
|
| 1181 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 1182 |
-
* to search for multiple values in a ordered range.
|
| 1183 |
-
*
|
| 1184 |
-
* \code
|
| 1185 |
-
* #include <thrust/binary_search.h>
|
| 1186 |
-
* #include <thrust/device_vector.h>
|
| 1187 |
-
* #include <thrust/functional.h>
|
| 1188 |
-
* #include <thrust/execution_policy.h>
|
| 1189 |
-
* ...
|
| 1190 |
-
* thrust::device_vector<int> input(5);
|
| 1191 |
-
*
|
| 1192 |
-
* input[0] = 0;
|
| 1193 |
-
* input[1] = 2;
|
| 1194 |
-
* input[2] = 5;
|
| 1195 |
-
* input[3] = 7;
|
| 1196 |
-
* input[4] = 8;
|
| 1197 |
-
*
|
| 1198 |
-
* thrust::device_vector<int> values(6);
|
| 1199 |
-
* values[0] = 0;
|
| 1200 |
-
* values[1] = 1;
|
| 1201 |
-
* values[2] = 2;
|
| 1202 |
-
* values[3] = 3;
|
| 1203 |
-
* values[4] = 8;
|
| 1204 |
-
* values[5] = 9;
|
| 1205 |
-
*
|
| 1206 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1207 |
-
*
|
| 1208 |
-
* thrust::lower_bound(input.begin(), input.end(),
|
| 1209 |
-
* values.begin(), values.end(),
|
| 1210 |
-
* output.begin(),
|
| 1211 |
-
* thrust::less<int>());
|
| 1212 |
-
*
|
| 1213 |
-
* // output is now [0, 1, 1, 2, 4, 5]
|
| 1214 |
-
* \endcode
|
| 1215 |
-
*
|
| 1216 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 1217 |
-
* \see \p upper_bound
|
| 1218 |
-
* \see \p equal_range
|
| 1219 |
-
* \see \p binary_search
|
| 1220 |
-
*/
|
| 1221 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator, typename StrictWeakOrdering>
|
| 1222 |
-
__host__ __device__
|
| 1223 |
-
OutputIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 1224 |
-
ForwardIterator first,
|
| 1225 |
-
ForwardIterator last,
|
| 1226 |
-
InputIterator values_first,
|
| 1227 |
-
InputIterator values_last,
|
| 1228 |
-
OutputIterator result,
|
| 1229 |
-
StrictWeakOrdering comp);
|
| 1230 |
-
|
| 1231 |
-
|
| 1232 |
-
/*! \p lower_bound is a vectorized version of binary search: for each
|
| 1233 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1234 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1235 |
-
* Specifically, it returns the index of first position where value could
|
| 1236 |
-
* be inserted without violating the ordering. This version of
|
| 1237 |
-
* \p lower_bound uses function object \c comp for comparison.
|
| 1238 |
-
*
|
| 1239 |
-
* \param first The beginning of the ordered sequence.
|
| 1240 |
-
* \param last The end of the ordered sequence.
|
| 1241 |
-
* \param values_first The beginning of the search values sequence.
|
| 1242 |
-
* \param values_last The end of the search values sequence.
|
| 1243 |
-
* \param result The beginning of the output sequence.
|
| 1244 |
-
* \param comp The comparison operator.
|
| 1245 |
-
*
|
| 1246 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1247 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1248 |
-
* and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
|
| 1249 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1250 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1251 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 1252 |
-
*
|
| 1253 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1254 |
-
*
|
| 1255 |
-
* The following code snippet demonstrates how to use \p lower_bound
|
| 1256 |
-
* to search for multiple values in a ordered range.
|
| 1257 |
-
*
|
| 1258 |
-
* \code
|
| 1259 |
-
* #include <thrust/binary_search.h>
|
| 1260 |
-
* #include <thrust/device_vector.h>
|
| 1261 |
-
* #include <thrust/functional.h>
|
| 1262 |
-
* ...
|
| 1263 |
-
* thrust::device_vector<int> input(5);
|
| 1264 |
-
*
|
| 1265 |
-
* input[0] = 0;
|
| 1266 |
-
* input[1] = 2;
|
| 1267 |
-
* input[2] = 5;
|
| 1268 |
-
* input[3] = 7;
|
| 1269 |
-
* input[4] = 8;
|
| 1270 |
-
*
|
| 1271 |
-
* thrust::device_vector<int> values(6);
|
| 1272 |
-
* values[0] = 0;
|
| 1273 |
-
* values[1] = 1;
|
| 1274 |
-
* values[2] = 2;
|
| 1275 |
-
* values[3] = 3;
|
| 1276 |
-
* values[4] = 8;
|
| 1277 |
-
* values[5] = 9;
|
| 1278 |
-
*
|
| 1279 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1280 |
-
*
|
| 1281 |
-
* thrust::lower_bound(input.begin(), input.end(),
|
| 1282 |
-
* values.begin(), values.end(),
|
| 1283 |
-
* output.begin(),
|
| 1284 |
-
* thrust::less<int>());
|
| 1285 |
-
*
|
| 1286 |
-
* // output is now [0, 1, 1, 2, 4, 5]
|
| 1287 |
-
* \endcode
|
| 1288 |
-
*
|
| 1289 |
-
* \see http://www.sgi.com/tech/stl/lower_bound.html
|
| 1290 |
-
* \see \p upper_bound
|
| 1291 |
-
* \see \p equal_range
|
| 1292 |
-
* \see \p binary_search
|
| 1293 |
-
*/
|
| 1294 |
-
template <class ForwardIterator, class InputIterator, class OutputIterator, class StrictWeakOrdering>
|
| 1295 |
-
OutputIterator lower_bound(ForwardIterator first,
|
| 1296 |
-
ForwardIterator last,
|
| 1297 |
-
InputIterator values_first,
|
| 1298 |
-
InputIterator values_last,
|
| 1299 |
-
OutputIterator result,
|
| 1300 |
-
StrictWeakOrdering comp);
|
| 1301 |
-
|
| 1302 |
-
|
| 1303 |
-
/*! \p upper_bound is a vectorized version of binary search: for each
|
| 1304 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1305 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1306 |
-
* Specifically, it returns the index of last position where value could
|
| 1307 |
-
* be inserted without violating the ordering.
|
| 1308 |
-
*
|
| 1309 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 1310 |
-
*
|
| 1311 |
-
* \param exec The execution policy to use for parallelization.
|
| 1312 |
-
* \param first The beginning of the ordered sequence.
|
| 1313 |
-
* \param last The end of the ordered sequence.
|
| 1314 |
-
* \param values_first The beginning of the search values sequence.
|
| 1315 |
-
* \param values_last The end of the search values sequence.
|
| 1316 |
-
* \param result The beginning of the output sequence.
|
| 1317 |
-
*
|
| 1318 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 1319 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1320 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1321 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1322 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1323 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1324 |
-
*
|
| 1325 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1326 |
-
*
|
| 1327 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 1328 |
-
* to search for multiple values in a ordered range using the \p thrust::device execution policy for
|
| 1329 |
-
* parallelization:
|
| 1330 |
-
*
|
| 1331 |
-
* \code
|
| 1332 |
-
* #include <thrust/binary_search.h>
|
| 1333 |
-
* #include <thrust/device_vector.h>
|
| 1334 |
-
* #include <thrust/execution_policy.h>
|
| 1335 |
-
* ...
|
| 1336 |
-
* thrust::device_vector<int> input(5);
|
| 1337 |
-
*
|
| 1338 |
-
* input[0] = 0;
|
| 1339 |
-
* input[1] = 2;
|
| 1340 |
-
* input[2] = 5;
|
| 1341 |
-
* input[3] = 7;
|
| 1342 |
-
* input[4] = 8;
|
| 1343 |
-
*
|
| 1344 |
-
* thrust::device_vector<int> values(6);
|
| 1345 |
-
* values[0] = 0;
|
| 1346 |
-
* values[1] = 1;
|
| 1347 |
-
* values[2] = 2;
|
| 1348 |
-
* values[3] = 3;
|
| 1349 |
-
* values[4] = 8;
|
| 1350 |
-
* values[5] = 9;
|
| 1351 |
-
*
|
| 1352 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1353 |
-
*
|
| 1354 |
-
* thrust::upper_bound(thrust::device,
|
| 1355 |
-
* input.begin(), input.end(),
|
| 1356 |
-
* values.begin(), values.end(),
|
| 1357 |
-
* output.begin());
|
| 1358 |
-
*
|
| 1359 |
-
* // output is now [1, 1, 2, 2, 5, 5]
|
| 1360 |
-
* \endcode
|
| 1361 |
-
*
|
| 1362 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 1363 |
-
* \see \p upper_bound
|
| 1364 |
-
* \see \p equal_range
|
| 1365 |
-
* \see \p binary_search
|
| 1366 |
-
*/
|
| 1367 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator>
|
| 1368 |
-
__host__ __device__
|
| 1369 |
-
OutputIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 1370 |
-
ForwardIterator first,
|
| 1371 |
-
ForwardIterator last,
|
| 1372 |
-
InputIterator values_first,
|
| 1373 |
-
InputIterator values_last,
|
| 1374 |
-
OutputIterator result);
|
| 1375 |
-
|
| 1376 |
-
|
| 1377 |
-
/*! \p upper_bound is a vectorized version of binary search: for each
|
| 1378 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1379 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1380 |
-
* Specifically, it returns the index of last position where value could
|
| 1381 |
-
* be inserted without violating the ordering.
|
| 1382 |
-
*
|
| 1383 |
-
* \param first The beginning of the ordered sequence.
|
| 1384 |
-
* \param last The end of the ordered sequence.
|
| 1385 |
-
* \param values_first The beginning of the search values sequence.
|
| 1386 |
-
* \param values_last The end of the search values sequence.
|
| 1387 |
-
* \param result The beginning of the output sequence.
|
| 1388 |
-
*
|
| 1389 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1390 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1391 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1392 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1393 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1394 |
-
*
|
| 1395 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1396 |
-
*
|
| 1397 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 1398 |
-
* to search for multiple values in a ordered range.
|
| 1399 |
-
*
|
| 1400 |
-
* \code
|
| 1401 |
-
* #include <thrust/binary_search.h>
|
| 1402 |
-
* #include <thrust/device_vector.h>
|
| 1403 |
-
* ...
|
| 1404 |
-
* thrust::device_vector<int> input(5);
|
| 1405 |
-
*
|
| 1406 |
-
* input[0] = 0;
|
| 1407 |
-
* input[1] = 2;
|
| 1408 |
-
* input[2] = 5;
|
| 1409 |
-
* input[3] = 7;
|
| 1410 |
-
* input[4] = 8;
|
| 1411 |
-
*
|
| 1412 |
-
* thrust::device_vector<int> values(6);
|
| 1413 |
-
* values[0] = 0;
|
| 1414 |
-
* values[1] = 1;
|
| 1415 |
-
* values[2] = 2;
|
| 1416 |
-
* values[3] = 3;
|
| 1417 |
-
* values[4] = 8;
|
| 1418 |
-
* values[5] = 9;
|
| 1419 |
-
*
|
| 1420 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1421 |
-
*
|
| 1422 |
-
* thrust::upper_bound(input.begin(), input.end(),
|
| 1423 |
-
* values.begin(), values.end(),
|
| 1424 |
-
* output.begin());
|
| 1425 |
-
*
|
| 1426 |
-
* // output is now [1, 1, 2, 2, 5, 5]
|
| 1427 |
-
* \endcode
|
| 1428 |
-
*
|
| 1429 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 1430 |
-
* \see \p upper_bound
|
| 1431 |
-
* \see \p equal_range
|
| 1432 |
-
* \see \p binary_search
|
| 1433 |
-
*/
|
| 1434 |
-
template <class ForwardIterator, class InputIterator, class OutputIterator>
|
| 1435 |
-
OutputIterator upper_bound(ForwardIterator first,
|
| 1436 |
-
ForwardIterator last,
|
| 1437 |
-
InputIterator values_first,
|
| 1438 |
-
InputIterator values_last,
|
| 1439 |
-
OutputIterator result);
|
| 1440 |
-
|
| 1441 |
-
|
| 1442 |
-
/*! \p upper_bound is a vectorized version of binary search: for each
|
| 1443 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1444 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1445 |
-
* Specifically, it returns the index of first position where value could
|
| 1446 |
-
* be inserted without violating the ordering. This version of
|
| 1447 |
-
* \p upper_bound uses function object \c comp for comparison.
|
| 1448 |
-
*
|
| 1449 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 1450 |
-
*
|
| 1451 |
-
* \param exec The execution policy to use for parallelization.
|
| 1452 |
-
* \param first The beginning of the ordered sequence.
|
| 1453 |
-
* \param last The end of the ordered sequence.
|
| 1454 |
-
* \param values_first The beginning of the search values sequence.
|
| 1455 |
-
* \param values_last The end of the search values sequence.
|
| 1456 |
-
* \param result The beginning of the output sequence.
|
| 1457 |
-
* \param comp The comparison operator.
|
| 1458 |
-
*
|
| 1459 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 1460 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1461 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1462 |
-
* and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
|
| 1463 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1464 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1465 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 1466 |
-
*
|
| 1467 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1468 |
-
*
|
| 1469 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 1470 |
-
* to search for multiple values in a ordered range using the \p thrust::device execution policy for
|
| 1471 |
-
* parallelization:
|
| 1472 |
-
*
|
| 1473 |
-
* \code
|
| 1474 |
-
* #include <thrust/binary_search.h>
|
| 1475 |
-
* #include <thrust/device_vector.h>
|
| 1476 |
-
* #include <thrust/functional.h>
|
| 1477 |
-
* #include <thrust/execution_policy.h>
|
| 1478 |
-
* ...
|
| 1479 |
-
* thrust::device_vector<int> input(5);
|
| 1480 |
-
*
|
| 1481 |
-
* input[0] = 0;
|
| 1482 |
-
* input[1] = 2;
|
| 1483 |
-
* input[2] = 5;
|
| 1484 |
-
* input[3] = 7;
|
| 1485 |
-
* input[4] = 8;
|
| 1486 |
-
*
|
| 1487 |
-
* thrust::device_vector<int> values(6);
|
| 1488 |
-
* values[0] = 0;
|
| 1489 |
-
* values[1] = 1;
|
| 1490 |
-
* values[2] = 2;
|
| 1491 |
-
* values[3] = 3;
|
| 1492 |
-
* values[4] = 8;
|
| 1493 |
-
* values[5] = 9;
|
| 1494 |
-
*
|
| 1495 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1496 |
-
*
|
| 1497 |
-
* thrust::upper_bound(thrust::device,
|
| 1498 |
-
* input.begin(), input.end(),
|
| 1499 |
-
* values.begin(), values.end(),
|
| 1500 |
-
* output.begin(),
|
| 1501 |
-
* thrust::less<int>());
|
| 1502 |
-
*
|
| 1503 |
-
* // output is now [1, 1, 2, 2, 5, 5]
|
| 1504 |
-
* \endcode
|
| 1505 |
-
*
|
| 1506 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 1507 |
-
* \see \p lower_bound
|
| 1508 |
-
* \see \p equal_range
|
| 1509 |
-
* \see \p binary_search
|
| 1510 |
-
*/
|
| 1511 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator, typename StrictWeakOrdering>
|
| 1512 |
-
__host__ __device__
|
| 1513 |
-
OutputIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 1514 |
-
ForwardIterator first,
|
| 1515 |
-
ForwardIterator last,
|
| 1516 |
-
InputIterator values_first,
|
| 1517 |
-
InputIterator values_last,
|
| 1518 |
-
OutputIterator result,
|
| 1519 |
-
StrictWeakOrdering comp);
|
| 1520 |
-
|
| 1521 |
-
|
| 1522 |
-
/*! \p upper_bound is a vectorized version of binary search: for each
|
| 1523 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1524 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1525 |
-
* Specifically, it returns the index of first position where value could
|
| 1526 |
-
* be inserted without violating the ordering. This version of
|
| 1527 |
-
* \p upper_bound uses function object \c comp for comparison.
|
| 1528 |
-
*
|
| 1529 |
-
* \param first The beginning of the ordered sequence.
|
| 1530 |
-
* \param last The end of the ordered sequence.
|
| 1531 |
-
* \param values_first The beginning of the search values sequence.
|
| 1532 |
-
* \param values_last The end of the search values sequence.
|
| 1533 |
-
* \param result The beginning of the output sequence.
|
| 1534 |
-
* \param comp The comparison operator.
|
| 1535 |
-
*
|
| 1536 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1537 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1538 |
-
* and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
|
| 1539 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1540 |
-
* and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
|
| 1541 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 1542 |
-
*
|
| 1543 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1544 |
-
*
|
| 1545 |
-
* The following code snippet demonstrates how to use \p upper_bound
|
| 1546 |
-
* to search for multiple values in a ordered range.
|
| 1547 |
-
*
|
| 1548 |
-
* \code
|
| 1549 |
-
* #include <thrust/binary_search.h>
|
| 1550 |
-
* #include <thrust/device_vector.h>
|
| 1551 |
-
* #include <thrust/functional.h>
|
| 1552 |
-
* ...
|
| 1553 |
-
* thrust::device_vector<int> input(5);
|
| 1554 |
-
*
|
| 1555 |
-
* input[0] = 0;
|
| 1556 |
-
* input[1] = 2;
|
| 1557 |
-
* input[2] = 5;
|
| 1558 |
-
* input[3] = 7;
|
| 1559 |
-
* input[4] = 8;
|
| 1560 |
-
*
|
| 1561 |
-
* thrust::device_vector<int> values(6);
|
| 1562 |
-
* values[0] = 0;
|
| 1563 |
-
* values[1] = 1;
|
| 1564 |
-
* values[2] = 2;
|
| 1565 |
-
* values[3] = 3;
|
| 1566 |
-
* values[4] = 8;
|
| 1567 |
-
* values[5] = 9;
|
| 1568 |
-
*
|
| 1569 |
-
* thrust::device_vector<unsigned int> output(6);
|
| 1570 |
-
*
|
| 1571 |
-
* thrust::upper_bound(input.begin(), input.end(),
|
| 1572 |
-
* values.begin(), values.end(),
|
| 1573 |
-
* output.begin(),
|
| 1574 |
-
* thrust::less<int>());
|
| 1575 |
-
*
|
| 1576 |
-
* // output is now [1, 1, 2, 2, 5, 5]
|
| 1577 |
-
* \endcode
|
| 1578 |
-
*
|
| 1579 |
-
* \see http://www.sgi.com/tech/stl/upper_bound.html
|
| 1580 |
-
* \see \p lower_bound
|
| 1581 |
-
* \see \p equal_range
|
| 1582 |
-
* \see \p binary_search
|
| 1583 |
-
*/
|
| 1584 |
-
template <class ForwardIterator, class InputIterator, class OutputIterator, class StrictWeakOrdering>
|
| 1585 |
-
OutputIterator upper_bound(ForwardIterator first,
|
| 1586 |
-
ForwardIterator last,
|
| 1587 |
-
InputIterator values_first,
|
| 1588 |
-
InputIterator values_last,
|
| 1589 |
-
OutputIterator result,
|
| 1590 |
-
StrictWeakOrdering comp);
|
| 1591 |
-
|
| 1592 |
-
|
| 1593 |
-
/*! \p binary_search is a vectorized version of binary search: for each
|
| 1594 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1595 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1596 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 1597 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 1598 |
-
* exists.
|
| 1599 |
-
*
|
| 1600 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 1601 |
-
*
|
| 1602 |
-
* \param exec The execution policy to use for parallelization.
|
| 1603 |
-
* \param first The beginning of the ordered sequence.
|
| 1604 |
-
* \param last The end of the ordered sequence.
|
| 1605 |
-
* \param values_first The beginning of the search values sequence.
|
| 1606 |
-
* \param values_last The end of the search values sequence.
|
| 1607 |
-
* \param result The beginning of the output sequence.
|
| 1608 |
-
*
|
| 1609 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 1610 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1611 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1612 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1613 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1614 |
-
* and bool is convertible to \c OutputIterator's \c value_type.
|
| 1615 |
-
*
|
| 1616 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1617 |
-
*
|
| 1618 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 1619 |
-
* to search for multiple values in a ordered range using the \p thrust::device execution policy for
|
| 1620 |
-
* parallelization:
|
| 1621 |
-
*
|
| 1622 |
-
* \code
|
| 1623 |
-
* #include <thrust/binary_search.h>
|
| 1624 |
-
* #include <thrust/device_vector.h>
|
| 1625 |
-
* #include <thrust/execution_policy.h>
|
| 1626 |
-
* ...
|
| 1627 |
-
* thrust::device_vector<int> input(5);
|
| 1628 |
-
*
|
| 1629 |
-
* input[0] = 0;
|
| 1630 |
-
* input[1] = 2;
|
| 1631 |
-
* input[2] = 5;
|
| 1632 |
-
* input[3] = 7;
|
| 1633 |
-
* input[4] = 8;
|
| 1634 |
-
*
|
| 1635 |
-
* thrust::device_vector<int> values(6);
|
| 1636 |
-
* values[0] = 0;
|
| 1637 |
-
* values[1] = 1;
|
| 1638 |
-
* values[2] = 2;
|
| 1639 |
-
* values[3] = 3;
|
| 1640 |
-
* values[4] = 8;
|
| 1641 |
-
* values[5] = 9;
|
| 1642 |
-
*
|
| 1643 |
-
* thrust::device_vector<bool> output(6);
|
| 1644 |
-
*
|
| 1645 |
-
* thrust::binary_search(thrust::device,
|
| 1646 |
-
* input.begin(), input.end(),
|
| 1647 |
-
* values.begin(), values.end(),
|
| 1648 |
-
* output.begin());
|
| 1649 |
-
*
|
| 1650 |
-
* // output is now [true, false, true, false, true, false]
|
| 1651 |
-
* \endcode
|
| 1652 |
-
*
|
| 1653 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 1654 |
-
* \see \p lower_bound
|
| 1655 |
-
* \see \p upper_bound
|
| 1656 |
-
* \see \p equal_range
|
| 1657 |
-
*/
|
| 1658 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator>
|
| 1659 |
-
__host__ __device__
|
| 1660 |
-
OutputIterator binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 1661 |
-
ForwardIterator first,
|
| 1662 |
-
ForwardIterator last,
|
| 1663 |
-
InputIterator values_first,
|
| 1664 |
-
InputIterator values_last,
|
| 1665 |
-
OutputIterator result);
|
| 1666 |
-
|
| 1667 |
-
|
| 1668 |
-
/*! \p binary_search is a vectorized version of binary search: for each
|
| 1669 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1670 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1671 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 1672 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 1673 |
-
* exists.
|
| 1674 |
-
*
|
| 1675 |
-
* \param first The beginning of the ordered sequence.
|
| 1676 |
-
* \param last The end of the ordered sequence.
|
| 1677 |
-
* \param values_first The beginning of the search values sequence.
|
| 1678 |
-
* \param values_last The end of the search values sequence.
|
| 1679 |
-
* \param result The beginning of the output sequence.
|
| 1680 |
-
*
|
| 1681 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1682 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1683 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1684 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1685 |
-
* and bool is convertible to \c OutputIterator's \c value_type.
|
| 1686 |
-
*
|
| 1687 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1688 |
-
*
|
| 1689 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 1690 |
-
* to search for multiple values in a ordered range.
|
| 1691 |
-
*
|
| 1692 |
-
* \code
|
| 1693 |
-
* #include <thrust/binary_search.h>
|
| 1694 |
-
* #include <thrust/device_vector.h>
|
| 1695 |
-
* ...
|
| 1696 |
-
* thrust::device_vector<int> input(5);
|
| 1697 |
-
*
|
| 1698 |
-
* input[0] = 0;
|
| 1699 |
-
* input[1] = 2;
|
| 1700 |
-
* input[2] = 5;
|
| 1701 |
-
* input[3] = 7;
|
| 1702 |
-
* input[4] = 8;
|
| 1703 |
-
*
|
| 1704 |
-
* thrust::device_vector<int> values(6);
|
| 1705 |
-
* values[0] = 0;
|
| 1706 |
-
* values[1] = 1;
|
| 1707 |
-
* values[2] = 2;
|
| 1708 |
-
* values[3] = 3;
|
| 1709 |
-
* values[4] = 8;
|
| 1710 |
-
* values[5] = 9;
|
| 1711 |
-
*
|
| 1712 |
-
* thrust::device_vector<bool> output(6);
|
| 1713 |
-
*
|
| 1714 |
-
* thrust::binary_search(input.begin(), input.end(),
|
| 1715 |
-
* values.begin(), values.end(),
|
| 1716 |
-
* output.begin());
|
| 1717 |
-
*
|
| 1718 |
-
* // output is now [true, false, true, false, true, false]
|
| 1719 |
-
* \endcode
|
| 1720 |
-
*
|
| 1721 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 1722 |
-
* \see \p lower_bound
|
| 1723 |
-
* \see \p upper_bound
|
| 1724 |
-
* \see \p equal_range
|
| 1725 |
-
*/
|
| 1726 |
-
template <class ForwardIterator, class InputIterator, class OutputIterator>
|
| 1727 |
-
OutputIterator binary_search(ForwardIterator first,
|
| 1728 |
-
ForwardIterator last,
|
| 1729 |
-
InputIterator values_first,
|
| 1730 |
-
InputIterator values_last,
|
| 1731 |
-
OutputIterator result);
|
| 1732 |
-
|
| 1733 |
-
|
| 1734 |
-
/*! \p binary_search is a vectorized version of binary search: for each
|
| 1735 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1736 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1737 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 1738 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 1739 |
-
* exists. This version of \p binary_search uses function object
|
| 1740 |
-
* \c comp for comparison.
|
| 1741 |
-
*
|
| 1742 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 1743 |
-
*
|
| 1744 |
-
* \param exec The execution policy to use for parallelization.
|
| 1745 |
-
* \param first The beginning of the ordered sequence.
|
| 1746 |
-
* \param last The end of the ordered sequence.
|
| 1747 |
-
* \param values_first The beginning of the search values sequence.
|
| 1748 |
-
* \param values_last The end of the search values sequence.
|
| 1749 |
-
* \param result The beginning of the output sequence.
|
| 1750 |
-
* \param comp The comparison operator.
|
| 1751 |
-
*
|
| 1752 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 1753 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1754 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1755 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1756 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1757 |
-
* and bool is convertible to \c OutputIterator's \c value_type.
|
| 1758 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 1759 |
-
*
|
| 1760 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1761 |
-
*
|
| 1762 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 1763 |
-
* to search for multiple values in a ordered range using the \p thrust::device execution policy for
|
| 1764 |
-
* parallelization:
|
| 1765 |
-
*
|
| 1766 |
-
* \code
|
| 1767 |
-
* #include <thrust/binary_search.h>
|
| 1768 |
-
* #include <thrust/device_vector.h>
|
| 1769 |
-
* #include <thrust/functional.h>
|
| 1770 |
-
* #include <thrust/execution_policy.h>
|
| 1771 |
-
* ...
|
| 1772 |
-
* thrust::device_vector<int> input(5);
|
| 1773 |
-
*
|
| 1774 |
-
* input[0] = 0;
|
| 1775 |
-
* input[1] = 2;
|
| 1776 |
-
* input[2] = 5;
|
| 1777 |
-
* input[3] = 7;
|
| 1778 |
-
* input[4] = 8;
|
| 1779 |
-
*
|
| 1780 |
-
* thrust::device_vector<int> values(6);
|
| 1781 |
-
* values[0] = 0;
|
| 1782 |
-
* values[1] = 1;
|
| 1783 |
-
* values[2] = 2;
|
| 1784 |
-
* values[3] = 3;
|
| 1785 |
-
* values[4] = 8;
|
| 1786 |
-
* values[5] = 9;
|
| 1787 |
-
*
|
| 1788 |
-
* thrust::device_vector<bool> output(6);
|
| 1789 |
-
*
|
| 1790 |
-
* thrust::binary_search(thrust::device,
|
| 1791 |
-
* input.begin(), input.end(),
|
| 1792 |
-
* values.begin(), values.end(),
|
| 1793 |
-
* output.begin(),
|
| 1794 |
-
* thrust::less<T>());
|
| 1795 |
-
*
|
| 1796 |
-
* // output is now [true, false, true, false, true, false]
|
| 1797 |
-
* \endcode
|
| 1798 |
-
*
|
| 1799 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 1800 |
-
* \see \p lower_bound
|
| 1801 |
-
* \see \p upper_bound
|
| 1802 |
-
* \see \p equal_range
|
| 1803 |
-
*/
|
| 1804 |
-
template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator, typename StrictWeakOrdering>
|
| 1805 |
-
__host__ __device__
|
| 1806 |
-
OutputIterator binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 1807 |
-
ForwardIterator first,
|
| 1808 |
-
ForwardIterator last,
|
| 1809 |
-
InputIterator values_first,
|
| 1810 |
-
InputIterator values_last,
|
| 1811 |
-
OutputIterator result,
|
| 1812 |
-
StrictWeakOrdering comp);
|
| 1813 |
-
|
| 1814 |
-
|
| 1815 |
-
/*! \p binary_search is a vectorized version of binary search: for each
|
| 1816 |
-
* iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
|
| 1817 |
-
* find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
|
| 1818 |
-
* It returns \c true if an element that is equivalent to \c value
|
| 1819 |
-
* is present in <tt>[first, last)</tt> and \c false if no such element
|
| 1820 |
-
* exists. This version of \p binary_search uses function object
|
| 1821 |
-
* \c comp for comparison.
|
| 1822 |
-
*
|
| 1823 |
-
* \param first The beginning of the ordered sequence.
|
| 1824 |
-
* \param last The end of the ordered sequence.
|
| 1825 |
-
* \param values_first The beginning of the search values sequence.
|
| 1826 |
-
* \param values_last The end of the search values sequence.
|
| 1827 |
-
* \param result The beginning of the output sequence.
|
| 1828 |
-
* \param comp The comparison operator.
|
| 1829 |
-
*
|
| 1830 |
-
* \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
|
| 1831 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
|
| 1832 |
-
* and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
|
| 1833 |
-
* \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
|
| 1834 |
-
* and bool is convertible to \c OutputIterator's \c value_type.
|
| 1835 |
-
* \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
|
| 1836 |
-
*
|
| 1837 |
-
* \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
|
| 1838 |
-
*
|
| 1839 |
-
* The following code snippet demonstrates how to use \p binary_search
|
| 1840 |
-
* to search for multiple values in a ordered range.
|
| 1841 |
-
*
|
| 1842 |
-
* \code
|
| 1843 |
-
* #include <thrust/binary_search.h>
|
| 1844 |
-
* #include <thrust/device_vector.h>
|
| 1845 |
-
* #include <thrust/functional.h>
|
| 1846 |
-
* ...
|
| 1847 |
-
* thrust::device_vector<int> input(5);
|
| 1848 |
-
*
|
| 1849 |
-
* input[0] = 0;
|
| 1850 |
-
* input[1] = 2;
|
| 1851 |
-
* input[2] = 5;
|
| 1852 |
-
* input[3] = 7;
|
| 1853 |
-
* input[4] = 8;
|
| 1854 |
-
*
|
| 1855 |
-
* thrust::device_vector<int> values(6);
|
| 1856 |
-
* values[0] = 0;
|
| 1857 |
-
* values[1] = 1;
|
| 1858 |
-
* values[2] = 2;
|
| 1859 |
-
* values[3] = 3;
|
| 1860 |
-
* values[4] = 8;
|
| 1861 |
-
* values[5] = 9;
|
| 1862 |
-
*
|
| 1863 |
-
* thrust::device_vector<bool> output(6);
|
| 1864 |
-
*
|
| 1865 |
-
* thrust::binary_search(input.begin(), input.end(),
|
| 1866 |
-
* values.begin(), values.end(),
|
| 1867 |
-
* output.begin(),
|
| 1868 |
-
* thrust::less<T>());
|
| 1869 |
-
*
|
| 1870 |
-
* // output is now [true, false, true, false, true, false]
|
| 1871 |
-
* \endcode
|
| 1872 |
-
*
|
| 1873 |
-
* \see http://www.sgi.com/tech/stl/binary_search.html
|
| 1874 |
-
* \see \p lower_bound
|
| 1875 |
-
* \see \p upper_bound
|
| 1876 |
-
* \see \p equal_range
|
| 1877 |
-
*/
|
| 1878 |
-
template <class ForwardIterator, class InputIterator, class OutputIterator, class StrictWeakOrdering>
|
| 1879 |
-
OutputIterator binary_search(ForwardIterator first,
|
| 1880 |
-
ForwardIterator last,
|
| 1881 |
-
InputIterator values_first,
|
| 1882 |
-
InputIterator values_last,
|
| 1883 |
-
OutputIterator result,
|
| 1884 |
-
StrictWeakOrdering comp);
|
| 1885 |
-
|
| 1886 |
-
|
| 1887 |
-
/*! \} // end vectorized_binary_search
|
| 1888 |
-
*/
|
| 1889 |
-
|
| 1890 |
-
|
| 1891 |
-
/*! \} // end binary_search
|
| 1892 |
-
*/
|
| 1893 |
-
|
| 1894 |
-
|
| 1895 |
-
/*! \} // end searching
|
| 1896 |
-
*/
|
| 1897 |
-
|
| 1898 |
-
|
| 1899 |
-
} // end namespace thrust
|
| 1900 |
-
|
| 1901 |
-
#include <thrust/detail/binary_search.inl>
|
| 1902 |
-
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spaces/ClearLove443/Robby-chatbot/modules/utils.py
DELETED
|
@@ -1,105 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import pandas as pd
|
| 3 |
-
import streamlit as st
|
| 4 |
-
import pdfplumber
|
| 5 |
-
|
| 6 |
-
from modules.chatbot import Chatbot
|
| 7 |
-
from modules.embedder import Embedder
|
| 8 |
-
|
| 9 |
-
class Utilities:
|
| 10 |
-
|
| 11 |
-
@staticmethod
|
| 12 |
-
def load_api_key():
|
| 13 |
-
"""
|
| 14 |
-
Loads the OpenAI API key from the .env file or
|
| 15 |
-
from the user's input and returns it
|
| 16 |
-
"""
|
| 17 |
-
if not hasattr(st.session_state, "api_key"):
|
| 18 |
-
st.session_state.api_key = None
|
| 19 |
-
#you can define your API key in .env directly
|
| 20 |
-
if os.path.exists(".env") and os.environ.get("OPENAI_API_KEY") is not None:
|
| 21 |
-
user_api_key = os.environ["OPENAI_API_KEY"]
|
| 22 |
-
st.sidebar.success("API key loaded from .env", icon="🚀")
|
| 23 |
-
else:
|
| 24 |
-
if st.session_state.api_key is not None:
|
| 25 |
-
user_api_key = st.session_state.api_key
|
| 26 |
-
st.sidebar.success("API key loaded from previous input", icon="🚀")
|
| 27 |
-
else:
|
| 28 |
-
user_api_key = st.sidebar.text_input(
|
| 29 |
-
label="#### Your OpenAI API key 👇", placeholder="sk-...", type="password"
|
| 30 |
-
)
|
| 31 |
-
if user_api_key:
|
| 32 |
-
st.session_state.api_key = user_api_key
|
| 33 |
-
|
| 34 |
-
return user_api_key
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
@staticmethod
|
| 38 |
-
def handle_upload(file_types):
|
| 39 |
-
"""
|
| 40 |
-
Handles and display uploaded_file
|
| 41 |
-
:param file_types: List of accepted file types, e.g., ["csv", "pdf", "txt"]
|
| 42 |
-
"""
|
| 43 |
-
uploaded_file = st.sidebar.file_uploader("upload", type=file_types, label_visibility="collapsed")
|
| 44 |
-
if uploaded_file is not None:
|
| 45 |
-
|
| 46 |
-
def show_csv_file(uploaded_file):
|
| 47 |
-
file_container = st.expander("Your CSV file :")
|
| 48 |
-
uploaded_file.seek(0)
|
| 49 |
-
shows = pd.read_csv(uploaded_file)
|
| 50 |
-
file_container.write(shows)
|
| 51 |
-
|
| 52 |
-
def show_pdf_file(uploaded_file):
|
| 53 |
-
file_container = st.expander("Your PDF file :")
|
| 54 |
-
with pdfplumber.open(uploaded_file) as pdf:
|
| 55 |
-
pdf_text = ""
|
| 56 |
-
for page in pdf.pages:
|
| 57 |
-
pdf_text += page.extract_text() + "\n\n"
|
| 58 |
-
file_container.write(pdf_text)
|
| 59 |
-
|
| 60 |
-
def show_txt_file(uploaded_file):
|
| 61 |
-
file_container = st.expander("Your TXT file:")
|
| 62 |
-
uploaded_file.seek(0)
|
| 63 |
-
content = uploaded_file.read().decode("utf-8")
|
| 64 |
-
file_container.write(content)
|
| 65 |
-
|
| 66 |
-
def get_file_extension(uploaded_file):
|
| 67 |
-
return os.path.splitext(uploaded_file)[1].lower()
|
| 68 |
-
|
| 69 |
-
file_extension = get_file_extension(uploaded_file.name)
|
| 70 |
-
|
| 71 |
-
# Show the contents of the file based on its extension
|
| 72 |
-
#if file_extension == ".csv" :
|
| 73 |
-
# show_csv_file(uploaded_file)
|
| 74 |
-
if file_extension== ".pdf" :
|
| 75 |
-
show_pdf_file(uploaded_file)
|
| 76 |
-
elif file_extension== ".txt" :
|
| 77 |
-
show_txt_file(uploaded_file)
|
| 78 |
-
|
| 79 |
-
else:
|
| 80 |
-
st.session_state["reset_chat"] = True
|
| 81 |
-
|
| 82 |
-
#print(uploaded_file)
|
| 83 |
-
return uploaded_file
|
| 84 |
-
|
| 85 |
-
@staticmethod
|
| 86 |
-
def setup_chatbot(uploaded_file, model, temperature):
|
| 87 |
-
"""
|
| 88 |
-
Sets up the chatbot with the uploaded file, model, and temperature
|
| 89 |
-
"""
|
| 90 |
-
embeds = Embedder()
|
| 91 |
-
|
| 92 |
-
with st.spinner("Processing..."):
|
| 93 |
-
uploaded_file.seek(0)
|
| 94 |
-
file = uploaded_file.read()
|
| 95 |
-
# Get the document embeddings for the uploaded file
|
| 96 |
-
vectors = embeds.getDocEmbeds(file, uploaded_file.name)
|
| 97 |
-
|
| 98 |
-
# Create a Chatbot instance with the specified model and temperature
|
| 99 |
-
chatbot = Chatbot(model, temperature,vectors)
|
| 100 |
-
st.session_state["ready"] = True
|
| 101 |
-
|
| 102 |
-
return chatbot
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
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|
spaces/Cong723/gpt-academic-public/crazy_functions/总结word文档.py
DELETED
|
@@ -1,127 +0,0 @@
|
|
| 1 |
-
from toolbox import update_ui
|
| 2 |
-
from toolbox import CatchException, report_execption, write_results_to_file
|
| 3 |
-
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
| 4 |
-
fast_debug = False
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
| 8 |
-
import time, os
|
| 9 |
-
# pip install python-docx 用于docx格式，跨平台
|
| 10 |
-
# pip install pywin32 用于doc格式，仅支持Win平台
|
| 11 |
-
for index, fp in enumerate(file_manifest):
|
| 12 |
-
if fp.split(".")[-1] == "docx":
|
| 13 |
-
from docx import Document
|
| 14 |
-
doc = Document(fp)
|
| 15 |
-
file_content = "\n".join([para.text for para in doc.paragraphs])
|
| 16 |
-
else:
|
| 17 |
-
import win32com.client
|
| 18 |
-
word = win32com.client.Dispatch("Word.Application")
|
| 19 |
-
word.visible = False
|
| 20 |
-
# 打开文件
|
| 21 |
-
print('fp', os.getcwd())
|
| 22 |
-
doc = word.Documents.Open(os.getcwd() + '/' + fp)
|
| 23 |
-
# file_content = doc.Content.Text
|
| 24 |
-
doc = word.ActiveDocument
|
| 25 |
-
file_content = doc.Range().Text
|
| 26 |
-
doc.Close()
|
| 27 |
-
word.Quit()
|
| 28 |
-
|
| 29 |
-
print(file_content)
|
| 30 |
-
# private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
|
| 31 |
-
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
| 32 |
-
from request_llm.bridge_all import model_info
|
| 33 |
-
max_token = model_info[llm_kwargs['llm_model']]['max_token']
|
| 34 |
-
TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
|
| 35 |
-
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
| 36 |
-
txt=file_content,
|
| 37 |
-
get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'],
|
| 38 |
-
limit=TOKEN_LIMIT_PER_FRAGMENT
|
| 39 |
-
)
|
| 40 |
-
this_paper_history = []
|
| 41 |
-
for i, paper_frag in enumerate(paper_fragments):
|
| 42 |
-
i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
|
| 43 |
-
i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
|
| 44 |
-
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
| 45 |
-
inputs=i_say,
|
| 46 |
-
inputs_show_user=i_say_show_user,
|
| 47 |
-
llm_kwargs=llm_kwargs,
|
| 48 |
-
chatbot=chatbot,
|
| 49 |
-
history=[],
|
| 50 |
-
sys_prompt="总结文章。"
|
| 51 |
-
)
|
| 52 |
-
|
| 53 |
-
chatbot[-1] = (i_say_show_user, gpt_say)
|
| 54 |
-
history.extend([i_say_show_user,gpt_say])
|
| 55 |
-
this_paper_history.extend([i_say_show_user,gpt_say])
|
| 56 |
-
|
| 57 |
-
# 已经对该文章的所有片段总结完毕，如果文章被切分了，
|
| 58 |
-
if len(paper_fragments) > 1:
|
| 59 |
-
i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
|
| 60 |
-
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
| 61 |
-
inputs=i_say,
|
| 62 |
-
inputs_show_user=i_say,
|
| 63 |
-
llm_kwargs=llm_kwargs,
|
| 64 |
-
chatbot=chatbot,
|
| 65 |
-
history=this_paper_history,
|
| 66 |
-
sys_prompt="总结文章。"
|
| 67 |
-
)
|
| 68 |
-
|
| 69 |
-
history.extend([i_say,gpt_say])
|
| 70 |
-
this_paper_history.extend([i_say,gpt_say])
|
| 71 |
-
|
| 72 |
-
res = write_results_to_file(history)
|
| 73 |
-
chatbot.append(("完成了吗？", res))
|
| 74 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 75 |
-
|
| 76 |
-
res = write_results_to_file(history)
|
| 77 |
-
chatbot.append(("所有文件都总结完成了吗？", res))
|
| 78 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
@CatchException
|
| 82 |
-
def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
| 83 |
-
import glob, os
|
| 84 |
-
|
| 85 |
-
# 基本信息：功能、贡献者
|
| 86 |
-
chatbot.append([
|
| 87 |
-
"函数插件功能？",
|
| 88 |
-
"批量总结Word文档。函数插件贡献者: JasonGuo1"])
|
| 89 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 90 |
-
|
| 91 |
-
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
| 92 |
-
try:
|
| 93 |
-
from docx import Document
|
| 94 |
-
except:
|
| 95 |
-
report_execption(chatbot, history,
|
| 96 |
-
a=f"解析项目: {txt}",
|
| 97 |
-
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
|
| 98 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 99 |
-
return
|
| 100 |
-
|
| 101 |
-
# 清空历史，以免输入溢出
|
| 102 |
-
history = []
|
| 103 |
-
|
| 104 |
-
# 检测输入参数，如没有给定输入参数，直接退出
|
| 105 |
-
if os.path.exists(txt):
|
| 106 |
-
project_folder = txt
|
| 107 |
-
else:
|
| 108 |
-
if txt == "": txt = '空空如也的输入栏'
|
| 109 |
-
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
| 110 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 111 |
-
return
|
| 112 |
-
|
| 113 |
-
# 搜索需要处理的文件清单
|
| 114 |
-
if txt.endswith('.docx') or txt.endswith('.doc'):
|
| 115 |
-
file_manifest = [txt]
|
| 116 |
-
else:
|
| 117 |
-
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
|
| 118 |
-
[f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
|
| 119 |
-
|
| 120 |
-
# 如果没找到任何文件
|
| 121 |
-
if len(file_manifest) == 0:
|
| 122 |
-
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
|
| 123 |
-
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
| 124 |
-
return
|
| 125 |
-
|
| 126 |
-
# 开始正式执行任务
|
| 127 |
-
yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
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spaces/Curranj/FlowerDiffusion/app.py
DELETED
|
@@ -1,72 +0,0 @@
|
|
| 1 |
-
import io
|
| 2 |
-
import os
|
| 3 |
-
import warnings
|
| 4 |
-
|
| 5 |
-
from PIL import Image
|
| 6 |
-
from stability_sdk import client
|
| 7 |
-
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
|
| 8 |
-
|
| 9 |
-
import gradio as gr
|
| 10 |
-
stability_api = client.StabilityInference(
|
| 11 |
-
key=os.environ["Secret"],
|
| 12 |
-
verbose=True,
|
| 13 |
-
)
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
def infer(prompt):
|
| 17 |
-
# the object returned is a python generator
|
| 18 |
-
answers = stability_api.generate(
|
| 19 |
-
prompt=f"Beautiful Portait of a {prompt} made out of flowers 💐 🌺 🌸 , artstation winner by Victo Ngai, Kilian Eng, vibrant colors, winning-award masterpiece, aesthetic octane render, 8K HD",
|
| 20 |
-
height =640
|
| 21 |
-
)
|
| 22 |
-
|
| 23 |
-
# iterating over the generator produces the api response
|
| 24 |
-
for resp in answers:
|
| 25 |
-
for artifact in resp.artifacts:
|
| 26 |
-
if artifact.finish_reason == generation.FILTER:
|
| 27 |
-
warnings.warn(
|
| 28 |
-
"Your request activated the API's safety filters and could not be processed."
|
| 29 |
-
"Please modify the prompt and try again.")
|
| 30 |
-
if artifact.type == generation.ARTIFACT_IMAGE:
|
| 31 |
-
img = Image.open(io.BytesIO(artifact.binary))
|
| 32 |
-
return img
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
block = gr.Blocks(css=".container { max-width: 600px; margin: auto; }")
|
| 36 |
-
|
| 37 |
-
num_samples = 1
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
with block as demo:
|
| 42 |
-
gr.Markdown("<h1><center>Flower Diffusion</center></h1>")
|
| 43 |
-
gr.Markdown(
|
| 44 |
-
"Get a pretty flowery image from any prompt - keep it simple!"
|
| 45 |
-
)
|
| 46 |
-
with gr.Group():
|
| 47 |
-
with gr.Box():
|
| 48 |
-
with gr.Row().style(mobile_collapse=False, equal_height=True):
|
| 49 |
-
|
| 50 |
-
text = gr.Textbox(
|
| 51 |
-
value = "Kitty cat",
|
| 52 |
-
label="Enter your prompt", show_label=False, max_lines=1
|
| 53 |
-
).style(
|
| 54 |
-
border=(True, False, True, True),
|
| 55 |
-
rounded=(True, False, False, True),
|
| 56 |
-
container=False,
|
| 57 |
-
)
|
| 58 |
-
btn = gr.Button("Run").style(
|
| 59 |
-
margin=False,
|
| 60 |
-
rounded=(False, True, True, False),
|
| 61 |
-
)
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
gallery = gr.Image()
|
| 65 |
-
text.submit(infer, inputs=[text], outputs=gallery)
|
| 66 |
-
btn.click(infer, inputs=[text], outputs=gallery)
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
demo.launch(debug=True, enable_queue = True)
|
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/BufrStubImagePlugin.py
DELETED
|
@@ -1,73 +0,0 @@
|
|
| 1 |
-
#
|
| 2 |
-
# The Python Imaging Library
|
| 3 |
-
# $Id$
|
| 4 |
-
#
|
| 5 |
-
# BUFR stub adapter
|
| 6 |
-
#
|
| 7 |
-
# Copyright (c) 1996-2003 by Fredrik Lundh
|
| 8 |
-
#
|
| 9 |
-
# See the README file for information on usage and redistribution.
|
| 10 |
-
#
|
| 11 |
-
|
| 12 |
-
from . import Image, ImageFile
|
| 13 |
-
|
| 14 |
-
_handler = None
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def register_handler(handler):
|
| 18 |
-
"""
|
| 19 |
-
Install application-specific BUFR image handler.
|
| 20 |
-
|
| 21 |
-
:param handler: Handler object.
|
| 22 |
-
"""
|
| 23 |
-
global _handler
|
| 24 |
-
_handler = handler
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
# --------------------------------------------------------------------
|
| 28 |
-
# Image adapter
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
def _accept(prefix):
|
| 32 |
-
return prefix[:4] == b"BUFR" or prefix[:4] == b"ZCZC"
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
class BufrStubImageFile(ImageFile.StubImageFile):
|
| 36 |
-
format = "BUFR"
|
| 37 |
-
format_description = "BUFR"
|
| 38 |
-
|
| 39 |
-
def _open(self):
|
| 40 |
-
offset = self.fp.tell()
|
| 41 |
-
|
| 42 |
-
if not _accept(self.fp.read(4)):
|
| 43 |
-
msg = "Not a BUFR file"
|
| 44 |
-
raise SyntaxError(msg)
|
| 45 |
-
|
| 46 |
-
self.fp.seek(offset)
|
| 47 |
-
|
| 48 |
-
# make something up
|
| 49 |
-
self.mode = "F"
|
| 50 |
-
self._size = 1, 1
|
| 51 |
-
|
| 52 |
-
loader = self._load()
|
| 53 |
-
if loader:
|
| 54 |
-
loader.open(self)
|
| 55 |
-
|
| 56 |
-
def _load(self):
|
| 57 |
-
return _handler
|
| 58 |
-
|
| 59 |
-
|
| 60 |
-
def _save(im, fp, filename):
|
| 61 |
-
if _handler is None or not hasattr(_handler, "save"):
|
| 62 |
-
msg = "BUFR save handler not installed"
|
| 63 |
-
raise OSError(msg)
|
| 64 |
-
_handler.save(im, fp, filename)
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
# --------------------------------------------------------------------
|
| 68 |
-
# Registry
|
| 69 |
-
|
| 70 |
-
Image.register_open(BufrStubImageFile.format, BufrStubImageFile, _accept)
|
| 71 |
-
Image.register_save(BufrStubImageFile.format, _save)
|
| 72 |
-
|
| 73 |
-
Image.register_extension(BufrStubImageFile.format, ".bufr")
|
|
|
|
|
|
|
|
|
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/subset/__main__.py
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
-
import sys
|
| 2 |
-
from fontTools.subset import main
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
if __name__ == "__main__":
|
| 6 |
-
sys.exit(main())
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Model3D-98fc2b2c.css
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
.gallery.svelte-1ayixqk{padding:var(--size-1) var(--size-2)}
|
|
|
|
|
|
spaces/DaleChen/AutoGPT/autogpt/commands/web_playwright.py
DELETED
|
@@ -1,80 +0,0 @@
|
|
| 1 |
-
"""Web scraping commands using Playwright"""
|
| 2 |
-
from __future__ import annotations
|
| 3 |
-
|
| 4 |
-
try:
|
| 5 |
-
from playwright.sync_api import sync_playwright
|
| 6 |
-
except ImportError:
|
| 7 |
-
print(
|
| 8 |
-
"Playwright not installed. Please install it with 'pip install playwright' to use."
|
| 9 |
-
)
|
| 10 |
-
from bs4 import BeautifulSoup
|
| 11 |
-
|
| 12 |
-
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def scrape_text(url: str) -> str:
|
| 16 |
-
"""Scrape text from a webpage
|
| 17 |
-
|
| 18 |
-
Args:
|
| 19 |
-
url (str): The URL to scrape text from
|
| 20 |
-
|
| 21 |
-
Returns:
|
| 22 |
-
str: The scraped text
|
| 23 |
-
"""
|
| 24 |
-
with sync_playwright() as p:
|
| 25 |
-
browser = p.chromium.launch()
|
| 26 |
-
page = browser.new_page()
|
| 27 |
-
|
| 28 |
-
try:
|
| 29 |
-
page.goto(url)
|
| 30 |
-
html_content = page.content()
|
| 31 |
-
soup = BeautifulSoup(html_content, "html.parser")
|
| 32 |
-
|
| 33 |
-
for script in soup(["script", "style"]):
|
| 34 |
-
script.extract()
|
| 35 |
-
|
| 36 |
-
text = soup.get_text()
|
| 37 |
-
lines = (line.strip() for line in text.splitlines())
|
| 38 |
-
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
|
| 39 |
-
text = "\n".join(chunk for chunk in chunks if chunk)
|
| 40 |
-
|
| 41 |
-
except Exception as e:
|
| 42 |
-
text = f"Error: {str(e)}"
|
| 43 |
-
|
| 44 |
-
finally:
|
| 45 |
-
browser.close()
|
| 46 |
-
|
| 47 |
-
return text
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
def scrape_links(url: str) -> str | list[str]:
|
| 51 |
-
"""Scrape links from a webpage
|
| 52 |
-
|
| 53 |
-
Args:
|
| 54 |
-
url (str): The URL to scrape links from
|
| 55 |
-
|
| 56 |
-
Returns:
|
| 57 |
-
Union[str, List[str]]: The scraped links
|
| 58 |
-
"""
|
| 59 |
-
with sync_playwright() as p:
|
| 60 |
-
browser = p.chromium.launch()
|
| 61 |
-
page = browser.new_page()
|
| 62 |
-
|
| 63 |
-
try:
|
| 64 |
-
page.goto(url)
|
| 65 |
-
html_content = page.content()
|
| 66 |
-
soup = BeautifulSoup(html_content, "html.parser")
|
| 67 |
-
|
| 68 |
-
for script in soup(["script", "style"]):
|
| 69 |
-
script.extract()
|
| 70 |
-
|
| 71 |
-
hyperlinks = extract_hyperlinks(soup, url)
|
| 72 |
-
formatted_links = format_hyperlinks(hyperlinks)
|
| 73 |
-
|
| 74 |
-
except Exception as e:
|
| 75 |
-
formatted_links = f"Error: {str(e)}"
|
| 76 |
-
|
| 77 |
-
finally:
|
| 78 |
-
browser.close()
|
| 79 |
-
|
| 80 |
-
return formatted_links
|
|
|
|
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spaces/DamianMH/Mlove/Dockerfile
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FROM node:18-bullseye-slim
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-
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RUN apt-get update && \
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-
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apt-get install -y git
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RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
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| 8 |
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| 9 |
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WORKDIR /app
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| 10 |
-
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| 11 |
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RUN npm install
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| 13 |
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COPY Dockerfile greeting.md* .env* ./
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| 14 |
-
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| 15 |
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RUN npm run build
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| 16 |
-
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| 17 |
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EXPOSE 7860
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| 18 |
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| 19 |
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ENV NODE_ENV=production
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| 20 |
-
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| 21 |
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CMD [ "npm", "start" ]
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spaces/Datasculptor/DescriptionGPT/detic/modeling/meta_arch/d2_deformable_detr.py
DELETED
|
@@ -1,308 +0,0 @@
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|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
import torch
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
from torch import nn
|
| 5 |
-
import math
|
| 6 |
-
|
| 7 |
-
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone
|
| 8 |
-
from detectron2.structures import Boxes, Instances
|
| 9 |
-
from ..utils import load_class_freq, get_fed_loss_inds
|
| 10 |
-
|
| 11 |
-
from models.backbone import Joiner
|
| 12 |
-
from models.deformable_detr import DeformableDETR, SetCriterion, MLP
|
| 13 |
-
from models.deformable_detr import _get_clones
|
| 14 |
-
from models.matcher import HungarianMatcher
|
| 15 |
-
from models.position_encoding import PositionEmbeddingSine
|
| 16 |
-
from models.deformable_transformer import DeformableTransformer
|
| 17 |
-
from models.segmentation import sigmoid_focal_loss
|
| 18 |
-
from util.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
|
| 19 |
-
from util.misc import NestedTensor, accuracy
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
__all__ = ["DeformableDetr"]
|
| 23 |
-
|
| 24 |
-
class CustomSetCriterion(SetCriterion):
|
| 25 |
-
def __init__(self, num_classes, matcher, weight_dict, losses, \
|
| 26 |
-
focal_alpha=0.25, use_fed_loss=False):
|
| 27 |
-
super().__init__(num_classes, matcher, weight_dict, losses, focal_alpha)
|
| 28 |
-
self.use_fed_loss = use_fed_loss
|
| 29 |
-
if self.use_fed_loss:
|
| 30 |
-
self.register_buffer(
|
| 31 |
-
'fed_loss_weight', load_class_freq(freq_weight=0.5))
|
| 32 |
-
|
| 33 |
-
def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
|
| 34 |
-
"""Classification loss (NLL)
|
| 35 |
-
targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
|
| 36 |
-
"""
|
| 37 |
-
assert 'pred_logits' in outputs
|
| 38 |
-
src_logits = outputs['pred_logits']
|
| 39 |
-
|
| 40 |
-
idx = self._get_src_permutation_idx(indices)
|
| 41 |
-
target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
|
| 42 |
-
target_classes = torch.full(src_logits.shape[:2], self.num_classes,
|
| 43 |
-
dtype=torch.int64, device=src_logits.device)
|
| 44 |
-
target_classes[idx] = target_classes_o
|
| 45 |
-
|
| 46 |
-
target_classes_onehot = torch.zeros(
|
| 47 |
-
[src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1],
|
| 48 |
-
dtype=src_logits.dtype, layout=src_logits.layout,
|
| 49 |
-
device=src_logits.device)
|
| 50 |
-
target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)
|
| 51 |
-
|
| 52 |
-
target_classes_onehot = target_classes_onehot[:,:,:-1] # B x N x C
|
| 53 |
-
if self.use_fed_loss:
|
| 54 |
-
inds = get_fed_loss_inds(
|
| 55 |
-
gt_classes=target_classes_o,
|
| 56 |
-
num_sample_cats=50,
|
| 57 |
-
weight=self.fed_loss_weight,
|
| 58 |
-
C=target_classes_onehot.shape[2])
|
| 59 |
-
loss_ce = sigmoid_focal_loss(
|
| 60 |
-
src_logits[:, :, inds],
|
| 61 |
-
target_classes_onehot[:, :, inds],
|
| 62 |
-
num_boxes,
|
| 63 |
-
alpha=self.focal_alpha,
|
| 64 |
-
gamma=2) * src_logits.shape[1]
|
| 65 |
-
else:
|
| 66 |
-
loss_ce = sigmoid_focal_loss(
|
| 67 |
-
src_logits, target_classes_onehot, num_boxes,
|
| 68 |
-
alpha=self.focal_alpha,
|
| 69 |
-
gamma=2) * src_logits.shape[1]
|
| 70 |
-
losses = {'loss_ce': loss_ce}
|
| 71 |
-
|
| 72 |
-
if log:
|
| 73 |
-
# TODO this should probably be a separate loss, not hacked in this one here
|
| 74 |
-
losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]
|
| 75 |
-
return losses
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
class MaskedBackbone(nn.Module):
|
| 79 |
-
""" This is a thin wrapper around D2's backbone to provide padding masking"""
|
| 80 |
-
|
| 81 |
-
def __init__(self, cfg):
|
| 82 |
-
super().__init__()
|
| 83 |
-
self.backbone = build_backbone(cfg)
|
| 84 |
-
backbone_shape = self.backbone.output_shape()
|
| 85 |
-
self.feature_strides = [backbone_shape[f].stride for f in backbone_shape.keys()]
|
| 86 |
-
self.strides = [backbone_shape[f].stride for f in backbone_shape.keys()]
|
| 87 |
-
self.num_channels = [backbone_shape[x].channels for x in backbone_shape.keys()]
|
| 88 |
-
|
| 89 |
-
def forward(self, tensor_list: NestedTensor):
|
| 90 |
-
xs = self.backbone(tensor_list.tensors)
|
| 91 |
-
out = {}
|
| 92 |
-
for name, x in xs.items():
|
| 93 |
-
m = tensor_list.mask
|
| 94 |
-
assert m is not None
|
| 95 |
-
mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
|
| 96 |
-
out[name] = NestedTensor(x, mask)
|
| 97 |
-
return out
|
| 98 |
-
|
| 99 |
-
@META_ARCH_REGISTRY.register()
|
| 100 |
-
class DeformableDetr(nn.Module):
|
| 101 |
-
"""
|
| 102 |
-
Implement Deformable Detr
|
| 103 |
-
"""
|
| 104 |
-
|
| 105 |
-
def __init__(self, cfg):
|
| 106 |
-
super().__init__()
|
| 107 |
-
self.with_image_labels = cfg.WITH_IMAGE_LABELS
|
| 108 |
-
self.weak_weight = cfg.MODEL.DETR.WEAK_WEIGHT
|
| 109 |
-
|
| 110 |
-
self.device = torch.device(cfg.MODEL.DEVICE)
|
| 111 |
-
self.test_topk = cfg.TEST.DETECTIONS_PER_IMAGE
|
| 112 |
-
self.num_classes = cfg.MODEL.DETR.NUM_CLASSES
|
| 113 |
-
self.mask_on = cfg.MODEL.MASK_ON
|
| 114 |
-
hidden_dim = cfg.MODEL.DETR.HIDDEN_DIM
|
| 115 |
-
num_queries = cfg.MODEL.DETR.NUM_OBJECT_QUERIES
|
| 116 |
-
|
| 117 |
-
# Transformer parameters:
|
| 118 |
-
nheads = cfg.MODEL.DETR.NHEADS
|
| 119 |
-
dropout = cfg.MODEL.DETR.DROPOUT
|
| 120 |
-
dim_feedforward = cfg.MODEL.DETR.DIM_FEEDFORWARD
|
| 121 |
-
enc_layers = cfg.MODEL.DETR.ENC_LAYERS
|
| 122 |
-
dec_layers = cfg.MODEL.DETR.DEC_LAYERS
|
| 123 |
-
num_feature_levels = cfg.MODEL.DETR.NUM_FEATURE_LEVELS
|
| 124 |
-
two_stage = cfg.MODEL.DETR.TWO_STAGE
|
| 125 |
-
with_box_refine = cfg.MODEL.DETR.WITH_BOX_REFINE
|
| 126 |
-
|
| 127 |
-
# Loss parameters:
|
| 128 |
-
giou_weight = cfg.MODEL.DETR.GIOU_WEIGHT
|
| 129 |
-
l1_weight = cfg.MODEL.DETR.L1_WEIGHT
|
| 130 |
-
deep_supervision = cfg.MODEL.DETR.DEEP_SUPERVISION
|
| 131 |
-
cls_weight = cfg.MODEL.DETR.CLS_WEIGHT
|
| 132 |
-
focal_alpha = cfg.MODEL.DETR.FOCAL_ALPHA
|
| 133 |
-
|
| 134 |
-
N_steps = hidden_dim // 2
|
| 135 |
-
d2_backbone = MaskedBackbone(cfg)
|
| 136 |
-
backbone = Joiner(d2_backbone, PositionEmbeddingSine(N_steps, normalize=True))
|
| 137 |
-
|
| 138 |
-
transformer = DeformableTransformer(
|
| 139 |
-
d_model=hidden_dim,
|
| 140 |
-
nhead=nheads,
|
| 141 |
-
num_encoder_layers=enc_layers,
|
| 142 |
-
num_decoder_layers=dec_layers,
|
| 143 |
-
dim_feedforward=dim_feedforward,
|
| 144 |
-
dropout=dropout,
|
| 145 |
-
activation="relu",
|
| 146 |
-
return_intermediate_dec=True,
|
| 147 |
-
num_feature_levels=num_feature_levels,
|
| 148 |
-
dec_n_points=4,
|
| 149 |
-
enc_n_points=4,
|
| 150 |
-
two_stage=two_stage,
|
| 151 |
-
two_stage_num_proposals=num_queries)
|
| 152 |
-
|
| 153 |
-
self.detr = DeformableDETR(
|
| 154 |
-
backbone, transformer, num_classes=self.num_classes,
|
| 155 |
-
num_queries=num_queries,
|
| 156 |
-
num_feature_levels=num_feature_levels,
|
| 157 |
-
aux_loss=deep_supervision,
|
| 158 |
-
with_box_refine=with_box_refine,
|
| 159 |
-
two_stage=two_stage,
|
| 160 |
-
)
|
| 161 |
-
|
| 162 |
-
if self.mask_on:
|
| 163 |
-
assert 0, 'Mask is not supported yet :('
|
| 164 |
-
|
| 165 |
-
matcher = HungarianMatcher(
|
| 166 |
-
cost_class=cls_weight, cost_bbox=l1_weight, cost_giou=giou_weight)
|
| 167 |
-
weight_dict = {"loss_ce": cls_weight, "loss_bbox": l1_weight}
|
| 168 |
-
weight_dict["loss_giou"] = giou_weight
|
| 169 |
-
if deep_supervision:
|
| 170 |
-
aux_weight_dict = {}
|
| 171 |
-
for i in range(dec_layers - 1):
|
| 172 |
-
aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
|
| 173 |
-
weight_dict.update(aux_weight_dict)
|
| 174 |
-
print('weight_dict', weight_dict)
|
| 175 |
-
losses = ["labels", "boxes", "cardinality"]
|
| 176 |
-
if self.mask_on:
|
| 177 |
-
losses += ["masks"]
|
| 178 |
-
self.criterion = CustomSetCriterion(
|
| 179 |
-
self.num_classes, matcher=matcher, weight_dict=weight_dict,
|
| 180 |
-
focal_alpha=focal_alpha,
|
| 181 |
-
losses=losses,
|
| 182 |
-
use_fed_loss=cfg.MODEL.DETR.USE_FED_LOSS
|
| 183 |
-
)
|
| 184 |
-
pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1)
|
| 185 |
-
pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1)
|
| 186 |
-
self.normalizer = lambda x: (x - pixel_mean) / pixel_std
|
| 187 |
-
|
| 188 |
-
|
| 189 |
-
def forward(self, batched_inputs):
|
| 190 |
-
"""
|
| 191 |
-
Args:
|
| 192 |
-
Returns:
|
| 193 |
-
dict[str: Tensor]:
|
| 194 |
-
mapping from a named loss to a tensor storing the loss. Used during training only.
|
| 195 |
-
"""
|
| 196 |
-
images = self.preprocess_image(batched_inputs)
|
| 197 |
-
output = self.detr(images)
|
| 198 |
-
if self.training:
|
| 199 |
-
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
|
| 200 |
-
targets = self.prepare_targets(gt_instances)
|
| 201 |
-
loss_dict = self.criterion(output, targets)
|
| 202 |
-
weight_dict = self.criterion.weight_dict
|
| 203 |
-
for k in loss_dict.keys():
|
| 204 |
-
if k in weight_dict:
|
| 205 |
-
loss_dict[k] *= weight_dict[k]
|
| 206 |
-
if self.with_image_labels:
|
| 207 |
-
if batched_inputs[0]['ann_type'] in ['image', 'captiontag']:
|
| 208 |
-
loss_dict['loss_image'] = self.weak_weight * self._weak_loss(
|
| 209 |
-
output, batched_inputs)
|
| 210 |
-
else:
|
| 211 |
-
loss_dict['loss_image'] = images[0].new_zeros(
|
| 212 |
-
[1], dtype=torch.float32)[0]
|
| 213 |
-
# import pdb; pdb.set_trace()
|
| 214 |
-
return loss_dict
|
| 215 |
-
else:
|
| 216 |
-
image_sizes = output["pred_boxes"].new_tensor(
|
| 217 |
-
[(t["height"], t["width"]) for t in batched_inputs])
|
| 218 |
-
results = self.post_process(output, image_sizes)
|
| 219 |
-
return results
|
| 220 |
-
|
| 221 |
-
|
| 222 |
-
def prepare_targets(self, targets):
|
| 223 |
-
new_targets = []
|
| 224 |
-
for targets_per_image in targets:
|
| 225 |
-
h, w = targets_per_image.image_size
|
| 226 |
-
image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device)
|
| 227 |
-
gt_classes = targets_per_image.gt_classes
|
| 228 |
-
gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy
|
| 229 |
-
gt_boxes = box_xyxy_to_cxcywh(gt_boxes)
|
| 230 |
-
new_targets.append({"labels": gt_classes, "boxes": gt_boxes})
|
| 231 |
-
if self.mask_on and hasattr(targets_per_image, 'gt_masks'):
|
| 232 |
-
assert 0, 'Mask is not supported yet :('
|
| 233 |
-
gt_masks = targets_per_image.gt_masks
|
| 234 |
-
gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)
|
| 235 |
-
new_targets[-1].update({'masks': gt_masks})
|
| 236 |
-
return new_targets
|
| 237 |
-
|
| 238 |
-
|
| 239 |
-
def post_process(self, outputs, target_sizes):
|
| 240 |
-
"""
|
| 241 |
-
"""
|
| 242 |
-
out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']
|
| 243 |
-
assert len(out_logits) == len(target_sizes)
|
| 244 |
-
assert target_sizes.shape[1] == 2
|
| 245 |
-
|
| 246 |
-
prob = out_logits.sigmoid()
|
| 247 |
-
topk_values, topk_indexes = torch.topk(
|
| 248 |
-
prob.view(out_logits.shape[0], -1), self.test_topk, dim=1)
|
| 249 |
-
scores = topk_values
|
| 250 |
-
topk_boxes = topk_indexes // out_logits.shape[2]
|
| 251 |
-
labels = topk_indexes % out_logits.shape[2]
|
| 252 |
-
boxes = box_cxcywh_to_xyxy(out_bbox)
|
| 253 |
-
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1,1,4))
|
| 254 |
-
|
| 255 |
-
# and from relative [0, 1] to absolute [0, height] coordinates
|
| 256 |
-
img_h, img_w = target_sizes.unbind(1)
|
| 257 |
-
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
| 258 |
-
boxes = boxes * scale_fct[:, None, :]
|
| 259 |
-
|
| 260 |
-
results = []
|
| 261 |
-
for s, l, b, size in zip(scores, labels, boxes, target_sizes):
|
| 262 |
-
r = Instances((size[0], size[1]))
|
| 263 |
-
r.pred_boxes = Boxes(b)
|
| 264 |
-
r.scores = s
|
| 265 |
-
r.pred_classes = l
|
| 266 |
-
results.append({'instances': r})
|
| 267 |
-
return results
|
| 268 |
-
|
| 269 |
-
|
| 270 |
-
def preprocess_image(self, batched_inputs):
|
| 271 |
-
"""
|
| 272 |
-
Normalize, pad and batch the input images.
|
| 273 |
-
"""
|
| 274 |
-
images = [self.normalizer(x["image"].to(self.device)) for x in batched_inputs]
|
| 275 |
-
return images
|
| 276 |
-
|
| 277 |
-
|
| 278 |
-
def _weak_loss(self, outputs, batched_inputs):
|
| 279 |
-
loss = 0
|
| 280 |
-
for b, x in enumerate(batched_inputs):
|
| 281 |
-
labels = x['pos_category_ids']
|
| 282 |
-
pred_logits = [outputs['pred_logits'][b]]
|
| 283 |
-
pred_boxes = [outputs['pred_boxes'][b]]
|
| 284 |
-
for xx in outputs['aux_outputs']:
|
| 285 |
-
pred_logits.append(xx['pred_logits'][b])
|
| 286 |
-
pred_boxes.append(xx['pred_boxes'][b])
|
| 287 |
-
pred_logits = torch.stack(pred_logits, dim=0) # L x N x C
|
| 288 |
-
pred_boxes = torch.stack(pred_boxes, dim=0) # L x N x 4
|
| 289 |
-
for label in labels:
|
| 290 |
-
loss += self._max_size_loss(
|
| 291 |
-
pred_logits, pred_boxes, label) / len(labels)
|
| 292 |
-
loss = loss / len(batched_inputs)
|
| 293 |
-
return loss
|
| 294 |
-
|
| 295 |
-
|
| 296 |
-
def _max_size_loss(self, logits, boxes, label):
|
| 297 |
-
'''
|
| 298 |
-
Inputs:
|
| 299 |
-
logits: L x N x C
|
| 300 |
-
boxes: L x N x 4
|
| 301 |
-
'''
|
| 302 |
-
target = logits.new_zeros((logits.shape[0], logits.shape[2]))
|
| 303 |
-
target[:, label] = 1.
|
| 304 |
-
sizes = boxes[..., 2] * boxes[..., 3] # L x N
|
| 305 |
-
ind = sizes.argmax(dim=1) # L
|
| 306 |
-
loss = F.binary_cross_entropy_with_logits(
|
| 307 |
-
logits[range(len(ind)), ind], target, reduction='sum')
|
| 308 |
-
return loss
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|
spaces/Datasculptor/StyleGAN-NADA/e4e/options/__init__.py
DELETED
|
File without changes
|
spaces/Dinoking/Guccio-AI-Designer/decomposition.py
DELETED
|
@@ -1,402 +0,0 @@
|
|
| 1 |
-
# Copyright 2020 Erik Härkönen. All rights reserved.
|
| 2 |
-
# This file is licensed to you under the Apache License, Version 2.0 (the "License");
|
| 3 |
-
# you may not use this file except in compliance with the License. You may obtain a copy
|
| 4 |
-
# of the License at http://www.apache.org/licenses/LICENSE-2.0
|
| 5 |
-
|
| 6 |
-
# Unless required by applicable law or agreed to in writing, software distributed under
|
| 7 |
-
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
|
| 8 |
-
# OF ANY KIND, either express or implied. See the License for the specific language
|
| 9 |
-
# governing permissions and limitations under the License.
|
| 10 |
-
|
| 11 |
-
# Patch for broken CTRL+C handler
|
| 12 |
-
# https://github.com/ContinuumIO/anaconda-issues/issues/905
|
| 13 |
-
import os
|
| 14 |
-
os.environ['FOR_DISABLE_CONSOLE_CTRL_HANDLER'] = '1'
|
| 15 |
-
|
| 16 |
-
import numpy as np
|
| 17 |
-
import os
|
| 18 |
-
from pathlib import Path
|
| 19 |
-
import re
|
| 20 |
-
import sys
|
| 21 |
-
import datetime
|
| 22 |
-
import argparse
|
| 23 |
-
import torch
|
| 24 |
-
import json
|
| 25 |
-
from types import SimpleNamespace
|
| 26 |
-
import scipy
|
| 27 |
-
from scipy.cluster.vq import kmeans
|
| 28 |
-
from tqdm import trange
|
| 29 |
-
from netdissect.nethook import InstrumentedModel
|
| 30 |
-
from config import Config
|
| 31 |
-
from estimators import get_estimator
|
| 32 |
-
from models import get_instrumented_model
|
| 33 |
-
|
| 34 |
-
SEED_SAMPLING = 1
|
| 35 |
-
SEED_RANDOM_DIRS = 2
|
| 36 |
-
SEED_LINREG = 3
|
| 37 |
-
SEED_VISUALIZATION = 5
|
| 38 |
-
|
| 39 |
-
B = 20
|
| 40 |
-
n_clusters = 500
|
| 41 |
-
|
| 42 |
-
def get_random_dirs(components, dimensions):
|
| 43 |
-
gen = np.random.RandomState(seed=SEED_RANDOM_DIRS)
|
| 44 |
-
dirs = gen.normal(size=(components, dimensions))
|
| 45 |
-
dirs /= np.sqrt(np.sum(dirs**2, axis=1, keepdims=True))
|
| 46 |
-
return dirs.astype(np.float32)
|
| 47 |
-
|
| 48 |
-
# Compute maximum batch size for given VRAM and network
|
| 49 |
-
def get_max_batch_size(inst, device, layer_name=None):
|
| 50 |
-
inst.remove_edits()
|
| 51 |
-
|
| 52 |
-
# Reset statistics
|
| 53 |
-
torch.cuda.reset_max_memory_cached(device)
|
| 54 |
-
torch.cuda.reset_max_memory_allocated(device)
|
| 55 |
-
total_mem = torch.cuda.get_device_properties(device).total_memory
|
| 56 |
-
|
| 57 |
-
B_max = 20
|
| 58 |
-
|
| 59 |
-
# Measure actual usage
|
| 60 |
-
for i in range(2, B_max, 2):
|
| 61 |
-
z = inst.model.sample_latent(n_samples=i)
|
| 62 |
-
if layer_name:
|
| 63 |
-
inst.model.partial_forward(z, layer_name)
|
| 64 |
-
else:
|
| 65 |
-
inst.model.forward(z)
|
| 66 |
-
|
| 67 |
-
maxmem = torch.cuda.max_memory_allocated(device)
|
| 68 |
-
del z
|
| 69 |
-
|
| 70 |
-
if maxmem > 0.5*total_mem:
|
| 71 |
-
print('Batch size {:d}: memory usage {:.0f}MB'.format(i, maxmem / 1e6))
|
| 72 |
-
return i
|
| 73 |
-
|
| 74 |
-
return B_max
|
| 75 |
-
|
| 76 |
-
# Solve for directions in latent space that match PCs in activaiton space
|
| 77 |
-
def linreg_lstsq(comp_np, mean_np, stdev_np, inst, config):
|
| 78 |
-
print('Performing least squares regression', flush=True)
|
| 79 |
-
|
| 80 |
-
torch.manual_seed(SEED_LINREG)
|
| 81 |
-
np.random.seed(SEED_LINREG)
|
| 82 |
-
|
| 83 |
-
comp = torch.from_numpy(comp_np).float().to(inst.model.device)
|
| 84 |
-
mean = torch.from_numpy(mean_np).float().to(inst.model.device)
|
| 85 |
-
stdev = torch.from_numpy(stdev_np).float().to(inst.model.device)
|
| 86 |
-
|
| 87 |
-
n_samp = max(10_000, config.n) // B * B # make divisible
|
| 88 |
-
n_comp = comp.shape[0]
|
| 89 |
-
latent_dims = inst.model.get_latent_dims()
|
| 90 |
-
|
| 91 |
-
# We're looking for M s.t. M*P*G'(Z) = Z => M*A = Z
|
| 92 |
-
# Z = batch of latent vectors (n_samples x latent_dims)
|
| 93 |
-
# G'(Z) = batch of activations at intermediate layer
|
| 94 |
-
# A = P*G'(Z) = projected activations (n_samples x pca_coords)
|
| 95 |
-
# M = linear mapping (pca_coords x latent_dims)
|
| 96 |
-
|
| 97 |
-
# Minimization min_M ||MA - Z||_l2 rewritten as min_M.T ||A.T*M.T - Z.T||_l2
|
| 98 |
-
# to match format expected by pytorch.lstsq
|
| 99 |
-
|
| 100 |
-
# TODO: regression on pixel-space outputs? (using nonlinear optimizer)
|
| 101 |
-
# min_M lpips(G_full(MA), G_full(Z))
|
| 102 |
-
|
| 103 |
-
# Tensors to fill with data
|
| 104 |
-
# Dimensions other way around, so these are actually the transposes
|
| 105 |
-
A = np.zeros((n_samp, n_comp), dtype=np.float32)
|
| 106 |
-
Z = np.zeros((n_samp, latent_dims), dtype=np.float32)
|
| 107 |
-
|
| 108 |
-
# Project tensor X onto PCs, return coordinates
|
| 109 |
-
def project(X, comp):
|
| 110 |
-
N = X.shape[0]
|
| 111 |
-
K = comp.shape[0]
|
| 112 |
-
coords = torch.bmm(comp.expand([N]+[-1]*comp.ndim), X.view(N, -1, 1))
|
| 113 |
-
return coords.reshape(N, K)
|
| 114 |
-
|
| 115 |
-
for i in trange(n_samp // B, desc='Collecting samples', ascii=True):
|
| 116 |
-
z = inst.model.sample_latent(B)
|
| 117 |
-
inst.model.partial_forward(z, config.layer)
|
| 118 |
-
act = inst.retained_features()[config.layer].reshape(B, -1)
|
| 119 |
-
|
| 120 |
-
# Project onto basis
|
| 121 |
-
act = act - mean
|
| 122 |
-
coords = project(act, comp)
|
| 123 |
-
coords_scaled = coords / stdev
|
| 124 |
-
|
| 125 |
-
A[i*B:(i+1)*B] = coords_scaled.detach().cpu().numpy()
|
| 126 |
-
Z[i*B:(i+1)*B] = z.detach().cpu().numpy().reshape(B, -1)
|
| 127 |
-
|
| 128 |
-
# Solve least squares fit
|
| 129 |
-
|
| 130 |
-
# gelsd = divide-and-conquer SVD; good default
|
| 131 |
-
# gelsy = complete orthogonal factorization; sometimes faster
|
| 132 |
-
# gelss = SVD; slow but less memory hungry
|
| 133 |
-
M_t = scipy.linalg.lstsq(A, Z, lapack_driver='gelsd')[0] # torch.lstsq(Z, A)[0][:n_comp, :]
|
| 134 |
-
|
| 135 |
-
# Solution given by rows of M_t
|
| 136 |
-
Z_comp = M_t[:n_comp, :]
|
| 137 |
-
Z_mean = np.mean(Z, axis=0, keepdims=True)
|
| 138 |
-
|
| 139 |
-
return Z_comp, Z_mean
|
| 140 |
-
|
| 141 |
-
def regression(comp, mean, stdev, inst, config):
|
| 142 |
-
# Sanity check: verify orthonormality
|
| 143 |
-
M = np.dot(comp, comp.T)
|
| 144 |
-
if not np.allclose(M, np.identity(M.shape[0])):
|
| 145 |
-
det = np.linalg.det(M)
|
| 146 |
-
print(f'WARNING: Computed basis is not orthonormal (determinant={det})')
|
| 147 |
-
|
| 148 |
-
return linreg_lstsq(comp, mean, stdev, inst, config)
|
| 149 |
-
|
| 150 |
-
def compute(config, dump_name, instrumented_model):
|
| 151 |
-
global B
|
| 152 |
-
|
| 153 |
-
timestamp = lambda : datetime.datetime.now().strftime("%d.%m %H:%M")
|
| 154 |
-
print(f'[{timestamp()}] Computing', dump_name.name)
|
| 155 |
-
|
| 156 |
-
# Ensure reproducibility
|
| 157 |
-
torch.manual_seed(0) # also sets cuda seeds
|
| 158 |
-
np.random.seed(0)
|
| 159 |
-
|
| 160 |
-
# Speed up backend
|
| 161 |
-
torch.backends.cudnn.benchmark = True
|
| 162 |
-
|
| 163 |
-
has_gpu = torch.cuda.is_available()
|
| 164 |
-
device = torch.device('cuda' if has_gpu else 'cpu')
|
| 165 |
-
layer_key = config.layer
|
| 166 |
-
|
| 167 |
-
if instrumented_model is None:
|
| 168 |
-
inst = get_instrumented_model(config.model, config.output_class, layer_key, device)
|
| 169 |
-
model = inst.model
|
| 170 |
-
else:
|
| 171 |
-
print('Reusing InstrumentedModel instance')
|
| 172 |
-
inst = instrumented_model
|
| 173 |
-
model = inst.model
|
| 174 |
-
inst.remove_edits()
|
| 175 |
-
model.set_output_class(config.output_class)
|
| 176 |
-
|
| 177 |
-
# Regress back to w space
|
| 178 |
-
if config.use_w:
|
| 179 |
-
print('Using W latent space')
|
| 180 |
-
model.use_w()
|
| 181 |
-
|
| 182 |
-
inst.retain_layer(layer_key)
|
| 183 |
-
model.partial_forward(model.sample_latent(1), layer_key)
|
| 184 |
-
sample_shape = inst.retained_features()[layer_key].shape
|
| 185 |
-
sample_dims = np.prod(sample_shape)
|
| 186 |
-
print('Feature shape:', sample_shape)
|
| 187 |
-
|
| 188 |
-
input_shape = inst.model.get_latent_shape()
|
| 189 |
-
input_dims = inst.model.get_latent_dims()
|
| 190 |
-
|
| 191 |
-
config.components = min(config.components, sample_dims)
|
| 192 |
-
transformer = get_estimator(config.estimator, config.components, config.sparsity)
|
| 193 |
-
|
| 194 |
-
X = None
|
| 195 |
-
X_global_mean = None
|
| 196 |
-
|
| 197 |
-
# Figure out batch size if not provided
|
| 198 |
-
B = config.batch_size or get_max_batch_size(inst, device, layer_key)
|
| 199 |
-
|
| 200 |
-
# Divisible by B (ignored in output name)
|
| 201 |
-
N = config.n // B * B
|
| 202 |
-
|
| 203 |
-
# Compute maximum batch size based on RAM + pagefile budget
|
| 204 |
-
target_bytes = 20 * 1_000_000_000 # GB
|
| 205 |
-
feat_size_bytes = sample_dims * np.dtype('float64').itemsize
|
| 206 |
-
N_limit_RAM = np.floor_divide(target_bytes, feat_size_bytes)
|
| 207 |
-
if not transformer.batch_support and N > N_limit_RAM:
|
| 208 |
-
print('WARNING: estimator does not support batching, ' \
|
| 209 |
-
'given config will use {:.1f} GB memory.'.format(feat_size_bytes / 1_000_000_000 * N))
|
| 210 |
-
|
| 211 |
-
# 32-bit LAPACK gets very unhappy about huge matrices (in linalg.svd)
|
| 212 |
-
if config.estimator == 'ica':
|
| 213 |
-
lapack_max_N = np.floor_divide(np.iinfo(np.int32).max // 4, sample_dims) # 4x extra buffer
|
| 214 |
-
if N > lapack_max_N:
|
| 215 |
-
raise RuntimeError(f'Matrices too large for ICA, please use N <= {lapack_max_N}')
|
| 216 |
-
|
| 217 |
-
print('B={}, N={}, dims={}, N/dims={:.1f}'.format(B, N, sample_dims, N/sample_dims), flush=True)
|
| 218 |
-
|
| 219 |
-
# Must not depend on chosen batch size (reproducibility)
|
| 220 |
-
NB = max(B, max(2_000, 3*config.components)) # ipca: as large as possible!
|
| 221 |
-
|
| 222 |
-
samples = None
|
| 223 |
-
if not transformer.batch_support:
|
| 224 |
-
samples = np.zeros((N + NB, sample_dims), dtype=np.float32)
|
| 225 |
-
|
| 226 |
-
torch.manual_seed(config.seed or SEED_SAMPLING)
|
| 227 |
-
np.random.seed(config.seed or SEED_SAMPLING)
|
| 228 |
-
|
| 229 |
-
# Use exactly the same latents regardless of batch size
|
| 230 |
-
# Store in main memory, since N might be huge (1M+)
|
| 231 |
-
# Run in batches, since sample_latent() might perform Z -> W mapping
|
| 232 |
-
n_lat = ((N + NB - 1) // B + 1) * B
|
| 233 |
-
latents = np.zeros((n_lat, *input_shape[1:]), dtype=np.float32)
|
| 234 |
-
with torch.no_grad():
|
| 235 |
-
for i in trange(n_lat // B, desc='Sampling latents'):
|
| 236 |
-
latents[i*B:(i+1)*B] = model.sample_latent(n_samples=B).cpu().numpy()
|
| 237 |
-
|
| 238 |
-
# Decomposition on non-Gaussian latent space
|
| 239 |
-
samples_are_latents = layer_key in ['g_mapping', 'style'] and inst.model.latent_space_name() == 'W'
|
| 240 |
-
|
| 241 |
-
canceled = False
|
| 242 |
-
try:
|
| 243 |
-
X = np.ones((NB, sample_dims), dtype=np.float32)
|
| 244 |
-
action = 'Fitting' if transformer.batch_support else 'Collecting'
|
| 245 |
-
for gi in trange(0, N, NB, desc=f'{action} batches (NB={NB})', ascii=True):
|
| 246 |
-
for mb in range(0, NB, B):
|
| 247 |
-
z = torch.from_numpy(latents[gi+mb:gi+mb+B]).to(device)
|
| 248 |
-
|
| 249 |
-
if samples_are_latents:
|
| 250 |
-
# Decomposition on latents directly (e.g. StyleGAN W)
|
| 251 |
-
batch = z.reshape((B, -1))
|
| 252 |
-
else:
|
| 253 |
-
# Decomposition on intermediate layer
|
| 254 |
-
with torch.no_grad():
|
| 255 |
-
model.partial_forward(z, layer_key)
|
| 256 |
-
|
| 257 |
-
# Permuted to place PCA dimensions last
|
| 258 |
-
batch = inst.retained_features()[layer_key].reshape((B, -1))
|
| 259 |
-
|
| 260 |
-
space_left = min(B, NB - mb)
|
| 261 |
-
X[mb:mb+space_left] = batch.cpu().numpy()[:space_left]
|
| 262 |
-
|
| 263 |
-
if transformer.batch_support:
|
| 264 |
-
if not transformer.fit_partial(X.reshape(-1, sample_dims)):
|
| 265 |
-
break
|
| 266 |
-
else:
|
| 267 |
-
samples[gi:gi+NB, :] = X.copy()
|
| 268 |
-
except KeyboardInterrupt:
|
| 269 |
-
if not transformer.batch_support:
|
| 270 |
-
sys.exit(1) # no progress yet
|
| 271 |
-
|
| 272 |
-
dump_name = dump_name.parent / dump_name.name.replace(f'n{N}', f'n{gi}')
|
| 273 |
-
print(f'Saving current state to "{dump_name.name}" before exiting')
|
| 274 |
-
canceled = True
|
| 275 |
-
|
| 276 |
-
if not transformer.batch_support:
|
| 277 |
-
X = samples # Use all samples
|
| 278 |
-
X_global_mean = X.mean(axis=0, keepdims=True, dtype=np.float32) # TODO: activations surely multi-modal...!
|
| 279 |
-
X -= X_global_mean
|
| 280 |
-
|
| 281 |
-
print(f'[{timestamp()}] Fitting whole batch')
|
| 282 |
-
t_start_fit = datetime.datetime.now()
|
| 283 |
-
|
| 284 |
-
transformer.fit(X)
|
| 285 |
-
|
| 286 |
-
print(f'[{timestamp()}] Done in {datetime.datetime.now() - t_start_fit}')
|
| 287 |
-
assert np.all(transformer.transformer.mean_ < 1e-3), 'Mean of normalized data should be zero'
|
| 288 |
-
else:
|
| 289 |
-
X_global_mean = transformer.transformer.mean_.reshape((1, sample_dims))
|
| 290 |
-
X = X.reshape(-1, sample_dims)
|
| 291 |
-
X -= X_global_mean
|
| 292 |
-
|
| 293 |
-
X_comp, X_stdev, X_var_ratio = transformer.get_components()
|
| 294 |
-
|
| 295 |
-
assert X_comp.shape[1] == sample_dims \
|
| 296 |
-
and X_comp.shape[0] == config.components \
|
| 297 |
-
and X_global_mean.shape[1] == sample_dims \
|
| 298 |
-
and X_stdev.shape[0] == config.components, 'Invalid shape'
|
| 299 |
-
|
| 300 |
-
# 'Activations' are really latents in a secondary latent space
|
| 301 |
-
if samples_are_latents:
|
| 302 |
-
Z_comp = X_comp
|
| 303 |
-
Z_global_mean = X_global_mean
|
| 304 |
-
else:
|
| 305 |
-
Z_comp, Z_global_mean = regression(X_comp, X_global_mean, X_stdev, inst, config)
|
| 306 |
-
|
| 307 |
-
# Normalize
|
| 308 |
-
Z_comp /= np.linalg.norm(Z_comp, axis=-1, keepdims=True)
|
| 309 |
-
|
| 310 |
-
# Random projections
|
| 311 |
-
# We expect these to explain much less of the variance
|
| 312 |
-
random_dirs = get_random_dirs(config.components, np.prod(sample_shape))
|
| 313 |
-
n_rand_samples = min(5000, X.shape[0])
|
| 314 |
-
X_view = X[:n_rand_samples, :].T
|
| 315 |
-
assert np.shares_memory(X_view, X), "Error: slice produced copy"
|
| 316 |
-
X_stdev_random = np.dot(random_dirs, X_view).std(axis=1)
|
| 317 |
-
|
| 318 |
-
# Inflate back to proper shapes (for easier broadcasting)
|
| 319 |
-
X_comp = X_comp.reshape(-1, *sample_shape)
|
| 320 |
-
X_global_mean = X_global_mean.reshape(sample_shape)
|
| 321 |
-
Z_comp = Z_comp.reshape(-1, *input_shape)
|
| 322 |
-
Z_global_mean = Z_global_mean.reshape(input_shape)
|
| 323 |
-
|
| 324 |
-
# Compute stdev in latent space if non-Gaussian
|
| 325 |
-
lat_stdev = np.ones_like(X_stdev)
|
| 326 |
-
if config.use_w:
|
| 327 |
-
samples = model.sample_latent(5000).reshape(5000, input_dims).detach().cpu().numpy()
|
| 328 |
-
coords = np.dot(Z_comp.reshape(-1, input_dims), samples.T)
|
| 329 |
-
lat_stdev = coords.std(axis=1)
|
| 330 |
-
|
| 331 |
-
os.makedirs(dump_name.parent, exist_ok=True)
|
| 332 |
-
np.savez_compressed(dump_name, **{
|
| 333 |
-
'act_comp': X_comp.astype(np.float32),
|
| 334 |
-
'act_mean': X_global_mean.astype(np.float32),
|
| 335 |
-
'act_stdev': X_stdev.astype(np.float32),
|
| 336 |
-
'lat_comp': Z_comp.astype(np.float32),
|
| 337 |
-
'lat_mean': Z_global_mean.astype(np.float32),
|
| 338 |
-
'lat_stdev': lat_stdev.astype(np.float32),
|
| 339 |
-
'var_ratio': X_var_ratio.astype(np.float32),
|
| 340 |
-
'random_stdevs': X_stdev_random.astype(np.float32),
|
| 341 |
-
})
|
| 342 |
-
|
| 343 |
-
if canceled:
|
| 344 |
-
sys.exit(1)
|
| 345 |
-
|
| 346 |
-
# Don't shutdown if passed as param
|
| 347 |
-
if instrumented_model is None:
|
| 348 |
-
inst.close()
|
| 349 |
-
del inst
|
| 350 |
-
del model
|
| 351 |
-
|
| 352 |
-
del X
|
| 353 |
-
del X_comp
|
| 354 |
-
del random_dirs
|
| 355 |
-
del batch
|
| 356 |
-
del samples
|
| 357 |
-
del latents
|
| 358 |
-
torch.cuda.empty_cache()
|
| 359 |
-
|
| 360 |
-
# Return cached results or commpute if needed
|
| 361 |
-
# Pass existing InstrumentedModel instance to reuse it
|
| 362 |
-
def get_or_compute(config, model=None, submit_config=None, force_recompute=False):
|
| 363 |
-
if submit_config is None:
|
| 364 |
-
wrkdir = str(Path(__file__).parent.resolve())
|
| 365 |
-
submit_config = SimpleNamespace(run_dir_root = wrkdir, run_dir = wrkdir)
|
| 366 |
-
|
| 367 |
-
# Called directly by run.py
|
| 368 |
-
return _compute(submit_config, config, model, force_recompute)
|
| 369 |
-
|
| 370 |
-
def _compute(submit_config, config, model=None, force_recompute=False):
|
| 371 |
-
basedir = Path(submit_config.run_dir)
|
| 372 |
-
outdir = basedir / 'out'
|
| 373 |
-
|
| 374 |
-
if config.n is None:
|
| 375 |
-
raise RuntimeError('Must specify number of samples with -n=XXX')
|
| 376 |
-
|
| 377 |
-
if model and not isinstance(model, InstrumentedModel):
|
| 378 |
-
raise RuntimeError('Passed model has to be wrapped in "InstrumentedModel"')
|
| 379 |
-
|
| 380 |
-
if config.use_w and not 'StyleGAN' in config.model:
|
| 381 |
-
raise RuntimeError(f'Cannot change latent space of non-StyleGAN model {config.model}')
|
| 382 |
-
|
| 383 |
-
transformer = get_estimator(config.estimator, config.components, config.sparsity)
|
| 384 |
-
dump_name = "{}-{}_{}_{}_n{}{}{}.npz".format(
|
| 385 |
-
config.model.lower(),
|
| 386 |
-
config.output_class.replace(' ', '_'),
|
| 387 |
-
config.layer.lower(),
|
| 388 |
-
transformer.get_param_str(),
|
| 389 |
-
config.n,
|
| 390 |
-
'_w' if config.use_w else '',
|
| 391 |
-
f'_seed{config.seed}' if config.seed else ''
|
| 392 |
-
)
|
| 393 |
-
|
| 394 |
-
dump_path = basedir / 'cache' / 'components' / dump_name
|
| 395 |
-
|
| 396 |
-
if not dump_path.is_file() or force_recompute:
|
| 397 |
-
print('Not cached')
|
| 398 |
-
t_start = datetime.datetime.now()
|
| 399 |
-
compute(config, dump_path, model)
|
| 400 |
-
print('Total time:', datetime.datetime.now() - t_start)
|
| 401 |
-
|
| 402 |
-
return dump_path
|
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|
spaces/Disguised/anime_character_recognizer/app.py
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from fastai.vision.all import *
|
| 3 |
-
import gradio as gr
|
| 4 |
-
import re
|
| 5 |
-
from glob import glob
|
| 6 |
-
|
| 7 |
-
learn = load_learner('model_ft15(extra).pkl')
|
| 8 |
-
|
| 9 |
-
categories = learn.dls.vocab
|
| 10 |
-
|
| 11 |
-
def classify_image(img):
|
| 12 |
-
pred,idx,probs = learn.predict(img)
|
| 13 |
-
return dict(zip(categories, map(float,probs)))
|
| 14 |
-
|
| 15 |
-
image = gr.inputs.Image(shape=(192, 192))
|
| 16 |
-
label = gr.outputs.Label()
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples='./examples')
|
| 20 |
-
intf.launch(inline=False)
|
|
|
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|
spaces/Dorado607/ChuanhuChatGPT/modules/config.py
DELETED
|
@@ -1,269 +0,0 @@
|
|
| 1 |
-
from collections import defaultdict
|
| 2 |
-
from contextlib import contextmanager
|
| 3 |
-
import os
|
| 4 |
-
import logging
|
| 5 |
-
import sys
|
| 6 |
-
import commentjson as json
|
| 7 |
-
|
| 8 |
-
from . import shared
|
| 9 |
-
from . import presets
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
__all__ = [
|
| 13 |
-
"my_api_key",
|
| 14 |
-
"sensitive_id",
|
| 15 |
-
"authflag",
|
| 16 |
-
"auth_list",
|
| 17 |
-
"dockerflag",
|
| 18 |
-
"retrieve_proxy",
|
| 19 |
-
"log_level",
|
| 20 |
-
"advance_docs",
|
| 21 |
-
"update_doc_config",
|
| 22 |
-
"usage_limit",
|
| 23 |
-
"multi_api_key",
|
| 24 |
-
"server_name",
|
| 25 |
-
"server_port",
|
| 26 |
-
"share",
|
| 27 |
-
"check_update",
|
| 28 |
-
"latex_delimiters_set",
|
| 29 |
-
"hide_history_when_not_logged_in",
|
| 30 |
-
"default_chuanhu_assistant_model",
|
| 31 |
-
"show_api_billing"
|
| 32 |
-
]
|
| 33 |
-
|
| 34 |
-
# 添加一个统一的config文件，避免文件过多造成的疑惑（优先级最低）
|
| 35 |
-
# 同时，也可以为后续支持自定义功能提供config的帮助
|
| 36 |
-
if os.path.exists("config.json"):
|
| 37 |
-
with open("config.json", "r", encoding='utf-8') as f:
|
| 38 |
-
config = json.load(f)
|
| 39 |
-
else:
|
| 40 |
-
config = {}
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
def load_config_to_environ(key_list):
|
| 44 |
-
global config
|
| 45 |
-
for key in key_list:
|
| 46 |
-
if key in config:
|
| 47 |
-
os.environ[key.upper()] = os.environ.get(key.upper(), config[key])
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
lang_config = config.get("language", "auto")
|
| 51 |
-
language = os.environ.get("LANGUAGE", lang_config)
|
| 52 |
-
|
| 53 |
-
hide_history_when_not_logged_in = config.get(
|
| 54 |
-
"hide_history_when_not_logged_in", False)
|
| 55 |
-
check_update = config.get("check_update", True)
|
| 56 |
-
show_api_billing = config.get("show_api_billing", False)
|
| 57 |
-
show_api_billing = bool(os.environ.get("SHOW_API_BILLING", show_api_billing))
|
| 58 |
-
|
| 59 |
-
if os.path.exists("api_key.txt"):
|
| 60 |
-
logging.info("检测到api_key.txt文件，正在进行迁移...")
|
| 61 |
-
with open("api_key.txt", "r", encoding="utf-8") as f:
|
| 62 |
-
config["openai_api_key"] = f.read().strip()
|
| 63 |
-
os.rename("api_key.txt", "api_key(deprecated).txt")
|
| 64 |
-
with open("config.json", "w", encoding='utf-8') as f:
|
| 65 |
-
json.dump(config, f, indent=4, ensure_ascii=False)
|
| 66 |
-
|
| 67 |
-
if os.path.exists("auth.json"):
|
| 68 |
-
logging.info("检测到auth.json文件，正在进行迁移...")
|
| 69 |
-
auth_list = []
|
| 70 |
-
with open("auth.json", "r", encoding='utf-8') as f:
|
| 71 |
-
auth = json.load(f)
|
| 72 |
-
for _ in auth:
|
| 73 |
-
if auth[_]["username"] and auth[_]["password"]:
|
| 74 |
-
auth_list.append((auth[_]["username"], auth[_]["password"]))
|
| 75 |
-
else:
|
| 76 |
-
logging.error("请检查auth.json文件中的用户名和密码！")
|
| 77 |
-
sys.exit(1)
|
| 78 |
-
config["users"] = auth_list
|
| 79 |
-
os.rename("auth.json", "auth(deprecated).json")
|
| 80 |
-
with open("config.json", "w", encoding='utf-8') as f:
|
| 81 |
-
json.dump(config, f, indent=4, ensure_ascii=False)
|
| 82 |
-
|
| 83 |
-
# 处理docker if we are running in Docker
|
| 84 |
-
dockerflag = config.get("dockerflag", False)
|
| 85 |
-
if os.environ.get("dockerrun") == "yes":
|
| 86 |
-
dockerflag = True
|
| 87 |
-
|
| 88 |
-
# 处理 api-key 以及 允许的用户列表
|
| 89 |
-
my_api_key = config.get("openai_api_key", "")
|
| 90 |
-
my_api_key = os.environ.get("OPENAI_API_KEY", my_api_key)
|
| 91 |
-
os.environ["OPENAI_API_KEY"] = my_api_key
|
| 92 |
-
os.environ["OPENAI_EMBEDDING_API_KEY"] = my_api_key
|
| 93 |
-
|
| 94 |
-
if config.get("legacy_api_usage", False):
|
| 95 |
-
sensitive_id = config.get("sensitive_id", "")
|
| 96 |
-
sensitive_id = os.environ.get("SENSITIVE_ID", sensitive_id)
|
| 97 |
-
else:
|
| 98 |
-
sensitive_id = my_api_key
|
| 99 |
-
|
| 100 |
-
google_palm_api_key = config.get("google_palm_api_key", "")
|
| 101 |
-
google_palm_api_key = os.environ.get(
|
| 102 |
-
"GOOGLE_PALM_API_KEY", google_palm_api_key)
|
| 103 |
-
os.environ["GOOGLE_PALM_API_KEY"] = google_palm_api_key
|
| 104 |
-
|
| 105 |
-
xmchat_api_key = config.get("xmchat_api_key", "")
|
| 106 |
-
os.environ["XMCHAT_API_KEY"] = xmchat_api_key
|
| 107 |
-
|
| 108 |
-
minimax_api_key = config.get("minimax_api_key", "")
|
| 109 |
-
os.environ["MINIMAX_API_KEY"] = minimax_api_key
|
| 110 |
-
minimax_group_id = config.get("minimax_group_id", "")
|
| 111 |
-
os.environ["MINIMAX_GROUP_ID"] = minimax_group_id
|
| 112 |
-
|
| 113 |
-
load_config_to_environ(["openai_api_type", "azure_openai_api_key", "azure_openai_api_base_url",
|
| 114 |
-
"azure_openai_api_version", "azure_deployment_name", "azure_embedding_deployment_name", "azure_embedding_model_name"])
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
usage_limit = os.environ.get("USAGE_LIMIT", config.get("usage_limit", 120))
|
| 118 |
-
|
| 119 |
-
# 多账户机制
|
| 120 |
-
multi_api_key = config.get("multi_api_key", False) # 是否开启多账户机制
|
| 121 |
-
if multi_api_key:
|
| 122 |
-
api_key_list = config.get("api_key_list", [])
|
| 123 |
-
if len(api_key_list) == 0:
|
| 124 |
-
logging.error("多账号模式已开启，但api_key_list为空，请检查config.json")
|
| 125 |
-
sys.exit(1)
|
| 126 |
-
shared.state.set_api_key_queue(api_key_list)
|
| 127 |
-
|
| 128 |
-
auth_list = config.get("users", []) # 实际上是使用者的列表
|
| 129 |
-
authflag = len(auth_list) > 0 # 是否开启认证的状态值，改为判断auth_list长度
|
| 130 |
-
|
| 131 |
-
# 处理自定义的api_host，优先读环境变量的配置，如果存在则自动装配
|
| 132 |
-
api_host = os.environ.get(
|
| 133 |
-
"OPENAI_API_BASE", config.get("openai_api_base", None))
|
| 134 |
-
if api_host is not None:
|
| 135 |
-
shared.state.set_api_host(api_host)
|
| 136 |
-
os.environ["OPENAI_API_BASE"] = f"{api_host}/v1"
|
| 137 |
-
logging.info(f"OpenAI API Base set to: {os.environ['OPENAI_API_BASE']}")
|
| 138 |
-
|
| 139 |
-
default_chuanhu_assistant_model = config.get(
|
| 140 |
-
"default_chuanhu_assistant_model", "gpt-3.5-turbo")
|
| 141 |
-
for x in ["GOOGLE_CSE_ID", "GOOGLE_API_KEY", "WOLFRAM_ALPHA_APPID", "SERPAPI_API_KEY"]:
|
| 142 |
-
if config.get(x, None) is not None:
|
| 143 |
-
os.environ[x] = config[x]
|
| 144 |
-
|
| 145 |
-
|
| 146 |
-
@contextmanager
|
| 147 |
-
def retrieve_openai_api(api_key=None):
|
| 148 |
-
old_api_key = os.environ.get("OPENAI_API_KEY", "")
|
| 149 |
-
if api_key is None:
|
| 150 |
-
os.environ["OPENAI_API_KEY"] = my_api_key
|
| 151 |
-
yield my_api_key
|
| 152 |
-
else:
|
| 153 |
-
os.environ["OPENAI_API_KEY"] = api_key
|
| 154 |
-
yield api_key
|
| 155 |
-
os.environ["OPENAI_API_KEY"] = old_api_key
|
| 156 |
-
|
| 157 |
-
|
| 158 |
-
# 处理log
|
| 159 |
-
log_level = config.get("log_level", "INFO")
|
| 160 |
-
logging.basicConfig(
|
| 161 |
-
level=log_level,
|
| 162 |
-
format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
|
| 163 |
-
)
|
| 164 |
-
|
| 165 |
-
# 处理代理：
|
| 166 |
-
http_proxy = os.environ.get("HTTP_PROXY", "")
|
| 167 |
-
https_proxy = os.environ.get("HTTPS_PROXY", "")
|
| 168 |
-
http_proxy = config.get("http_proxy", http_proxy)
|
| 169 |
-
https_proxy = config.get("https_proxy", https_proxy)
|
| 170 |
-
|
| 171 |
-
# 重置系统变量，在不需要设置的时候不设置环境变量，以免引起全局代理报错
|
| 172 |
-
os.environ["HTTP_PROXY"] = ""
|
| 173 |
-
os.environ["HTTPS_PROXY"] = ""
|
| 174 |
-
|
| 175 |
-
local_embedding = config.get("local_embedding", False) # 是否使用本地embedding
|
| 176 |
-
|
| 177 |
-
|
| 178 |
-
@contextmanager
|
| 179 |
-
def retrieve_proxy(proxy=None):
|
| 180 |
-
"""
|
| 181 |
-
1, 如果proxy = NONE，设置环境变量，并返回最新设置的代理
|
| 182 |
-
2，如果proxy ！= NONE，更新当前的代理配置，但是不更新环境变量
|
| 183 |
-
"""
|
| 184 |
-
global http_proxy, https_proxy
|
| 185 |
-
if proxy is not None:
|
| 186 |
-
http_proxy = proxy
|
| 187 |
-
https_proxy = proxy
|
| 188 |
-
yield http_proxy, https_proxy
|
| 189 |
-
else:
|
| 190 |
-
old_var = os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"]
|
| 191 |
-
os.environ["HTTP_PROXY"] = http_proxy
|
| 192 |
-
os.environ["HTTPS_PROXY"] = https_proxy
|
| 193 |
-
yield http_proxy, https_proxy # return new proxy
|
| 194 |
-
|
| 195 |
-
# return old proxy
|
| 196 |
-
os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] = old_var
|
| 197 |
-
|
| 198 |
-
|
| 199 |
-
# 处理latex options
|
| 200 |
-
user_latex_option = config.get("latex_option", "default")
|
| 201 |
-
if user_latex_option == "default":
|
| 202 |
-
latex_delimiters_set = [
|
| 203 |
-
{"left": "$$", "right": "$$", "display": True},
|
| 204 |
-
{"left": "$", "right": "$", "display": False},
|
| 205 |
-
{"left": "\\(", "right": "\\)", "display": False},
|
| 206 |
-
{"left": "\\[", "right": "\\]", "display": True},
|
| 207 |
-
]
|
| 208 |
-
elif user_latex_option == "strict":
|
| 209 |
-
latex_delimiters_set = [
|
| 210 |
-
{"left": "$$", "right": "$$", "display": True},
|
| 211 |
-
{"left": "\\(", "right": "\\)", "display": False},
|
| 212 |
-
{"left": "\\[", "right": "\\]", "display": True},
|
| 213 |
-
]
|
| 214 |
-
elif user_latex_option == "all":
|
| 215 |
-
latex_delimiters_set = [
|
| 216 |
-
{"left": "$$", "right": "$$", "display": True},
|
| 217 |
-
{"left": "$", "right": "$", "display": False},
|
| 218 |
-
{"left": "\\(", "right": "\\)", "display": False},
|
| 219 |
-
{"left": "\\[", "right": "\\]", "display": True},
|
| 220 |
-
{"left": "\\begin{equation}", "right": "\\end{equation}", "display": True},
|
| 221 |
-
{"left": "\\begin{align}", "right": "\\end{align}", "display": True},
|
| 222 |
-
{"left": "\\begin{alignat}", "right": "\\end{alignat}", "display": True},
|
| 223 |
-
{"left": "\\begin{gather}", "right": "\\end{gather}", "display": True},
|
| 224 |
-
{"left": "\\begin{CD}", "right": "\\end{CD}", "display": True},
|
| 225 |
-
]
|
| 226 |
-
elif user_latex_option == "disabled":
|
| 227 |
-
latex_delimiters_set = []
|
| 228 |
-
else:
|
| 229 |
-
latex_delimiters_set = [
|
| 230 |
-
{"left": "$$", "right": "$$", "display": True},
|
| 231 |
-
{"left": "$", "right": "$", "display": False},
|
| 232 |
-
{"left": "\\(", "right": "\\)", "display": False},
|
| 233 |
-
{"left": "\\[", "right": "\\]", "display": True},
|
| 234 |
-
]
|
| 235 |
-
|
| 236 |
-
# 处理advance docs
|
| 237 |
-
advance_docs = defaultdict(lambda: defaultdict(dict))
|
| 238 |
-
advance_docs.update(config.get("advance_docs", {}))
|
| 239 |
-
|
| 240 |
-
|
| 241 |
-
def update_doc_config(two_column_pdf):
|
| 242 |
-
global advance_docs
|
| 243 |
-
advance_docs["pdf"]["two_column"] = two_column_pdf
|
| 244 |
-
|
| 245 |
-
logging.info(f"更新后的文件参数为：{advance_docs}")
|
| 246 |
-
|
| 247 |
-
|
| 248 |
-
# 处理gradio.launch参数
|
| 249 |
-
server_name = config.get("server_name", None)
|
| 250 |
-
server_port = config.get("server_port", None)
|
| 251 |
-
if server_name is None:
|
| 252 |
-
if dockerflag:
|
| 253 |
-
server_name = "0.0.0.0"
|
| 254 |
-
else:
|
| 255 |
-
server_name = "127.0.0.1"
|
| 256 |
-
if server_port is None:
|
| 257 |
-
if dockerflag:
|
| 258 |
-
server_port = 7860
|
| 259 |
-
|
| 260 |
-
assert server_port is None or type(server_port) == int, "要求port设置为int类型"
|
| 261 |
-
|
| 262 |
-
# 设置默认model
|
| 263 |
-
default_model = config.get("default_model", "")
|
| 264 |
-
try:
|
| 265 |
-
presets.DEFAULT_MODEL = presets.MODELS.index(default_model)
|
| 266 |
-
except ValueError:
|
| 267 |
-
pass
|
| 268 |
-
|
| 269 |
-
share = config.get("share", False)
|
|
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spaces/Drac77/hakurei-waifu-diffusion/app.py
DELETED
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@@ -1,3 +0,0 @@
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import gradio as gr
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gr.Interface.load("models/hakurei/waifu-diffusion").launch()
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