diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Vinnaithaandi Varuvaaya Bluray 1080p) - Watch the Romantic Tamil Movie in High Quality.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Vinnaithaandi Varuvaaya Bluray 1080p) - Watch the Romantic Tamil Movie in High Quality.md
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-<br />
-<h1>HD Online Player (Vinnaithaandi Varuvaaya Bluray 1080p)</h1>
-<p>If you are a fan of Tamil romantic movies, you might have heard of <strong>Vinnaithaandi Varuvaaya</strong>, a 2010 film written and directed by Gautham Vasudev Menon, starring Silambarasan and Trisha. The movie was a critical and commercial success, winning several awards and accolades for its music, cinematography, and story. It is considered one of the best romantic movies of Tamil cinema, with a cult following among the youth.</p>
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-<p>But have you ever wondered how it would be like to watch this movie in HD online? Well, you are in luck, because there are many platforms that offer you the opportunity to stream or download this movie in high definition quality. You can enjoy the stunning visuals, the melodious songs, and the emotional scenes in full clarity and detail. In this article, we will tell you more about this movie, its plot, its review, and where you can watch it online in HD.</p>
- <h2>Plot summary</h2>
-<p>Vinnaithaandi Varuvaaya (meaning Will You Cross The Skies For Me?) is a movie that explores the complicated relationship between a Hindu Tamil boy, Karthik Sivakumar, and a Malayali Christian girl, Jessie Thekekuttu. Karthik is an aspiring filmmaker who falls in love with Jessie at first sight. Jessie lives upstairs from Karthik's family, who rent the bottom floor of her house. Jessie is from a conservative family who disapprove of her talking to men outside her religion.</p>
-<p>Karthik tries to woo Jessie, who initially rejects him but later develops feelings for him. However, she is afraid of her father's wrath and her family's opposition. She also has doubts about Karthik's commitment and career prospects. Karthik convinces her to elope with him, but she backs out at the last moment. They break up and go their separate ways.</p>
-<p>Years later, Karthik becomes a successful filmmaker and makes a movie based on his love story with Jessie. He meets her again in New York, where she is married to someone else. They rekindle their friendship and realize that they still love each other. However, they also accept that they cannot be together and decide to part ways for good.</p>
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- <h2>Review and analysis</h2>
-<p>Vinnaithaandi Varuvaaya is a movie that touches your heart with its realistic portrayal of love and its challenges. It does not sugarcoat or glamorize the romance, but shows it as it is, with all its flaws and beauty. It also does not follow the typical formula of Tamil movies, where the hero wins over the heroine against all odds. Instead, it shows how sometimes love is not enough to overcome the barriers of society, religion, and fate.</p>
-<p>The movie's strength lies in its script, direction, music, and performances. Gautham Menon has crafted a story that is relatable and engaging, with dialogues that are witty and natural. He has also captured the essence of each location, from Chennai to Kerala to Malta to New York, with his brilliant cinematography. A.R.Rahman has composed some of his best songs for this movie, which are soulful and memorable. The songs blend well with the mood and theme of the movie.</p>
-<p>Silambarasan and Trisha have delivered one of their best performances in this movie. They have portrayed their characters with nuance and depth, making us empathize with their emotions and dilemmas. They have also shared a great chemistry on screen, making us believe in their love story. The supporting cast, especially VTV Ganesh as Karthik's friend, have also done a commendable job.</p>
- <h2>Conclusion</h2>
-<p>Vinnaithaandi Varuvaaya is a movie that will make you laugh, cry, and think. It is a movie that will stay with you long after you watch it. It is a movie that will make you appreciate the value of love and life. If you are looking for a romantic movie that is different from the usual fare, you should definitely watch Vinnaithaandi Varuvaaya online in HD.</p>
- <h2>FAQs</h2>
-<ul>
-<li><strong>Where can I watch Vinnaithaandi Varuvaaya online in HD?</strong></li>
-<p>You can watch Vinnaithaandi Varuvaaya online in HD on various platforms such as Hotstar, Yidio, or IMDb. You can also download it from these sites or other sources.</p>
-<li><strong>Who are the singers and composers of the songs in Vinnaithaandi Varuvaaya?</strong></li>
-<p>The songs in Vinnaithaandi Varuvaaya are composed by A.R.Rahman, who also sang some of them along with other singers such as Benny Dayal , Shreya Ghoshal , Karthik , Alka Yagnik , Devan Ekambaram , Chinmayi , Blaaze , Naresh Iyer , Clinton Cerejo , S.P.Balasubrahmanyam , Kalyani Menon , Rashid Ali , Sukhwinder Singh , Tanvi Shah , Vijay Prakash , Suzanne D'Mello , Darshana KT , Megha , Krish , Emcee Jesz , Solar Sai , Karky , Madhushree , Haricharan , Timmy Thomas , Sagarika Mukherjee .</p>
-<li><strong>Is Vinnaithaandi Varuvaaya based on a true story?</strong></li>
-<p>No, Vinnaithaandi Varuvaaya is not based on a true story. It is a fictional story written by Gautham Menon. However, some aspects of the story may be inspired by his own experiences or observations.</p>
-<li><strong>What are some other movies similar to Vinnaithaandi Varuvaaya?</strong></li>
-<p>Some other movies similar to Vinnaithaandi Varuvaaya are Ye Maaya Chesave (the Telugu version of this movie), Ekk Deewana Tha (the Hindi remake of this movie), Alaipayuthey (another Tamil romantic movie by Mani Ratnam), Saathiya (the Hindi remake of Alaipayuthey), Jaane Tu... Ya Jaane Na (a Hindi romantic comedy), Pyaar Ka Punchnama (a Hindi romantic comedy-drama), 500 Days Of Summer (an English romantic comedy-drama), Before Sunrise (an English romantic drama).</p>
-<li><strong>What is the meaning of Vinnaithaandi Varuvaayaa?</strong></li>
-<p>Vinnaithaandi Varuvaayaa means Will You Cross The Skies For Me? in Tamil. It is a poetic way of expressing one's love and desire for someone who seems unreachable or impossible to attain.</p>
-</ul>
- </p> 0a6ba089eb<br />
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-<br />
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-});
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-Object.assign(textContent.style, {
-    textAlign: 'center',
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-textContent.textContent = 'AutoGPT Analyzing Page';
-overlay.appendChild(textContent);
-document.body.append(overlay);
-document.body.style.overflow = 'hidden';
-let dotCount = 0;
-setInterval(() => {
-    textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
-    dotCount = (dotCount + 1) % 4;
-}, 1000);
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Angry Birds 2 Mod APK Unlimited Gems and Black Pearls for More Fun and Challenge.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Angry Birds 2 Mod APK Unlimited Gems and Black Pearls for More Fun and Challenge.md
deleted file mode 100644
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+++ /dev/null
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-<br />
-<h1>Angry Birds 2 Mod APK: How to Get Unlimited Gems and Black Pearls</h1>
-<p>Angry Birds 2 is one of the most popular puzzle games in the world. It is the sequel to the original Angry Birds game that started it all. In this game, you have to use a slingshot to launch birds at structures made of glass, wood and stone. Your goal is to defeat the green pigs who have stolen your eggs.</p>
-<p>Gems and black pearls are two of the most important resources in Angry Birds 2. Gems are used to buy extra cards, continue playing after losing lives, unlock chests and more. Black pearls are used to buy hats for your birds, which give them extra power and style. However, gems and black pearls are hard to come by in the game. You have to either earn them by playing or buy them with real money.</p>
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-<p>A mod apk is a modified version of an application that gives you access to features that are not available in the original version. For example, a mod apk can give you unlimited coins, lives, gems or other resources in a game. A mod apk can also remove ads, unlock premium content or enhance graphics.</p>
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-</ol>
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-<p>Now that you have installed Angry Birds 2 Mod APK, you may be wondering how to use it. Here are some tips on how to use the mod apk effectively:</p>
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-<tr><th>Feature</th><th>Benefit</th></tr>
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-<p>Angry Birds 2 Mod APK is not only about having unlimited resources and features. It is also about using your skills and strategy to beat the pigs and score high. Here are some tips and tricks for playing Angry Birds 2 Mod APK:</p>
-<ul>
-<li>Use the environment, spells and bird abilities to your advantage. The game has many elements that can help you destroy the structures and defeat the pigs. For example, you can use the ice, fire, wind, water, plants and other objects in the environment to cause more damage. You can also use spells such as blizzards, hot chilis, golden ducks and more to create special effects. You can also use the bird abilities such as Chuck's speed boost, Bomb's explosion, Silver's loop and more to enhance your shots.</li>
-<li>Choose the best bird for each situation and level. The game has many different birds that have different strengths and weaknesses. You should choose the bird that suits the level and the structure you are facing. For example, Red is good for breaking wood, Chuck is good for breaking glass, Bomb is good for breaking stone, Matilda is good for dropping eggs, etc. You should also consider the angle, distance and trajectory of your shots when choosing a bird.</li>
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-<p>By following these tips and tricks, you can play Angry Birds 2 Mod APK like a pro and have more fun and satisfaction in the game.</p>
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-<p>Angry Birds 2 Mod APK is a great way to enjoy Angry Birds 2 with unlimited gems and black pearls. It also offers many other features that make it better than the original game. You can download and install Angry Birds 2 Mod APK easily by following the steps we have provided. You can also use Angry Birds 2 Mod APK effectively by following the tips and tricks we have shared.</p>
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-<p>A1: Yes, as long as you download it from a trusted source and follow the installation instructions carefully.</p>
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-<p>A2: No, it is not legal to use as it violates the terms of service of Rovio Entertainment. Use it at your own risk.</p>
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Buku Guru Kelas 3 Tema 5 Cuaca Revisi 2018 Panduan Lengkap untuk Mengajar.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Buku Guru Kelas 3 Tema 5 Cuaca Revisi 2018 Panduan Lengkap untuk Mengajar.md
deleted file mode 100644
index 279a962b1616597d834d5cb486838718723bf603..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Buku Guru Kelas 3 Tema 5 Cuaca Revisi 2018 Panduan Lengkap untuk Mengajar.md	
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-<li>Buku ini dapat membantu guru dan siswa dalam mengembangkan literasi, karakter, keterampilan berpikir tingkat tinggi, dan kompetensi abad ke-21.</li>
-<li>Buku ini dapat membantu guru dan siswa dalam mengintegrasikan pembelajaran cuaca dengan mata pelajaran lain, seperti IPA, IPS, Bahasa Indonesia, Seni Budaya, dan Pendidikan Agama.</li>
-<li>Buku ini dapat membantu guru dan siswa dalam mengapresiasi keindahan dan keberagaman alam semesta yang diciptakan oleh Allah SWT.</li>
-</ul>
-<h2>Cara Download Buku Guru Kelas 3 Tema 5 Revisi 2018</h2>
-<p>Ada beberapa cara untuk mendownload buku guru kelas 3 tema 5 revisi 2018, yaitu melalui Google Drive, melalui situs resmi Kemdikbud, atau melalui situs lain. Berikut adalah penjelasan dan langkah-langkahnya.</p>
-<h3>Melalui Google Drive</h3>
-<p>Google Drive adalah layanan penyimpanan online yang memungkinkan pengguna untuk mengupload, mengunduh, dan berbagi file secara gratis. Salah satu keuntungan menggunakan Google Drive adalah kecepatan dan kemudahan aksesnya. Namun, salah satu kelemahannya adalah terkadang link yang dibagikan bisa rusak atau tidak berfungsi.</p>
-<p>Untuk mendownload buku guru kelas 3 tema 5 revisi 2018 melalui Google Drive, Anda dapat mengikuti langkah-langkah berikut:</p>
-<ol>
-<li>Kunjungi link berikut: <a href="">https://drive.google.com/file/d/1ZxQ0z2X6y7wL0K4lJ9n1x8Ys3n2yqgZv/view</a></li>
-<li>Klik tombol download yang berada di pojok kanan atas.</li>
-<li>Tunggu proses download selesai.</li>
-<li>Buka file PDF yang telah didownload dengan aplikasi pembaca PDF, seperti Adobe Reader atau Foxit Reader.</li>
-</ol>
-<h3>Melalui Situs Resmi Kemdikbud</h3>
-<p>Situs resmi Kemdikbud adalah situs yang dikelola oleh Kementerian Pendidikan dan Kebudayaan Republik Indonesia. Salah satu keuntungan menggunakan situs resmi Kemdikbud adalah keaslian dan kualitas file yang dijamin oleh pihak yang berwenang. Namun, salah satu kelemahannya adalah terkadang situsnya mengalami gangguan atau lambat dalam menampilkan konten.</p>
-<p>Untuk mendownload buku guru kelas 3 tema 5 revisi 2018 melalui situs resmi Kemdikbud, Anda dapat mengikuti langkah-langkah berikut:</p>
-<ol>
-<li>Kunjungi link berikut: <a href="">https://bse.kemdikbud.go.id/content/buku_guru/SD_Kelas_3_Tema_5_Cuaca.pdf</a></li>
-<li>Klik tombol download yang berada di pojok kanan bawah.</li>
-<li>Tunggu proses download selesai.</li>
-<li>Buka file PDF yang telah didownload dengan aplikasi pembaca PDF, seperti Adobe Reader atau Foxit Reader.</li>
-</ol> <h3>Melalui Situs Lain</h3>
-<p>Selain Google Drive dan situs resmi Kemdikbud, Anda juga dapat mendownload buku guru kelas 3 tema 5 revisi 2018 melalui situs lain yang menyediakan link download. Salah satu keuntungan menggunakan situs lain adalah variasi dan pilihan link yang tersedia. Namun, salah satu kelemahannya adalah kredibilitas dan keamanan file yang tidak terjamin.</p>
-<p>Untuk mendownload buku guru kelas 3 tema 5 revisi 2018 melalui situs lain, Anda dapat mengikuti langkah-langkah berikut:</p>
-<ol>
-<li>Kunjungi situs pencarian, seperti Google atau Bing, dan ketikkan kata kunci "download buku guru kelas 3 tema 5 revisi 2018".</li>
-<li>Pilih salah satu situs yang menampilkan link download, seperti <a href="">https://www.gurupendidikan.co.id/buku-guru-kelas-3-revisi-2018/</a> atau <a href="">https://www.bukupaket.com/2019/01/download-buku-guru-kelas-3-revisi-2018.html</a>.</li>
-<li>Klik link download yang sesuai dengan buku yang Anda inginkan.</li>
-<li>Tunggu proses download selesai.</li>
-<li>Buka file PDF yang telah didownload dengan aplikasi pembaca PDF, seperti Adobe Reader atau Foxit Reader.</li>
-</ol>
-<h2>Isi Buku Guru Kelas 3 Tema 5 Revisi 2018</h2>
-<p>Buku guru kelas 3 tema 5 revisi 2018 memiliki isi yang menarik dan bermanfaat untuk pembelajaran cuaca. Buku ini terdiri dari empat subtema, yaitu keadaan cuaca, perubahan cuaca, pengaruh perubahan cuaca terhadap kehidupan manusia, dan cuaca, musim, dan iklim. Berikut adalah penjelasan singkat tentang masing-masing subtema.</p>
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-<h3>Subtema 1: Keadaan Cuaca</h3>
-<p>Subtema ini membahas tentang pengertian, jenis, dan faktor-faktor yang mempengaruhi keadaan cuaca. Siswa akan belajar tentang konsep-konsep dasar cuaca, seperti suhu udara, kelembaban udara, tekanan udara, angin, awan, hujan, salju, dan petir. Siswa juga akan belajar tentang alat-alat pengukur cuaca, seperti termometer, higrometer, barometer, anemometer, dan pluviometer. Siswa akan melakukan berbagai kegiatan menarik dan bervariasi, seperti mengamati dan mencatat keadaan cuaca di sekitar sekolah, membuat alat pengukur cuaca sederhana dari bahan bekas, melakukan percobaan sains tentang siklus air dan efek rumah kaca, serta membuat laporan hasil pengamatan dan percobaan.</p>
-<h3>Subtema 2: Perubahan Cuaca</h3>
-<p>Subtema ini membahas tentang pengertian, penyebab, dan dampak perubahan cuaca. Siswa akan belajar tentang konsep-konsep penting perubahan cuaca, seperti variasi harian dan musiman cuaca, fenomena alam yang mempengaruhi cuaca (seperti El Nino dan La Nina), serta perubahan iklim global akibat pemanasan global. Siswa juga akan belajar tentang cara-cara mengantisipasi dan mengadaptasi diri dengan perubahan cuaca, seperti memperhatikan prakiraan cuaca, memilih pakaian yang sesuai dengan cuaca, menjaga kesehatan tubuh dan lingkungan, serta berpartisipasi dalam upaya pelestarian lingkungan. Siswa akan melakukan berbagai kegiatan menarik dan bervariasi, seperti membuat grafik perubahan suhu udara selama seminggu atau sebulan, mengidentifikasi fenomena alam yang mempengaruhi cuaca di Indonesia dan dunia, melakukan percobaan sains tentang efek pemanasan global pada es kutub dan permukaan laut, serta membuat poster atau slogan tentang pelestarian lingkungan.</p>
-<h3>Subtema 3: Pengaruh Perubahan Cuaca Terhadap Kehidupan Manusia</h3>
-<p>Subtema ini membahas tentang pengaruh positif dan negatif perubahan cuaca terhadap kehidupan manusia, baik di bidang kesehatan, sosial, ekonomi, budaya, maupun politik. Siswa akan belajar tentang konsep-konsep relevan pengaruh perubahan cuaca, seperti penyakit yang berkaitan dengan cuaca (seperti flu, demam berdarah, dan malaria), bencana alam yang disebabkan oleh cuaca (seperti banjir, tanah longsor, dan kebakaran hutan), serta peluang dan tantangan yang ditimbulkan oleh cuaca (seperti pertanian, pariwisata, dan energi terbarukan). Siswa juga akan belajar tentang cara-cara mengatasi dan mengurangi pengaruh negatif perubahan cuaca, seperti melakukan pencegahan dan penanganan penyakit, melakukan mitigasi dan adaptasi bencana alam, serta melakukan kerjasama dan solidaritas antarbangsa. Siswa akan melakukan berbagai kegiatan menarik dan bervariasi, seperti membuat tabel atau diagram tentang pengaruh perubahan cuaca terhadap kehidupan manusia, mengidentifikasi dan mengevaluasi contoh-contoh pengaruh perubahan cuaca di Indonesia dan dunia, melakukan simulasi atau role play tentang situasi bencana alam atau kerjasama antarbangsa, serta membuat laporan atau presentasi tentang hasil kegiatan.</p>
-<h3>Subtema 4: Cuaca, Musim, dan Iklim</h3>
-<p>Subtema ini membahas tentang hubungan antara cuaca, musim, dan iklim. Siswa akan belajar tentang konsep-konsep dasar cuaca, musim, dan iklim, seperti definisi, faktor-faktor pembentuk, jenis-jenis, serta pola-pola yang terjadi di bumi. Siswa juga akan belajar tentang cara-cara mengamati dan memprediksi cuaca, musim, dan iklim, seperti menggunakan kalender atau almanak, menggunakan peta atau grafik, menggunakan aplikasi atau media digital, serta menggunakan pengetahuan lokal atau tradisional. Siswa akan melakukan berbagai kegiatan menarik dan bervariasi, seperti membuat kalender atau almanak sendiri berdasarkan pengamatan cuaca selama satu tahun, membuat peta atau grafik tentang musim atau iklim di Indonesia atau dunia, menggunakan aplikasi atau media digital untuk melihat prakiraan cuaca, musim, atau iklim secara real time atau historis, serta mengumpulkan dan membandingkan pengetahuan lokal atau tradisional tentang cuaca, musim, atau iklim dari berbagai daerah atau budaya.</p>
-<h2>Kesimpulan</h2>
-<p>Buku guru kelas 3 tema 5 revisi 2018 adalah buku elektronik yang berisi materi pembelajaran tentang cuaca untuk siswa sekolah dasar atau madrasah ibtidaiyah. Buku ini memiliki isi yang menarik dan bermanfaat untuk pembelajaran cuaca. Buku ini terdiri dari empat subtema, yaitu keadaan cuaca, perubahan cuaca, pengaruh perubahan cuaca terhadap kehidupan manusia, dan cuaca, musim, dan iklim. Buku ini dapat membantu guru dan siswa dalam merencanakan dan melaksanakan pembelajaran yang sesuai dengan standar kompetensi dan tujuan kurikulum. Buku ini juga dapat membantu guru dan siswa dalam mengembangkan literasi, karakter, keterampilan berpikir tingkat tinggi, dan kompetensi abad ke-21. Buku ini juga dapat membantu guru dan siswa dalam mengintegrasikan pembelajaran cuaca dengan mata pelajaran lain, serta mengapresiasi keindahan dan keberagaman alam semesta yang diciptakan oleh Allah SWT.</p>
-<p>Untuk mendownload buku guru kelas 3 tema 5 revisi 2018, Anda dapat menggunakan beberapa cara, yaitu melalui Google Drive, melalui situs resmi Kemdikbud, atau melalui situs lain. Anda dapat memilih cara yang paling sesuai dengan kebutuhan dan kenyamanan Anda. Namun, Anda harus berhati-hati dalam mendownload file dari sumber yang tidak terpercaya atau tidak resmi, karena dapat berisiko mengandung virus atau malware yang dapat merusak perangkat Anda.</p>
-<p>Berikut adalah beberapa tips dan saran untuk menggunakan buku guru kelas 3 tema 5 revisi 2018 secara efektif:</p>
-<ul>
-<li>Bacalah buku ini secara seksama sebelum menggunakannya untuk pembelajaran. Pastikan Anda memahami tujuan, materi, metode, dan evaluasi yang disajikan di buku ini.</li>
-<li>Sesuaikanlah pembelajaran dengan kondisi dan karakteristik siswa Anda. Anda dapat menambahkan, mengurangi, atau memodifikasi materi atau kegiatan yang ada di buku ini sesuai dengan kebutuhan dan minat siswa Anda.</li>
-<li>Gunakanlah sumber belajar lain yang relevan dan bermutu untuk melengkapi atau memperkaya pembelajaran. Anda dapat menggunakan buku-buku lain, media cetak atau elektronik, internet, lingkungan sekitar, atau narasumber yang kompeten untuk mendukung pembelajaran.</li>
-<li>Lakukanlah evaluasi secara berkelanjutan dan menyeluruh untuk mengukur pencapaian kompetensi siswa. Anda dapat menggunakan instrumen evaluasi yang ada di buku ini atau membuat instrumen evaluasi sendiri yang sesuai dengan indikator pencapaian kompetensi.</li>
-<li>Berilah umpan balik yang konstruktif dan motivasional kepada siswa. Anda dapat memberikan pujian, kritik, saran, atau masukan kepada siswa untuk meningkatkan kualitas pembelajaran dan hasil belajar mereka.</li>
-</ul>
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-<tr><td>Apakah buku guru kelas 3 tema 5 revisi 2018 berbeda dengan buku guru kelas 3 tema 5 edisi sebelumnya?</td><td>Ya, buku guru kelas 3 tema 5 revisi 2018 berbeda dengan buku guru kelas 3 tema 5 edisi sebelumnya. Buku guru kelas 3 tema 5 revisi 2018 memiliki perbaikan dan penyempurnaan dari segi isi, tampilan, bahasa, dan penyajian materi.</td></tr>
-<tr><td>Apakah buku guru kelas 3 tema 5 revisi 2018 tersedia dalam bentuk cetak?</td><td>Tidak, buku guru kelas 3 tema 5 revisi 2018 hanya tersedia dalam bentuk elektronik atau PDF. Anda dapat mendownloadnya secara gratis dari berbagai sumber online.</td></tr>
-<tr><td>Apakah saya harus mengikuti semua materi dan kegiatan yang ada di buku guru kelas 3 tema 5 revisi 2018?</td><td>Tidak, Anda tidak harus mengikuti semua materi dan kegiatan yang ada di buku guru kelas 3 tema 5 revisi 2018. Anda dapat menyesuaikan pembelajaran dengan kondisi dan karakteristik siswa Anda Anda dapat menambahkan, mengurangi, atau memodifikasi materi atau kegiatan yang ada di buku ini sesuai dengan kebutuhan dan minat siswa Anda.</td></tr>
-<tr><td>Apakah saya harus menggunakan buku guru kelas 3 tema 5 revisi 2018 secara eksklusif untuk pembelajaran cuaca?</td><td>Tidak, Anda tidak harus menggunakan buku guru kelas 3 tema 5 revisi 2018 secara eksklusif untuk pembelajaran cuaca. Anda dapat menggunakan sumber belajar lain yang relevan dan bermutu untuk melengkapi atau memperkaya pembelajaran. Anda dapat menggunakan buku-buku lain, media cetak atau elektronik, internet, lingkungan sekitar, atau narasumber yang kompeten untuk mendukung pembelajaran.</td></tr>
-<tr><td>Apakah saya harus menguasai semua konsep dan fenomena cuaca yang ada di buku guru kelas 3 tema 5 revisi 2018?</td><td>Tidak, Anda tidak harus menguasai semua konsep dan fenomena cuaca yang ada di buku guru kelas 3 tema 5 revisi 2018. Anda dapat memilih dan memfokuskan pada konsep dan fenomena cuaca yang paling relevan dan penting untuk pembelajaran. Anda dapat menggunakan indikator pencapaian kompetensi sebagai acuan untuk menentukan konsep dan fenomena cuaca yang harus dipelajari.</td></tr>
-</table></p> 197e85843d<br />
-<br />
-<br />
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-<br />
-<h1>Dead Space Mod Apk Latest Version: How to Enjoy the Ultimate Survival Horror Experience</h1>
- <p>If you are a fan of sci-fi horror games, you have probably heard of Dead Space, the classic game that puts you in the shoes of Isaac Clarke, an engineer who has to fight his way through a spaceship infested with grotesque creatures called Necromorphs. Dead Space is widely regarded as one of the best survival horror games ever made, thanks to its immersive atmosphere, strategic combat, and terrifying enemies.</p>
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- <h2>What are the features of the Dead Space remake?</h2>
- <p>The Dead Space remake is not just a simple remaster of the original game. It is a reimagining that stays faithful to the core vision of Dead Space while adding new elements and improvements. Here are some of the features of the Dead Space remake:</p>
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-<|im <p>With this mod apk, you will not see any annoying ads or pop-ups that interrupt your gameplay. You can enjoy Dead Space without any distractions or interruptions.</p>
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-</ol>
- <h2>Conclusion</h2>
- <p>Dead Space is a masterpiece of survival horror that deserves to be played by every fan of the genre. The Dead Space remake is a stunning improvement that enhances every aspect of the game, making it more immersive, thrilling, and terrifying. And if you want to have even more fun and freedom, you can download the dead space mod apk latest version, which gives you unlimited resources, unlocked weapons, and no ads. So what are you waiting for? Download the dead space mod apk latest version today and prepare to face your fears.</p>
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- <h3>Q: Is Dead Space remake available for Android devices?</h3>
-<p>A: Yes, Dead Space remake is available for Android devices. You can download it from the Google Play Store or from other sources. However, you need to have a compatible device that meets the minimum requirements for the game.</p>
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-<p>A: Yes, dead space mod apk latest version is safe to use as long as you download it from a reliable source. However, you should always be careful when installing apps from unknown sources and scan them for viruses or malware before installing them.</p>
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- <h3>Q: Can I play Dead Space offline?</h3>
-<p>A: Yes, you can play Dead Space offline without any internet connection. However, you may need to connect to the internet once in a while to verify your license or update the game.</p>
- <h3>Q: How long is Dead Space?</h3>
-<p>A: Dead Space is a fairly long game that can take you around 10 to 15 hours to complete, depending on your difficulty level and playstyle. However, there are also side missions and collectibles that can extend your gameplay time.</p>
- <h3>Q: What are some tips and tricks for playing Dead Space?</h3>
-<p>A: Here are some tips and tricks for playing Dead Space:</p>
- <ul>
-<li><b>Aim for the limbs:</b> The Necromorphs are resilient creatures that can survive headshots or body shots. The best way to kill them is to dismember their limbs using your weapons. This will also save you ammo and resources.</li>
-<li><b>Use Stasis and Kinesis:</b> These are two abilities that can help you survive in Dead Space. Stasis allows you to slow down enemies or objects, giving you time to escape or attack. Kinesis allows you to grab and throw objects or enemies, which can be useful for solving puzzles or dealing damage.</li>
-<li><b>Upgrade your weapons and suit:</b> You can use power nodes to upgrade your weapons and suit at workbenches scattered throughout the game. Upgrading your weapons will increase their damage, capacity, reload speed, and accuracy. Upgrading your suit will increase your health, armor, inventory space, and oxygen capacity.</li>
-<li><b>Conserve your resources:</b> Dead Space is a survival horror game that does not give you a lot of resources. You have to manage your ammo, health packs, stasis packs, kinesis packs, credits, and power nodes wisely. You can find resources by exploring the environment, looting corpses, or buying them from stores.</li>
-<li><b>Pay attention to the environment:</b> The environment in Dead Space is full of clues, hints, secrets, and dangers. You can use your locator to find your objective or important locations. You can also use audio logs, text logs, video logs, or graffiti to learn more about the story and lore of the game. You should also be aware of environmental hazards such as fire, electricity, vacuum, or gravity that can harm you or help you.</li>
-</ul></p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Download JioTV Live APK and Never Miss Your Favourite Shows Again.md b/spaces/1phancelerku/anime-remove-background/Download JioTV Live APK and Never Miss Your Favourite Shows Again.md
deleted file mode 100644
index 255be7d814b9c211a68e47baf8a7957b4a5a51ca..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download JioTV Live APK and Never Miss Your Favourite Shows Again.md	
+++ /dev/null
@@ -1,162 +0,0 @@
-<br />
-<h1>Jio TV Live APK: A Complete Guide</h1>
-<p>If you are looking for a way to watch live TV on your smartphone, tablet, or computer, you might want to check out Jio TV Live APK. This is an app that lets you stream over 1000+ TV channels, including 300+ HD channels, in 15+ languages, for free. You can also watch the latest TV shows, movies, sports, news, devotional, and more on this app. In this article, we will tell you everything you need to know about Jio TV Live APK, such as its features, benefits, how to download and install it, how to use it, alternatives, reviews, and FAQs.</p>
-<h2>jio tv live apk</h2><br /><p><b><b>Download File</b> &gt; <a href="https://jinyurl.com/2uNQeQ">https://jinyurl.com/2uNQeQ</a></b></p><br /><br />
- <h2>What is Jio TV Live APK?</h2>
-<p>Jio TV Live APK is an Android app that allows you to watch live TV on your device. It is developed by Jio Platforms Limited, a subsidiary of Reliance Industries Limited, which also offers other digital services such as Jio SIM, Jio Fiber, Jio Cinema, Jio Music, and more. Jio TV Live APK is one of the most popular entertainment apps in India, with over 100 million downloads on Google Play Store. It is also available for iOS users and web users.</p>
- <h3>Features and benefits of Jio TV Live APK</h3>
-<p>Some of the features and benefits of Jio TV Live APK are:</p>
-<ul>
-<li>It offers over 1000+ live TV channels across various genres and languages, including entertainment, movies, sports, news, music, devotional, kids, lifestyle, infotainment, education, and more.</li>
-<li>It has a rich collection of on-demand movies and shows from various platforms such as Eros Now, Hungama Play, YouTube, Hooq, Zee5, Sony Liv, Voot, Alt Balaji, Shemaroo Me, Sun NXT, etc.</li>
-<li>It allows you to watch live sports events such as cricket, football, hockey, tennis, basketball, F1, WWE, UFC, Moto GP, NBA, PGA Tour, etc. with exclusive access to Jio Sports channels.</li>
-<li>It lets you catch up on the latest shows and episodes that you missed with the 7 days catch-up feature.</li>
-<li>It enables you to pause and play live TV channels at your convenience.</li>
-<li>It lets you set reminders for your favorite shows and never miss them.</li>
-<li>It allows you to record your favorite shows and watch them later.</li>
-<li>It supports multiple audio and language options for different channels.</li>
-<li>It lets you mark channels or programs as favorites for easy access.</li>
-<li>It lets you find all the popular and trending shows in the ‘Featured’ tab.</li>
-<li>It lets you browse through the top stories of the day in the ‘News’ tab.</li>
-<li>It lets you listen to your favorite tunes on the go with the ‘Music’ tab.</li>
-<li>It lets you connect spiritually with live darshans, poojas, aartis, and more with the ‘Jio Darshan’ tab.</li>
-<li>It lets you play exciting games and have unlimited fun with the ‘Games’ tab.</li>
-</ul>
- <h3>How to download and install Jio TV Live APK</h3>
-<h2>How to use Jio TV Live APK</h2>
-<p>Once you have downloaded and installed Jio TV Live APK on your device, you can start using it to watch live TV channels and programs. Here are some steps to help you use Jio TV Live APK:</p>
- <h3>How to watch live TV channels on Jio TV Live APK</h3>
-<ol>
-<li>Open the Jio TV Live APK app on your device and sign in with your Jio ID and password. If you don't have a Jio ID, you can create one using your Jio number or email address.</li>
-<li>On the home screen, you will see different tabs such as Featured, News, Music, Games, etc. You can swipe left or right to browse through them.</li>
-<li>To watch live TV channels, tap on the 'Live TV' tab at the bottom of the screen. You will see a list of channels categorized by genres and languages.</li>
-<li>You can scroll up or down to find your preferred channel or use the search bar at the top to look for a specific channel or program.</li>
-<li>Tap on the channel icon to start watching it live. You can also swipe left or right on the screen to switch between channels.</li>
-<li>To adjust the video quality, audio language, or subtitles, tap on the settings icon at the top right corner of the screen.</li>
-<li>To pause or play the live stream, tap on the screen once. You can also rewind or fast forward up to 30 seconds by tapping on the left or right edge of the screen.</li>
-</ol>
- <h3>How to catch up on missed shows on Jio TV Live APK</h3>
-<ol>
-<li>If you missed any show or episode that was aired in the last 7 days, you can watch it using the catch-up feature of Jio TV Live APK.</li>
-<li>To access the catch-up feature, tap on the 'Live TV' tab at the bottom of the screen and then tap on the 'Catch Up' icon at the top left corner of the screen.</li>
-<li>You will see a calendar with dates marked in blue. Tap on any date to see the list of shows and episodes that were aired on that day.</li>
-<li>You can also filter the list by genre, language, or channel using the icons at the top of the screen.</li>
-<li>Tap on any show or episode to start watching it. You can also pause, play, rewind, or fast forward as you wish.</li>
-</ol>
- <h3>How to record and watch later on Jio TV Live APK</h3>
-<ol>
-<li>If you want to record any show or episode and watch it later, you can use the record feature of Jio TV Live APK.</li>
-<li>To access the record feature, tap on the 'Live TV' tab at the bottom of the screen and then tap on any channel icon.</li>
-<li>You will see a list of programs that are currently airing or scheduled to air on that channel.</li>
-<li>Tap on any program that you want to record and then tap on the 'Record' icon at the bottom of the screen.</li>
-<li>You can choose to record only that program or all episodes of that program. You can also set a reminder for that program if you want.</li>
-<li>The recorded program will be saved in your device storage and you can watch it later by tapping on the 'Recordings' tab at the bottom of the screen.</li>
-<li>You can also delete any recording by tapping on it and then tapping on the 'Delete' icon at the bottom of the screen.</li>
-</ol>
- <h3>How to set reminders and favorites on Jio TV Live APK</h3>
-<ol>
-<li>If you want to get notified about any upcoming show or episode that you don't want to miss, you can set a reminder for it using Jio TV Live APK.</li>
-<li>To set a reminder, tap on any program that is scheduled to air in future and then tap on the 'Reminder' icon at the bottom of the screen.</li>
-<li>You will get a notification when that program is about to start. You can also cancel any reminder by tapping on it and then tapping on the 'Cancel' icon at the bottom of the screen.</li>
-<li>If you want to mark any channel or program as your favorite for easy access, you can do so using Jio TV Live APK.</li>
-<li>To mark a channel as your favorite, tap and hold on any channel icon until a star appears on it. To unmark it, tap and hold again until the star disappears.</li>
-<li>To mark a <p>program as your favorite, tap on any program and then tap on the 'Favorite' icon at the bottom of the screen. To unmark it, tap on it again and then tap on the 'Unfavorite' icon at the bottom of the screen.</li>
-<li>You can access your favorite channels and programs by tapping on the 'Favorites' tab at the bottom of the screen.</li>
-</ol>
- <h2>Alternatives to Jio TV Live APK</h2>
-<p>While Jio TV Live APK is a great app for watching live TV on your device, it is not the only one. There are some other apps that offer similar or better features and services. Here are some of the alternatives to Jio TV Live APK that you can try:</p>
- <h3>Airtel Xstream TV</h3>
-<p>Airtel Xstream TV is an app that lets you watch live TV, movies, shows, and more on your device. It is offered by Airtel, one of the leading telecom operators in India. It has over 400+ live TV channels, 10000+ movies and shows, and exclusive content from platforms such as Zee5, Eros Now, Hooq, Hungama Play, etc. It also has a dedicated kids section with educational and fun content. You can also download and watch offline any content that you like. Airtel Xstream TV is free for Airtel users and requires a subscription for non-Airtel users.</p>
- <h3>Disney+ Hotstar</h3>
-<p>Disney+ Hotstar is an app that lets you watch live TV, movies, shows, sports, news, and more on your device. It is offered by Star India, a subsidiary of The Walt Disney Company. It has over 300+ live TV channels, 100000+ hours of content, and exclusive access to Disney+ originals, Marvel movies, Star Wars series, Pixar animations, etc. It also has a huge collection of Indian movies and shows in various languages. You can also watch live sports events such as IPL, ICC Cricket World Cup, Premier League, Formula 1, etc. Disney+ Hotstar requires a subscription to access its premium content.</p>
- <h3>Vodafone Play</h3>
-<p>Vodafone Play is an app that lets you watch live TV, movies, shows, and more on your device. It is offered by Vodafone Idea Limited, another leading telecom operator in India. It has over 450+ live TV channels, 15000+ movies and shows, and exclusive content from platforms such as Zee5, Sony Liv, Lionsgate Play, Shemaroo Me, Sun NXT, etc. It also has a curated section for kids with educational and entertaining content. You can also download and watch offline any content that you like. Vodafone Play is free for Vodafone Idea users and requires a subscription for non-Vodafone Idea users.</p>
- <h2>Reviews of Jio TV Live APK</h2>
-<p>Jio TV Live APK has received mixed reviews from its users. Some users have praised its features, quality, variety, and user interface. Some users have complained about its bugs, errors, <p>Some of the user reviews of Jio TV Live APK are:</p>
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- <h3>Pros and cons of Jio TV Live APK</h3>
-<p>According to the user ratings and feedback on Google Play Store, some of the pros and cons of Jio TV Live APK are:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>It has a large variety of channels and content in different languages and genres.</td>
-<td>It sometimes crashes or freezes while streaming.</td>
-</tr>
-<tr>
-<td>It has a user-friendly interface and easy navigation.</td>
-<td>It consumes a lot of data and battery while streaming.</td>
-</tr>
-<tr>
-<td>It has a catch-up, record, and reminder feature for convenience.</td>
-<td>It does not support casting or mirroring to other devices.</td>
-</tr>
-<tr>
-<td>It has exclusive access to Jio Sports channels and events.</td>
-<td>It requires a Jio SIM or ID to access the app.</td>
-</tr>
-<tr>
-<td>It is free to download and use for Jio users.</td>
-<td>It has ads that can be annoying or intrusive.</td>
-</tr>
-</table>
- <h3>User ratings and feedback of Jio TV Live APK</h3>
-<p>Here are some of the user ratings and feedback of Jio TV Live APK from Google Play Store:</p>
- <ul>
-<li>"I love this app. It has all the channels that I want to watch. The quality is good and the catch-up feature is very useful. I can watch my favorite shows anytime, anywhere. Thank you Jio TV." - 5 stars</li>
-<li>"This app is good but it needs improvement. It often stops working or shows error messages. It also consumes a lot of data and battery. Please fix these issues and make it more stable." - 3 stars</li>
-<li>"This app is very bad. It does not work properly on my device. It always buffers or shows a black screen. It also does not have some of the channels that I like. It is a waste of time and space." - 1 star</li>
-<li>"This app is amazing. It has a lot of channels and content in different languages and genres. The interface is simple and easy to use. The record and reminder feature is very helpful. I enjoy watching live sports on this app." - 4 stars</li>
-<li>"This app is okay but it could be better. It does not support casting or mirroring to other devices. It also has ads that are annoying or intrusive. It should have more options for video quality, audio language, and subtitles." - 2 stars</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Jio TV Live APK is an app that lets you watch live TV on your device. It has over 1000+ live TV channels, 10000+ movies and shows, and exclusive content from various platforms. It also has features such as catch-up, record, reminder, favorites, etc. It is free for Jio users and requires a subscription for non-Jio users. It has received mixed reviews from its users, with some praising its features, quality, variety, and user interface, and some complaining about its bugs, errors, data consumption, battery drain, casting support, ads, etc. Overall, it is a good app for watching live TV on your device, but it needs improvement in some areas.</p>
- <h2>FAQs</h2>
- <ol>
-<li>What are the requirements to use Jio TV Live APK?</li>
-<p>To use Jio TV Live APK, you need an Android device with Android 4.4 or above, a Jio SIM or ID, and an internet connection.</p>
- <li>How can I watch Jio TV Live APK on my PC or laptop?</li>
-<p>To watch Jio TV Live APK on your PC or laptop, you need to visit the official website of Jio TV and sign in with your Jio ID and password. You can also use an Android emulator such as BlueStacks or Nox Player to run the app on your PC or laptop.</p>
- <li>How can I watch Jio TV Live APK on my smart TV?</li>
-<p>To watch Jio TV Live APK on your smart TV, you need to connect your device with the app to your smart TV using a Chromecast device or a similar device that supports screen mirroring or casting. You can also use an HDMI cable to connect your device with the app to your smart TV.</p>
- <li>How can I update Jio TV Live APK?</li>
-<p>To update Jio TV Live APK, you need to visit the Google Play Store or the official website of Jio TV <p>and download the latest version of the app. You can also enable the auto-update option in the settings of the app to get the updates automatically.</p>
- <li>How can I contact the customer support of Jio TV Live APK?</li>
-<p>To contact the customer support of Jio TV Live APK, you can call the toll-free number 1800-889-9999 or email at care@jio.com. You can also visit the help and support section of the app or the website to get answers to your queries.</p>
-</ol></p> 401be4b1e0<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r50.py b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r50.py
deleted file mode 100644
index 08ba55dbbea6df0afffddbb3d1ed173efad99604..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r50.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/AIFILMS/StyleGANEX/models/__init__.py b/spaces/AIFILMS/StyleGANEX/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/distributions/__init__.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/distributions/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AISuperheroes/05GR-Image-To-Multilingual-OCR/README.md b/spaces/AISuperheroes/05GR-Image-To-Multilingual-OCR/README.md
deleted file mode 100644
index d8ce53c4da0a538d2259b795dec4e0b0296c2bab..0000000000000000000000000000000000000000
--- a/spaces/AISuperheroes/05GR-Image-To-Multilingual-OCR/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 05GR Image To Multilingual OCR
-emoji: 🐢
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AIZero2HeroBootcamp/MultiPDF-QA-ChatGPT-Langchain/htmlTemplates.py b/spaces/AIZero2HeroBootcamp/MultiPDF-QA-ChatGPT-Langchain/htmlTemplates.py
deleted file mode 100644
index 3d55ded9c07afe6b571a99f57d50883c63cc37b3..0000000000000000000000000000000000000000
--- a/spaces/AIZero2HeroBootcamp/MultiPDF-QA-ChatGPT-Langchain/htmlTemplates.py
+++ /dev/null
@@ -1,44 +0,0 @@
-css = '''
-<style>
-.chat-message {
-    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
-}
-.chat-message.user {
-    background-color: #2b313e
-}
-.chat-message.bot {
-    background-color: #475063
-}
-.chat-message .avatar {
-  width: 20%;
-}
-.chat-message .avatar img {
-  max-width: 78px;
-  max-height: 78px;
-  border-radius: 50%;
-  object-fit: cover;
-}
-.chat-message .message {
-  width: 80%;
-  padding: 0 1.5rem;
-  color: #fff;
-}
-'''
-
-bot_template = '''
-<div class="chat-message bot">
-    <div class="avatar">
-        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
-    </div>
-    <div class="message">{{MSG}}</div>
-</div>
-'''
-
-user_template = '''
-<div class="chat-message user">
-    <div class="avatar">
-        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
-    </div>    
-    <div class="message">{{MSG}}</div>
-</div>
-'''
\ No newline at end of file
diff --git a/spaces/ASJMO/freegpt/client/css/message-input.css b/spaces/ASJMO/freegpt/client/css/message-input.css
deleted file mode 100644
index de5f58388133bd3b2b2333dd99cecf0110002367..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/client/css/message-input.css
+++ /dev/null
@@ -1,27 +0,0 @@
-#message-input {
-	margin-right: 30px;
-	height: 64px;
-}
-
-#message-input::-webkit-scrollbar {
-	width: 5px;
-}
-
-#message-input::-webkit-scrollbar-track {
-	background: #f1f1f1;
-}
-
-#message-input::-webkit-scrollbar-thumb {
-	background: #c7a2ff;
-}
-
-#message-input::-webkit-scrollbar-thumb:hover {
-	background: #8b3dff;
-}
-
-@media screen and (max-width: 360px) {
-	#message-input {
-		margin: 0;
-	}
-}
-
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/methods/SwapPage.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/methods/SwapPage.js
deleted file mode 100644
index d3c030073fedc83f07ef2586e7427aa0aac3165c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/methods/SwapPage.js
+++ /dev/null
@@ -1,38 +0,0 @@
-import Container from '../../container/Container.js';
-
-const ContainerSetChildVisible = Container.prototype.setChildVisible;
-
-var SwapPage = function (key, fadeInDuration) {
-    this._previousKey = this._currentKey;
-    var prevoiusPage = this.previousPage;
-    if (prevoiusPage) {
-        if (this.swapMode === 0) { // Invisible
-            ContainerSetChildVisible.call(this, prevoiusPage, false);
-            this.emit('pageinvisible', prevoiusPage, this._previousKey, this);
-        } else { // Destroy
-            prevoiusPage.destroy();
-        }
-    }
-
-    if (key && !this.sizerChildren.hasOwnProperty(key)) {
-        this.emit('createpage', key, this);
-    }
-
-    this._currentKey = key;
-    var currentPage = this.currentPage;
-    if (currentPage) {
-        ContainerSetChildVisible.call(this, currentPage, true);
-        this.emit('pagevisible', currentPage, this._currentKey, this);
-
-        if (fadeInDuration === undefined) {
-            fadeInDuration = this.fadeInDuration;
-        }
-
-        if (fadeInDuration > 0) {
-            currentPage.setAlpha(0).fadeIn(fadeInDuration, 1);
-        }
-    }
-    return this;
-}
-
-export default SwapPage;
\ No newline at end of file
diff --git a/spaces/AiMimicry/sovits-models/modules/attentions.py b/spaces/AiMimicry/sovits-models/modules/attentions.py
deleted file mode 100644
index f9c11ca4a3acb86bf1abc04d9dcfa82a4ed4061f..0000000000000000000000000000000000000000
--- a/spaces/AiMimicry/sovits-models/modules/attentions.py
+++ /dev/null
@@ -1,349 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.commons as commons
-import modules.modules as modules
-from modules.modules import LayerNorm
-
-
-class FFT(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
-               proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(
-        MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
-                           proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(
-        FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/AlexWang/lama/saicinpainting/__init__.py b/spaces/AlexWang/lama/saicinpainting/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Alican/pixera/util/image_pool.py b/spaces/Alican/pixera/util/image_pool.py
deleted file mode 100644
index 6d086f882bc3d1b90c529fce6cddaaa75f2005d7..0000000000000000000000000000000000000000
--- a/spaces/Alican/pixera/util/image_pool.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import random
-import torch
-
-
-class ImagePool():
-    """This class implements an image buffer that stores previously generated images.
-
-    This buffer enables us to update discriminators using a history of generated images
-    rather than the ones produced by the latest generators.
-    """
-
-    def __init__(self, pool_size):
-        """Initialize the ImagePool class
-
-        Parameters:
-            pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
-        """
-        self.pool_size = pool_size
-        if self.pool_size > 0:  # create an empty pool
-            self.num_imgs = 0
-            self.images = []
-
-    def query(self, images):
-        """Return an image from the pool.
-
-        Parameters:
-            images: the latest generated images from the generator
-
-        Returns images from the buffer.
-
-        By 50/100, the buffer will return input images.
-        By 50/100, the buffer will return images previously stored in the buffer,
-        and insert the current images to the buffer.
-        """
-        if self.pool_size == 0:  # if the buffer size is 0, do nothing
-            return images
-        return_images = []
-        for image in images:
-            image = torch.unsqueeze(image.data, 0)
-            if self.num_imgs < self.pool_size:   # if the buffer is not full; keep inserting current images to the buffer
-                self.num_imgs = self.num_imgs + 1
-                self.images.append(image)
-                return_images.append(image)
-            else:
-                p = random.uniform(0, 1)
-                if p > 0.5:  # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
-                    random_id = random.randint(0, self.pool_size - 1)  # randint is inclusive
-                    tmp = self.images[random_id].clone()
-                    self.images[random_id] = image
-                    return_images.append(tmp)
-                else:       # by another 50% chance, the buffer will return the current image
-                    return_images.append(image)
-        return_images = torch.cat(return_images, 0)   # collect all the images and return
-        return return_images
diff --git a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/cpp/longcode/jpge.cpp b/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/cpp/longcode/jpge.cpp
deleted file mode 100644
index 2e26b71ed5aad0d46478fdbcd3a880be1401f946..0000000000000000000000000000000000000000
--- a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/cpp/longcode/jpge.cpp
+++ /dev/null
@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include <stdlib.h>
-#include <string.h>
-#if PLATFORM_WINDOWS
-#include <malloc.h>
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES]  =
-{
-  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
-  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
-  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
-  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
-  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
-  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
-  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
-  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
-  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
-  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-
-// Low-level helper functions.
-template <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
-  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS  13
-#define ROW_BITS    2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
-  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
-  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
-  int32 u1 = DCT_MUL(t12 + t13, 4433); \
-  s2 = u1 + DCT_MUL(t13, 6270); \
-  s6 = u1 + DCT_MUL(t12, -15137); \
-  u1 = t4 + t7; \
-  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
-  int32 z5 = DCT_MUL(u3 + u4, 9633); \
-  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
-  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
-  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
-  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
-  u3 += z5; u4 += z5; \
-  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
-  int32 c, *q = p;
-  for (c = 7; c >= 0; c--, q += 8)
-  {
-    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
-    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
-  }
-  for (q = p, c = 7; c >= 0; c--, q++)
-  {
-    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
-    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
-  }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
-  const uint cMaxPasses = 4;
-  uint32 hist[256 * cMaxPasses]; clear_obj(hist);
-  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
-  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
-  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
-  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
-  {
-    const uint32* pHist = &hist[pass << 8];
-    uint offsets[256], cur_ofs = 0;
-    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
-    for (uint i = 0; i < num_syms; i++)
-      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
-    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
-  }
-  return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
-  int root, leaf, next, avbl, used, dpth;
-  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
-  A[0].m_key += A[1].m_key; root = 0; leaf = 2;
-  for (next=1; next < n-1; next++)
-  {
-    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;
-    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;
-  }
-  A[n-2].m_key = 0;
-  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
-  avbl = 1; used = dpth = 0; root = n-2; next = n-1;
-  while (avbl>0)
-  {
-    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
-    while (avbl>used) { A[next--].m_key = dpth; avbl--; }
-    avbl = 2*used; dpth++; used = 0;
-  }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
-  if (code_list_len <= 1) return;
-
-  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
-  uint32 total = 0;
-  for (int i = max_code_size; i > 0; i--)
-    total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
-  while (total != (1UL << max_code_size))
-  {
-    pNum_codes[max_code_size]--;
-    for (int i = max_code_size - 1; i > 0; i--)
-    {
-      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
-    }
-    total--;
-  }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
-  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
-  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's
-  int num_used_syms = 1;
-  const uint32 *pSym_count = &m_huff_count[table_num][0];
-  for (int i = 0; i < table_len; i++)
-    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
-  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
-  calculate_minimum_redundancy(pSyms, num_used_syms);
-
-  // Count the # of symbols of each code size.
-  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
-  for (int i = 0; i < num_used_syms; i++)
-    num_codes[pSyms[i].m_key]++;
-
-  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
-  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
-  // Compute m_huff_bits array, which contains the # of symbols per code size.
-  clear_obj(m_huff_bits[table_num]);
-  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
-    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);
-
-  // Remove the dummy symbol added above, which must be in largest bucket.
-  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
-  {
-    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
-  }
-
-  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
-  for (int i = num_used_syms - 1; i >= 1; i--)
-    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
-  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
-  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
-  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
-  emit_marker(M_APP0);
-  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
-  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
-  emit_byte(0);
-  emit_byte(1);      /* Major version */
-  emit_byte(1);      /* Minor version */
-  emit_byte(0);      /* Density unit */
-  emit_word(1);
-  emit_word(1);
-  emit_byte(0);      /* No thumbnail image */
-  emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
-  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
-  {
-    emit_marker(M_DQT);
-    emit_word(64 + 1 + 2);
-    emit_byte(static_cast<uint8>(i));
-    for (int j = 0; j < 64; j++)
-      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));
-  }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
-  emit_marker(M_SOF0);                           /* baseline */
-  emit_word(3 * m_num_components + 2 + 5 + 1);
-  emit_byte(8);                                  /* precision */
-  emit_word(m_image_y);
-  emit_word(m_image_x);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */
-    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */
-    emit_byte(i > 0);                                   /* quant. table num */
-  }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
-  emit_marker(M_DHT);
-
-  int length = 0;
-  for (int i = 1; i <= 16; i++)
-    length += bits[i];
-
-  emit_word(length + 2 + 1 + 16);
-  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));
-
-  for (int i = 1; i <= 16; i++)
-    emit_byte(bits[i]);
-
-  for (int i = 0; i < length; i++)
-    emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
-  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
-  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
-  if (m_num_components == 3)
-  {
-    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
-    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
-  }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
-  emit_marker(M_SOS);
-  emit_word(2 * m_num_components + 2 + 1 + 3);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));
-    if (i == 0)
-      emit_byte((0 << 4) + 0);
-    else
-      emit_byte((1 << 4) + 1);
-  }
-  emit_byte(0);     /* spectral selection */
-  emit_byte(63);
-  emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
-  emit_marker(M_SOI);
-  emit_jfif_app0();
-  emit_dqt();
-  emit_sof();
-  emit_dhts();
-  emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
-  int i, l, last_p, si;
-  uint8 huff_size[257];
-  uint huff_code[257];
-  uint code;
-
-  int p = 0;
-  for (l = 1; l <= 16; l++)
-    for (i = 1; i <= bits[l]; i++)
-      huff_size[p++] = (char)l;
-
-  huff_size[p] = 0; last_p = p; // write sentinel
-
-  code = 0; si = huff_size[0]; p = 0;
-
-  while (huff_size[p])
-  {
-    while (huff_size[p] == si)
-      huff_code[p++] = code++;
-    code <<= 1;
-    si++;
-  }
-
-  memset(codes, 0, sizeof(codes[0])*256);
-  memset(code_sizes, 0, sizeof(code_sizes[0])*256);
-  for (p = 0; p < last_p; p++)
-  {
-    codes[val[p]]      = huff_code[p];
-    code_sizes[val[p]] = huff_size[p];
-  }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
-  int32 q;
-  if (m_params.m_quality < 50)
-    q = 5000 / m_params.m_quality;
-  else
-    q = 200 - m_params.m_quality * 2;
-  for (int i = 0; i < 64; i++)
-  {
-    int32 j = *pSrc++; j = (j * q + 50L) / 100L;
-    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
-  }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
-  m_bit_buffer = 0; m_bits_in = 0;
-  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
-  m_mcu_y_ofs = 0;
-  m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
-  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
-  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
-  if (m_num_components > 1)
-  {
-    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
-    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
-  }
-  first_pass_init();
-  emit_markers();
-  m_pass_num = 2;
-  return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
-  m_num_components = 3;
-  switch (m_params.m_subsampling)
-  {
-    case Y_ONLY:
-    {
-      m_num_components = 1;
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H1V1:
-    {
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H2V1:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 8;
-      break;
-    }
-    case H2V2:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 16;
-    }
-  }
-
-  m_image_x        = p_x_res; m_image_y = p_y_res;
-  m_image_bpp      = src_channels;
-  m_image_bpl      = m_image_x * src_channels;
-  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
-  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
-  m_image_bpl_xlt  = m_image_x * m_num_components;
-  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;
-  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;
-
-  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
-  for (int i = 1; i < m_mcu_y; i++)
-    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
-  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
-  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-  m_pOut_buf = m_out_buf;
-
-  if (m_params.m_two_pass_flag)
-  {
-    clear_obj(m_huff_count);
-    first_pass_init();
-  }
-  else
-  {
-    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
-    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
-    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
-    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
-    if (!second_pass_init()) return false;   // in effect, skip over the first pass
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[i] + x;
-    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
-    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (8 * 3)) + c;
-  y <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[y + i] + x;
-    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
-    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
-  uint8 *pSrc1, *pSrc2;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  int a = 0, b = 2;
-  for (int i = 0; i < 16; i += 2, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pSrc2 = m_mcu_lines[i + 1] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
-    int temp = a; a = b; b = temp;
-  }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
-  uint8 *pSrc1;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
-  }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
-  int32 *q = m_quantization_tables[component_num > 0];
-  int16 *pDst = m_coefficient_array;
-  for (int i = 0; i < 64; i++)
-  {
-    sample_array_t j = m_sample_array[s_zag[i]];
-    if (j < 0)
-    {
-      if ((j = -j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>(-(j / *q));
-    }
-    else
-    {
-      if ((j = j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>((j / *q));
-    }
-    q++;
-  }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
-  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
-    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
-  m_pOut_buf = m_out_buf;
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
-  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
-  while (m_bits_in >= 8)
-  {
-    uint8 c;
-    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
-    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
-    if (c == 0xFF) JPGE_PUT_BYTE(0);
-    m_bit_buffer <<= 8;
-    m_bits_in -= 8;
-  }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
-  if (component_num >= 3) return; // just to shut up static analysis
-  int i, run_len, nbits, temp1;
-  int16 *src = m_coefficient_array;
-  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
-  temp1 = src[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = src[0];
-  if (temp1 < 0) temp1 = -temp1;
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  dc_count[nbits]++;
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        ac_count[0xF0]++;
-        run_len -= 16;
-      }
-      if (temp1 < 0) temp1 = -temp1;
-      nbits = 1;
-      while (temp1 >>= 1) nbits++;
-      ac_count[(run_len << 4) + nbits]++;
-      run_len = 0;
-    }
-  }
-  if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
-  int i, j, run_len, nbits, temp1, temp2;
-  int16 *pSrc = m_coefficient_array;
-  uint *codes[2];
-  uint8 *code_sizes[2];
-
-  if (component_num == 0)
-  {
-    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
-    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
-  }
-  else
-  {
-    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
-    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
-  }
-
-  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = pSrc[0];
-
-  if (temp1 < 0)
-  {
-    temp1 = -temp1; temp2--;
-  }
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  put_bits(codes[0][nbits], code_sizes[0][nbits]);
-  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
-        run_len -= 16;
-      }
-      if ((temp2 = temp1) < 0)
-      {
-        temp1 = -temp1;
-        temp2--;
-      }
-      nbits = 1;
-      while (temp1 >>= 1)
-        nbits++;
-      j = (run_len << 4) + nbits;
-      put_bits(codes[1][j], code_sizes[1][j]);
-      put_bits(temp2 & ((1 << nbits) - 1), nbits);
-      run_len = 0;
-    }
-  }
-  if (run_len)
-    put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
-  DCT2D(m_sample_array);
-  load_quantized_coefficients(component_num);
-  if (m_pass_num == 1)
-    code_coefficients_pass_one(component_num);
-  else
-    code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
-  if (m_num_components == 1)
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8_grey(i); code_block(0);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
-      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
-    }
-  }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
-  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
-  if (m_num_components > 1)
-  {
-    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
-  }
-  return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
-  put_bits(0x7F, 7);
-  flush_output_buffer();
-  emit_marker(M_EOI);
-  m_pass_num++; // purposely bump up m_pass_num, for debugging
-  return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
-  if (m_mcu_y_ofs)
-  {
-    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
-    {
-      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
-        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
-    }
-
-    process_mcu_row();
-  }
-
-  if (m_pass_num == 1)
-    return terminate_pass_one();
-  else
-    return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
-  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);
-
-  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
-  if (m_num_components == 1)
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_Y(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_Y(pDst, Psrc, m_image_x);
-    else
-      memcpy(pDst, Psrc, m_image_x);
-  }
-  else
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_YCC(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_YCC(pDst, Psrc, m_image_x);
-    else
-      Y_to_YCC(pDst, Psrc, m_image_x);
-  }
-
-  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
-  if (m_num_components == 1)
-    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
-  else
-  {
-    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
-    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
-    for (int i = m_image_x; i < m_image_x_mcu; i++)
-    {
-      *q++ = y; *q++ = cb; *q++ = cr;
-    }
-  }
-
-  if (++m_mcu_y_ofs == m_mcu_y)
-  {
-    process_mcu_row();
-    m_mcu_y_ofs = 0;
-  }
-}
-
-void jpeg_encoder::clear()
-{
-  m_mcu_lines[0] = NULL;
-  m_pass_num = 0;
-  m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
-  clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
-  deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
-  deinit();
-  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
-  m_pStream = pStream;
-  m_params = comp_params;
-  return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
-  jpge_free(m_mcu_lines[0]);
-  clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
-  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
-  if (m_all_stream_writes_succeeded)
-  {
-    if (!pScanline)
-    {
-      if (!process_end_of_image()) return false;
-    }
-    else
-    {
-      load_mcu(pScanline);
-    }
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include <stdio.h>
-
-class cfile_stream : public output_stream
-{
-   cfile_stream(const cfile_stream &);
-   cfile_stream &operator= (const cfile_stream &);
-
-   FILE* m_pFile;
-   bool m_bStatus;
-
-public:
-   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
-   virtual ~cfile_stream()
-   {
-      close();
-   }
-
-   bool open(const char *pFilename)
-   {
-      close();
-#if defined(_MSC_VER)
-      if (fopen_s(&m_pFile, pFilename, "wb") != 0)
-	  {
-		  return false;
-	  }
-#else
-      m_pFile = fopen(pFilename, "wb");
-#endif
-      m_bStatus = (m_pFile != NULL);
-      return m_bStatus;
-   }
-
-   bool close()
-   {
-      if (m_pFile)
-      {
-         if (fclose(m_pFile) == EOF)
-         {
-            m_bStatus = false;
-         }
-         m_pFile = NULL;
-      }
-      return m_bStatus;
-   }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
-      return m_bStatus;
-   }
-
-   uint get_size() const
-   {
-      return m_pFile ? ftell(m_pFile) : 0;
-   }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-  cfile_stream dst_stream;
-  if (!dst_stream.open(pFilename))
-    return false;
-
-  jpge::jpeg_encoder dst_image;
-  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-    return false;
-
-  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-  {
-    for (int64_t i = 0; i < height; i++)
-    {
-		// i, width, and num_channels are all 64bit
-       const uint8* pBuf = pImage_data + i * width * num_channels;
-       if (!dst_image.process_scanline(pBuf))
-          return false;
-    }
-    if (!dst_image.process_scanline(NULL))
-       return false;
-  }
-
-  dst_image.deinit();
-
-  return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
-   memory_stream(const memory_stream &);
-   memory_stream &operator= (const memory_stream &);
-
-   uint8 *m_pBuf;
-   uint64_t m_buf_size, m_buf_ofs;
-
-public:
-   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
-   virtual ~memory_stream() { }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      uint64_t buf_remaining = m_buf_size - m_buf_ofs;
-      if ((uint64_t)len > buf_remaining)
-         return false;
-      memcpy(m_pBuf + m_buf_ofs, pBuf, len);
-      m_buf_ofs += len;
-      return true;
-   }
-
-   uint64_t get_size() const
-   {
-      return m_buf_ofs;
-   }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-   if ((!pDstBuf) || (!buf_size))
-      return false;
-
-   memory_stream dst_stream(pDstBuf, buf_size);
-
-   buf_size = 0;
-
-   jpge::jpeg_encoder dst_image;
-   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-      return false;
-
-   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-   {
-     for (int64_t i = 0; i < height; i++)
-     {
-        const uint8* pScanline = pImage_data + i * width * num_channels;
-        if (!dst_image.process_scanline(pScanline))
-           return false;
-     }
-     if (!dst_image.process_scanline(NULL))
-        return false;
-   }
-
-   dst_image.deinit();
-
-   buf_size = dst_stream.get_size();
-   return true;
-}
-
-} // namespace jpge
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/transformer2d.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/transformer2d.md
deleted file mode 100644
index 4ad2b00b6f2377bac9192a41970c17cd6084be93..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/transformer2d.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Transformer2D
-
-A Transformer model for image-like data from [CompVis](https://huggingface.co/CompVis) that is based on the [Vision Transformer](https://huggingface.co/papers/2010.11929) introduced by Dosovitskiy et al. The [`Transformer2DModel`] accepts discrete (classes of vector embeddings) or continuous (actual embeddings) inputs.
-
-When the input is **continuous**:
-
-1. Project the input and reshape it to `(batch_size, sequence_length, feature_dimension)`.
-2. Apply the Transformer blocks in the standard way.
-3. Reshape to image.
-
-When the input is **discrete**:
-
-<Tip>
-
-It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
-
-</Tip>
-
-1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings.
-2. Apply the Transformer blocks in the standard way.
-3. Predict classes of unnoised image.
-
-## Transformer2DModel
-
-[[autodoc]] Transformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.transformer_2d.Transformer2DModelOutput
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/optimization/habana.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/optimization/habana.md
deleted file mode 100644
index 0f076245fb1c69b83026a36b820105d5de15c85a..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/optimization/habana.md
+++ /dev/null
@@ -1,71 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Habana Gaudi에서 Stable Diffusion을 사용하는 방법
-
-🤗 Diffusers는 🤗 [Optimum Habana](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion)를 통해서 Habana Gaudi와 호환됩니다.
-
-## 요구 사항
-
-- Optimum Habana 1.4 또는 이후, [여기](https://huggingface.co/docs/optimum/habana/installation)에 설치하는 방법이 있습니다.
-- SynapseAI 1.8.
-
-
-## 추론 파이프라인
-
-Gaudi에서 Stable Diffusion 1 및 2로 이미지를 생성하려면 두 인스턴스를 인스턴스화해야 합니다:
-- [`GaudiStableDiffusionPipeline`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline)이 포함된 파이프라인. 이 파이프라인은 *텍스트-이미지 생성*을 지원합니다.
-- [`GaudiDDIMScheduler`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiDDIMScheduler)이 포함된 스케줄러. 이 스케줄러는 Habana Gaudi에 최적화되어 있습니다.
-
-파이프라인을 초기화할 때, HPU에 배포하기 위해 `use_habana=True`를 지정해야 합니다.
-또한 가능한 가장 빠른 생성을 위해 `use_hpu_graphs=True`로 **HPU 그래프**를 활성화해야 합니다.
-마지막으로, [Hugging Face Hub](https://huggingface.co/Habana)에서 다운로드할 수 있는 [Gaudi configuration](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config)을 지정해야 합니다.
-
-```python
-from optimum.habana import GaudiConfig
-from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
-
-model_name = "stabilityai/stable-diffusion-2-base"
-scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
-pipeline = GaudiStableDiffusionPipeline.from_pretrained(
-    model_name,
-    scheduler=scheduler,
-    use_habana=True,
-    use_hpu_graphs=True,
-    gaudi_config="Habana/stable-diffusion",
-)
-```
-
-파이프라인을 호출하여 하나 이상의 프롬프트에서 배치별로 이미지를 생성할 수 있습니다.
-
-```python
-outputs = pipeline(
-    prompt=[
-        "High quality photo of an astronaut riding a horse in space",
-        "Face of a yellow cat, high resolution, sitting on a park bench",
-    ],
-    num_images_per_prompt=10,
-    batch_size=4,
-)
-```
-
-더 많은 정보를 얻기 위해, Optimum Habana의 [문서](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion)와 공식 Github 저장소에 제공된 [예시](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion)를 확인하세요.
-
-
-## 벤치마크
-
-다음은 [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) Gaudi 구성(혼합 정밀도 bf16/fp32)을 사용하는 Habana first-generation Gaudi 및 Gaudi2의 지연 시간입니다:
-
-|                        | Latency (배치 크기 = 1) | Throughput (배치 크기 = 8) |
-| ---------------------- |:------------------------:|:---------------------------:|
-| first-generation Gaudi | 4.29s                    | 0.283 images/s              |
-| Gaudi2                 | 1.54s                    | 0.904 images/s              |
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/README.md b/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/README.md
deleted file mode 100644
index 4c3c86cd8c30a4d2006b6c8ae509d49e9bb96358..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/README.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# Region Proposal by Guided Anchoring
-
-## Introduction
-
-[ALGORITHM]
-
-We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278).
-
-```latex
-@inproceedings{wang2019region,
-    title={Region Proposal by Guided Anchoring},
-    author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin},
-    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-    year={2019}
-}
-```
-
-## Results and Models
-
-The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).
-
-| Method |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 | Config | Download |
-| :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :------: | :--------: |
-| GA-RPN |    R-50-FPN     |  caffe  |   1x    |   5.3    |      15.8      |  68.4   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json)   |
-| GA-RPN |    R-101-FPN    |  caffe  |   1x    |   7.3    |      13.0      |  69.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) |
-| GA-RPN | X-101-32x4d-FPN | pytorch |   1x    |   8.5    |      10.0      |  70.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) |
-| GA-RPN | X-101-64x4d-FPN | pytorch |   1x    |   7.1    |      7.5       |  71.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) |
-
-|     Method     |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-| :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
-| GA-Faster RCNN |    R-50-FPN     |  caffe  |   1x    |   5.5    |                |  39.6  |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json)           |
-| GA-Faster RCNN |    R-101-FPN    |  caffe  |   1x    |   7.5    |                |  41.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) |
-| GA-Faster RCNN | X-101-32x4d-FPN | pytorch |   1x    |   8.7    |      9.7       |  43.0  |            [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json)            |
-| GA-Faster RCNN | X-101-64x4d-FPN | pytorch |   1x    |   11.8   |      7.3       |  43.9  |            [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json)            |
-|  GA-RetinaNet  |    R-50-FPN     |  caffe  |   1x    |   3.5    |      16.8      |  36.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json)       |
-|  GA-RetinaNet  |    R-101-FPN    |  caffe  |   1x    |   5.5    |      12.9      |  39.0  |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json)      |
-|  GA-RetinaNet  | X-101-32x4d-FPN | pytorch |   1x    |   6.9    |      10.6      |  40.5  |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json)      |
-|  GA-RetinaNet  | X-101-64x4d-FPN | pytorch |   1x    |   9.9    |      7.7       |  41.3  |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json)      |
-
-- In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.
-
-- Performance on COCO test-dev benchmark are shown as follows.
-
-|     Method     | Backbone  | Style | Lr schd | Aug Train | Score thr |  AP   | AP_50 | AP_75 | AP_small | AP_medium | AP_large | Download |
-| :------------: | :-------: | :---: | :-----: | :-------: | :-------: | :---: | :---: | :---: | :------: | :-------: | :------: | :------: |
-| GA-Faster RCNN | R-101-FPN | caffe |   1x    |     F     |   0.05    |       |       |       |          |           |          |          |
-| GA-Faster RCNN | R-101-FPN | caffe |   1x    |     F     |   0.001   |       |       |       |          |           |          |          |
-|  GA-RetinaNet  | R-101-FPN | caffe |   1x    |     F     |   0.05    |       |       |       |          |           |          |          |
-|  GA-RetinaNet  | R-101-FPN | caffe |   2x    |     T     |   0.05    |       |       |       |          |           |          |          |
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/yolact_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/yolact_head.py
deleted file mode 100644
index 10d311f94ee99e1bf65ee3e5827f1699c28a23e3..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/yolact_head.py
+++ /dev/null
@@ -1,943 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, xavier_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-
-@HEADS.register_module()
-class YOLACTHead(AnchorHead):
-    """YOLACT box head used in https://arxiv.org/abs/1904.02689.
-
-    Note that YOLACT head is a light version of RetinaNet head.
-    Four differences are described as follows:
-
-    1. YOLACT box head has three-times fewer anchors.
-    2. YOLACT box head shares the convs for box and cls branches.
-    3. YOLACT box head uses OHEM instead of Focal loss.
-    4. YOLACT box head predicts a set of mask coefficients for each box.
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        anchor_generator (dict): Config dict for anchor generator
-        loss_cls (dict): Config of classification loss.
-        loss_bbox (dict): Config of localization loss.
-        num_head_convs (int): Number of the conv layers shared by
-            box and cls branches.
-        num_protos (int): Number of the mask coefficients.
-        use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
-            cls loss calculation. If false, ``loss_single`` will be used.
-        conv_cfg (dict): Dictionary to construct and config conv layer.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 anchor_generator=dict(
-                     type='AnchorGenerator',
-                     octave_base_scale=3,
-                     scales_per_octave=1,
-                     ratios=[0.5, 1.0, 2.0],
-                     strides=[8, 16, 32, 64, 128]),
-                 loss_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=False,
-                     reduction='none',
-                     loss_weight=1.0),
-                 loss_bbox=dict(
-                     type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
-                 num_head_convs=1,
-                 num_protos=32,
-                 use_ohem=True,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 **kwargs):
-        self.num_head_convs = num_head_convs
-        self.num_protos = num_protos
-        self.use_ohem = use_ohem
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        super(YOLACTHead, self).__init__(
-            num_classes,
-            in_channels,
-            loss_cls=loss_cls,
-            loss_bbox=loss_bbox,
-            anchor_generator=anchor_generator,
-            **kwargs)
-        if self.use_ohem:
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-            self.sampling = False
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.head_convs = nn.ModuleList()
-        for i in range(self.num_head_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.head_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.conv_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.conv_reg = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 4, 3, padding=1)
-        self.conv_coeff = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.num_protos,
-            3,
-            padding=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.head_convs:
-            xavier_init(m.conv, distribution='uniform', bias=0)
-        xavier_init(self.conv_cls, distribution='uniform', bias=0)
-        xavier_init(self.conv_reg, distribution='uniform', bias=0)
-        xavier_init(self.conv_coeff, distribution='uniform', bias=0)
-
-    def forward_single(self, x):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-
-        Returns:
-            tuple:
-                cls_score (Tensor): Cls scores for a single scale level \
-                    the channels number is num_anchors * num_classes.
-                bbox_pred (Tensor): Box energies / deltas for a single scale \
-                    level, the channels number is num_anchors * 4.
-                coeff_pred (Tensor): Mask coefficients for a single scale \
-                    level, the channels number is num_anchors * num_protos.
-        """
-        for head_conv in self.head_convs:
-            x = head_conv(x)
-        cls_score = self.conv_cls(x)
-        bbox_pred = self.conv_reg(x)
-        coeff_pred = self.conv_coeff(x).tanh()
-        return cls_score, bbox_pred, coeff_pred
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """A combination of the func:``AnchorHead.loss`` and
-        func:``SSDHead.loss``.
-
-        When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
-        otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
-        returns ``sampling_results``.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
-                boxes can be ignored when computing the loss. Default: None
-
-        Returns:
-            tuple:
-                dict[str, Tensor]: A dictionary of loss components.
-                List[:obj:``SamplingResult``]: Sampler results for each image.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-            unmap_outputs=not self.use_ohem,
-            return_sampling_results=True)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         num_total_pos, num_total_neg, sampling_results) = cls_reg_targets
-
-        if self.use_ohem:
-            num_images = len(img_metas)
-            all_cls_scores = torch.cat([
-                s.permute(0, 2, 3, 1).reshape(
-                    num_images, -1, self.cls_out_channels) for s in cls_scores
-            ], 1)
-            all_labels = torch.cat(labels_list, -1).view(num_images, -1)
-            all_label_weights = torch.cat(label_weights_list,
-                                          -1).view(num_images, -1)
-            all_bbox_preds = torch.cat([
-                b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
-                for b in bbox_preds
-            ], -2)
-            all_bbox_targets = torch.cat(bbox_targets_list,
-                                         -2).view(num_images, -1, 4)
-            all_bbox_weights = torch.cat(bbox_weights_list,
-                                         -2).view(num_images, -1, 4)
-
-            # concat all level anchors to a single tensor
-            all_anchors = []
-            for i in range(num_images):
-                all_anchors.append(torch.cat(anchor_list[i]))
-
-            # check NaN and Inf
-            assert torch.isfinite(all_cls_scores).all().item(), \
-                'classification scores become infinite or NaN!'
-            assert torch.isfinite(all_bbox_preds).all().item(), \
-                'bbox predications become infinite or NaN!'
-
-            losses_cls, losses_bbox = multi_apply(
-                self.loss_single_OHEM,
-                all_cls_scores,
-                all_bbox_preds,
-                all_anchors,
-                all_labels,
-                all_label_weights,
-                all_bbox_targets,
-                all_bbox_weights,
-                num_total_samples=num_total_pos)
-        else:
-            num_total_samples = (
-                num_total_pos +
-                num_total_neg if self.sampling else num_total_pos)
-
-            # anchor number of multi levels
-            num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-            # concat all level anchors and flags to a single tensor
-            concat_anchor_list = []
-            for i in range(len(anchor_list)):
-                concat_anchor_list.append(torch.cat(anchor_list[i]))
-            all_anchor_list = images_to_levels(concat_anchor_list,
-                                               num_level_anchors)
-            losses_cls, losses_bbox = multi_apply(
-                self.loss_single,
-                cls_scores,
-                bbox_preds,
-                all_anchor_list,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                bbox_weights_list,
-                num_total_samples=num_total_samples)
-
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results
-
-    def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels,
-                         label_weights, bbox_targets, bbox_weights,
-                         num_total_samples):
-        """"See func:``SSDHead.loss``."""
-        loss_cls_all = self.loss_cls(cls_score, labels, label_weights)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
-            as_tuple=False).reshape(-1)
-        neg_inds = (labels == self.num_classes).nonzero(
-            as_tuple=False).view(-1)
-
-        num_pos_samples = pos_inds.size(0)
-        if num_pos_samples == 0:
-            num_neg_samples = neg_inds.size(0)
-        else:
-            num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
-            if num_neg_samples > neg_inds.size(0):
-                num_neg_samples = neg_inds.size(0)
-        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
-        loss_cls_pos = loss_cls_all[pos_inds].sum()
-        loss_cls_neg = topk_loss_cls_neg.sum()
-        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
-        if self.reg_decoded_bbox:
-            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-            # is applied directly on the decoded bounding boxes, it
-            # decodes the already encoded coordinates to absolute format.
-            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
-        loss_bbox = self.loss_bbox(
-            bbox_pred,
-            bbox_targets,
-            bbox_weights,
-            avg_factor=num_total_samples)
-        return loss_cls[None], loss_bbox
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   coeff_preds,
-                   img_metas,
-                   cfg=None,
-                   rescale=False):
-        """"Similiar to func:``AnchorHead.get_bboxes``, but additionally
-        processes coeff_preds.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                with shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            coeff_preds (list[Tensor]): Mask coefficients for each scale
-                level with shape (N, num_anchors * num_protos, H, W)
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-
-        Returns:
-            list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
-                a 3-tuple. The first item is an (n, 5) tensor, where the
-                first 4 columns are bounding box positions
-                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
-                between 0 and 1. The second item is an (n,) tensor where each
-                item is the predicted class label of the corresponding box.
-                The third item is an (n, num_protos) tensor where each item
-                is the predicted mask coefficients of instance inside the
-                corresponding box.
-        """
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-
-        device = cls_scores[0].device
-        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
-        mlvl_anchors = self.anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-
-        det_bboxes = []
-        det_labels = []
-        det_coeffs = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_pred_list = [
-                bbox_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            coeff_pred_list = [
-                coeff_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list,
-                                               coeff_pred_list, mlvl_anchors,
-                                               img_shape, scale_factor, cfg,
-                                               rescale)
-            det_bboxes.append(bbox_res[0])
-            det_labels.append(bbox_res[1])
-            det_coeffs.append(bbox_res[2])
-        return det_bboxes, det_labels, det_coeffs
-
-    def _get_bboxes_single(self,
-                           cls_score_list,
-                           bbox_pred_list,
-                           coeff_preds_list,
-                           mlvl_anchors,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        """"Similiar to func:``AnchorHead._get_bboxes_single``, but
-        additionally processes coeff_preds_list and uses fast NMS instead of
-        traditional NMS.
-
-        Args:
-            cls_score_list (list[Tensor]): Box scores for a single scale level
-                Has shape (num_anchors * num_classes, H, W).
-            bbox_pred_list (list[Tensor]): Box energies / deltas for a single
-                scale level with shape (num_anchors * 4, H, W).
-            coeff_preds_list (list[Tensor]): Mask coefficients for a single
-                scale level with shape (num_anchors * num_protos, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shape (tuple[int]): Shape of the input image,
-                (height, width, 3).
-            scale_factor (ndarray): Scale factor of the image arange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-
-        Returns:
-            tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor,
-                where the first 4 columns are bounding box positions
-                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between
-                0 and 1. The second item is an (n,) tensor where each item is
-                the predicted class label of the corresponding box. The third
-                item is an (n, num_protos) tensor where each item is the
-                predicted mask coefficients of instance inside the
-                corresponding box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_coeffs = []
-        for cls_score, bbox_pred, coeff_pred, anchors in \
-                zip(cls_score_list, bbox_pred_list,
-                    coeff_preds_list, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            cls_score = cls_score.permute(1, 2,
-                                          0).reshape(-1, self.cls_out_channels)
-            if self.use_sigmoid_cls:
-                scores = cls_score.sigmoid()
-            else:
-                scores = cls_score.softmax(-1)
-            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
-            coeff_pred = coeff_pred.permute(1, 2,
-                                            0).reshape(-1, self.num_protos)
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[0] > nms_pre:
-                # Get maximum scores for foreground classes.
-                if self.use_sigmoid_cls:
-                    max_scores, _ = scores.max(dim=1)
-                else:
-                    # remind that we set FG labels to [0, num_class-1]
-                    # since mmdet v2.0
-                    # BG cat_id: num_class
-                    max_scores, _ = scores[:, :-1].max(dim=1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[topk_inds, :]
-                scores = scores[topk_inds, :]
-                coeff_pred = coeff_pred[topk_inds, :]
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shape)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_coeffs.append(coeff_pred)
-        mlvl_bboxes = torch.cat(mlvl_bboxes)
-        if rescale:
-            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
-        mlvl_scores = torch.cat(mlvl_scores)
-        mlvl_coeffs = torch.cat(mlvl_coeffs)
-        if self.use_sigmoid_cls:
-            # Add a dummy background class to the backend when using sigmoid
-            # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-            # BG cat_id: num_class
-            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
-            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
-        det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores,
-                                                      mlvl_coeffs,
-                                                      cfg.score_thr,
-                                                      cfg.iou_thr, cfg.top_k,
-                                                      cfg.max_per_img)
-        return det_bboxes, det_labels, det_coeffs
-
-
-@HEADS.register_module()
-class YOLACTSegmHead(nn.Module):
-    """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
-
-    Apply a semantic segmentation loss on feature space using layers that are
-    only evaluated during training to increase performance with no speed
-    penalty.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        num_classes (int): Number of categories excluding the background
-            category.
-        loss_segm (dict): Config of semantic segmentation loss.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels=256,
-                 loss_segm=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0)):
-        super(YOLACTSegmHead, self).__init__()
-        self.in_channels = in_channels
-        self.num_classes = num_classes
-        self.loss_segm = build_loss(loss_segm)
-        self._init_layers()
-        self.fp16_enabled = False
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.segm_conv = nn.Conv2d(
-            self.in_channels, self.num_classes, kernel_size=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        xavier_init(self.segm_conv, distribution='uniform')
-
-    def forward(self, x):
-        """Forward feature from the upstream network.
-
-        Args:
-            x (Tensor): Feature from the upstream network, which is
-                a 4D-tensor.
-
-        Returns:
-            Tensor: Predicted semantic segmentation map with shape
-                (N, num_classes, H, W).
-        """
-        return self.segm_conv(x)
-
-    @force_fp32(apply_to=('segm_pred', ))
-    def loss(self, segm_pred, gt_masks, gt_labels):
-        """Compute loss of the head.
-
-        Args:
-            segm_pred (list[Tensor]): Predicted semantic segmentation map
-                with shape (N, num_classes, H, W).
-            gt_masks (list[Tensor]): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_labels (list[Tensor]): Class indices corresponding to each box.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        loss_segm = []
-        num_imgs, num_classes, mask_h, mask_w = segm_pred.size()
-        for idx in range(num_imgs):
-            cur_segm_pred = segm_pred[idx]
-            cur_gt_masks = gt_masks[idx].float()
-            cur_gt_labels = gt_labels[idx]
-            segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks,
-                                            cur_gt_labels)
-            if segm_targets is None:
-                loss = self.loss_segm(cur_segm_pred,
-                                      torch.zeros_like(cur_segm_pred),
-                                      torch.zeros_like(cur_segm_pred))
-            else:
-                loss = self.loss_segm(
-                    cur_segm_pred,
-                    segm_targets,
-                    avg_factor=num_imgs * mask_h * mask_w)
-            loss_segm.append(loss)
-        return dict(loss_segm=loss_segm)
-
-    def get_targets(self, segm_pred, gt_masks, gt_labels):
-        """Compute semantic segmentation targets for each image.
-
-        Args:
-            segm_pred (Tensor): Predicted semantic segmentation map
-                with shape (num_classes, H, W).
-            gt_masks (Tensor): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_labels (Tensor): Class indices corresponding to each box.
-
-        Returns:
-            Tensor: Semantic segmentation targets with shape
-                (num_classes, H, W).
-        """
-        if gt_masks.size(0) == 0:
-            return None
-        num_classes, mask_h, mask_w = segm_pred.size()
-        with torch.no_grad():
-            downsampled_masks = F.interpolate(
-                gt_masks.unsqueeze(0), (mask_h, mask_w),
-                mode='bilinear',
-                align_corners=False).squeeze(0)
-            downsampled_masks = downsampled_masks.gt(0.5).float()
-            segm_targets = torch.zeros_like(segm_pred, requires_grad=False)
-            for obj_idx in range(downsampled_masks.size(0)):
-                segm_targets[gt_labels[obj_idx] - 1] = torch.max(
-                    segm_targets[gt_labels[obj_idx] - 1],
-                    downsampled_masks[obj_idx])
-            return segm_targets
-
-
-@HEADS.register_module()
-class YOLACTProtonet(nn.Module):
-    """YOLACT mask head used in https://arxiv.org/abs/1904.02689.
-
-    This head outputs the mask prototypes for YOLACT.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        proto_channels (tuple[int]): Output channels of protonet convs.
-        proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs.
-        include_last_relu (Bool): If keep the last relu of protonet.
-        num_protos (int): Number of prototypes.
-        num_classes (int): Number of categories excluding the background
-            category.
-        loss_mask_weight (float): Reweight the mask loss by this factor.
-        max_masks_to_train (int): Maximum number of masks to train for
-            each image.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels=256,
-                 proto_channels=(256, 256, 256, None, 256, 32),
-                 proto_kernel_sizes=(3, 3, 3, -2, 3, 1),
-                 include_last_relu=True,
-                 num_protos=32,
-                 loss_mask_weight=1.0,
-                 max_masks_to_train=100):
-        super(YOLACTProtonet, self).__init__()
-        self.in_channels = in_channels
-        self.proto_channels = proto_channels
-        self.proto_kernel_sizes = proto_kernel_sizes
-        self.include_last_relu = include_last_relu
-        self.protonet = self._init_layers()
-
-        self.loss_mask_weight = loss_mask_weight
-        self.num_protos = num_protos
-        self.num_classes = num_classes
-        self.max_masks_to_train = max_masks_to_train
-        self.fp16_enabled = False
-
-    def _init_layers(self):
-        """A helper function to take a config setting and turn it into a
-        network."""
-        # Possible patterns:
-        # ( 256, 3) -> conv
-        # ( 256,-2) -> deconv
-        # (None,-2) -> bilinear interpolate
-        in_channels = self.in_channels
-        protonets = nn.ModuleList()
-        for num_channels, kernel_size in zip(self.proto_channels,
-                                             self.proto_kernel_sizes):
-            if kernel_size > 0:
-                layer = nn.Conv2d(
-                    in_channels,
-                    num_channels,
-                    kernel_size,
-                    padding=kernel_size // 2)
-            else:
-                if num_channels is None:
-                    layer = InterpolateModule(
-                        scale_factor=-kernel_size,
-                        mode='bilinear',
-                        align_corners=False)
-                else:
-                    layer = nn.ConvTranspose2d(
-                        in_channels,
-                        num_channels,
-                        -kernel_size,
-                        padding=kernel_size // 2)
-            protonets.append(layer)
-            protonets.append(nn.ReLU(inplace=True))
-            in_channels = num_channels if num_channels is not None \
-                else in_channels
-        if not self.include_last_relu:
-            protonets = protonets[:-1]
-        return nn.Sequential(*protonets)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.protonet:
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-
-    def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None):
-        """Forward feature from the upstream network to get prototypes and
-        linearly combine the prototypes, using masks coefficients, into
-        instance masks. Finally, crop the instance masks with given bboxes.
-
-        Args:
-            x (Tensor): Feature from the upstream network, which is
-                a 4D-tensor.
-            coeff_pred (list[Tensor]): Mask coefficients for each scale
-                level with shape (N, num_anchors * num_protos, H, W).
-            bboxes (list[Tensor]): Box used for cropping with shape
-                (N, num_anchors * 4, H, W). During training, they are
-                ground truth boxes. During testing, they are predicted
-                boxes.
-            img_meta (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            sampling_results (List[:obj:``SamplingResult``]): Sampler results
-                for each image.
-
-        Returns:
-            list[Tensor]: Predicted instance segmentation masks.
-        """
-        prototypes = self.protonet(x)
-        prototypes = prototypes.permute(0, 2, 3, 1).contiguous()
-
-        num_imgs = x.size(0)
-        # Training state
-        if self.training:
-            coeff_pred_list = []
-            for coeff_pred_per_level in coeff_pred:
-                coeff_pred_per_level = \
-                    coeff_pred_per_level.permute(0, 2, 3, 1)\
-                    .reshape(num_imgs, -1, self.num_protos)
-                coeff_pred_list.append(coeff_pred_per_level)
-            coeff_pred = torch.cat(coeff_pred_list, dim=1)
-
-        mask_pred_list = []
-        for idx in range(num_imgs):
-            cur_prototypes = prototypes[idx]
-            cur_coeff_pred = coeff_pred[idx]
-            cur_bboxes = bboxes[idx]
-            cur_img_meta = img_meta[idx]
-
-            # Testing state
-            if not self.training:
-                bboxes_for_cropping = cur_bboxes
-            else:
-                cur_sampling_results = sampling_results[idx]
-                pos_assigned_gt_inds = \
-                    cur_sampling_results.pos_assigned_gt_inds
-                bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone()
-                pos_inds = cur_sampling_results.pos_inds
-                cur_coeff_pred = cur_coeff_pred[pos_inds]
-
-            # Linearly combine the prototypes with the mask coefficients
-            mask_pred = cur_prototypes @ cur_coeff_pred.t()
-            mask_pred = torch.sigmoid(mask_pred)
-
-            h, w = cur_img_meta['img_shape'][:2]
-            bboxes_for_cropping[:, 0] /= w
-            bboxes_for_cropping[:, 1] /= h
-            bboxes_for_cropping[:, 2] /= w
-            bboxes_for_cropping[:, 3] /= h
-
-            mask_pred = self.crop(mask_pred, bboxes_for_cropping)
-            mask_pred = mask_pred.permute(2, 0, 1).contiguous()
-            mask_pred_list.append(mask_pred)
-        return mask_pred_list
-
-    @force_fp32(apply_to=('mask_pred', ))
-    def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results):
-        """Compute loss of the head.
-
-        Args:
-            mask_pred (list[Tensor]): Predicted prototypes with shape
-                (num_classes, H, W).
-            gt_masks (list[Tensor]): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            img_meta (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            sampling_results (List[:obj:``SamplingResult``]): Sampler results
-                for each image.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        loss_mask = []
-        num_imgs = len(mask_pred)
-        total_pos = 0
-        for idx in range(num_imgs):
-            cur_mask_pred = mask_pred[idx]
-            cur_gt_masks = gt_masks[idx].float()
-            cur_gt_bboxes = gt_bboxes[idx]
-            cur_img_meta = img_meta[idx]
-            cur_sampling_results = sampling_results[idx]
-
-            pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds
-            num_pos = pos_assigned_gt_inds.size(0)
-            # Since we're producing (near) full image masks,
-            # it'd take too much vram to backprop on every single mask.
-            # Thus we select only a subset.
-            if num_pos > self.max_masks_to_train:
-                perm = torch.randperm(num_pos)
-                select = perm[:self.max_masks_to_train]
-                cur_mask_pred = cur_mask_pred[select]
-                pos_assigned_gt_inds = pos_assigned_gt_inds[select]
-                num_pos = self.max_masks_to_train
-            total_pos += num_pos
-
-            gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds]
-
-            mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks,
-                                            pos_assigned_gt_inds)
-            if num_pos == 0:
-                loss = cur_mask_pred.sum() * 0.
-            elif mask_targets is None:
-                loss = F.binary_cross_entropy(cur_mask_pred,
-                                              torch.zeros_like(cur_mask_pred),
-                                              torch.zeros_like(cur_mask_pred))
-            else:
-                cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1)
-                loss = F.binary_cross_entropy(
-                    cur_mask_pred, mask_targets,
-                    reduction='none') * self.loss_mask_weight
-
-                h, w = cur_img_meta['img_shape'][:2]
-                gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] -
-                                   gt_bboxes_for_reweight[:, 0]) / w
-                gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] -
-                                    gt_bboxes_for_reweight[:, 1]) / h
-                loss = loss.mean(dim=(1,
-                                      2)) / gt_bboxes_width / gt_bboxes_height
-                loss = torch.sum(loss)
-            loss_mask.append(loss)
-
-        if total_pos == 0:
-            total_pos += 1  # avoid nan
-        loss_mask = [x / total_pos for x in loss_mask]
-
-        return dict(loss_mask=loss_mask)
-
-    def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds):
-        """Compute instance segmentation targets for each image.
-
-        Args:
-            mask_pred (Tensor): Predicted prototypes with shape
-                (num_classes, H, W).
-            gt_masks (Tensor): Ground truth masks for each image with
-                the same shape of the input image.
-            pos_assigned_gt_inds (Tensor): GT indices of the corresponding
-                positive samples.
-        Returns:
-            Tensor: Instance segmentation targets with shape
-                (num_instances, H, W).
-        """
-        if gt_masks.size(0) == 0:
-            return None
-        mask_h, mask_w = mask_pred.shape[-2:]
-        gt_masks = F.interpolate(
-            gt_masks.unsqueeze(0), (mask_h, mask_w),
-            mode='bilinear',
-            align_corners=False).squeeze(0)
-        gt_masks = gt_masks.gt(0.5).float()
-        mask_targets = gt_masks[pos_assigned_gt_inds]
-        return mask_targets
-
-    def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale):
-        """Resize, binarize, and format the instance mask predictions.
-
-        Args:
-            mask_pred (Tensor): shape (N, H, W).
-            label_pred (Tensor): shape (N, ).
-            img_meta (dict): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            rescale (bool): If rescale is False, then returned masks will
-                fit the scale of imgs[0].
-        Returns:
-            list[ndarray]: Mask predictions grouped by their predicted classes.
-        """
-        ori_shape = img_meta['ori_shape']
-        scale_factor = img_meta['scale_factor']
-        if rescale:
-            img_h, img_w = ori_shape[:2]
-        else:
-            img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32)
-            img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32)
-
-        cls_segms = [[] for _ in range(self.num_classes)]
-        if mask_pred.size(0) == 0:
-            return cls_segms
-
-        mask_pred = F.interpolate(
-            mask_pred.unsqueeze(0), (img_h, img_w),
-            mode='bilinear',
-            align_corners=False).squeeze(0) > 0.5
-        mask_pred = mask_pred.cpu().numpy().astype(np.uint8)
-
-        for m, l in zip(mask_pred, label_pred):
-            cls_segms[l].append(m)
-        return cls_segms
-
-    def crop(self, masks, boxes, padding=1):
-        """Crop predicted masks by zeroing out everything not in the predicted
-        bbox.
-
-        Args:
-            masks (Tensor): shape [H, W, N].
-            boxes (Tensor): bbox coords in relative point form with
-                shape [N, 4].
-
-        Return:
-            Tensor: The cropped masks.
-        """
-        h, w, n = masks.size()
-        x1, x2 = self.sanitize_coordinates(
-            boxes[:, 0], boxes[:, 2], w, padding, cast=False)
-        y1, y2 = self.sanitize_coordinates(
-            boxes[:, 1], boxes[:, 3], h, padding, cast=False)
-
-        rows = torch.arange(
-            w, device=masks.device, dtype=x1.dtype).view(1, -1,
-                                                         1).expand(h, w, n)
-        cols = torch.arange(
-            h, device=masks.device, dtype=x1.dtype).view(-1, 1,
-                                                         1).expand(h, w, n)
-
-        masks_left = rows >= x1.view(1, 1, -1)
-        masks_right = rows < x2.view(1, 1, -1)
-        masks_up = cols >= y1.view(1, 1, -1)
-        masks_down = cols < y2.view(1, 1, -1)
-
-        crop_mask = masks_left * masks_right * masks_up * masks_down
-
-        return masks * crop_mask.float()
-
-    def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True):
-        """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0,
-        and x2 <= image_size. Also converts from relative to absolute
-        coordinates and casts the results to long tensors.
-
-        Warning: this does things in-place behind the scenes so
-        copy if necessary.
-
-        Args:
-            _x1 (Tensor): shape (N, ).
-            _x2 (Tensor): shape (N, ).
-            img_size (int): Size of the input image.
-            padding (int): x1 >= padding, x2 <= image_size-padding.
-            cast (bool): If cast is false, the result won't be cast to longs.
-
-        Returns:
-            tuple:
-                x1 (Tensor): Sanitized _x1.
-                x2 (Tensor): Sanitized _x2.
-        """
-        x1 = x1 * img_size
-        x2 = x2 * img_size
-        if cast:
-            x1 = x1.long()
-            x2 = x2.long()
-        x1 = torch.min(x1, x2)
-        x2 = torch.max(x1, x2)
-        x1 = torch.clamp(x1 - padding, min=0)
-        x2 = torch.clamp(x2 + padding, max=img_size)
-        return x1, x2
-
-
-class InterpolateModule(nn.Module):
-    """This is a module version of F.interpolate.
-
-    Any arguments you give it just get passed along for the ride.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-
-        self.args = args
-        self.kwargs = kwargs
-
-    def forward(self, x):
-        """Forward features from the upstream network."""
-        return F.interpolate(x, *self.args, **self.kwargs)
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/resnet.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/resnet.py
deleted file mode 100644
index 1cb3ac057ee2d52c46fc94685b5d4e698aad8d5f..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/resnet.py
+++ /dev/null
@@ -1,316 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-
-from .utils import constant_init, kaiming_init
-
-
-def conv3x3(in_planes, out_planes, stride=1, dilation=1):
-    """3x3 convolution with padding."""
-    return nn.Conv2d(
-        in_planes,
-        out_planes,
-        kernel_size=3,
-        stride=stride,
-        padding=dilation,
-        dilation=dilation,
-        bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 stride=1,
-                 dilation=1,
-                 downsample=None,
-                 style='pytorch',
-                 with_cp=False):
-        super(BasicBlock, self).__init__()
-        assert style in ['pytorch', 'caffe']
-        self.conv1 = conv3x3(inplanes, planes, stride, dilation)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.dilation = dilation
-        assert not with_cp
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 stride=1,
-                 dilation=1,
-                 downsample=None,
-                 style='pytorch',
-                 with_cp=False):
-        """Bottleneck block.
-
-        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
-        it is "caffe", the stride-two layer is the first 1x1 conv layer.
-        """
-        super(Bottleneck, self).__init__()
-        assert style in ['pytorch', 'caffe']
-        if style == 'pytorch':
-            conv1_stride = 1
-            conv2_stride = stride
-        else:
-            conv1_stride = stride
-            conv2_stride = 1
-        self.conv1 = nn.Conv2d(
-            inplanes, planes, kernel_size=1, stride=conv1_stride, bias=False)
-        self.conv2 = nn.Conv2d(
-            planes,
-            planes,
-            kernel_size=3,
-            stride=conv2_stride,
-            padding=dilation,
-            dilation=dilation,
-            bias=False)
-
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(
-            planes, planes * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-        self.dilation = dilation
-        self.with_cp = with_cp
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            residual = x
-
-            out = self.conv1(x)
-            out = self.bn1(out)
-            out = self.relu(out)
-
-            out = self.conv2(out)
-            out = self.bn2(out)
-            out = self.relu(out)
-
-            out = self.conv3(out)
-            out = self.bn3(out)
-
-            if self.downsample is not None:
-                residual = self.downsample(x)
-
-            out += residual
-
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        out = self.relu(out)
-
-        return out
-
-
-def make_res_layer(block,
-                   inplanes,
-                   planes,
-                   blocks,
-                   stride=1,
-                   dilation=1,
-                   style='pytorch',
-                   with_cp=False):
-    downsample = None
-    if stride != 1 or inplanes != planes * block.expansion:
-        downsample = nn.Sequential(
-            nn.Conv2d(
-                inplanes,
-                planes * block.expansion,
-                kernel_size=1,
-                stride=stride,
-                bias=False),
-            nn.BatchNorm2d(planes * block.expansion),
-        )
-
-    layers = []
-    layers.append(
-        block(
-            inplanes,
-            planes,
-            stride,
-            dilation,
-            downsample,
-            style=style,
-            with_cp=with_cp))
-    inplanes = planes * block.expansion
-    for _ in range(1, blocks):
-        layers.append(
-            block(inplanes, planes, 1, dilation, style=style, with_cp=with_cp))
-
-    return nn.Sequential(*layers)
-
-
-class ResNet(nn.Module):
-    """ResNet backbone.
-
-    Args:
-        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
-        num_stages (int): Resnet stages, normally 4.
-        strides (Sequence[int]): Strides of the first block of each stage.
-        dilations (Sequence[int]): Dilation of each stage.
-        out_indices (Sequence[int]): Output from which stages.
-        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
-            layer is the 3x3 conv layer, otherwise the stride-two layer is
-            the first 1x1 conv layer.
-        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
-            not freezing any parameters.
-        bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
-            running stats (mean and var).
-        bn_frozen (bool): Whether to freeze weight and bias of BN layers.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-    """
-
-    arch_settings = {
-        18: (BasicBlock, (2, 2, 2, 2)),
-        34: (BasicBlock, (3, 4, 6, 3)),
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3))
-    }
-
-    def __init__(self,
-                 depth,
-                 num_stages=4,
-                 strides=(1, 2, 2, 2),
-                 dilations=(1, 1, 1, 1),
-                 out_indices=(0, 1, 2, 3),
-                 style='pytorch',
-                 frozen_stages=-1,
-                 bn_eval=True,
-                 bn_frozen=False,
-                 with_cp=False):
-        super(ResNet, self).__init__()
-        if depth not in self.arch_settings:
-            raise KeyError(f'invalid depth {depth} for resnet')
-        assert num_stages >= 1 and num_stages <= 4
-        block, stage_blocks = self.arch_settings[depth]
-        stage_blocks = stage_blocks[:num_stages]
-        assert len(strides) == len(dilations) == num_stages
-        assert max(out_indices) < num_stages
-
-        self.out_indices = out_indices
-        self.style = style
-        self.frozen_stages = frozen_stages
-        self.bn_eval = bn_eval
-        self.bn_frozen = bn_frozen
-        self.with_cp = with_cp
-
-        self.inplanes = 64
-        self.conv1 = nn.Conv2d(
-            3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.res_layers = []
-        for i, num_blocks in enumerate(stage_blocks):
-            stride = strides[i]
-            dilation = dilations[i]
-            planes = 64 * 2**i
-            res_layer = make_res_layer(
-                block,
-                self.inplanes,
-                planes,
-                num_blocks,
-                stride=stride,
-                dilation=dilation,
-                style=self.style,
-                with_cp=with_cp)
-            self.inplanes = planes * block.expansion
-            layer_name = f'layer{i + 1}'
-            self.add_module(layer_name, res_layer)
-            self.res_layers.append(layer_name)
-
-        self.feat_dim = block.expansion * 64 * 2**(len(stage_blocks) - 1)
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            from ..runner import load_checkpoint
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, nn.BatchNorm2d):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-        outs = []
-        for i, layer_name in enumerate(self.res_layers):
-            res_layer = getattr(self, layer_name)
-            x = res_layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-        if len(outs) == 1:
-            return outs[0]
-        else:
-            return tuple(outs)
-
-    def train(self, mode=True):
-        super(ResNet, self).train(mode)
-        if self.bn_eval:
-            for m in self.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eval()
-                    if self.bn_frozen:
-                        for params in m.parameters():
-                            params.requires_grad = False
-        if mode and self.frozen_stages >= 0:
-            for param in self.conv1.parameters():
-                param.requires_grad = False
-            for param in self.bn1.parameters():
-                param.requires_grad = False
-            self.bn1.eval()
-            self.bn1.weight.requires_grad = False
-            self.bn1.bias.requires_grad = False
-            for i in range(1, self.frozen_stages + 1):
-                mod = getattr(self, f'layer{i}')
-                mod.eval()
-                for param in mod.parameters():
-                    param.requires_grad = False
diff --git a/spaces/Apex-X/ROOPOK/app.py b/spaces/Apex-X/ROOPOK/app.py
deleted file mode 100644
index ac81150d2a4acd6d7124fd9c15115ab12892b61a..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/ROOPOK/app.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# -* coding:UTF-8 -*
-# !/usr/bin/env python
-import numpy as np
-import gradio as gr
-import roop.globals
-from roop.core import (
-    start,
-    decode_execution_providers,
-    suggest_max_memory,
-    suggest_execution_threads,
-)
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import normalize_output_path
-import os
-from PIL import Image
-
-
-def swap_face(source_file, target_file):
-
-    source_path = "input.jpg"
-    target_path = "target.jpg"
-
-    source_image = Image.fromarray(source_file)
-    source_image.save(source_path)
-    target_image = Image.fromarray(target_file)
-    target_image.save(target_path)
-
-    print("source_path: ", source_path)
-    print("target_path: ", target_path)
-
-    roop.globals.source_path = source_path
-    roop.globals.target_path = target_path
-    output_path = "output.jpg"
-    roop.globals.output_path = normalize_output_path(
-        roop.globals.source_path, roop.globals.target_path, output_path
-    )
-    roop.globals.frame_processors = ["face_swapper"]
-    roop.globals.headless = True
-    roop.globals.keep_fps = True
-    roop.globals.keep_audio = True
-    roop.globals.keep_frames = False
-    roop.globals.many_faces = False
-    roop.globals.video_encoder = "libx264"
-    roop.globals.video_quality = 18
-    roop.globals.max_memory = suggest_max_memory()
-    roop.globals.execution_providers = decode_execution_providers(["cpu"])
-    roop.globals.execution_threads = suggest_execution_threads()
-
-    print(
-        "start process",
-        roop.globals.source_path,
-        roop.globals.target_path,
-        roop.globals.output_path,
-    )
-
-    for frame_processor in get_frame_processors_modules(
-        roop.globals.frame_processors
-    ):
-        if not frame_processor.pre_check():
-            return
-
-    start()
-    return output_path
-
-
-app = gr.Interface(
-    fn=swap_face, inputs=[gr.Image(), gr.Image()], outputs="image"
-)
-app.launch()
diff --git a/spaces/Apex-X/Tm/roop/processors/frame/__init__.py b/spaces/Apex-X/Tm/roop/processors/frame/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ArkanDash/rvc-models-new/lib/infer_pack/modules/F0Predictor/__init__.py b/spaces/ArkanDash/rvc-models-new/lib/infer_pack/modules/F0Predictor/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/openai.py b/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/openai.py
deleted file mode 100644
index 3f4eb8b55fe960e1792b3da804b60b3d8f70fe26..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/openai.py
+++ /dev/null
@@ -1,156 +0,0 @@
-""" OpenAI pretrained model functions
-
-Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-
-import os
-import warnings
-from typing import Union, List
-
-import torch
-
-from .model import build_model_from_openai_state_dict
-from .pretrained import (
-    get_pretrained_url,
-    list_pretrained_tag_models,
-    download_pretrained,
-)
-
-__all__ = ["list_openai_models", "load_openai_model"]
-
-
-def list_openai_models() -> List[str]:
-    """Returns the names of available CLIP models"""
-    return list_pretrained_tag_models("openai")
-
-
-def load_openai_model(
-    name: str,
-    model_cfg,
-    device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
-    jit=True,
-    cache_dir=os.path.expanduser("~/.cache/clip"),
-    enable_fusion: bool = False,
-    fusion_type: str = "None",
-):
-    """Load a CLIP model, preserve its text pretrained part, and set in the CLAP model
-
-    Parameters
-    ----------
-    name : str
-        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
-    device : Union[str, torch.device]
-        The device to put the loaded model
-    jit : bool
-        Whether to load the optimized JIT model (default) or more hackable non-JIT model.
-
-    Returns
-    -------
-    model : torch.nn.Module
-        The CLAP model
-    preprocess : Callable[[PIL.Image], torch.Tensor]
-        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
-    """
-    if get_pretrained_url(name, "openai"):
-        model_path = download_pretrained(
-            get_pretrained_url(name, "openai"), root=cache_dir
-        )
-    elif os.path.isfile(name):
-        model_path = name
-    else:
-        raise RuntimeError(
-            f"Model {name} not found; available models = {list_openai_models()}"
-        )
-
-    try:
-        # loading JIT archive
-        model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
-        state_dict = None
-    except RuntimeError:
-        # loading saved state dict
-        if jit:
-            warnings.warn(
-                f"File {model_path} is not a JIT archive. Loading as a state dict instead"
-            )
-            jit = False
-        state_dict = torch.load(model_path, map_location="cpu")
-
-    if not jit:
-        try:
-            model = build_model_from_openai_state_dict(
-                state_dict or model.state_dict(), model_cfg, enable_fusion, fusion_type
-            ).to(device)
-        except KeyError:
-            sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
-            model = build_model_from_openai_state_dict(
-                sd, model_cfg, enable_fusion, fusion_type
-            ).to(device)
-
-        if str(device) == "cpu":
-            model.float()
-        return model
-
-    # patch the device names
-    device_holder = torch.jit.trace(
-        lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]
-    )
-    device_node = [
-        n
-        for n in device_holder.graph.findAllNodes("prim::Constant")
-        if "Device" in repr(n)
-    ][-1]
-
-    def patch_device(module):
-        try:
-            graphs = [module.graph] if hasattr(module, "graph") else []
-        except RuntimeError:
-            graphs = []
-
-        if hasattr(module, "forward1"):
-            graphs.append(module.forward1.graph)
-
-        for graph in graphs:
-            for node in graph.findAllNodes("prim::Constant"):
-                if "value" in node.attributeNames() and str(node["value"]).startswith(
-                    "cuda"
-                ):
-                    node.copyAttributes(device_node)
-
-    model.apply(patch_device)
-    patch_device(model.encode_audio)
-    patch_device(model.encode_text)
-
-    # patch dtype to float32 on CPU
-    if str(device) == "cpu":
-        float_holder = torch.jit.trace(
-            lambda: torch.ones([]).float(), example_inputs=[]
-        )
-        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
-        float_node = float_input.node()
-
-        def patch_float(module):
-            try:
-                graphs = [module.graph] if hasattr(module, "graph") else []
-            except RuntimeError:
-                graphs = []
-
-            if hasattr(module, "forward1"):
-                graphs.append(module.forward1.graph)
-
-            for graph in graphs:
-                for node in graph.findAllNodes("aten::to"):
-                    inputs = list(node.inputs())
-                    for i in [
-                        1,
-                        2,
-                    ]:  # dtype can be the second or third argument to aten::to()
-                        if inputs[i].node()["value"] == 5:
-                            inputs[i].node().copyAttributes(float_node)
-
-        model.apply(patch_float)
-        patch_float(model.encode_audio)
-        patch_float(model.encode_text)
-        model.float()
-
-    model.audio_branch.audio_length = model.audio_cfg.audio_length
-    return model
diff --git a/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/pann_model.py b/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/pann_model.py
deleted file mode 100644
index 0d9a8eb0bf897ad6ec04923361b01e5de433b2ef..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/pann_model.py
+++ /dev/null
@@ -1,704 +0,0 @@
-# PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
-# Reference from https://github.com/qiuqiangkong/audioset_tagging_cnn
-# Some layers are re-designed for CLAP
-import os
-
-os.environ["NUMBA_CACHE_DIR"] = "/tmp/"
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchlibrosa.stft import Spectrogram, LogmelFilterBank
-from torchlibrosa.augmentation import SpecAugmentation
-
-from .utils import do_mixup, interpolate, pad_framewise_output
-from .feature_fusion import iAFF, AFF, DAF
-
-
-def init_layer(layer):
-    """Initialize a Linear or Convolutional layer."""
-    nn.init.xavier_uniform_(layer.weight)
-
-    if hasattr(layer, "bias"):
-        if layer.bias is not None:
-            layer.bias.data.fill_(0.0)
-
-
-def init_bn(bn):
-    """Initialize a Batchnorm layer."""
-    bn.bias.data.fill_(0.0)
-    bn.weight.data.fill_(1.0)
-
-
-class ConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels):
-
-        super(ConvBlock, self).__init__()
-
-        self.conv1 = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=(3, 3),
-            stride=(1, 1),
-            padding=(1, 1),
-            bias=False,
-        )
-
-        self.conv2 = nn.Conv2d(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=(3, 3),
-            stride=(1, 1),
-            padding=(1, 1),
-            bias=False,
-        )
-
-        self.bn1 = nn.BatchNorm2d(out_channels)
-        self.bn2 = nn.BatchNorm2d(out_channels)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_layer(self.conv2)
-        init_bn(self.bn1)
-        init_bn(self.bn2)
-
-    def forward(self, input, pool_size=(2, 2), pool_type="avg"):
-
-        x = input
-        x = F.relu_(self.bn1(self.conv1(x)))
-        x = F.relu_(self.bn2(self.conv2(x)))
-        if pool_type == "max":
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg":
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg+max":
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        else:
-            raise Exception("Incorrect argument!")
-
-        return x
-
-
-class ConvBlock5x5(nn.Module):
-    def __init__(self, in_channels, out_channels):
-
-        super(ConvBlock5x5, self).__init__()
-
-        self.conv1 = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=(5, 5),
-            stride=(1, 1),
-            padding=(2, 2),
-            bias=False,
-        )
-
-        self.bn1 = nn.BatchNorm2d(out_channels)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_bn(self.bn1)
-
-    def forward(self, input, pool_size=(2, 2), pool_type="avg"):
-
-        x = input
-        x = F.relu_(self.bn1(self.conv1(x)))
-        if pool_type == "max":
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg":
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg+max":
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        else:
-            raise Exception("Incorrect argument!")
-
-        return x
-
-
-class AttBlock(nn.Module):
-    def __init__(self, n_in, n_out, activation="linear", temperature=1.0):
-        super(AttBlock, self).__init__()
-
-        self.activation = activation
-        self.temperature = temperature
-        self.att = nn.Conv1d(
-            in_channels=n_in,
-            out_channels=n_out,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=True,
-        )
-        self.cla = nn.Conv1d(
-            in_channels=n_in,
-            out_channels=n_out,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=True,
-        )
-
-        self.bn_att = nn.BatchNorm1d(n_out)
-        self.init_weights()
-
-    def init_weights(self):
-        init_layer(self.att)
-        init_layer(self.cla)
-        init_bn(self.bn_att)
-
-    def forward(self, x):
-        # x: (n_samples, n_in, n_time)
-        norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1)
-        cla = self.nonlinear_transform(self.cla(x))
-        x = torch.sum(norm_att * cla, dim=2)
-        return x, norm_att, cla
-
-    def nonlinear_transform(self, x):
-        if self.activation == "linear":
-            return x
-        elif self.activation == "sigmoid":
-            return torch.sigmoid(x)
-
-
-class Cnn14(nn.Module):
-    def __init__(
-        self,
-        sample_rate,
-        window_size,
-        hop_size,
-        mel_bins,
-        fmin,
-        fmax,
-        classes_num,
-        enable_fusion=False,
-        fusion_type="None",
-    ):
-
-        super(Cnn14, self).__init__()
-
-        window = "hann"
-        center = True
-        pad_mode = "reflect"
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(
-            n_fft=window_size,
-            hop_length=hop_size,
-            win_length=window_size,
-            window=window,
-            center=center,
-            pad_mode=pad_mode,
-            freeze_parameters=True,
-        )
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(
-            sr=sample_rate,
-            n_fft=window_size,
-            n_mels=mel_bins,
-            fmin=fmin,
-            fmax=fmax,
-            ref=ref,
-            amin=amin,
-            top_db=top_db,
-            freeze_parameters=True,
-        )
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(
-            time_drop_width=64,
-            time_stripes_num=2,
-            freq_drop_width=8,
-            freq_stripes_num=2,
-        )
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        if (self.enable_fusion) and (self.fusion_type == "channel_map"):
-            self.conv_block1 = ConvBlock(in_channels=4, out_channels=64)
-        else:
-            self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
-        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
-
-        self.fc1 = nn.Linear(2048, 2048, bias=True)
-        self.fc_audioset = nn.Linear(2048, classes_num, bias=True)
-
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]
-        ):
-            self.mel_conv1d = nn.Sequential(
-                nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2),
-                nn.BatchNorm1d(64),  # No Relu
-            )
-            if self.fusion_type == "daf_1d":
-                self.fusion_model = DAF()
-            elif self.fusion_type == "aff_1d":
-                self.fusion_model = AFF(channels=64, type="1D")
-            elif self.fusion_type == "iaff_1d":
-                self.fusion_model = iAFF(channels=64, type="1D")
-
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
-        ):
-            self.mel_conv2d = nn.Sequential(
-                nn.Conv2d(1, 64, kernel_size=(5, 5), stride=(6, 2), padding=(2, 2)),
-                nn.BatchNorm2d(64),
-                nn.ReLU(inplace=True),
-            )
-
-            if self.fusion_type == "daf_2d":
-                self.fusion_model = DAF()
-            elif self.fusion_type == "aff_2d":
-                self.fusion_model = AFF(channels=64, type="2D")
-            elif self.fusion_type == "iaff_2d":
-                self.fusion_model = iAFF(channels=64, type="2D")
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        if self.enable_fusion and input["longer"].sum() == 0:
-            # if no audio is longer than 10s, then randomly select one audio to be longer
-            input["longer"][torch.randint(0, input["longer"].shape[0], (1,))] = True
-
-        if not self.enable_fusion:
-            x = self.spectrogram_extractor(
-                input["waveform"].to(device=device, non_blocking=True)
-            )  # (batch_size, 1, time_steps, freq_bins)
-            x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)
-
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-        else:
-            longer_list = input["longer"].to(device=device, non_blocking=True)
-            x = input["mel_fusion"].to(device=device, non_blocking=True)
-            longer_list_idx = torch.where(longer_list)[0]
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-            if self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]:
-                new_x = x[:, 0:1, :, :].clone().contiguous()
-                # local processing
-                if len(longer_list_idx) > 0:
-                    fusion_x_local = x[longer_list_idx, 1:, :, :].clone().contiguous()
-                    FB, FC, FT, FF = fusion_x_local.size()
-                    fusion_x_local = fusion_x_local.view(FB * FC, FT, FF)
-                    fusion_x_local = torch.permute(
-                        fusion_x_local, (0, 2, 1)
-                    ).contiguous()
-                    fusion_x_local = self.mel_conv1d(fusion_x_local)
-                    fusion_x_local = fusion_x_local.view(
-                        FB, FC, FF, fusion_x_local.size(-1)
-                    )
-                    fusion_x_local = (
-                        torch.permute(fusion_x_local, (0, 2, 1, 3))
-                        .contiguous()
-                        .flatten(2)
-                    )
-                    if fusion_x_local.size(-1) < FT:
-                        fusion_x_local = torch.cat(
-                            [
-                                fusion_x_local,
-                                torch.zeros(
-                                    (FB, FF, FT - fusion_x_local.size(-1)),
-                                    device=device,
-                                ),
-                            ],
-                            dim=-1,
-                        )
-                    else:
-                        fusion_x_local = fusion_x_local[:, :, :FT]
-                    # 1D fusion
-                    new_x = new_x.squeeze(1).permute((0, 2, 1)).contiguous()
-                    new_x[longer_list_idx] = self.fusion_model(
-                        new_x[longer_list_idx], fusion_x_local
-                    )
-                    x = new_x.permute((0, 2, 1)).contiguous()[:, None, :, :]
-                else:
-                    x = new_x
-            elif self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d", "channel_map"]:
-                x = x  # no change
-
-        if self.training:
-            x = self.spec_augmenter(x)
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
-        ):
-            global_x = x[:, 0:1, :, :]
-
-            # global processing
-            B, C, H, W = global_x.shape
-            global_x = self.conv_block1(global_x, pool_size=(2, 2), pool_type="avg")
-            if len(longer_list_idx) > 0:
-                local_x = x[longer_list_idx, 1:, :, :].contiguous()
-                TH = global_x.size(-2)
-                # local processing
-                B, C, H, W = local_x.shape
-                local_x = local_x.view(B * C, 1, H, W)
-                local_x = self.mel_conv2d(local_x)
-                local_x = local_x.view(
-                    B, C, local_x.size(1), local_x.size(2), local_x.size(3)
-                )
-                local_x = local_x.permute((0, 2, 1, 3, 4)).contiguous().flatten(2, 3)
-                TB, TC, _, TW = local_x.size()
-                if local_x.size(-2) < TH:
-                    local_x = torch.cat(
-                        [
-                            local_x,
-                            torch.zeros(
-                                (TB, TC, TH - local_x.size(-2), TW),
-                                device=global_x.device,
-                            ),
-                        ],
-                        dim=-2,
-                    )
-                else:
-                    local_x = local_x[:, :, :TH, :]
-
-                global_x[longer_list_idx] = self.fusion_model(
-                    global_x[longer_list_idx], local_x
-                )
-            x = global_x
-        else:
-            x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
-
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block6(x, pool_size=(1, 1), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 32)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {
-            "clipwise_output": clipwise_output,
-            "embedding": embedding,
-            "fine_grained_embedding": latent_output,
-        }
-        return output_dict
-
-
-class Cnn6(nn.Module):
-    def __init__(
-        self,
-        sample_rate,
-        window_size,
-        hop_size,
-        mel_bins,
-        fmin,
-        fmax,
-        classes_num,
-        enable_fusion=False,
-        fusion_type="None",
-    ):
-
-        super(Cnn6, self).__init__()
-
-        window = "hann"
-        center = True
-        pad_mode = "reflect"
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(
-            n_fft=window_size,
-            hop_length=hop_size,
-            win_length=window_size,
-            window=window,
-            center=center,
-            pad_mode=pad_mode,
-            freeze_parameters=True,
-        )
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(
-            sr=sample_rate,
-            n_fft=window_size,
-            n_mels=mel_bins,
-            fmin=fmin,
-            fmax=fmax,
-            ref=ref,
-            amin=amin,
-            top_db=top_db,
-            freeze_parameters=True,
-        )
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(
-            time_drop_width=64,
-            time_stripes_num=2,
-            freq_drop_width=8,
-            freq_stripes_num=2,
-        )
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512)
-
-        self.fc1 = nn.Linear(512, 512, bias=True)
-        self.fc_audioset = nn.Linear(512, classes_num, bias=True)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        x = self.spectrogram_extractor(input)  # (batch_size, 1, time_steps, freq_bins)
-        x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)
-
-        x = x.transpose(1, 3)
-        x = self.bn0(x)
-        x = x.transpose(1, 3)
-
-        if self.training:
-            x = self.spec_augmenter(x)
-
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 16)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {
-            "clipwise_output": clipwise_output,
-            "embedding": embedding,
-            "fine_grained_embedding": latent_output,
-        }
-
-        return output_dict
-
-
-class Cnn10(nn.Module):
-    def __init__(
-        self,
-        sample_rate,
-        window_size,
-        hop_size,
-        mel_bins,
-        fmin,
-        fmax,
-        classes_num,
-        enable_fusion=False,
-        fusion_type="None",
-    ):
-
-        super(Cnn10, self).__init__()
-
-        window = "hann"
-        center = True
-        pad_mode = "reflect"
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(
-            n_fft=window_size,
-            hop_length=hop_size,
-            win_length=window_size,
-            window=window,
-            center=center,
-            pad_mode=pad_mode,
-            freeze_parameters=True,
-        )
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(
-            sr=sample_rate,
-            n_fft=window_size,
-            n_mels=mel_bins,
-            fmin=fmin,
-            fmax=fmax,
-            ref=ref,
-            amin=amin,
-            top_db=top_db,
-            freeze_parameters=True,
-        )
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(
-            time_drop_width=64,
-            time_stripes_num=2,
-            freq_drop_width=8,
-            freq_stripes_num=2,
-        )
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
-
-        self.fc1 = nn.Linear(1024, 1024, bias=True)
-        self.fc_audioset = nn.Linear(1024, classes_num, bias=True)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        x = self.spectrogram_extractor(input)  # (batch_size, 1, time_steps, freq_bins)
-        x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)
-
-        x = x.transpose(1, 3)
-        x = self.bn0(x)
-        x = x.transpose(1, 3)
-
-        if self.training:
-            x = self.spec_augmenter(x)
-
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 32)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {
-            "clipwise_output": clipwise_output,
-            "embedding": embedding,
-            "fine_grained_embedding": latent_output,
-        }
-
-        return output_dict
-
-
-def create_pann_model(audio_cfg, enable_fusion=False, fusion_type="None"):
-    try:
-        ModelProto = eval(audio_cfg.model_name)
-        model = ModelProto(
-            sample_rate=audio_cfg.sample_rate,
-            window_size=audio_cfg.window_size,
-            hop_size=audio_cfg.hop_size,
-            mel_bins=audio_cfg.mel_bins,
-            fmin=audio_cfg.fmin,
-            fmax=audio_cfg.fmax,
-            classes_num=audio_cfg.class_num,
-            enable_fusion=enable_fusion,
-            fusion_type=fusion_type,
-        )
-        return model
-    except:
-        raise RuntimeError(
-            f"Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough."
-        )
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py
deleted file mode 100644
index 163462e1f194e1e4100da92d76d9516f7cc22e35..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from torch import nn
-from torchvision.ops import roi_align
-
-
-# NOTE: torchvision's RoIAlign has a different default aligned=False
-class ROIAlign(nn.Module):
-    def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
-        """
-        Args:
-            output_size (tuple): h, w
-            spatial_scale (float): scale the input boxes by this number
-            sampling_ratio (int): number of inputs samples to take for each output
-                sample. 0 to take samples densely.
-            aligned (bool): if False, use the legacy implementation in
-                Detectron. If True, align the results more perfectly.
-
-        Note:
-            The meaning of aligned=True:
-
-            Given a continuous coordinate c, its two neighboring pixel indices (in our
-            pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
-            c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
-            from the underlying signal at continuous coordinates 0.5 and 1.5). But the original
-            roi_align (aligned=False) does not subtract the 0.5 when computing neighboring
-            pixel indices and therefore it uses pixels with a slightly incorrect alignment
-            (relative to our pixel model) when performing bilinear interpolation.
-
-            With `aligned=True`,
-            we first appropriately scale the ROI and then shift it by -0.5
-            prior to calling roi_align. This produces the correct neighbors; see
-            detectron2/tests/test_roi_align.py for verification.
-
-            The difference does not make a difference to the model's performance if
-            ROIAlign is used together with conv layers.
-        """
-        super().__init__()
-        self.output_size = output_size
-        self.spatial_scale = spatial_scale
-        self.sampling_ratio = sampling_ratio
-        self.aligned = aligned
-
-        from torchvision import __version__
-
-        version = tuple(int(x) for x in __version__.split(".")[:2])
-        # https://github.com/pytorch/vision/pull/2438
-        assert version >= (0, 7), "Require torchvision >= 0.7"
-
-    def forward(self, input, rois):
-        """
-        Args:
-            input: NCHW images
-            rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
-        """
-        assert rois.dim() == 2 and rois.size(1) == 5
-        if input.is_quantized:
-            input = input.dequantize()
-        return roi_align(
-            input,
-            rois.to(dtype=input.dtype),
-            self.output_size,
-            self.spatial_scale,
-            self.sampling_ratio,
-            self.aligned,
-        )
-
-    def __repr__(self):
-        tmpstr = self.__class__.__name__ + "("
-        tmpstr += "output_size=" + str(self.output_size)
-        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
-        tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
-        tmpstr += ", aligned=" + str(self.aligned)
-        tmpstr += ")"
-        return tmpstr
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py
deleted file mode 100644
index b1eedeebf8e3bde80722fc4acf51be6ca212cb3d..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-import torch
-
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec, batched_nms_rotated
-from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.events import get_event_storage
-
-from ..box_regression import Box2BoxTransformRotated
-from ..poolers import ROIPooler
-from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals
-from .box_head import build_box_head
-from .fast_rcnn import FastRCNNOutputLayers
-from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads
-
-logger = logging.getLogger(__name__)
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    R: number of ROIs, combined over all images, in the minibatch
-    Ri: number of ROIs in image i
-    K: number of foreground classes. E.g.,there are 80 foreground classes in COCO.
-
-Naming convention:
-
-    deltas: refers to the 5-d (dx, dy, dw, dh, da) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransformRotated`).
-
-    pred_class_logits: predicted class scores in [-inf, +inf]; use
-        softmax(pred_class_logits) to estimate P(class).
-
-    gt_classes: ground-truth classification labels in [0, K], where [0, K) represent
-        foreground object classes and K represents the background class.
-
-    pred_proposal_deltas: predicted rotated box2box transform deltas for transforming proposals
-        to detection box predictions.
-
-    gt_proposal_deltas: ground-truth rotated box2box transform deltas
-"""
-
-
-def fast_rcnn_inference_rotated(
-    boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image
-):
-    """
-    Call `fast_rcnn_inference_single_image_rotated` for all images.
-
-    Args:
-        boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
-            boxes for each image. Element i has shape (Ri, K * 5) if doing
-            class-specific regression, or (Ri, 5) if doing class-agnostic
-            regression, where Ri is the number of predicted objects for image i.
-            This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`.
-        scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
-            Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
-            for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`.
-        image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.
-        score_thresh (float): Only return detections with a confidence score exceeding this
-            threshold.
-        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].
-        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
-            all detections.
-
-    Returns:
-        instances: (list[Instances]): A list of N instances, one for each image in the batch,
-            that stores the topk most confidence detections.
-        kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates
-            the corresponding boxes/scores index in [0, Ri) from the input, for image i.
-    """
-    result_per_image = [
-        fast_rcnn_inference_single_image_rotated(
-            boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image
-        )
-        for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes)
-    ]
-    return [x[0] for x in result_per_image], [x[1] for x in result_per_image]
-
-
-def fast_rcnn_inference_single_image_rotated(
-    boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image
-):
-    """
-    Single-image inference. Return rotated bounding-box detection results by thresholding
-    on scores and applying rotated non-maximum suppression (Rotated NMS).
-
-    Args:
-        Same as `fast_rcnn_inference_rotated`, but with rotated boxes, scores, and image shapes
-        per image.
-
-    Returns:
-        Same as `fast_rcnn_inference_rotated`, but for only one image.
-    """
-    valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
-    if not valid_mask.all():
-        boxes = boxes[valid_mask]
-        scores = scores[valid_mask]
-
-    B = 5  # box dimension
-    scores = scores[:, :-1]
-    num_bbox_reg_classes = boxes.shape[1] // B
-    # Convert to Boxes to use the `clip` function ...
-    boxes = RotatedBoxes(boxes.reshape(-1, B))
-    boxes.clip(image_shape)
-    boxes = boxes.tensor.view(-1, num_bbox_reg_classes, B)  # R x C x B
-    # Filter results based on detection scores
-    filter_mask = scores > score_thresh  # R x K
-    # R' x 2. First column contains indices of the R predictions;
-    # Second column contains indices of classes.
-    filter_inds = filter_mask.nonzero()
-    if num_bbox_reg_classes == 1:
-        boxes = boxes[filter_inds[:, 0], 0]
-    else:
-        boxes = boxes[filter_mask]
-    scores = scores[filter_mask]
-
-    # Apply per-class Rotated NMS
-    keep = batched_nms_rotated(boxes, scores, filter_inds[:, 1], nms_thresh)
-    if topk_per_image >= 0:
-        keep = keep[:topk_per_image]
-    boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
-
-    result = Instances(image_shape)
-    result.pred_boxes = RotatedBoxes(boxes)
-    result.scores = scores
-    result.pred_classes = filter_inds[:, 1]
-
-    return result, filter_inds[:, 0]
-
-
-class RotatedFastRCNNOutputLayers(FastRCNNOutputLayers):
-    """
-    Two linear layers for predicting Rotated Fast R-CNN outputs.
-    """
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        args = super().from_config(cfg, input_shape)
-        args["box2box_transform"] = Box2BoxTransformRotated(
-            weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS
-        )
-        return args
-
-    def inference(self, predictions, proposals):
-        """
-        Returns:
-            list[Instances]: same as `fast_rcnn_inference_rotated`.
-            list[Tensor]: same as `fast_rcnn_inference_rotated`.
-        """
-        boxes = self.predict_boxes(predictions, proposals)
-        scores = self.predict_probs(predictions, proposals)
-        image_shapes = [x.image_size for x in proposals]
-
-        return fast_rcnn_inference_rotated(
-            boxes,
-            scores,
-            image_shapes,
-            self.test_score_thresh,
-            self.test_nms_thresh,
-            self.test_topk_per_image,
-        )
-
-
-@ROI_HEADS_REGISTRY.register()
-class RROIHeads(StandardROIHeads):
-    """
-    This class is used by Rotated Fast R-CNN to detect rotated boxes.
-    For now, it only supports box predictions but not mask or keypoints.
-    """
-
-    @configurable
-    def __init__(self, **kwargs):
-        """
-        NOTE: this interface is experimental.
-        """
-        super().__init__(**kwargs)
-        assert (
-            not self.mask_on and not self.keypoint_on
-        ), "Mask/Keypoints not supported in Rotated ROIHeads."
-        assert not self.train_on_pred_boxes, "train_on_pred_boxes not implemented for RROIHeads!"
-
-    @classmethod
-    def _init_box_head(cls, cfg, input_shape):
-        # fmt: off
-        in_features       = cfg.MODEL.ROI_HEADS.IN_FEATURES
-        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_scales     = tuple(1.0 / input_shape[k].stride for k in in_features)
-        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        # fmt: on
-        assert pooler_type in ["ROIAlignRotated"], pooler_type
-        # assume all channel counts are equal
-        in_channels = [input_shape[f].channels for f in in_features][0]
-
-        box_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        box_head = build_box_head(
-            cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution)
-        )
-        # This line is the only difference v.s. StandardROIHeads
-        box_predictor = RotatedFastRCNNOutputLayers(cfg, box_head.output_shape)
-        return {
-            "box_in_features": in_features,
-            "box_pooler": box_pooler,
-            "box_head": box_head,
-            "box_predictor": box_predictor,
-        }
-
-    @torch.no_grad()
-    def label_and_sample_proposals(self, proposals, targets):
-        """
-        Prepare some proposals to be used to train the RROI heads.
-        It performs box matching between `proposals` and `targets`, and assigns
-        training labels to the proposals.
-        It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes,
-        with a fraction of positives that is no larger than `self.positive_sample_fraction.
-
-        Args:
-            See :meth:`StandardROIHeads.forward`
-
-        Returns:
-            list[Instances]: length `N` list of `Instances`s containing the proposals
-                sampled for training. Each `Instances` has the following fields:
-                - proposal_boxes: the rotated proposal boxes
-                - gt_boxes: the ground-truth rotated boxes that the proposal is assigned to
-                  (this is only meaningful if the proposal has a label > 0; if label = 0
-                   then the ground-truth box is random)
-                - gt_classes: the ground-truth classification lable for each proposal
-        """
-        if self.proposal_append_gt:
-            proposals = add_ground_truth_to_proposals(targets, proposals)
-
-        proposals_with_gt = []
-
-        num_fg_samples = []
-        num_bg_samples = []
-        for proposals_per_image, targets_per_image in zip(proposals, targets):
-            has_gt = len(targets_per_image) > 0
-            match_quality_matrix = pairwise_iou_rotated(
-                targets_per_image.gt_boxes, proposals_per_image.proposal_boxes
-            )
-            matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix)
-            sampled_idxs, gt_classes = self._sample_proposals(
-                matched_idxs, matched_labels, targets_per_image.gt_classes
-            )
-
-            proposals_per_image = proposals_per_image[sampled_idxs]
-            proposals_per_image.gt_classes = gt_classes
-
-            if has_gt:
-                sampled_targets = matched_idxs[sampled_idxs]
-                proposals_per_image.gt_boxes = targets_per_image.gt_boxes[sampled_targets]
-
-            num_bg_samples.append((gt_classes == self.num_classes).sum().item())
-            num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1])
-            proposals_with_gt.append(proposals_per_image)
-
-        # Log the number of fg/bg samples that are selected for training ROI heads
-        storage = get_event_storage()
-        storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples))
-        storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples))
-
-        return proposals_with_gt
diff --git a/spaces/Banbri/zcvzcv/src/components/icons/hugging-clap.tsx b/spaces/Banbri/zcvzcv/src/components/icons/hugging-clap.tsx
deleted file mode 100644
index ffb37ae6183cd8ce7fe7c212e383a6510eba2485..0000000000000000000000000000000000000000
--- a/spaces/Banbri/zcvzcv/src/components/icons/hugging-clap.tsx
+++ /dev/null
@@ -1,8 +0,0 @@
-export function HuggingClap() {
-  return (
-    <svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-      <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-      <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-    </svg>
-  )
-}
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Apk Descargar Templo Ejecutar 3.md b/spaces/Benson/text-generation/Examples/Apk Descargar Templo Ejecutar 3.md
deleted file mode 100644
index 1bf3594b9e91effaed9aaa93dec40e82b00c4680..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Apk Descargar Templo Ejecutar 3.md	
+++ /dev/null
@@ -1,122 +0,0 @@
-
-<h1>Descargar APK Temple Run 3: Cómo jugar la última versión del popular juego Endless Runner</h1>
-<p>Temple Run es uno de los juegos más exitosos y adictivos en dispositivos móviles. Ha sido descargado más de 500 millones de veces y ha generado varios spin-offs y secuelas. La última entrega, Temple Run 3, está aquí y promete ofrecer más diversión, maravilla, peligro y aventura que nunca. En este artículo, le mostraremos cómo descargar e instalar Temple Run 3 APK en su dispositivo Android, cómo jugar el juego y disfrutar de sus nuevas características, y cómo obtener altas puntuaciones y toneladas de monedas en sus carreras. </p>
-<h2>apk descargar templo ejecutar 3</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733; <a href="https://bltlly.com/2v6KZ7">https://bltlly.com/2v6KZ7</a></b></p><br /><br />
- <h2>¿Qué es Temple Run 3?</h2>
-<p>Temple Run 3 es la secuela de los juegos de éxito Temple Run y Temple Run 2, desarrollado por Imangi Studios. Es un juego de corredor sin fin en 3D donde controlas a un explorador que ha robado un ídolo maldito de un antiguo templo y debe correr por su vida de los malvados monos demoníacos que lo persiguen. Tienes que probar tus reflejos mientras corres por las antiguas paredes del templo y a lo largo de escarpados acantilados, deslizando para girar, saltar y deslizarte para evitar obstáculos, recoger monedas y comprar power-ups, desbloquear nuevos personajes y ver hasta dónde puedes correr. </p>
- <h3>Las características y mejoras de Temple Run 3</h3>
-<p>Temple Run 3 se basa en los sólidos cimientos de sus predecesores, pero también añade algunas nuevas características y mejoras que lo hacen más emocionante y desafiante. Algunos de ellos son:</p>
-<ul>
-<li><b>Gráficos mejorados:</b> El juego tiene colores vibrantes y entornos más detallados que se suman al atractivo de la ciudad perdida en el cielo. El juego también tiene efectos más realistas como cascadas, fuego, sombras y reflexiones. </li>
-<li><b>Nuevos obstáculos:</b> El juego introduce nuevos obstáculos que requieren más habilidad y tiempo para superarse, como estatuas que respiran fuego, ejes oscilantes, picos giratorios, rocas que caen, brechas y más. </li>
-
-<li><b>Nuevos potenciadores:</b> El juego también tiene algunos nuevos potenciadores que pueden ayudarte en tu carrera, como la invisibilidad que te hace inmune a los obstáculos por un corto tiempo, impulso que te da una ráfaga de velocidad e invencibilidad por una corta distancia, escudo que te protege de un solo golpe, bono de gema que duplica el valor de las gemas que recoger, bono de moneda que duplica el valor de las monedas que recoger, bono de puntuación que aumenta su multiplicador de puntuación, y el imán que atrae a todas las monedas y gemas en su vecindad. </li>
-<li><b>Nuevos personajes:</b> El juego también tiene algunos nuevos personajes que puedes desbloquear y jugar como, cada uno con sus propias habilidades y fondos. Estos incluyen Scarlett Fox, un ladrón astuto y ágil que puede utilizar su gancho de agarre para balancearse a través de los huecos, Barry Bones, un policía duro y valiente que puede usar su bastón para romper obstáculos, Karma Lee, un artista marcial rápido y elegante que puede usar sus nunchucks para desviar obstáculos, Montana Smith, un explorador robusto y aventurero que puede usar su látigo para agarrar monedas y gemas, Zack Wonder, un doble de riesgo audaz y carismático que puede usar su casco para protegerse de un solo golpe, y Francisco Montoya, un conquistador encantador y extravagante que puede usar su estoque para cortar obstáculos. </li>
-</ul>
- <h2> ¿Cómo descargar e instalar Temple Run 3 APK en su dispositivo Android? </h2>
-<p>Si quieres jugar Temple Run 3 en tu dispositivo Android, tendrás que descargar e instalar el archivo APK del juego. APK significa Android Package Kit, y es un formato de archivo que contiene todos los elementos de una aplicación Android. Al instalar un archivo APK, puedes disfrutar de aplicaciones que no están disponibles en Google Play Store o que no son compatibles con tu dispositivo. Sin embargo, también tendrá que tomar algunas precauciones y cumplir con algunos requisitos antes de instalar un archivo APK. </p>
- <h3>Los pasos para descargar e instalar el archivo APK</h3>
-<p>Aquí están los pasos para descargar e instalar el archivo APK Temple Run 3 en su dispositivo Android:</p>
-<ol>
-
-<li><b>Descargar el archivo APK:</b> Una vez que haya encontrado una fuente confiable, haga clic en el botón de descarga o enlace y guarde el archivo APK en su dispositivo. Es posible que necesite permitir descargas de fuentes desconocidas en la configuración de su navegador. </li>
-<li><b>Habilitar fuentes desconocidas en la configuración del dispositivo:</b> Antes de instalar el archivo APK, tendrá que habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. Puede ver un mensaje de advertencia de que instalar aplicaciones de fuentes desconocidas puede dañar su dispositivo. Pulse Aceptar para continuar. </li>
-<li><b>Instalar el archivo APK:</b> Después de haber habilitado fuentes desconocidas, busque el archivo APK en el administrador de archivos o la carpeta de descargas de su dispositivo. Pulse sobre él y siga las instrucciones en la pantalla para instalarlo. Puede ver un mensaje que le pide que conceda permisos a la aplicación. Pulse Instalar para continuar. </li>
-<li><b>Iniciar el juego y disfrutar:</b> Una vez que la instalación se ha completado, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio. Puede ver un mensaje que le pide que verifique su edad y acepte los términos del servicio. Toque OK para comenzar a jugar Temple Run 3.</li>
-</ol>
- <h3>Las precauciones y requisitos para instalar el archivo APK</h3>
-<p>Si bien instalar un archivo APK puede ser una forma conveniente de disfrutar de aplicaciones que no están disponibles o compatibles con su dispositivo, también viene con algunos riesgos y limitaciones. Estas son algunas de las precauciones y requisitos que debe tener en cuenta antes de instalar un archivo APK:</p>
-<p></p>
-<ul>
-
-<li><b>Escanear el archivo APK en busca de virus y malware:</b> No todos los archivos APK son seguros. Algunos pueden contener virus, malware, spyware u otro código dañino que puede dañar su dispositivo o comprometer su privacidad. Antes de instalar un archivo APK, asegúrese de escanearlo con una aplicación antivirus o anti-malware de buena reputación. También puede utilizar herramientas en línea como VirusTotal o MetaDefender para comprobar el archivo APK para cualquier amenaza. </li>
-<li><b>Copia de seguridad de los datos y el dispositivo:</b> La instalación de un archivo APK puede causar algunos problemas o errores en el dispositivo, como bloqueos, bloqueos, fallos o pérdida de datos. Para evitar perder datos o configuraciones importantes, asegúrese de hacer una copia de seguridad de su dispositivo antes de instalar un archivo APK. Puede usar aplicaciones como Google Drive, Dropbox , o Titanium Backup para realizar copias de seguridad de sus datos y dispositivos en la nube o su tarjeta SD. </li>
-<li><b>Actualizar la aplicación regularmente:</b> Instalar un archivo APK puede no darle acceso a las últimas actualizaciones y características de la aplicación. Algunos archivos APK pueden estar desactualizados o tener errores, y pueden no funcionar correctamente en su dispositivo. Para asegurarte de que tienes la mejor experiencia con la aplicación, asegúrate de actualizarla regularmente. Puede hacer esto comprobando el sitio web donde descargó el archivo APK para cualquier versión nueva, o utilizando aplicaciones como APKUpdater o APKPure que pueden notificarle de cualquier actualización disponible para sus aplicaciones instaladas. </li>
-</ul>
- <h2>Cómo jugar Temple Run 3 y disfrutar de sus nuevos elementos de juego? </h2>
-<p>Ahora que has descargado e instalado Temple Run 3 en tu dispositivo Android, estás listo para jugar el juego y disfrutar de sus nuevos elementos de juego. Temple Run 3 es fácil de aprender, pero difícil de dominar, y te mantendrá enganchado durante horas con su juego adictivo y emocionante. Estos son algunos de los conceptos básicos y consejos sobre cómo jugar Temple Run 3 y divertirse con sus nuevas características. </p>
- <h3>Los controles y objetivos básicos de Temple Run 3</h3>
-
- <h3>Las nuevas secciones de zipwire y carro de la mina de Temple Run 3</h3>
-<p>Una de las nuevas características de Temple Run 3 es la adición de zipwire y secciones de carro de la mina que añaden más variedad y desafío al juego. Estas secciones aparecen aleatoriamente en tu carrera, y requieren diferentes habilidades y estrategias para completarse. </p>
-<ul>
-<li><b>Secciones de Zipwire:</b> En estas secciones, tienes que deslizarte por una tirolina de cuerda mientras evitas obstáculos como ramas, rocas, bolas de fuego y monos demonios. Puedes deslizar hacia la izquierda o hacia la derecha para moverte hacia la izquierda o hacia la derecha en la tirolina, deslizar hacia arriba para saltar sobre los obstáculos, deslizar hacia abajo para agacharte bajo los obstáculos e inclinar tu dispositivo para recoger monedas y gemas. También puede tocar la pantalla para usar potenciadores si tiene alguno. </li>
-<li><b>Secciones del carro de la mina:</b> En estas secciones, usted tiene que saltar a un carro de minas y navegar por una red de pistas de minería en las profundidades de las cuevas. Puede deslizar hacia la izquierda o hacia la derecha para cambiar las pistas, deslizar hacia arriba para saltar sobre los huecos u obstáculos, deslizar hacia abajo para agacharse en el carro de minas, e inclinar el dispositivo para recoger monedas y gemas. También puede tocar la pantalla para usar potenciadores si tiene alguno. </li>
-</ul>
- <h3>Los potenciadores y personajes de Temple Run 3</h3>
-<p>Otra nueva característica de Temple Run 3 es la introducción de algunos nuevos power-ups y personajes que pueden mejorar su experiencia de juego. Los power-ups son artículos especiales que pueden darte una ventaja en tu carrera, como aumento de velocidad, invencibilidad, imán de monedas, etc. Puedes comprar power-ups en la tienda del juego usando monedas o gemas, o encontrarlos aleatoriamente en tu carrera. Puede activarlos tocando la pantalla cuando aparecen en la esquina superior izquierda. Los personajes son diferentes avatares que puedes jugar como en el juego, cada uno con sus propias habilidades y antecedentes. Puedes desbloquear personajes en la tienda del juego usando monedas o gemas, o completando ciertos logros. Puede cambiar caracteres en el menú principal antes de iniciar una ejecución. </p>
-<tabla>
-<tr><th>Encendido</th><th>Descripción</th></tr>
-
-<tr><td>Boost</td><td>Te da una ráfaga de velocidad e invencibilidad para una distancia corta</td></tr>
-<tr><td>Shield</td><td>Te protege de un golpe</td></tr>
-<tr><td>Gem Bonus</td><td>Duplica el valor de las gemas que recopilas</td></tr>
-<tr><td>Coin Bonus</td><td>Duplica el valor de las monedas que recoges</td></tr>
-<tr><td>Bonus de puntuación</td><td>Aumenta el multiplicador de puntuación</td></tr>
-<tr><td>Magnet</td><td>Atrae a todas las monedas y gemas en su vecindad</td></tr>
-</tabla>
- <tabla <tabla>
-<tr><th>Carácter</th><th>Descripción</th></tr>
-<tr><td>Scarlett Fox</td><td>Una ladrona astuta y ágil que puede usar su gancho para balancearse a través de los huecos</td></tr>
-<tr><td>Barry Bones</td><td>Un policía duro y valiente que puede usar su bastón para romper obstáculos</td></tr>
-<tr><td>Karma Lee</td><td>Una artista marcial rápida y elegante que puede usar sus nunchucks para desviar los obstáculos</td></tr>
-<tr><td>Montana Smith</td><td>Un explorador robusto y aventurero que puede usar su látigo para agarrar monedas y gemas</td></tr>
-<tr><td>Zack Wonder</td><td>Un especialista audaz y carismático que puede usar su casco para protegerse de un golpe</td></tr>
-<tr><td>Francisco Montoya</td><td>Un conquistador encantador y extravagante que puede usar su estoque para cortar obstáculos</td></tr>
-</tabla>
- <h2>¿Cómo obtener puntuaciones altas y toneladas de monedas en Temple Run 3?</h2>
-<p>Temple Run 3 no es solo un juego de supervivencia, sino también un juego de competición. Usted querrá obtener altas puntuaciones y toneladas de monedas en sus carreras, para que pueda mostrar sus habilidades, desbloquear más artículos, y desafiar a sus amigos. Aquí hay algunos consejos y trucos sobre cómo lograrlo. </p>
- <h3>Los consejos y trucos para evitar obstáculos y recoger monedas</h3>
-<p>La forma más básica de conseguir altas puntuaciones y toneladas de monedas en Temple Run 3 es evitar obstáculos y recoger monedas tanto como sea posible. Aquí hay algunos consejos y trucos sobre cómo hacerlo:</p>
-<ul>
-
-<li><b>Usa los power-ups correctos en el momento adecuado:</b> Los power-ups pueden darte una ventaja en tu carrera, pero tienes que usarlos sabiamente. No los desperdicie en secciones fáciles o cuando no los necesite. Guárdelos para secciones difíciles o peligrosas o cuando esté en problemas. Por ejemplo, utilice la invisibilidad cuando se encuentra con un montón de obstáculos, utilice impulso cuando se está quedando atrás o necesita un escape rápido, utilice el escudo cuando está a punto de golpear algo, utilice la gema de bonificación o bono de moneda cuando vea un montón de gemas o monedas, utilice la prima de la cuenta cuando usted tiene un multiplicador alto, y utilice el imán cuando usted desea recoger todo en su trayectoria. </li>
-<li><b>Sigue los rastros de monedas:</b> El juego a menudo te guiará con rastros de monedas que te llevarán al mejor camino o la ruta más segura. Síguelos tanto como sea posible, ya que te ayudarán a evitar obstáculos, recoger monedas, y encontrar power-ups. Sin embargo, no los sigas ciegamente si te llevan a un callejón sin salida o a una trampa. Usa tu juicio e intuición para decidir cuándo seguirlos o desviarte de ellos. </li>
-<li><b>Evita los bordes:</b> El juego también intentará engañarte con caminos que te lleven a los bordes de los acantilados o las paredes. Evítalos tanto como sea posible, ya que te harán más vulnerable a caerte o golpear algo. Manténgase en el centro o en el lado interno del camino tanto como sea posible, ya que le darán más espacio para maniobrar y recoger monedas. </li>
-<li><b>No seas codicioso:</b> El juego también te tentará con monedas y gemas que se colocan en lugares arriesgados o difíciles de alcanzar. No se vuelva codicioso y vaya por ellos si son demasiado arriesgados o demasiado lejos. Puede terminar perdiendo más de lo que gana. Enfócate en mantenerte vivo y recoger lo que es fácil y seguro. </li>
-</ul>
- <h3>Las mejoras y utilidades que pueden ayudarte en tu ejecución</h3>
-
-<tabla <tabla>
-<tr><th>Actualización</th><th>Descripción</th></tr>
-<tr><td>Duración de invisibilidad</td><td>Aumenta la duración del encendido de invisibilidad</td></tr>
-<tr><td>Aumentar la distancia</td><td>Aumenta la distancia del aumento de potencia</td></tr>
-<tr><td>Duración del escudo</td><td>Aumenta la duración del encendido del escudo</td></tr>
-<tr><td>Gem Value</td><td>Aumenta el valor de las gemas que recopilas</td></tr>
-<tr><td>Valor de la moneda</td><td>Aumenta el valor de las monedas que recoge</td></tr>
-<tr><td>Multiplicador de puntuación</td><td>Aumenta el multiplicador de puntuación</td></tr>
-<tr><td>Duración del imán</td><td>Aumenta la duración del encendido del imán</td></tr>
-</tabla>
- <tabla>
-<tr><th>Utilidad</th><th>Descripción</th></tr>
-<tr><td>Vida extra</td><td>Te da una vida extra al inicio de tu carrera</td></tr>
-<tr><td>Head Start</td><td>Le da ventaja al inicio de su carrera</td></tr>
-<tr><td>Mega Head Start</td><td>Le da una ventaja mega al inicio de su carrera</td></tr>
-<tr><td>Save Me</td><td>Te salva de la muerte una vez en tu carrera</td></tr>
-<tr><td>Mega Save Me</td><td>Te salva de la muerte dos veces en tu carrera</td></tr>
-<tr><td>Gem Pack</td><td>Le da un paquete de gemas para usar en su ejecución</td></tr>
-<tr><td>Coin Pack</td><td>Te da un paquete de monedas para usar en tu carrera</td></tr>
-</tabla>
- <h3>Los logros y tablas de clasificación que pueden motivar a jugar más</h3>
-
- <h2>Conclusión</h2>
-<p>Temple Run 3 es un juego divertido y adictivo que te mantendrá entretenido durante horas. Ha mejorado los gráficos, nuevos obstáculos, nuevas secciones, nuevos potenciadores y nuevos personajes que lo hacen más emocionante y desafiante que nunca. Puede descargar e instalar Temple Run 3 APK en su dispositivo Android siguiendo los pasos y precauciones que hemos proporcionado en este artículo. También puedes jugar a Temple Run 3 y disfrutar de sus nuevas características siguiendo los consejos y trucos que hemos compartido en este artículo. También puede obtener altas puntuaciones y toneladas de monedas en Temple Run 3 mediante el uso de las mejoras y utilidades que hemos sugerido en este artículo. También puede utilizar los logros y tablas de clasificación para motivarse a jugar más y mejorar sus habilidades. Esperamos que este artículo te haya ayudado a aprender más sobre Temple Run 3 y cómo jugarlo. Ahora, ¿qué estás esperando? Descargar Temple Run 3 APK hoy y empezar a correr por su vida! </p>
- <h2>Preguntas frecuentes</h2>
-<h4>¿Temple Run 3 es gratis? </h4>
-<p>Sí, Temple Run 3 es gratis para jugar. Sin embargo, también contiene algunas compras opcionales en la aplicación que pueden mejorar tu experiencia de juego, como monedas, gemas, potenciadores, mejoras, utilidades, personajes y fondos de pantalla. Puedes comprar estos artículos con dinero real o ganarlos jugando el juego. </p>
- <h4>¿Temple Run 3 es compatible con mi dispositivo? </h4>
-<p>Temple Run 3 es compatible con la mayoría de dispositivos Android que tienen Android 4.1 o superior. Sin embargo, es posible que algunos dispositivos no admitan algunas características o que experimenten algunos problemas de rendimiento debido a limitaciones de hardware. Puede comprobar la compatibilidad de su dispositivo leyendo la descripción de la aplicación o mirando los detalles de la aplicación en el sitio web donde descargó el archivo APK. </p>
- <h4>¿Es seguro descargar e instalar Temple Run 3? </h4>
-
- <h4>¿Cómo puedo desbloquear más personajes y fondos de pantalla en Temple Run 3?</h4>
-<p>Puedes desbloquear más personajes y fondos de pantalla en Temple Run 3 comprándolos en la tienda del juego usando monedas o gemas, o completando ciertos logros. Cada personaje y fondo de pantalla tiene un precio y un requisito diferentes. Puedes verlos en la tienda del juego o en el menú de logros. </p>
- <h4>¿Cómo puedo contactar a los desarrolladores de Temple Run 3?</h4>
-<p>Si tiene alguna pregunta, comentario, sugerencia o problema con respecto a Temple Run 3, puede ponerse en contacto con los desarrolladores del juego visitando su sitio web, siguiendo sus cuentas de redes sociales o enviándoles un correo electrónico. Estas son algunas de las formas de contactarlos:</p>
-<ul>
-<li><b>Sitio web:</b> https://www.imangistudios.com/</li>
-<li><b>Facebook:</b> https://www.facebook.com/TempleRun</li>
-<li><b>Twitter:</b> https://twitter.com/TempleRun</li>
-<li><b>Correo electrónico:</b> support@imangistudios.com</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
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-
-<h1>Fundamentos de Baldi en línea: Un juego de aprendizaje divertido y aterrador</h1>
-<p>Si estás buscando un juego que combine educación, horror y humor, entonces deberías probar <strong>Baldi’s Basics Online</strong>. Este juego es una parodia del software de edutainment barato de los 90 que te hará reír y gritar al mismo tiempo. En este artículo, te diremos todo lo que necesitas saber sobre Baldi’s Basics Online, incluyendo qué es, por qué es popular y cómo jugarlo. </p>
-<h2>baldi 39;s conceptos básicos en línea</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://bltlly.com/2v6K3D">https://bltlly.com/2v6K3D</a></b></p><br /><br />
- <h2>¿Qué es lo básico de Baldi en línea? </h2>
-<p>Baldi’s Basics Online es un juego de terror de supervivencia creado por Basically Games en 2018. Fue inspirado por juegos como Sonic’s Schoolhouse, I.M. Meen, y 3D Dinosaur Adventure. El juego tiene un estilo gráfico simple y retro que contrasta con su atmósfera espeluznante y tensa. </p>
- <h3>La historia y el juego de Baldi’s Basics Online</h3>
-<p>El juego comienza con tu amigo pidiéndote que recojas sus siete cuadernos que dejó en la escuela. Sin embargo, tan pronto como entras en la escuela, te das cuenta de que algo está mal. El profesor, Baldi, no es amable en absoluto. Quiere jugar al escondite contigo, pero si te atrapa, te castigará con su gobernante. Para escapar de él, tienes que resolver problemas matemáticos que son fáciles o imposibles. Cuantos más errores cometes, más enojado y rápido se vuelve Baldi. También tienes que lidiar con otros obstáculos, como puertas cerradas, eventos aleatorios y otros personajes que pueden ayudarte o dificultarte. </p>
- <h3>Los diferentes modos de juego de Baldi’s Basics Online</h3>
-<p>Baldi’s Basics Online ofrece dos modos de juego: el modo historia y el modo sin fin. En el modo Historia, tu objetivo es encontrar los siete cuadernos y luego huir de la escuela. En el modo Endless, tu objetivo es encontrar tantos cuadernos como sea posible sin ser atrapado por Baldi. También puedes personalizar el nivel de dificultad cambiando la velocidad y la inteligencia de Baldi y sus amigos. </p>
-<p></p>
- <h3>Los personajes y los elementos de Baldi’s Basics Online</h3>
-
-<ul>
-<li><strong>Baldi</strong>: El antagonista principal del juego. Es un profesor calvo que ama las matemáticas y odia los errores. Te perseguirá con su regla si tienes algún problema. </li>
-<li><strong>Director de la Cosa</strong>: El director de la escuela que hace cumplir las reglas. Él te enviará a detención si te ve rompiendo alguna regla, como correr, comer o entrar en las salas de la facultad. </li>
-<li><strong>Playtime</strong>: Una chica que quiere jugar contigo. Te obligará a saltar la cuerda con ella cinco veces si te atrapa. Puedes usar tijeras para cortar su cuerda y escapar. </li>
-<li><strong>Gotta Sweep</strong>: Una escoba gigante que barre todo a su manera. Puede alejarte a ti o a Baldi de tu destino. </li>
-<li><strong>Artes y Artesanos</strong>: Un títere de calcetín al que le gustan las cosas brillantes. Te teletransportará a Baldi si te ve recogiendo demasiados cuadernos. </li>
-<li><strong>1st Prize</strong>: Un robot que ama abrazar a la gente. Te empujará en la dirección que está enfrentando <p>Algunos de los elementos son:</p>
-<ul>
-<li><strong>BSODA</strong>: Un refresco que puede ser usado para rociar Baldi u otros caracteres lejos de ti. </li>
-<li><strong>Quarter</strong>: Una moneda que se puede utilizar para comprar artículos de la máquina expendedora o pagar al matón. </li>
-<li><strong>Despertador</strong>: Un reloj que puede ser usado para distraer a Baldi u otros personajes por unos segundos. </li>
-<li><strong>Zesty Bar</strong>: Una barra de caramelo que puede restaurar tu resistencia y hacerte correr más rápido. </li>
-<li><strong>Cinta Anti Audición y Desorientación Baldi</strong>: Una cinta que puede ser usada para hacer que Baldi no pueda escucharte por un tiempo. </li>
-</ul>
- <h2>¿Por qué es tan popular Baldi’s Basics Online? </h2>
-<p>Baldi’s Basics Online es un juego que ha ganado mucha popularidad y atención desde su lanzamiento. Hay varias razones por las que este juego es tan atractivo para muchos jugadores, como:</p>
- <h3>La parodia y el humor de Baldi’s Basics Online</h3>
-
- <h3>El horror y el desafío de los Fundamentos de Baldi Online</h3>
-<p>Baldi’s Basics Online es también un juego que ofrece una genuina sensación de miedo y dificultad. El juego utiliza sustos de salto, música de suspenso y eventos impredecibles para crear una atmósfera tensa e inmersiva. El juego también prueba las habilidades y los nervios de los jugadores, ya que tienen que resolver problemas de matemáticas, evitar obstáculos y escapar de Baldi. El juego no es fácil y requiere estrategia, paciencia y suerte para vencerlo. </p>
- <h3>La comunidad de fans y los mods de Baldi’s Basics Online</h3>
-<p>Baldi’s Basics Online es también un juego que tiene una base de fans leal y creativa. El juego ha inspirado muchas creaciones de fans, tales como fan art, fan fiction, juegos de fans y videos de fans. El juego también tiene muchos mods que añaden nuevas características, personajes, niveles y modos al juego. Algunos de los mods más populares son Baldi’s Basics Plus, Baldi’s Basics in Education and Learning Remastered, Baldi’s Basics Random Maps Edition y Baldi’s Basics Birthday Bash.</p>
- <h2>¿Cómo se juega Baldi’s Basics en línea? </h2>
-<p>Si quieres jugar Baldi’s Basics Online, tendrás que seguir estos pasos:</p>
- <h3>Los requisitos del sistema y las plataformas de Baldi’s Basics Online</h3>
-<p>Baldi’s Basics Online es un juego que puede ejecutarse en la mayoría de dispositivos y plataformas. El juego no requiere especificaciones de alta gama o conexión a Internet. El juego está disponible para Windows, Mac, Linux, Android, iOS y navegadores web. Puede descargar el juego de forma gratuita desde su sitio web oficial o desde otras fuentes como itch.io o Game Jolt. También puede jugar el juego en línea en varios sitios web como Crazy Games o Poki. </p>
- <h3>Los controles y los consejos de Baldi’s Basics Online</h3>
-
-<p>Algunos de los consejos que pueden ayudarte a jugar mejor son:</p>
-<ul>
-<li>Presta atención a los sonidos y las señales en el juego. Pueden darte pistas sobre dónde están Baldi u otros personajes. </li>
-<li>Usa los objetos sabiamente. Pueden salvarte la vida o meterte en problemas dependiendo de cómo los uses. </li>
-<li>No seas demasiado codicioso. Coleccionar demasiados cuadernos o artículos puede hacer que Baldi sea más rápido o desencadenar otros eventos. </li>
-<li>No te rindas. El juego es difícil pero no imposible. Puedes aprender de tus errores e intentarlo de nuevo. </li>
-</ul>
- <h3>Los mejores sitios web para jugar Baldi’s Basics Online</h3>
-<p>Si quieres jugar Baldi’s Basics Online en línea sin descargar nada, puedes visitar algunos de estos sitios web:</p>
- | Sitio web | URL | | Sitio web | URL | --- - | Lagged | [https://lagged.com/en/g/baldis-basics]( 1 ) | Snokido | [https:/www.snokido.com/game/baldis-basics]( 2 ) | Play-Games | [https:>>/w.games.-games.-baldis-games.html]) | | Juegos | Conclusión
-<p>Baldi’s Basics Online es un juego que te hará reír y gritar al mismo tiempo. Es una parodia de juegos educativos que se convierte en un juego de terror de supervivencia. Tienes que encontrar cuadernos, resolver problemas de matemáticas, y escapar de Baldi y sus amigos. El juego tiene un estilo de gráficos simple y retro, pero una atmósfera espeluznante y tensa. El juego es popular debido a su humor, horror y desafío. También tiene una comunidad de fans leal y creativa que hace mods y creaciones hechas por fans. Puede jugar el juego en línea en varios sitios web, o descargarlo de forma gratuita desde su sitio web oficial u otras fuentes. </p>
- <h4>Resumen de los puntos principales</h4>
-<ul>
-<li>Baldi’s Basics Online es un juego de terror de supervivencia que parodia el software de entretenimiento educativo barato de los 90. </li>
-<li>El juego tiene un estilo gráfico simple y retro, pero una atmósfera espeluznante y tensa. </li>
-
-<li>El juego es popular debido a su humor, horror y desafío. También tiene una comunidad de fans leal y creativa que hace mods y creaciones hechas por fans. </li>
-<li> Puede jugar el juego en línea en varios sitios web, o descargarlo de forma gratuita desde su sitio web oficial u otras fuentes. </li>
-</ul>
- <h4>Llamada a la acción</h4>
-<p>Si usted está listo para tener un poco de diversión y algunos sustos, entonces usted debe probar Baldi’s Basics Online hoy. Usted no se arrepentirá. Solo ten cuidado de no hacer enojar a Baldi, o te perseguirá con su gobernante. ¡Diviértete y buena suerte! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Baldi’s Basics Online:</p>
- <ol>
-<li><strong>¿Quién hizo los conceptos básicos de Baldi en línea? </strong></li>
-<p>Baldi’s Basics Online fue creado por Basically Games, un desarrollador de juegos independiente con sede en los Estados Unidos. El juego fue hecho originalmente para el Meta Game Jam en 2018, donde ganó el primer lugar. </p>
- <li><strong>Lo básico de Baldi es aterrador en línea? </strong></li>
-<p>Baldi’s Basics Online es un juego que mezcla horror y humor. El juego utiliza sustos de salto, música de suspenso y eventos impredecibles para crear una atmósfera tensa e inmersiva. El juego también prueba las habilidades y los nervios de los jugadores, ya que tienen que resolver problemas de matemáticas, evitar obstáculos y escapar de Baldi. El juego no es para los débiles de corazón, pero también es muy divertido y agradable. </p>
- <li><strong>¿Es Baldi’s Basics en línea educativo? </strong></li>
-<p>Baldi’s Basics Online no es un juego educativo, sino una parodia de juegos educativos. El juego no enseña nada útil o preciso, sino que se burla de los tópicos y tropos del software de entretenimiento educativo de los años 90. El juego utiliza la lógica absurda, problemas matemáticos imposibles, y la actuación de voz cursi para crear un contraste hilarante con sus elementos de terror. El juego no se toma en serio e invita a los jugadores a divertirse con su ridiculez. </p>
- <li><strong>Es Baldi’s Basics multijugador en línea? </strong></li>
-
- <li><strong>¿Es Baldi’s Basics Online gratis? </strong></li>
-<p>Baldi’s Basics Online es un juego gratuito que se puede jugar en línea o descargar desde su sitio web oficial u otras fuentes. El juego no requiere ningún pago o registro para jugar. Sin embargo, el juego acepta donaciones de jugadores que quieren apoyar al desarrollador y al proyecto. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
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-<h1>Caramelo Crush Saga Mod Apk ilimitadas barras de oro: Todo lo que necesita saber</h1>
- <p>Si usted es un fan de los juegos de puzzle casual, es probable que haya oído hablar o jugado Candy Crush Saga. Este adictivo juego ha existido durante casi una década y tiene millones de jugadores en todo el mundo. Pero ¿sabías que hay una manera de hacer este juego aún más divertido y gratificante? Sí, estamos hablando de Candy Crush Saga Mod Apk ilimitadas barras de oro.</p>
-<h2>caramelo crush saga mod apk barras de oro ilimitadas</h2><br /><p><b><b>Download</b> &#10004; <a href="https://bltlly.com/2v6LfC">https://bltlly.com/2v6LfC</a></b></p><br /><br />
- <p>En este artículo, le diremos todo lo que necesita saber acerca de este increíble mod apk que le permite disfrutar de barras de oro ilimitadas, boosters, vidas, movimientos, y más en Candy Crush Saga. También le mostraremos cómo descargar e instalar en su dispositivo, cómo jugar el juego con consejos y trucos, y cuáles son algunas alternativas a este apk mod. Así que, vamos a empezar! </p>
- <h2>¿Qué es Candy Crush Saga y por qué es popular? </h2>
- <p>Candy Crush Saga es un videojuego gratuito desarrollado por King. Fue lanzado en 2012 para Facebook, y más tarde para iOS, Android, Windows Phone, Windows 10 y otras plataformas. Es una variación de su juego de navegador Candy Crush.</p>
- <p>La premisa del juego es simple. Tienes un nivel lleno de dulces. Combina tres o más dulces del mismo color para eliminarlos del tablero. Tienes un número limitado de movimientos o tiempo para completar el objetivo de cada nivel, como anotar un cierto número de puntos, limpiar todos los bloques de gelatina, recoger todos los ingredientes, etc.</p>
- <p>El juego se divide en muchos episodios, cada uno con un tema y una historia diferentes. Hay miles de niveles en el juego, con los nuevos añadidos cada dos semanas. El juego también cuenta con dulces especiales que tienen diferentes efectos cuando se combina o se activa, tales como caramelos a rayas, caramelos envueltos, bombas de color, etc.</p>
-<p></p>
-
- <p>Algunas de las razones por las que Candy Crush Saga es tan popular son:</p>
-<ul>
-<li> Tiene un juego simple pero adictivo que atrae a una amplia gama de jugadores. </li>
-<li> Tiene gráficos coloridos y efectos de sonido que crean una atmósfera dulce y divertida. </li>
-<h2>¿Qué es Candy Crush Saga Mod Apk y cuáles son sus beneficios? </h2>
- <p>Un apk mod es una versión modificada de una aplicación original que ha sido alterada para proporcionar algunas características adicionales o beneficios que no están disponibles en la aplicación oficial. Un apk mod puede ser creado por cualquiera que tenga las habilidades y herramientas para modificar el código y los archivos de la aplicación. </p>
- <p>Candy Crush Saga Mod Apk ilimitadas barras de oro es uno de los más populares y buscados apks mod para Candy Crush Saga. Es un apk mod que le da acceso ilimitado a barras de oro, boosters, vidas, movimientos, y otros recursos en el juego. Con este mod apk, se puede disfrutar de jugar Candy Crush Saga sin limitaciones o restricciones. </p>
- <p>Algunos de los beneficios de usar Candy Crush Saga Mod Apk ilimitadas barras de oro son:</p>
-<ul>
-<li>Puedes obtener barras de oro ilimitadas, que son la moneda premium en el juego. Puedes usar barras de oro para comprar potenciadores, movimientos adicionales, vidas adicionales y otros elementos en el juego. </li>
-<li>Usted puede obtener impulsores ilimitados, que son elementos especiales que pueden ayudarle a borrar los niveles más rápido y más fácil. Algunos de los potenciadores son martillo de piruleta, caramelos a rayas y envueltos, bomba de color, interruptor libre, etc.</li>
-<li>Puedes obtener vidas ilimitadas, que son el número de veces que puedes jugar un nivel antes de tener que esperar a que se llenen. Con vidas ilimitadas, puedes jugar todo lo que quieras sin interrupciones. </li>
-<li>Puedes obtener movimientos ilimitados, que son el número de veces que puedes intercambiar dulces en un nivel. Con movimientos ilimitados, puedes hacer tantas coincidencias como quieras sin preocuparte por quedarte sin ellas. </li>
-
-<li> Puede eliminar todos los anuncios y ventanas emergentes en el juego, lo que significa que puede jugar sin distracciones o molestias. </li>
-</ul>
- <p>Como se puede ver, Candy Crush Saga Mod Apk ilimitadas barras de oro es una gran manera de mejorar su experiencia de juego y divertirse más con Candy Crush Saga. Sin embargo, antes de descargarlo e instalarlo en su dispositivo, necesita saber algunas cosas importantes sobre él. </p> <h2>Cómo descargar e instalar Candy Crush Saga Mod Apk ilimitadas barras de oro? </h2>
- <p>Ahora que sabes lo que es Candy Crush Saga Mod Apk ilimitadas barras de oro y cuáles son sus beneficios, es posible que se pregunte cómo descargar e instalar en su dispositivo. Bueno, no es muy difícil, pero necesitas seguir algunos pasos cuidadosamente para evitar errores o problemas. </p>
- <p>Estos son los pasos que debe tomar para descargar e instalar Candy Crush Saga Mod Apk ilimitadas barras de oro:</p>
- <h3>Paso 1: Encontrar el archivo Apk Mod y verificar su seguridad</h3>
- <p>Lo primero que tienes que hacer es encontrar el archivo apk mod que desea descargar. Hay muchos sitios web que ofrecen archivos apk mod para varias aplicaciones y juegos, pero no todos ellos son seguros y fiables. Algunos de ellos pueden contener malware, virus o archivos falsos que pueden dañar tu dispositivo o robar tus datos. </p>
- <p>Por lo tanto, es necesario tener cuidado y elegir una fuente de confianza para descargar el archivo apk mod. Uno de los mejores sitios para las descargas seguras de Android APK es APKMirror. Es propiedad y está operado por el mismo equipo que creó el sitio de noticias de Android ampliamente leído, Android Police, que debería asegurarle que está en manos seguras. </p>
-
- <p>Antes de instalar el archivo apk mod, necesita verificar su seguridad. Hay dos maneras de hacer eso:</p>
- <h4>El primer método: Comprobación del hash del archivo</h4>
- <p>Un hash es un código único que identifica un archivo y su contenido. Si dos archivos tienen el mismo hash, significa que son idénticos. Si tienen diferentes hashes, significa que son diferentes o modificados. </p>
- <p>Puede utilizar una herramienta como Hashdroid para comprobar y comparar los hashes de la aplicación. Puede hacer coincidir el resultado con otro servicio como Apktovi checker Tool para ver si son consistentes. Si lo son, significa que el archivo apk mod es seguro y original. Si no lo son, significa que el archivo apk mod está manipulado o falso. </p>
- <h4>El segundo método: Usar un conjunto de herramientas de seguridad</h4>
- <p>También puede utilizar un kit de herramientas de seguridad como VirusTotal para escanear el archivo apk mod en busca de virus y malware. VirusTotal analiza sus archivos APK y ayuda a detectar todo tipo de virus y malware que puedan estar presentes. El servicio es fácil de usar: solo sube tus archivos y espera los resultados. </p>
- <p>Si VirusTotal muestra que el archivo apk mod está limpio y seguro, puede proceder a instalarlo. Si muestra que el archivo apk mod está infectado o sospechoso, debe eliminarlo y buscar otra fuente. </p> <h3>Paso 2: Habilitar fuentes desconocidas e instalar el archivo apk Mod</h3>
- <p>Una vez que haya descargado y verificado el archivo apk mod, es necesario habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de Google Play Store. De forma predeterminada, esta configuración está desactivada, por lo que debe habilitarla manualmente. </p>
- <p>Para habilitar fuentes desconocidas en su dispositivo, debe seguir estos pasos:</p>
-<ul>
-<li>Vaya a la configuración de su dispositivo y toque en Seguridad o Privacidad.</li>
-<li>Encontrar la opción que dice Fuentes desconocidas o Instalar aplicaciones desconocidas y alternar en. </li>
-
-</ul>
- <p>Ahora, ya está listo para instalar el archivo apk mod. Para hacer eso, debe seguir estos pasos:</p>
-<ul>
-<li>Vaya a la carpeta Administrador de archivos o descargas de su dispositivo y encuentre el archivo apk mod que descargó. </li>
-<li>Toque en el archivo apk mod y aparecerá un aviso, preguntándole si desea instalar la aplicación. Toque en Instalar y espere a que termine el proceso de instalación. </li>
-<li>Una vez que la instalación se realiza, verá un mensaje que dice App Installed. Toque en Abrir para iniciar el juego o Hecho para salir. </li>
-</ul>
- <h3>Paso 3: Iniciar y jugar el juego con el Mod Apk Características</h3>
- <p>Felicidades! Usted ha instalado con éxito Candy Crush Saga Mod Apk barras de oro ilimitadas en su dispositivo. Ahora, se puede iniciar y jugar el juego con todas las características apk mod habilitado. </p>
- <p>Para iniciar y jugar el juego, debe seguir estos pasos:</p>
-<ul>
-<li>Ve a la pantalla de inicio de tu dispositivo o al cajón de aplicaciones y encuentra el icono de la saga Candy Crush. Toca en él para abrir el juego. </li>
-<li>El juego se cargará y le pedirá que inicie sesión con su cuenta de Facebook o jugar como invitado. Puedes elegir cualquiera de las dos opciones, pero te recomendamos iniciar sesión con tu cuenta de Facebook para guardar tu progreso y sincronizar tus datos entre dispositivos. </li>
-<li> El juego se iniciará y verá el menú principal. Puede elegir jugar el juego desde el principio o reanudar desde donde lo dejó. </li>
-<li>Te darás cuenta de que tienes barras de oro ilimitadas, potenciadores, vidas, movimientos y otros recursos en el juego. Puedes usarlos tanto como quieras sin limitaciones o restricciones. </li>
-<li>También notarás que todos los niveles y episodios están desbloqueados en el juego. Puedes jugar a cualquier nivel sin tener que completar los anteriores. </li>
-<li>También notarás que no hay anuncios ni ventanas emergentes en el juego. Puedes jugar sin distracciones ni molestias. </li>
-</ul>
-
- <p>Candy Crush Saga es un juego simple pero desafiante que requiere estrategia, habilidad y suerte. Puede ser frustrante a veces, especialmente cuando se queda atascado en un nivel difícil o se queda sin recursos. Pero no te preocupes, tenemos algunos consejos y trucos que pueden ayudarte a jugar mejor y superar cualquier obstáculo. </p>
- <p>Aquí están algunos de los mejores consejos y trucos para jugar Candy Crush Saga:</p>
- <h3>Consejo 1: Conozca los diferentes tipos de niveles y objetivos</h3>
- <p>Candy Crush Saga tiene diferentes tipos de niveles y objetivos que necesitas completar para progresar en el juego. Algunos de los tipos más comunes son:</p>
-<ul>
-<li>Niveles de puntuación: Es necesario anotar un cierto número de puntos dentro de un número determinado de movimientos o tiempo. </li>
-<li>Niveles de jalea: Es necesario limpiar todos los bloques de jalea de la junta haciendo coincidir los caramelos en la parte superior de ellos. </li>
-<li>Niveles de ingredientes: Es necesario bajar todos los ingredientes (cerezas o avellanas) a la parte inferior de la placa por la limpieza de los dulces por debajo de ellos. </li>
-<li>Niveles de pedido: Es necesario recoger un número específico y el tipo de dulces o dulces especiales haciendo coincidir. </li>
-<li>Niveles mixtos: Necesitas completar dos o más objetivos diferentes en un nivel. </li>
-</ul>
- <p>Puedes saber qué tipo de nivel y objetivo estás jugando mirando los iconos en la esquina superior izquierda de la pantalla. También puedes ver cuántos movimientos o tiempo te queda, cuántas estrellas has ganado y cuánto progreso has hecho hacia el objetivo. </p>
-
- <h3>Consejo 2: Conozca los dulces especiales y cómo crearlos y usarlos</h3>
- <p>Los dulces especiales son dulces que tienen diferentes efectos cuando se combinan o se activan. Pueden ayudarte a limpiar más dulces, crear más combos y lograr tus objetivos más rápido. Hay cuatro tipos principales de dulces especiales en Candy Crush Saga:</p>
-<ul>
-<li>Caramelos a rayas: Estos son caramelos que tienen rayas horizontales o verticales en ellos. Se crean haciendo coincidir cuatro caramelos del mismo color en una fila o columna. Cuando coincidan, limpiarán toda una fila o columna de dulces, dependiendo de la dirección de sus rayas. </li>
-<li>Caramelos envueltos: Estos son caramelos que tienen una envoltura de colores alrededor de ellos. Se crean haciendo coincidir cinco caramelos del mismo color en forma de L o T. Cuando coinciden, explotarán dos veces, despejando un área de 3x3 de dulces a su alrededor cada vez. </li>
-<li>Bombas de color: Estos son caramelos que tienen chispas de chocolate en ellos. Se crean haciendo coincidir cinco caramelos del mismo color en una fila o columna. Cuando se combina con otro caramelo, borrarán todos los caramelos de ese color del tablero. </li>
-<li>Caramelos de pescado: Estos son caramelos que parecen peces. Se crean haciendo coincidir cuatro caramelos del mismo color en forma cuadrada. Cuando coincidan, nadarán a un caramelo al azar en el tablero y lo limpiarán. También pueden apuntar a bloques de gelatina, ingredientes o bloqueadores dependiendo del objetivo del nivel. </li>
-</ul>
- <p>También puedes combinar dos dulces especiales para crear efectos aún más potentes. Por ejemplo, si combinas un caramelo a rayas y un caramelo envuelto, crearás un caramelo gigante que limpiará tres filas y tres columnas de dulces. Si combinas una bomba de color y un caramelo de pescado, crearás un banco de peces que limpiará todos los caramelos de un color del tablero. </p>
-
- <p>Los combos son cuando combinas más de tres caramelos del mismo color en un solo movimiento. Pueden ayudarte a limpiar más dulces, crear dulces más especiales y obtener más puntos. Sin embargo, no todos los combos son igualmente efectivos. Algunos combos son mejores que otros, dependiendo del objetivo y diseño del nivel. </p>
- <p>Aquí están algunos de los mejores y peores combos para usar en el juego:</p>
- <h4>Los mejores combos:</h4>
-<ul>
-<li>Rayas + rayas: Este combo limpiará dos filas y dos columnas de caramelos, creando una explosión en forma de cruz. Puede ayudarte a limpiar una gran área del tablero, especialmente si las rayas son perpendiculares entre sí. </li>
-<li>Envuelto + Envuelto: Este combo creará una gran explosión que despejará un área de 5x5 de dulces a su alrededor. Puede ayudarlo a eliminar bloqueadores, bloques de jalea o ingredientes que son difíciles de alcanzar. </li>
-<li>Color Bomb + Color Bomb: Este combo borrará todos los caramelos del tablero, dejando solo dulces especiales y bloqueadores. Puede ayudarte a completar cualquier objetivo que requiera limpiar muchos dulces o crear muchos dulces especiales. </li>
-</ul>
- <h4>Los peores combos:</h4>
-<ul>
-<li>Pescado + Pescado: Este combo creará dos peces que nadarán a dos caramelos al azar en el tablero y limpiarlos. Puede ser muy impredecible e ineficaz, especialmente si los peces se dirigen a dulces irrelevantes o aislados. </li>
-<li>Rayas + Pescado: Este combo creará un pez rayado que nadará a un caramelo al azar en el tablero y despejar una fila o columna de dulces, dependiendo de la dirección de sus rayas. También puede ser muy impredecible e ineficaz, especialmente si el pez rayado se dirige a un caramelo que está lejos del objetivo o bloqueado por otros dulces. </li>
-
-</ul>
- <p>Usted debe tratar de utilizar los mejores combos tanto como sea posible porque pueden ayudar a borrar los niveles más rápido y más fácil. Sin embargo, también debes evitar usar los peores combos tanto como sea posible porque pueden desperdiciar tus movimientos y recursos. </p>
- <h3>Consejo 4: Cómo obtener barras de oro gratis y refuerzos en el juego</h3>
- <p>Las barras de oro y los boosters son recursos muy útiles en Candy Crush Saga. Pueden ayudarte a comprar movimientos adicionales, vidas u objetos en el juego. Sin embargo, también son muy limitadas y caras. Solo puedes obtener unas cuantas barras de oro y refuerzos de forma gratuita al completar ciertas tareas o logros en el juego. También puedes comprarlos con dinero real, pero eso puede ser muy costoso y no todos pueden permitírselo. </p>
- <p>Afortunadamente, hay otra manera de obtener barras de oro gratis y refuerzos en el juego sin gastar dinero o usar cualquier apk mod. Esto es mediante el uso de algunos trucos y hacks que están disponibles en línea. Aquí están algunos de ellos:</p>
- <h4>El primer truco: Usar lapso de tiempo</h4>
- <p>Este truco consiste en cambiar la configuración de fecha y hora del dispositivo para engañar al juego a pensar que ha pasado más tiempo del que realmente tiene. De esta manera, puede rellenar sus vidas más rápido, obtener más recompensas diarias, y restablecer sus vueltas de la rueda. </p>
- <p>Para usar este truco, debes seguir estos pasos:</p>
-<ul>
-<li>Cierra el juego completamente y asegúrate de que no se ejecuta en segundo plano. </li>
-<li>Vaya a la configuración de su dispositivo y apague su conexión a Internet (Wi-Fi o datos móviles). </li>
-<li>Vaya a la configuración de fecha y hora de su dispositivo y cámbiela a unas pocas horas o días antes de su hora actual. </li>
-<li>Vuelve al juego y ábrelo. Verás que tus vidas están llenas, tus recompensas diarias están listas, y tus giros de rueda se restablecen. </li>
-<li>Recoge tus recompensas y juega el juego como de costumbre. </li>
-<li>Cuando haya terminado de jugar, cierre el juego completamente de nuevo y encienda su conexión a Internet. </li>
-
-</ul>
- <p>Nota: Este truco puede no funcionar para algunos dispositivos o versiones del juego. También puede causar algunos fallos o errores en el juego. Úsalo bajo tu propio riesgo. </p>
- <h4>El segundo truco: Usar generadores en línea</h4>
- <p>Este truco implica el uso de generadores en línea que pretenden darle barras de oro gratis y refuerzos en Candy Crush Saga. Estos generadores son sitios web que le piden que introduzca su nombre de usuario o dirección de correo electrónico, seleccione el tipo de dispositivo, elija cuántas barras de oro y refuerzos que desea, y haga clic en un botón que dice Generar o Hack. También pueden pedirle que complete algunas encuestas, ofertas o pasos de verificación humana antes de darle los recursos. </p>
- <p>Para usar este truco, debes seguir estos pasos:</p>
-<ul>
-<li>Ir a su navegador y buscar Candy Crush Saga generadores en línea o hacks. Verá una lista de resultados con diferentes sitios web que ofrecen este servicio. </li>
-<li>Elija uno de los sitios web que se ve confiable y legítimo. Evite los que se ven sombrío o estafa. </li>
-<li>Introduzca su nombre de usuario o dirección de correo electrónico que utiliza para jugar Candy Crush Saga. Seleccione el tipo de dispositivo (Android o iOS) y elija cuántas barras de oro y boosters desea. </li>
-<li>Haga clic en el botón Generar o Hack y espere a que el proceso termine. Se le puede pedir que complete algunas encuestas, ofertas o pasos de verificación humana antes de obtener los recursos. Siga las instrucciones y complételas según sea necesario. </li>
-<li>Una vez que recibas el mensaje de confirmación que dice que tus recursos están listos, vuelve al juego y ábrelo. Verá que sus barras de oro y refuerzos se añaden a su cuenta. </li>
-</ul>
- <p>Nota: Este truco puede no funcionar para algunos dispositivos o versiones del juego. También puede ser arriesgado e ilegal de usar. Usted puede ser expulsado del juego o perder su cuenta si la usa. Utilícela bajo su propio riesgo. </p>
- <h2>Alternativas a Candy Crush Saga Mod Apk barras de oro ilimitadas</h2>
-
- <p>Aquí están algunas de las alternativas a Candy Crush Saga Mod Apk ilimitadas barras de oro:</p>
- <h3>Alternativa 1: Jugar otros juegos similares</h3>
- <p>Si te gusta Candy Crush Saga, es posible que también te gusten otros juegos similares que tienen el mismo juego, gráficos y características. Hay muchos otros juegos de puzzle casual que puedes jugar en tu dispositivo, como:</p>
-<ul>
-<li>Candy Crush Soda Saga: Este es un spin-off de Candy Crush Saga que introduce nuevos elementos como botellas de soda, glaseado, miel, etc. Tiene más de 6000 niveles y nuevos añadidos cada semana. </li>
-<li>Candy Crush Jelly Saga: Este es otro spin-off de Candy Crush Saga que introduce nuevos elementos como jelly queens, pufflers, etc. Tiene más de 3000 niveles y nuevos añadidos cada semana. </li>
-<li>Candy Crush Friends Saga: Este es el último spin-off de Candy Crush Saga que introduce nuevos elementos como amigos, disfraces, etc. Tiene más de 1000 niveles y nuevos añadidos cada semana. </li>
-<li>Toon Blast: Este es un juego que tiene un juego similar a Candy Crush Saga, pero con personajes de dibujos animados y temas. Tiene más de 6000 niveles y nuevos añadidos cada semana. </li>
-<li>Homescapes: Este es un juego que combina rompecabezas de match-3 con el diseño y la renovación del hogar. Tiene más de 6000 niveles y nuevos añadidos cada semana. </li>
-</ul>
- <p>Puedes descargar y jugar estos juegos gratis desde la Google Play Store o la App Store. Son juegos divertidos, desafiantes y adictivos que pueden mantenerte entretenido durante horas. </p>
- <h3>Alternativa 2: Utilice otras formas de obtener barras de oro ilimitadas y potenciadores</h3>
- <p>Si usted no quiere utilizar un apk mod o un generador en línea para obtener barras de oro ilimitadas y refuerzos en Candy Crush Saga, hay algunas otras maneras que usted puede probar. Estas formas son legales y seguras de usar, pero pueden requerir más tiempo y esfuerzo. Estas son algunas de ellas:</p>
-<ul>
-
-<li>Conectar con Facebook: El juego le permite conectarse con su cuenta de Facebook y sincronizar su progreso a través de dispositivos. Al hacerlo, también puede obtener algunos lingotes de oro gratis y refuerzos de sus amigos o invitándolos a jugar el juego. </li>
-<li>Ver anuncios: El juego a veces le ofrece la opción de ver anuncios a cambio de algunas barras de oro gratis y refuerzos. Puede aprovechar esta opción siempre que esté disponible. </li>
-<li>Participar en eventos y concursos: El juego a menudo alberga eventos y concursos que le dan la oportunidad de ganar barras de oro y refuerzos jugando ciertos niveles o completar ciertos objetivos. </li>
-</ul>
- <p>Puedes usar estas formas para obtener más barras de oro y potenciadores en Candy Crush Saga sin usar ningún mod apk o hack. Sin embargo , pueden no darle tantos lingotes de oro y boosters como un apk mod o un hack haría. </p>
- <h2>Conclusión</h2>
- <p>Candy Crush Saga es un juego divertido y adictivo que puede mantenerte entretenido durante horas. Sin embargo, también puede ser frustrante y desafiante a veces, especialmente cuando te quedas sin recursos o te quedas atascado en un nivel difícil. Es por eso que algunas personas utilizan Candy Crush Saga Mod Apk ilimitadas Barras de Oro para disfrutar del juego con más características y beneficios. </p>
- <p>En este artículo, te hemos dicho todo lo que necesitas saber sobre Candy Crush Saga Mod Apk ilimitadas Barras de Oro. Hemos explicado qué es, cuáles son sus beneficios, cómo descargarlo e instalarlo, cómo jugar el juego con consejos y trucos, y cuáles son algunas alternativas. Esperamos que haya encontrado este artículo útil e informativo. </p>
- <p>Ahora, es su turno para decidir si desea utilizar Candy Crush Saga Mod Apk ilimitadas barras de oro o no. Si lo hace, asegúrese de seguir los pasos y precauciones que hemos mencionado en este artículo. Si no lo haces, todavía puedes disfrutar del juego con otras formas que hemos sugerido en este artículo. </p>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes que la gente tiene acerca de Candy Crush Saga Mod Apk ilimitadas barras de oro:</p>
- <h3>Q: ¿Es Candy Crush Saga Mod Apk barras de oro ilimitadas seguro de usar? </h3>
-<p>A: Depende de dónde lo descargues y cómo lo verifiques. Si lo descarga desde una fuente confiable y comprueba su seguridad con una herramienta como Hashdroid o VirusTotal, debería ser seguro de usar. Sin embargo, si lo descarga desde una fuente oscura o desconocida y no lo verifica, puede ser inseguro de usar. Puede contener malware, virus o archivos falsos que pueden dañar tu dispositivo o robar tus datos. </p>
- <h3>Q: ¿Es Candy Crush Saga Mod Apk barras de oro ilimitadas legal de usar? </h3>
-<p>A: No, no es legal usarlo. Es una violación de los términos del juego de servicio y los derechos de propiedad intelectual. También es injusto para otros jugadores que juegan el juego sin usar cualquier apk mod o hack. Si utiliza Candy Crush Saga Mod Apk ilimitadas Barras de Oro, usted puede conseguir prohibido en el juego o perder su cuenta. </p>
- <h3>Q: ¿Candy Crush Saga Mod Apk barras de oro ilimitadas funcionan en mi dispositivo? </h3>
-<p>A: Depende de su tipo de dispositivo y la versión. Candy Crush Saga Mod Apk ilimitado Barras de Oro está diseñado para dispositivos Android solamente. Puede que no funcione en dispositivos iOS u otras plataformas. También puede no funcionar en algunos dispositivos Android o versiones que son incompatibles con el archivo apk mod. </p>
- <h3>Q: ¿Cómo puedo actualizar Candy Crush Saga Mod Apk ilimitadas barras de oro? </h3>
-<p>A: Usted puede actualizar Candy Crush Saga Mod Apk ilimitadas barras de oro mediante la descarga e instalación de la última versión del archivo apk mod de la misma fuente que lo descargó de antes. Sin embargo, puede perder su progreso y los datos si se actualiza el archivo apk mod sin hacer una copia de seguridad de ellos primero. </p>
- <h3>Q: ¿Puedo jugar Candy Crush Saga Mod Apk ilimitadas barras de oro en línea o fuera de línea? </h3> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Betacuckgpt/togethercomputer-GPT-JT-Moderation-6B/app.py b/spaces/Betacuckgpt/togethercomputer-GPT-JT-Moderation-6B/app.py
deleted file mode 100644
index 4423ecbbd598bb3bfe5d4e3abd28f7fed65125a8..0000000000000000000000000000000000000000
--- a/spaces/Betacuckgpt/togethercomputer-GPT-JT-Moderation-6B/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/togethercomputer/GPT-JT-Moderation-6B").launch()
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/req_command.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/req_command.py
deleted file mode 100644
index c2f4e38bed82c32bf5e45657fd8658cff1710f13..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/req_command.py
+++ /dev/null
@@ -1,505 +0,0 @@
-"""Contains the Command base classes that depend on PipSession.
-
-The classes in this module are in a separate module so the commands not
-needing download / PackageFinder capability don't unnecessarily import the
-PackageFinder machinery and all its vendored dependencies, etc.
-"""
-
-import logging
-import os
-import sys
-from functools import partial
-from optparse import Values
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple
-
-from pip._internal.cache import WheelCache
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.command_context import CommandContextMixIn
-from pip._internal.exceptions import CommandError, PreviousBuildDirError
-from pip._internal.index.collector import LinkCollector
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.models.selection_prefs import SelectionPreferences
-from pip._internal.models.target_python import TargetPython
-from pip._internal.network.session import PipSession
-from pip._internal.operations.build.build_tracker import BuildTracker
-from pip._internal.operations.prepare import RequirementPreparer
-from pip._internal.req.constructors import (
-    install_req_from_editable,
-    install_req_from_line,
-    install_req_from_parsed_requirement,
-    install_req_from_req_string,
-)
-from pip._internal.req.req_file import parse_requirements
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.resolution.base import BaseResolver
-from pip._internal.self_outdated_check import pip_self_version_check
-from pip._internal.utils.temp_dir import (
-    TempDirectory,
-    TempDirectoryTypeRegistry,
-    tempdir_kinds,
-)
-from pip._internal.utils.virtualenv import running_under_virtualenv
-
-if TYPE_CHECKING:
-    from ssl import SSLContext
-
-logger = logging.getLogger(__name__)
-
-
-def _create_truststore_ssl_context() -> Optional["SSLContext"]:
-    if sys.version_info < (3, 10):
-        raise CommandError("The truststore feature is only available for Python 3.10+")
-
-    try:
-        import ssl
-    except ImportError:
-        logger.warning("Disabling truststore since ssl support is missing")
-        return None
-
-    try:
-        import truststore
-    except ImportError:
-        raise CommandError(
-            "To use the truststore feature, 'truststore' must be installed into "
-            "pip's current environment."
-        )
-
-    return truststore.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
-
-
-class SessionCommandMixin(CommandContextMixIn):
-
-    """
-    A class mixin for command classes needing _build_session().
-    """
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._session: Optional[PipSession] = None
-
-    @classmethod
-    def _get_index_urls(cls, options: Values) -> Optional[List[str]]:
-        """Return a list of index urls from user-provided options."""
-        index_urls = []
-        if not getattr(options, "no_index", False):
-            url = getattr(options, "index_url", None)
-            if url:
-                index_urls.append(url)
-        urls = getattr(options, "extra_index_urls", None)
-        if urls:
-            index_urls.extend(urls)
-        # Return None rather than an empty list
-        return index_urls or None
-
-    def get_default_session(self, options: Values) -> PipSession:
-        """Get a default-managed session."""
-        if self._session is None:
-            self._session = self.enter_context(self._build_session(options))
-            # there's no type annotation on requests.Session, so it's
-            # automatically ContextManager[Any] and self._session becomes Any,
-            # then https://github.com/python/mypy/issues/7696 kicks in
-            assert self._session is not None
-        return self._session
-
-    def _build_session(
-        self,
-        options: Values,
-        retries: Optional[int] = None,
-        timeout: Optional[int] = None,
-        fallback_to_certifi: bool = False,
-    ) -> PipSession:
-        cache_dir = options.cache_dir
-        assert not cache_dir or os.path.isabs(cache_dir)
-
-        if "truststore" in options.features_enabled:
-            try:
-                ssl_context = _create_truststore_ssl_context()
-            except Exception:
-                if not fallback_to_certifi:
-                    raise
-                ssl_context = None
-        else:
-            ssl_context = None
-
-        session = PipSession(
-            cache=os.path.join(cache_dir, "http") if cache_dir else None,
-            retries=retries if retries is not None else options.retries,
-            trusted_hosts=options.trusted_hosts,
-            index_urls=self._get_index_urls(options),
-            ssl_context=ssl_context,
-        )
-
-        # Handle custom ca-bundles from the user
-        if options.cert:
-            session.verify = options.cert
-
-        # Handle SSL client certificate
-        if options.client_cert:
-            session.cert = options.client_cert
-
-        # Handle timeouts
-        if options.timeout or timeout:
-            session.timeout = timeout if timeout is not None else options.timeout
-
-        # Handle configured proxies
-        if options.proxy:
-            session.proxies = {
-                "http": options.proxy,
-                "https": options.proxy,
-            }
-
-        # Determine if we can prompt the user for authentication or not
-        session.auth.prompting = not options.no_input
-        session.auth.keyring_provider = options.keyring_provider
-
-        return session
-
-
-class IndexGroupCommand(Command, SessionCommandMixin):
-
-    """
-    Abstract base class for commands with the index_group options.
-
-    This also corresponds to the commands that permit the pip version check.
-    """
-
-    def handle_pip_version_check(self, options: Values) -> None:
-        """
-        Do the pip version check if not disabled.
-
-        This overrides the default behavior of not doing the check.
-        """
-        # Make sure the index_group options are present.
-        assert hasattr(options, "no_index")
-
-        if options.disable_pip_version_check or options.no_index:
-            return
-
-        # Otherwise, check if we're using the latest version of pip available.
-        session = self._build_session(
-            options,
-            retries=0,
-            timeout=min(5, options.timeout),
-            # This is set to ensure the function does not fail when truststore is
-            # specified in use-feature but cannot be loaded. This usually raises a
-            # CommandError and shows a nice user-facing error, but this function is not
-            # called in that try-except block.
-            fallback_to_certifi=True,
-        )
-        with session:
-            pip_self_version_check(session, options)
-
-
-KEEPABLE_TEMPDIR_TYPES = [
-    tempdir_kinds.BUILD_ENV,
-    tempdir_kinds.EPHEM_WHEEL_CACHE,
-    tempdir_kinds.REQ_BUILD,
-]
-
-
-def warn_if_run_as_root() -> None:
-    """Output a warning for sudo users on Unix.
-
-    In a virtual environment, sudo pip still writes to virtualenv.
-    On Windows, users may run pip as Administrator without issues.
-    This warning only applies to Unix root users outside of virtualenv.
-    """
-    if running_under_virtualenv():
-        return
-    if not hasattr(os, "getuid"):
-        return
-    # On Windows, there are no "system managed" Python packages. Installing as
-    # Administrator via pip is the correct way of updating system environments.
-    #
-    # We choose sys.platform over utils.compat.WINDOWS here to enable Mypy platform
-    # checks: https://mypy.readthedocs.io/en/stable/common_issues.html
-    if sys.platform == "win32" or sys.platform == "cygwin":
-        return
-
-    if os.getuid() != 0:
-        return
-
-    logger.warning(
-        "Running pip as the 'root' user can result in broken permissions and "
-        "conflicting behaviour with the system package manager. "
-        "It is recommended to use a virtual environment instead: "
-        "https://pip.pypa.io/warnings/venv"
-    )
-
-
-def with_cleanup(func: Any) -> Any:
-    """Decorator for common logic related to managing temporary
-    directories.
-    """
-
-    def configure_tempdir_registry(registry: TempDirectoryTypeRegistry) -> None:
-        for t in KEEPABLE_TEMPDIR_TYPES:
-            registry.set_delete(t, False)
-
-    def wrapper(
-        self: RequirementCommand, options: Values, args: List[Any]
-    ) -> Optional[int]:
-        assert self.tempdir_registry is not None
-        if options.no_clean:
-            configure_tempdir_registry(self.tempdir_registry)
-
-        try:
-            return func(self, options, args)
-        except PreviousBuildDirError:
-            # This kind of conflict can occur when the user passes an explicit
-            # build directory with a pre-existing folder. In that case we do
-            # not want to accidentally remove it.
-            configure_tempdir_registry(self.tempdir_registry)
-            raise
-
-    return wrapper
-
-
-class RequirementCommand(IndexGroupCommand):
-    def __init__(self, *args: Any, **kw: Any) -> None:
-        super().__init__(*args, **kw)
-
-        self.cmd_opts.add_option(cmdoptions.no_clean())
-
-    @staticmethod
-    def determine_resolver_variant(options: Values) -> str:
-        """Determines which resolver should be used, based on the given options."""
-        if "legacy-resolver" in options.deprecated_features_enabled:
-            return "legacy"
-
-        return "2020-resolver"
-
-    @classmethod
-    def make_requirement_preparer(
-        cls,
-        temp_build_dir: TempDirectory,
-        options: Values,
-        build_tracker: BuildTracker,
-        session: PipSession,
-        finder: PackageFinder,
-        use_user_site: bool,
-        download_dir: Optional[str] = None,
-        verbosity: int = 0,
-    ) -> RequirementPreparer:
-        """
-        Create a RequirementPreparer instance for the given parameters.
-        """
-        temp_build_dir_path = temp_build_dir.path
-        assert temp_build_dir_path is not None
-
-        resolver_variant = cls.determine_resolver_variant(options)
-        if resolver_variant == "2020-resolver":
-            lazy_wheel = "fast-deps" in options.features_enabled
-            if lazy_wheel:
-                logger.warning(
-                    "pip is using lazily downloaded wheels using HTTP "
-                    "range requests to obtain dependency information. "
-                    "This experimental feature is enabled through "
-                    "--use-feature=fast-deps and it is not ready for "
-                    "production."
-                )
-        else:
-            lazy_wheel = False
-            if "fast-deps" in options.features_enabled:
-                logger.warning(
-                    "fast-deps has no effect when used with the legacy resolver."
-                )
-
-        return RequirementPreparer(
-            build_dir=temp_build_dir_path,
-            src_dir=options.src_dir,
-            download_dir=download_dir,
-            build_isolation=options.build_isolation,
-            check_build_deps=options.check_build_deps,
-            build_tracker=build_tracker,
-            session=session,
-            progress_bar=options.progress_bar,
-            finder=finder,
-            require_hashes=options.require_hashes,
-            use_user_site=use_user_site,
-            lazy_wheel=lazy_wheel,
-            verbosity=verbosity,
-        )
-
-    @classmethod
-    def make_resolver(
-        cls,
-        preparer: RequirementPreparer,
-        finder: PackageFinder,
-        options: Values,
-        wheel_cache: Optional[WheelCache] = None,
-        use_user_site: bool = False,
-        ignore_installed: bool = True,
-        ignore_requires_python: bool = False,
-        force_reinstall: bool = False,
-        upgrade_strategy: str = "to-satisfy-only",
-        use_pep517: Optional[bool] = None,
-        py_version_info: Optional[Tuple[int, ...]] = None,
-    ) -> BaseResolver:
-        """
-        Create a Resolver instance for the given parameters.
-        """
-        make_install_req = partial(
-            install_req_from_req_string,
-            isolated=options.isolated_mode,
-            use_pep517=use_pep517,
-        )
-        resolver_variant = cls.determine_resolver_variant(options)
-        # The long import name and duplicated invocation is needed to convince
-        # Mypy into correctly typechecking. Otherwise it would complain the
-        # "Resolver" class being redefined.
-        if resolver_variant == "2020-resolver":
-            import pip._internal.resolution.resolvelib.resolver
-
-            return pip._internal.resolution.resolvelib.resolver.Resolver(
-                preparer=preparer,
-                finder=finder,
-                wheel_cache=wheel_cache,
-                make_install_req=make_install_req,
-                use_user_site=use_user_site,
-                ignore_dependencies=options.ignore_dependencies,
-                ignore_installed=ignore_installed,
-                ignore_requires_python=ignore_requires_python,
-                force_reinstall=force_reinstall,
-                upgrade_strategy=upgrade_strategy,
-                py_version_info=py_version_info,
-            )
-        import pip._internal.resolution.legacy.resolver
-
-        return pip._internal.resolution.legacy.resolver.Resolver(
-            preparer=preparer,
-            finder=finder,
-            wheel_cache=wheel_cache,
-            make_install_req=make_install_req,
-            use_user_site=use_user_site,
-            ignore_dependencies=options.ignore_dependencies,
-            ignore_installed=ignore_installed,
-            ignore_requires_python=ignore_requires_python,
-            force_reinstall=force_reinstall,
-            upgrade_strategy=upgrade_strategy,
-            py_version_info=py_version_info,
-        )
-
-    def get_requirements(
-        self,
-        args: List[str],
-        options: Values,
-        finder: PackageFinder,
-        session: PipSession,
-    ) -> List[InstallRequirement]:
-        """
-        Parse command-line arguments into the corresponding requirements.
-        """
-        requirements: List[InstallRequirement] = []
-        for filename in options.constraints:
-            for parsed_req in parse_requirements(
-                filename,
-                constraint=True,
-                finder=finder,
-                options=options,
-                session=session,
-            ):
-                req_to_add = install_req_from_parsed_requirement(
-                    parsed_req,
-                    isolated=options.isolated_mode,
-                    user_supplied=False,
-                )
-                requirements.append(req_to_add)
-
-        for req in args:
-            req_to_add = install_req_from_line(
-                req,
-                comes_from=None,
-                isolated=options.isolated_mode,
-                use_pep517=options.use_pep517,
-                user_supplied=True,
-                config_settings=getattr(options, "config_settings", None),
-            )
-            requirements.append(req_to_add)
-
-        for req in options.editables:
-            req_to_add = install_req_from_editable(
-                req,
-                user_supplied=True,
-                isolated=options.isolated_mode,
-                use_pep517=options.use_pep517,
-                config_settings=getattr(options, "config_settings", None),
-            )
-            requirements.append(req_to_add)
-
-        # NOTE: options.require_hashes may be set if --require-hashes is True
-        for filename in options.requirements:
-            for parsed_req in parse_requirements(
-                filename, finder=finder, options=options, session=session
-            ):
-                req_to_add = install_req_from_parsed_requirement(
-                    parsed_req,
-                    isolated=options.isolated_mode,
-                    use_pep517=options.use_pep517,
-                    user_supplied=True,
-                    config_settings=parsed_req.options.get("config_settings")
-                    if parsed_req.options
-                    else None,
-                )
-                requirements.append(req_to_add)
-
-        # If any requirement has hash options, enable hash checking.
-        if any(req.has_hash_options for req in requirements):
-            options.require_hashes = True
-
-        if not (args or options.editables or options.requirements):
-            opts = {"name": self.name}
-            if options.find_links:
-                raise CommandError(
-                    "You must give at least one requirement to {name} "
-                    '(maybe you meant "pip {name} {links}"?)'.format(
-                        **dict(opts, links=" ".join(options.find_links))
-                    )
-                )
-            else:
-                raise CommandError(
-                    "You must give at least one requirement to {name} "
-                    '(see "pip help {name}")'.format(**opts)
-                )
-
-        return requirements
-
-    @staticmethod
-    def trace_basic_info(finder: PackageFinder) -> None:
-        """
-        Trace basic information about the provided objects.
-        """
-        # Display where finder is looking for packages
-        search_scope = finder.search_scope
-        locations = search_scope.get_formatted_locations()
-        if locations:
-            logger.info(locations)
-
-    def _build_package_finder(
-        self,
-        options: Values,
-        session: PipSession,
-        target_python: Optional[TargetPython] = None,
-        ignore_requires_python: Optional[bool] = None,
-    ) -> PackageFinder:
-        """
-        Create a package finder appropriate to this requirement command.
-
-        :param ignore_requires_python: Whether to ignore incompatible
-            "Requires-Python" values in links. Defaults to False.
-        """
-        link_collector = LinkCollector.create(session, options=options)
-        selection_prefs = SelectionPreferences(
-            allow_yanked=True,
-            format_control=options.format_control,
-            allow_all_prereleases=options.pre,
-            prefer_binary=options.prefer_binary,
-            ignore_requires_python=ignore_requires_python,
-        )
-
-        return PackageFinder.create(
-            link_collector=link_collector,
-            selection_prefs=selection_prefs,
-            target_python=target_python,
-        )
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/utf8prober.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/utf8prober.py
deleted file mode 100644
index d96354d97c2195320d0acc1717a5876eafbea2af..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/utf8prober.py
+++ /dev/null
@@ -1,82 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from typing import Union
-
-from .charsetprober import CharSetProber
-from .codingstatemachine import CodingStateMachine
-from .enums import MachineState, ProbingState
-from .mbcssm import UTF8_SM_MODEL
-
-
-class UTF8Prober(CharSetProber):
-    ONE_CHAR_PROB = 0.5
-
-    def __init__(self) -> None:
-        super().__init__()
-        self.coding_sm = CodingStateMachine(UTF8_SM_MODEL)
-        self._num_mb_chars = 0
-        self.reset()
-
-    def reset(self) -> None:
-        super().reset()
-        self.coding_sm.reset()
-        self._num_mb_chars = 0
-
-    @property
-    def charset_name(self) -> str:
-        return "utf-8"
-
-    @property
-    def language(self) -> str:
-        return ""
-
-    def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
-        for c in byte_str:
-            coding_state = self.coding_sm.next_state(c)
-            if coding_state == MachineState.ERROR:
-                self._state = ProbingState.NOT_ME
-                break
-            if coding_state == MachineState.ITS_ME:
-                self._state = ProbingState.FOUND_IT
-                break
-            if coding_state == MachineState.START:
-                if self.coding_sm.get_current_charlen() >= 2:
-                    self._num_mb_chars += 1
-
-        if self.state == ProbingState.DETECTING:
-            if self.get_confidence() > self.SHORTCUT_THRESHOLD:
-                self._state = ProbingState.FOUND_IT
-
-        return self.state
-
-    def get_confidence(self) -> float:
-        unlike = 0.99
-        if self._num_mb_chars < 6:
-            unlike *= self.ONE_CHAR_PROB**self._num_mb_chars
-            return 1.0 - unlike
-        return unlike
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_resources/abc.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_resources/abc.py
deleted file mode 100644
index d39dc1adba0f00d2f7bdf6fa2cd1abcd82475e2e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_resources/abc.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import abc
-from typing import BinaryIO, Iterable, Text
-
-from ._compat import runtime_checkable, Protocol
-
-
-class ResourceReader(metaclass=abc.ABCMeta):
-    """Abstract base class for loaders to provide resource reading support."""
-
-    @abc.abstractmethod
-    def open_resource(self, resource: Text) -> BinaryIO:
-        """Return an opened, file-like object for binary reading.
-
-        The 'resource' argument is expected to represent only a file name.
-        If the resource cannot be found, FileNotFoundError is raised.
-        """
-        # This deliberately raises FileNotFoundError instead of
-        # NotImplementedError so that if this method is accidentally called,
-        # it'll still do the right thing.
-        raise FileNotFoundError
-
-    @abc.abstractmethod
-    def resource_path(self, resource: Text) -> Text:
-        """Return the file system path to the specified resource.
-
-        The 'resource' argument is expected to represent only a file name.
-        If the resource does not exist on the file system, raise
-        FileNotFoundError.
-        """
-        # This deliberately raises FileNotFoundError instead of
-        # NotImplementedError so that if this method is accidentally called,
-        # it'll still do the right thing.
-        raise FileNotFoundError
-
-    @abc.abstractmethod
-    def is_resource(self, path: Text) -> bool:
-        """Return True if the named 'path' is a resource.
-
-        Files are resources, directories are not.
-        """
-        raise FileNotFoundError
-
-    @abc.abstractmethod
-    def contents(self) -> Iterable[str]:
-        """Return an iterable of entries in `package`."""
-        raise FileNotFoundError
-
-
-@runtime_checkable
-class Traversable(Protocol):
-    """
-    An object with a subset of pathlib.Path methods suitable for
-    traversing directories and opening files.
-    """
-
-    @abc.abstractmethod
-    def iterdir(self):
-        """
-        Yield Traversable objects in self
-        """
-
-    def read_bytes(self):
-        """
-        Read contents of self as bytes
-        """
-        with self.open('rb') as strm:
-            return strm.read()
-
-    def read_text(self, encoding=None):
-        """
-        Read contents of self as text
-        """
-        with self.open(encoding=encoding) as strm:
-            return strm.read()
-
-    @abc.abstractmethod
-    def is_dir(self) -> bool:
-        """
-        Return True if self is a directory
-        """
-
-    @abc.abstractmethod
-    def is_file(self) -> bool:
-        """
-        Return True if self is a file
-        """
-
-    @abc.abstractmethod
-    def joinpath(self, child):
-        """
-        Return Traversable child in self
-        """
-
-    def __truediv__(self, child):
-        """
-        Return Traversable child in self
-        """
-        return self.joinpath(child)
-
-    @abc.abstractmethod
-    def open(self, mode='r', *args, **kwargs):
-        """
-        mode may be 'r' or 'rb' to open as text or binary. Return a handle
-        suitable for reading (same as pathlib.Path.open).
-
-        When opening as text, accepts encoding parameters such as those
-        accepted by io.TextIOWrapper.
-        """
-
-    @abc.abstractproperty
-    def name(self) -> str:
-        """
-        The base name of this object without any parent references.
-        """
-
-
-class TraversableResources(ResourceReader):
-    """
-    The required interface for providing traversable
-    resources.
-    """
-
-    @abc.abstractmethod
-    def files(self):
-        """Return a Traversable object for the loaded package."""
-
-    def open_resource(self, resource):
-        return self.files().joinpath(resource).open('rb')
-
-    def resource_path(self, resource):
-        raise FileNotFoundError(resource)
-
-    def is_resource(self, path):
-        return self.files().joinpath(path).is_file()
-
-    def contents(self):
-        return (item.name for item in self.files().iterdir())
diff --git a/spaces/Boadiwaa/Recipes/openai/api_resources/experimental/__init__.py b/spaces/Boadiwaa/Recipes/openai/api_resources/experimental/__init__.py
deleted file mode 100644
index d24c7b0cb5cf27867b4671444354980e04dc49c1..0000000000000000000000000000000000000000
--- a/spaces/Boadiwaa/Recipes/openai/api_resources/experimental/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from openai.api_resources.experimental.completion_config import (  # noqa: F401
-    CompletionConfig,
-)
diff --git a/spaces/CVH-vn1210/make_hair/minigpt4/datasets/datasets/caption_datasets.py b/spaces/CVH-vn1210/make_hair/minigpt4/datasets/datasets/caption_datasets.py
deleted file mode 100644
index 8c8fb2014794a2800e5bea480ba3ecd353512915..0000000000000000000000000000000000000000
--- a/spaces/CVH-vn1210/make_hair/minigpt4/datasets/datasets/caption_datasets.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import os
-from collections import OrderedDict
-
-from minigpt4.datasets.datasets.base_dataset import BaseDataset
-from PIL import Image
-
-
-class __DisplMixin:
-    def displ_item(self, index):
-        sample, ann = self.__getitem__(index), self.annotation[index]
-
-        return OrderedDict(
-            {
-                "file": ann["image"],
-                "caption": ann["caption"],
-                "image": sample["image"],
-            }
-        )
-
-
-class CaptionDataset(BaseDataset, __DisplMixin):
-    def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
-        """
-        vis_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        """
-        super().__init__(vis_processor, text_processor, vis_root, ann_paths)
-
-        self.img_ids = {}
-        n = 0
-        for ann in self.annotation:
-            img_id = ann["image_id"]
-            if img_id not in self.img_ids.keys():
-                self.img_ids[img_id] = n
-                n += 1
-
-    def __getitem__(self, index):
-
-        # TODO this assumes image input, not general enough
-        ann = self.annotation[index]
-
-        img_file = '{:0>12}.jpg'.format(ann["image_id"])
-        image_path = os.path.join(self.vis_root, img_file)
-        image = Image.open(image_path).convert("RGB")
-
-        image = self.vis_processor(image)
-        caption = self.text_processor(ann["caption"])
-
-        return {
-            "image": image,
-            "text_input": caption,
-            "image_id": self.img_ids[ann["image_id"]],
-        }
-
-
-class CaptionEvalDataset(BaseDataset, __DisplMixin):
-    def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
-        """
-        vis_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        """
-        super().__init__(vis_processor, text_processor, vis_root, ann_paths)
-
-    def __getitem__(self, index):
-
-        ann = self.annotation[index]
-
-        image_path = os.path.join(self.vis_root, ann["image"])
-        image = Image.open(image_path).convert("RGB")
-
-        image = self.vis_processor(image)
-
-        return {
-            "image": image,
-            "image_id": ann["image_id"],
-            "instance_id": ann["instance_id"],
-        }
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/__init__.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/__init__.py
deleted file mode 100644
index 64fb6d46359c05ed3d7aa1ec91fdd6e15b14c932..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .build import PROPOSAL_GENERATOR_REGISTRY, build_proposal_generator
-from .rpn import RPN_HEAD_REGISTRY, build_rpn_head, RPN
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/box_head.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/box_head.py
deleted file mode 100644
index 8d618dc0e4b0f1a57ad52d54741f2162b1595406..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/box_head.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.layers import Conv2d, Linear, ShapeSpec, get_norm
-from detectron2.utils.registry import Registry
-
-ROI_BOX_HEAD_REGISTRY = Registry("ROI_BOX_HEAD")
-ROI_BOX_HEAD_REGISTRY.__doc__ = """
-Registry for box heads, which make box predictions from per-region features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-@ROI_BOX_HEAD_REGISTRY.register()
-class FastRCNNConvFCHead(nn.Module):
-    """
-    A head with several 3x3 conv layers (each followed by norm & relu) and
-    several fc layers (each followed by relu).
-    """
-
-    def __init__(self, cfg, input_shape: ShapeSpec):
-        """
-        The following attributes are parsed from config:
-            num_conv, num_fc: the number of conv/fc layers
-            conv_dim/fc_dim: the dimension of the conv/fc layers
-            norm: normalization for the conv layers
-        """
-        super().__init__()
-
-        # fmt: off
-        num_conv   = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV
-        conv_dim   = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM
-        num_fc     = cfg.MODEL.ROI_BOX_HEAD.NUM_FC
-        fc_dim     = cfg.MODEL.ROI_BOX_HEAD.FC_DIM
-        norm       = cfg.MODEL.ROI_BOX_HEAD.NORM
-        # fmt: on
-        assert num_conv + num_fc > 0
-
-        self._output_size = (input_shape.channels, input_shape.height, input_shape.width)
-
-        self.conv_norm_relus = []
-        for k in range(num_conv):
-            conv = Conv2d(
-                self._output_size[0],
-                conv_dim,
-                kernel_size=3,
-                padding=1,
-                bias=not norm,
-                norm=get_norm(norm, conv_dim),
-                activation=F.relu,
-            )
-            self.add_module("conv{}".format(k + 1), conv)
-            self.conv_norm_relus.append(conv)
-            self._output_size = (conv_dim, self._output_size[1], self._output_size[2])
-
-        self.fcs = []
-        for k in range(num_fc):
-            fc = Linear(np.prod(self._output_size), fc_dim)
-            self.add_module("fc{}".format(k + 1), fc)
-            self.fcs.append(fc)
-            self._output_size = fc_dim
-
-        for layer in self.conv_norm_relus:
-            weight_init.c2_msra_fill(layer)
-        for layer in self.fcs:
-            weight_init.c2_xavier_fill(layer)
-
-    def forward(self, x):
-        for layer in self.conv_norm_relus:
-            x = layer(x)
-        if len(self.fcs):
-            if x.dim() > 2:
-                x = torch.flatten(x, start_dim=1)
-            for layer in self.fcs:
-                x = F.relu(layer(x))
-        return x
-
-    @property
-    def output_shape(self):
-        """
-        Returns:
-            ShapeSpec: the output feature shape
-        """
-        o = self._output_size
-        if isinstance(o, int):
-            return ShapeSpec(channels=o)
-        else:
-            return ShapeSpec(channels=o[0], height=o[1], width=o[2])
-
-
-def build_box_head(cfg, input_shape):
-    """
-    Build a box head defined by `cfg.MODEL.ROI_BOX_HEAD.NAME`.
-    """
-    name = cfg.MODEL.ROI_BOX_HEAD.NAME
-    return ROI_BOX_HEAD_REGISTRY.get(name)(cfg, input_shape)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/butd/model_cfgs.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/butd/model_cfgs.py
deleted file mode 100644
index 7d5208fbb0e5e77c8af5e923cc55bc9ca3500680..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/butd/model_cfgs.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Zhenwei Shao https://github.com/ParadoxZW
-# --------------------------------------------------------
-
-from openvqa.core.base_cfgs import BaseCfgs
-
-
-class Cfgs(BaseCfgs):
-    def __init__(self):
-        super(Cfgs, self).__init__()
-
-        self.IMG_FEAT_SIZE = 2048
-        self.HIDDEN_SIZE = 512
-        self.DROPOUT_R = 0.2
-        self.CLASSIFER_DROPOUT_R = 0.5
-        self.FLAT_OUT_SIZE = 1024
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/temporary_buffer.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/temporary_buffer.h
deleted file mode 100644
index 2adfaf2810c67462e41f271e43ad0aff9cfbf75f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/temporary_buffer.h
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special temporary buffer functions
-
diff --git a/spaces/CVPR/WALT/cwalt/clustering_utils.py b/spaces/CVPR/WALT/cwalt/clustering_utils.py
deleted file mode 100644
index 7463bfce84ae1c1089d9cf2a0e97de8e7397ce7a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/cwalt/clustering_utils.py
+++ /dev/null
@@ -1,132 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri May 20 15:18:20 2022
-
-@author: dinesh
-"""
-
-# 0 - Import related libraries
-
-import urllib
-import zipfile
-import os
-import scipy.io
-import math
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-from scipy.spatial.distance import directed_hausdorff
-from sklearn.cluster import DBSCAN
-from sklearn.metrics.pairwise import pairwise_distances
-import scipy.spatial.distance
-
-from .kmedoid import kMedoids # kMedoids code is adapted from https://github.com/letiantian/kmedoids
-
-# Some visualization stuff, not so important
-# sns.set()
-plt.rcParams['figure.figsize'] = (12, 12)
-
-# Utility Functions
-
-color_lst = plt.rcParams['axes.prop_cycle'].by_key()['color']
-color_lst.extend(['firebrick', 'olive', 'indigo', 'khaki', 'teal', 'saddlebrown',
-                  'skyblue', 'coral', 'darkorange', 'lime', 'darkorchid', 'dimgray'])
-
-
-def plot_cluster(image, traj_lst, cluster_lst):
-    '''
-    Plots given trajectories with a color that is specific for every trajectory's own cluster index.
-    Outlier trajectories which are specified with -1 in `cluster_lst` are plotted dashed with black color
-    '''
-    cluster_count = np.max(cluster_lst) + 1
-
-    for traj, cluster in zip(traj_lst, cluster_lst):
-
-        # if cluster == -1:
-        #     # Means it it a noisy trajectory, paint it black
-        #     plt.plot(traj[:, 0], traj[:, 1], c='k', linestyle='dashed')
-        #
-        # else:
-        plt.plot(traj[:, 0], traj[:, 1], c=color_lst[cluster % len(color_lst)])
-
-    plt.imshow(image)
-    # plt.show()
-    plt.axis('off')
-    plt.savefig('trajectory.png', bbox_inches='tight')
-    plt.show()
-
-
-# 3 - Distance matrix
-
-def hausdorff( u, v):
-    d = max(directed_hausdorff(u, v)[0], directed_hausdorff(v, u)[0])
-    return d
-
-
-def build_distance_matrix(traj_lst):
-    # 2 - Trajectory segmentation
-
-    print('Running trajectory segmentation...')
-    degree_threshold = 5
-
-    for traj_index, traj in enumerate(traj_lst):
-
-        hold_index_lst = []
-        previous_azimuth = 1000
-
-        for point_index, point in enumerate(traj[:-1]):
-            next_point = traj[point_index + 1]
-            diff_vector = next_point - point
-            azimuth = (math.degrees(math.atan2(*diff_vector)) + 360) % 360
-
-            if abs(azimuth - previous_azimuth) > degree_threshold:
-                hold_index_lst.append(point_index)
-                previous_azimuth = azimuth
-        hold_index_lst.append(traj.shape[0] - 1)  # Last point of trajectory is always added
-
-        traj_lst[traj_index] = traj[hold_index_lst, :]
-
-    print('Building distance matrix...')
-    traj_count = len(traj_lst)
-    D = np.zeros((traj_count, traj_count))
-
-    # This may take a while
-    for i in range(traj_count):
-        if i % 20 == 0:
-            print(i)
-        for j in range(i + 1, traj_count):
-            distance = hausdorff(traj_lst[i], traj_lst[j])
-            D[i, j] = distance
-            D[j, i] = distance
-
-    return D
-
-
-def run_kmedoids(image, traj_lst, D):
-    # 4 - Different clustering methods
-
-    # 4.1 - kmedoids
-
-    traj_count = len(traj_lst)
-
-    k = 3  # The number of clusters
-    medoid_center_lst, cluster2index_lst = kMedoids(D, k)
-
-    cluster_lst = np.empty((traj_count,), dtype=int)
-
-    for cluster in cluster2index_lst:
-        cluster_lst[cluster2index_lst[cluster]] = cluster
-
-    plot_cluster(image, traj_lst, cluster_lst)
-
-
-def run_dbscan(image, traj_lst, D):
-    mdl = DBSCAN(eps=400, min_samples=10)
-    cluster_lst = mdl.fit_predict(D)
-
-    plot_cluster(image, traj_lst, cluster_lst)
-
-
-
diff --git a/spaces/CVPR/WALT/mmdet/core/bbox/samplers/__init__.py b/spaces/CVPR/WALT/mmdet/core/bbox/samplers/__init__.py
deleted file mode 100644
index 0b06303fe1000e11c5486c40c70606a34a5208e3..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/core/bbox/samplers/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from .base_sampler import BaseSampler
-from .combined_sampler import CombinedSampler
-from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
-from .iou_balanced_neg_sampler import IoUBalancedNegSampler
-from .ohem_sampler import OHEMSampler
-from .pseudo_sampler import PseudoSampler
-from .random_sampler import RandomSampler
-from .sampling_result import SamplingResult
-from .score_hlr_sampler import ScoreHLRSampler
-
-__all__ = [
-    'BaseSampler', 'PseudoSampler', 'RandomSampler',
-    'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler',
-    'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler'
-]
diff --git a/spaces/CVPR/lama-example/bin/evaluate_predicts.py b/spaces/CVPR/lama-example/bin/evaluate_predicts.py
deleted file mode 100644
index a4c182a50bc0cc3e2e03c713c2c0be2a804b04b8..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/bin/evaluate_predicts.py
+++ /dev/null
@@ -1,79 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import pandas as pd
-
-from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
-from saicinpainting.evaluation.evaluator import InpaintingEvaluator, lpips_fid100_f1
-from saicinpainting.evaluation.losses.base_loss import SegmentationAwareSSIM, \
-    SegmentationClassStats, SSIMScore, LPIPSScore, FIDScore, SegmentationAwareLPIPS, SegmentationAwareFID
-from saicinpainting.evaluation.utils import load_yaml
-
-
-def main(args):
-    config = load_yaml(args.config)
-
-    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
-
-    metrics = {
-        'ssim': SSIMScore(),
-        'lpips': LPIPSScore(),
-        'fid': FIDScore()
-    }
-    enable_segm = config.get('segmentation', dict(enable=False)).get('enable', False)
-    if enable_segm:
-        weights_path = os.path.expandvars(config.segmentation.weights_path)
-        metrics.update(dict(
-            segm_stats=SegmentationClassStats(weights_path=weights_path),
-            segm_ssim=SegmentationAwareSSIM(weights_path=weights_path),
-            segm_lpips=SegmentationAwareLPIPS(weights_path=weights_path),
-            segm_fid=SegmentationAwareFID(weights_path=weights_path)
-        ))
-    evaluator = InpaintingEvaluator(dataset, scores=metrics,
-                                    integral_title='lpips_fid100_f1', integral_func=lpips_fid100_f1,
-                                    **config.evaluator_kwargs)
-
-    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)
-
-    results = evaluator.evaluate()
-
-    results = pd.DataFrame(results).stack(1).unstack(0)
-    results.dropna(axis=1, how='all', inplace=True)
-    results.to_csv(args.outpath, sep='\t', float_format='%.4f')
-
-    if enable_segm:
-        only_short_results = results[[c for c in results.columns if not c[0].startswith('segm_')]].dropna(axis=1, how='all')
-        only_short_results.to_csv(args.outpath + '_short', sep='\t', float_format='%.4f')
-
-        print(only_short_results)
-
-        segm_metrics_results = results[['segm_ssim', 'segm_lpips', 'segm_fid']].dropna(axis=1, how='all').transpose().unstack(0).reorder_levels([1, 0], axis=1)
-        segm_metrics_results.drop(['mean', 'std'], axis=0, inplace=True)
-
-        segm_stats_results = results['segm_stats'].dropna(axis=1, how='all').transpose()
-        segm_stats_results.index = pd.MultiIndex.from_tuples(n.split('/') for n in segm_stats_results.index)
-        segm_stats_results = segm_stats_results.unstack(0).reorder_levels([1, 0], axis=1)
-        segm_stats_results.sort_index(axis=1, inplace=True)
-        segm_stats_results.dropna(axis=0, how='all', inplace=True)
-
-        segm_results = pd.concat([segm_metrics_results, segm_stats_results], axis=1, sort=True)
-        segm_results.sort_values(('mask_freq', 'total'), ascending=False, inplace=True)
-
-        segm_results.to_csv(args.outpath + '_segm', sep='\t', float_format='%.4f')
-    else:
-        print(results)
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('config', type=str, help='Path to evaluation config')
-    aparser.add_argument('datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('predictdir', type=str,
-                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
-    aparser.add_argument('outpath', type=str, help='Where to put results')
-
-    main(aparser.parse_args())
diff --git a/spaces/CVPR/regionclip-demo/detectron2/layers/roi_align.py b/spaces/CVPR/regionclip-demo/detectron2/layers/roi_align.py
deleted file mode 100644
index 163462e1f194e1e4100da92d76d9516f7cc22e35..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/layers/roi_align.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from torch import nn
-from torchvision.ops import roi_align
-
-
-# NOTE: torchvision's RoIAlign has a different default aligned=False
-class ROIAlign(nn.Module):
-    def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
-        """
-        Args:
-            output_size (tuple): h, w
-            spatial_scale (float): scale the input boxes by this number
-            sampling_ratio (int): number of inputs samples to take for each output
-                sample. 0 to take samples densely.
-            aligned (bool): if False, use the legacy implementation in
-                Detectron. If True, align the results more perfectly.
-
-        Note:
-            The meaning of aligned=True:
-
-            Given a continuous coordinate c, its two neighboring pixel indices (in our
-            pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
-            c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
-            from the underlying signal at continuous coordinates 0.5 and 1.5). But the original
-            roi_align (aligned=False) does not subtract the 0.5 when computing neighboring
-            pixel indices and therefore it uses pixels with a slightly incorrect alignment
-            (relative to our pixel model) when performing bilinear interpolation.
-
-            With `aligned=True`,
-            we first appropriately scale the ROI and then shift it by -0.5
-            prior to calling roi_align. This produces the correct neighbors; see
-            detectron2/tests/test_roi_align.py for verification.
-
-            The difference does not make a difference to the model's performance if
-            ROIAlign is used together with conv layers.
-        """
-        super().__init__()
-        self.output_size = output_size
-        self.spatial_scale = spatial_scale
-        self.sampling_ratio = sampling_ratio
-        self.aligned = aligned
-
-        from torchvision import __version__
-
-        version = tuple(int(x) for x in __version__.split(".")[:2])
-        # https://github.com/pytorch/vision/pull/2438
-        assert version >= (0, 7), "Require torchvision >= 0.7"
-
-    def forward(self, input, rois):
-        """
-        Args:
-            input: NCHW images
-            rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
-        """
-        assert rois.dim() == 2 and rois.size(1) == 5
-        if input.is_quantized:
-            input = input.dequantize()
-        return roi_align(
-            input,
-            rois.to(dtype=input.dtype),
-            self.output_size,
-            self.spatial_scale,
-            self.sampling_ratio,
-            self.aligned,
-        )
-
-    def __repr__(self):
-        tmpstr = self.__class__.__name__ + "("
-        tmpstr += "output_size=" + str(self.output_size)
-        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
-        tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
-        tmpstr += ", aligned=" + str(self.aligned)
-        tmpstr += ")"
-        return tmpstr
diff --git a/spaces/Cartinoe5930/LLMAgora/app.py b/spaces/Cartinoe5930/LLMAgora/app.py
deleted file mode 100644
index 00a8783898ecdbbe0499f9782df8eb112f020834..0000000000000000000000000000000000000000
--- a/spaces/Cartinoe5930/LLMAgora/app.py
+++ /dev/null
@@ -1,364 +0,0 @@
-import gradio as gr
-import json
-import requests
-import os
-from model_inference import Inference
-import time
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-question_selector_map = {}
-
-every_model = ["llama2", "llama2-chat", "vicuna", "falcon", "falcon-instruct", "orca", "wizardlm"]
-
-with open("src/inference_endpoint.json", "r") as f:
-    inference_endpoint = json.load(f)
-
-for i in range(len(every_model)):
-    inference_endpoint[every_model[i]]["headers"]["Authorization"] += HF_TOKEN
-
-def build_question_selector_map(questions):
-    question_selector_map = {}
-
-    # Build question selector map
-    for q in questions:
-        preview = f"{q['question_id']+1}: " + q["question"][:128] + "..."
-        question_selector_map[preview] = q
-
-    return question_selector_map
-
-def math_display_question_answer(question, cot, request: gr.Request):
-    if cot:
-        q = math_cot_question_selector_map[question]
-    else:
-        q = math_question_selector_map[question]
-
-    return q["agent_response"]["llama"][0], q["agent_response"]["wizardlm"][0], q["agent_response"]["orca"][0], q["summarization"][0], q["agent_response"]["llama"][1], q["agent_response"]["wizardlm"][1], q["agent_response"]["orca"][1], q["summarization"][1],  q["agent_response"]["llama"][2], q["agent_response"]["wizardlm"][2], q["agent_response"]["orca"][2]
-
-def gsm_display_question_answer(question, cot, request: gr.Request):
-    if cot:
-        q = gsm_cot_question_selector_map[question]
-    else:
-        q = gsm_question_selector_map[question]
-
-    return q["agent_response"]["llama"][0], q["agent_response"]["wizardlm"][0], q["agent_response"]["orca"][0], q["summarization"][0], q["agent_response"]["llama"][1], q["agent_response"]["wizardlm"][1], q["agent_response"]["orca"][1], q["summarization"][1],  q["agent_response"]["llama"][2], q["agent_response"]["wizardlm"][2], q["agent_response"]["orca"][2]
-
-def mmlu_display_question_answer(question, cot, request: gr.Request):
-    if cot:
-        q = mmlu_cot_question_selector_map[question]
-    else:
-        q = mmlu_question_selector_map[question]
-
-    return q["agent_response"]["llama"][0], q["agent_response"]["wizardlm"][0], q["agent_response"]["orca"][0], q["summarization"][0], q["agent_response"]["llama"][1], q["agent_response"]["wizardlm"][1], q["agent_response"]["orca"][1], q["summarization"][1], q["agent_response"]["llama"][2], q["agent_response"]["wizardlm"][2], q["agent_response"]["orca"][2]
-    
-def warmup(list_model, model_inference_endpoints=inference_endpoint):
-    for model in list_model:
-        model = model.lower()
-        API_URL = model_inference_endpoints[model]["API_URL"]
-        headers = model_inference_endpoints[model]["headers"]
-        headers["Authorization"] += HF_TOKEN
-
-        def query(payload):
-            return requests.post(API_URL, headers=headers, json=payload)
-            
-        output = query({
-            "inputs": "Hello. "
-        })
-        
-    time.sleep(300)
-    return {
-        model_list: gr.update(visible=False),
-        options: gr.update(visible=True),
-        inputbox: gr.update(visible=True),
-        submit: gr.update(visible=True),
-        warmup_button: gr.update(visible=False),
-        welcome_message: gr.update(visible=True)
-    }
-
-def inference(model_list, question, API_KEY, cot, hf_token=HF_TOKEN):
-    if len(model_list) != 3:
-        raise gr.Error("Please choose just '3' models! Neither more nor less!")
-
-    for i in range(len(model_list)):
-        model_list[i] = model_list[i].lower()
-
-    model_response = Inference(model_list, question, API_KEY, cot, hf_token)
-
-    return {
-        output_msg: gr.update(visible=True),
-        output_col: gr.update(visible=True),
-        model1_output1: model_response["agent_response"][model_list[0]][0],
-        model2_output1: model_response["agent_response"][model_list[1]][0],
-        model3_output1: model_response["agent_response"][model_list[2]][0],
-        summarization_text1: model_response["summarization"][0],
-        model1_output2: model_response["agent_response"][model_list[0]][1],
-        model2_output2: model_response["agent_response"][model_list[1]][1],
-        model3_output2: model_response["agent_response"][model_list[2]][1],
-        summarization_text2: model_response["summarization"][1],
-        model1_output3: model_response["agent_response"][model_list[0]][2],
-        model2_output3: model_response["agent_response"][model_list[1]][2],
-        model3_output3: model_response["agent_response"][model_list[2]][2]
-    }
-
-def load_responses():
-    with open("result/Math/math_result.json", "r") as math_file:
-        math_responses = json.load(math_file)
-
-    with open("result/Math/math_result_cot.json", "r") as math_cot_file:
-        math_cot_responses = json.load(math_cot_file)
-
-    with open("result/GSM8K/gsm_result.json", "r") as gsm_file:
-        gsm_responses = json.load(gsm_file)
-
-    with open("result/GSM8K/gsm_result_cot.json", "r") as gsm_cot_file:
-        gsm_cot_responses = json.load(gsm_cot_file)
-
-    with open("result/MMLU/mmlu_result.json", "r") as mmlu_file:
-        mmlu_responses = json.load(mmlu_file)
-
-    with open("result/MMLU/mmlu_result_cot.json", "r") as mmlu_cot_file:
-        mmlu_cot_responses = json.load(mmlu_cot_file)
-    
-    return math_responses, math_cot_responses, gsm_responses, gsm_cot_responses, mmlu_responses, mmlu_cot_responses
-
-def load_questions(math, gsm, mmlu):
-    math_questions = []
-    gsm_questions = []
-    mmlu_questions = []
-    for i in range(100):
-        math_questions.append(f"{i+1}: " + math[i]["question"][:128] + "...")
-        gsm_questions.append(f"{i+1}: " + gsm[i]["question"][:128] + "...")
-        mmlu_questions.append(f"{i+1}: " + mmlu[i]["question"][:128] + "...")
-
-    return math_questions, gsm_questions, mmlu_questions
-
-math_result, math_cot_result, gsm_result, gsm_cot_result, mmlu_result, mmlu_cot_result = load_responses()
-
-math_questions, gsm_questions, mmlu_questions = load_questions(math_result, gsm_result, mmlu_result)
-
-math_question_selector_map = build_question_selector_map(math_result)
-math_cot_question_selector_map = build_question_selector_map(math_cot_result)
-gsm_question_selector_map = build_question_selector_map(gsm_result)
-gsm_cot_question_selector_map = build_question_selector_map(gsm_cot_result)
-mmlu_question_selector_map = build_question_selector_map(mmlu_result)
-mmlu_cot_question_selector_map = build_question_selector_map(mmlu_cot_result)
-
-
-TITLE = """<h1 align="center">LLM Agora 🗣️🏦</h1>"""
-
-INTRODUCTION_TEXT = """
-The **LLM Agora** 🗣️🏦 aims to improve the quality of open-source LMs' responses through debate & revision introduced in [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://arxiv.org/abs/2305.14325).
-Thank you to the authors of this paper for suggesting a great idea!
-
-Do you know that? 🤔 **LLMs can also improve their responses by debating with other LLMs**! 😮 We applied this concept to several open-source LMs to verify that the open-source model, not the proprietary one, can sufficiently improve the response through discussion. 🤗
-For more details, please refer to the [GitHub Repository](https://github.com/gauss5930/LLM-Agora).
-You can also check the results in this Space!
-
-You can use LLM Agora with your own questions if the response of open-source LM is not satisfactory and you want to improve the quality!
-The Math, GSM8K, and MMLU Tabs show the results of the experiment(Llama2, WizardLM2, Orca2), and for inference, please use the 'Inference' tab.
-
-Here's how to use LLM Agora!
-
-1. Before starting, choose just 3 models and click the 'Warm-up LLM Agora 🔥' button and wait until '🤗🔥 Welcome to LLM Agora 🔥🤗' appears. (Suggest to go grab a coffee☕ since it takes 5 minutes!)
-2. Once the interaction space is available, proceed with the following process.
-3. Check the CoT box if you want to utilize the Chain-of-Thought while inferencing.
-4. Please fill in your OpenAI API KEY, it will be used to use ChatGPT to summarize the responses.
-5. Type your question in the Question box and click the 'Submit' button! If you do so, LLM Agora will show you improved answers! 🤗 (It will take roughly a minute! Please wait for an answer!)
-
-For more detailed information, please check '※ Specific information about LLM Agora' at the bottom of the page.
-
-※ Due to quota limitations, 'Llama2-Chat' and 'Falcon-Instruct' are currently unavailable. We will provide additional updates in the future.
-"""
-
-WELCOME_TEXT = """<h1 align="center">🤗🔥 Welcome to LLM Agora 🔥🤗</h1>"""
-
-RESPONSE_TEXT = """<h1 align="center">🤗 Here are the responses to each model!! 🤗</h1>"""
-
-SPECIFIC_INFORMATION = """
-This is the specific information about LLM Agora!
-
-**Tasks**
-
-- Math: The problem of arithmetic operations on six randomly selected numbers. The format is '{}+{}*{}+{}-{}*{}=?'
-- GSM8K: GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers.
-- MMLU: MMLU (Massive Multitask Language Understanding) is a new benchmark designed to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings.
-
-**Model size**
-
-Besides Falcon, all other models are based on Llama2.
-
-|Model name|Model size|
-|---|---|
-|Llama2|13B|
-|Llama2-Chat|13B|
-|Vicuna|13B|
-|Falcon|7B|
-|Falcon-Instruct|7B|
-|WizardLM|13B|
-|Orca|13B|
-
-**Agent numbers & Debate rounds**
-
-- We limit the number of agents and debate rounds because of the limitation of resources. As a result, we decided to use 3 agents and 2 rounds of debate!
-
-**GitHub Repository**
-
-- If you want to see more specific information, please check the [GitHub Repository](https://github.com/gauss5930/LLM-Agora) of LLM Agora!
-
-**Citation**
-
-```
-@article{du2023improving,
-  title={Improving Factuality and Reasoning in Language Models through Multiagent Debate},
-  author={Du, Yilun and Li, Shuang and Torralba, Antonio and Tenenbaum, Joshua B and Mordatch, Igor},
-  journal={arXiv preprint arXiv:2305.14325},
-  year={2023}
-}
-```
-"""
-
-with gr.Blocks() as demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT)
-    with gr.Column():
-        with gr.Tab("Inference"):
-            model_list = gr.CheckboxGroup(["Llama2", "Vicuna", "Falcon", "WizardLM", "Orca"], label="Model Selection", info="Choose 3 LMs to participate in LLM Agora.", type="value", visible=True)
-            warmup_button = gr.Button("Warm-up LLM Agora 🔥", visible=True)
-            welcome_message = gr.HTML(WELCOME_TEXT, visible=False)
-            with gr.Row(visible=False) as options:
-                    cot = gr.Checkbox(label="CoT", info="Do you want to use CoT for inference?")
-                    API_KEY = gr.Textbox(label="OpenAI API Key", value="", info="Please fill in your OpenAI API token.", placeholder="sk..", type="password")
-            with gr.Column(visible=False) as inputbox:
-                question = gr.Textbox(label="Question", value="", info="Please type your question!", placeholder="")
-            submit = gr.Button("Submit", visible=False)
-
-            with gr.Row(visible=False) as output_msg:
-                gr.HTML(RESPONSE_TEXT)
-
-            with gr.Column(visible=False) as output_col:
-                with gr.Row(elem_id="model1_response"):
-                    model1_output1 = gr.Textbox(label="1️⃣ model's initial response")
-                    model2_output1 = gr.Textbox(label="2️⃣ model's initial response")
-                    model3_output1 = gr.Textbox(label="3️⃣ model's initial response")
-                summarization_text1 = gr.Textbox(label="Summarization 1")
-                with gr.Row(elem_id="model2_response"):
-                    model1_output2 = gr.Textbox(label="1️⃣ model's revised response")
-                    model2_output2 = gr.Textbox(label="2️⃣ model's revised response")
-                    model3_output2 = gr.Textbox(label="3️⃣ model's revised response")
-                summarization_text2 = gr.Textbox(label="Summarization 2")
-                with gr.Row(elem_id="model3_response"):
-                    model1_output3 = gr.Textbox(label="1️⃣ model's final response")
-                    model2_output3 = gr.Textbox(label="2️⃣ model's final response")
-                    model3_output3 = gr.Textbox(label="3️⃣ model's final response")
-
-        
-        with gr.Tab("Math"):
-            math_cot = gr.Checkbox(label="CoT", info="If you want to see CoT result, please check the box.")
-            math_question_list = gr.Dropdown(math_questions, label="Math Question")
-
-            with gr.Column():
-                with gr.Row(elem_id="model1_response"):
-                    math_model1_output1 = gr.Textbox(label="Llama2🦙's 1️⃣st response")
-                    math_model2_output1 = gr.Textbox(label="WizardLM🧙‍♂️'s 1️⃣st response")
-                    math_model3_output1 = gr.Textbox(label="Orca🐬's 1️⃣st response")
-                math_summarization_text1 = gr.Textbox(label="Summarization 1️⃣")
-                with gr.Row(elem_id="model2_response"):
-                    math_model1_output2 = gr.Textbox(label="Llama2🦙's 2️⃣nd response")
-                    math_model2_output2 = gr.Textbox(label="WizardLM🧙‍♂️'s 2️⃣nd response")
-                    math_model3_output2 = gr.Textbox(label="Orca🐬's 2️⃣nd response")
-                math_summarization_text2 = gr.Textbox(label="Summarization 2️⃣")
-                with gr.Row(elem_id="model3_response"):
-                    math_model1_output3 = gr.Textbox(label="Llama2🦙's 3️⃣rd response")
-                    math_model2_output3 = gr.Textbox(label="WizardLM🧙‍♂️'s 3️⃣rd response")
-                    math_model3_output3 = gr.Textbox(label="Orca🐬's 3️⃣rd response")
-
-            gr.HTML("""<h1 align="center"> The result of Math </h1>""")
-            gr.HTML("""<p align="center"><img src='https://github.com/gauss5930/LLM-Agora/assets/80087878/4fc22896-1306-4a93-bd54-a7a2ff184c98'></p>""")
-
-        math_cot.select(
-            math_display_question_answer,
-            [math_question_list, math_cot],
-            [math_model1_output1, math_model2_output1, math_model3_output1, math_summarization_text1, math_model1_output2, math_model2_output2, math_model3_output2, math_summarization_text2, math_model1_output3, math_model2_output3, math_model3_output3]
-        )
-        math_question_list.change(
-            math_display_question_answer,
-            [math_question_list, math_cot],
-            [math_model1_output1, math_model2_output1, math_model3_output1, math_summarization_text1, math_model1_output2, math_model2_output2, math_model3_output2, math_summarization_text2, math_model1_output3, math_model2_output3, math_model3_output3]
-        )
-
-
-        with gr.Tab("GSM8K"):
-            gsm_cot = gr.Checkbox(label="CoT", info="If you want to see CoT result, please check the box.")
-            gsm_question_list = gr.Dropdown(gsm_questions, label="GSM8K Question")
-
-            with gr.Column():
-                with gr.Row(elem_id="model1_response"):
-                    gsm_model1_output1 = gr.Textbox(label="Llama2🦙's 1️⃣st response")
-                    gsm_model2_output1 = gr.Textbox(label="WizardLM🧙‍♂️'s 1️⃣st response")
-                    gsm_model3_output1 = gr.Textbox(label="Orca🐬's 1️⃣st response")
-                gsm_summarization_text1 = gr.Textbox(label="Summarization 1️⃣")
-                with gr.Row(elem_id="model2_response"):
-                    gsm_model1_output2 = gr.Textbox(label="Llama2🦙's 2️⃣nd response")
-                    gsm_model2_output2 = gr.Textbox(label="WizardLM🧙‍♂️'s 2️⃣nd response")
-                    gsm_model3_output2 = gr.Textbox(label="Orca🐬's 2️⃣nd response")
-                gsm_summarization_text2 = gr.Textbox(label="Summarization 2️⃣")
-                with gr.Row(elem_id="model3_response"):
-                    gsm_model1_output3 = gr.Textbox(label="Llama2🦙's 3️⃣rd response")
-                    gsm_model2_output3 = gr.Textbox(label="WizardLM🧙‍♂️'s 3️⃣rd response")
-                    gsm_model3_output3 = gr.Textbox(label="Orca🐬's 3️⃣rd response")
-
-            gr.HTML("""<h1 align="center"> The result of GSM8K </h1>""")
-            gr.HTML("""<p align="center"><img src="https://github.com/gauss5930/LLM-Agora/assets/80087878/64f05ea4-5bec-41e4-83d7-d8855e753290"></p>""")
-
-        gsm_cot.select(
-            gsm_display_question_answer,
-            [gsm_question_list, gsm_cot],
-            [gsm_model1_output1, gsm_model2_output1, gsm_model3_output1, gsm_summarization_text1, gsm_model1_output2, gsm_model2_output2, gsm_model3_output2, gsm_summarization_text2, gsm_model1_output3, gsm_model2_output3, gsm_model3_output3]
-        )
-        gsm_question_list.change(
-            gsm_display_question_answer,
-            [gsm_question_list, gsm_cot],
-            [gsm_model1_output1, gsm_model2_output1, gsm_model3_output1, gsm_summarization_text1, gsm_model1_output2, gsm_model2_output2, gsm_model3_output2, gsm_summarization_text2, gsm_model1_output3, gsm_model2_output3, gsm_model3_output3]
-        )
-
-
-        with gr.Tab("MMLU"):
-            mmlu_cot = gr.Checkbox(label="CoT", info="If you want to see CoT result, please check the box.")
-            mmlu_question_list = gr.Dropdown(mmlu_questions, label="MMLU Question")
-            
-            with gr.Column():
-                with gr.Row(elem_id="model1_response"):
-                    mmlu_model1_output1 = gr.Textbox(label="Llama2🦙's 1️⃣st response")
-                    mmlu_model2_output1 = gr.Textbox(label="WizardLM🧙‍♂️'s 1️⃣st response")
-                    mmlu_model3_output1 = gr.Textbox(label="Orca🐬's 1️⃣st response")
-                mmlu_summarization_text1 = gr.Textbox(label="Summarization 1️⃣")
-                with gr.Row(elem_id="model2_response"):
-                    mmlu_model1_output2 = gr.Textbox(label="Llama2🦙's 2️⃣nd response")
-                    mmlu_model2_output2 = gr.Textbox(label="WizardLM🧙‍♂️'s 2️⃣nd response")
-                    mmlu_model3_output2 = gr.Textbox(label="Orca🐬's 2️⃣nd response")
-                mmlu_summarization_text2 = gr.Textbox(label="Summarization 2️⃣")
-                with gr.Row(elem_id="model3_response"):
-                    mmlu_model1_output3 = gr.Textbox(label="Llama2🦙's 3️⃣rd response")
-                    mmlu_model2_output3 = gr.Textbox(label="WizardLM🧙‍♂️'s 3️⃣rd response")
-                    mmlu_model3_output3 = gr.Textbox(label="Orca🐬's 3️⃣rd response")
-
-            gr.HTML("""<h1 align="center"> The result of MMLU </h1>""")
-            gr.HTML("""<p align="center"><img src="https://github.com/composable-models/llm_multiagent_debate/assets/80087878/963571aa-228b-4d73-9082-5f528552383e"></p>""")
-
-        mmlu_cot.select(
-            mmlu_display_question_answer,
-            [mmlu_question_list, mmlu_cot],
-            [mmlu_model1_output1, mmlu_model2_output1, mmlu_model3_output1, mmlu_summarization_text1, mmlu_model1_output2, mmlu_model2_output2, mmlu_model3_output2, mmlu_summarization_text2, mmlu_model1_output3, mmlu_model2_output3, mmlu_model3_output3]
-        )
-        mmlu_question_list.change(
-            mmlu_display_question_answer,
-            [mmlu_question_list, mmlu_cot],
-            [mmlu_model1_output1, mmlu_model2_output1, mmlu_model3_output1, mmlu_summarization_text1, mmlu_model1_output2, mmlu_model2_output2, mmlu_model3_output2, mmlu_summarization_text2, mmlu_model1_output3, mmlu_model2_output3, mmlu_model3_output3]
-        )
-
-        with gr.Accordion("※ Specific information about LLM Agora", open=False):
-            gr.Markdown(SPECIFIC_INFORMATION)
-
-    warmup_button.click(warmup, [model_list], [model_list, options, inputbox, submit, warmup_button, welcome_message])
-    submit.click(inference, [model_list, question, API_KEY, cot], [output_msg, output_col, model1_output1, model2_output1, model3_output1, summarization_text1, model1_output2, model2_output2, model3_output2, summarization_text2, model1_output3, model2_output3, model3_output3])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/processing/__init__.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/processing/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/red/index.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/red/index.js
deleted file mode 100644
index 47664d813dcdf14993566508b168c4f9045b06c9..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/red/index.js
+++ /dev/null
@@ -1,161 +0,0 @@
-import { QQRedBot } from "./bot.js"
-import { getToken } from './tool.js'
-import { toQQRedMsg } from './message.js'
-import { Version, allSocketList, setAllSocketList } from '../../components/index.js'
-import WebSocket from 'ws'
-import fetch from "node-fetch"
-
-logger.info(logger.yellow("- 正在加载 Chronocat(red) 适配器插件"))
-
-export const redAdapter = new class RedAdapter {
-    constructor() {
-        this.id = "QQ"
-        this.name = 'chronocat'
-    }
-
-    reconnectCount = 1
-
-    async connect(data) {
-        if (data.closed) return
-        const [host, port] = data.address.split(':')
-        let token = data.accessToken
-        if (!token) {
-            token = getToken()
-            if (!token) return
-        }
-        const bot = {
-            host,
-            port,
-            token
-        }
-        bot.sendApi = async (method, api, body) => {
-            const controller = new AbortController()
-            const signal = controller.signal
-            const timeout = 30000
-            setTimeout(() => {
-                controller.abort()
-            }, timeout);
-            return await fetch(`http://${bot.host}:${bot.port}/api/${api}`, {
-                signal,
-                method,
-                body,
-                headers: {
-                    Authorization: 'Bearer ' + bot.token
-                }
-            }).then(r => {
-                if (!r.ok) throw r
-                const contentType = r.headers.get('content-type');
-                if (contentType.includes('application/json')) {
-                    return r.json();
-                } else if (contentType.includes('text/plain')) {
-                    return r.text();
-                } else {
-                    return r
-                }
-            }).catch(error => {
-                if (error.name === 'AbortError') {
-                    return { error: `${logger.red(`[${this.uin}] ${api} 请求超时, 请检查账号状态或重启QQ！`)}` }
-                } else {
-                    return { error }
-                }
-            })
-        }
-        const reconnect = () => {
-            if (!data.stopReconnect && ((this.reconnectCount < data.maxReconnectAttempts) || data.maxReconnectAttempts <= 0)) {
-                logger.warn(`${this.name} 开始尝试重新连接第${this.reconnectCount}次`);
-                this.reconnectCount++
-                setTimeout(() => {
-                    this.connect(data)
-                }, data.reconnectInterval * 1000);
-            } else {
-                this.stopReconnect = false
-                logger.warn(`${this.name} 达到最大重连次数或关闭连接,停止重连`);
-            }
-        }
-        let info = await bot.sendApi('get', 'getSelfProfile')
-        if (info.error) {
-            if (info.error.code == 'ECONNREFUSED') {
-                logger.error(`${this.name} 请检查是否安装Chronocat并启动QQNT`)
-                reconnect()
-                return
-            }
-            logger.error(`${this.name} Token错误或其他未知原因`)
-            logger.error(await info.error.text?.() || info.error)
-            return
-        }
-        if (!info.uin) {
-            logger.error(`${this.name} 请点击登录`)
-            reconnect()
-            return
-        }
-        bot.info = {
-            ...info,
-            user_id: info.uin,
-            self_id: info.uin,
-            nickname: info.nick,
-            username: info.nick
-        }
-        bot.nickname = info.nick
-        bot.self_id = Number(info.uin)
-        this.uin = bot.self_id
-        bot.uin = bot.self_id
-        bot.ws = new WebSocket(`ws://${bot.host}:${bot.port}`)
-        bot.send = (type, payload) => bot.ws.send(JSON.stringify({ type, payload }))
-        bot.ws.on('open', () => bot.send('meta::connect', { token: bot.token }))
-        bot.ws.on('message', data => toQQRedMsg(bot, data))
-        bot.ws.on('close', (code) => {
-            delete Bot[bot.self_id]
-            switch (code) {
-                case 1005:
-                    logger.error(`${this.name}(${bot.self_id}) 主动断开连接`)
-                    return
-                case 1006:
-                    logger.error(`${this.name}(${bot.self_id}) QQNT被关闭`)
-                    reconnect()
-                    return
-                default:
-                    return
-            }
-        })
-        Bot[bot.self_id] = new QQRedBot(bot)
-        if (!Version.isTrss) {
-            /** 米游社主动推送、椰奶状态pro */
-            if (!Bot?.adapter) {
-                Bot.adapter = [Bot.uin]
-                Bot.adapter.push(bot.self_id)
-            } else {
-                Bot.adapter.push(bot.self_id)
-                /** 去重防止断连后出现多个重复的id */
-                Bot.adapter = Array.from(new Set(Bot.adapter.map(JSON.stringify))).map(JSON.parse)
-            }
-        }
-        logger.mark(`${logger.blue(`[${bot.self_id}]`)} ${this.name}(${this.id}) 已连接`)
-        data.ws = {
-            close: () => {
-                bot.ws.close()
-            }
-        }
-        data.status = 1
-        data.uin = this.uin
-        setAllSocketList(data)
-        this.reconnectCount = 1
-        Bot.em(`connect.${bot.self_id}`, Bot[bot.self_id])
-        return true
-    }
-
-    async load() {
-        for (const i of allSocketList) {
-            if (i.type == 4) {
-                await new Promise(resolve => {
-                    redAdapter.connect(i).then(resolve)
-                    setTimeout(resolve, 5000)
-                })
-            }
-        }
-    }
-}
-
-if (Version.isTrss) {
-    Bot.adapter.push(redAdapter)
-}
-logger.info(logger.green("- Chronocat(red) 适配器插件 加载完成"))
\ No newline at end of file
diff --git a/spaces/Cong723/gpt-academic-public/docs/waifu_plugin/autoload.js b/spaces/Cong723/gpt-academic-public/docs/waifu_plugin/autoload.js
deleted file mode 100644
index 6922fff83423ad51d621595486cf6f1f8ccba4e8..0000000000000000000000000000000000000000
--- a/spaces/Cong723/gpt-academic-public/docs/waifu_plugin/autoload.js
+++ /dev/null
@@ -1,23 +0,0 @@
-try {
-    $("<link>").attr({href: "file=docs/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css"}).appendTo('head');
-    $('body').append('<div class="waifu"><div class="waifu-tips"></div><canvas id="live2d" class="live2d"></canvas><div class="waifu-tool"><span class="fui-home"></span> <span class="fui-chat"></span> <span class="fui-eye"></span> <span class="fui-user"></span> <span class="fui-photo"></span> <span class="fui-info-circle"></span> <span class="fui-cross"></span></div></div>');
-    $.ajax({url: "file=docs/waifu_plugin/waifu-tips.js", dataType:"script", cache: true, success: function() {
-        $.ajax({url: "file=docs/waifu_plugin/live2d.js", dataType:"script", cache: true, success: function() {
-            /* 可直接修改部分参数 */
-            live2d_settings['hitokotoAPI'] = "hitokoto.cn";  // 一言 API
-            live2d_settings['modelId'] = 5;                  // 默认模型 ID
-            live2d_settings['modelTexturesId'] = 1;          // 默认材质 ID
-            live2d_settings['modelStorage'] = false;         // 不储存模型 ID
-            live2d_settings['waifuSize']            = '210x187';  
-            live2d_settings['waifuTipsSize']        = '187x52';  
-            live2d_settings['canSwitchModel']       = true;
-            live2d_settings['canSwitchTextures']    = true;
-            live2d_settings['canSwitchHitokoto']    = false;
-            live2d_settings['canTakeScreenshot']    = false;
-            live2d_settings['canTurnToHomePage']    = false;
-            live2d_settings['canTurnToAboutPage']   = false;
-            /* 在 initModel 前添加 */
-            initModel("file=docs/waifu_plugin/waifu-tips.json");
-        }});
-    }});
-} catch(err) { console.log("[Error] JQuery is not defined.") }
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/ke.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/ke.py
deleted file mode 100644
index 41e777e803f0ad206bb7042ddd35395c107a84eb..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/ke.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import torch
-
-
-# transpose
-FLIP_LEFT_RIGHT = 0
-FLIP_TOP_BOTTOM = 1
-
-class KES(object):
-    def __init__(self, kes, size, mode=None):
-        # FIXME remove check once we have better integration with device
-        # in my version this would consistently return a CPU tensor
-        device = kes.device if isinstance(kes, torch.Tensor) else torch.device('cpu')
-        kes = torch.as_tensor(kes, dtype=torch.float32, device=device)
-        if len(kes.size()) == 2:
-            kes = kes.unsqueeze(2)
-            if not kes.size()[0] ==0:
-                assert(kes.size()[-2] == 12), str(kes.size()) # 12kes
-
-        num_kes = kes.shape[0]
-        kes_x = kes[:, :6, 0] # 4+2=6
-        kes_y = kes[:, 6:, 0]
-        # TODO remove once support or zero in dim is in
-        if not kes.size()[0] ==0:
-            assert(kes_x.size() == kes_y.size()), str(kes_x.size())+' '+str(kes_y.size())
-
-        if num_kes > 0:
-            kes = kes.view(num_kes, -1, 1)
-            kes_x = kes_x.view(num_kes, -1, 1)
-            kes_y = kes_y.view(num_kes, -1, 1)
-
-        # TODO should I split them?
-        self.kes = kes
-        self.kes_x = kes_x
-        self.kes_y = kes_y
-
-        self.size = size
-        self.mode = mode
-
-    def crop(self, box):
-        w, h = box[2] - box[0], box[3] - box[1]
-        k = self.kes.clone()
-        k[:, :6, 0] -= box[0]
-        k[:, 6:, 0] -= box[1]
-        return type(self)(k, (w, h), self.mode)
-
-    def resize(self, size, *args, **kwargs):
-        ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
-        ratio_w, ratio_h = ratios
-        resized_data_x = self.kes_x.clone()
-        resized_data_x[..., :] *= ratio_w
-
-        resized_data_y = self.kes_y.clone()
-        resized_data_y[..., :] *= ratio_h
-
-        resized_data = torch.cat((resized_data_x, resized_data_y), dim=-2)
-        return type(self)(resized_data, size, self.mode)
-
-    def transpose(self, method):
-        if method not in (FLIP_LEFT_RIGHT,):
-            raise NotImplementedError(
-                    "Only FLIP_LEFT_RIGHT implemented")
-
-        flip_inds = type(self).FLIP_INDS
-        flipped_data_x = self.kes_x[:, flip_inds]
-        width = self.size[0]
-        TO_REMOVE = 1
-        # Flip x coordinates
-        flipped_data_x[..., :] = width - flipped_data_x[..., :] - TO_REMOVE
-
-        flipped_data_y = self.kes_y.clone()
-        flipped_data = torch.cat((flipped_data_x, flipped_data_y), dim=-2)
-        return type(self)(flipped_data, self.size, self.mode)
-
-    def to(self, *args, **kwargs):
-        return type(self)(self.kes.to(*args, **kwargs), self.size, self.mode)
-
-    def __getitem__(self, item):
-        return type(self)(self.kes[item], self.size, self.mode)
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += 'num_instances_x={}, '.format(len(self.kes_x))
-        s += 'num_instances_y={}, '.format(len(self.kes_y))
-        s += 'image_width={}, '.format(self.size[0])
-        s += 'image_height={})'.format(self.size[1])
-        return s
-
-
-def _create_flip_indices(names, flip_map):
-    full_flip_map = flip_map.copy()
-    full_flip_map.update({v: k for k, v in flip_map.items()})
-    flipped_names = [i if i not in full_flip_map else full_flip_map[i] for i in names]
-    flip_indices = [names.index(i) for i in flipped_names]
-    return torch.tensor(flip_indices)
-
-
-class textKES(KES):
-    NAMES = [ # x and y
-        'meanx', 
-        'xmin',
-        'x2',
-        'x3',
-        'xmax',
-        'cx'
-        # 'meany',
-        # 'ymin',
-        # 'y2',
-        # 'y3',
-        # 'ymax',
-        # 'cy'
-    ]
-    FLIP_MAP = {
-        'xmin': 'xmax',
-        'x2': 'x3',
-    }
-
-
-# TODO this doesn't look great
-textKES.FLIP_INDS = _create_flip_indices(textKES.NAMES, textKES.FLIP_MAP)
-
-
-# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop)
-def kes_to_heat_map(kes_x, kes_y, mty, rois, heatmap_size):
-    if rois.numel() == 0:
-        return rois.new().long(), rois.new().long()
-    offset_x = rois[:, 0]
-    offset_y = rois[:, 1]
-    scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])
-    scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])
-
-    offset_x = offset_x[:, None]
-    offset_y = offset_y[:, None]
-    scale_x = scale_x[:, None]
-    scale_y = scale_y[:, None]
-
-    x = kes_x[..., 0]
-    y = kes_y[..., 0]
-
-    x_boundary_inds = x == rois[:, 2][:, None]
-    y_boundary_inds = y == rois[:, 3][:, None]
-
-    x = (x - offset_x) * scale_x
-    x = x.floor().long()
-    y = (y - offset_y) * scale_y
-    y = y.floor().long()
-
-    x[x_boundary_inds] = heatmap_size - 1
-    y[y_boundary_inds] = heatmap_size - 1
-
-    valid_loc_x = (x >= 0) & (x < heatmap_size)
-    valid_x = (valid_loc_x).long()
-
-    valid_loc_y = (y >= 0) & (y < heatmap_size)
-    valid_y = (valid_loc_y).long()
-
-    valid_mty = ((x >= 0) & (x < heatmap_size)) & ((y >= 0) & (y < heatmap_size))
-    valid_mty = valid_mty.sum(dim=1)>0
-    valid_mty = (valid_mty).long()
-
-    heatmap_x = x
-    heatmap_y = y
-
-    mty = mty
-    return heatmap_x, heatmap_y, valid_x, valid_y, mty, valid_mty
diff --git a/spaces/DGSpitzer/DGS-Diffusion-Space/app.py b/spaces/DGSpitzer/DGS-Diffusion-Space/app.py
deleted file mode 100644
index 406a0c07d29d9c1f23cfae9b20b2361def5ba2ff..0000000000000000000000000000000000000000
--- a/spaces/DGSpitzer/DGS-Diffusion-Space/app.py
+++ /dev/null
@@ -1,432 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-from diffusers import EulerAncestralDiscreteScheduler
-import gradio as gr
-import torch
-import os
-from PIL import Image
-import datetime
-import time
-import psutil
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-def is_google_colab():
-    try:
-        import google.colab
-        return True
-    except:
-        return False
-
-is_colab = is_google_colab()
-        
-start_time = time.time()
-
-
-if torch.cuda.is_available():
-  torchfloat = torch.float16
-else:
-  torchfloat = torch.float32
-
-class Model:
-    def __init__(self, name, path="", prefix=""):
-        self.name = name
-        self.path = path
-        self.prefix = prefix
-        self.pipe_t2i = None
-        self.pipe_i2i = None
-
-
-models = [
-   Model("Cyberpunk Anime Diffusion", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
-   Model("DGSpitzer Art Diffusion", "DGSpitzer/DGSpitzer-Art-Diffusion", "dgspitzer painting,"),
-   Model("Guan Yu Diffusion", "DGSpitzer/Guan-Yu-Diffusion", "")
-]
-
-custom_model = None
-if is_colab:
-  models.insert(0, Model("Custom model"))
-  custom_model = models[0]
-
-last_mode = "txt2img"
-current_model = models[1] if is_colab else models[0]
-current_model_path = current_model.path
-
-if is_colab:
-  pipe0 = StableDiffusionPipeline.from_pretrained(
-      current_model.path,
-      torch_dtype=torchfloat,
-      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
-      safety_checker=lambda images, clip_input: (images, False)
-      )
-    
-  if torch.cuda.is_available():
-    pipe0 = pipe0.to("cuda")
-
-elif torch.cuda.is_available() == False:
-  pipe0 = StableDiffusionPipeline.from_pretrained(
-    current_model.path,
-    torch_dtype=torchfloat,
-    scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
-    )
-
-else:
-  eulera = EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
-  pipe0 = StableDiffusionPipeline.from_pretrained(
-      models[0].path,
-      torch_dtype=torchfloat,
-      scheduler=eulera
-      )
-  pipe1 = StableDiffusionPipeline.from_pretrained(
-      models[1].path,
-      torch_dtype=torchfloat,
-      scheduler=eulera,
-      use_auth_token=os.environ.get("HF")
-      )
-#  pipe1 = StableDiffusionPipeline.from_pretrained(
-#      models[1].path,
-#      torch_dtype=torchfloat,
-#      scheduler=eulera
-#      )
-  pipe2 = StableDiffusionPipeline.from_pretrained(
-      models[2].path,
-      torch_dtype=torchfloat,
-      scheduler=eulera
-      )
-  if torch.cuda.is_available():
-    pipe0 = pipe0.to("cuda")
-    pipe1 = pipe1.to("cuda")
-    pipe2 = pipe2.to("cuda")
-      
-  #pipe.enable_xformers_memory_efficient_attention()
-
-device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def custom_model_changed(path):
-  models[0].path = path
-  global current_model
-  current_model = models[0]
-
-def on_model_change(model_name):
-  
-  prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
-
-  return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
-
-def inference(model_name, prompt, neg_prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, n_images=1):
-  print("Generated image with prompt: " + prompt)
-  if neg_prompt != "":
-    print("Negative prompt: " + neg_prompt)
-      
-  print(psutil.virtual_memory()) # print memory usage
-
-  global current_model
-  for model in models:
-    if model.name == model_name:
-      current_model = model
-      model_path = current_model.path
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  
-  try:
-    if img is not None:
-      return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, n_images, generator), None, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-    else:
-      return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, n_images, generator), None, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-  except Exception as e:
-    return None, error_str(e), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-def inference_example(model_name, prompt, neg_prompt, guidance, steps, width=512, height=512):
-    _image, _error, _bool1, _bool2, _bool3 = inference(model_name, prompt, neg_prompt, guidance, steps, width, height)
-    return _image
-
-
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, n_images, generator):
-
-    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
-
-    global last_mode
-    global pipe0
-    global pipe1
-    global pipe2
-    global current_model_path
-    if model_path != current_model_path or last_mode != "txt2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe0 = StableDiffusionPipeline.from_pretrained(
-              current_model_path,
-              torch_dtype=torchfloat,
-              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
-              safety_checker=lambda images, clip_input: (images, False)
-              )
-          if torch.cuda.is_available():
-            pipe0 = pipe0.to("cuda")
-            # pipe.enable_xformers_memory_efficient_attention()
-        elif torch.cuda.is_available() == False:
-          pipe0 = StableDiffusionPipeline.from_pretrained(
-            current_model_path,
-            torch_dtype=torchfloat,
-            scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
-            )
-        last_mode = "txt2img"
-
-    prompt = current_model.prefix + prompt  
-    if current_model == models[0] or is_colab or torch.cuda.is_available() == False:
-        result = pipe0(
-          prompt,
-          negative_prompt = neg_prompt,
-          num_images_per_prompt=n_images,
-          num_inference_steps = int(steps),
-          guidance_scale = guidance,
-          width = width,
-          height = height,
-          generator = generator)
-    elif current_model == models[1]:
-        result = pipe1(
-          prompt,
-          negative_prompt = neg_prompt,
-          num_images_per_prompt=n_images,
-          num_inference_steps = int(steps),
-          guidance_scale = guidance,
-          width = width,
-          height = height,
-          generator = generator)
-    elif current_model == models[2]:
-        result = pipe2(
-          prompt,
-          negative_prompt = neg_prompt,
-          num_images_per_prompt=n_images,
-          num_inference_steps = int(steps),
-          guidance_scale = guidance,
-          width = width,
-          height = height,
-          generator = generator)
-    
-    print("Job done! Filtering nsfw content")
-    
-    return replace_nsfw_images(result)
-
-def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, n_images, generator):
-
-    print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
-
-    global last_mode
-    global pipe0
-    global pipe1
-    global current_model_path
-    if model_path != current_model_path or last_mode != "img2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe0 = StableDiffusionImg2ImgPipeline.from_pretrained(
-              current_model_path,
-              torch_dtype=torchfloat,
-              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
-              safety_checker=lambda images, clip_input: (images, False)
-              )
-        else:
-          pipe0 = StableDiffusionImg2ImgPipeline.from_pretrained(
-              current_model_path,
-              torch_dtype=torchfloat,
-              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
-              )
-          # pipe = pipe.to("cpu")
-          # pipe = current_model.pipe_i2i
-        
-        if torch.cuda.is_available():
-          pipe0 = pipe0.to("cuda")
-          #pipe0.enable_xformers_memory_efficient_attention()
-        last_mode = "img2img"
-
-    prompt = current_model.prefix + prompt
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe0(
-        prompt,
-        negative_prompt = neg_prompt,
-        num_images_per_prompt=n_images,
-        image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        # width = width,
-        # height = height,
-        generator = generator)
-        
-    return replace_nsfw_images(result)
-
-def replace_nsfw_images(results):
-    #Only return 1 image for community sharing
-    if is_colab:
-      return results.images[0]
-
-    for i in range(len(results.images)):
-      if results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw_placeholder.jpg")
-          
-    return results.images[0]
-
-
-css = """
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {
-    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-}
-#share-btn {
-    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-}
-#share-btn * {
-    all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
-    width: auto !important;
-    min-height: 0px !important;
-}
-#share-btn-container .wrap {
-    display: none !important;
-}
-#gallery{min-height:20rem}
-"""
-
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-                "
-              >
-                <h1 style="font-weight: 900; margin-bottom: 7px;">
-                  DGSpitzer Diffusion Space
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-               Online Demo for <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion" target="_blank">Cyberpunk Anime Diffusion</a> & <a href="https://huggingface.co/DGSpitzer/DGSpitzer-Art-Diffusion" target="_blank">DGSpitzer Art Diffusion</a> & <a href="https://huggingface.co/DGSpitzer/Guan-Yu-Diffusion" target="_blank">Guan Yu Diffusion</a>. Based of the projects by anzorq <a href="https://twitter.com/hahahahohohe"></a> and fffiloni <a href="https://twitter.com/fffiloni"></a>
-              </p>
-            </div>
-            <div
-               style="
-                  gap: 1.2rem;
-                  font-size: 1rem;
-                "
-             >
-              <p>Another online version without queue: <a href="https://colab.research.google.com/github/HelixNGC7293/cyberpunk-anime-diffusion/blob/main/cyberpunk_anime_diffusion.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
-            </div>
-        """
-    )
-    gr.Markdown('''
-      👇 Buy me a coffee if you like ♥ this project!
-      [![Buy me a coffee](https://badgen.net/badge/icon/Buy%20Me%20A%20Coffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/dgspitzer)
-    ''')
-    with gr.Row():
-        
-        with gr.Column():
-            model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
-            #prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2, placeholder="Enter your prompt~", elem_id="input-prompt").style(container=False)
-            prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2, placeholder="Enter your prompt~", elem_id="input-prompt")
-            neg_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter what you don't want to generate", elem_id="input-neg-prompt")
-            width_input = gr.Slider(label="Width", value=576, maximum=768, minimum=384, step=64)
-            height_input = gr.Slider(label="Height", value=576, maximum=768, minimum=384, step=64)
-            #n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
-            guidance = gr.Slider(label="Guidance scale", value=7, maximum=10)
-            steps = gr.Slider(label="Steps", value=20, maximum=50, minimum=2)
-            seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-            run = gr.Button(value="Run")
-            gr.Markdown(f"Running on: {device}")
-            
-            # with gr.Column(scale=45):
-            #     with gr.Tab("Text to Image"):
-            #         with gr.Group():
-            #             gr.Markdown(f"Current in text to Image Mode, ")
-            #             #seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-                
-            #     with gr.Tab("Image to Image"):
-            #         with gr.Group():
-            #             gr.Markdown(f"Image to Image Mode")
-            #             image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-            #             strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-                      
-        with gr.Column():
-            image_out = gr.Image(height=512, type="filepath", elem_id="output-img")
-            error_output = gr.Markdown()
-            #gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
-        
-
-    with gr.Column(elem_id="col-container"):
-        with gr.Group(elem_id="share-btn-container"):
-          community_icon = gr.HTML(community_icon_html, visible=False)
-          loading_icon = gr.HTML(loading_icon_html, visible=False)
-          share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-    
-    if is_colab:
-      model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
-      custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
-    
-    #outputs = [gallery, error_output]
-    run.click(inference, inputs=[model_name, prompt, neg_prompt, guidance, steps, width_input, height_input, seed], outputs=[image_out, error_output, share_button, community_icon, loading_icon])
-    
-    share_button.click(None, [], [], _js=share_js)
-    
-    gr.Examples([
-        [models[0].name, "perfect face portrait of beautiful smile girl,  clean face, wears hoody, half body, soldier working in a cyberpunk city, cleavage, intricate, 8k, highly detailed, digital painting, intense, sharp focus", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 512, 704],
-        [models[0].name, "portrait of a beautiful fancy gorgeous anime girl, intricate details", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 448, 640],
-        [models[0].name, "city landscape with fancy car, racing on the road, gopro, intricate details, 4k, cyberpunk", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 704, 704],
-        [models[0].name, "portrait of liu yifei girl, soldier working in a cyberpunk city, cleavage, intricate, 8k, highly detailed, digital painting, intense, sharp focus", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 704, 704],
-        [models[0].name, "portrait of a muscular beard male in dgs illustration style, half-body, holding robot arms, strong chest", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 512, 640],
-        [models[1].name, "portrait of professional photo of a cute girl, brown hair, freckles, perfect nose, fringe, green eyes, space clothing, space suit, glowing dragon logo on chest, dragon clothes, wearing orange dragon robe, blue dragon belt, dragon pants, dragon cape, action scene, dramatic pose, medium_breasts, perfect face, perfect eyes, pores, soft light,strong body, intricate detail,sharp focus, detailed background, masterpiece, highest quality, 4k, thick thighs, strong legs, tall, High detail RAW color, in spaceship, metal walls, feminine, highly detailed, white neon lights, skin pores, smile, high detail hair, sharp body, detailed body, gorgeous face, realistic, real, fit", "lowres, low quality, twisted,unappealing,uneven,unprofessional,draft,fake face, fake,uneven body, unnatural face, plastic face,out of focus,out of frame, poorly drawn, crippled, crooked, broken, weird, distorted, erased, cut, mutilated, sloppy, ugly, pixelated, bad hands, aliasing, poorly drawn, sloppy, over exposed, over saturated, burnt image, sloppy, fuzzy, poor quality, pixelated, sleepy, closed-eyes, pixelated, ugly, bad anatomy, hideous, deformed, mutant, sloppy, poorly drawn, poorly detailed, smudged, sketch, pencil, doll, plastic, disfigured, close up, topless, nude, naked, cleavage, tits, belly", 7, 30, 576, 576],
-        [models[1].name, "professional photo portrait of muscular man male, brown hair, freckles, fringe, brown eyes, space clothing, space suit, glowing UNSC logo on chest, wearing blue astronaut robe, blue space belt, dragon pants, action scene, ", "lowres, low quality, twisted,unappealing,uneven,unprofessional,draft,fake face, fake,uneven body, unnatural face, plastic face,out of focus,out of frame, poorly drawn, crippled, crooked, broken, weird, distorted, erased, cut, mutilated, sloppy, ugly, pixelated, bad hands, aliasing, poorly drawn, sloppy, over exposed, over saturated, burnt image, sloppy, fuzzy, poor quality, pixelated, sleepy, closed-eyes, pixelated, ugly, bad anatomy, hideous, deformed, mutant, sloppy, poorly drawn, poorly detailed, smudged, sketch, pencil, doll, plastic, disfigured, close up, topless, nude, naked, cleavage, tits, belly", 7, 30, 576, 576],
-        [models[1].name, "a painting of professional portrait steampunk lady doctor, smart eyes,high-tec huge glasses, very thick dark eyebrow, brown eyes, tan skin, short fancy braid hair, intricate deails, aquiline nose", "cat ear, body out of frame, deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limbs", 7, 30, 576, 576],
-        [models[1].name, "a whiteshape professional portrait steampunk fancy bun very short hair girl, gears clothes, heavy armor, masterpiece, 4k, action scene, dramatic pose, small_breasts, perfect face, perfect eyes, pores, soft light,strong body, intricate detail,sharp focus, detailed background, masterpiece, highest quality, 4k, thick thighs, strong legs, tall, High detail RAW color, in spaceship, metal walls, feminine, highly detailed, white neon lights, skin pores, smile, high detail hair, sharp body, detailed body, gorgeous face, realistic, real, fit", "cat ear, body out of frame, deformed, blurry, bad anatomy, ugly, disfigured, poorly drawn face, mutation, mutated, extra limbs", 7, 30, 576, 576],
-        [models[1].name, "outline anime portrait of a beautiful martial art girl", "saturation, purple, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[1].name, "a beautiful mech girl, 4k, photorealistic", "saturation", 7, 30, 576, 576],
-        [models[1].name, "portrait of a girl, photorealistic painting paladin priest working in a magical tavern, cleavage, perfect face, curvy hair, intricate, 8k, highly detailed, intense, sharp focus, Anime, outline", "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, bad feet", 7, 30, 576, 576],
-        [models[1].name, "a mech robot", "purple", 7, 30, 576, 576],
-        [models[1].name, "portrait of a beautiful very young teen cowboy girl who embodies the rugged and independent spirit of the Old West. She should be strong-willed and confident, with a sharp sense of justice and a heart of gold. Her style should be a blend of traditional Western fashion and modern flair, with a touch of glamour and femininity. Whether she's taming wild horses, fighting off bandits, or just enjoying the sunset on the prairie, this cowboy girl is a force to be reckoned with and a sight to behold.", "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, bad feet", 7, 30, 576, 576],
-        [models[1].name, "a muscular man from space", "blurry, out of focus", 7, 30, 576, 576],
-        [models[1].name, "landscape of a cyberpunk soldier man in the battlefield, dark light, far away", "blurry, out of focus", 7, 30, 704, 576],
-        [models[2].name, "Portrait of guanyu walking in ancient battlefield, close up shot", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "Portrait of a man holding very cute panda plushie, Guanyu", "", 7, 30, 576, 576],
-        [models[2].name, "taking selfie on ancient battlefield of China, guanyu, gopro, sharp focus, old scratched photo, three kingdoms warriors everywhere, masterpiece", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "superman, portrait of fancy superhero guanyu, golden spiderman, mech, robot, high tech, shining core, intricate details, 4k", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "batman, portrait of fancy superhero guanyu, golden spiderman, mech, robot, high tech, shining core, intricate details, 4k", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "a huge gundam mech fighting against guanyu", "blurry, out of focus", 7, 30, 576, 576],
-        [models[2].name, "a cute nendoroid figure toy guanyu", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "a cute funko figure toy guanyu", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "a lego guanyu", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "a gorgeous wuxia girl standing in the palace", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "a handsome wuxia man", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-        [models[2].name, "a wuxia girl diving underwater, surrounded by a shark", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 576, 576],
-        [models[2].name, "a cute nendoroid wuxia figure toy", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
-    ], [model_name, prompt, neg_prompt, guidance, steps, width_input, height_input], outputs=image_out, fn=inference_example, cache_examples=torch.cuda.is_available())
-    gr.Markdown('''
-      Models and Space by [@DGSpitzer](https://www.youtube.com/channel/UCzzsYBF4qwtMwJaPJZ5SuPg)❤️ [@大谷的游戏创作小屋](https://space.bilibili.com/176003)
-      [![Twitter Follow](https://img.shields.io/twitter/follow/DGSpitzer?label=%40DGSpitzer&style=social)](https://twitter.com/DGSpitzer)
-      ![visitors](https://visitor-badge.glitch.me/badge?page_id=dgspitzer_DGS_Diffusion_Space)
-      
-      ![Model Views](https://visitor-badge.glitch.me/badge?page_id=DGspitzer_Art_Diffusion)
-      
-    ''')
-
-if not is_colab:
-  demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/code.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/code.py
deleted file mode 100644
index 134877a4036db4c07fa4427c29694ebc680f65aa..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/code.py
+++ /dev/null
@@ -1,157 +0,0 @@
-"""gr.Code() component"""
-
-from __future__ import annotations
-
-from typing import Literal
-
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import StringSerializable
-
-from gradio.components.base import IOComponent, _Keywords
-from gradio.events import Changeable, Inputable
-
-set_documentation_group("component")
-
-
-@document("languages")
-class Code(Changeable, Inputable, IOComponent, StringSerializable):
-    """
-    Creates a Code editor for entering, editing or viewing code.
-    Preprocessing: passes a {str} of code into the function.
-    Postprocessing: expects the function to return a {str} of code or a single-elment {tuple}: (string filepath,)
-    """
-
-    languages = [
-        "python",
-        "markdown",
-        "json",
-        "html",
-        "css",
-        "javascript",
-        "typescript",
-        "yaml",
-        "dockerfile",
-        "shell",
-        "r",
-        None,
-    ]
-
-    def __init__(
-        self,
-        value: str | tuple[str] | None = None,
-        language: Literal[
-            "python",
-            "markdown",
-            "json",
-            "html",
-            "css",
-            "javascript",
-            "typescript",
-            "yaml",
-            "dockerfile",
-            "shell",
-            "r",
-        ]
-        | None = None,
-        *,
-        lines: int = 5,
-        label: str | None = None,
-        interactive: bool | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: Default value to show in the code editor. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            language: The language to display the code as. Supported languages listed in `gr.Code.languages`.
-            label: component name in interface.
-            interactive: Whether user should be able to enter code or only view it.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        assert language in Code.languages, f"Language {language} not supported."
-        self.language = language
-        self.lines = lines
-        IOComponent.__init__(
-            self,
-            label=label,
-            interactive=interactive,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            **kwargs,
-        )
-
-    def get_config(self):
-        return {
-            "value": self.value,
-            "language": self.language,
-            "lines": self.lines,
-            **IOComponent.get_config(self),
-        }
-
-    def postprocess(self, y):
-        if y is None:
-            return None
-        elif isinstance(y, tuple):
-            with open(y[0]) as file_data:
-                return file_data.read()
-        else:
-            return y.strip()
-
-    @staticmethod
-    def update(
-        value: str
-        | tuple[str]
-        | None
-        | Literal[_Keywords.NO_VALUE] = _Keywords.NO_VALUE,
-        label: str | None = None,
-        show_label: bool | None = None,
-        container: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        visible: bool | None = None,
-        language: Literal[
-            "python",
-            "markdown",
-            "json",
-            "html",
-            "css",
-            "javascript",
-            "typescript",
-            "yaml",
-            "dockerfile",
-            "shell",
-            "r",
-        ]
-        | None = None,
-        interactive: bool | None = None,
-    ):
-        return {
-            "label": label,
-            "show_label": show_label,
-            "container": container,
-            "scale": scale,
-            "min_width": min_width,
-            "visible": visible,
-            "value": value,
-            "language": language,
-            "interactive": interactive,
-            "__type__": "update",
-        }
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/sync.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/sync.py
deleted file mode 100644
index a4c85f0449af4e1150b21f36606597ddb00775d9..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/sync.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import socket
-import ssl
-import sys
-import typing
-
-from .._exceptions import (
-    ConnectError,
-    ConnectTimeout,
-    ExceptionMapping,
-    ReadError,
-    ReadTimeout,
-    WriteError,
-    WriteTimeout,
-    map_exceptions,
-)
-from .._utils import is_socket_readable
-from .base import SOCKET_OPTION, NetworkBackend, NetworkStream
-
-
-class SyncStream(NetworkStream):
-    def __init__(self, sock: socket.socket) -> None:
-        self._sock = sock
-
-    def read(self, max_bytes: int, timeout: typing.Optional[float] = None) -> bytes:
-        exc_map: ExceptionMapping = {socket.timeout: ReadTimeout, OSError: ReadError}
-        with map_exceptions(exc_map):
-            self._sock.settimeout(timeout)
-            return self._sock.recv(max_bytes)
-
-    def write(self, buffer: bytes, timeout: typing.Optional[float] = None) -> None:
-        if not buffer:
-            return
-
-        exc_map: ExceptionMapping = {socket.timeout: WriteTimeout, OSError: WriteError}
-        with map_exceptions(exc_map):
-            while buffer:
-                self._sock.settimeout(timeout)
-                n = self._sock.send(buffer)
-                buffer = buffer[n:]
-
-    def close(self) -> None:
-        self._sock.close()
-
-    def start_tls(
-        self,
-        ssl_context: ssl.SSLContext,
-        server_hostname: typing.Optional[str] = None,
-        timeout: typing.Optional[float] = None,
-    ) -> NetworkStream:
-        exc_map: ExceptionMapping = {
-            socket.timeout: ConnectTimeout,
-            OSError: ConnectError,
-        }
-        with map_exceptions(exc_map):
-            try:
-                self._sock.settimeout(timeout)
-                sock = ssl_context.wrap_socket(
-                    self._sock, server_hostname=server_hostname
-                )
-            except Exception as exc:  # pragma: nocover
-                self.close()
-                raise exc
-        return SyncStream(sock)
-
-    def get_extra_info(self, info: str) -> typing.Any:
-        if info == "ssl_object" and isinstance(self._sock, ssl.SSLSocket):
-            return self._sock._sslobj  # type: ignore
-        if info == "client_addr":
-            return self._sock.getsockname()
-        if info == "server_addr":
-            return self._sock.getpeername()
-        if info == "socket":
-            return self._sock
-        if info == "is_readable":
-            return is_socket_readable(self._sock)
-        return None
-
-
-class SyncBackend(NetworkBackend):
-    def connect_tcp(
-        self,
-        host: str,
-        port: int,
-        timeout: typing.Optional[float] = None,
-        local_address: typing.Optional[str] = None,
-        socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None,
-    ) -> NetworkStream:
-        # Note that we automatically include `TCP_NODELAY`
-        # in addition to any other custom socket options.
-        if socket_options is None:
-            socket_options = []  # pragma: no cover
-        address = (host, port)
-        source_address = None if local_address is None else (local_address, 0)
-        exc_map: ExceptionMapping = {
-            socket.timeout: ConnectTimeout,
-            OSError: ConnectError,
-        }
-
-        with map_exceptions(exc_map):
-            sock = socket.create_connection(
-                address,
-                timeout,
-                source_address=source_address,
-            )
-            for option in socket_options:
-                sock.setsockopt(*option)  # pragma: no cover
-            sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
-        return SyncStream(sock)
-
-    def connect_unix_socket(
-        self,
-        path: str,
-        timeout: typing.Optional[float] = None,
-        socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None,
-    ) -> NetworkStream:  # pragma: nocover
-        if sys.platform == "win32":
-            raise RuntimeError(
-                "Attempted to connect to a UNIX socket on a Windows system."
-            )
-        if socket_options is None:
-            socket_options = []
-
-        exc_map: ExceptionMapping = {
-            socket.timeout: ConnectTimeout,
-            OSError: ConnectError,
-        }
-        with map_exceptions(exc_map):
-            sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
-            for option in socket_options:
-                sock.setsockopt(*option)
-            sock.settimeout(timeout)
-            sock.connect(path)
-        return SyncStream(sock)
diff --git a/spaces/DataHunter/ostris-crayon_style_lora_sdxl/app.py b/spaces/DataHunter/ostris-crayon_style_lora_sdxl/app.py
deleted file mode 100644
index 1d96740ce812429490ac3fc7252484e5ff4eae79..0000000000000000000000000000000000000000
--- a/spaces/DataHunter/ostris-crayon_style_lora_sdxl/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/ostris/crayon_style_lora_sdxl").launch()
\ No newline at end of file
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/communal/data_augmentation.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/communal/data_augmentation.py
deleted file mode 100644
index d4656acf518be6972276e7cb4e42dcf402a79c98..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/communal/data_augmentation.py
+++ /dev/null
@@ -1,279 +0,0 @@
-""" 
-@Date: 2021/07/27
-@description:
-"""
-import numpy as np
-import cv2
-import functools
-
-from utils.conversion import pixel2lonlat, lonlat2pixel, uv2lonlat, lonlat2uv, pixel2uv
-
-
-@functools.lru_cache()
-def prepare_stretch(w, h):
-    lon = pixel2lonlat(np.array(range(w)), w=w, axis=0)
-    lat = pixel2lonlat(np.array(range(h)), h=h, axis=1)
-    sin_lon = np.sin(lon)
-    cos_lon = np.cos(lon)
-    tan_lat = np.tan(lat)
-    return sin_lon, cos_lon, tan_lat
-
-
-def pano_stretch_image(pano_img, kx, ky, kz):
-    """
-    Note that this is the inverse mapping, which refers to Equation 3 in HorizonNet paper (the coordinate system in
-    the paper is different from here, xz needs to be swapped)
-    :param pano_img: a panorama image, shape must be [h,w,c]
-    :param kx: stretching along left-right direction
-    :param ky: stretching along up-down direction
-    :param kz: stretching along front-back direction
-    :return:
-    """
-    w = pano_img.shape[1]
-    h = pano_img.shape[0]
-
-    sin_lon, cos_lon, tan_lat = prepare_stretch(w, h)
-
-    n_lon = np.arctan2(sin_lon * kz / kx, cos_lon)
-    n_lat = np.arctan(tan_lat[..., None] * np.sin(n_lon) / sin_lon * kx / ky)
-    n_pu = lonlat2pixel(n_lon, w=w, axis=0, need_round=False)
-    n_pv = lonlat2pixel(n_lat, h=h, axis=1, need_round=False)
-
-    pixel_map = np.empty((h, w, 2), dtype=np.float32)
-    pixel_map[..., 0] = n_pu
-    pixel_map[..., 1] = n_pv
-    map1 = pixel_map[..., 0]
-    map2 = pixel_map[..., 1]
-    # using wrap mode because it is continues at left or right of panorama
-    new_img = cv2.remap(pano_img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_WRAP)
-    return new_img
-
-
-def pano_stretch_conner(corners, kx, ky, kz):
-    """
-    :param corners:
-    :param kx: stretching along left-right direction
-    :param ky: stretching along up-down direction
-    :param kz: stretching along front-back direction
-    :return:
-    """
-
-    lonlat = uv2lonlat(corners)
-    sin_lon = np.sin(lonlat[..., 0:1])
-    cos_lon = np.cos(lonlat[..., 0:1])
-    tan_lat = np.tan(lonlat[..., 1:2])
-
-    n_lon = np.arctan2(sin_lon * kx / kz, cos_lon)
-
-    a = np.bitwise_or(corners[..., 0] == 0.5, corners[..., 0] == 1)
-    b = np.bitwise_not(a)
-    w = np.zeros_like(n_lon)
-    w[b] = np.sin(n_lon[b]) / sin_lon[b]
-    w[a] = kx / kz
-
-    n_lat = np.arctan(tan_lat * w / kx * ky)
-
-    lst = [n_lon, n_lat]
-    lonlat = np.concatenate(lst, axis=-1)
-    new_corners = lonlat2uv(lonlat)
-    return new_corners
-
-
-def pano_stretch(pano_img, corners, kx, ky, kz):
-    """
-    :param pano_img: a panorama image, shape must be [h,w,c]
-    :param corners:
-    :param kx: stretching along left-right direction
-    :param ky: stretching along up-down direction
-    :param kz: stretching along front-back direction
-    :return:
-    """
-    new_img = pano_stretch_image(pano_img, kx, ky, kz)
-    new_corners = pano_stretch_conner(corners, kx, ky, kz)
-    return new_img, new_corners
-
-
-class PanoDataAugmentation:
-    def __init__(self, aug):
-        self.aug = aug
-        self.parameters = {}
-
-    def need_aug(self, name):
-        return name in self.aug and self.aug[name]
-
-    def execute_space_aug(self, corners, image):
-        if image is None:
-            return image
-
-        if self.aug is None:
-            return corners, image
-        w = image.shape[1]
-        h = image.shape[0]
-
-        if self.need_aug('STRETCH'):
-            kx = np.random.uniform(1, 2)
-            kx = 1 / kx if np.random.randint(2) == 0 else kx
-            # we found that the ky transform may cause IoU to drop (HorizonNet also only x and z transform)
-            # ky = np.random.uniform(1, 2)
-            # ky = 1 / ky if np.random.randint(2) == 0 else ky
-            ky = 1
-            kz = np.random.uniform(1, 2)
-            kz = 1 / kz if np.random.randint(2) == 0 else kz
-            image, corners = pano_stretch(image, corners, kx, ky, kz)
-            self.parameters['STRETCH'] = {'kx': kx, 'ky': ky, 'kz': kz}
-        else:
-            self.parameters['STRETCH'] = None
-
-        if self.need_aug('ROTATE'):
-            d_pu = np.random.randint(w)
-            image = np.roll(image, d_pu, axis=1)
-            corners[..., 0] = (corners[..., 0] + pixel2uv(np.array([d_pu]), w, h)) % pixel2uv(np.array([w]), w, h)
-            self.parameters['ROTATE'] = d_pu
-        else:
-            self.parameters['ROTATE'] = None
-
-        if self.need_aug('FLIP') and np.random.randint(2) == 0:
-            image = np.flip(image, axis=1).copy()
-            corners[..., 0] = pixel2uv(np.array([w]), w, h) - corners[..., 0]
-            corners = corners[::-1]
-            self.parameters['FLIP'] = True
-        else:
-            self.parameters['FLIP'] = None
-
-        return corners, image
-
-    def execute_visual_aug(self, image):
-        if self.need_aug('GAMMA'):
-            p = np.random.uniform(1, 2)
-            if np.random.randint(2) == 0:
-                p = 1 / p
-            image = image ** p
-            self.parameters['GAMMA'] = p
-        else:
-            self.parameters['GAMMA'] = None
-
-        # The following visual augmentation methods are only implemented but not tested
-        if self.need_aug('HUE') or self.need_aug('SATURATION'):
-            image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
-
-            if self.need_aug('HUE') and np.random.randint(2) == 0:
-                p = np.random.uniform(-0.1, 0.1)
-                image[..., 0] = np.mod(image[..., 0] + p * 180, 180)
-                self.parameters['HUE'] = p
-            else:
-                self.parameters['HUE'] = None
-
-            if self.need_aug('SATURATION') and np.random.randint(2) == 0:
-                p = np.random.uniform(0.5, 1.5)
-                image[..., 1] = np.clip(image[..., 1] * p, 0, 1)
-                self.parameters['SATURATION'] = p
-            else:
-                self.parameters['SATURATION'] = None
-
-            image = cv2.cvtColor(image, cv2.COLOR_HSV2RGB)
-
-        if self.need_aug('CONTRAST') and np.random.randint(2) == 0:
-            p = np.random.uniform(0.9, 1.1)
-            mean = image.mean(axis=0).mean(axis=0)
-            image = (image - mean) * p + mean
-            image = np.clip(image, 0, 1)
-            self.parameters['CONTRAST'] = p
-        else:
-            self.parameters['CONTRAST'] = None
-
-        return image
-
-    def execute_aug(self, corners, image):
-        corners, image = self.execute_space_aug(corners, image)
-        if image is not None:
-            image = self.execute_visual_aug(image)
-        return corners, image
-
-
-if __name__ == '__main__1':
-    from tqdm import trange
-    from visualization.floorplan import draw_floorplan
-    from dataset.communal.read import read_image, read_label
-    from utils.time_watch import TimeWatch
-    from utils.conversion import uv2xyz
-    from utils.boundary import corners2boundary
-
-    np.random.seed(123)
-    pano_img_path = "../../src/dataset/mp3d/image/TbHJrupSAjP_f320ae084f3a447da3e8ab11dd5f9320.png"
-    label_path = "../../src/dataset/mp3d/label/TbHJrupSAjP_f320ae084f3a447da3e8ab11dd5f9320.json"
-    pano_img = read_image(pano_img_path)
-    label = read_label(label_path)
-
-    corners = label['corners']
-    ratio = label['ratio']
-
-    pano_aug = PanoDataAugmentation(aug={
-        'STRETCH': True,
-        'ROTATE': True,
-        'FLIP': True,
-        'GAMMA': True,
-        # 'HUE': True,
-        # 'SATURATION': True,
-        # 'CONTRAST': True
-    })
-
-    # draw_floorplan(corners, show=True, marker_color=0.5, center_color=0.8, plan_y=1.6, show_radius=8)
-    # draw_boundaries(pano_img, corners_list=[corners], show=True, length=1024, ratio=ratio)
-
-    w = TimeWatch("test")
-    for i in trange(50000):
-        new_corners, new_pano_img = pano_aug.execute_aug(corners.copy(), pano_img.copy())
-        # draw_floorplan(uv2xyz(new_corners, plan_y=1.6)[..., ::2], show=True, marker_color=0.5, center_color=0.8,
-        #                show_radius=8)
-        # draw_boundaries(new_pano_img, corners_list=[new_corners], show=True, length=1024, ratio=ratio)
-
-
-if __name__ == '__main__':
-    from utils.boundary import corners2boundary
-    from visualization.floorplan import draw_floorplan
-    from utils.boundary import visibility_corners
-
-    corners = np.array([[0.7664539, 0.7416811],
-                        [0.06641078, 0.6521386],
-                        [0.30997428, 0.57855356],
-                        [0.383300784, 0.58726823],
-                        [0.383300775, 0.8005296],
-                        [0.5062902, 0.74822706]])
-    corners = visibility_corners(corners)
-    print(corners)
-    # draw_floorplan(uv2xyz(corners, plan_y=1.6)[..., ::2], show=True, marker_color=0.5, center_color=0.8,
-    #                show_radius=8)
-    visible_floor_boundary = corners2boundary(corners, length=256, visible=True)
-    # visible_depth = xyz2depth(uv2xyz(visible_floor_boundary, 1), 1)
-    print(len(visible_floor_boundary))
-
-
-if __name__ == '__main__0':
-    from visualization.floorplan import draw_floorplan
-
-    from dataset.communal.read import read_image, read_label
-    from utils.time_watch import TimeWatch
-    from utils.conversion import uv2xyz
-
-    # np.random.seed(1234)
-    pano_img_path = "../../src/dataset/mp3d/image/VVfe2KiqLaN_35b41dcbfcf84f96878f6ca28c70e5af.png"
-    label_path = "../../src/dataset/mp3d/label/VVfe2KiqLaN_35b41dcbfcf84f96878f6ca28c70e5af.json"
-    pano_img = read_image(pano_img_path)
-    label = read_label(label_path)
-
-    corners = label['corners']
-    ratio = label['ratio']
-
-    # draw_floorplan(corners, show=True, marker_color=0.5, center_color=0.8, plan_y=1.6, show_radius=8)
-
-    w = TimeWatch()
-    for i in range(5):
-        kx = np.random.uniform(1, 2)
-        kx = 1 / kx if np.random.randint(2) == 0 else kx
-        ky = np.random.uniform(1, 2)
-        ky = 1 / ky if np.random.randint(2) == 0 else ky
-        kz = np.random.uniform(1, 2)
-        kz = 1 / kz if np.random.randint(2) == 0 else kz
-        new_corners = pano_stretch_conner(corners.copy(), kx, ky, kz)
-        draw_floorplan(uv2xyz(new_corners, plan_y=1.6)[..., ::2], show=True, marker_color=0.5, center_color=0.8,
-                       show_radius=8)
diff --git a/spaces/Dorado607/ChuanhuChatGPT/README_origin.md b/spaces/Dorado607/ChuanhuChatGPT/README_origin.md
deleted file mode 100644
index cf809eaa7623a04523680b5b8ce5c4ca4ea48894..0000000000000000000000000000000000000000
--- a/spaces/Dorado607/ChuanhuChatGPT/README_origin.md
+++ /dev/null
@@ -1,142 +0,0 @@
-<div align="right">
-  <!-- 语言: -->
-  简体中文 | <a title="English" href="./readme/README_en.md">English</a> | <a title="Japanese" href="./readme/README_ja.md">日本語</a>
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://github.com/GaiZhenbiao/ChuanhuChatGPT/assets/70903329/aca3a7ec-4f1d-4667-890c-a6f47bf08f63" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>为ChatGPT等多种LLM提供了一个轻快好用的Web图形界面和众多附加功能</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        流式传输 / 无限对话 / 保存对话 / 预设Prompt集 / 联网搜索 / 根据文件回答 <br />
-        渲染LaTeX / 渲染表格 / 代码高亮 / 自动亮暗色切换 / 自适应界面 / “小而美”的体验 <br />
-        自定义api-Host / 多参数可调 / 多API Key均衡负载 / 多用户显示 / 适配GPT-4 / 支持本地部署LLM
-      </p>
-      <a href="https://www.bilibili.com/video/BV1mo4y1r7eE"><strong>视频教程</strong></a>
-        ·
-      <a href="https://www.bilibili.com/video/BV1184y1w7aP"><strong>2.0介绍视频</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>在线体验</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>一键部署</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## 目录
-
-| [支持模型](#支持模型) | [使用技巧](#使用技巧) | [安装方式](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) | [常见问题](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题) | [给作者买可乐🥤](#捐款) |
-| ------------------ | ------------------ | -------------------------------------------------------------------- | -------------------------------------------------------------------- | -------------------- |
-
-## 支持模型
-
-**通过API调用的语言模型**：
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**本地部署语言模型**：
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## 使用技巧
-
-- 使用System Prompt可以很有效地设定前提条件。
-- 使用Prompt模板功能时，选择Prompt模板集合文件，然后从下拉菜单中选择想要的prompt。
-- 如果回答不满意，可以使用 `重新生成`按钮再试一次
-- 输入框支持换行，按 `shift enter`即可。
-- 可以在输入框按上下箭头在输入历史之间切换
-- 部署到服务器：在 `config.json` 中设置 `"server_name": "0.0.0.0", "server_port": <你的端口号>,`。
-- 获取公共链接：在 `config.json` 中设置 `"share": true,`。注意程序必须在运行，才能通过公共链接访问。
-- 在Hugging Face上使用：建议在右上角 **复制Space** 再使用，这样App反应可能会快一点。
-
-## 快速上手
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-然后，在项目文件夹中复制一份 `config_example.json`，并将其重命名为 `config.json`，在其中填入 `API-Key` 等设置。
-
-```shell
-python ChuanhuChatbot.py
-```
-
-一个浏览器窗口将会自动打开，此时您将可以使用 **川虎Chat** 与ChatGPT或其他模型进行对话。
-
-> **Note**
->
-> 具体详尽的安装教程和使用教程请查看[本项目的wiki页面](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)。
-
-## 疑难杂症解决
-
-在遇到各种问题查阅相关信息前，您可以先尝试手动拉取本项目的最新更改并更新依赖库，然后重试。步骤为：
-
-1. 点击网页上的 `Download ZIP` 下载最新代码，或
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 尝试再次安装依赖（可能本项目引入了新的依赖）
-   ```
-   pip install -r requirements.txt
-   ```
-
-很多时候，这样就可以解决问题。
-
-如果问题仍然存在，请查阅该页面：[常见问题](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-该页面列出了**几乎所有**您可能遇到的各种问题，包括如何配置代理，以及遇到问题后您该采取的措施，**请务必认真阅读**。
-
-## 了解更多
-
-若需了解更多信息，请查看我们的 [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki)：
-
-- [想要做出贡献？](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [项目更新情况？](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [二次开发许可？](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-- [如何引用项目？](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## 捐款
-
-🐯如果觉得这个软件对你有所帮助，欢迎请作者喝可乐、喝咖啡～
-
-联系作者：请去[我的bilibili账号](https://space.bilibili.com/29125536)私信我。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/Ebo010/hot-dog/app.py b/spaces/Ebo010/hot-dog/app.py
deleted file mode 100644
index adf57ddb6e2c6969ba5a457f58a1c26ba3f8cea2..0000000000000000000000000000000000000000
--- a/spaces/Ebo010/hot-dog/app.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-
-def predict(image):
-    predictions = pipeline(image)
-    return {p["label"]: p["score"] for p in predictions}
-
-gr.Interface(
-    predict,
-    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
-    outputs=gr.outputs.Label(num_top_classes=2),
-    title="Hot Dog? Or Not?",
-).launch()
\ No newline at end of file
diff --git a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/app.py b/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/app.py
deleted file mode 100644
index 97c59221c429e335c3a2e3413c11cc155d5b6122..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/app.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import os
-os.system("pip install gradio==2.9b23")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-from random import randint
-import sys
-from subprocess import call
-import psutil
-
-
-
-
-torch.hub.download_url_to_file('http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution_files/100075_lowres.jpg', 'bear.jpg')
-  
-    
-def run_cmd(command):
-    try:
-        print(command)
-        call(command, shell=True)
-    except KeyboardInterrupt:
-        print("Process interrupted")
-        sys.exit(1)
-run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
-run_cmd("pip install basicsr")
-run_cmd("pip freeze")
-
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")
-
-
-def inference(img,mode):
-    _id = randint(1, 10000)
-    INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
-    OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
-    run_cmd("rm -rf " + INPUT_DIR)
-    run_cmd("rm -rf " + OUTPUT_DIR)
-    run_cmd("mkdir " + INPUT_DIR)
-    run_cmd("mkdir " + OUTPUT_DIR)
-    basewidth = 256
-    wpercent = (basewidth/float(img.size[0]))
-    hsize = int((float(img.size[1])*float(wpercent)))
-    img = img.resize((basewidth,hsize), Image.ANTIALIAS)
-    img.save(INPUT_DIR + "1.jpg", "JPEG")
-    if mode == "base":
-        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
-    else:
-        os.system("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
-    return os.path.join(OUTPUT_DIR, "1_out.jpg")
-
-
-
-        
-title = "Real-ESRGAN"
-description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>"
-
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(["base","anime"], type="value", default="base", label="model type")], 
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-    ['bear.jpg','base'],
-    ['anime.png','anime']
-    ]).launch()
\ No newline at end of file
diff --git a/spaces/EnigmaOfTheWorld/ChanakyaNeeti/app.py b/spaces/EnigmaOfTheWorld/ChanakyaNeeti/app.py
deleted file mode 100644
index 93e8231d19041bc1a4861d1c2ab46db733cf2624..0000000000000000000000000000000000000000
--- a/spaces/EnigmaOfTheWorld/ChanakyaNeeti/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from PyPDF2 import PdfReader
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import OpenAI
-import gradio as gr
-
-import os
-
-
-
-embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
-docsearch = FAISS.load_local(folder_path = 'chanakyaNeeti',embeddings=embeddings)
-
-chain = load_qa_chain(OpenAI(openai_api_key=os.environ['OPENAI_API_KEY']), chain_type="stuff")
-
-def learn_from_chanakya_neeti(chat_history,query):
-  greetings = ['hi', 'hello', 'hey', 'good morning', 'good afternoon', 'good evening', 'greetings', 'salutations', 'yo', 'howdy', 'hola', 'bonjour', 'konnichiwa', 'ni hao', 'ciao', 'salaam', 'shalom', 'namaste']
-  if query.lower() in greetings:
-      result = f"Hello!I am here to help with your question from Chanakya Neeti today."
-  elif query.lower() in ["what are you?","who are you?"]:
-      result = f'I am an AI chatbot available to assist you with any inquiries you may have about Chanakya Neeti.'
-  else:
-      docs = docsearch.similarity_search(query)
-      result =  result if "i don’t know" not in (result:=chain.run(input_documents=docs, question=query)).lower().strip() else "Please only ask questions from chanakya neeti."
-    
-  return chat_history + [(query,result)]
-
-with gr.Blocks() as demo:
-  gr.HTML(value="""<h1 style="text-align: center;text-decoration: underline">Chankya Neeti</h1>""")
-  gr.HTML(value="""<p style="font-style: italic;">Chanakya Neeti is the essence of various scriptures Chanakya carefully studied to come up with a code of conduct that will benefit all mankind. It is a book written by Chanakya, a great scholar, to simplify complicated scriptures and provide a practical application of moral codes and ideal conduct.<p>""")
-  chatbot = gr.Chatbot()
-  textbox = gr.Textbox(label="Ask your question.",placeholder="How can one build a strong and successful team?")
-
-  textbox.submit(fn=learn_from_chanakya_neeti,inputs=[chatbot,textbox],outputs=[chatbot])
-
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/EronSamez/RVC_HFmeu/demucs/__init__.py b/spaces/EronSamez/RVC_HFmeu/demucs/__init__.py
deleted file mode 100644
index d4182e356427e1b05a79f8da641c70bb732514fa..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/demucs/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-__version__ = "2.0.3"
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/lib/train/losses.py b/spaces/EronSamez/RVC_HFmeu/infer/lib/train/losses.py
deleted file mode 100644
index b1b263e4c205e78ffe970f622ab6ff68f36d3b17..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/lib/train/losses.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            rl = rl.float().detach()
-            gl = gl.float()
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        dr = dr.float()
-        dg = dg.float()
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        dg = dg.float()
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-    """
-    z_p, logs_q: [b, h, t_t]
-    m_p, logs_p: [b, h, t_t]
-    """
-    z_p = z_p.float()
-    logs_q = logs_q.float()
-    m_p = m_p.float()
-    logs_p = logs_p.float()
-    z_mask = z_mask.float()
-
-    kl = logs_p - logs_q - 0.5
-    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
-    kl = torch.sum(kl * z_mask)
-    l = kl / torch.sum(z_mask)
-    return l
diff --git a/spaces/EvgenyK/Text-To-Image/app.py b/spaces/EvgenyK/Text-To-Image/app.py
deleted file mode 100644
index 05cc32b30cf9d06a7d6697f4a27db893afe788bb..0000000000000000000000000000000000000000
--- a/spaces/EvgenyK/Text-To-Image/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#Acknowledgments:
-#This project is inspired by:
-#1. https://github.com/haltakov/natural-language-image-search by Vladimir Haltakov
-#2. DrishtiSharma/Text-to-Image-search-using-CLIP
-
-
-import torch
-import requests
-import numpy as np
-import pandas as pd
-import gradio as gr
-from io import BytesIO
-from PIL import Image as PILIMAGE
-from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer
-
-#Selecting device based on availability of GPUs
-device = "cuda" if torch.cuda.is_available() else "cpu"
-    
-#Defining model, processor and tokenizer
-model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
-
-    
-#Loading the data
-photos = pd.read_csv("./items_data.csv")
-photo_features = np.load("./features.npy")
-photo_ids = pd.read_csv("./photo_ids.csv")
-photo_ids = list(photo_ids['photo_id'])
-    
-def find_best_matches(text):
-    
-    #Inference
-    with torch.no_grad():
-        # Encode and normalize the description using CLIP
-        inputs = tokenizer([text],  padding=True, return_tensors="pt")
-        inputs = processor(text=[text], images=None, return_tensors="pt", padding=True)
-    text_encoded =  model.get_text_features(**inputs).detach().numpy()
-  
-    
-    # Finding Cosine similarity
-    similarities = list((text_encoded @ photo_features.T).squeeze(0))
-    
-    #Block of code for displaying top 3 best matches (images)
-    matched_images = []
-    for i in range(3):
-      idx = sorted(zip(similarities, range(photo_features.shape[0])), key=lambda x: x[0], reverse=True)[i][1]
-      photo_id = photo_ids[idx]
-      photo_data = photos[photos["Uniq Id"] == photo_id].iloc[0]
-      response = requests.get(photo_data["Image"] + "?w=640")
-      img = PILIMAGE.open(BytesIO(response.content))
-      matched_images.append(img)
-    return matched_images
-    
-    
-#Gradio app    
-with gr.Blocks() as demo:
-    with gr.Column(variant="panel"):
-        with gr.Row(variant="compact"):
-            text = gr.Textbox(
-                label="Search product",
-                show_label=False,
-                max_lines=1,
-                placeholder="Type product",
-            ).style(
-                container=False,
-            )
-            btn = gr.Button("Search").style(full_width=False)
-
-        gallery = gr.Gallery(
-            label="Products", show_label=False, elem_id="gallery"
-        ).style(grid=[3], height="auto")
-
-    btn.click(find_best_matches, inputs = text, outputs = gallery)
-
-demo.launch(show_api=False)
diff --git a/spaces/Fernando22/freegpt-webui/client/css/style.css b/spaces/Fernando22/freegpt-webui/client/css/style.css
deleted file mode 100644
index 918cf83eb9a36bf07c861e4476c60af65f5bf91d..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/client/css/style.css
+++ /dev/null
@@ -1,18 +0,0 @@
-@import "./global.css";
-@import "./hljs.css";
-@import "./main.css";
-@import "./sidebar.css";
-@import "./conversation.css";
-@import "./message.css";
-@import "./stop-generating.css";
-@import "./typing.css";
-@import "./checkbox.css";
-@import "./label.css";
-@import "./button.css";
-@import "./buttons.css";
-@import "./dropdown.css";
-@import "./field.css";
-@import "./select.css";
-@import "./options.css";
-@import "./settings.css";
-@import "./message-input.css";
diff --git a/spaces/FredMagick/Stable-diffusion-Bias-test/README.md b/spaces/FredMagick/Stable-diffusion-Bias-test/README.md
deleted file mode 100644
index cbc691556d4ea167fb8743161d9299b97aed2de2..0000000000000000000000000000000000000000
--- a/spaces/FredMagick/Stable-diffusion-Bias-test/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stable Diffusion Bias Test
-emoji: 📊
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
-license: bigscience-bloom-rail-1.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/FridaZuley/RVC_HFKawaii/lib/uvr5_pack/lib_v5/layers_537238KB.py b/spaces/FridaZuley/RVC_HFKawaii/lib/uvr5_pack/lib_v5/layers_537238KB.py
deleted file mode 100644
index a38b7bb3ae3136b07eadfc2db445fef4c2de186b..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/lib/uvr5_pack/lib_v5/layers_537238KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/FridaZuley/RVC_HFKawaii/mdx.py b/spaces/FridaZuley/RVC_HFKawaii/mdx.py
deleted file mode 100644
index 4cc7c08b37bc371294f2f82b3382424a5455b7c2..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/mdx.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import torch
-import onnxruntime as ort
-from tqdm import tqdm
-import warnings
-import numpy as np
-import hashlib
-import queue
-import threading
-
-warnings.filterwarnings("ignore")
-
-class MDX_Model:
-    def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
-        self.dim_f = dim_f
-        self.dim_t = dim_t
-        self.dim_c = 4
-        self.n_fft = n_fft
-        self.hop = hop
-        self.stem_name = stem_name
-        self.compensation = compensation
-
-        self.n_bins = self.n_fft//2+1
-        self.chunk_size = hop * (self.dim_t-1)
-        self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
-        out_c = self.dim_c
-
-        self.freq_pad = torch.zeros([1, out_c, self.n_bins-self.dim_f, self.dim_t]).to(device)
-
-    def stft(self, x):
-        x = x.reshape([-1, self.chunk_size])
-        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
-        x = torch.view_as_real(x)
-        x = x.permute([0,3,1,2])
-        x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,4,self.n_bins,self.dim_t])
-        return x[:,:,:self.dim_f]
-
-    def istft(self, x, freq_pad=None):
-        freq_pad = self.freq_pad.repeat([x.shape[0],1,1,1]) if freq_pad is None else freq_pad
-        x = torch.cat([x, freq_pad], -2)
-        # c = 4*2 if self.target_name=='*' else 2
-        x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,2,self.n_bins,self.dim_t])
-        x = x.permute([0,2,3,1])
-        x = x.contiguous()
-        x = torch.view_as_complex(x)
-        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
-        return x.reshape([-1,2,self.chunk_size])
-
-
-class MDX:
-
-    DEFAULT_SR = 44100
-    # Unit: seconds
-    DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
-    DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
-    DEFAULT_PROCESSOR = 0
-
-    def __init__(self, model_path:str, params:MDX_Model, processor=DEFAULT_PROCESSOR):
-
-        # Set the device and the provider (CPU or CUDA)
-        self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
-        self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
-        self.model = params
-
-        # Load the ONNX model using ONNX Runtime
-        self.ort = ort.InferenceSession(model_path, providers=self.provider)
-        # Preload the model for faster performance
-        self.ort.run(None, {'input':torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
-        self.process = lambda spec:self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
-        self.prog = None
-
-    @staticmethod
-    def get_hash(model_path):
-        try:
-            with open(model_path, 'rb') as f:
-                f.seek(- 10000 * 1024, 2)
-                model_hash = hashlib.md5(f.read()).hexdigest()
-        except:
-            model_hash = hashlib.md5(open(model_path,'rb').read()).hexdigest()
-            
-        return model_hash
-    
-    @staticmethod
-    def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
-        """
-        Segment or join segmented wave array
-
-        Args:
-            wave: (np.array) Wave array to be segmented or joined
-            combine: (bool) If True, combines segmented wave array. If False, segments wave array.
-            chunk_size: (int) Size of each segment (in samples)
-            margin_size: (int) Size of margin between segments (in samples)
-
-        Returns:
-            numpy array: Segmented or joined wave array
-        """
-        
-        if combine:
-            processed_wave = None  # Initializing as None instead of [] for later numpy array concatenation
-            for segment_count, segment in enumerate(wave):
-                start = 0 if segment_count == 0 else margin_size
-                end = None if segment_count == len(wave)-1 else -margin_size
-                if margin_size == 0:
-                    end = None
-                if processed_wave is None:  # Create array for first segment
-                    processed_wave = segment[:, start:end]
-                else:  # Concatenate to existing array for subsequent segments
-                    processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
-        else:
-            processed_wave = []
-            sample_count = wave.shape[-1]
-
-            if chunk_size <= 0 or chunk_size > sample_count:
-                chunk_size = sample_count
-
-            if margin_size > chunk_size:
-                margin_size = chunk_size
-
-            for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
-                margin = 0 if segment_count == 0 else margin_size
-                end = min(skip+chunk_size+margin_size, sample_count)
-                start = skip-margin
-
-                cut = wave[:,start:end].copy()
-                processed_wave.append(cut)
-
-                if end == sample_count:
-                    break
-        
-        return processed_wave
-
-    def pad_wave(self, wave):
-        """
-        Pad the wave array to match the required chunk size
-
-        Args:
-            wave: (np.array) Wave array to be padded
-
-        Returns:
-            tuple: (padded_wave, pad, trim)
-                - padded_wave: Padded wave array
-                - pad: Number of samples that were padded
-                - trim: Number of samples that were trimmed
-        """
-        n_sample = wave.shape[1]
-        trim = self.model.n_fft//2
-        gen_size = self.model.chunk_size-2*trim
-        pad = gen_size - n_sample%gen_size
-
-        # Padded wave
-        wave_p = np.concatenate((np.zeros((2,trim)), wave, np.zeros((2,pad)), np.zeros((2,trim))), 1)
-
-        mix_waves = []
-        for i in range(0, n_sample+pad, gen_size):
-            waves = np.array(wave_p[:, i:i+self.model.chunk_size])
-            mix_waves.append(waves)
-
-        mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
-        return mix_waves, pad, trim
-
-    def _process_wave(self, mix_waves, trim, pad, q:queue.Queue, _id:int):
-        """
-        Process each wave segment in a multi-threaded environment
-
-        Args:
-            mix_waves: (torch.Tensor) Wave segments to be processed
-            trim: (int) Number of samples trimmed during padding
-            pad: (int) Number of samples padded during padding
-            q: (queue.Queue) Queue to hold the processed wave segments
-            _id: (int) Identifier of the processed wave segment
-
-        Returns:
-            numpy array: Processed wave segment
-        """
-        mix_waves = mix_waves.split(1)
-        with torch.no_grad():
-            pw = []
-            for mix_wave in mix_waves:
-                self.prog.update()
-                spec = self.model.stft(mix_wave)
-                processed_spec = torch.tensor(self.process(spec))
-                processed_wav = self.model.istft(processed_spec.to(self.device))
-                processed_wav = processed_wav[:,:,trim:-trim].transpose(0,1).reshape(2, -1).cpu().numpy()
-                pw.append(processed_wav)
-        processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
-        q.put({_id:processed_signal})
-        return processed_signal
-
-    def process_wave(self, wave:np.array, mt_threads=1):
-        """
-        Process the wave array in a multi-threaded environment
-
-        Args:
-            wave: (np.array) Wave array to be processed
-            mt_threads: (int) Number of threads to be used for processing
-
-        Returns:
-            numpy array: Processed wave array
-        """
-        self.prog = tqdm(total=0)
-        chunk = wave.shape[-1]//mt_threads
-        waves = self.segment(wave, False, chunk)
-
-        # Create a queue to hold the processed wave segments
-        q = queue.Queue()
-        threads = []
-        for c, batch in enumerate(waves):
-            mix_waves, pad, trim = self.pad_wave(batch)
-            self.prog.total = len(mix_waves)*mt_threads
-            thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
-            thread.start()
-            threads.append(thread)
-        for thread in threads:
-            thread.join()
-        self.prog.close()
-
-        processed_batches = []
-        while not q.empty():
-            processed_batches.append(q.get())
-        processed_batches = [list(wave.values())[0] for wave in sorted(processed_batches, key=lambda d: list(d.keys())[0])]
-        assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
-        return self.segment(processed_batches, True, chunk)
\ No newline at end of file
diff --git a/spaces/GAIR/Factool/factool/scientific/tool.py b/spaces/GAIR/Factool/factool/scientific/tool.py
deleted file mode 100644
index afffa4ad5677c6e4c68828eee9f9033ebdeaf715..0000000000000000000000000000000000000000
--- a/spaces/GAIR/Factool/factool/scientific/tool.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import yaml
-
-from scholarly import scholarly
-from scholarly import ProxyGenerator
-import os
-
-from factool.env_config import factool_env_config
-
-# env
-# scraper_api_key = factool_env_config.scraper_api_key
-
-class google_scholar():
-    def __init__(self):
-        pg = ProxyGenerator()
-        scraper_api_key = os.environ.get("SCRAPER_API_KEY", None)
-        assert scraper_api_key is not None, "Please set the SCRAPER_API_KEY environment variable."
-        success = pg.ScraperAPI(scraper_api_key)
-        scholarly.use_proxy(pg)
-
-    def run(self, query):
-        try:
-            results = scholarly.search_pubs(query)
-            paper_info = next(results)
-            paper_info_subset = {key: paper_info['bib'][key] for key in ['title', 'author', 'pub_year']}
-            return paper_info_subset
-        except StopIteration:
-            return {'title': "no match!", "author": "no match!", "pub_year": "no match!"}
diff --git a/spaces/GIZ/vulnerability_analysis/utils/uploadAndExample.py b/spaces/GIZ/vulnerability_analysis/utils/uploadAndExample.py
deleted file mode 100644
index 68e5f0c2970b102765f98f4185a965c8d7f48182..0000000000000000000000000000000000000000
--- a/spaces/GIZ/vulnerability_analysis/utils/uploadAndExample.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import streamlit as st
-import tempfile
-import json
-
-def add_upload(choice):
-    """
-    Provdies the user with choice to either 'Upload Document' or 'Try Example'.
-    Based on user choice runs streamlit processes and save the path and name of
-    the 'file' to streamlit session_state which then can be fetched later.
-
-    """
-    
-    if choice == 'Upload Document':
-        
-       # if 'filename' in st.session_state:
-          # Delete all the items in Session state
-        #    for key in st.session_state.keys():
-         #       del st.session_state[key]
-
-        uploaded_file = st.sidebar.file_uploader('Upload the File',
-                            type=['pdf', 'docx', 'txt'])
-        if uploaded_file is not None:
-            with tempfile.NamedTemporaryFile(mode="wb", delete = False) as temp:
-                bytes_data = uploaded_file.getvalue()
-                temp.write(bytes_data)
-                st.session_state['filename'] = uploaded_file.name
-                st.session_state['filepath'] = temp.name
-
-                
-    else:
-        # listing the options
-        with open('docStore/sample/files.json','r') as json_file:
-            files = json.load(json_file)
-
-        option = st.sidebar.selectbox('Select the example document',
-                              list(files.keys()))
-        file_name = file_path  = files[option]
-        st.session_state['filename'] = file_name
-        st.session_state['filepath'] = file_path
diff --git a/spaces/Giedrius/mood_detector/app.py b/spaces/Giedrius/mood_detector/app.py
deleted file mode 100644
index 3da34c00e094b7364aeb16d390336498668a8962..0000000000000000000000000000000000000000
--- a/spaces/Giedrius/mood_detector/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-
-IMG_SIZE = (128, 128)
-
-# load keras model
-model_path = 'my_model.h5'
-model = tf.keras.models.load_model(model_path)
-
-categories = ('happy', 'sad')
-
-def classify_image(img):
-    img_array_expanded_dims = img.reshape((-1, 128, 128, 3))
-    prediction = model.predict(img_array_expanded_dims)
-    prediction_prob = float(tf.nn.sigmoid(prediction))
-    probs = [1-prediction_prob, prediction_prob]
-    return dict(zip(categories, probs))
-
-gr_image = gr.inputs.Image(shape=IMG_SIZE)
-label = gr.outputs.Label()
-examples = ['happy.jpg', 'sad.jpg']
-
-iface = gr.Interface(fn=classify_image, inputs=gr_image, outputs=label, examples=examples)
-iface.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/yolact/yolact_r101_1x8_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/yolact/yolact_r101_1x8_coco.py
deleted file mode 100644
index 2864b590b5538b735a16df3b2690b29a95384df8..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/yolact/yolact_r101_1x8_coco.py
+++ /dev/null
@@ -1,3 +0,0 @@
-_base_ = './yolact_r50_1x8_coco.py'
-
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/score_hlr_sampler.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/score_hlr_sampler.py
deleted file mode 100644
index 11d46b97705db60fb6a4eb5fa7da10ac78acb8bc..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/score_hlr_sampler.py
+++ /dev/null
@@ -1,264 +0,0 @@
-import torch
-from mmcv.ops import nms_match
-
-from ..builder import BBOX_SAMPLERS
-from ..transforms import bbox2roi
-from .base_sampler import BaseSampler
-from .sampling_result import SamplingResult
-
-
-@BBOX_SAMPLERS.register_module()
-class ScoreHLRSampler(BaseSampler):
-    r"""Importance-based Sample Reweighting (ISR_N), described in `Prime Sample
-    Attention in Object Detection <https://arxiv.org/abs/1904.04821>`_.
-
-    Score hierarchical local rank (HLR) differentiates with RandomSampler in
-    negative part. It firstly computes Score-HLR in a two-step way,
-    then linearly maps score hlr to the loss weights.
-
-    Args:
-        num (int): Total number of sampled RoIs.
-        pos_fraction (float): Fraction of positive samples.
-        context (:class:`BaseRoIHead`): RoI head that the sampler belongs to.
-        neg_pos_ub (int): Upper bound of the ratio of num negative to num
-            positive, -1 means no upper bound.
-        add_gt_as_proposals (bool): Whether to add ground truth as proposals.
-        k (float): Power of the non-linear mapping.
-        bias (float): Shift of the non-linear mapping.
-        score_thr (float): Minimum score that a negative sample is to be
-            considered as valid bbox.
-    """
-
-    def __init__(self,
-                 num,
-                 pos_fraction,
-                 context,
-                 neg_pos_ub=-1,
-                 add_gt_as_proposals=True,
-                 k=0.5,
-                 bias=0,
-                 score_thr=0.05,
-                 iou_thr=0.5,
-                 **kwargs):
-        super().__init__(num, pos_fraction, neg_pos_ub, add_gt_as_proposals)
-        self.k = k
-        self.bias = bias
-        self.score_thr = score_thr
-        self.iou_thr = iou_thr
-        self.context = context
-        # context of cascade detectors is a list, so distinguish them here.
-        if not hasattr(context, 'num_stages'):
-            self.bbox_roi_extractor = context.bbox_roi_extractor
-            self.bbox_head = context.bbox_head
-            self.with_shared_head = context.with_shared_head
-            if self.with_shared_head:
-                self.shared_head = context.shared_head
-        else:
-            self.bbox_roi_extractor = context.bbox_roi_extractor[
-                context.current_stage]
-            self.bbox_head = context.bbox_head[context.current_stage]
-
-    @staticmethod
-    def random_choice(gallery, num):
-        """Randomly select some elements from the gallery.
-
-        If `gallery` is a Tensor, the returned indices will be a Tensor;
-        If `gallery` is a ndarray or list, the returned indices will be a
-        ndarray.
-
-        Args:
-            gallery (Tensor | ndarray | list): indices pool.
-            num (int): expected sample num.
-
-        Returns:
-            Tensor or ndarray: sampled indices.
-        """
-        assert len(gallery) >= num
-
-        is_tensor = isinstance(gallery, torch.Tensor)
-        if not is_tensor:
-            if torch.cuda.is_available():
-                device = torch.cuda.current_device()
-            else:
-                device = 'cpu'
-            gallery = torch.tensor(gallery, dtype=torch.long, device=device)
-        perm = torch.randperm(gallery.numel(), device=gallery.device)[:num]
-        rand_inds = gallery[perm]
-        if not is_tensor:
-            rand_inds = rand_inds.cpu().numpy()
-        return rand_inds
-
-    def _sample_pos(self, assign_result, num_expected, **kwargs):
-        """Randomly sample some positive samples."""
-        pos_inds = torch.nonzero(assign_result.gt_inds > 0).flatten()
-        if pos_inds.numel() <= num_expected:
-            return pos_inds
-        else:
-            return self.random_choice(pos_inds, num_expected)
-
-    def _sample_neg(self,
-                    assign_result,
-                    num_expected,
-                    bboxes,
-                    feats=None,
-                    img_meta=None,
-                    **kwargs):
-        """Sample negative samples.
-
-        Score-HLR sampler is done in the following steps:
-        1. Take the maximum positive score prediction of each negative samples
-            as s_i.
-        2. Filter out negative samples whose s_i <= score_thr, the left samples
-            are called valid samples.
-        3. Use NMS-Match to divide valid samples into different groups,
-            samples in the same group will greatly overlap with each other
-        4. Rank the matched samples in two-steps to get Score-HLR.
-            (1) In the same group, rank samples with their scores.
-            (2) In the same score rank across different groups,
-                rank samples with their scores again.
-        5. Linearly map Score-HLR to the final label weights.
-
-        Args:
-            assign_result (:obj:`AssignResult`): result of assigner.
-            num_expected (int): Expected number of samples.
-            bboxes (Tensor): bbox to be sampled.
-            feats (Tensor): Features come from FPN.
-            img_meta (dict): Meta information dictionary.
-        """
-        neg_inds = torch.nonzero(assign_result.gt_inds == 0).flatten()
-        num_neg = neg_inds.size(0)
-        if num_neg == 0:
-            return neg_inds, None
-        with torch.no_grad():
-            neg_bboxes = bboxes[neg_inds]
-            neg_rois = bbox2roi([neg_bboxes])
-            bbox_result = self.context._bbox_forward(feats, neg_rois)
-            cls_score, bbox_pred = bbox_result['cls_score'], bbox_result[
-                'bbox_pred']
-
-            ori_loss = self.bbox_head.loss(
-                cls_score=cls_score,
-                bbox_pred=None,
-                rois=None,
-                labels=neg_inds.new_full((num_neg, ),
-                                         self.bbox_head.num_classes),
-                label_weights=cls_score.new_ones(num_neg),
-                bbox_targets=None,
-                bbox_weights=None,
-                reduction_override='none')['loss_cls']
-
-            # filter out samples with the max score lower than score_thr
-            max_score, argmax_score = cls_score.softmax(-1)[:, :-1].max(-1)
-            valid_inds = (max_score > self.score_thr).nonzero().view(-1)
-            invalid_inds = (max_score <= self.score_thr).nonzero().view(-1)
-            num_valid = valid_inds.size(0)
-            num_invalid = invalid_inds.size(0)
-
-            num_expected = min(num_neg, num_expected)
-            num_hlr = min(num_valid, num_expected)
-            num_rand = num_expected - num_hlr
-            if num_valid > 0:
-                valid_rois = neg_rois[valid_inds]
-                valid_max_score = max_score[valid_inds]
-                valid_argmax_score = argmax_score[valid_inds]
-                valid_bbox_pred = bbox_pred[valid_inds]
-
-                # valid_bbox_pred shape: [num_valid, #num_classes, 4]
-                valid_bbox_pred = valid_bbox_pred.view(
-                    valid_bbox_pred.size(0), -1, 4)
-                selected_bbox_pred = valid_bbox_pred[range(num_valid),
-                                                     valid_argmax_score]
-                pred_bboxes = self.bbox_head.bbox_coder.decode(
-                    valid_rois[:, 1:], selected_bbox_pred)
-                pred_bboxes_with_score = torch.cat(
-                    [pred_bboxes, valid_max_score[:, None]], -1)
-                group = nms_match(pred_bboxes_with_score, self.iou_thr)
-
-                # imp: importance
-                imp = cls_score.new_zeros(num_valid)
-                for g in group:
-                    g_score = valid_max_score[g]
-                    # g_score has already sorted
-                    rank = g_score.new_tensor(range(g_score.size(0)))
-                    imp[g] = num_valid - rank + g_score
-                _, imp_rank_inds = imp.sort(descending=True)
-                _, imp_rank = imp_rank_inds.sort()
-                hlr_inds = imp_rank_inds[:num_expected]
-
-                if num_rand > 0:
-                    rand_inds = torch.randperm(num_invalid)[:num_rand]
-                    select_inds = torch.cat(
-                        [valid_inds[hlr_inds], invalid_inds[rand_inds]])
-                else:
-                    select_inds = valid_inds[hlr_inds]
-
-                neg_label_weights = cls_score.new_ones(num_expected)
-
-                up_bound = max(num_expected, num_valid)
-                imp_weights = (up_bound -
-                               imp_rank[hlr_inds].float()) / up_bound
-                neg_label_weights[:num_hlr] = imp_weights
-                neg_label_weights[num_hlr:] = imp_weights.min()
-                neg_label_weights = (self.bias +
-                                     (1 - self.bias) * neg_label_weights).pow(
-                                         self.k)
-                ori_selected_loss = ori_loss[select_inds]
-                new_loss = ori_selected_loss * neg_label_weights
-                norm_ratio = ori_selected_loss.sum() / new_loss.sum()
-                neg_label_weights *= norm_ratio
-            else:
-                neg_label_weights = cls_score.new_ones(num_expected)
-                select_inds = torch.randperm(num_neg)[:num_expected]
-
-            return neg_inds[select_inds], neg_label_weights
-
-    def sample(self,
-               assign_result,
-               bboxes,
-               gt_bboxes,
-               gt_labels=None,
-               img_meta=None,
-               **kwargs):
-        """Sample positive and negative bboxes.
-
-        This is a simple implementation of bbox sampling given candidates,
-        assigning results and ground truth bboxes.
-
-        Args:
-            assign_result (:obj:`AssignResult`): Bbox assigning results.
-            bboxes (Tensor): Boxes to be sampled from.
-            gt_bboxes (Tensor): Ground truth bboxes.
-            gt_labels (Tensor, optional): Class labels of ground truth bboxes.
-
-        Returns:
-            tuple[:obj:`SamplingResult`, Tensor]: Sampling result and negetive
-                label weights.
-        """
-        bboxes = bboxes[:, :4]
-
-        gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8)
-        if self.add_gt_as_proposals:
-            bboxes = torch.cat([gt_bboxes, bboxes], dim=0)
-            assign_result.add_gt_(gt_labels)
-            gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8)
-            gt_flags = torch.cat([gt_ones, gt_flags])
-
-        num_expected_pos = int(self.num * self.pos_fraction)
-        pos_inds = self.pos_sampler._sample_pos(
-            assign_result, num_expected_pos, bboxes=bboxes, **kwargs)
-        num_sampled_pos = pos_inds.numel()
-        num_expected_neg = self.num - num_sampled_pos
-        if self.neg_pos_ub >= 0:
-            _pos = max(1, num_sampled_pos)
-            neg_upper_bound = int(self.neg_pos_ub * _pos)
-            if num_expected_neg > neg_upper_bound:
-                num_expected_neg = neg_upper_bound
-        neg_inds, neg_label_weights = self.neg_sampler._sample_neg(
-            assign_result,
-            num_expected_neg,
-            bboxes,
-            img_meta=img_meta,
-            **kwargs)
-
-        return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
-                              assign_result, gt_flags), neg_label_weights
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 59b8c6dd5ef234334bcdfa3d5e3594b7a9989b17..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/HaloMaster/chinesesummary/app.py b/spaces/HaloMaster/chinesesummary/app.py
deleted file mode 100644
index b2efbb86660eaf243134e175e58709976f97d748..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import sys
-sys.path.append("./fengshen/examples/pegasus/")
-
-import fengshen
-import gradio as gr
-from transformers import PegasusForConditionalGeneration
-from tokenizers_pegasus import PegasusTokenizer
-
-model = PegasusForConditionalGeneration.from_pretrained("IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese")
-tokenizer = PegasusTokenizer.from_pretrained("IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese")
-
-
-#def greet(name):
-#    return "Hello " + name + "!!"
-
-#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-def generator(text, max_length, num_return_sequences):
-  inputs = tokenizer(text, max_length=1024, return_tensors="pt")
-  # Generate Summary
-  summary_ids = model.generate(inputs["input_ids"], max_length)
-  return tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[:num_return_sequences]
-
-def generate(text):
-    result = generator(text, max_length=300, num_return_sequences=1)
-    return result
-
-examples = [
-    ["""现在乌军的迫击炮都可以开始轰击库皮扬斯克的俄军目标了，双方相距只有几公里。
-最重要的是该地区俄军背后就是奥斯科尔河-北顿涅茨河。而为了确保第聂伯河上赫尔松城的后勤保障，俄军已经把舟桥部队主力调到赫尔松去了，现在是远水解不了近渴。
-乌军很有可能暂时不会直接攻击伊久姆城区，现在还是要先拿下库皮扬斯克和奥斯科尔河上的两座桥梁。
-切断俄军从别尔哥罗德向库皮扬斯克的物资输送，切断库皮扬斯克-伊久姆公路，断其粮道。
-现在要看俄军有没有预备队。库皮扬斯克没援军的话大概率守不住。但是现在打了三天，俄军还没有援军抵达战场。
-现在，俄军最主要任务是守住库皮扬斯克，同时要确保库皮扬斯克-伊久姆高速公路的安全。"""],
-    ["在北京冬奥会自由式滑雪女子坡面障碍技巧决赛中，中国选手谷爱凌夺得银牌。祝贺谷爱凌！今天上午，自由式滑雪女子坡面障碍技巧决赛举行。决赛分三轮进行，取选手最佳成绩排名决出奖牌。第一跳，中国选手谷爱凌获得69.90分。在12位选手中排名第三。完成动作后，谷爱凌又扮了个鬼脸，甚是可爱。第二轮中，谷爱凌在道具区第三个障碍处失误，落地时摔倒。获得16.98分。网友：摔倒了也没关系，继续加油！在第二跳失误摔倒的情况下，谷爱凌顶住压力，第三跳稳稳发挥，流畅落地！获得86.23分！此轮比赛，共12位选手参赛，谷爱凌第10位出场。网友：看比赛时我比谷爱凌紧张，加油！"],
-]
-
-iface = gr.Interface(
-    fn=generate,
-    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
-    outputs=gr.outputs.Textbox(label="Generated Text"),
-    examples=examples
-)
-# 
-
-iface.launch()
-
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/pointer_generator/postprocess.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/pointer_generator/postprocess.py
deleted file mode 100644
index b213aed80fd1e3d86f975256fcb7d9d4c16ca857..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/pointer_generator/postprocess.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import re
-import sys
-
-
-class OOVIndexError(IndexError):
-    def __init__(self, pos, source_seq, target_seq):
-        super(OOVIndexError, self).__init__(
-            "A <unk-N> tag in the target sequence refers to a position that is "
-            "outside the source sequence. Most likely there was a mismatch in "
-            "provided source and target sequences. Otherwise this would mean that "
-            "the pointing mechanism somehow attended to a position that is past "
-            "the actual sequence end."
-        )
-        self.source_pos = pos
-        self.source_seq = source_seq
-        self.target_seq = target_seq
-
-
-def replace_oovs(source_in, target_in, target_out):
-    """Replaces <unk-N> tokens in the target text with the corresponding word in
-    the source text.
-    """
-
-    oov_re = re.compile("^<unk-([0-9]+)>$")
-
-    for source_seq, target_seq in zip(source_in, target_in):
-        target_seq_out = []
-
-        pos_to_word = source_seq.strip().split()
-        for token in target_seq.strip().split():
-            m = oov_re.match(token)
-            if m:
-                pos = int(m.group(1))
-                if pos >= len(pos_to_word):
-                    raise OOVIndexError(pos, source_seq, target_seq)
-                token_out = pos_to_word[pos]
-            else:
-                token_out = token
-            target_seq_out.append(token_out)
-        target_out.write(" ".join(target_seq_out) + "\n")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Replaces <unk-N> tokens in target sequences with words from "
-        "the corresponding position in the source sequence."
-    )
-    parser.add_argument(
-        "--source", type=str, help="text file with source sequences", required=True
-    )
-    parser.add_argument(
-        "--target", type=str, help="text file with target sequences", required=True
-    )
-    parser.add_argument(
-        "--target-out",
-        type=str,
-        help="where to write target sequences without <unk-N> " "entries",
-        required=True,
-    )
-    args = parser.parse_args()
-
-    target_in = (
-        open(args.target, "r", encoding="utf-8") if args.target is not None else None
-    )
-    target_out = (
-        open(args.target_out, "w", encoding="utf-8")
-        if args.target_out is not None
-        else None
-    )
-    with open(args.source, "r", encoding="utf-8") as source_in, open(
-        args.target, "r", encoding="utf-8"
-    ) as target_in, open(args.target_out, "w", encoding="utf-8") as target_out:
-        replace_oovs(source_in, target_in, target_out)
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except OOVIndexError as e:
-        print(e, file=sys.stderr)
-        print("Source sequence:", e.source_seq.strip(), file=sys.stderr)
-        print("Target sequence:", e.target_seq.strip(), file=sys.stderr)
-        print(
-            "Source sequence length:",
-            len(e.source_seq.strip().split()),
-            file=sys.stderr,
-        )
-        print("The offending tag points to:", e.source_pos)
-        sys.exit(2)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh
deleted file mode 100644
index c1276bbe4d0e02deb984c7c10d6c0486dce09a5f..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/bin/bash
-
-# prepare word WFSTs, reference data, and decode
-
-set -eu
-
-w2v_dir=  # same as in train.sh
-out_dir=  # same as in train.sh
-lexicon=  # word to phone mapping
-wrd_arpa_lm=  # word LM
-wrd_arpa_lm_bin=  # word LM for KenLM, used in unsupervised selection
-
-dec_exp=  # what HMM stage to decode (e.g., tri3b)
-dec_script=  # what decoding script to use (e.g., steps/decode_fmllr.sh)
-phn_label=phnc
-wrd_label=wrd
-dec_suffix=word
-dec_splits="train valid"
-valid_split="valid"
-
-data_dir=$out_dir/data
-wrd_data_dir=$out_dir/data_word
-
-lexicon_clean=$(mktemp)
-cat $lexicon | sort | uniq > $lexicon_clean
-local/prepare_lang_word.sh $w2v_dir/dict.${phn_label}.txt $data_dir $lexicon_clean && rm $lexicon_clean
-local/prepare_lm.sh --langdir $data_dir/lang_word --lmdir $data_dir/lang_test_word $wrd_arpa_lm $data_dir
-
-for x in $dec_splits; do
-  x_gt=${x}_gt
-  mkdir -p $wrd_data_dir/$x_gt
-  cp $data_dir/$x_gt/{feats.scp,cmvn.scp,utt2spk,spk2utt} $wrd_data_dir/$x_gt/
-  python local/copy_aligned_text.py < $w2v_dir/$x.$wrd_label > $wrd_data_dir/$x_gt/text
-done
-
-local/decode.sh --nj 40 --graph_name graph${dec_suffix} --decode_suffix $dec_suffix \
-  --val_sets "$dec_splits" --decode_script $dec_script \
-  $out_dir/exp/$dec_exp $data_dir $data_dir/lang_test_word
-
-local/unsup_select_decode_word.sh \
-  --split $valid_split --kenlm_path $wrd_arpa_lm_bin \
-  --ref_txt $wrd_data_dir/${valid_split}_gt/text \
-  --psd_txt $data_dir/${valid_split}/text \
-  --dec_name decode${dec_suffix} --graph_name graph${dec_suffix} \
-  --phonemize_lexicon $data_dir/local/dict_word/lexicon.txt \
-  $out_dir/exp
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/text/cleaners.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/text/cleaners.py
deleted file mode 100644
index a7d4e029baa436e88e4d68090e886afdd998a68d..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/text/cleaners.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import re
-
-from unidecode import unidecode
-from .numbers import normalize_numbers
-
-
-
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r"\s+")
-
-def lowercase(text):
-    return text.lower()
-
-def collapse_whitespace(text):
-    return re.sub(_whitespace_re, " ", text)
-
-def basic_indic_cleaners(text):
-    """Basic pipeline that collapses whitespace without transliteration."""
-    text = collapse_whitespace(text)
-    return text
-
-
-def english_cleaner(text):
-    text = text.lower().replace('‘','\'').replace('’','\'')
-    return text
-
-
-def lowercase(text):
-    return text.lower()
-
-def convert_to_ascii(text):
-    return unidecode(text)
-
-def expand_numbers(text):
-    return normalize_numbers(text)
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'missus'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-  ('pvt', 'private'),
-  ('rs', 'Rupees')
-]]
-
-
-
-
-
-
-def english_cleaners(text):
-    '''Pipeline for English text, including number and abbreviation expansion.'''
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = expand_numbers(text)
-    text = expand_abbreviations(text)
-    text = collapse_whitespace(text)
-    return text
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/tts_infer/num_to_word_on_sent.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/tts_infer/num_to_word_on_sent.py
deleted file mode 100644
index de571c2be63fa467491d01daf0e2f38dada67de9..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/tts_infer/num_to_word_on_sent.py
+++ /dev/null
@@ -1,1319 +0,0 @@
-import re
-import string
-
-# ----------------------------- indic_num.py -----------------------------
-supported_lang = {"en", "hi", "gu", "mr", "bn", "te", "ta", "kn", "or", "pa"}
-# supported_lang = {'eng', 'hin', 'guj', 'mar', 'ben', 'tel', 'tam', 'kan', 'ori', 'pan'}  # Three alphabet lang code
-
-all_num = {
-    "en": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
-    "hi": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
-    "gu": ["૦", "૧", "૨", "૩", "૪", "૫", "૬", "૭", "૮", "૯"],
-    "mr": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
-    "bn": ["০", "১", "২", "৩", "৪", "৫", "৬", "৭", "৮", "৯"],
-    "te": ["౦", "౧", "౨", "౩", "౪", "౫", "౬", "౭", "౮", "౯"],
-    "ta": ["0", "௧", "௨", "௩", "௪", "௫", "௬", "௭", "௮", "௯", "௰"],
-    "kn": ["೦", "೧", "೨", "೩", "೪", "೫", "೬", "೭", "೮", "೯"],
-    "or": ["୦", "୧", "୨", "୩", "୪", "୫", "୬", "୭", "୮", "୯"],
-    "pa": ["੦", "੧", "੨", "੩", "੪", "੫", "੬", "੭", "੮", "੯"],
-}
-
-num_dict = dict()
-num_dict["en"] = {
-    "0": "zero",
-    "1": "one",
-    "2": "two",
-    "3": "three",
-    "4": "four",
-    "5": "five",
-    "6": "six",
-    "7": "seven",
-    "8": "eight",
-    "9": "nine",
-    "10": "ten",
-    "11": "eleven",
-    "12": "twelve",
-    "13": "thirteen",
-    "14": "fourteen",
-    "15": "fifteen",
-    "16": "sixteen",
-    "17": "seventeen",
-    "18": "eighteen",
-    "19": "nineteen",
-    "20": "twenty",
-    "21": "twenty-one",
-    "22": "twenty-two",
-    "23": "twenty-three",
-    "24": "twenty-four",
-    "25": "twenty-five",
-    "26": "twenty-six",
-    "27": "twenty-seven",
-    "28": "twenty-eight",
-    "29": "twenty-nine",
-    "30": "thirty",
-    "31": "thirty-one",
-    "32": "thirty-two",
-    "33": "thirty-three",
-    "34": "thirty-four",
-    "35": "thirty-five",
-    "36": "thirty-six",
-    "37": "thirty-seven",
-    "38": "thirty-eight",
-    "39": "thirty-nine",
-    "40": "forty",
-    "41": "forty-one",
-    "42": "forty-two",
-    "43": "forty-three",
-    "44": "forty-four",
-    "45": "forty-five",
-    "46": "forty-six",
-    "47": "forty-seven",
-    "48": "forty-eight",
-    "49": "forty-nine",
-    "50": "fifty",
-    "51": "fifty-one",
-    "52": "fifty-two",
-    "53": "fifty-three",
-    "54": "fifty-four",
-    "55": "fifty-five",
-    "56": "fifty-six",
-    "57": "fifty-seven",
-    "58": "fifty-eight",
-    "59": "fifty-nine",
-    "60": "sixty",
-    "61": "sixty-one",
-    "62": "sixty-two",
-    "63": "sixty-three",
-    "64": "sixty-four",
-    "65": "sixty-five",
-    "66": "sixty-six",
-    "67": "sixty-seven",
-    "68": "sixty-eight",
-    "69": "sixty-nine",
-    "70": "seventy",
-    "71": "seventy-one",
-    "72": "seventy-two",
-    "73": "seventy-three",
-    "74": "seventy-four",
-    "75": "seventy-five",
-    "76": "seventy-six",
-    "77": "seventy-seven",
-    "78": "seventy-eight",
-    "79": "seventy-nine",
-    "80": "eighty",
-    "81": "eighty-one",
-    "82": "eighty-two",
-    "83": "eighty-three",
-    "84": "eighty-four",
-    "85": "eighty-five",
-    "86": "eighty-six",
-    "87": "eighty-seven",
-    "88": "eighty-eight",
-    "89": "eighty-nine",
-    "90": "ninety",
-    "91": "ninety-one",
-    "92": "ninety-two",
-    "93": "ninety-three",
-    "94": "ninety-four",
-    "95": "ninety-five",
-    "96": "ninety-six",
-    "97": "ninety-seven",
-    "98": "ninety-eight",
-    "99": "ninety-nine",
-    "100": "hundred",
-    "1000": "thousand",
-    "100000": "lac",
-    "10000000": "crore",
-    "1000000000": "arab",
-}  # English-India
-num_dict["hi"] = {
-    "0": "शून्य",
-    "1": "एक",
-    "2": "दो",
-    "3": "तीन",
-    "4": "चार",
-    "5": "पाँच",
-    "6": "छः",
-    "7": "सात",
-    "8": "आठ",
-    "9": "नौ",
-    "10": "दस",
-    "11": "ग्यारह",
-    "12": "बारह",
-    "13": "तेरह",
-    "14": "चौदह",
-    "15": "पंद्रह",
-    "16": "सोलह",
-    "17": "सत्रह",
-    "18": "अट्ठारह",
-    "19": "उन्नीस",
-    "20": "बीस",
-    "21": "इक्कीस",
-    "22": "बाईस",
-    "23": "तेईस",
-    "24": "चौबिस",
-    "25": "पच्चीस",
-    "26": "छब्बीस",
-    "27": "सत्ताईस",
-    "28": "अट्ठाईस",
-    "29": "उनतीस",
-    "30": "तीस",
-    "31": "इकतीस",
-    "32": "बत्तीस",
-    "33": "तैंतीस",
-    "34": "चौंतीस",
-    "35": "पैंतीस",
-    "36": "छत्तीस",
-    "37": "सैंतीस",
-    "38": "अड़तीस",
-    "39": "उनतालीस",
-    "40": "चालीस",
-    "41": "इकतालीस",
-    "42": "बयालीस",
-    "43": "तैंतालीस",
-    "44": "चौंतालीस",
-    "45": "पैंतालीस",
-    "46": "छियालीस",
-    "47": "सैंतालीस",
-    "48": "अड़तालीस",
-    "49": "उनचास",
-    "50": "पचास",
-    "51": "इक्यावन​",
-    "52": "बावन",
-    "53": "तिरेपन",
-    "54": "चौवन",
-    "55": "पचपन",
-    "56": "छप्पन",
-    "57": "सत्तावन",
-    "58": "अट्ठावन",
-    "59": "उनसठ",
-    "60": "साठ",
-    "61": "इकसठ",
-    "62": "बासठ",
-    "63": "तिरेसठ",
-    "64": "चौंसठ",
-    "65": "पैंसठ",
-    "66": "छयासठ",
-    "67": "सरसठ​",
-    "68": "अड़सठ",
-    "69": "उनहत्तर",
-    "70": "सत्तर",
-    "71": "इकहत्तर",
-    "72": "बहत्तर",
-    "73": "तिहत्तर",
-    "74": "चौहत्तर",
-    "75": "पचहत्तर",
-    "76": "छिहत्तर",
-    "77": "सतहत्तर",
-    "78": "अठहत्तर",
-    "79": "उन्यासी",
-    "80": "अस्सी",
-    "81": "इक्यासी",
-    "82": "बयासी",
-    "83": "तिरासी",
-    "84": "चौरासी",
-    "85": "पचासी",
-    "86": "छियासी",
-    "87": "सत्तासी",
-    "88": "अठासी",
-    "89": "नवासी",
-    "90": "नब्बे",
-    "91": "इक्यानवे",
-    "92": "बानवे",
-    "93": "तिरानवे",
-    "94": "चौरानवे",
-    "95": "पचानवे",
-    "96": "छियानवे",
-    "97": "सत्तानवे",
-    "98": "अट्ठानवे",
-    "99": "निन्यानवे",
-    "100": "सौ",
-    "1000": "हज़ार",
-    "100000": "लाख",
-    "10000000": "करोड़",
-    "1000000000": "अरब",
-}  # Hindi
-num_dict["gu"] = {
-    "0": "શૂન્ય",
-    "1": "એક",
-    "2": "બે",
-    "3": "ત્રણ",
-    "4": "ચાર",
-    "5": "પાંચ",
-    "6": "છ",
-    "7": "સાત",
-    "8": "આઠ",
-    "9": "નવ",
-    "10": "દસ",
-    "11": "અગિયાર",
-    "12": "બાર",
-    "13": "તેર",
-    "14": "ચૌદ",
-    "15": "પંદર",
-    "16": "સોળ",
-    "17": "સત્તર",
-    "18": "અઢાર",
-    "19": "ઓગણિસ",
-    "20": "વીસ",
-    "21": "એકવીસ",
-    "22": "બાવીસ",
-    "23": "તેવીસ",
-    "24": "ચોવીસ",
-    "25": "પચ્ચીસ",
-    "26": "છવીસ",
-    "27": "સત્તાવીસ",
-    "28": "અઠ્ઠાવીસ",
-    "29": "ઓગણત્રીસ",
-    "30": "ત્રીસ",
-    "31": "એકત્રીસ",
-    "32": "બત્રીસ",
-    "33": "તેત્રીસ",
-    "34": "ચોત્રીસ",
-    "35": "પાંત્રીસ",
-    "36": "છત્રીસ",
-    "37": "સડત્રીસ",
-    "38": "અડત્રીસ",
-    "39": "ઓગણચાલીસ",
-    "40": "ચાલીસ",
-    "41": "એકતાલીસ",
-    "42": "બેતાલીસ",
-    "43": "ત્રેતાલીસ",
-    "44": "ચુંમાલીસ",
-    "45": "પિસ્તાલીસ",
-    "46": "છેતાલીસ",
-    "47": "સુડતાલીસ",
-    "48": "અડતાલીસ",
-    "49": "ઓગણપચાસ",
-    "50": "પચાસ",
-    "51": "એકાવન",
-    "52": "બાવન",
-    "53": "ત્રેપન",
-    "54": "ચોપન",
-    "55": "પંચાવન",
-    "56": "છપ્પન",
-    "57": "સત્તાવન",
-    "58": "અઠ્ઠાવન",
-    "59": "ઓગણસાઠ",
-    "60": "સાઈઠ",
-    "61": "એકસઠ",
-    "62": "બાસઠ",
-    "63": "ત્રેસઠ",
-    "64": "ચોસઠ",
-    "65": "પાંસઠ",
-    "66": "છાસઠ",
-    "67": "સડસઠ",
-    "68": "અડસઠ",
-    "69": "અગણોસિત્તેર",
-    "70": "સિત્તેર",
-    "71": "એકોતેર",
-    "72": "બોતેર",
-    "73": "તોતેર",
-    "74": "ચુમોતેર",
-    "75": "પંચોતેર",
-    "76": "છોતેર",
-    "77": "સિત્યોતેર",
-    "78": "ઇઠ્યોતેર",
-    "79": "ઓગણાએંસી",
-    "80": "એંસી",
-    "81": "એક્યાસી",
-    "82": "બ્યાસી",
-    "83": "ત્યાસી",
-    "84": "ચોર્યાસી",
-    "85": "પંચાસી",
-    "86": "છ્યાસી",
-    "87": "સિત્યાસી",
-    "88": "ઈઠ્યાસી",
-    "89": "નેવ્યાસી",
-    "90": "નેવું",
-    "91": "એકાણું",
-    "92": "બાણું",
-    "93": "ત્રાણું",
-    "94": "ચોરાણું",
-    "95": "પંચાણું",
-    "96": "છન્નું",
-    "97": "સત્તાણું",
-    "98": "અઠ્ઠાણું",
-    "99": "નવ્વાણું",
-    "100": "સો",
-    "1000": "હજાર",
-    "100000": "લાખ",
-    "1000000": "દસ લાખ",
-    "10000000": "કરોડ઼",
-}  # Gujarati
-num_dict["mr"] = {
-    "0": "शून्य",
-    "1": "एक",
-    "2": "दोन",
-    "3": "तीन",
-    "4": "चार",
-    "5": "पाच",
-    "6": "सहा",
-    "7": "सात",
-    "8": "आठ",
-    "9": "नऊ",
-    "10": "दहा",
-    "11": "अकरा",
-    "12": "बारा",
-    "13": "तेरा",
-    "14": "चौदा",
-    "15": "पंधरा",
-    "16": "सोळा",
-    "17": "सतरा",
-    "18": "अठरा",
-    "19": "एकोणीस",
-    "20": "वीस",
-    "21": "एकवीस",
-    "22": "बावीस",
-    "23": "तेवीस",
-    "24": "चोवीस",
-    "25": "पंचवीस",
-    "26": "सव्वीस",
-    "27": "सत्तावीस",
-    "28": "अठ्ठावीस",
-    "29": "एकोणतीस",
-    "30": "तीस",
-    "31": "एकतीस",
-    "32": "बत्तीस",
-    "33": "तेहेतीस",
-    "34": "चौतीस",
-    "35": "पस्तीस",
-    "36": "छत्तीस",
-    "37": "सदतीस",
-    "38": "अडतीस",
-    "39": "एकोणचाळीस",
-    "40": "चाळीस",
-    "41": "एक्केचाळीस",
-    "42": "बेचाळीस",
-    "43": "त्रेचाळीस",
-    "44": "चव्वेचाळीस",
-    "45": "पंचेचाळीस",
-    "46": "सेहेचाळीस",
-    "47": "सत्तेचाळीस",
-    "48": "अठ्ठेचाळीस",
-    "49": "एकोणपन्नास",
-    "50": "पन्नास",
-    "51": "एक्कावन्न",
-    "52": "बावन्न",
-    "53": "त्रेपन्न",
-    "54": "चोपन्न",
-    "55": "पंचावन्न",
-    "56": "छप्पन्न",
-    "57": "सत्तावन्न",
-    "58": "अठ्ठावन्न",
-    "59": "एकोणसाठ",
-    "60": "साठ",
-    "61": "एकसष्ठ",
-    "62": "बासष्ठ",
-    "63": "त्रेसष्ठ",
-    "64": "चौसष्ठ",
-    "65": "पासष्ठ",
-    "66": "सहासष्ठ",
-    "67": "सदुसष्ठ",
-    "68": "अडुसष्ठ",
-    "69": "एकोणसत्तर",
-    "70": "सत्तर",
-    "71": "एक्काहत्तर",
-    "72": "बाहत्तर",
-    "73": "त्र्याहत्तर",
-    "74": "चौर्‍याहत्तर",
-    "75": "पंच्याहत्तर",
-    "76": "शहात्तर",
-    "77": "सत्याहत्तर",
-    "78": "अठ्ठ्याहत्तर",
-    "79": "एकोण ऐंशी",
-    "80": "ऐंशी",
-    "81": "एक्क्याऐंशी",
-    "82": "ब्याऐंशी",
-    "83": "त्र्याऐंशी",
-    "84": "चौऱ्याऐंशी",
-    "85": "पंच्याऐंशी",
-    "86": "शहाऐंशी",
-    "87": "सत्त्याऐंशी",
-    "88": "अठ्ठ्याऐंशी",
-    "89": "एकोणनव्वद",
-    "90": "नव्वद",
-    "91": "एक्क्याण्णव",
-    "92": "ब्याण्णव",
-    "93": "त्र्याण्णव",
-    "94": "चौऱ्याण्णव",
-    "95": "पंच्याण्णव",
-    "96": "शहाण्णव",
-    "97": "सत्त्याण्णव",
-    "98": "अठ्ठ्याण्णव",
-    "99": "नव्व्याण्णव",
-    "100": "शे",
-    "1000": "हजार",
-    "100000": "लाख",
-    "10000000": "कोटी",
-    "1000000000": "अब्ज",
-}  # Marathi
-num_dict["bn"] = {
-    "0": "শূন্য",
-    "1": "এক",
-    "2": "দুই",
-    "3": "তিন",
-    "4": "চার",
-    "5": "পাঁচ",
-    "6": "ছয়",
-    "7": "সাত",
-    "8": "আট",
-    "9": "নয়",
-    "10": "দশ",
-    "11": "এগার",
-    "12": "বার",
-    "13": "তের",
-    "14": "চৌদ্দ",
-    "15": "পনের",
-    "16": "ষোল",
-    "17": "সতের",
-    "18": "আঠার",
-    "19": "ঊনিশ",
-    "20": "বিশ",
-    "21": "একুশ",
-    "22": "বাইশ",
-    "23": "তেইশ",
-    "24": "চব্বিশ",
-    "25": "পঁচিশ",
-    "26": "ছাব্বিশ",
-    "27": "সাতাশ",
-    "28": "আঠাশ",
-    "29": "ঊনত্রিশ",
-    "30": "ত্রিশ",
-    "31": "একত্রিশ",
-    "32": "বত্রিশ",
-    "33": "তেত্রিশ",
-    "34": "চৌত্রিশ",
-    "35": "পঁয়ত্রিশ",
-    "36": "ছত্রিশ",
-    "37": "সাঁইত্রিশ",
-    "38": "আটত্রিশ",
-    "39": "ঊনচল্লিশ",
-    "40": "চল্লিশ",
-    "41": "একচল্লিশ",
-    "42": "বিয়াল্লিশ",
-    "43": "তেতাল্লিশ",
-    "44": "চুয়াল্লিশ",
-    "45": "পঁয়তাল্লিশ",
-    "46": "ছেচল্লিশ",
-    "47": "সাতচল্লিশ",
-    "48": "আটচল্লিশ",
-    "49": "ঊনপঞ্চাশ",
-    "50": "পঞ্চাশ",
-    "51": "একান্ন",
-    "52": "বায়ান্ন",
-    "53": "তিপ্পান্ন",
-    "54": "চুয়ান্ন",
-    "55": "পঞ্চান্ন",
-    "56": "ছাপ্পান্ন",
-    "57": "সাতান্ন",
-    "58": "আটান্ন",
-    "59": "ঊনষাট",
-    "60": "ষাট",
-    "61": "একষট্টি",
-    "62": "বাষট্টি",
-    "63": "তেষট্টি",
-    "64": "চৌষট্টি",
-    "65": "পঁয়ষট্টি",
-    "66": "ছেষট্টি",
-    "67": "সাতষট্টি",
-    "68": "আটষট্টি",
-    "69": "ঊনসত্তর",
-    "70": "সত্তর",
-    "71": "একাত্তর",
-    "72": "বাহাত্তর",
-    "73": "তিয়াত্তর",
-    "74": "চুয়াত্তর",
-    "75": "পঁচাত্তর",
-    "76": "ছিয়াত্তর",
-    "77": "সাতাত্তর",
-    "78": "আটাত্তর",
-    "79": "ঊনআশি",
-    "80": "আশি",
-    "81": "একাশি",
-    "82": "বিরাশি",
-    "83": "তিরাশি",
-    "84": "চুরাশি",
-    "85": "পঁচাশি",
-    "86": "ছিয়াশি",
-    "87": "সাতাশি",
-    "88": "আটাশি",
-    "89": "ঊননব্বই",
-    "90": "নব্বই",
-    "91": "একানব্বই",
-    "92": "বিরানব্বই",
-    "93": "তিরানব্বই",
-    "94": "চুরানব্বই",
-    "95": "পঁচানব্বই",
-    "96": "ছিয়ানব্বই",
-    "97": "সাতানব্বই",
-    "98": "আটানব্বই",
-    "99": "নিরানব্বই",
-    "100": "শো",
-    "1000": "হাজার",
-    "100000": "লাখ",
-    "10000000": "কোটি",
-    "1000000000": "একশ’ কোটি",
-}  # Bengali
-num_dict["te"] = {
-    "0": "సున్నా",
-    "1": "ఒకటి",
-    "2": "రెండు",
-    "3": "మూడు",
-    "4": "నాలుగు",
-    "5": "ఐదు",
-    "6": "ఆరు",
-    "7": "ఏడు",
-    "8": "ఎనిమిది",
-    "9": "తొమ్మిది",
-    "10": "పది",
-    "11": "పదకొండు",
-    "12": "పన్నెండు",
-    "13": "పదమూడు",
-    "14": "పద్నాలుగు",
-    "15": "పదిహేను",
-    "16": "పదహారు",
-    "17": "పదిహేడు",
-    "18": "పద్దెనిమిది",
-    "19": "పందొమ్మిది",
-    "20": "ఇరవై",
-    "21": "ఇరవై ఒకటి",
-    "22": "ఇరవై రెండు",
-    "23": "ఇరవై మూడు",
-    "24": "ఇరవై నాలుగు",
-    "25": "ఇరవై ఐదు",
-    "26": "ఇరవై ఆరు",
-    "27": "ఇరవై ఏడు",
-    "28": "ఇరవై ఎనిమిది",
-    "29": "ఇరవై తొమ్మిది",
-    "30": "ముప్పై",
-    "31": "ముప్పై ఒకటి",
-    "32": "ముప్పై రెండు",
-    "33": "ముప్పై మూడు",
-    "34": "ముప్పై నాలుగు",
-    "35": "ముప్పై ఐదు",
-    "36": "ముప్పై ఆరు",
-    "37": "ముప్పై ఏడు",
-    "38": "ముప్పై ఎనిమిది",
-    "39": "ముప్పై తొమ్మిది",
-    "40": "నలభై",
-    "41": "నలభై ఒకటి",
-    "42": "నలభై రెండు",
-    "43": "నలభై మూడు",
-    "44": "నలభై నాలుగు",
-    "45": "నలభై ఐదు",
-    "46": "నలభై ఆరు",
-    "47": "నలభై ఏడు",
-    "48": "నలభై ఎనిమిది",
-    "49": "నలభై తొమ్మిది",
-    "50": "యాభై",
-    "51": "యాభై ఒకటి",
-    "52": "యాభై రెండు",
-    "53": "యాభై మూడు",
-    "54": "యాభై నాలుగు",
-    "55": "యాభై ఐదు",
-    "56": "యాభై ఆరు",
-    "57": "యాభై ఏడు",
-    "58": "యాభై ఎనిమిది",
-    "59": "యాభై తొమ్మిది",
-    "60": "అరవై",
-    "61": "అరవై ఒకటి",
-    "62": "అరవై రెండు",
-    "63": "అరవై మూడు",
-    "64": "అరవై నాలుగు",
-    "65": "అరవై ఐదు",
-    "66": "అరవై ఆరు",
-    "67": "అరవై ఏడు",
-    "68": "అరవై ఎనిమిది",
-    "69": "అరవై తొమ్మిది",
-    "70": "డెబ్బై",
-    "71": "డెబ్బై ఒకటి",
-    "72": "డెబ్బై రెండు",
-    "73": "డెబ్బై మూడు",
-    "74": "డెబ్బై నాలుగు",
-    "75": "డెబ్బై ఐదు",
-    "76": "డెబ్బై ఆరు",
-    "77": "డెబ్బై ఏడు",
-    "78": "డెబ్బై ఎనిమిది",
-    "79": "డెబ్బై తొమ్మిది",
-    "80": "ఎనభై",
-    "81": "ఎనభై ఒకటి",
-    "82": "ఎనభై రెండు",
-    "83": "ఎనభై మూడు",
-    "84": "ఎనభై నాలుగు",
-    "85": "ఎనభై ఐదు",
-    "86": "ఎనభై ఆరు",
-    "87": "ఎనభై ఏడు",
-    "88": "ఎనభై ఎనిమిది",
-    "89": "ఎనభై తొమ్మిది",
-    "90": "తొంభై",
-    "91": "తొంభై ఒకటి",
-    "92": "తొంభై రెండు",
-    "93": "తొంభై మూడు",
-    "94": "తొంభై నాలుగు",
-    "95": "తొంభై ఐదు",
-    "96": "తొంభై ఆరు",
-    "97": "తొంభై ఏడు",
-    "98": "తొంభై ఎనిమిది",
-    "99": "తొంభై తొమ్మిది",
-    "100": "వందల",
-    "1000": "వేల",
-    "100000": "లక్షల",
-    "10000000": "కోట్ల",
-    "1000000000": "బిలియన్",
-}  # Telugu
-num_dict["ta"] = {
-    "0": "பூஜ்ஜியம்",
-    "1": "ஒன்று",
-    "2": "இரண்டு",
-    "3": "மூன்று",
-    "4": "நான்கு",
-    "5": "ஐந்து",
-    "6": "ஆறு",
-    "7": "ஏழு",
-    "8": "எட்டு",
-    "9": "ஒன்பது",
-    "10": "பத்து",
-    "11": "பதினொன்று",
-    "12": "பன்னிரண்டு",
-    "13": "பதிமூன்று",
-    "14": "பதினான்கு",
-    "15": "பதினைந்து",
-    "16": "பதினாறு",
-    "17": "பதினேழு",
-    "18": "பதினெட்டு",
-    "19": "பத்தொன்பது",
-    "20": "இருபது",
-    "21": "இருபது ஒன்று",
-    "22": "இருபத்து இரண்டு",
-    "23": "இருபத்து மூன்று",
-    "24": "இருபத்து நான்கு",
-    "25": "இருபத்து ஐந்து",
-    "26": "இருபத்து ஆறு",
-    "27": "இருபத்து ஏழு",
-    "28": "இருபத்து எட்டு",
-    "29": "இருபத்து ஒன்பது",
-    "30": "முப்பது",
-    "31": "முப்பத்து ஒன்று",
-    "32": "முப்பத்து இரண்டு",
-    "33": "முப்பத்து மூன்று",
-    "34": "முப்பத்து நான்கு",
-    "35": "முப்பத்து ஐந்து",
-    "36": "முப்பத்து ஆறு",
-    "37": "முப்பத்து ஏழு",
-    "38": "முப்பத்து எட்டு",
-    "39": "முப்பத்து ஒன்பது",
-    "40": "நாற்பது",
-    "41": "நாற்பத்து ஒன்று",
-    "42": "நாற்பத்து இரண்டு",
-    "43": "நாற்பத்து மூன்று",
-    "44": "நாற்பத்து நான்கு",
-    "45": "நாற்பத்து ஐந்து",
-    "46": "நாற்பத்து ஆறு",
-    "47": " நாற்பத்து ஏழு",
-    "48": "நாற்பத்து எட்டு",
-    "49": "நாற்பத்து ஒன்பது",
-    "50": "ஐம்பது",
-    "51": "ஐம்பத்து ஒன்று",
-    "52": "ஐம்பத்து இரண்டு",
-    "53": "ஐம்பத்து மூன்று",
-    "54": "ஐம்பத்து நான்கு",
-    "55": "ஐம்பத்து ஐந்து",
-    "56": "ஐம்பத்து ஆறு",
-    "57": "ஐம்பத்து ஏழு",
-    "58": "ஐம்பத்து எட்டு",
-    "59": "ஐம்பத்து ஒன்பது",
-    "60": "அறுபது",
-    "61": "அறுபத்து ஒன்று",
-    "62": "அறுபத்து இரண்டு",
-    "63": "அறுபத்து மூன்று",
-    "64": "அறுபத்து நான்கு",
-    "65": "அறுபத்து ஐந்து",
-    "66": "அறுபத்து ஆறு",
-    "67": "அறுபத்து ஏழு",
-    "68": "அறுபத்து எட்டு",
-    "69": "அறுபத்து ஒன்பது",
-    "70": "எழுபது",
-    "71": "எழுபத்தி ஒன்று",
-    "72": "எழுபத்தி இரண்டு",
-    "73": "எழுபத்தி முச்சக்கர",
-    "74": "எழுபத்தி நான்கு",
-    "75": "எழுபத்தி ஐந்து",
-    "76": "எழுபத்தி ஆறு",
-    "77": "எழுபத்தி ஏழு",
-    "78": "எழுபத்தி எட்டு",
-    "79": "எழுபத்தி ஒன்பது",
-    "80": "எண்பது",
-    "81": "எண்பத்தியொன்று",
-    "82": "எண்பத்திரண்டு",
-    "83": "எண்பத்திமூன்று",
-    "84": "என்பதினான்கு",
-    "85": "என்பதினைந்து",
-    "86": "எண்பத்திஆறு",
-    "87": "எண்பத்திஏழு",
-    "88": "எண்பத்தியெட்டு",
-    "89": "எண்பத்தியொன்பது",
-    "90": "தொன்னூறு",
-    "91": "தொண்ணூற்றியொன்று",
-    "92": "தொண்ணூற்றிரண்டு",
-    "93": "தொண்ணூற்றிமூன்று",
-    "94": "தொண்ணூற்றிநான்கு",
-    "95": "தொண்ணூற்றிஐந்து",
-    "96": "தொண்ணூற்றியாறு",
-    "97": "தொண்ணூற்றியேழு",
-    "98": "தொண்ணூற்றியெட்டு",
-    "99": "தொண்ணூற்றிஒன்பது",
-    "100": "நூறு",
-    "1000": "ஆயிரம்",
-    "100000": "இலட்சம்",
-    "10000000": "கோடி",
-    "1000000000": "பில்லியன்",
-}  # Tamil
-num_dict["kn"] = {
-    "0": "ಸೊನ್ನೆ",
-    "1": "ಒಂದು",
-    "2": "ಎರಡು",
-    "3": "ಮೂರು",
-    "4": "ನಾಲ್ಕು",
-    "5": "ಅಯ್ದು",
-    "6": "ಆರು",
-    "7": "ಏಳು",
-    "8": "ಎಂಟು",
-    "9": "ಒಂಬತ್ತು",
-    "10": "ಹತ್ತು",
-    "11": "ಹನ್ನೊಂದು",
-    "12": "ಹನ್ನೆರಡು",
-    "13": "ಹದಿಮೂರು",
-    "14": "ಹದಿನಾಲ್ಕು",
-    "15": "ಹದಿನೈದು",
-    "16": "ಹದಿನಾರು",
-    "17": "ಹದಿನೇಳು",
-    "18": "ಹದಿನೆಂಟು",
-    "19": "ಹತ್ತೊಂಬತ್ತು",
-    "20": "ಇಪ್ಪತ್ತು",
-    "21": "ಇಪ್ಪತ್ತ್’ಒಂದು",
-    "22": "ಇಪ್ಪತ್ತ್’ಎರಡು",
-    "23": "ಇಪ್ಪತ್ತ್’ಮೂರು",
-    "24": "ಇಪ್ಪತ್ತ್’ನಾಲ್ಕು",
-    "25": "ಇಪ್ಪತ್ತ್’ಐದು",
-    "26": "ಇಪ್ಪತ್ತ್’ಆರು",
-    "27": "ಇಪ್ಪತ್ತ್’ಏಳು",
-    "28": "ಇಪ್ಪತ್ತ್’ಎಂಟು",
-    "29": "ಇಪ್ಪತ್ತ್’ಒಂಬತ್ತು",
-    "30": "ಮೂವತ್ತು",
-    "31": "ಮುವತ್ತ್’ಒಂದು",
-    "32": "ಮುವತ್ತ್’ಎರಡು",
-    "33": "ಮುವತ್ತ್’ಮೂರು",
-    "34": "ಮೂವತ್ತ್’ನಾಲ್ಕು",
-    "35": "ಮೂವತ್ತ್’ಐದು",
-    "36": "ಮೂವತ್ತ್’ಆರು",
-    "37": "ಮೂವತ್ತ್’ಏಳು",
-    "38": "ಮೂವತ್ತ್’ಎಂಟು",
-    "39": "ಮೂವತ್ತ್’ಒಂಬತ್ತು",
-    "40": "ನಲವತ್ತು",
-    "41": "ನಲವತ್ತೊಂದು",
-    "42": "ನಲವತ್ತ್ ಎರಡು",
-    "43": "ನಲವತ್ತ್ ಮೂರು",
-    "44": "ನಲವತ್ತ್ ನಾಲ್ಕು",
-    "45": "ನಲವತ್ತೈದು",
-    "46": "ನಲವತ್ತಾರು",
-    "47": "ನಲವತ್ತೇಳು",
-    "48": "ನಲವತ್ತೆಂಟು",
-    "49": "ನಲವತ್ತೊಂಬತ್ತು",
-    "50": "ಐವತ್ತು",
-    "51": "ಐವತ್ತೊಂದು",
-    "52": "ಐವತ್ತೆರಡು",
-    "53": "ಐವತ್ತಮೂರು",
-    "54": "ಐವತ್ತ್ನಾಲ್ಕು",
-    "55": "ಐವತ್ತೈದು",
-    "56": "ಐವತ್ತಾರು",
-    "57": "ಐವತ್ತೇಳು",
-    "58": "ಐವತ್ತೆಂಟು",
-    "59": "ಐವತ್ತೊಂಬತ್ತು",
-    "60": "ಅರವತ್ತು",
-    "61": "ಅರವತ್ತೊಂದು",
-    "62": "ಅರವತ್ತೆರಡು",
-    "63": "ಅರವತ್ತ್ ಮೂರು",
-    "64": "ಅರವತ್ತ್ ನಾಲ್ಕು",
-    "65": "ಅರವತ್ತೈದು",
-    "66": "ಅರವತ್ತಾರು",
-    "67": "ಅರವತ್ತೇಳು",
-    "68": "ಅರವತ್ತೆಂಟು",
-    "69": "ಅರವತ್ತೊಂಬತ್ತು",
-    "70": "ಎಪ್ಪತ್ತು",
-    "71": "ಎಪ್ಪತ್ತೊಂದು",
-    "72": "ಎಪ್ಪತ್ತೆರಡು",
-    "73": "ಎಪ್ಪತ್ತ್ ಮೂರು",
-    "74": "ಎಪ್ಪತ್ತ್ ನಾಲ್ಕು",
-    "75": "ಎಪ್ಪತ್ತೈದು",
-    "76": "ಎಪ್ಪತ್ತಾರು",
-    "77": "ಎಪ್ಪತ್ತೇಳು",
-    "78": "ಎಪ್ಪತ್ತೆಂಟು",
-    "79": "ಎಪ್ಪತ್ತೊಂಬತ್ತು",
-    "80": "ಎಂಬತ್ತು",
-    "81": "ಎಂಬತ್ತೊಂದು",
-    "82": "ಎಂಬತ್ತೆರಡು",
-    "83": "ಎಂಬತ್ತ್ ಮೂರು",
-    "84": "ಎಂಬತ್ತ್ ನಾಲ್ಕು",
-    "85": "ಎಂಬತ್ತೈದು",
-    "86": "ಎಂಬತ್ತಾರು",
-    "87": "ಎಂಬತ್ತೇಳು",
-    "88": "ಎಂಬತ್ತೆಂಟು",
-    "89": "ಎಂಬತ್ತೊಂಬತ್ತು",
-    "90": "ತೊಂಬತ್ತು",
-    "91": "ತೊಂಬತ್ತೊಂದು",
-    "92": "ತೊಂಬತ್ತೆರಡು",
-    "93": "ತೊಂಬತ್ತ ಮೂರು",
-    "94": "ತೊಂಬತ್ತ ನಾಲ್ಕು",
-    "95": "ತೊಂಬತ್ತೈದು",
-    "96": "ತೊಂಬತ್ತಾರು",
-    "97": "ತೊಂಬತ್ತೇಳು",
-    "98": "ತೊಂಬತ್ತೆಂಟು",
-    "99": "ತೊಂಬತ್ತೊಂಬತ್ತು",
-    "100": "ನೂರ",
-    "1000": "ಸಾವಿರದ",
-    "100000": "ಲಕ್ಷದ",
-    "10000000": "ಕೋಟಿ",
-    "1000000000": "ಶತಕೋಟಿ",
-}  # Kannada
-num_dict["or"] = {
-    "0": "ଶୁନ୍ୟ",
-    "1": "ଏକ",
-    "2": "ଦୁଇ",
-    "3": "ତିନି",
-    "4": "ଚାରି",
-    "5": "ପାଞ୍ଚ",
-    "6": "ଛଅ",
-    "7": "ସାତ",
-    "8": "ଆଠ",
-    "9": "ନଅ",
-    "10": "ନଅ",
-    "11": "ଏଗାର",
-    "12": "ବାର",
-    "13": "ତେର",
-    "14": "ଚଉଦ",
-    "15": "ପନ୍ଦର",
-    "16": "ଷୋହଳ",
-    "17": "ସତର",
-    "18": "ଅଠର",
-    "19": "ଊଣାଇଶ",
-    "20": "କୋଡିଏ",
-    "21": "ଏକୋଇଶି",
-    "22": "ବାଇଶି",
-    "23": "ତେଇଶି",
-    "24": "ଚବିଶି",
-    "25": "ପଚିଶି",
-    "26": "ଛବିଶି",
-    "27": "ସତାଇଶି",
-    "28": "ଅଠାଇଶି",
-    "29": "ଅଣତିରିଶି",
-    "30": "ତିରିଶି",
-    "31": "ଏକତିରିଶି",
-    "32": "ବତିଶି",
-    "33": "ତେତିଶି",
-    "34": "ଚଉତିରିଶି",
-    "35": "ପଞ୍ଚତିରିଶି",
-    "36": "ଛତିଶି",
-    "37": "ସଂଇତିରିଶି",
-    "38": "ଅଠତିରିଶି",
-    "39": "ଅଣଚାଳିଶି",
-    "40": "ଚାଳିଶି",
-    "41": "ଏକଚାଳିଶି",
-    "42": "ବୟାଳିଶି",
-    "43": "ତେୟାଳିଶି",
-    "44": "ଚଉରାଳିଶି",
-    "45": "ପଞ୍ଚଚାଳିଶି",
-    "46": "ଛୟାଳିଶି",
-    "47": "ସତଚାଳିଶି",
-    "48": "ଅଠଚାଳିଶି",
-    "49": "ଅଣଚାଶ",
-    "50": "ପଚାଶ",
-    "51": "ଏକାବନ",
-    "52": "ବାଉନ",
-    "53": "ତେପନ",
-    "54": "ଚଉବନ",
-    "55": "ପଞ୍ଚାବନ",
-    "56": "ଛପନ",
-    "57": "ସତାବନ",
-    "58": "ଅଠାବନ",
-    "59": "ଅଣଷଠି",
-    "60": "ଷାଠିଏ",
-    "61": "ଏକଷଠି",
-    "62": "ବାଷଠି",
-    "63": "ତେଷଠି",
-    "64": "ଚଉଷଠି",
-    "65": "ପଞ୍ଚଷଠି",
-    "66": "ଛଅଷଠି",
-    "67": "ସତଷଠି",
-    "68": "ଅଠଷଠି",
-    "69": "ଅଣସ୍ତରୀ",
-    "70": "ସତୂରୀ",
-    "71": "ଏକସ୍ତରୀ",
-    "72": "ବାସ୍ତରୀ",
-    "73": "ତେସ୍ତରୀ",
-    "74": "ଚଉସ୍ତରୀ",
-    "75": "ପଞ୍ଚସ୍ତରୀ",
-    "76": "ଛଅସ୍ତରୀ",
-    "77": "ସତସ୍ତରୀ",
-    "78": "ଅଠସ୍ତରୀ",
-    "79": "ଅଣାଅଶୀ",
-    "80": "ଅଶୀ",
-    "81": "ଏକାଅଶୀ",
-    "82": "ବୟାଅଶୀ",
-    "83": "ତେୟାଅଶୀ",
-    "84": "ଚଉରାଅଶୀ",
-    "85": "ପଞ୍ଚାଅଶୀ",
-    "86": "ଛୟାଅଶୀ",
-    "87": "ସତାଅଶୀ",
-    "88": "ଅଠାଅଶୀ",
-    "89": "ଅଣାନବେ",
-    "90": "ନବେ",
-    "91": "ଏକାନବେ",
-    "92": "ବୟାନବେ",
-    "93": "ତେୟାନବେ",
-    "94": "ଚଉରାନବେ",
-    "95": "ପଞ୍ଚାନବେ",
-    "96": "ଛୟାନବେ",
-    "97": "ସତାନବେ",
-    "98": "ଅଠାନବେ",
-    "99": "ଅନେଶତ",
-    "100": "ଶହେ",
-    "1000": "ହଜାର",
-    "100000": "ଲକ୍ଷ",
-    "10000000": "କୋଟି",
-    "1000000000": "କୋଟି",
-}  # Oriya
-num_dict["pa"] = {
-    "0": "ਸਿਫਰ ",
-    "1": "ਇੱਕ",
-    "2": "ਦੋ",
-    "3": "ਤਿੰਨ",
-    "4": "ਚਾਰ",
-    "5": "ਪੰਜ",
-    "6": "ਛੇ",
-    "7": "ਸੱਤ",
-    "8": "ਅੱਠ",
-    "9": "ਨੌਂ",
-    "10": "ਦੱਸ",
-    "11": "ਗਿਆਰਾਂ",
-    "12": "ਬਾਰਾਂ",
-    "13": "ਤੇਰਾਂ",
-    "14": "ਚੌਦਾਂ",
-    "15": "ਪੰਦਰਾਂ",
-    "16": "ਸੋਲ਼ਾਂ",
-    "17": "ਸਤਾਰਾਂ",
-    "18": "ਅਠਾਰਾਂ",
-    "19": "ਉਨੀ",
-    "20": "ਵੀਹ",
-    "21": "ਇੱਕੀ",
-    "22": "ਬਾਈ",
-    "23": "ਤੇਈ",
-    "24": "ਚੌਵੀ",
-    "25": "ਪੰਝੀ",
-    "26": "ਛੱਬੀ",
-    "27": "ਸਤਾਈ",
-    "28": "ਅਠਾਈ",
-    "29": "ਉਨੱਤੀ",
-    "30": "ਤੀਹ",
-    "31": "ਇਕੱਤੀ",
-    "32": "ਬੱਤੀ",
-    "33": "ਤੇਤੀ",
-    "34": "ਚੌਂਤੀ",
-    "35": "ਪੈਂਤੀ",
-    "36": "ਛੱਤੀ",
-    "37": "ਸੈਂਤੀ",
-    "38": "ਅਠੱਤੀ",
-    "39": "ਉਨਤਾਲੀ",
-    "40": "ਚਾਲੀ",
-    "41": "ਇਕਤਾਲੀ",
-    "42": "ਬਤਾਲੀ",
-    "43": "ਤਰਤਾਲੀ",
-    "44": "ਚੌਤਾਲੀ",
-    "45": "ਪੰਜਤਾਲੀ",
-    "46": "ਛਿਆਲੀ",
-    "47": "ਸੰਤਾਲੀ",
-    "48": "ਅੱਠਤਾਲੀ",
-    "49": "ਉਣਿੰਜਾ",
-    "50": "ਪੰਜਾਹ",
-    "51": "ਇਕਵਿੰਜਾ",
-    "52": "ਬਵਿੰਜਾ",
-    "53": "ਤਰਵਿੰਜਾ",
-    "54": "ਚਰਿੰਜਾ",
-    "55": "ਪਚਵਿੰਜਾ",
-    "56": "ਛਪਿੰਜਾ",
-    "57": "ਸਤਵਿੰਜਾ",
-    "58": "ਅੱਠਵਿੰਜਾ",
-    "59": "ਉਣਾਠ",
-    "60": "ਸੱਠ",
-    "61": "ਇਕਾਠ",
-    "62": "ਬਾਠ੍ਹ",
-    "63": "ਤਰੇਠ੍ਹ",
-    "64": "ਚੌਠ੍ਹ",
-    "65": "ਪੈਂਠ",
-    "66": "ਛਿਆਠ",
-    "67": "ਸਤਾਹਠ",
-    "68": "ਅੱਠਾਠ",
-    "69": "ਉਣੱਤਰ",
-    "70": "ਸੱਤਰ",
-    "71": "ਇਕ੍ਹੱਤਰ",
-    "72": "ਬਹੱਤਰ",
-    "73": "ਤਹੱਤਰ",
-    "74": "ਚੌਹੱਤਰ",
-    "75": "ਪੰਜੱਤਰ",
-    "76": "ਛਿਹੱਤਰ",
-    "77": "ਸਤੱਤਰ",
-    "78": "ਅਠੱਤਰ",
-    "79": "ਉਣਾਸੀ",
-    "80": "ਅੱਸੀ",
-    "81": "ਇਕਾਸੀ",
-    "82": "ਬਿਆਸੀ",
-    "83": "ਤਰਾਸੀ",
-    "84": "ਚਰਾਸੀ",
-    "85": "ਪੰਜਾਸੀ",
-    "86": "ਛਿਆਸੀ",
-    "87": "ਸਤਾਸੀ",
-    "88": "ਅਠਾਸੀ",
-    "89": "ਉਣਾਨਵੇਂ",
-    "90": "ਨੱਬੇ",
-    "91": "ਇਕਾਨਵੇਂ",
-    "92": "ਬਿਆਨਵੇਂ",
-    "93": "ਤਰਾਨਵੇਂ",
-    "94": "ਚਰਾਨਵੇਂ",
-    "95": "ਪਚਾਨਵੇਂ",
-    "96": "ਛਿਆਨਵੇਂ",
-    "97": "ਸਤਾਨਵੇਂ",
-    "98": "ਅਠਾਨਵੇਂ",
-    "99": "ਨਿੜਾਨਵੇਂ",
-    "100": "ਸੌ",
-    "1000": "ਹਜਾਰ",
-    "100000": "ਲੱਖ",
-    "10000000": "ਕਰੋੜ",
-    "1000000000": "ਅਰਬ",
-}  # Punjabi
-
-# --------------------------- num_to_word.py ------------------------------
-"""
-Method to convert Numbers to Words
-for indian languages
-
-Use cases:-
-1) Speech recognition pre-processing
-2) Language modeling Data pre-processing
-
--------------------------
-check indic_numbers.py to add support
-for any indian language
-"""
-
-
-def language_specific_exception(words, lang, combiner):
-    """
-    Language Specific Exception will come here
-    """
-
-    def occurs_at_end(piece):
-        return words[-len(piece) :] == piece
-
-    if lang == "mr":
-        words = words.replace("एक" + combiner + "शे", "शंभर")
-    elif lang == "gu":
-        words = words.replace("બે" + combiner + "સો", "બસ્સો")
-    elif lang == "te":
-        exception_dict = {
-            "1": "ఒక",
-            "100": "వంద",
-            "100+": "వందలు",
-            "1000": "వెయ్యి",
-            "1000+": "వేలు",
-            "100000": "లక్ష",
-            "100000+": "లక్షలు",
-            "10000000": "కోటి",
-            "10000000+": "కోట్లు",
-        }
-
-        test_case = ["100", "1000", "100000", "10000000"]
-        for test in test_case:
-            test_word = num_dict["te"][test]
-            match = num_dict["te"]["1"] + combiner + test_word
-            # for numbers like : 100, 1000, 100000
-            if words == match:
-                return exception_dict[test]
-            # for numbers like : 200, 4000, 800000
-            elif occurs_at_end(test_word):
-                words = words.replace(test_word, exception_dict[test + "+"])
-            # for numbers like : 105, 1076, 123993
-            elif not occurs_at_end(match):
-                replacement = exception_dict["1"] + combiner + exception_dict[test]
-                words = words.replace(match, replacement)
-
-        # Exception case for 101...199
-        special_case = "ఒక" + combiner + "వంద"
-        words = words.replace(special_case, "నూట")
-    elif lang == "kn":
-        # special case for 100
-        if words == ("ಒಂದು" + combiner + "ನೂರ"):
-            return "ನೂರು"
-        exception_dict = {
-            "ನೂರ": "ನೂರು",
-            "ಸಾವಿರದ": "ಸಾವಿರ",
-            "ಲಕ್ಷದ": "ಲಕ್ಷ",
-            "ಕೋಟಿಯ": "ಕೋಟಿ",
-        }
-        for expt in exception_dict:
-            if occurs_at_end(expt):
-                words = words.replace(expt, exception_dict[expt])
-    return words
-
-
-def num_to_word(num, lang, separator=", ", combiner=" "):
-    """
-    Main Method
-    :param num: Number digits from any indian language
-    :param lang: Language Code from supported Language
-    :param separator: Separator character i.e. separator = '-' --> 'two hundred-sixty'
-    :param combiner: combine number with position i.e. combiner = '-' --> 'two-hundred sixty'
-    :return: UTF-8 String of numbers in words
-    """
-    lang = lang.lower()
-    num = str(num)
-
-    # Load dictionary according to language code
-    assert lang in supported_lang, "Language not supported"
-    num_dic = num_dict[lang]
-
-    # dash default combiner for english-india
-    if (lang == "en") & (combiner == " "):
-        combiner = "-"
-
-    # Remove punctuations from numbers
-    num = str(num).replace(",", "").replace(" ", "")
-    
-    # return word as it is if not number
-    if not num.isdecimal():
-        return num
-
-    # Replace native language numbers with english digits
-    for language in supported_lang:
-        for num_index in range(10):
-            num = num.replace(all_num[language][num_index], all_num["en"][num_index])
-
-    # Assert that input contains only integer number
-    for digit in num:
-        assert digit in all_num["en"], "Give proper input"
-
-    # Process
-    # For Number longer than 9 digits
-    def all_two_digit(digits_2):
-        if len(digits_2) <= 1:  # Provided only one/zero digit
-            return num_dic.get(digits_2, "")
-        elif digits_2 == "00":  # Two Zero provided
-            return num_dic["0"] + separator + num_dic["0"]
-        elif digits_2[0] == "0":  # First digit is zero
-            return num_dic["0"] + separator + num_dic[digits_2[1]]
-        else:  # Both digit provided
-            return num_dic[digits_2]
-
-    # For Number less than 9 digits
-    def two_digit(digits_2):
-        digits_2 = digits_2.lstrip("0")
-        if len(digits_2) != 0:
-            return num_dic[digits_2]
-        else:
-            return ""
-
-    def all_digit(digits):
-        digits = digits.lstrip("0")
-        digit_len = len(digits)
-        if digit_len > 3:
-            num_of_digits_to_process = (digit_len % 2) + 1
-            process_digits = digits[:num_of_digits_to_process]
-            base = str(10 ** (int(digit_len / 2) * 2 - 1))
-            remain_digits = digits[num_of_digits_to_process:]
-            return (
-                num_dic[process_digits]
-                + combiner
-                + num_dic[base]
-                + separator
-                + all_digit(remain_digits)
-            )
-        elif len(digits) == 3:
-            return (
-                num_dic[digits[:1]]
-                + combiner
-                + num_dic["100"]
-                + separator
-                + two_digit(digits[1:])
-            )
-        else:
-            return two_digit(digits)
-
-    num = num.lstrip("0")
-    full_digit_len = len(num)
-
-    if full_digit_len == 0:
-        output = num_dic["0"]
-    elif full_digit_len <= 9:
-        output = all_digit(num)
-    else:
-        iteration = round(full_digit_len / 2)
-        output = all_two_digit(num[:2])  # First to digit
-        for i in range(1, iteration):
-            output = (
-                output + separator + all_two_digit(num[i * 2 : (i + 1) * 2])
-            )  # Next two digit pairs
-        remaining_digits = num[iteration * 2 :]
-        if not all_two_digit(remaining_digits) == "":
-            output = (
-                output + separator + all_two_digit(remaining_digits)
-            )  # remaining Last one/two digits
-
-    output = output.strip(separator)
-
-    output = language_specific_exception(output, lang, combiner)
-
-    return output
-
-
-# --------------------------------- num_to_word_on_a_sent ---------------------------------
-
-
-def is_digit(word, digit_pattern):
-    return re.search(digit_pattern, word)
-
-
-def remove_punct(sent):
-    clean = re.sub("[%s]" % re.escape(string.punctuation), " ", sent)
-    return " ".join([word for word in clean.split() if word])
-
-
-def normalize_nums(text, lang):
-    """
-    text: str (eg)
-    lang: lang code ['en', 'hi']
-
-    returns: str
-    (eg)
-    """
-
-    if lang in supported_lang:
-        text = text.replace('-',' - ') # space separate hyphen
-        words = text.split()
-        lang_digits = [str(i) for i in range(0, 10)]
-
-        digit_pattern = "[" + "".join(lang_digits) + "]"
-        num_indices = [
-            ind for ind, word in enumerate(words) if is_digit(word, digit_pattern)
-        ]
-
-        words_up = [
-            num_to_word(word, lang, separator=" ", combiner=" ")
-            if ind in num_indices
-            else word
-            for ind, word in enumerate(words)
-        ]
-        return " ".join(words_up)
-    else:
-        return text
-
-
-if __name__ == "__main__":
-    print(normalize_nums("रीटा के पास 16 बिल्लियाँ हैं।", "hi"))
diff --git a/spaces/Hellisotherpeople/Unsupervised_Extractive_Summarization/app.py b/spaces/Hellisotherpeople/Unsupervised_Extractive_Summarization/app.py
deleted file mode 100644
index c1386136a442dd361672304900a10db16c6c97b3..0000000000000000000000000000000000000000
--- a/spaces/Hellisotherpeople/Unsupervised_Extractive_Summarization/app.py
+++ /dev/null
@@ -1,152 +0,0 @@
-from re import split
-import streamlit as st
-import more_itertools
-import spacy
-from sentence_transformers import SentenceTransformer, util
-
-st.set_page_config(
-page_title="Semantic_Search/Summarization")
-
-
-choclate_example = """You’d be hard-pressed to find anyone who doesn’t like chocolate. While it's mostly known for its taste (and the associated cravings), it’s also a good source of nutrients when in its pure form and eaten in moderation.
-
-This well-loved food, once called the "drink of the gods" by the Maya people, has a rich history as well. Chocolate comes from the seeds of the cacao pod, which grows on the cacao tree. Theobroma cacao is native to the tropical rainforests of Central America, where it has grown for thousands of years. 
-
-"""
-
-
-
-nlp = spacy.load('en_core_web_sm')
-
-
-
-wit_text = """
-
-                                                              .             .   
-                                                                             . .
-                                                                               .
-                                      . .. . .*/,            .         .       .
-                             .*&#&@@@&@@@@&%&&&@&(.                            .
-                         *#&&@&&@@@@@@@@@%@@@@@@&,...        .  .  .     .     .
-                        #@@@@@@@@@@@@@@@@@@@@@@@@##...   .. ..     .            
-                      ,&@&&@@&&&@@@@@%*........,&@@@&(,,...... .                
-                     .&&&@@&/,......  .    ......(@@@@@&#......   .      .  .   
-                      .&@@&,... ...  .. .   ......#@@@@@&.......     .   .      
-                      .&@@&,... .. .............,,*@@@@@(.......   .         .  
-                      .@@@@/*..****,,*,*(&@@@@%(,...&@@@/.......    . ....      
-                       ,@@&,,/&(&@@@@(..(%(,&%,((,...&@&......... .     . .. .  
-                        (@(..(%**,,(/....,,.............%/........  ..  .  .    
-                        ../...........................(#..........   .  .  .   .
-                       .(.(...,....,/@@&&&#......,,,............ .. .....       
-                        ..,.,,,,.....,,,*,........,..,.......... .. .. .  .     
-                         ../,**,,,.../@@@@@%,.,.,,,,............ . . .       .  
-                        .   .,**,,*#,**#%%/,,,,,,,,,,. ...... .   ..      .     
-                         .  ..**********,......,,,**,. .  ......       .      . 
-                    .     ....,/(/***,,,**,,,,*/((**,...... ..     .            
-                       ..  ....,/#&@@@@@@@@@@&%%#/**,........               .   
-                      ....,/*,.,,**/#%&@@@@%#(%#/*,%..(*#(*..                   
-                   .,(/*,(/#/(..,,,*///(////(((***,..*###%/%/(/,,.       .      
-            ....,,*(((**/**/#(...,.,,*/(##/,,***/.@..%&#%%///(##/,*,,,.  .      
-         .....,,,*%(,&%(/////#*....,.,,***,,,*/(.@..&&%&#%/***#%(,(((*,*,,. ..  
-     ..*...,,,*,#%,*%(/*(/*#(##..&,..,,.,**,,*/,.#.&&&&&###((//%%/*****,,,,*.,, 
-   ...,,(*,,.,.*&&&%((**#((/(/&,.&..../(////,,..@@@@&&&&%(//,*(##(**/*,*,,/****,
-   ,,/**/#(//*(%%/%@@@%(*/(/%##/,@.....,,*,...(@@@&%%(##(*/@@&((/(#/*/((%/*//***
-  ,,/&/(#(###*/#&**#/%#///*//#//&@(...  ....&@&&&###(%#####/%((*(#%%(#&&#%,((%&/
- .,(,,*&%*/%&#(&((//#/,*/*/(/*//#&&/,.&.. ./&(((%%%#((%/(#(##,%*#/((%@%(##%&&*/(
-.*,,**@#**%%&&#%%#(#/*/***/*/**(//&... ,  .%####%%&%(#/(%/(/(%&%#(#%@%%(&@(,*%%(
-***(@%,,&@&##&%(@(/(//,,///(//*/**%.. ....#((###%&##(/%(&,(&@&#%###&&%@/@&(&%#&#
-**/#&(((&&#@@@#%%&*/(**///(/,/,**/*.. ,*##/(((&%(%%#((%%&@@@#####&&@@&(%&@&#/(%%
-(%#(*,%@@@(@@@&%%@&//(//(////**,/(#(,*(&%(%#%%&(%%#&(%(#@@@@&&%%%&@@@&/(&&&/*%&%
-,*#&@@@@@&/(%@@%&&&%#(#//#/#/****##%*/*/*%&&%&&%%#%%(%@@@&@#%%&&@@@&&@@*#%&#(***
-#(/***//%(((((&@&&&@&(/#(**(*((//**#/,/%&%&%&&%&##%%&&&@@@&#%&&&@@%(#*(*((%(%%&%
-*,#%&&&@%&&&%(&@@&&&%&/%//(***/(///%(@&#&&&%&&&%&%@&&&##@@@&&&@@@&&((((###%(#/(*
-/#**..,,*,/*#&@&@@&%&@%#(((#**(/(//%@@#&@#&&%#%%@@&&@@@@@@&@@@@@&#(###%##%%(%&#/
-#%&%(,*,##&&&&&@@@&&@&&#*/*/(/*((//#@&(@&&%&&%&@&&&&@&@@@@@@&&@&&%#%(%(%#&%%#/(%
-***,/((&%(/(**//&@@&@@@@(*/(,((#/((&@%%&#%&&&@&&#&&@@@@@@&@&@@@&((#&%#%%////(###
-*,/*/#&&@@&@&*,%@@@@@@@&&(**#*(*#%#&@%%&%#&@@@@&@&&&@@@@@@@@@@@%%%%#//(*/#/#(%%%
-#%#%&#&%%*,/##&@@@@@@&&&&&#(*#//#%&&&%&(/&@&@@@@@@&&&&@@@@@@@@@&%(%%(%(##%*//(%&
-&&%(**/%&&&%&&@&@&@@@%&&@@@#####%&&@##//&%%%&@&&@&@@&@&&@@@@@@&&&%#/##(%##%&&&&#
-**(#&%&%%&&%&&@@@@@@@@@@@@@@&%&%@@&&#%(@&&&&%&&&&@@@&&&@@@@@@@@/*%%%&&&&&%%%%##/
-%&&%&%/***%&@@@&&@@@@@@&&@&&&@&&@@@%(%&&&&%%&&%&&&%@@@@@@@@@@@@&&&@&&%/,***,***/
-
-The limits of my language means the limits of my world
-
- - Ludwig Wittgenstein
-"""
-
-
-st.title("Unsupervised Extractive Text Summarization and Semantic Search - by Allen Roush!")
-st.caption("Under active development! Please contact me or drop an issue on https://github.com/Hellisotherpeople/CX_DB8")
-
-with st.expander("Dedicated to the late..."):
-    st.text(wit_text)
-
-
-model_name = st.text_area("Enter the name of the pre-trained model from sentence transformers that we are using for summarization", value = "paraphrase-MiniLM-L3-v2")
-st.caption("This will download a new model, so it may take awhile or even break if the model is too large")
-st.caption("See the list of pre-trained models that are available here! https://www.sbert.net/docs/pretrained_models.html")
-embedder = SentenceTransformer(model_name)
-
-
-granularity = st.radio("What level of granularity do you want to summarize at?", ('Sentence', 'Word', 'Paragraph'))
-
-y = st.text_area('enter a query', value = "I love chocolate")
-x = st.text_area('Enter a document', value = choclate_example)
-percentage = st.number_input("Enter the percentage of the text you want highlighted", max_value = 0.99, min_value = 0.01, value = 0.3)
-
-query_embedding = embedder.encode(y, convert_to_tensor=True)
-
-if granularity == "Sentence":
-    doc = nlp(x)
-    doc_sents = [str(sent) for sent in doc.sents]
-    corpus_embeddings = embedder.encode(doc_sents, convert_to_tensor=True)
-    len_doc = len(doc_sents)
-elif granularity == "Word":
-    split_words = x.split()
-    ww_size = st.number_input("What size do you want the word window to be?", value = 3, min_value = 1, max_value=int(len(split_words)/4))
-    window_words = list(more_itertools.windowed(split_words, ww_size)) ###Doesn't seem to be impacting anything??? Must be related to implementation of semantic search by sentence transformers... 
-    len_doc = len(window_words)
-    corpus_embeddings = embedder.encode(window_words, convert_to_tensor=True)
-elif granularity == "Paragraph":
-    split_lines = x.splitlines()
-    corpus_embeddings = embedder.encode(split_lines, convert_to_tensor=True)
-    len_doc = len(split_lines)
-
-semantic_search_results = util.semantic_search(query_embedding, corpus_embeddings, top_k= int(percentage * len_doc))
-
-list_of_selected_extracts = []
-for extract in semantic_search_results[0]:
-    list_of_selected_extracts.append(extract["corpus_id"])
-
-if granularity == "Sentence":
-    original_granularity = doc_sents
-elif granularity == "Word":
-    original_granularity = split_words
-elif granularity == "Paragraph":
-    original_granularity = split_lines
-
-string_to_print = ""
-for count, chunk in enumerate(original_granularity):
-    if count in list_of_selected_extracts:
-        string_to_add = "\u0332".join(" " + chunk)
-        string_to_print += string_to_add
-    else:
-        string_to_print += " "
-        string_to_print += chunk
-
-st.subheader("Output summary")
-st.write(string_to_print)
-
-st.subheader("Raw results")
-st.caption("corpus_id is the number of the word, sentence, or paragraph. Score is the raw cosine similarty score between the document and the query")
-st.write(semantic_search_results[0])
-
-st.subheader("Results of segmentation/tokenization")
-st.caption("This shows the representation that the webapp gets of the input document. Useful for debugging if you get strange output")
-
-if granularity == "Sentence":
-    st.write(doc_sents)
-elif granularity == "Word":
-    st.write(window_words)
-elif granularity == "Paragraph":
-    st.write(split_lines)
\ No newline at end of file
diff --git a/spaces/IDKiro/DehazeFormer_Demo/README.md b/spaces/IDKiro/DehazeFormer_Demo/README.md
deleted file mode 100644
index f151c30777b4d9ee168bc3bb62581b67833600bc..0000000000000000000000000000000000000000
--- a/spaces/IDKiro/DehazeFormer_Demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: DehazeFormer Demo
-emoji: 🌖
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/preprocess_flist_config.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/preprocess_flist_config.py
deleted file mode 100644
index 6e3dd0bd9390a509c282bbde4ff2631ac94404e4..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/preprocess_flist_config.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import os
-import argparse
-import re
-
-from tqdm import tqdm
-from random import shuffle
-import json
-
-config_template = json.load(open("configs/config.json"))
-
-pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
-    parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
-    parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
-    parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
-    args = parser.parse_args()
-    
-    train = []
-    val = []
-    test = []
-    idx = 0
-    spk_dict = {}
-    spk_id = 0
-    for speaker in tqdm(os.listdir(args.source_dir)):
-        spk_dict[speaker] = spk_id
-        spk_id += 1
-        wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
-        for wavpath in wavs:
-            if not pattern.match(wavpath):
-                print(f"warning：文件名{wavpath}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
-        if len(wavs) < 10:
-            print(f"warning：{speaker}数据集数量小于10条，请补充数据")
-        wavs = [i for i in wavs if i.endswith("wav")]
-        shuffle(wavs)
-        train += wavs[2:-2]
-        val += wavs[:2]
-        test += wavs[-2:]
-
-    shuffle(train)
-    shuffle(val)
-    shuffle(test)
-            
-    print("Writing", args.train_list)
-    with open(args.train_list, "w") as f:
-        for fname in tqdm(train):
-            wavpath = fname
-            f.write(wavpath + "\n")
-        
-    print("Writing", args.val_list)
-    with open(args.val_list, "w") as f:
-        for fname in tqdm(val):
-            wavpath = fname
-            f.write(wavpath + "\n")
-            
-    print("Writing", args.test_list)
-    with open(args.test_list, "w") as f:
-        for fname in tqdm(test):
-            wavpath = fname
-            f.write(wavpath + "\n")
-
-    config_template["spk"] = spk_dict
-    print("Writing configs/config.json")
-    with open("configs/config.json", "w") as f:
-        json.dump(config_template, f, indent=2)
diff --git a/spaces/Illumotion/Koboldcpp/gguf-py/README.md b/spaces/Illumotion/Koboldcpp/gguf-py/README.md
deleted file mode 100644
index a28d8c57adc7d010a108a94a150b6511c64c9eca..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/gguf-py/README.md
+++ /dev/null
@@ -1,71 +0,0 @@
-## gguf
-
-This is a Python package for writing binary files in the [GGUF](https://github.com/ggerganov/ggml/pull/302)
-(GGML Universal File) format.
-
-See [convert-llama-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-llama-hf-to-gguf.py)
-as an example for its usage.
-
-## Installation
-```sh
-pip install gguf
-```
-
-## Development
-Maintainers who participate in development of this package are advised to install it in editable mode:
-
-```sh
-cd /path/to/llama.cpp/gguf-py
-
-pip install --editable .
-```
-
-**Note**: This may require to upgrade your Pip installation, with a message saying that editable installation currently requires `setup.py`.
-In this case, upgrade Pip to the latest:
-
-```sh
-pip install --upgrade pip
-```
-
-## Automatic publishing with CI
-
-There's a GitHub workflow to make a release automatically upon creation of tags in a specified format.
-
-1. Bump the version in `pyproject.toml`.
-2. Create a tag named `gguf-vx.x.x` where `x.x.x` is the semantic version number.
-
-```sh
-git tag -a gguf-v1.0.0 -m "Version 1.0 release"
-```
-
-3. Push the tags.
-
-```sh
-git push origin --tags
-```
-
-## Manual publishing
-If you want to publish the package manually for any reason, you need to have `twine` and `build` installed:
-
-```sh
-pip install build twine
-```
-
-Then, folow these steps to release a new version:
-
-1. Bump the version in `pyproject.toml`.
-2. Build the package:
-
-```sh
-python -m build
-```
-
-3. Upload the generated distribution archives:
-
-```sh
-python -m twine upload dist/*
-```
-
-## TODO
-- [ ] Add tests
-- [ ] Include conversion scripts as command line entry points in this package.
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/data/datasets.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/data/datasets.py
deleted file mode 100644
index c4f503dafffb970d8dbaca33934da417036d1e55..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/data/datasets.py
+++ /dev/null
@@ -1,304 +0,0 @@
-import glob
-import logging
-import os
-import random
-
-import albumentations as A
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-import webdataset
-from omegaconf import open_dict, OmegaConf
-from skimage.feature import canny
-from skimage.transform import rescale, resize
-from torch.utils.data import Dataset, IterableDataset, DataLoader, DistributedSampler, ConcatDataset
-
-from saicinpainting.evaluation.data import InpaintingDataset as InpaintingEvaluationDataset, \
-    OurInpaintingDataset as OurInpaintingEvaluationDataset, ceil_modulo, InpaintingEvalOnlineDataset
-from saicinpainting.training.data.aug import IAAAffine2, IAAPerspective2
-from saicinpainting.training.data.masks import get_mask_generator
-
-LOGGER = logging.getLogger(__name__)
-
-
-class InpaintingTrainDataset(Dataset):
-    def __init__(self, indir, mask_generator, transform):
-        self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True))
-        self.mask_generator = mask_generator
-        self.transform = transform
-        self.iter_i = 0
-
-    def __len__(self):
-        return len(self.in_files)
-
-    def __getitem__(self, item):
-        path = self.in_files[item]
-        img = cv2.imread(path)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = self.transform(image=img)['image']
-        img = np.transpose(img, (2, 0, 1))
-        # TODO: maybe generate mask before augmentations? slower, but better for segmentation-based masks
-        mask = self.mask_generator(img, iter_i=self.iter_i)
-        self.iter_i += 1
-        return dict(image=img,
-                    mask=mask)
-
-
-class InpaintingTrainWebDataset(IterableDataset):
-    def __init__(self, indir, mask_generator, transform, shuffle_buffer=200):
-        self.impl = webdataset.Dataset(indir).shuffle(shuffle_buffer).decode('rgb').to_tuple('jpg')
-        self.mask_generator = mask_generator
-        self.transform = transform
-
-    def __iter__(self):
-        for iter_i, (img,) in enumerate(self.impl):
-            img = np.clip(img * 255, 0, 255).astype('uint8')
-            img = self.transform(image=img)['image']
-            img = np.transpose(img, (2, 0, 1))
-            mask = self.mask_generator(img, iter_i=iter_i)
-            yield dict(image=img,
-                       mask=mask)
-
-
-class ImgSegmentationDataset(Dataset):
-    def __init__(self, indir, mask_generator, transform, out_size, segm_indir, semantic_seg_n_classes):
-        self.indir = indir
-        self.segm_indir = segm_indir
-        self.mask_generator = mask_generator
-        self.transform = transform
-        self.out_size = out_size
-        self.semantic_seg_n_classes = semantic_seg_n_classes
-        self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True))
-
-    def __len__(self):
-        return len(self.in_files)
-
-    def __getitem__(self, item):
-        path = self.in_files[item]
-        img = cv2.imread(path)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = cv2.resize(img, (self.out_size, self.out_size))
-        img = self.transform(image=img)['image']
-        img = np.transpose(img, (2, 0, 1))
-        mask = self.mask_generator(img)
-        segm, segm_classes= self.load_semantic_segm(path)
-        result = dict(image=img,
-                      mask=mask,
-                      segm=segm,
-                      segm_classes=segm_classes)
-        return result
-
-    def load_semantic_segm(self, img_path):
-        segm_path = img_path.replace(self.indir, self.segm_indir).replace(".jpg", ".png")
-        mask = cv2.imread(segm_path, cv2.IMREAD_GRAYSCALE)
-        mask = cv2.resize(mask, (self.out_size, self.out_size))
-        tensor = torch.from_numpy(np.clip(mask.astype(int)-1, 0, None))
-        ohe = F.one_hot(tensor.long(), num_classes=self.semantic_seg_n_classes) # w x h x n_classes
-        return ohe.permute(2, 0, 1).float(), tensor.unsqueeze(0)
-
-
-def get_transforms(transform_variant, out_size):
-    if transform_variant == 'default':
-        transform = A.Compose([
-            A.RandomScale(scale_limit=0.2),  # +/- 20%
-            A.PadIfNeeded(min_height=out_size, min_width=out_size),
-            A.RandomCrop(height=out_size, width=out_size),
-            A.HorizontalFlip(),
-            A.CLAHE(),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),
-            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),
-            A.ToFloat()
-        ])
-    elif transform_variant == 'distortions':
-        transform = A.Compose([
-            IAAPerspective2(scale=(0.0, 0.06)),
-            IAAAffine2(scale=(0.7, 1.3),
-                       rotate=(-40, 40),
-                       shear=(-0.1, 0.1)),
-            A.PadIfNeeded(min_height=out_size, min_width=out_size),
-            A.OpticalDistortion(),
-            A.RandomCrop(height=out_size, width=out_size),
-            A.HorizontalFlip(),
-            A.CLAHE(),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),
-            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),
-            A.ToFloat()
-        ])
-    elif transform_variant == 'distortions_scale05_1':
-        transform = A.Compose([
-            IAAPerspective2(scale=(0.0, 0.06)),
-            IAAAffine2(scale=(0.5, 1.0),
-                       rotate=(-40, 40),
-                       shear=(-0.1, 0.1),
-                       p=1),
-            A.PadIfNeeded(min_height=out_size, min_width=out_size),
-            A.OpticalDistortion(),
-            A.RandomCrop(height=out_size, width=out_size),
-            A.HorizontalFlip(),
-            A.CLAHE(),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),
-            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),
-            A.ToFloat()
-        ])
-    elif transform_variant == 'distortions_scale03_12':
-        transform = A.Compose([
-            IAAPerspective2(scale=(0.0, 0.06)),
-            IAAAffine2(scale=(0.3, 1.2),
-                       rotate=(-40, 40),
-                       shear=(-0.1, 0.1),
-                       p=1),
-            A.PadIfNeeded(min_height=out_size, min_width=out_size),
-            A.OpticalDistortion(),
-            A.RandomCrop(height=out_size, width=out_size),
-            A.HorizontalFlip(),
-            A.CLAHE(),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),
-            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),
-            A.ToFloat()
-        ])
-    elif transform_variant == 'distortions_scale03_07':
-        transform = A.Compose([
-            IAAPerspective2(scale=(0.0, 0.06)),
-            IAAAffine2(scale=(0.3, 0.7),  # scale 512 to 256 in average
-                       rotate=(-40, 40),
-                       shear=(-0.1, 0.1),
-                       p=1),
-            A.PadIfNeeded(min_height=out_size, min_width=out_size),
-            A.OpticalDistortion(),
-            A.RandomCrop(height=out_size, width=out_size),
-            A.HorizontalFlip(),
-            A.CLAHE(),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),
-            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),
-            A.ToFloat()
-        ])
-    elif transform_variant == 'distortions_light':
-        transform = A.Compose([
-            IAAPerspective2(scale=(0.0, 0.02)),
-            IAAAffine2(scale=(0.8, 1.8),
-                       rotate=(-20, 20),
-                       shear=(-0.03, 0.03)),
-            A.PadIfNeeded(min_height=out_size, min_width=out_size),
-            A.RandomCrop(height=out_size, width=out_size),
-            A.HorizontalFlip(),
-            A.CLAHE(),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),
-            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),
-            A.ToFloat()
-        ])
-    elif transform_variant == 'non_space_transform':
-        transform = A.Compose([
-            A.CLAHE(),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),
-            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),
-            A.ToFloat()
-        ])
-    elif transform_variant == 'no_augs':
-        transform = A.Compose([
-            A.ToFloat()
-        ])
-    else:
-        raise ValueError(f'Unexpected transform_variant {transform_variant}')
-    return transform
-
-
-def make_default_train_dataloader(indir, kind='default', out_size=512, mask_gen_kwargs=None, transform_variant='default',
-                                  mask_generator_kind="mixed", dataloader_kwargs=None, ddp_kwargs=None, **kwargs):
-    LOGGER.info(f'Make train dataloader {kind} from {indir}. Using mask generator={mask_generator_kind}')
-
-    mask_generator = get_mask_generator(kind=mask_generator_kind, kwargs=mask_gen_kwargs)
-    transform = get_transforms(transform_variant, out_size)
-
-    if kind == 'default':
-        dataset = InpaintingTrainDataset(indir=indir,
-                                         mask_generator=mask_generator,
-                                         transform=transform,
-                                         **kwargs)
-    elif kind == 'default_web':
-        dataset = InpaintingTrainWebDataset(indir=indir,
-                                            mask_generator=mask_generator,
-                                            transform=transform,
-                                            **kwargs)
-    elif kind == 'img_with_segm':
-        dataset = ImgSegmentationDataset(indir=indir,
-                                         mask_generator=mask_generator,
-                                         transform=transform,
-                                         out_size=out_size,
-                                         **kwargs)
-    else:
-        raise ValueError(f'Unknown train dataset kind {kind}')
-
-    if dataloader_kwargs is None:
-        dataloader_kwargs = {}
-
-    is_dataset_only_iterable = kind in ('default_web',)
-
-    if ddp_kwargs is not None and not is_dataset_only_iterable:
-        dataloader_kwargs['shuffle'] = False
-        dataloader_kwargs['sampler'] = DistributedSampler(dataset, **ddp_kwargs)
-
-    if is_dataset_only_iterable and 'shuffle' in dataloader_kwargs:
-        with open_dict(dataloader_kwargs):
-            del dataloader_kwargs['shuffle']
-
-    dataloader = DataLoader(dataset, **dataloader_kwargs)
-    return dataloader
-
-
-def make_default_val_dataset(indir, kind='default', out_size=512, transform_variant='default', **kwargs):
-    if OmegaConf.is_list(indir) or isinstance(indir, (tuple, list)):
-        return ConcatDataset([
-            make_default_val_dataset(idir, kind=kind, out_size=out_size, transform_variant=transform_variant, **kwargs) for idir in indir 
-        ])
-
-    LOGGER.info(f'Make val dataloader {kind} from {indir}')
-    mask_generator = get_mask_generator(kind=kwargs.get("mask_generator_kind"), kwargs=kwargs.get("mask_gen_kwargs"))
-
-    if transform_variant is not None:
-        transform = get_transforms(transform_variant, out_size)
-
-    if kind == 'default':
-        dataset = InpaintingEvaluationDataset(indir, **kwargs)
-    elif kind == 'our_eval':
-        dataset = OurInpaintingEvaluationDataset(indir, **kwargs)
-    elif kind == 'img_with_segm':
-        dataset = ImgSegmentationDataset(indir=indir,
-                                         mask_generator=mask_generator,
-                                         transform=transform,
-                                         out_size=out_size,
-                                         **kwargs)
-    elif kind == 'online':
-        dataset = InpaintingEvalOnlineDataset(indir=indir,
-                                              mask_generator=mask_generator,
-                                              transform=transform,
-                                              out_size=out_size,
-                                              **kwargs)
-    else:
-        raise ValueError(f'Unknown val dataset kind {kind}')
-
-    return dataset
-
-
-def make_default_val_dataloader(*args, dataloader_kwargs=None, **kwargs):
-    dataset = make_default_val_dataset(*args, **kwargs)
-
-    if dataloader_kwargs is None:
-        dataloader_kwargs = {}
-    dataloader = DataLoader(dataset, **dataloader_kwargs)
-    return dataloader
-
-
-def make_constant_area_crop_params(img_height, img_width, min_size=128, max_size=512, area=256*256, round_to_mod=16):
-    min_size = min(img_height, img_width, min_size)
-    max_size = min(img_height, img_width, max_size)
-    if random.random() < 0.5:
-        out_height = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod))
-        out_width = min(max_size, ceil_modulo(area // out_height, round_to_mod))
-    else:
-        out_width = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod))
-        out_height = min(max_size, ceil_modulo(area // out_width, round_to_mod))
-
-    start_y = random.randint(0, img_height - out_height)
-    start_x = random.randint(0, img_width - out_width)
-    return (start_y, start_x, out_height, out_width)
diff --git a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/data/merge.py b/spaces/Intel/NeuralChat-ICX-INT4/fastchat/data/merge.py
deleted file mode 100644
index ea5b8a93b3872f20e8a285465709c09549b76b89..0000000000000000000000000000000000000000
--- a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/data/merge.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""
-Merge two conversation files into one
-
-Usage: python3 -m fastchat.data.merge --in file1.json file2.json --out merged.json
-"""
-
-import argparse
-import json
-from typing import Dict, Sequence, Optional
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--in-file", type=str, required=True, nargs="+")
-    parser.add_argument("--out-file", type=str, default="merged.json")
-    args = parser.parse_args()
-
-    new_content = []
-    for in_file in args.in_file:
-        content = json.load(open(in_file, "r"))
-        new_content.extend(content)
-
-    json.dump(new_content, open(args.out_file, "w"), indent=2)
diff --git a/spaces/IoMa/stable-diffusion-webui-cpu/app.py b/spaces/IoMa/stable-diffusion-webui-cpu/app.py
deleted file mode 100644
index 97ae384e8328400efae790b1ac23c035e070d7db..0000000000000000000000000000000000000000
--- a/spaces/IoMa/stable-diffusion-webui-cpu/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import os
-from sys import executable as pyexecutable
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:str = "") -> int :
-  if(ClonePath == "") :
-    while True:
-      i=subprocess.run([r"git",r"clone",URI])
-      if(i.returncode == 0 ): 
-       del i
-       gc.collect()
-       return 0
-      else :
-       del i
-  else: 
-    while True:
-      i=subprocess.run([r"git",r"clone",URI,ClonePath])
-      if(i.returncode == 0 ): 
-       del i
-       gc.collect()
-       return 0
-      else :
-       del i
-def DownLoad(URI:str,DownloadPath:str,DownLoadFileName:str ) -> int:
-  while (True):
-    i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",DownloadPath,r"-o",DownLoadFileName,URI]);
-    if(i.returncode == 0 ): 
-      del i
-      gc.collect()
-      return 0
-    else :
-      del i
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",str(user_home / r"stable-diffusion-webui"))
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard 89f9faa63388756314e8a1d96cf86bf5e0663045")
-#
-
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",str(user_home / r"stable-diffusion-webui" / r"embeddings"  / r"negative"))
-Gitclone(r"https://huggingface.co/embed/lora",str(user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive"))
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",str(user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN") ,r"4x-UltraSharp.pth")
-while True: 
-  if(subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")]).returncode == 0):
-    break
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" ))
-Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",str(user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser"))
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface"))
-Gitclone(r"https://github.com/camenduru/sd-civitai-browser",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-civitai-browser"))
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks"))
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet"))
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor"))
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib"))
-Gitclone(r"https://github.com/hnmr293/posex",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"posex"))
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor"))
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN"))
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete"))
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels"))
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui"))
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin"))
-
-#Gitclone(r"https://github.com/KohakuBueleaf/a1111-sd-webui-locon",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-locon" ))
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg"))
-Gitclone(r"https://github.com/ashen-sensored/stable-diffusion-webui-two-shot",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-two-shot"))
-Gitclone(r"https://github.com/camenduru/sd_webui_stealth_pnginfo",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_webui_stealth_pnginfo"))
-
-os.chdir(user_home / r"stable-diffusion-webui")
-
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],str(user_home / "stable-diffusion-webui" / "extensions" / "sd-webui-controlnet" / "models"),pathlib.Path(dList[i]).name)
-del dList
-
-#download model    
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.5-pruned.ckpt")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.0.vae.pt")
-DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"Counterfeit-V3.0_fp16.safetensors")
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1B_orangemixs.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"AOM3A1B_orangemixs.safetensors")
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Without%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_WithoutVAE.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/9474",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"chilloutmix_NiPrunedFp16.safetensors")
-
-#My customly added models
-DownLoad(r"https://civitai.com/api/download/models/105674?", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"realisticVisionV30_v30VAE.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/94640", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"majicmixRealistic_v6.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/109123", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamshaper_7.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/27392", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"openjourney_V4.ckpt")
-DownLoad(r"https://civitai.com/api/download/models/95489", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anyloraCheckpoint_bakedvaeBlessedFp16.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/90854", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"AnythingV5Ink_ink.safetensors")
-
-#LoRa ?
-DownLoad(r"https://civitai.com/api/download/models/39885",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"Better_light.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39164",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"backlighting.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/62833",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"add_detail.safetensors")
-
-#strt webui
-
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-while True:
-  ret=subprocess.run([r"python3" ,r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
-  if(ret.returncode == 0 ): 
-    del ret
-    gc.collect()
-  else :
-    del ret
-
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipeline_flax_utils.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipeline_flax_utils.py
deleted file mode 100644
index f8fd304776d785fc11df5f93f5c2443fb78bceef..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipeline_flax_utils.py
+++ /dev/null
@@ -1,506 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import importlib
-import inspect
-import os
-from typing import Dict, List, Optional, Union
-
-import numpy as np
-
-import flax
-import PIL
-from flax.core.frozen_dict import FrozenDict
-from huggingface_hub import snapshot_download
-from PIL import Image
-from tqdm.auto import tqdm
-
-from .configuration_utils import ConfigMixin
-from .hub_utils import http_user_agent
-from .modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin
-from .schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin
-from .utils import CONFIG_NAME, DIFFUSERS_CACHE, BaseOutput, is_transformers_available, logging
-
-
-if is_transformers_available():
-    from transformers import FlaxPreTrainedModel
-
-INDEX_FILE = "diffusion_flax_model.bin"
-
-
-logger = logging.get_logger(__name__)
-
-
-LOADABLE_CLASSES = {
-    "diffusers": {
-        "FlaxModelMixin": ["save_pretrained", "from_pretrained"],
-        "FlaxSchedulerMixin": ["save_pretrained", "from_pretrained"],
-        "FlaxDiffusionPipeline": ["save_pretrained", "from_pretrained"],
-    },
-    "transformers": {
-        "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
-        "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
-        "FlaxPreTrainedModel": ["save_pretrained", "from_pretrained"],
-        "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
-        "ProcessorMixin": ["save_pretrained", "from_pretrained"],
-        "ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
-    },
-}
-
-ALL_IMPORTABLE_CLASSES = {}
-for library in LOADABLE_CLASSES:
-    ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
-
-
-def import_flax_or_no_model(module, class_name):
-    try:
-        # 1. First make sure that if a Flax object is present, import this one
-        class_obj = getattr(module, "Flax" + class_name)
-    except AttributeError:
-        # 2. If this doesn't work, it's not a model and we don't append "Flax"
-        class_obj = getattr(module, class_name)
-    except AttributeError:
-        raise ValueError(f"Neither Flax{class_name} nor {class_name} exist in {module}")
-
-    return class_obj
-
-
-@flax.struct.dataclass
-class FlaxImagePipelineOutput(BaseOutput):
-    """
-    Output class for image pipelines.
-
-    Args:
-        images (`List[PIL.Image.Image]` or `np.ndarray`)
-            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
-            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
-    """
-
-    images: Union[List[PIL.Image.Image], np.ndarray]
-
-
-class FlaxDiffusionPipeline(ConfigMixin):
-    r"""
-    Base class for all models.
-
-    [`FlaxDiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion
-    pipelines and handles methods for loading, downloading and saving models as well as a few methods common to all
-    pipelines to:
-
-        - enabling/disabling the progress bar for the denoising iteration
-
-    Class attributes:
-
-        - **config_name** ([`str`]) -- name of the config file that will store the class and module names of all
-          components of the diffusion pipeline.
-    """
-    config_name = "model_index.json"
-
-    def register_modules(self, **kwargs):
-        # import it here to avoid circular import
-        from diffusers import pipelines
-
-        for name, module in kwargs.items():
-            if module is None:
-                register_dict = {name: (None, None)}
-            else:
-                # retrieve library
-                library = module.__module__.split(".")[0]
-
-                # check if the module is a pipeline module
-                pipeline_dir = module.__module__.split(".")[-2]
-                path = module.__module__.split(".")
-                is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
-
-                # if library is not in LOADABLE_CLASSES, then it is a custom module.
-                # Or if it's a pipeline module, then the module is inside the pipeline
-                # folder so we set the library to module name.
-                if library not in LOADABLE_CLASSES or is_pipeline_module:
-                    library = pipeline_dir
-
-                # retrieve class_name
-                class_name = module.__class__.__name__
-
-                register_dict = {name: (library, class_name)}
-
-            # save model index config
-            self.register_to_config(**register_dict)
-
-            # set models
-            setattr(self, name, module)
-
-    def save_pretrained(self, save_directory: Union[str, os.PathLike], params: Union[Dict, FrozenDict]):
-        # TODO: handle inference_state
-        """
-        Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to
-        a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading
-        method. The pipeline can easily be re-loaded using the `[`~FlaxDiffusionPipeline.from_pretrained`]` class
-        method.
-
-        Arguments:
-            save_directory (`str` or `os.PathLike`):
-                Directory to which to save. Will be created if it doesn't exist.
-        """
-        self.save_config(save_directory)
-
-        model_index_dict = dict(self.config)
-        model_index_dict.pop("_class_name")
-        model_index_dict.pop("_diffusers_version")
-        model_index_dict.pop("_module", None)
-
-        for pipeline_component_name in model_index_dict.keys():
-            sub_model = getattr(self, pipeline_component_name)
-            if sub_model is None:
-                # edge case for saving a pipeline with safety_checker=None
-                continue
-
-            model_cls = sub_model.__class__
-
-            save_method_name = None
-            # search for the model's base class in LOADABLE_CLASSES
-            for library_name, library_classes in LOADABLE_CLASSES.items():
-                library = importlib.import_module(library_name)
-                for base_class, save_load_methods in library_classes.items():
-                    class_candidate = getattr(library, base_class, None)
-                    if class_candidate is not None and issubclass(model_cls, class_candidate):
-                        # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
-                        save_method_name = save_load_methods[0]
-                        break
-                if save_method_name is not None:
-                    break
-
-            save_method = getattr(sub_model, save_method_name)
-            expects_params = "params" in set(inspect.signature(save_method).parameters.keys())
-
-            if expects_params:
-                save_method(
-                    os.path.join(save_directory, pipeline_component_name), params=params[pipeline_component_name]
-                )
-            else:
-                save_method(os.path.join(save_directory, pipeline_component_name))
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
-        r"""
-        Instantiate a Flax diffusion pipeline from pre-trained pipeline weights.
-
-        The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
-
-        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
-        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
-        task.
-
-        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
-        weights are discarded.
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-
-                    - A string, the *repo id* of a pretrained pipeline hosted inside a model repo on
-                      https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like
-                      `CompVis/ldm-text2im-large-256`.
-                    - A path to a *directory* containing pipeline weights saved using
-                      [`~FlaxDiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`.
-            dtype (`str` or `jnp.dtype`, *optional*):
-                Override the default `jnp.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
-                will be automatically derived from the model's weights.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
-                file exists.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only(`bool`, *optional*, defaults to `False`):
-                Whether or not to only look at local files (i.e., do not try to download the model).
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-                when running `huggingface-cli login` (stored in `~/.huggingface`).
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-            mirror (`str`, *optional*):
-                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
-                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
-                Please refer to the mirror site for more information. specify the folder name here.
-
-            kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
-                specific pipeline class. The overwritten components are then directly passed to the pipelines
-                `__init__` method. See example below for more information.
-
-        <Tip>
-
-         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
-         models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"runwayml/stable-diffusion-v1-5"`
-
-        </Tip>
-
-        <Tip>
-
-        Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
-        this method in a firewalled environment.
-
-        </Tip>
-
-        Examples:
-
-        ```py
-        >>> from diffusers import FlaxDiffusionPipeline
-
-        >>> # Download pipeline from huggingface.co and cache.
-        >>> # Requires to be logged in to Hugging Face hub,
-        >>> # see more in [the documentation](https://huggingface.co/docs/hub/security-tokens)
-        >>> pipeline, params = FlaxDiffusionPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5",
-        ...     revision="bf16",
-        ...     dtype=jnp.bfloat16,
-        ... )
-
-        >>> # Download pipeline, but use a different scheduler
-        >>> from diffusers import FlaxDPMSolverMultistepScheduler
-
-        >>> model_id = "runwayml/stable-diffusion-v1-5"
-        >>> sched, sched_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
-        ...     model_id,
-        ...     subfolder="scheduler",
-        ... )
-
-        >>> dpm_pipe, dpm_params = FlaxStableDiffusionPipeline.from_pretrained(
-        ...     model_id, revision="bf16", dtype=jnp.bfloat16, scheduler=dpmpp
-        ... )
-        >>> dpm_params["scheduler"] = dpmpp_state
-        ```
-        """
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        revision = kwargs.pop("revision", None)
-        from_pt = kwargs.pop("from_pt", False)
-        dtype = kwargs.pop("dtype", None)
-
-        # 1. Download the checkpoints and configs
-        # use snapshot download here to get it working from from_pretrained
-        if not os.path.isdir(pretrained_model_name_or_path):
-            config_dict = cls.load_config(
-                pretrained_model_name_or_path,
-                cache_dir=cache_dir,
-                resume_download=resume_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-            )
-            # make sure we only download sub-folders and `diffusers` filenames
-            folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
-            allow_patterns = [os.path.join(k, "*") for k in folder_names]
-            allow_patterns += [FLAX_WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, cls.config_name]
-
-            # make sure we don't download PyTorch weights, unless when using from_pt
-            ignore_patterns = "*.bin" if not from_pt else []
-
-            if cls != FlaxDiffusionPipeline:
-                requested_pipeline_class = cls.__name__
-            else:
-                requested_pipeline_class = config_dict.get("_class_name", cls.__name__)
-                requested_pipeline_class = (
-                    requested_pipeline_class
-                    if requested_pipeline_class.startswith("Flax")
-                    else "Flax" + requested_pipeline_class
-                )
-
-            user_agent = {"pipeline_class": requested_pipeline_class}
-            user_agent = http_user_agent(user_agent)
-
-            # download all allow_patterns
-            cached_folder = snapshot_download(
-                pretrained_model_name_or_path,
-                cache_dir=cache_dir,
-                resume_download=resume_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-                allow_patterns=allow_patterns,
-                ignore_patterns=ignore_patterns,
-                user_agent=user_agent,
-            )
-        else:
-            cached_folder = pretrained_model_name_or_path
-
-        config_dict = cls.load_config(cached_folder)
-
-        # 2. Load the pipeline class, if using custom module then load it from the hub
-        # if we load from explicit class, let's use it
-        if cls != FlaxDiffusionPipeline:
-            pipeline_class = cls
-        else:
-            diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
-            class_name = (
-                config_dict["_class_name"]
-                if config_dict["_class_name"].startswith("Flax")
-                else "Flax" + config_dict["_class_name"]
-            )
-            pipeline_class = getattr(diffusers_module, class_name)
-
-        # some modules can be passed directly to the init
-        # in this case they are already instantiated in `kwargs`
-        # extract them here
-        expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys())
-        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
-
-        init_dict, _, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
-
-        init_kwargs = {}
-
-        # inference_params
-        params = {}
-
-        # import it here to avoid circular import
-        from diffusers import pipelines
-
-        # 3. Load each module in the pipeline
-        for name, (library_name, class_name) in init_dict.items():
-            if class_name is None:
-                # edge case for when the pipeline was saved with safety_checker=None
-                init_kwargs[name] = None
-                continue
-
-            is_pipeline_module = hasattr(pipelines, library_name)
-            loaded_sub_model = None
-            sub_model_should_be_defined = True
-
-            # if the model is in a pipeline module, then we load it from the pipeline
-            if name in passed_class_obj:
-                # 1. check that passed_class_obj has correct parent class
-                if not is_pipeline_module:
-                    library = importlib.import_module(library_name)
-                    class_obj = getattr(library, class_name)
-                    importable_classes = LOADABLE_CLASSES[library_name]
-                    class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
-
-                    expected_class_obj = None
-                    for class_name, class_candidate in class_candidates.items():
-                        if class_candidate is not None and issubclass(class_obj, class_candidate):
-                            expected_class_obj = class_candidate
-
-                    if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
-                        raise ValueError(
-                            f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
-                            f" {expected_class_obj}"
-                        )
-                elif passed_class_obj[name] is None:
-                    logger.warning(
-                        f"You have passed `None` for {name} to disable its functionality in {pipeline_class}. Note"
-                        f" that this might lead to problems when using {pipeline_class} and is not recommended."
-                    )
-                    sub_model_should_be_defined = False
-                else:
-                    logger.warning(
-                        f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
-                        " has the correct type"
-                    )
-
-                # set passed class object
-                loaded_sub_model = passed_class_obj[name]
-            elif is_pipeline_module:
-                pipeline_module = getattr(pipelines, library_name)
-                class_obj = import_flax_or_no_model(pipeline_module, class_name)
-
-                importable_classes = ALL_IMPORTABLE_CLASSES
-                class_candidates = {c: class_obj for c in importable_classes.keys()}
-            else:
-                # else we just import it from the library.
-                library = importlib.import_module(library_name)
-                class_obj = import_flax_or_no_model(library, class_name)
-
-                importable_classes = LOADABLE_CLASSES[library_name]
-                class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
-
-            if loaded_sub_model is None and sub_model_should_be_defined:
-                load_method_name = None
-                for class_name, class_candidate in class_candidates.items():
-                    if class_candidate is not None and issubclass(class_obj, class_candidate):
-                        load_method_name = importable_classes[class_name][1]
-
-                load_method = getattr(class_obj, load_method_name)
-
-                # check if the module is in a subdirectory
-                if os.path.isdir(os.path.join(cached_folder, name)):
-                    loadable_folder = os.path.join(cached_folder, name)
-                else:
-                    loaded_sub_model = cached_folder
-
-                if issubclass(class_obj, FlaxModelMixin):
-                    loaded_sub_model, loaded_params = load_method(loadable_folder, from_pt=from_pt, dtype=dtype)
-                    params[name] = loaded_params
-                elif is_transformers_available() and issubclass(class_obj, FlaxPreTrainedModel):
-                    if from_pt:
-                        # TODO(Suraj): Fix this in Transformers. We should be able to use `_do_init=False` here
-                        loaded_sub_model = load_method(loadable_folder, from_pt=from_pt)
-                        loaded_params = loaded_sub_model.params
-                        del loaded_sub_model._params
-                    else:
-                        loaded_sub_model, loaded_params = load_method(loadable_folder, _do_init=False)
-                    params[name] = loaded_params
-                elif issubclass(class_obj, FlaxSchedulerMixin):
-                    loaded_sub_model, scheduler_state = load_method(loadable_folder)
-                    params[name] = scheduler_state
-                else:
-                    loaded_sub_model = load_method(loadable_folder)
-
-            init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
-
-        model = pipeline_class(**init_kwargs, dtype=dtype)
-        return model, params
-
-    @staticmethod
-    def numpy_to_pil(images):
-        """
-        Convert a numpy image or a batch of images to a PIL image.
-        """
-        if images.ndim == 3:
-            images = images[None, ...]
-        images = (images * 255).round().astype("uint8")
-        if images.shape[-1] == 1:
-            # special case for grayscale (single channel) images
-            pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
-        else:
-            pil_images = [Image.fromarray(image) for image in images]
-
-        return pil_images
-
-    # TODO: make it compatible with jax.lax
-    def progress_bar(self, iterable):
-        if not hasattr(self, "_progress_bar_config"):
-            self._progress_bar_config = {}
-        elif not isinstance(self._progress_bar_config, dict):
-            raise ValueError(
-                f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
-            )
-
-        return tqdm(iterable, **self._progress_bar_config)
-
-    def set_progress_bar_config(self, **kwargs):
-        self._progress_bar_config = kwargs
diff --git a/spaces/Jorgerv97/Herramienta_interactiva_ensenyanza_tecnicas_aprendizaje_supervisado_salud/GeneralInfo/cleaning.py b/spaces/Jorgerv97/Herramienta_interactiva_ensenyanza_tecnicas_aprendizaje_supervisado_salud/GeneralInfo/cleaning.py
deleted file mode 100644
index 4c83aae3f6db5d8317fce7d453a556092db0cef6..0000000000000000000000000000000000000000
--- a/spaces/Jorgerv97/Herramienta_interactiva_ensenyanza_tecnicas_aprendizaje_supervisado_salud/GeneralInfo/cleaning.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from shiny import module, ui, reactive
-
-@module.ui
-def cleaning_1_ui():
-    return ui.div(
-        ui.div(
-            ui.markdown("La limpieza de la base de datos es uno de los pasos más importantes del proceso de creación de un modelo ya que afectará notablemente a nuestros resultados finales. Debemos asegurarnos de que **no existe ningún dato erróneo, valores nulos o simplemente que no aporte información relevante** para realizar la predicción final. También nos debemos asegurar de que todos los datos se encuentren en el formato correcto para poder ser introducidos a nuestros algoritmos de aprendizaje supervisado.")
-            , style="padding-right:50px; padding-top:20px; text-align: justify; text-justify: inter-word;" 
-        ),
-        ui.tags.p("Esta base de datos requiere únicamente dos cambios:"
-                    , style="padding-right:50px; text-align: justify; text-justify: inter-word;"),
-        ui.div(
-            ui.markdown("- **Modificar los datos de 'diagnosis'**. Muchos algoritmos sólo permiten datos cuantitativos para trabajar. Deberíamos habernos dado cuenta en la tabla de tipos de datos originales que los datos de 'diagnosis' no son cuantitativos, sino que son categóricos (en formato de texto). Por tanto deberemos **convertirla a valores numéricos**, asignando por ejemplo los valores 'B' (benigno) al número 0 y los valores 'M' (maligno) al número 1. ¡Prueba a hacerlo con el botón de abajo!")
-            , style="padding-right:50px; text-align: justify; text-justify: inter-word;" 
-        ),
-    )
-
-@module.ui
-def cleaning_2_ui():
-    return ui.div(
-        ui.div(
-            ui.markdown("- También podemos encontrar en nuestra base de datos un **dato claramente irrelevante** para la predicción de 'diagnosis': la columna de 'id'. Únicamente sirve para localizar la muestra fuera del modelo de aprendizaje supervisado. La podemos eliminar con el selector y el botón que encontramos a continuación.")
-            , style="padding-right:50px; padding-top:30px; text-align: justify; text-justify: inter-word;" 
-        ),
-    )
-
-@module.ui
-def cleaning_extra_ui():
-    return ui.div(
-        {"id": "clean-more-info"},
-        ui.input_action_button("show_more_cleaning", "Otras operaciones de limpieza ▽"
-                                , style="padding: 20px 0px 10px 0px; background: white; border: none; font-weight: bold; text-decoration: underline; border: 0 !important; box-shadow: 0 0 !important; transition: 0.1s !important; background-color: transparent !important;"),
-    )
-
-
-@module.server
-def cleaning_server(input, output, session):
-
-    @reactive.Effect
-    @reactive.event(input.show_more_cleaning)
-    def _():
-        show_more_cleaning_button = input.show_more_cleaning()
-        if show_more_cleaning_button % 2 == 1:
-            ui.update_action_button("show_more_cleaning", label="Otras operaciones de limpieza △")
-            ui.insert_ui(
-                ui.div({"id": "inserted-clean-info"},
-                    ui.markdown("""En otras ocasiones puede ser necesario:
-- Eliminar otras columnas con información irrelevante.
-- Convertir otras columnas a valores numéricos.
-- Eliminar filas con datos erróneos o sin posibilidad de ser tratados correctamente.
-- Convertir los datos nulos existentes de columnas o de toda la base de datos a 0.
-- Convertir ciertos valores o valores nulos a otro tipo de valor manualmente."""
-                    ),
-                    style="border: solid 0px grey; border-radius: 10px; background:#eceef1 ;margin-right:50px; padding:15px 20px 10px 20px; text-align: justify; text-justify: inter-word;",
-                ),
-                selector="#clean-more-info",
-                where="beforeEnd",
-            )
-        else:
-            ui.update_action_button("show_more_cleaning", label="Otras operaciones de limpieza ▽")
-            ui.remove_ui("#inserted-clean-info")
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/deep_sort/sort/preprocessing.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/deep_sort/sort/preprocessing.py
deleted file mode 100644
index 3e708ee5fff2474f15c8f6d113e535fa007090e3..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/deep_sort/sort/preprocessing.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-import cv2
-
-
-def non_max_suppression(boxes, max_bbox_overlap, scores=None):
-    """Suppress overlapping detections.
-
-    Original code from [1]_ has been adapted to include confidence score.
-
-    .. [1] http://www.pyimagesearch.com/2015/02/16/
-           faster-non-maximum-suppression-python/
-
-    Examples
-    --------
-
-        >>> boxes = [d.roi for d in detections]
-        >>> scores = [d.confidence for d in detections]
-        >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
-        >>> detections = [detections[i] for i in indices]
-
-    Parameters
-    ----------
-    boxes : ndarray
-        Array of ROIs (x, y, width, height).
-    max_bbox_overlap : float
-        ROIs that overlap more than this values are suppressed.
-    scores : Optional[array_like]
-        Detector confidence score.
-
-    Returns
-    -------
-    List[int]
-        Returns indices of detections that have survived non-maxima suppression.
-
-    """
-    if len(boxes) == 0:
-        return []
-
-    boxes = boxes.astype(np.float32)
-    pick = []
-
-    x1 = boxes[:, 0]
-    y1 = boxes[:, 1]
-    x2 = boxes[:, 2] + boxes[:, 0]
-    y2 = boxes[:, 3] + boxes[:, 1]
-
-    area = (x2 - x1 + 1) * (y2 - y1 + 1)
-    if scores is not None:
-        idxs = np.argsort(scores)
-    else:
-        idxs = np.argsort(y2)
-
-    while len(idxs) > 0:
-        last = len(idxs) - 1
-        i = idxs[last]
-        pick.append(i)
-
-        xx1 = np.maximum(x1[i], x1[idxs[:last]])
-        yy1 = np.maximum(y1[i], y1[idxs[:last]])
-        xx2 = np.minimum(x2[i], x2[idxs[:last]])
-        yy2 = np.minimum(y2[i], y2[idxs[:last]])
-
-        w = np.maximum(0, xx2 - xx1 + 1)
-        h = np.maximum(0, yy2 - yy1 + 1)
-
-        overlap = (w * h) / area[idxs[:last]] # IOU 
-
-        idxs = np.delete(
-            idxs, np.concatenate(
-                ([last], np.where(overlap > max_bbox_overlap)[0])))
-
-    return pick
diff --git a/spaces/Kuachi/ai-voice/transforms.py b/spaces/Kuachi/ai-voice/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Kuachi/ai-voice/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/margin_head_hooks.py b/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/margin_head_hooks.py
deleted file mode 100644
index fbeae7a347453153ff4ab3bef958acb549623f6f..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/margin_head_hooks.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved
-import numpy as np
-from mmengine.hooks import Hook
-from mmengine.model import is_model_wrapper
-
-from mmpretrain.models.heads import ArcFaceClsHead
-from mmpretrain.registry import HOOKS
-
-
-@HOOKS.register_module()
-class SetAdaptiveMarginsHook(Hook):
-    r"""Set adaptive-margins in ArcFaceClsHead based on the power of
-    category-wise count.
-
-    A PyTorch implementation of paper `Google Landmark Recognition 2020
-    Competition Third Place Solution <https://arxiv.org/abs/2010.05350>`_.
-    The margins will be
-    :math:`\text{f}(n) = (marginMax - marginMin) · norm(n^p) + marginMin`.
-    The `n` indicates the number of occurrences of a category.
-
-    Args:
-        margin_min (float): Lower bound of margins. Defaults to 0.05.
-        margin_max (float): Upper bound of margins. Defaults to 0.5.
-        power (float): The power of category freqercy. Defaults to -0.25.
-    """
-
-    def __init__(self, margin_min=0.05, margin_max=0.5, power=-0.25) -> None:
-        self.margin_min = margin_min
-        self.margin_max = margin_max
-        self.margin_range = margin_max - margin_min
-        self.p = power
-
-    def before_train(self, runner):
-        """change the margins in ArcFaceClsHead.
-
-        Args:
-            runner (obj: `Runner`): Runner.
-        """
-        model = runner.model
-        if is_model_wrapper(model):
-            model = model.module
-
-        if (hasattr(model, 'head')
-                and not isinstance(model.head, ArcFaceClsHead)):
-            raise ValueError(
-                'Hook ``SetFreqPowAdvMarginsHook`` could only be used '
-                f'for ``ArcFaceClsHead``, but get {type(model.head)}')
-
-        # generate margins base on the dataset.
-        gt_labels = runner.train_dataloader.dataset.get_gt_labels()
-        label_count = np.bincount(gt_labels)
-        label_count[label_count == 0] = 1  # At least one occurrence
-        pow_freq = np.power(label_count, self.p)
-
-        min_f, max_f = pow_freq.min(), pow_freq.max()
-        normized_pow_freq = (pow_freq - min_f) / (max_f - min_f)
-        margins = normized_pow_freq * self.margin_range + self.margin_min
-
-        assert len(margins) == runner.model.head.num_classes
-
-        model.head.set_margins(margins)
diff --git a/spaces/Lamai/LAMAIGPT/autogpt/commands/google_search.py b/spaces/Lamai/LAMAIGPT/autogpt/commands/google_search.py
deleted file mode 100644
index 7d38ce7568d2de207d521b077cfebd72527c9795..0000000000000000000000000000000000000000
--- a/spaces/Lamai/LAMAIGPT/autogpt/commands/google_search.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""Google search command for Autogpt."""
-from __future__ import annotations
-
-import json
-
-from duckduckgo_search import ddg
-
-from autogpt.config import Config
-
-CFG = Config()
-
-
-def google_search(query: str, num_results: int = 8) -> str:
-    """Return the results of a Google search
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-    search_results = []
-    if not query:
-        return json.dumps(search_results)
-
-    results = ddg(query, max_results=num_results)
-    if not results:
-        return json.dumps(search_results)
-
-    for j in results:
-        search_results.append(j)
-
-    return json.dumps(search_results, ensure_ascii=False, indent=4)
-
-
-def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
-    """Return the results of a Google search using the official Google API
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-
-    from googleapiclient.discovery import build
-    from googleapiclient.errors import HttpError
-
-    try:
-        # Get the Google API key and Custom Search Engine ID from the config file
-        api_key = CFG.google_api_key
-        custom_search_engine_id = CFG.custom_search_engine_id
-
-        # Initialize the Custom Search API service
-        service = build("customsearch", "v1", developerKey=api_key)
-
-        # Send the search query and retrieve the results
-        result = (
-            service.cse()
-            .list(q=query, cx=custom_search_engine_id, num=num_results)
-            .execute()
-        )
-
-        # Extract the search result items from the response
-        search_results = result.get("items", [])
-
-        # Create a list of only the URLs from the search results
-        search_results_links = [item["link"] for item in search_results]
-
-    except HttpError as e:
-        # Handle errors in the API call
-        error_details = json.loads(e.content.decode())
-
-        # Check if the error is related to an invalid or missing API key
-        if error_details.get("error", {}).get(
-            "code"
-        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
-            "message", ""
-        ):
-            return "Error: The provided Google API key is invalid or missing."
-        else:
-            return f"Error: {e}"
-
-    # Return the list of search result URLs
-    return search_results_links
diff --git a/spaces/LanguageBind/LanguageBind/open_clip/tokenizer.py b/spaces/LanguageBind/LanguageBind/open_clip/tokenizer.py
deleted file mode 100644
index 97ac3e804fd1d421183c2764c020a723149825d1..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/open_clip/tokenizer.py
+++ /dev/null
@@ -1,214 +0,0 @@
-""" CLIP tokenizer
-
-Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-import gzip
-import html
-import os
-from functools import lru_cache
-from typing import Union, List
-
-import ftfy
-import regex as re
-import torch
-
-# https://stackoverflow.com/q/62691279
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a significant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        if not special_tokens:
-            special_tokens = ['<start_of_text>', '<end_of_text>']
-        else:
-            special_tokens = ['<start_of_text>', '<end_of_text>'] + special_tokens
-        vocab.extend(special_tokens)
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {t:t for t in special_tokens}
-        special = "|".join(special_tokens)
-        self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-        self.vocab_size = len(self.encoder)
-        self.all_special_ids = [self.encoder[t] for t in special_tokens]
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text
-
-
-_tokenizer = SimpleTokenizer()
-
-def decode(output_ids: torch.Tensor):
-    output_ids = output_ids.cpu().numpy()
-    return _tokenizer.decode(output_ids)
-
-def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:
-    """
-    Returns the tokenized representation of given input string(s)
-
-    Parameters
-    ----------
-    texts : Union[str, List[str]]
-        An input string or a list of input strings to tokenize
-    context_length : int
-        The context length to use; all CLIP models use 77 as the context length
-
-    Returns
-    -------
-    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
-    """
-    if isinstance(texts, str):
-        texts = [texts]
-
-    sot_token = _tokenizer.encoder["<start_of_text>"]
-    eot_token = _tokenizer.encoder["<end_of_text>"]
-    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
-    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-    for i, tokens in enumerate(all_tokens):
-        if len(tokens) > context_length:
-            tokens = tokens[:context_length]  # Truncate
-            tokens[-1] = eot_token
-        result[i, :len(tokens)] = torch.tensor(tokens)
-
-    return result
-
-
-class HFTokenizer:
-    """HuggingFace tokenizer wrapper"""
-
-    def __init__(self, tokenizer_name: str, cache_dir=None):
-        from transformers import AutoTokenizer
-        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, cache_dir=cache_dir)
-
-    def save_pretrained(self, dest):
-        self.tokenizer.save_pretrained(dest)
-
-    def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor:
-        # same cleaning as for default tokenizer, except lowercasing
-        # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance
-        if isinstance(texts, str):
-            texts = [texts]
-        texts = [whitespace_clean(basic_clean(text)) for text in texts]
-        output = self.tokenizer(
-            texts,
-            return_tensors='pt',
-            max_length=context_length,
-            padding='max_length',
-            truncation=True,
-        )
-        return output.input_ids, output.attention_mask
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/uvr5/mdxnet.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/uvr5/mdxnet.py
deleted file mode 100644
index 86a066893ad99cfed77788027a9deb8ed486a7f2..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/uvr5/mdxnet.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-import librosa
-import numpy as np
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-cpu = torch.device("cpu")
-
-
-class ConvTDFNetTrim:
-    def __init__(
-        self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
-    ):
-        super(ConvTDFNetTrim, self).__init__()
-
-        self.dim_f = dim_f
-        self.dim_t = 2**dim_t
-        self.n_fft = n_fft
-        self.hop = hop
-        self.n_bins = self.n_fft // 2 + 1
-        self.chunk_size = hop * (self.dim_t - 1)
-        self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
-            device
-        )
-        self.target_name = target_name
-        self.blender = "blender" in model_name
-
-        self.dim_c = 4
-        out_c = self.dim_c * 4 if target_name == "*" else self.dim_c
-        self.freq_pad = torch.zeros(
-            [1, out_c, self.n_bins - self.dim_f, self.dim_t]
-        ).to(device)
-
-        self.n = L // 2
-
-    def stft(self, x):
-        x = x.reshape([-1, self.chunk_size])
-        x = torch.stft(
-            x,
-            n_fft=self.n_fft,
-            hop_length=self.hop,
-            window=self.window,
-            center=True,
-            return_complex=True,
-        )
-        x = torch.view_as_real(x)
-        x = x.permute([0, 3, 1, 2])
-        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
-            [-1, self.dim_c, self.n_bins, self.dim_t]
-        )
-        return x[:, :, : self.dim_f]
-
-    def istft(self, x, freq_pad=None):
-        freq_pad = (
-            self.freq_pad.repeat([x.shape[0], 1, 1, 1])
-            if freq_pad is None
-            else freq_pad
-        )
-        x = torch.cat([x, freq_pad], -2)
-        c = 4 * 2 if self.target_name == "*" else 2
-        x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
-            [-1, 2, self.n_bins, self.dim_t]
-        )
-        x = x.permute([0, 2, 3, 1])
-        x = x.contiguous()
-        x = torch.view_as_complex(x)
-        x = torch.istft(
-            x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
-        )
-        return x.reshape([-1, c, self.chunk_size])
-
-
-def get_models(device, dim_f, dim_t, n_fft):
-    return ConvTDFNetTrim(
-        device=device,
-        model_name="Conv-TDF",
-        target_name="vocals",
-        L=11,
-        dim_f=dim_f,
-        dim_t=dim_t,
-        n_fft=n_fft,
-    )
-
-
-class Predictor:
-    def __init__(self, args):
-        import onnxruntime as ort
-
-        logger.info(ort.get_available_providers())
-        self.args = args
-        self.model_ = get_models(
-            device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
-        )
-        self.model = ort.InferenceSession(
-            os.path.join(args.onnx, self.model_.target_name + ".onnx"),
-            providers=[
-                "CUDAExecutionProvider",
-                "DmlExecutionProvider",
-                "CPUExecutionProvider",
-            ],
-        )
-        logger.info("ONNX load done")
-
-    def demix(self, mix):
-        samples = mix.shape[-1]
-        margin = self.args.margin
-        chunk_size = self.args.chunks * 44100
-        assert not margin == 0, "margin cannot be zero!"
-        if margin > chunk_size:
-            margin = chunk_size
-
-        segmented_mix = {}
-
-        if self.args.chunks == 0 or samples < chunk_size:
-            chunk_size = samples
-
-        counter = -1
-        for skip in range(0, samples, chunk_size):
-            counter += 1
-
-            s_margin = 0 if counter == 0 else margin
-            end = min(skip + chunk_size + margin, samples)
-
-            start = skip - s_margin
-
-            segmented_mix[skip] = mix[:, start:end].copy()
-            if end == samples:
-                break
-
-        sources = self.demix_base(segmented_mix, margin_size=margin)
-        """
-        mix:(2,big_sample)
-        segmented_mix:offset->(2,small_sample)
-        sources:(1,2,big_sample)
-        """
-        return sources
-
-    def demix_base(self, mixes, margin_size):
-        chunked_sources = []
-        progress_bar = tqdm(total=len(mixes))
-        progress_bar.set_description("Processing")
-        for mix in mixes:
-            cmix = mixes[mix]
-            sources = []
-            n_sample = cmix.shape[1]
-            model = self.model_
-            trim = model.n_fft // 2
-            gen_size = model.chunk_size - 2 * trim
-            pad = gen_size - n_sample % gen_size
-            mix_p = np.concatenate(
-                (np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
-            )
-            mix_waves = []
-            i = 0
-            while i < n_sample + pad:
-                waves = np.array(mix_p[:, i : i + model.chunk_size])
-                mix_waves.append(waves)
-                i += gen_size
-            mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
-            with torch.no_grad():
-                _ort = self.model
-                spek = model.stft(mix_waves)
-                if self.args.denoise:
-                    spec_pred = (
-                        -_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
-                        + _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
-                    )
-                    tar_waves = model.istft(torch.tensor(spec_pred))
-                else:
-                    tar_waves = model.istft(
-                        torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
-                    )
-                tar_signal = (
-                    tar_waves[:, :, trim:-trim]
-                    .transpose(0, 1)
-                    .reshape(2, -1)
-                    .numpy()[:, :-pad]
-                )
-
-                start = 0 if mix == 0 else margin_size
-                end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
-                if margin_size == 0:
-                    end = None
-                sources.append(tar_signal[:, start:end])
-
-                progress_bar.update(1)
-
-            chunked_sources.append(sources)
-        _sources = np.concatenate(chunked_sources, axis=-1)
-        # del self.model
-        progress_bar.close()
-        return _sources
-
-    def prediction(self, m, vocal_root, others_root, format):
-        os.makedirs(vocal_root, exist_ok=True)
-        os.makedirs(others_root, exist_ok=True)
-        basename = os.path.basename(m)
-        mix, rate = librosa.load(m, mono=False, sr=44100)
-        if mix.ndim == 1:
-            mix = np.asfortranarray([mix, mix])
-        mix = mix.T
-        sources = self.demix(mix.T)
-        opt = sources[0].T
-        if format in ["wav", "flac"]:
-            sf.write(
-                "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
-            )
-            sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
-        else:
-            path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename)
-            path_other = "%s/%s_others.wav" % (others_root, basename)
-            sf.write(path_vocal, mix - opt, rate)
-            sf.write(path_other, opt, rate)
-            if os.path.exists(path_vocal):
-                os.system(
-                    "ffmpeg -i %s -vn %s -q:a 2 -y"
-                    % (path_vocal, path_vocal[:-4] + ".%s" % format)
-                )
-            if os.path.exists(path_other):
-                os.system(
-                    "ffmpeg -i %s -vn %s -q:a 2 -y"
-                    % (path_other, path_other[:-4] + ".%s" % format)
-                )
-
-
-class MDXNetDereverb:
-    def __init__(self, chunks, device):
-        self.onnx = "assets/uvr5_weights/onnx_dereverb_By_FoxJoy"
-        self.shifts = 10  # 'Predict with randomised equivariant stabilisation'
-        self.mixing = "min_mag"  # ['default','min_mag','max_mag']
-        self.chunks = chunks
-        self.margin = 44100
-        self.dim_t = 9
-        self.dim_f = 3072
-        self.n_fft = 6144
-        self.denoise = True
-        self.pred = Predictor(self)
-        self.device = device
-
-    def path_audio(self, input, vocal_root, others_root, format):
-        self.pred.prediction(input, vocal_root, others_root, format)
diff --git a/spaces/LaynzKunz/RVC-Inference-webui-grado-colab-huggingafce/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/LaynzKunz/RVC-Inference-webui-grado-colab-huggingafce/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/RVC-Inference-webui-grado-colab-huggingafce/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/Lbin123/Lbingo/src/components/ui/sheet.tsx b/spaces/Lbin123/Lbingo/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/src/components/ui/sheet.tsx
+++ /dev/null
@@ -1,122 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SheetPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Sheet = SheetPrimitive.Root
-
-const SheetTrigger = SheetPrimitive.Trigger
-
-const SheetClose = SheetPrimitive.Close
-
-const SheetPortal = ({
-  className,
-  children,
-  ...props
-}: SheetPrimitive.DialogPortalProps) => (
-  <SheetPrimitive.Portal
-    className={cn('fixed inset-0 z-50 flex', className)}
-    {...props}
-  >
-    {children}
-  </SheetPrimitive.Portal>
-)
-SheetPortal.displayName = SheetPrimitive.Portal.displayName
-
-const SheetOverlay = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <SheetPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-SheetOverlay.displayName = SheetPrimitive.Overlay.displayName
-
-const SheetContent = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <SheetPortal>
-    <SheetPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed inset-y-0 left-0 z-50 h-full border-r bg-background p-6 shadow-lg transition ease-in-out data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:slide-out-to-left data-[state=open]:slide-in-from-left data-[state=closed]:duration-300 data-[state=open]:duration-500 sm:max-w-sm',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <SheetPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-secondary">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </SheetPrimitive.Close>
-    </SheetPrimitive.Content>
-  </SheetPortal>
-))
-SheetContent.displayName = SheetPrimitive.Content.displayName
-
-const SheetHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div className={cn('flex flex-col space-y-2', className)} {...props} />
-)
-SheetHeader.displayName = 'SheetHeader'
-
-const SheetFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-SheetFooter.displayName = 'SheetFooter'
-
-const SheetTitle = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold text-foreground', className)}
-    {...props}
-  />
-))
-SheetTitle.displayName = SheetPrimitive.Title.displayName
-
-const SheetDescription = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-SheetDescription.displayName = SheetPrimitive.Description.displayName
-
-export {
-  Sheet,
-  SheetTrigger,
-  SheetClose,
-  SheetContent,
-  SheetHeader,
-  SheetFooter,
-  SheetTitle,
-  SheetDescription
-}
diff --git a/spaces/Lbin123/Lbingo/src/lib/isomorphic/browser.ts b/spaces/Lbin123/Lbingo/src/lib/isomorphic/browser.ts
deleted file mode 100644
index de125b1f1786d1618cb1ff47f403d76c6784f4ce..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/src/lib/isomorphic/browser.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-'use client'
-
-const debug = console.info.bind(console)
-
-class WebSocketAlias extends WebSocket {
-  constructor(address: string | URL, ...args: any) {
-    super(address)
-  }
-}
-
-export default { fetch, WebSocket: WebSocketAlias, debug }
diff --git a/spaces/Liu-LAB/GPT-academic/request_llm/bridge_llama2.py b/spaces/Liu-LAB/GPT-academic/request_llm/bridge_llama2.py
deleted file mode 100644
index e236c94272b110b2d4e4eb72812a270a0c8b3226..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/request_llm/bridge_llama2.py
+++ /dev/null
@@ -1,91 +0,0 @@
-model_name = "LLaMA"
-cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
-
-
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-from toolbox import update_ui, get_conf, ProxyNetworkActivate
-from multiprocessing import Process, Pipe
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
-from threading import Thread
-
-
-# ------------------------------------------------------------------------------------------------------------------------
-# 🔌💻 Local Model
-# ------------------------------------------------------------------------------------------------------------------------
-@SingletonLocalLLM
-class GetONNXGLMHandle(LocalLLMHandle):
-
-    def load_model_info(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        self.model_name = model_name
-        self.cmd_to_install = cmd_to_install
-
-    def load_model_and_tokenizer(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        import os, glob
-        import os
-        import platform
-        huggingface_token, device = get_conf('HUGGINGFACE_ACCESS_TOKEN', 'LOCAL_MODEL_DEVICE')
-        assert len(huggingface_token) != 0, "没有填写 HUGGINGFACE_ACCESS_TOKEN"
-        with open(os.path.expanduser('~/.cache/huggingface/token'), 'w') as f:
-            f.write(huggingface_token)
-        model_id = 'meta-llama/Llama-2-7b-chat-hf'
-        with ProxyNetworkActivate():
-            self._tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=huggingface_token)
-            # use fp16
-            model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=huggingface_token).eval()
-            if device.startswith('cuda'): model = model.half().to(device)
-            self._model = model
-
-            return self._model, self._tokenizer
-
-    def llm_stream_generator(self, **kwargs):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        def adaptor(kwargs):
-            query = kwargs['query']
-            max_length = kwargs['max_length']
-            top_p = kwargs['top_p']
-            temperature = kwargs['temperature']
-            history = kwargs['history']
-            console_slience = kwargs.get('console_slience', True)
-            return query, max_length, top_p, temperature, history, console_slience
-        
-        def convert_messages_to_prompt(query, history):
-            prompt = ""
-            for a, b in history:
-                prompt += f"\n[INST]{a}[/INST]"
-                prompt += "\n{b}" + b
-            prompt += f"\n[INST]{query}[/INST]"
-            return prompt
-        
-        query, max_length, top_p, temperature, history, console_slience = adaptor(kwargs)
-        prompt = convert_messages_to_prompt(query, history)
-        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
-        # code from transformers.llama
-        streamer = TextIteratorStreamer(self._tokenizer)
-        # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
-        inputs = self._tokenizer([prompt], return_tensors="pt")
-        prompt_tk_back = self._tokenizer.batch_decode(inputs['input_ids'])[0]
-
-        generation_kwargs = dict(inputs.to(self._model.device), streamer=streamer, max_new_tokens=max_length)
-        thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
-        thread.start()
-        generated_text = ""
-        for new_text in streamer: 
-            generated_text += new_text
-            if not console_slience: print(new_text, end='')
-            yield generated_text.lstrip(prompt_tk_back).rstrip("</s>")
-        if not console_slience: print()
-        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
-        
-    def try_to_import_special_deps(self, **kwargs):
-        # import something that will raise error if the user does not install requirement_*.txt
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
-        import importlib
-        importlib.import_module('transformers')
-
-
-# ------------------------------------------------------------------------------------------------------------------------
-# 🔌💻 GPT-Academic Interface
-# ------------------------------------------------------------------------------------------------------------------------
-predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name)
\ No newline at end of file
diff --git a/spaces/MAGAer13/mPLUG-Owl2/mplug_owl2/serve/controller.py b/spaces/MAGAer13/mPLUG-Owl2/mplug_owl2/serve/controller.py
deleted file mode 100644
index aeef2b34ce2c5bddf1e9e8d72ff0a4425929c554..0000000000000000000000000000000000000000
--- a/spaces/MAGAer13/mPLUG-Owl2/mplug_owl2/serve/controller.py
+++ /dev/null
@@ -1,298 +0,0 @@
-"""
-A controller manages distributed workers.
-It sends worker addresses to clients.
-"""
-import argparse
-import asyncio
-import dataclasses
-from enum import Enum, auto
-import json
-import logging
-import time
-from typing import List, Union
-import threading
-
-from fastapi import FastAPI, Request
-from fastapi.responses import StreamingResponse
-import numpy as np
-import requests
-import uvicorn
-
-from mplug_owl2.constants import CONTROLLER_HEART_BEAT_EXPIRATION
-from mplug_owl2.utils import build_logger, server_error_msg
-
-
-logger = build_logger("controller", "controller.log")
-
-
-class DispatchMethod(Enum):
-    LOTTERY = auto()
-    SHORTEST_QUEUE = auto()
-
-    @classmethod
-    def from_str(cls, name):
-        if name == "lottery":
-            return cls.LOTTERY
-        elif name == "shortest_queue":
-            return cls.SHORTEST_QUEUE
-        else:
-            raise ValueError(f"Invalid dispatch method")
-
-
-@dataclasses.dataclass
-class WorkerInfo:
-    model_names: List[str]
-    speed: int
-    queue_length: int
-    check_heart_beat: bool
-    last_heart_beat: str
-
-
-def heart_beat_controller(controller):
-    while True:
-        time.sleep(CONTROLLER_HEART_BEAT_EXPIRATION)
-        controller.remove_stable_workers_by_expiration()
-
-
-class Controller:
-    def __init__(self, dispatch_method: str):
-        # Dict[str -> WorkerInfo]
-        self.worker_info = {}
-        self.dispatch_method = DispatchMethod.from_str(dispatch_method)
-
-        self.heart_beat_thread = threading.Thread(
-            target=heart_beat_controller, args=(self,))
-        self.heart_beat_thread.start()
-
-        logger.info("Init controller")
-
-    def register_worker(self, worker_name: str, check_heart_beat: bool,
-                        worker_status: dict):
-        if worker_name not in self.worker_info:
-            logger.info(f"Register a new worker: {worker_name}")
-        else:
-            logger.info(f"Register an existing worker: {worker_name}")
-
-        if not worker_status:
-            worker_status = self.get_worker_status(worker_name)
-        if not worker_status:
-            return False
-
-        self.worker_info[worker_name] = WorkerInfo(
-            worker_status["model_names"], worker_status["speed"], worker_status["queue_length"],
-            check_heart_beat, time.time())
-
-        logger.info(f"Register done: {worker_name}, {worker_status}")
-        return True
-
-    def get_worker_status(self, worker_name: str):
-        try:
-            r = requests.post(worker_name + "/worker_get_status", timeout=5)
-        except requests.exceptions.RequestException as e:
-            logger.error(f"Get status fails: {worker_name}, {e}")
-            return None
-
-        if r.status_code != 200:
-            logger.error(f"Get status fails: {worker_name}, {r}")
-            return None
-
-        return r.json()
-
-    def remove_worker(self, worker_name: str):
-        del self.worker_info[worker_name]
-
-    def refresh_all_workers(self):
-        old_info = dict(self.worker_info)
-        self.worker_info = {}
-
-        for w_name, w_info in old_info.items():
-            if not self.register_worker(w_name, w_info.check_heart_beat, None):
-                logger.info(f"Remove stale worker: {w_name}")
-
-    def list_models(self):
-        model_names = set()
-
-        for w_name, w_info in self.worker_info.items():
-            model_names.update(w_info.model_names)
-
-        return list(model_names)
-
-    def get_worker_address(self, model_name: str):
-        if self.dispatch_method == DispatchMethod.LOTTERY:
-            worker_names = []
-            worker_speeds = []
-            for w_name, w_info in self.worker_info.items():
-                if model_name in w_info.model_names:
-                    worker_names.append(w_name)
-                    worker_speeds.append(w_info.speed)
-            worker_speeds = np.array(worker_speeds, dtype=np.float32)
-            norm = np.sum(worker_speeds)
-            if norm < 1e-4:
-                return ""
-            worker_speeds = worker_speeds / norm
-            if True:  # Directly return address
-                pt = np.random.choice(np.arange(len(worker_names)),
-                    p=worker_speeds)
-                worker_name = worker_names[pt]
-                return worker_name
-
-            # Check status before returning
-            while True:
-                pt = np.random.choice(np.arange(len(worker_names)),
-                    p=worker_speeds)
-                worker_name = worker_names[pt]
-
-                if self.get_worker_status(worker_name):
-                    break
-                else:
-                    self.remove_worker(worker_name)
-                    worker_speeds[pt] = 0
-                    norm = np.sum(worker_speeds)
-                    if norm < 1e-4:
-                        return ""
-                    worker_speeds = worker_speeds / norm
-                    continue
-            return worker_name
-        elif self.dispatch_method == DispatchMethod.SHORTEST_QUEUE:
-            worker_names = []
-            worker_qlen = []
-            for w_name, w_info in self.worker_info.items():
-                if model_name in w_info.model_names:
-                    worker_names.append(w_name)
-                    worker_qlen.append(w_info.queue_length / w_info.speed)
-            if len(worker_names) == 0:
-                return ""
-            min_index = np.argmin(worker_qlen)
-            w_name = worker_names[min_index]
-            self.worker_info[w_name].queue_length += 1
-            logger.info(f"names: {worker_names}, queue_lens: {worker_qlen}, ret: {w_name}")
-            return w_name
-        else:
-            raise ValueError(f"Invalid dispatch method: {self.dispatch_method}")
-
-    def receive_heart_beat(self, worker_name: str, queue_length: int):
-        if worker_name not in self.worker_info:
-            logger.info(f"Receive unknown heart beat. {worker_name}")
-            return False
-
-        self.worker_info[worker_name].queue_length = queue_length
-        self.worker_info[worker_name].last_heart_beat = time.time()
-        logger.info(f"Receive heart beat. {worker_name}")
-        return True
-
-    def remove_stable_workers_by_expiration(self):
-        expire = time.time() - CONTROLLER_HEART_BEAT_EXPIRATION
-        to_delete = []
-        for worker_name, w_info in self.worker_info.items():
-            if w_info.check_heart_beat and w_info.last_heart_beat < expire:
-                to_delete.append(worker_name)
-
-        for worker_name in to_delete:
-            self.remove_worker(worker_name)
-
-    def worker_api_generate_stream(self, params):
-        worker_addr = self.get_worker_address(params["model"])
-        if not worker_addr:
-            logger.info(f"no worker: {params['model']}")
-            ret = {
-                "text": server_error_msg,
-                "error_code": 2,
-            }
-            yield json.dumps(ret).encode() + b"\0"
-
-        try:
-            response = requests.post(worker_addr + "/worker_generate_stream",
-                json=params, stream=True, timeout=5)
-            for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
-                if chunk:
-                    yield chunk + b"\0"
-        except requests.exceptions.RequestException as e:
-            logger.info(f"worker timeout: {worker_addr}")
-            ret = {
-                "text": server_error_msg,
-                "error_code": 3,
-            }
-            yield json.dumps(ret).encode() + b"\0"
-
-
-    # Let the controller act as a worker to achieve hierarchical
-    # management. This can be used to connect isolated sub networks.
-    def worker_api_get_status(self):
-        model_names = set()
-        speed = 0
-        queue_length = 0
-
-        for w_name in self.worker_info:
-            worker_status = self.get_worker_status(w_name)
-            if worker_status is not None:
-                model_names.update(worker_status["model_names"])
-                speed += worker_status["speed"]
-                queue_length += worker_status["queue_length"]
-
-        return {
-            "model_names": list(model_names),
-            "speed": speed,
-            "queue_length": queue_length,
-        }
-
-
-app = FastAPI()
-
-
-@app.post("/register_worker")
-async def register_worker(request: Request):
-    data = await request.json()
-    controller.register_worker(
-        data["worker_name"], data["check_heart_beat"],
-        data.get("worker_status", None))
-
-
-@app.post("/refresh_all_workers")
-async def refresh_all_workers():
-    models = controller.refresh_all_workers()
-
-
-@app.post("/list_models")
-async def list_models():
-    models = controller.list_models()
-    return {"models": models}
-
-
-@app.post("/get_worker_address")
-async def get_worker_address(request: Request):
-    data = await request.json()
-    addr = controller.get_worker_address(data["model"])
-    return {"address": addr}
-
-
-@app.post("/receive_heart_beat")
-async def receive_heart_beat(request: Request):
-    data = await request.json()
-    exist = controller.receive_heart_beat(
-        data["worker_name"], data["queue_length"])
-    return {"exist": exist}
-
-
-@app.post("/worker_generate_stream")
-async def worker_api_generate_stream(request: Request):
-    params = await request.json()
-    generator = controller.worker_api_generate_stream(params)
-    return StreamingResponse(generator)
-
-
-@app.post("/worker_get_status")
-async def worker_api_get_status(request: Request):
-    return controller.worker_api_get_status()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--host", type=str, default="localhost")
-    parser.add_argument("--port", type=int, default=21001)
-    parser.add_argument("--dispatch-method", type=str, choices=[
-        "lottery", "shortest_queue"], default="shortest_queue")
-    args = parser.parse_args()
-    logger.info(f"args: {args}")
-
-    controller = Controller(args.dispatch_method)
-    uvicorn.run(app, host=args.host, port=args.port, log_level="info")
\ No newline at end of file
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/inference.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/inference.py
deleted file mode 100644
index 843bee8ab464aaa0296f5361b4aa65c30f625c1b..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/inference.py
+++ /dev/null
@@ -1,286 +0,0 @@
-from __future__ import annotations
-
-import gc
-import pathlib
-import sys
-import tempfile
-import os
-import gradio as gr
-import imageio
-import PIL.Image
-import torch
-from diffusers.utils.import_utils import is_xformers_available
-from einops import rearrange
-from huggingface_hub import ModelCard
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, CLIPTextModelWithProjection
-from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, PNDMScheduler, ControlNetModel, PriorTransformer, UnCLIPScheduler
-from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
-from omegaconf import OmegaConf
-from typing import Any, Callable, Dict, List, Optional, Union, Tuple
-
-sys.path.append('Make-A-Protagonist')
-
-from makeaprotagonist.models.unet import UNet3DConditionModel
-from makeaprotagonist.pipelines.pipeline_stable_unclip_controlavideo import MakeAProtagonistStableUnCLIPPipeline, MultiControlNetModel
-from makeaprotagonist.dataset.dataset import MakeAProtagonistDataset
-from makeaprotagonist.util import save_videos_grid, ddim_inversion_unclip, ddim_inversion_prior
-from experts.grounded_sam_mask_out import mask_out_reference_image
-
-
-import ipdb
-
-class InferencePipeline:
-    def __init__(self, hf_token: str | None = None):
-        self.hf_token = hf_token
-        self.pipe = None
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.model_id = None
-
-        self.conditions = None
-        self.masks = None
-        self.ddim_inv_latent = None
-        self.train_dataset, self.sample_indices = None, None
-
-    def clear(self) -> None:
-        self.model_id = None
-        del self.pipe
-        self.pipe = None
-        self.conditions = None
-        self.masks = None
-        self.ddim_inv_latent = None
-        self.train_dataset, self.sample_indices = None, None
-
-        torch.cuda.empty_cache()
-        gc.collect()
-
-    @staticmethod
-    def check_if_model_is_local(model_id: str) -> bool:
-        return pathlib.Path(model_id).exists()
-
-    @staticmethod
-    def get_model_card(model_id: str,
-                       hf_token: str | None = None) -> ModelCard:
-        if InferencePipeline.check_if_model_is_local(model_id):
-            card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
-        else:
-            card_path = model_id
-        return ModelCard.load(card_path, token=hf_token)
-
-    @staticmethod
-    def get_base_model_info(model_id: str, hf_token: str | None = None) -> str:
-        card = InferencePipeline.get_model_card(model_id, hf_token)
-        return card.data.base_model
-
-    @torch.no_grad()
-    def load_pipe(self, model_id: str, n_steps, seed) -> None:
-        if model_id == self.model_id:
-            return self.conditions, self.masks, self.ddim_inv_latent, self.train_dataset, self.sample_indices
-
-        base_model_id = self.get_base_model_info(model_id, self.hf_token)
-        
-        pretrained_model_path = 'stabilityai/stable-diffusion-2-1-unclip-small'
-        # image encoding components
-        feature_extractor = CLIPImageProcessor.from_pretrained(pretrained_model_path, subfolder="feature_extractor")
-        image_encoder = CLIPVisionModelWithProjection.from_pretrained(pretrained_model_path, subfolder="image_encoder")
-        # image noising components
-        image_normalizer = StableUnCLIPImageNormalizer.from_pretrained(pretrained_model_path, subfolder="image_normalizer", torch_dtype=torch.float16,)
-        image_noising_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="image_noising_scheduler")
-        # regular denoising components
-        tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
-        text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder", torch_dtype=torch.float16,)
-        vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae", torch_dtype=torch.float16,)
-        self.ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
-        self.ddim_inv_scheduler.set_timesteps(n_steps)
-
-        prior_model_id = "kakaobrain/karlo-v1-alpha"
-        data_type = torch.float16
-        prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type)
-
-        prior_text_model_id = "openai/clip-vit-large-patch14"
-        prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
-        prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
-        prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler")
-        prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
-
-        controlnet_model_id = ['controlnet-2-1-unclip-small-openposefull', 'controlnet-2-1-unclip-small-depth']
-        controlnet = MultiControlNetModel( [ControlNetModel.from_pretrained('Make-A-Protagonist/controlnet-2-1-unclip-small', subfolder=subfolder_id, torch_dtype=torch.float16) for subfolder_id in controlnet_model_id] )
-
-        unet = UNet3DConditionModel.from_pretrained(
-            model_id,
-            subfolder='unet',
-            torch_dtype=torch.float16,
-            use_auth_token=self.hf_token)
-
-        # Freeze vae and text_encoder and adapter
-        vae.requires_grad_(False)
-        text_encoder.requires_grad_(False)
-
-        ## freeze image embed
-        image_encoder.requires_grad_(False)
-
-        unet.requires_grad_(False)
-        ## freeze controlnet
-        controlnet.requires_grad_(False)
-
-        ## freeze prior
-        prior.requires_grad_(False)
-        prior_text_model.requires_grad_(False)
-
-        config_file = os.path.join('Make-A-Protagonist/configs', model_id.split('/')[-1] + '.yaml')
-        self.cfg = OmegaConf.load(config_file)
-
-        # def source_parsing(self, n_steps):
-        # ipdb.set_trace()
-        train_dataset = MakeAProtagonistDataset(**self.cfg)
-        train_dataset.preprocess_img_embedding(feature_extractor, image_encoder)
-        train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=1, num_workers=0, 
-        )
-        image_encoder.to(dtype=data_type)
-        pipe = MakeAProtagonistStableUnCLIPPipeline(
-                prior_tokenizer=prior_tokenizer,
-                prior_text_encoder=prior_text_model,
-                prior=prior,
-                prior_scheduler=prior_scheduler,
-                feature_extractor=feature_extractor,
-                image_encoder=image_encoder,
-                image_normalizer=image_normalizer,
-                image_noising_scheduler=image_noising_scheduler,
-                vae=vae, 
-                text_encoder=text_encoder, 
-                tokenizer=tokenizer, 
-                unet=unet,
-                controlnet=controlnet,
-                scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
-            )
-
-        pipe = pipe.to(self.device)
-        
-        if is_xformers_available():
-            pipe.unet.enable_xformers_memory_efficient_attention()
-            pipe.controlnet.enable_xformers_memory_efficient_attention()
-        self.pipe = pipe
-        self.model_id = model_id  # type: ignore
-        self.vae = vae
-        # self.feature_extractor = feature_extractor
-        # self.image_encoder = image_encoder
-        ## ddim inverse for source video
-
-        batch = next(iter(train_dataloader))
-        weight_dtype = torch.float16
-        pixel_values = batch["pixel_values"].to(weight_dtype).to(self.device)
-        video_length = pixel_values.shape[1]
-        pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
-        latents = self.vae.encode(pixel_values).latent_dist.sample()
-        latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
-        latents = latents * self.vae.config.scaling_factor
-        # ControlNet
-        # ipdb.set_trace()
-        conditions = [_condition.to(weight_dtype).to(self.device) for _, _condition in batch["conditions"].items()] # b f c h w
-        masks = batch["masks"].to(weight_dtype).to(self.device) # b,f,1,h,w
-        emb_dim = train_dataset.img_embeddings[0].size(0)
-        key_frame_embed = torch.zeros((1, emb_dim)).to(device=latents.device, dtype=latents.dtype) ## this is dim 0
-        # ipdb.set_trace()
-        ddim_inv_latent = ddim_inversion_unclip(
-            self.pipe, self.ddim_inv_scheduler, video_latent=latents,
-            num_inv_steps=n_steps, prompt="", image_embed=key_frame_embed, noise_level=0, seed=seed)[-1].to(weight_dtype)
-        self.conditions = conditions
-        self.masks = masks
-        self.ddim_inv_latent = ddim_inv_latent
-        self.train_dataset = train_dataset
-        self.sample_indices = batch["sample_indices"][0]
-        return conditions, masks, ddim_inv_latent, train_dataset, batch["sample_indices"][0]
-                
-    def run(
-        self,
-        model_id: str,
-        prompt: str,
-        video_length: int,
-        fps: int,
-        seed: int,
-        n_steps: int,
-        guidance_scale: float,
-        ref_image: PIL.Image.Image,
-        ref_pro_prompt: str,
-        noise_level: int,
-        start_step: int, 
-        control_pose: float,
-        control_depth: float,
-        source_pro: int = 0, # 0 or 1
-        source_bg: int = 0,
-    ) -> PIL.Image.Image:
-
-        if not torch.cuda.is_available():
-            raise gr.Error('CUDA is not available.')
-
-        torch.cuda.empty_cache()
-        
-        conditions, masks, ddim_inv_latent, _, _ = self.load_pipe(model_id, n_steps, seed)
-        ## conditions [1,F,3,H,W]
-        ## masks [1,F,1,H,W]
-        ## ddim_inv_latent [1,4,F,H,W]
-        ## NOTE this is to deal with video length
-        conditions = [_condition[:,:video_length] for _condition in conditions]
-        masks = masks[:, :video_length]
-        ddim_inv_latent = ddim_inv_latent[:,:,:video_length]
-
-        generator = torch.Generator(device=self.device).manual_seed(seed)
-
-        ## TODO mask out reference image
-        # ipdb.set_trace()
-        ref_image = mask_out_reference_image(ref_image, ref_pro_prompt)
-        controlnet_conditioning_scale = [control_pose, control_depth]
-
-        prior_denoised_embeds = None
-        image_embed = None
-        if source_bg:
-            ## using source background and changing the protagonist
-            prior_denoised_embeds = self.train_dataset.img_embeddings[0][None].to(device=ddim_inv_latent.device, dtype=ddim_inv_latent.dtype) # 1, 768 for UnCLIP-small
-        
-        if source_pro:
-            # using source protagonist and changing the background
-            sample_indices = self.sample_indices
-            image_embed = [self.train_dataset.img_embeddings[idx] for idx in sample_indices]
-            image_embed = torch.stack(image_embed, dim=0).to(device=ddim_inv_latent.device, dtype=ddim_inv_latent.dtype) # F, 768 for UnCLIP-small # F,C
-            image_embed = image_embed[:video_length]
-            ref_image = None
-
-        # ipdb.set_trace()
-        out = self.pipe(
-            image=ref_image, 
-            prompt=prompt, 
-            control_image=conditions, 
-            video_length=video_length,
-            width=768,
-            height=768,
-            num_inference_steps=n_steps,
-            guidance_scale=guidance_scale,
-            generator=generator,
-            ## ddim inversion
-            latents=ddim_inv_latent, 
-            ## ref image embeds 
-            noise_level=noise_level, 
-            ## controlnet
-            controlnet_conditioning_scale=controlnet_conditioning_scale,
-            ## mask
-            masks=masks, 
-            mask_mode='all',
-            mask_latent_fuse_mode = 'all',
-            start_step=start_step,
-            ## edit bg and pro
-            prior_latents=None, 
-            image_embeds=image_embed, # keep pro
-            prior_denoised_embeds=prior_denoised_embeds # keep bg
-            )
-
-        frames = rearrange(out.videos[0], 'c t h w -> t h w c')
-        frames = (frames * 255).to(torch.uint8).numpy()
-
-        out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-        writer = imageio.get_writer(out_file.name, fps=fps)
-        for frame in frames:
-            writer.append_data(frame)
-        writer.close()
-
-        return out_file.name
diff --git a/spaces/MarkuzML/swap_face/app.py b/spaces/MarkuzML/swap_face/app.py
deleted file mode 100644
index 4552b8ae7520323f66f385c79e9f24b3d80d4781..0000000000000000000000000000000000000000
--- a/spaces/MarkuzML/swap_face/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import streamlit as st
-import face_recognition
-import os
-import cv2
-import insightface
-import pickle
-
-from insightface.app import FaceAnalysis
-
-
-PATH_TMP = 'tmp'
-PATH_MODEL = 'bin'
-DATA_IMAGE_PICKLE = 'data_images.pkl'
-ONNX_SWAPPER_MODEL = 'inswapper_128.onnx'
-
-
-def face_swapper(image_background, image_customer):
-    app = FaceAnalysis(name='buffalo_l')
-    app.prepare(ctx_id=0, det_size=(640, 640))
-    swapper = insightface.model_zoo.get_model(os.path.join(os.getcwd(), PATH_MODEL, ONNX_SWAPPER_MODEL), download=False)
-    face_customer = app.get(image_customer)[0]
-    faces = app.get(image_background)
-
-    for face in faces:
-        image_background = swapper.get(image_background, face, face_customer, paste_back=True)
-
-    return image_background
-
-def process(image):
-    with open(os.path.join(os.getcwd(), PATH_MODEL, DATA_IMAGE_PICKLE), 'rb') as file:
-        data_images = pickle.load(file)
-
-    images_background_encoding, images_background_contents = data_images['encodings'], data_images['content']
-    image_loaded = face_recognition.load_image_file(image)
-    face_encoding = face_recognition.face_encodings(image_loaded)[0]
-    face_distances = face_recognition.face_distance(images_background_encoding, face_encoding)
-
-    tmp_distance = face_distances[0]
-    tmp_content = images_background_contents[0]
-    for face_distance, images_background_content in zip(face_distances[1:], images_background_contents[1:]):
-        if tmp_distance > face_distance:
-            tmp_distance = face_distance
-            tmp_content = images_background_content
-
-    output_image = face_swapper(tmp_content, image_loaded)
-    return output_image
-
-image_output = None
-
-st.title('Change Faces')
-
-option = st.radio('How would you like to upload your image?', ('File', 'WebCam'), horizontal=True)
-
-if option=='File':
-    uploaded_file = st.file_uploader('Choose your image', type=['jpg', 'png', 'jpeg'])
-else:
-    uploaded_file = st.camera_input("Take a picture")
-
-
-if uploaded_file is not None:
-    bytes_data = uploaded_file.getvalue()
-    if option=='File':
-        st.image(uploaded_file)
-    if st.button('Process'):
-        image_output = process(uploaded_file)
-        st.image(image_output)
-
-if image_output is not None:
-    image_output_to_download = cv2.cvtColor(image_output, cv2.COLOR_BGR2RGB)
-    _, image_output_to_download = cv2.imencode('.jpg', image_output_to_download)
-    st.download_button('Download image', image_output_to_download.tobytes(), file_name=f'output_{uploaded_file.name}')
diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/modules/crepe.py b/spaces/MashiroSA/sovits-emu-voice-transform/modules/crepe.py
deleted file mode 100644
index 0bff0e3474de6483290b56993f9b845e91ef9702..0000000000000000000000000000000000000000
--- a/spaces/MashiroSA/sovits-emu-voice-transform/modules/crepe.py
+++ /dev/null
@@ -1,327 +0,0 @@
-from typing import Optional,Union
-try:
-    from typing import Literal
-except Exception as e:
-    from typing_extensions import Literal
-import numpy as np
-import torch
-import torchcrepe
-from torch import nn
-from torch.nn import functional as F
-import scipy
-
-#from:https://github.com/fishaudio/fish-diffusion
-
-def repeat_expand(
-    content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
-):
-    """Repeat content to target length.
-    This is a wrapper of torch.nn.functional.interpolate.
-
-    Args:
-        content (torch.Tensor): tensor
-        target_len (int): target length
-        mode (str, optional): interpolation mode. Defaults to "nearest".
-
-    Returns:
-        torch.Tensor: tensor
-    """
-
-    ndim = content.ndim
-
-    if content.ndim == 1:
-        content = content[None, None]
-    elif content.ndim == 2:
-        content = content[None]
-
-    assert content.ndim == 3
-
-    is_np = isinstance(content, np.ndarray)
-    if is_np:
-        content = torch.from_numpy(content)
-
-    results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
-
-    if is_np:
-        results = results.numpy()
-
-    if ndim == 1:
-        return results[0, 0]
-    elif ndim == 2:
-        return results[0]
-
-
-class BasePitchExtractor:
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        keep_zeros: bool = True,
-    ):
-        """Base pitch extractor.
-
-        Args:
-            hop_length (int, optional): Hop length. Defaults to 512.
-            f0_min (float, optional): Minimum f0. Defaults to 50.0.
-            f0_max (float, optional): Maximum f0. Defaults to 1100.0.
-            keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
-        """
-
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.keep_zeros = keep_zeros
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        raise NotImplementedError("BasePitchExtractor is not callable.")
-
-    def post_process(self, x, sampling_rate, f0, pad_to):
-        if isinstance(f0, np.ndarray):
-            f0 = torch.from_numpy(f0).float().to(x.device)
-
-        if pad_to is None:
-            return f0
-
-        f0 = repeat_expand(f0, pad_to)
-
-        if self.keep_zeros:
-            return f0
-        
-        vuv_vector = torch.zeros_like(f0)
-        vuv_vector[f0 > 0.0] = 1.0
-        vuv_vector[f0 <= 0.0] = 0.0
-        
-        # 去掉0频率, 并线性插值
-        nzindex = torch.nonzero(f0).squeeze()
-        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
-        time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
-        time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
-
-        if f0.shape[0] <= 0:
-            return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
-
-        if f0.shape[0] == 1:
-            return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
-    
-        # 大概可以用 torch 重写?
-        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
-        vuv_vector = vuv_vector.cpu().numpy()
-        vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
-        
-        return f0,vuv_vector
-
-
-class MaskedAvgPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of mean pooling that supports masked values.
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedAvgPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        # Apply the mask by setting masked elements to zero, or make NaNs zero
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        # Ensure mask has the same shape as the input tensor
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-        # Create a ones kernel with the same number of channels as the input tensor
-        ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
-
-        # Perform sum pooling
-        sum_pooled = nn.functional.conv1d(
-            masked_x,
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-
-        # Count the non-masked (valid) elements in each pooling window
-        valid_count = nn.functional.conv1d(
-            mask.float(),
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-        valid_count = valid_count.clamp(min=1)  # Avoid division by zero
-
-        # Perform masked average pooling
-        avg_pooled = sum_pooled / valid_count
-
-        # Fill zero values with NaNs
-        avg_pooled[avg_pooled == 0] = float("nan")
-
-        if ndim == 2:
-            return avg_pooled.squeeze(1)
-
-        return avg_pooled
-
-
-class MaskedMedianPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of median pooling that supports masked values.
-
-        This implementation is inspired by the median pooling implementation in
-        https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedMedianPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-
-        x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
-        mask = F.pad(
-            mask.float(), (self.padding, self.padding), mode="constant", value=0
-        )
-
-        x = x.unfold(2, self.kernel_size, self.stride)
-        mask = mask.unfold(2, self.kernel_size, self.stride)
-
-        x = x.contiguous().view(x.size()[:3] + (-1,))
-        mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
-
-        # Combine the mask with the input tensor
-        #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
-        x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
-
-        # Sort the masked tensor along the last dimension
-        x_sorted, _ = torch.sort(x_masked, dim=-1)
-
-        # Compute the count of non-masked (valid) values
-        valid_count = mask.sum(dim=-1)
-
-        # Calculate the index of the median value for each pooling window
-        median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
-
-        # Gather the median values using the calculated indices
-        median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
-
-        # Fill infinite values with NaNs
-        median_pooled[torch.isinf(median_pooled)] = float("nan")
-        
-        if ndim == 2:
-            return median_pooled.squeeze(1)
-
-        return median_pooled
-
-
-class CrepePitchExtractor(BasePitchExtractor):
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        threshold: float = 0.05,
-        keep_zeros: bool = False,
-        device = None,
-        model: Literal["full", "tiny"] = "full",
-        use_fast_filters: bool = True,
-    ):
-        super().__init__(hop_length, f0_min, f0_max, keep_zeros)
-
-        self.threshold = threshold
-        self.model = model
-        self.use_fast_filters = use_fast_filters
-        self.hop_length = hop_length
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        if self.use_fast_filters:
-            self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
-            self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        """Extract pitch using crepe.
-
-
-        Args:
-            x (torch.Tensor): Audio signal, shape (1, T).
-            sampling_rate (int, optional): Sampling rate. Defaults to 44100.
-            pad_to (int, optional): Pad to length. Defaults to None.
-
-        Returns:
-            torch.Tensor: Pitch, shape (T // hop_length,).
-        """
-
-        assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
-        assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
-
-        x = x.to(self.dev)
-        f0, pd = torchcrepe.predict(
-            x,
-            sampling_rate,
-            self.hop_length,
-            self.f0_min,
-            self.f0_max,
-            pad=True,
-            model=self.model,
-            batch_size=1024,
-            device=x.device,
-            return_periodicity=True,
-        )
-
-        # Filter, remove silence, set uv threshold, refer to the original warehouse readme
-        if self.use_fast_filters:
-            pd = self.median_filter(pd)
-        else:
-            pd = torchcrepe.filter.median(pd, 3)
-
-        pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
-        f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
-        
-        if self.use_fast_filters:
-            f0 = self.mean_filter(f0)
-        else:
-            f0 = torchcrepe.filter.mean(f0, 3)
-
-        f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
-
-        return self.post_process(x, sampling_rate, f0, pad_to)
diff --git a/spaces/MathysL/AutoGPT4/run.sh b/spaces/MathysL/AutoGPT4/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
-    echo Installing missing packages...
-    pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
deleted file mode 100644
index 722d5d8d71f75486e2db3008907c4eadfca41d63..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .conv_module import ConvModule
-
-
-class DepthwiseSeparableConvModule(nn.Module):
-    """Depthwise separable convolution module.
-
-    See https://arxiv.org/pdf/1704.04861.pdf for details.
-
-    This module can replace a ConvModule with the conv block replaced by two
-    conv block: depthwise conv block and pointwise conv block. The depthwise
-    conv block contains depthwise-conv/norm/activation layers. The pointwise
-    conv block contains pointwise-conv/norm/activation layers. It should be
-    noted that there will be norm/activation layer in the depthwise conv block
-    if `norm_cfg` and `act_cfg` are specified.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-            Same as that in ``nn._ConvNd``.
-        out_channels (int): Number of channels produced by the convolution.
-            Same as that in ``nn._ConvNd``.
-        kernel_size (int | tuple[int]): Size of the convolving kernel.
-            Same as that in ``nn._ConvNd``.
-        stride (int | tuple[int]): Stride of the convolution.
-            Same as that in ``nn._ConvNd``. Default: 1.
-        padding (int | tuple[int]): Zero-padding added to both sides of
-            the input. Same as that in ``nn._ConvNd``. Default: 0.
-        dilation (int | tuple[int]): Spacing between kernel elements.
-            Same as that in ``nn._ConvNd``. Default: 1.
-        norm_cfg (dict): Default norm config for both depthwise ConvModule and
-            pointwise ConvModule. Default: None.
-        act_cfg (dict): Default activation config for both depthwise ConvModule
-            and pointwise ConvModule. Default: dict(type='ReLU').
-        dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
-            'default', it will be the same as `norm_cfg`. Default: 'default'.
-        dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
-            'default', it will be the same as `act_cfg`. Default: 'default'.
-        pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
-            'default', it will be the same as `norm_cfg`. Default: 'default'.
-        pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
-            'default', it will be the same as `act_cfg`. Default: 'default'.
-        kwargs (optional): Other shared arguments for depthwise and pointwise
-            ConvModule. See ConvModule for ref.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 norm_cfg=None,
-                 act_cfg=dict(type='ReLU'),
-                 dw_norm_cfg='default',
-                 dw_act_cfg='default',
-                 pw_norm_cfg='default',
-                 pw_act_cfg='default',
-                 **kwargs):
-        super(DepthwiseSeparableConvModule, self).__init__()
-        assert 'groups' not in kwargs, 'groups should not be specified'
-
-        # if norm/activation config of depthwise/pointwise ConvModule is not
-        # specified, use default config.
-        dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
-        dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
-        pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
-        pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
-
-        # depthwise convolution
-        self.depthwise_conv = ConvModule(
-            in_channels,
-            in_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=in_channels,
-            norm_cfg=dw_norm_cfg,
-            act_cfg=dw_act_cfg,
-            **kwargs)
-
-        self.pointwise_conv = ConvModule(
-            in_channels,
-            out_channels,
-            1,
-            norm_cfg=pw_norm_cfg,
-            act_cfg=pw_act_cfg,
-            **kwargs)
-
-    def forward(self, x):
-        x = self.depthwise_conv(x)
-        x = self.pointwise_conv(x)
-        return x
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/dist_utils.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/dist_utils.py
deleted file mode 100644
index d3a1ef3fda5ceeb31bf15a73779da1b1903ab0fe..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/dist_utils.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-import os
-import subprocess
-from collections import OrderedDict
-
-import torch
-import torch.multiprocessing as mp
-from torch import distributed as dist
-from torch._utils import (_flatten_dense_tensors, _take_tensors,
-                          _unflatten_dense_tensors)
-
-
-def init_dist(launcher, backend='nccl', **kwargs):
-    if mp.get_start_method(allow_none=True) is None:
-        mp.set_start_method('spawn')
-    if launcher == 'pytorch':
-        _init_dist_pytorch(backend, **kwargs)
-    elif launcher == 'mpi':
-        _init_dist_mpi(backend, **kwargs)
-    elif launcher == 'slurm':
-        _init_dist_slurm(backend, **kwargs)
-    else:
-        raise ValueError(f'Invalid launcher type: {launcher}')
-
-
-def _init_dist_pytorch(backend, **kwargs):
-    # TODO: use local_rank instead of rank % num_gpus
-    rank = int(os.environ['RANK'])
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(rank % num_gpus)
-    dist.init_process_group(backend=backend, **kwargs)
-
-
-def _init_dist_mpi(backend, **kwargs):
-    # TODO: use local_rank instead of rank % num_gpus
-    rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(rank % num_gpus)
-    dist.init_process_group(backend=backend, **kwargs)
-
-
-def _init_dist_slurm(backend, port=None):
-    """Initialize slurm distributed training environment.
-
-    If argument ``port`` is not specified, then the master port will be system
-    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
-    environment variable, then a default port ``29500`` will be used.
-
-    Args:
-        backend (str): Backend of torch.distributed.
-        port (int, optional): Master port. Defaults to None.
-    """
-    proc_id = int(os.environ['SLURM_PROCID'])
-    ntasks = int(os.environ['SLURM_NTASKS'])
-    node_list = os.environ['SLURM_NODELIST']
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(proc_id % num_gpus)
-    addr = subprocess.getoutput(
-        f'scontrol show hostname {node_list} | head -n1')
-    # specify master port
-    if port is not None:
-        os.environ['MASTER_PORT'] = str(port)
-    elif 'MASTER_PORT' in os.environ:
-        pass  # use MASTER_PORT in the environment variable
-    else:
-        # 29500 is torch.distributed default port
-        os.environ['MASTER_PORT'] = '29500'
-    # use MASTER_ADDR in the environment variable if it already exists
-    if 'MASTER_ADDR' not in os.environ:
-        os.environ['MASTER_ADDR'] = addr
-    os.environ['WORLD_SIZE'] = str(ntasks)
-    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
-    os.environ['RANK'] = str(proc_id)
-    dist.init_process_group(backend=backend)
-
-
-def get_dist_info():
-    if dist.is_available() and dist.is_initialized():
-        rank = dist.get_rank()
-        world_size = dist.get_world_size()
-    else:
-        rank = 0
-        world_size = 1
-    return rank, world_size
-
-
-def master_only(func):
-
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        rank, _ = get_dist_info()
-        if rank == 0:
-            return func(*args, **kwargs)
-
-    return wrapper
-
-
-def allreduce_params(params, coalesce=True, bucket_size_mb=-1):
-    """Allreduce parameters.
-
-    Args:
-        params (list[torch.Parameters]): List of parameters or buffers of a
-            model.
-        coalesce (bool, optional): Whether allreduce parameters as a whole.
-            Defaults to True.
-        bucket_size_mb (int, optional): Size of bucket, the unit is MB.
-            Defaults to -1.
-    """
-    _, world_size = get_dist_info()
-    if world_size == 1:
-        return
-    params = [param.data for param in params]
-    if coalesce:
-        _allreduce_coalesced(params, world_size, bucket_size_mb)
-    else:
-        for tensor in params:
-            dist.all_reduce(tensor.div_(world_size))
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
-    """Allreduce gradients.
-
-    Args:
-        params (list[torch.Parameters]): List of parameters of a model
-        coalesce (bool, optional): Whether allreduce parameters as a whole.
-            Defaults to True.
-        bucket_size_mb (int, optional): Size of bucket, the unit is MB.
-            Defaults to -1.
-    """
-    grads = [
-        param.grad.data for param in params
-        if param.requires_grad and param.grad is not None
-    ]
-    _, world_size = get_dist_info()
-    if world_size == 1:
-        return
-    if coalesce:
-        _allreduce_coalesced(grads, world_size, bucket_size_mb)
-    else:
-        for tensor in grads:
-            dist.all_reduce(tensor.div_(world_size))
-
-
-def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
-    if bucket_size_mb > 0:
-        bucket_size_bytes = bucket_size_mb * 1024 * 1024
-        buckets = _take_tensors(tensors, bucket_size_bytes)
-    else:
-        buckets = OrderedDict()
-        for tensor in tensors:
-            tp = tensor.type()
-            if tp not in buckets:
-                buckets[tp] = []
-            buckets[tp].append(tensor)
-        buckets = buckets.values()
-
-    for bucket in buckets:
-        flat_tensors = _flatten_dense_tensors(bucket)
-        dist.all_reduce(flat_tensors)
-        flat_tensors.div_(world_size)
-        for tensor, synced in zip(
-                bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
-            tensor.copy_(synced)
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/logs.py b/spaces/MetaWabbit/Auto-GPT/autogpt/logs.py
deleted file mode 100644
index 35037404a98f7be9b7d577b625cc190ca27f4566..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/logs.py
+++ /dev/null
@@ -1,332 +0,0 @@
-"""Logging module for Auto-GPT."""
-import json
-import logging
-import os
-import random
-import re
-import time
-import traceback
-from logging import LogRecord
-
-from colorama import Fore, Style
-
-from autogpt.config import Config, Singleton
-from autogpt.speech import say_text
-
-CFG = Config()
-
-
-class Logger(metaclass=Singleton):
-    """
-    Logger that handle titles in different colors.
-    Outputs logs in console, activity.log, and errors.log
-    For console handler: simulates typing
-    """
-
-    def __init__(self):
-        # create log directory if it doesn't exist
-        this_files_dir_path = os.path.dirname(__file__)
-        log_dir = os.path.join(this_files_dir_path, "../logs")
-        if not os.path.exists(log_dir):
-            os.makedirs(log_dir)
-
-        log_file = "activity.log"
-        error_file = "error.log"
-
-        console_formatter = AutoGptFormatter("%(title_color)s %(message)s")
-
-        # Create a handler for console which simulate typing
-        self.typing_console_handler = TypingConsoleHandler()
-        self.typing_console_handler.setLevel(logging.INFO)
-        self.typing_console_handler.setFormatter(console_formatter)
-
-        # Create a handler for console without typing simulation
-        self.console_handler = ConsoleHandler()
-        self.console_handler.setLevel(logging.DEBUG)
-        self.console_handler.setFormatter(console_formatter)
-
-        # Info handler in activity.log
-        self.file_handler = logging.FileHandler(
-            os.path.join(log_dir, log_file), "a", "utf-8"
-        )
-        self.file_handler.setLevel(logging.DEBUG)
-        info_formatter = AutoGptFormatter(
-            "%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
-        )
-        self.file_handler.setFormatter(info_formatter)
-
-        # Error handler error.log
-        error_handler = logging.FileHandler(
-            os.path.join(log_dir, error_file), "a", "utf-8"
-        )
-        error_handler.setLevel(logging.ERROR)
-        error_formatter = AutoGptFormatter(
-            "%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
-            " %(message_no_color)s"
-        )
-        error_handler.setFormatter(error_formatter)
-
-        self.typing_logger = logging.getLogger("TYPER")
-        self.typing_logger.addHandler(self.typing_console_handler)
-        self.typing_logger.addHandler(self.file_handler)
-        self.typing_logger.addHandler(error_handler)
-        self.typing_logger.setLevel(logging.DEBUG)
-
-        self.logger = logging.getLogger("LOGGER")
-        self.logger.addHandler(self.console_handler)
-        self.logger.addHandler(self.file_handler)
-        self.logger.addHandler(error_handler)
-        self.logger.setLevel(logging.DEBUG)
-
-    def typewriter_log(
-        self, title="", title_color="", content="", speak_text=False, level=logging.INFO
-    ):
-        if speak_text and CFG.speak_mode:
-            say_text(f"{title}. {content}")
-
-        if content:
-            if isinstance(content, list):
-                content = " ".join(content)
-        else:
-            content = ""
-
-        self.typing_logger.log(
-            level, content, extra={"title": title, "color": title_color}
-        )
-
-    def debug(
-        self,
-        message,
-        title="",
-        title_color="",
-    ):
-        self._log(title, title_color, message, logging.DEBUG)
-
-    def warn(
-        self,
-        message,
-        title="",
-        title_color="",
-    ):
-        self._log(title, title_color, message, logging.WARN)
-
-    def error(self, title, message=""):
-        self._log(title, Fore.RED, message, logging.ERROR)
-
-    def _log(self, title="", title_color="", message="", level=logging.INFO):
-        if message:
-            if isinstance(message, list):
-                message = " ".join(message)
-        self.logger.log(level, message, extra={"title": title, "color": title_color})
-
-    def set_level(self, level):
-        self.logger.setLevel(level)
-        self.typing_logger.setLevel(level)
-
-    def double_check(self, additionalText=None):
-        if not additionalText:
-            additionalText = (
-                "Please ensure you've setup and configured everything"
-                " correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
-                "double check. You can also create a github issue or join the discord"
-                " and ask there!"
-            )
-
-        self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
-
-
-"""
-Output stream to console using simulated typing
-"""
-
-
-class TypingConsoleHandler(logging.StreamHandler):
-    def emit(self, record):
-        min_typing_speed = 0.05
-        max_typing_speed = 0.01
-
-        msg = self.format(record)
-        try:
-            words = msg.split()
-            for i, word in enumerate(words):
-                print(word, end="", flush=True)
-                if i < len(words) - 1:
-                    print(" ", end="", flush=True)
-                typing_speed = random.uniform(min_typing_speed, max_typing_speed)
-                time.sleep(typing_speed)
-                # type faster after each word
-                min_typing_speed = min_typing_speed * 0.95
-                max_typing_speed = max_typing_speed * 0.95
-            print()
-        except Exception:
-            self.handleError(record)
-
-
-class ConsoleHandler(logging.StreamHandler):
-    def emit(self, record) -> None:
-        msg = self.format(record)
-        try:
-            print(msg)
-        except Exception:
-            self.handleError(record)
-
-
-class AutoGptFormatter(logging.Formatter):
-    """
-    Allows to handle custom placeholders 'title_color' and 'message_no_color'.
-    To use this formatter, make sure to pass 'color', 'title' as log extras.
-    """
-
-    def format(self, record: LogRecord) -> str:
-        if hasattr(record, "color"):
-            record.title_color = (
-                getattr(record, "color")
-                + getattr(record, "title")
-                + " "
-                + Style.RESET_ALL
-            )
-        else:
-            record.title_color = getattr(record, "title")
-        if hasattr(record, "msg"):
-            record.message_no_color = remove_color_codes(getattr(record, "msg"))
-        else:
-            record.message_no_color = ""
-        return super().format(record)
-
-
-def remove_color_codes(s: str) -> str:
-    ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
-    return ansi_escape.sub("", s)
-
-
-logger = Logger()
-
-
-def print_assistant_thoughts(ai_name, assistant_reply):
-    """Prints the assistant's thoughts to the console"""
-    from autogpt.json_utils.json_fix_llm import (
-        attempt_to_fix_json_by_finding_outermost_brackets,
-        fix_and_parse_json,
-    )
-
-    try:
-        try:
-            # Parse and print Assistant response
-            assistant_reply_json = fix_and_parse_json(assistant_reply)
-        except json.JSONDecodeError:
-            logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
-            assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
-                assistant_reply
-            )
-            if isinstance(assistant_reply_json, str):
-                assistant_reply_json = fix_and_parse_json(assistant_reply_json)
-
-        # Check if assistant_reply_json is a string and attempt to parse
-        # it into a JSON object
-        if isinstance(assistant_reply_json, str):
-            try:
-                assistant_reply_json = json.loads(assistant_reply_json)
-            except json.JSONDecodeError:
-                logger.error("Error: Invalid JSON\n", assistant_reply)
-                assistant_reply_json = (
-                    attempt_to_fix_json_by_finding_outermost_brackets(
-                        assistant_reply_json
-                    )
-                )
-
-        assistant_thoughts_reasoning = None
-        assistant_thoughts_plan = None
-        assistant_thoughts_speak = None
-        assistant_thoughts_criticism = None
-        if not isinstance(assistant_reply_json, dict):
-            assistant_reply_json = {}
-        assistant_thoughts = assistant_reply_json.get("thoughts", {})
-        assistant_thoughts_text = assistant_thoughts.get("text")
-
-        if assistant_thoughts:
-            assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
-            assistant_thoughts_plan = assistant_thoughts.get("plan")
-            assistant_thoughts_criticism = assistant_thoughts.get("criticism")
-            assistant_thoughts_speak = assistant_thoughts.get("speak")
-
-        logger.typewriter_log(
-            f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
-        )
-        logger.typewriter_log(
-            "REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
-        )
-
-        if assistant_thoughts_plan:
-            logger.typewriter_log("PLAN:", Fore.YELLOW, "")
-            # If it's a list, join it into a string
-            if isinstance(assistant_thoughts_plan, list):
-                assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
-            elif isinstance(assistant_thoughts_plan, dict):
-                assistant_thoughts_plan = str(assistant_thoughts_plan)
-
-            # Split the input_string using the newline character and dashes
-            lines = assistant_thoughts_plan.split("\n")
-            for line in lines:
-                line = line.lstrip("- ")
-                logger.typewriter_log("- ", Fore.GREEN, line.strip())
-
-        logger.typewriter_log(
-            "CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
-        )
-        # Speak the assistant's thoughts
-        if CFG.speak_mode and assistant_thoughts_speak:
-            say_text(assistant_thoughts_speak)
-        else:
-            logger.typewriter_log("SPEAK:", Fore.YELLOW, f"{assistant_thoughts_speak}")
-
-        return assistant_reply_json
-    except json.decoder.JSONDecodeError:
-        logger.error("Error: Invalid JSON\n", assistant_reply)
-        if CFG.speak_mode:
-            say_text(
-                "I have received an invalid JSON response from the OpenAI API."
-                " I cannot ignore this response."
-            )
-
-    # All other errors, return "Error: + error message"
-    except Exception:
-        call_stack = traceback.format_exc()
-        logger.error("Error: \n", call_stack)
-
-
-def print_assistant_thoughts(
-    ai_name: object, assistant_reply_json_valid: object
-) -> None:
-    assistant_thoughts_reasoning = None
-    assistant_thoughts_plan = None
-    assistant_thoughts_speak = None
-    assistant_thoughts_criticism = None
-
-    assistant_thoughts = assistant_reply_json_valid.get("thoughts", {})
-    assistant_thoughts_text = assistant_thoughts.get("text")
-    if assistant_thoughts:
-        assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
-        assistant_thoughts_plan = assistant_thoughts.get("plan")
-        assistant_thoughts_criticism = assistant_thoughts.get("criticism")
-        assistant_thoughts_speak = assistant_thoughts.get("speak")
-    logger.typewriter_log(
-        f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
-    )
-    logger.typewriter_log("REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}")
-    if assistant_thoughts_plan:
-        logger.typewriter_log("PLAN:", Fore.YELLOW, "")
-        # If it's a list, join it into a string
-        if isinstance(assistant_thoughts_plan, list):
-            assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
-        elif isinstance(assistant_thoughts_plan, dict):
-            assistant_thoughts_plan = str(assistant_thoughts_plan)
-
-        # Split the input_string using the newline character and dashes
-        lines = assistant_thoughts_plan.split("\n")
-        for line in lines:
-            line = line.lstrip("- ")
-            logger.typewriter_log("- ", Fore.GREEN, line.strip())
-    logger.typewriter_log("CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}")
-    # Speak the assistant's thoughts
-    if CFG.speak_mode and assistant_thoughts_speak:
-        say_text(assistant_thoughts_speak)
diff --git a/spaces/MikeTrizna/bhl_clip_classifier/app.py b/spaces/MikeTrizna/bhl_clip_classifier/app.py
deleted file mode 100644
index 67adc3673f4e7474e30733ba67cab44adb766bd8..0000000000000000000000000000000000000000
--- a/spaces/MikeTrizna/bhl_clip_classifier/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import numpy as np
-from PIL import Image
-
-checkpoint = "openai/clip-vit-base-patch32"
-classifier = pipeline(model=checkpoint, 
-                      task="zero-shot-image-classification")
-
-def shot(image, labels_text):
-    if labels_text:
-        labels = labels_text.split(";")
-    else:
-        labels = ['A page of printed text',
-                 'A page of handwritten text',
-                 'A blank page with no text',
-                 'A cover of a book',
-                 'A page of a book that contains a large illustration',
-                 'A page that features a table with multiple columns and rows']
-    results = classifier(image,
-                         candidate_labels=labels)
-    return {result["label"]: result["score"] for result in results}
-    
-demo = gr.Interface(shot, 
-                    [gr.Image(type="pil"), 
-                    gr.Textbox(
-                        label="Labels",
-                        info="Separated by a semicolon (;)",
-                        lines=6,
-                        value="""A page of printed text; 
-A page of handwritten text;
-A blank page with no text;
-A cover of a book;
-A page of a book that contains a large illustration;
-A page that features a table with multiple columns and rows""",
-                    )], 
-                    outputs="label", 
-                    examples=[['Journalsdateboo00DeanZ_0177.jpg',None], 
-                              ["newmexicobotani00newmb_0084.jpg",None],
-                              ["easternareacrui00natic_0004.jpg",None],
-                              ["1945fieldnotesla00klau_0318.jpg",None],
-                              ["sturmsfiguresofp01stur_0263.jpg",None]],
-                    description="Upload an image of a scanned document page, or choose one of the examples below",
-                    title="Zero-shot Image Classification of BHL Images")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/MilaNLProc/wordify/src/wordifier.py b/spaces/MilaNLProc/wordify/src/wordifier.py
deleted file mode 100644
index 7bc7cd085e92fc1a6d4d7e5d1f76c758fac34577..0000000000000000000000000000000000000000
--- a/spaces/MilaNLProc/wordify/src/wordifier.py
+++ /dev/null
@@ -1,157 +0,0 @@
-from typing import Dict, List, Tuple
-
-import numpy as np
-import pandas as pd
-import streamlit as st
-from pandas.core.frame import DataFrame
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LogisticRegression
-from sklearn.preprocessing import LabelEncoder
-from sklearn.utils import resample
-
-from .configs import InputTransformConfigs, ModelConfigs
-
-
-def input_transform(
-    text: pd.Series, labels: pd.Series, configs=InputTransformConfigs
-) -> Dict[str, np.ndarray]:
-    """
-    Encodes text in mathematical object ameanable to training algorithm
-    """
-    tfidf_vectorizer = TfidfVectorizer(
-        input="content",  # default: file already in memory
-        encoding="utf-8",  # default
-        decode_error="strict",  # default
-        strip_accents=None,  # do nothing
-        lowercase=False,  # do nothing
-        preprocessor=None,  # do nothing - default
-        tokenizer=None,  # default
-        stop_words=None,  # do nothing
-        analyzer="word",
-        ngram_range=configs.NGRAM_RANGE.value,  # maximum 3-ngrams
-        min_df=configs.MIN_DF.value,
-        max_df=configs.MAX_DF.value,
-        sublinear_tf=configs.SUBLINEAR.value,
-    )
-    label_encoder = LabelEncoder()
-
-    X = tfidf_vectorizer.fit_transform(text.values)
-    y = label_encoder.fit_transform(labels.values)
-
-    return {
-        "X": X,
-        "y": y,
-        "X_names": np.array(tfidf_vectorizer.get_feature_names_out()),
-        "y_names": label_encoder.classes_,
-    }
-
-
-def wordifier(
-    X: np.ndarray,
-    y: np.ndarray,
-    X_names: List[str],
-    y_names: List[str],
-    configs=ModelConfigs,
-) -> List[Tuple[str, float, str]]:
-
-    n_instances, n_features = X.shape
-    n_classes = len(y_names)
-
-    # NOTE: the * 10 / 10 trick is to have "nice" round-ups
-    sample_fraction = np.ceil((n_features / n_instances) * 10) / 10
-
-    sample_size = min(
-        # this is the maximum supported
-        configs.MAX_SELECTION.value,
-        # at minimum you want MIN_SELECTION but in general you want
-        # n_instances * sample_fraction
-        max(configs.MIN_SELECTION.value, int(n_instances * sample_fraction)),
-        # however if previous one is bigger the the available instances take
-        # the number of available instances
-        n_instances,
-    )
-
-    # TODO: might want to try out something to subsample features at each iteration
-
-    # initialize coefficient matrices
-    pos_scores = np.zeros((n_classes, n_features), dtype=int)
-    neg_scores = np.zeros((n_classes, n_features), dtype=int)
-
-    pbar = st.progress(0)
-    for i, _ in enumerate(range(configs.NUM_ITERS.value)):
-
-        # run randomized regression
-        clf = LogisticRegression(
-            penalty="l1",
-            C=configs.PENALTIES.value[np.random.randint(len(configs.PENALTIES.value))],
-            solver="liblinear",
-            multi_class="auto",
-            max_iter=500,
-            class_weight="balanced",
-            random_state=42,
-        )
-
-        # sample indices to subsample matrix
-        selection = resample(
-            np.arange(n_instances), replace=True, stratify=y, n_samples=sample_size
-        )
-
-        # fit
-        try:
-            clf.fit(X[selection], y[selection])
-        except ValueError:
-            continue
-
-        # record coefficients
-        if n_classes == 2:
-            pos_scores[1] = pos_scores[1] + (clf.coef_ > 0.0)
-            neg_scores[1] = neg_scores[1] + (clf.coef_ < 0.0)
-            pos_scores[0] = pos_scores[0] + (clf.coef_ < 0.0)
-            neg_scores[0] = neg_scores[0] + (clf.coef_ > 0.0)
-        else:
-            pos_scores += clf.coef_ > 0
-            neg_scores += clf.coef_ < 0
-
-        pbar.progress(round(i / configs.NUM_ITERS.value, 1))
-
-    # normalize
-    pos_scores = pos_scores / configs.NUM_ITERS.value
-    neg_scores = neg_scores / configs.NUM_ITERS.value
-
-    # get only active features
-    pos_positions = np.where(
-        pos_scores >= configs.SELECTION_THRESHOLD.value, pos_scores, 0
-    )
-    neg_positions = np.where(
-        neg_scores >= configs.SELECTION_THRESHOLD.value, neg_scores, 0
-    )
-
-    # prepare DataFrame
-    pos = [
-        (X_names[i], pos_scores[c, i], y_names[c])
-        for c, i in zip(*pos_positions.nonzero())
-    ]
-    neg = [
-        (X_names[i], neg_scores[c, i], y_names[c])
-        for c, i in zip(*neg_positions.nonzero())
-    ]
-
-    return pos, neg
-
-
-def output_transform(
-    pos: List[Tuple[str, float, str]], neg: List[Tuple[str, float, str]]
-) -> DataFrame:
-    posdf = pd.DataFrame(pos, columns="word score label".split()).sort_values(
-        ["label", "score"], ascending=False
-    )
-    posdf["correlation"] = "positive"
-    negdf = pd.DataFrame(neg, columns="word score label".split()).sort_values(
-        ["label", "score"], ascending=False
-    )
-    negdf["correlation"] = "negative"
-
-    output = pd.concat([posdf, negdf], ignore_index=False, axis=0)
-    output.columns = output.columns.str.title()
-
-    return output
diff --git a/spaces/MrSashkaman/StyleTransfer/model.py b/spaces/MrSashkaman/StyleTransfer/model.py
deleted file mode 100644
index 9a47025ee2d306cacf4eae0edd3be9a5659157d8..0000000000000000000000000000000000000000
--- a/spaces/MrSashkaman/StyleTransfer/model.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-
-import subprocess
-import time
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from PIL import Image
-import torchvision.transforms as transforms
-import torchvision.models as models
-import copy
-
-import os.path
-from os.path import exists
-
-def gram_matrix(input):
-    a, b, c, d = input.size()  # a=batch size(=1)
-    #b=number of feature maps
-    #(c,d)=dimensions of a f. map (N=c*d)
-
-    features = input.view(a * b, c * d)  # resise F_XL into \hat F_XL
-
-    G = torch.mm(features, features.t())  # compute the gram product
-
-    # we 'normalize' the values of the gram matrix
-    # by dividing by the number of element in each feature maps.
-    return G.div(a * b * c * d)
-
-class Normalization(nn.Module):
-    def __init__(self, mean, std):
-        super(Normalization, self).__init__()
-        # .view the mean and std to make them [C x 1 x 1] so that they can
-        # directly work with image Tensor of shape [B x C x H x W].
-        # B is batch size. C is number of channels. H is height and W is width.
-        self.mean = torch.tensor(mean).view(-1, 1, 1)
-        self.std = torch.tensor(std).view(-1, 1, 1)
-
-    def forward(self, img):
-        # normalize img
-        return (img - self.mean) / self.std
-    
-class ContentLoss(nn.Module):
-
-    def __init__(self, target,):
-        super(ContentLoss, self).__init__()
-        # we 'detach' the target content from the tree used
-        # to dynamically compute the gradient: this is a stated value,
-        # not a variable. Otherwise the forward method of the criterion
-        # will throw an error.
-        self.target = target.detach()
-
-    def forward(self, input):
-        self.loss = F.mse_loss(input, self.target)
-        return input
-
-
-class StyleLoss(nn.Module):
-
-    def __init__(self, target_feature):
-        super(StyleLoss, self).__init__()
-        self.target = gram_matrix(target_feature).detach()
-
-    def forward(self, input):
-        G = gram_matrix(input)
-        self.loss = F.mse_loss(G, self.target)
-        return input
-
-class transfer_model:
-    def __init__(self,
-                 num_steps,
-                 style_weight,
-                 #content_weight,
-                learning_rate,
-                step_checkpoint):
-        
-        self.num_steps = num_steps
-        self.style_weight = style_weight
-        self.content_weight = 1
-        self.learning_rate = learning_rate
-        self.step_checkpoint = step_checkpoint
-        
-        
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.cnn = models.vgg19(pretrained=True).features.to(self.device).eval()
-        self.imsize = 512 if torch.cuda.is_available() else 200
-        
-        self.cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(self.device)
-        self.cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(self.device)
-        
-
-        
-    def image_loader(self, image_name):
-        
-        loader = transforms.Compose([
-            transforms.Resize((self.imsize, self.imsize)),
-            transforms.CenterCrop(self.imsize),  # scale imported image
-            transforms.ToTensor()])  # transform it into a torch tensor
-            
-        image = Image.open(image_name)
-        image = loader(image).unsqueeze(0)
-    
-        return image.to(self.device, torch.float)
-            
-    def get_input_optimizer(self, input_img):
-            # this line to show that input is a parameter that requires a gradient
-        optimizer = optim.LBFGS([input_img], lr=self.learning_rate)
-        return optimizer
-            
-            
-    def get_style_model_and_losses(self,
-                                   cnn,
-                                   normalization_mean,
-                                   normalization_std,
-                                   style_img,
-                                   content_img,
-                                   content_layers=['conv_4'],
-                                   style_layers=['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']):
-
-        device = self.device
-        
-        # normalization module
-        normalization = Normalization(normalization_mean, normalization_std).to(device)
-    
-        # just in order to have an iterable access to or list of content/syle
-        # losses
-        content_losses = []
-        style_losses = []
-    
-        # assuming that cnn is a nn.Sequential, so we make a new nn.Sequential
-        # to put in modules that are supposed to be activated sequentially
-        
-        model = nn.Sequential(normalization)
-    
-        i = 0  # increment every time we see a conv
-        for layer in cnn.children():
-            if isinstance(layer, nn.Conv2d):
-                i += 1
-                name = 'conv_{}'.format(i)
-            elif isinstance(layer, nn.ReLU):
-                name = 'relu_{}'.format(i)
-                # The in-place version doesn't play very nicely with the ContentLoss
-                # and StyleLoss we insert below. So we replace with out-of-place
-                # ones here.
-                layer = nn.ReLU(inplace=False)
-            elif isinstance(layer, nn.MaxPool2d):
-                name = 'pool_{}'.format(i)
-            elif isinstance(layer, nn.BatchNorm2d):
-                name = 'bn_{}'.format(i)
-            else:
-                raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
-    
-            model.add_module(name, layer)
-    
-            if name in content_layers:
-                # add content loss:
-                target = model(content_img).detach()
-                content_loss = ContentLoss(target)
-                model.add_module("content_loss_{}".format(i), content_loss)
-                content_losses.append(content_loss)
-    
-            if name in style_layers:
-                # add style loss:
-                target_feature = model(style_img).detach()
-                style_loss = StyleLoss(target_feature)
-                model.add_module("style_loss_{}".format(i), style_loss)
-                style_losses.append(style_loss)
-    
-        # now we trim off the layers after the last content and style losses
-        for i in range(len(model) - 1, -1, -1):
-            if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
-                break
-            
-        model = model[:(i + 1)] 
-        
-
-    
-        return model, style_losses, content_losses
-    
-    
-    def run_style_transfer(self):
-        
-        style_img = self.image_loader('images/style/style.png')
-        content_img = self.image_loader('images/original/img.png')
-        input_img = content_img.clone()
-        
-        """Run the style transfer."""
-        print('Building the style transfer model..')
-        model, style_losses, content_losses = self.get_style_model_and_losses(self.cnn,
-                                                                         self.cnn_normalization_mean,
-                                                                         self.cnn_normalization_std,
-                                                                         style_img,
-                                                                         content_img)
-    
-        # We want to optimize the input and not the model parameters so we
-        # update all the requires_grad fields accordingly
-    
-        unloader_pil = transforms.ToPILImage()
-    
-        input_img.requires_grad_(True)
-        model.requires_grad_(False)
-    
-        optimizer = self.get_input_optimizer(input_img)
-    
-        print('Optimizing..')
-        run = [0]
-    
-        output_list = []
-        while run[0] <= self.num_steps:
-        
-            def closure():
-                 # correct the values of updated input image
-                with torch.no_grad():
-                    input_img.clamp_(0, 1)
-    
-                optimizer.zero_grad()
-                model(input_img)
-                style_score = 0
-                content_score = 0
-    
-                for sl in style_losses:
-                    style_score += sl.loss
-                for cl in content_losses:
-                    content_score += cl.loss
-    
-                style_score *= self.style_weight
-                content_score *= self.content_weight
-    
-                loss = style_score + content_score
-                loss.backward()
-    
-    
-                run[0] += 1
-                if run[0] % self.step_checkpoint == 0:
-                    print("run {}:".format(run))
-                    print('Style Loss : {:4f} Content Loss: {:4f}'.format(
-                         style_score.item(), content_score.item()))
-                     
-    
-                    output_list.append((loss, input_img.cpu().clone()))
-    
-                    input_img_0 = input_img.cpu().clone()  # we clone the tensor to not do changes on it
-                    input_img_0 = input_img_0.squeeze(0)      # remove the fake batch dimension
-                    input_img_0 = unloader_pil(input_img_0)
-                    input_img_0.save(f'images/created/result_{run[0]}_iter.png')
-             
-                    print()
-    
-                return style_score + content_score
-    
-            optimizer.step(closure)
-         
-         # a last correction...
-        with torch.no_grad():
-            input_img.clamp(0, 1)
-    
-    
-         #output_list
-        best_img = sorted(output_list, key=lambda x:x[0], reverse=False)[0][1]
-        best_img = best_img.cpu().clone()    # we clone the tensor to not do changes on it
-        best_img = best_img.squeeze(0)       # remove the fake batch dimension
-        best_img = unloader_pil(best_img)
-        best_img.save(f'images/created/result_final.jpg')
-    
-        return  #best_img, output_list 
\ No newline at end of file
diff --git a/spaces/MrTitanicus/rvc-models/infer_pack/commons.py b/spaces/MrTitanicus/rvc-models/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/MrTitanicus/rvc-models/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/NeuralInternet/Text-Generation_Playground/modules/shared.py b/spaces/NeuralInternet/Text-Generation_Playground/modules/shared.py
deleted file mode 100644
index ea2eb50b7f586e5c562bf2e7c75429c91f21ec6c..0000000000000000000000000000000000000000
--- a/spaces/NeuralInternet/Text-Generation_Playground/modules/shared.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import argparse
-
-model = None
-tokenizer = None
-model_name = ""
-soft_prompt_tensor = None
-soft_prompt = False
-is_RWKV = False
-
-# Chat variables
-history = {'internal': [], 'visible': []}
-character = 'None'
-stop_everything = False
-processing_message = '*Is typing...*'
-
-# UI elements (buttons, sliders, HTML, etc)
-gradio = {}
-
-# Generation input parameters
-input_params = []
-
-settings = {
-    'max_new_tokens': 200,
-    'max_new_tokens_min': 1,
-    'max_new_tokens_max': 2000,
-    'name1': 'Person 1',
-    'name2': 'Person 2',
-    'context': 'This is a conversation between two people.',
-    'stop_at_newline': True,
-    'chat_prompt_size': 2048,
-    'chat_prompt_size_min': 0,
-    'chat_prompt_size_max': 2048,
-    'chat_generation_attempts': 1,
-    'chat_generation_attempts_min': 1,
-    'chat_generation_attempts_max': 5,
-    'name1_pygmalion': 'You',
-    'name2_pygmalion': 'Kawaii',
-    'context_pygmalion': "Kawaii's persona: Kawaii is a cheerful person who loves to make others smile. She is an optimist who loves to spread happiness and positivity wherever she goes.\n<START>",
-    'stop_at_newline_pygmalion': False,
-    'default_extensions': [],
-    'chat_default_extensions': ["gallery"],
-    'presets': {
-        'default': 'NovelAI-Sphinx Moth',
-        'pygmalion-*': 'Pygmalion',
-        'RWKV-*': 'Naive',
-    },
-    'prompts': {
-        'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
-        '^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n',
-        '(rosey|chip|joi)_.*_instruct.*': 'User: \n',
-        'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>'
-    }
-}
-
-def str2bool(v):
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ('yes', 'true', 't', 'y', '1'):
-        return True
-    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
-        return False
-    else:
-        raise argparse.ArgumentTypeError('Boolean value expected.')
-
-parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
-parser.add_argument('--model', type=str, help='Name of the model to load by default.')
-parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.')
-parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode.')
-parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
-parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
-parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
-parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
-parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.')
-parser.add_argument('--gptq-model-type', type=str, help='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
-parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
-parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
-parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
-parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".')
-parser.add_argument('--gpu-memory', type=int, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs.')
-parser.add_argument('--cpu-memory', type=int, help='Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.')
-parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
-parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).')
-parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.")
-parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).")
-parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
-parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.')
-parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.')
-parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".')
-parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.')
-parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.')
-parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.')
-parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
-parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
-parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
-parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
-parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
-parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
-args = parser.parse_args()
-
-# Provisional, this will be deleted later
-if args.load_in_4bit:
-    print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n")
-    args.gptq_bits = 4
diff --git a/spaces/Nooshinbr/story_generation/app.py b/spaces/Nooshinbr/story_generation/app.py
deleted file mode 100644
index 9d7dc84bcafc9826a1c0a845c2673968865835e1..0000000000000000000000000000000000000000
--- a/spaces/Nooshinbr/story_generation/app.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import gradio as gr
-
-description = "Story generation with GPT-2"
-title = "Generate your own story"
-examples = [["Adventurer is approached by a mysterious stranger in the tavern for a new quest."]]
-
-interface = gr.Interface.load("huggingface/pranavpsv/gpt2-genre-story-generator",
-            description=description,
-            examples=examples
-)
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/infer.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/infer.py
deleted file mode 100644
index 3fb67151e0dc425e02d090a62b1d83e6039e6ccb..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/infer.py
+++ /dev/null
@@ -1,471 +0,0 @@
-#!/usr/bin/env python -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import ast
-import hashlib
-import logging
-import os
-import shutil
-import sys
-from dataclasses import dataclass, field, is_dataclass
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import editdistance
-import torch
-import torch.distributed as dist
-from examples.speech_recognition.new.decoders.decoder_config import (
-    DecoderConfig,
-    FlashlightDecoderConfig,
-)
-from examples.speech_recognition.new.decoders.decoder import Decoder
-from fairseq import checkpoint_utils, distributed_utils, progress_bar, tasks, utils
-from fairseq.data.data_utils import post_process
-from fairseq.dataclass.configs import (
-    CheckpointConfig,
-    CommonConfig,
-    CommonEvalConfig,
-    DatasetConfig,
-    DistributedTrainingConfig,
-    FairseqDataclass,
-)
-from fairseq.logging.meters import StopwatchMeter, TimeMeter
-from fairseq.logging.progress_bar import BaseProgressBar
-from fairseq.models.fairseq_model import FairseqModel
-from omegaconf import OmegaConf
-
-import hydra
-from hydra.core.config_store import ConfigStore
-
-logging.root.setLevel(logging.INFO)
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-config_path = Path(__file__).resolve().parent / "conf"
-
-
-@dataclass
-class DecodingConfig(DecoderConfig, FlashlightDecoderConfig):
-    unique_wer_file: bool = field(
-        default=False,
-        metadata={"help": "If set, use a unique file for storing WER"},
-    )
-    results_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "If set, write hypothesis and reference sentences into this directory"
-        },
-    )
-
-
-@dataclass
-class InferConfig(FairseqDataclass):
-    task: Any = None
-    decoding: DecodingConfig = DecodingConfig()
-    common: CommonConfig = CommonConfig()
-    common_eval: CommonEvalConfig = CommonEvalConfig()
-    checkpoint: CheckpointConfig = CheckpointConfig()
-    distributed_training: DistributedTrainingConfig = DistributedTrainingConfig()
-    dataset: DatasetConfig = DatasetConfig()
-    is_ax: bool = field(
-        default=False,
-        metadata={
-            "help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume"
-        },
-    )
-
-
-def reset_logging():
-    root = logging.getLogger()
-    for handler in root.handlers:
-        root.removeHandler(handler)
-    root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper())
-    handler = logging.StreamHandler(sys.stdout)
-    handler.setFormatter(
-        logging.Formatter(
-            fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-            datefmt="%Y-%m-%d %H:%M:%S",
-        )
-    )
-    root.addHandler(handler)
-
-
-class InferenceProcessor:
-    cfg: InferConfig
-
-    def __init__(self, cfg: InferConfig) -> None:
-        self.cfg = cfg
-        self.task = tasks.setup_task(cfg.task)
-
-        models, saved_cfg = self.load_model_ensemble()
-        self.models = models
-        self.saved_cfg = saved_cfg
-        self.tgt_dict = self.task.target_dictionary
-
-        self.task.load_dataset(
-            self.cfg.dataset.gen_subset,
-            task_cfg=saved_cfg.task,
-        )
-        self.generator = Decoder(cfg.decoding, self.tgt_dict)
-        self.gen_timer = StopwatchMeter()
-        self.wps_meter = TimeMeter()
-        self.num_sentences = 0
-        self.total_errors = 0
-        self.total_length = 0
-
-        self.hypo_words_file = None
-        self.hypo_units_file = None
-        self.ref_words_file = None
-        self.ref_units_file = None
-
-        self.progress_bar = self.build_progress_bar()
-
-    def __enter__(self) -> "InferenceProcessor":
-        if self.cfg.decoding.results_path is not None:
-            self.hypo_words_file = self.get_res_file("hypo.word")
-            self.hypo_units_file = self.get_res_file("hypo.units")
-            self.ref_words_file = self.get_res_file("ref.word")
-            self.ref_units_file = self.get_res_file("ref.units")
-        return self
-
-    def __exit__(self, *exc) -> bool:
-        if self.cfg.decoding.results_path is not None:
-            self.hypo_words_file.close()
-            self.hypo_units_file.close()
-            self.ref_words_file.close()
-            self.ref_units_file.close()
-        return False
-
-    def __iter__(self) -> Any:
-        for sample in self.progress_bar:
-            if not self.cfg.common.cpu:
-                sample = utils.move_to_cuda(sample)
-
-            # Happens on the last batch.
-            if "net_input" not in sample:
-                continue
-            yield sample
-
-    def log(self, *args, **kwargs):
-        self.progress_bar.log(*args, **kwargs)
-
-    def print(self, *args, **kwargs):
-        self.progress_bar.print(*args, **kwargs)
-
-    def get_res_file(self, fname: str) -> None:
-        fname = os.path.join(self.cfg.decoding.results_path, fname)
-        if self.data_parallel_world_size > 1:
-            fname = f"{fname}.{self.data_parallel_rank}"
-        return open(fname, "w", buffering=1)
-
-    def merge_shards(self) -> None:
-        """Merges all shard files into shard 0, then removes shard suffix."""
-
-        shard_id = self.data_parallel_rank
-        num_shards = self.data_parallel_world_size
-
-        if self.data_parallel_world_size > 1:
-
-            def merge_shards_with_root(fname: str) -> None:
-                fname = os.path.join(self.cfg.decoding.results_path, fname)
-                logger.info("Merging %s on shard %d", fname, shard_id)
-                base_fpath = Path(f"{fname}.0")
-                with open(base_fpath, "a") as out_file:
-                    for s in range(1, num_shards):
-                        shard_fpath = Path(f"{fname}.{s}")
-                        with open(shard_fpath, "r") as in_file:
-                            for line in in_file:
-                                out_file.write(line)
-                        shard_fpath.unlink()
-                shutil.move(f"{fname}.0", fname)
-
-            dist.barrier()  # ensure all shards finished writing
-            if shard_id == (0 % num_shards):
-                merge_shards_with_root("hypo.word")
-            if shard_id == (1 % num_shards):
-                merge_shards_with_root("hypo.units")
-            if shard_id == (2 % num_shards):
-                merge_shards_with_root("ref.word")
-            if shard_id == (3 % num_shards):
-                merge_shards_with_root("ref.units")
-            dist.barrier()
-
-    def optimize_model(self, model: FairseqModel) -> None:
-        model.make_generation_fast_()
-        if self.cfg.common.fp16:
-            model.half()
-        if not self.cfg.common.cpu:
-            model.cuda()
-
-    def load_model_ensemble(self) -> Tuple[List[FairseqModel], FairseqDataclass]:
-        arg_overrides = ast.literal_eval(self.cfg.common_eval.model_overrides)
-        models, saved_cfg = checkpoint_utils.load_model_ensemble(
-            utils.split_paths(self.cfg.common_eval.path, separator="\\"),
-            arg_overrides=arg_overrides,
-            task=self.task,
-            suffix=self.cfg.checkpoint.checkpoint_suffix,
-            strict=(self.cfg.checkpoint.checkpoint_shard_count == 1),
-            num_shards=self.cfg.checkpoint.checkpoint_shard_count,
-        )
-        for model in models:
-            self.optimize_model(model)
-        return models, saved_cfg
-
-    def get_dataset_itr(self, disable_iterator_cache: bool = False) -> None:
-        return self.task.get_batch_iterator(
-            dataset=self.task.dataset(self.cfg.dataset.gen_subset),
-            max_tokens=self.cfg.dataset.max_tokens,
-            max_sentences=self.cfg.dataset.batch_size,
-            max_positions=(sys.maxsize, sys.maxsize),
-            ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test,
-            required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
-            seed=self.cfg.common.seed,
-            num_shards=self.data_parallel_world_size,
-            shard_id=self.data_parallel_rank,
-            num_workers=self.cfg.dataset.num_workers,
-            data_buffer_size=self.cfg.dataset.data_buffer_size,
-            disable_iterator_cache=disable_iterator_cache,
-        ).next_epoch_itr(shuffle=False)
-
-    def build_progress_bar(
-        self,
-        epoch: Optional[int] = None,
-        prefix: Optional[str] = None,
-        default_log_format: str = "tqdm",
-    ) -> BaseProgressBar:
-        return progress_bar.progress_bar(
-            iterator=self.get_dataset_itr(),
-            log_format=self.cfg.common.log_format,
-            log_interval=self.cfg.common.log_interval,
-            epoch=epoch,
-            prefix=prefix,
-            tensorboard_logdir=self.cfg.common.tensorboard_logdir,
-            default_log_format=default_log_format,
-        )
-
-    @property
-    def data_parallel_world_size(self):
-        if self.cfg.distributed_training.distributed_world_size == 1:
-            return 1
-        return distributed_utils.get_data_parallel_world_size()
-
-    @property
-    def data_parallel_rank(self):
-        if self.cfg.distributed_training.distributed_world_size == 1:
-            return 0
-        return distributed_utils.get_data_parallel_rank()
-
-    def process_sentence(
-        self,
-        sample: Dict[str, Any],
-        hypo: Dict[str, Any],
-        sid: int,
-        batch_id: int,
-    ) -> Tuple[int, int]:
-        speaker = None  # Speaker can't be parsed from dataset.
-
-        if "target_label" in sample:
-            toks = sample["target_label"]
-        else:
-            toks = sample["target"]
-        toks = toks[batch_id, :]
-
-        # Processes hypothesis.
-        hyp_pieces = self.tgt_dict.string(hypo["tokens"].int().cpu())
-        if "words" in hypo:
-            hyp_words = " ".join(hypo["words"])
-        else:
-            hyp_words = post_process(hyp_pieces, self.cfg.common_eval.post_process)
-
-        # Processes target.
-        target_tokens = utils.strip_pad(toks, self.tgt_dict.pad())
-        tgt_pieces = self.tgt_dict.string(target_tokens.int().cpu())
-        tgt_words = post_process(tgt_pieces, self.cfg.common_eval.post_process)
-
-        if self.cfg.decoding.results_path is not None:
-            print(f"{hyp_pieces} ({speaker}-{sid})", file=self.hypo_units_file)
-            print(f"{hyp_words} ({speaker}-{sid})", file=self.hypo_words_file)
-            print(f"{tgt_pieces} ({speaker}-{sid})", file=self.ref_units_file)
-            print(f"{tgt_words} ({speaker}-{sid})", file=self.ref_words_file)
-
-        if not self.cfg.common_eval.quiet:
-            logger.info(f"HYPO: {hyp_words}")
-            logger.info(f"REF: {tgt_words}")
-            logger.info("---------------------")
-
-        hyp_words, tgt_words = hyp_words.split(), tgt_words.split()
-
-        return editdistance.eval(hyp_words, tgt_words), len(tgt_words)
-
-    def process_sample(self, sample: Dict[str, Any]) -> None:
-        self.gen_timer.start()
-        hypos = self.task.inference_step(
-            generator=self.generator,
-            models=self.models,
-            sample=sample,
-        )
-        num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
-        self.gen_timer.stop(num_generated_tokens)
-        self.wps_meter.update(num_generated_tokens)
-
-        for batch_id, sample_id in enumerate(sample["id"].tolist()):
-            errs, length = self.process_sentence(
-                sample=sample,
-                sid=sample_id,
-                batch_id=batch_id,
-                hypo=hypos[batch_id][0],
-            )
-            self.total_errors += errs
-            self.total_length += length
-
-        self.log({"wps": round(self.wps_meter.avg)})
-        if "nsentences" in sample:
-            self.num_sentences += sample["nsentences"]
-        else:
-            self.num_sentences += sample["id"].numel()
-
-    def log_generation_time(self) -> None:
-        logger.info(
-            "Processed %d sentences (%d tokens) in %.1fs %.2f "
-            "sentences per second, %.2f tokens per second)",
-            self.num_sentences,
-            self.gen_timer.n,
-            self.gen_timer.sum,
-            self.num_sentences / self.gen_timer.sum,
-            1.0 / self.gen_timer.avg,
-        )
-
-
-def parse_wer(wer_file: Path) -> float:
-    with open(wer_file, "r") as f:
-        return float(f.readline().strip().split(" ")[1])
-
-
-def get_wer_file(cfg: InferConfig) -> Path:
-    """Hashes the decoding parameters to a unique file ID."""
-    base_path = "wer"
-    if cfg.decoding.results_path is not None:
-        base_path = os.path.join(cfg.decoding.results_path, base_path)
-
-    if cfg.decoding.unique_wer_file:
-        yaml_str = OmegaConf.to_yaml(cfg.decoding)
-        fid = int(hashlib.md5(yaml_str.encode("utf-8")).hexdigest(), 16)
-        return Path(f"{base_path}.{fid % 1000000}")
-    else:
-        return Path(base_path)
-
-
-def main(cfg: InferConfig) -> float:
-    """Entry point for main processing logic.
-
-    Args:
-        cfg: The inferance configuration to use.
-        wer: Optional shared memory pointer for returning the WER. If not None,
-            the final WER value will be written here instead of being returned.
-
-    Returns:
-        The final WER if `wer` is None, otherwise None.
-    """
-
-    yaml_str, wer_file = OmegaConf.to_yaml(cfg.decoding), get_wer_file(cfg)
-
-    # Validates the provided configuration.
-    if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
-        cfg.dataset.max_tokens = 4000000
-    if not cfg.common.cpu and not torch.cuda.is_available():
-        raise ValueError("CUDA not found; set `cpu=True` to run without CUDA")
-
-    with InferenceProcessor(cfg) as processor:
-        for sample in processor:
-            processor.process_sample(sample)
-
-        processor.log_generation_time()
-
-        if cfg.decoding.results_path is not None:
-            processor.merge_shards()
-
-        errs_t, leng_t = processor.total_errors, processor.total_length
-
-        if cfg.common.cpu:
-            logger.warning("Merging WER requires CUDA.")
-        elif processor.data_parallel_world_size > 1:
-            stats = torch.LongTensor([errs_t, leng_t]).cuda()
-            dist.all_reduce(stats, op=dist.ReduceOp.SUM)
-            errs_t, leng_t = stats[0].item(), stats[1].item()
-
-        wer = errs_t * 100.0 / leng_t
-
-        if distributed_utils.is_master(cfg.distributed_training):
-            with open(wer_file, "w") as f:
-                f.write(
-                    (
-                        f"WER: {wer}\n"
-                        f"err / num_ref_words = {errs_t} / {leng_t}\n\n"
-                        f"{yaml_str}"
-                    )
-                )
-
-        return wer
-
-
-@hydra.main(config_path=config_path, config_name="infer")
-def hydra_main(cfg: InferConfig) -> Union[float, Tuple[float, Optional[float]]]:
-    container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
-    cfg = OmegaConf.create(container)
-    OmegaConf.set_struct(cfg, True)
-
-    if cfg.common.reset_logging:
-        reset_logging()
-
-    # logger.info("Config:\n%s", OmegaConf.to_yaml(cfg))
-    wer = float("inf")
-
-    try:
-        if cfg.common.profile:
-            with torch.cuda.profiler.profile():
-                with torch.autograd.profiler.emit_nvtx():
-                    distributed_utils.call_main(cfg, main)
-        else:
-            distributed_utils.call_main(cfg, main)
-
-        wer = parse_wer(get_wer_file(cfg))
-    except BaseException as e:  # pylint: disable=broad-except
-        if not cfg.common.suppress_crashes:
-            raise
-        else:
-            logger.error("Crashed! %s", str(e))
-
-    logger.info("Word error rate: %.4f", wer)
-    if cfg.is_ax:
-        return wer, None
-
-    return wer
-
-
-def cli_main() -> None:
-    try:
-        from hydra._internal.utils import (
-            get_args,
-        )  # pylint: disable=import-outside-toplevel
-
-        cfg_name = get_args().config_name or "infer"
-    except ImportError:
-        logger.warning("Failed to get config name from hydra args")
-        cfg_name = "infer"
-
-    cs = ConfigStore.instance()
-    cs.store(name=cfg_name, node=InferConfig)
-
-    for k in InferConfig.__dataclass_fields__:
-        if is_dataclass(InferConfig.__dataclass_fields__[k].type):
-            v = InferConfig.__dataclass_fields__[k].default
-            cs.store(name=k, node=v)
-
-    hydra_main()  # pylint: disable=no-value-for-parameter
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/plasma_utils.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/plasma_utils.py
deleted file mode 100644
index b9fab3b739db46b685fa6859a2f851a14eef8407..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/plasma_utils.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import subprocess
-import json
-import tempfile
-import hashlib
-from typing import Hashable
-
-try:
-    import pyarrow.plasma as plasma
-
-    PYARROW_AVAILABLE = True
-except ImportError:
-    plasma = None
-    PYARROW_AVAILABLE = False
-
-
-class PlasmaArray:
-    """
-    Wrapper around numpy arrays that automatically moves the data to shared
-    memory upon serialization. This is particularly helpful when passing numpy
-    arrays through multiprocessing, so that data is not unnecessarily
-    duplicated or pickled.
-    """
-
-    def __init__(self, array):
-        super().__init__()
-        self.array = array
-        self.disable = array.nbytes < 134217728  # disable for arrays <128MB
-        self.object_id = None
-        self.path = None
-
-        # variables with underscores shouldn't be pickled
-        self._client = None
-        self._server = None
-        self._server_tmp = None
-        self._plasma = None
-
-    @property
-    def plasma(self):
-        if self._plasma is None and not self.disable:
-            self._plasma = plasma
-        return self._plasma
-
-    def start_server(self):
-        if self.plasma is None or self._server is not None:
-            return
-        assert self.object_id is None
-        assert self.path is None
-        self._server_tmp = tempfile.NamedTemporaryFile()
-        self.path = self._server_tmp.name
-        self._server = subprocess.Popen(
-            ["plasma_store", "-m", str(int(1.05 * self.array.nbytes)), "-s", self.path]
-        )
-
-    @property
-    def client(self):
-        if self._client is None:
-            assert self.path is not None
-            self._client = self.plasma.connect(self.path, num_retries=200)
-        return self._client
-
-    def __getstate__(self):
-        """Called on pickle load"""
-        if self.plasma is None:
-            return self.__dict__
-        if self.object_id is None:
-            self.start_server()
-            self.object_id = self.client.put(self.array)
-        state = self.__dict__.copy()
-        del state["array"]
-        state["_client"] = None
-        state["_server"] = None
-        state["_server_tmp"] = None
-        state["_plasma"] = None
-        return state
-
-    def __setstate__(self, state):
-        """Called on pickle save"""
-        self.__dict__.update(state)
-        if self.plasma is None:
-            return
-        self.array = self.client.get(self.object_id)
-
-    def __del__(self):
-        if self._server is not None:
-            self._server.kill()
-            self._server = None
-            self._server_tmp.close()
-            self._server_tmp = None
-
-
-DEFAULT_PLASMA_PATH = "/tmp/plasma"
-
-
-class PlasmaView:
-    """Interface to write and read from shared memory. Whereas PlasmaArray writes to plasma on serialization,
-    PlasmaView writes to shared memory on instantiation."""
-
-    def __init__(self, array, split_path: str, hash_data: Hashable, plasma_path=None):
-        """
-        Args:
-            array: numpy array to store. This can be read with ``PlasmaView().array``
-            split_path: the path whence the data was read, used for hashing
-            hash_data: other metadata about the array that can be used to create a unique key.
-                as of writing, the 3 callers in ``TokenBlockDataset`` use::
-
-                    hash_data = ((block_size, document_sep_len, str(break_mode), len(dataset)), 0|1|2)
-
-
-        """
-        assert PYARROW_AVAILABLE
-        assert split_path is not None
-        if plasma_path is None:
-            plasma_path = DEFAULT_PLASMA_PATH
-
-        self.path = plasma_path
-        self.split_path = split_path
-        self._client = None  # Initialize lazily for pickle. plasma clients should not be deep copied or serialized.
-        self._n = None
-
-        self.object_id = self.get_object_id(self.split_path, hash_data)
-        try:
-            self.client.put(array, object_id=self.object_id)
-        except plasma.PlasmaObjectExists:
-            pass
-
-    @property
-    def client(self):
-        if self._client is None:
-            self._client = plasma.connect(self.path, num_retries=200)
-        return self._client
-
-    @property
-    def array(self):
-        """Fetch a read only view of an np.array, stored in plasma."""
-        ret = self.client.get(self.object_id)
-        return ret
-
-    @staticmethod
-    def get_object_id(split_path: str, hash_data: Hashable):
-        """Returns plasma.ObjectID from hashing split_path and object_num."""
-        hash = hashlib.blake2b(bytes(split_path, "utf-8"), digest_size=20)
-        harg = json.dumps(hash_data).encode("utf-8")
-        hash.update(harg)
-        return plasma.ObjectID(hash.digest())
-
-    def __getstate__(self):
-        """Called on pickle save"""
-        self.disconnect()
-        state = self.__dict__.copy()
-        assert state["_client"] is None
-        assert "object_id" in state
-        return state
-
-    def __setstate__(self, state):
-        """Called on pickle load"""
-        self.__dict__.update(state)
-
-    def __del__(self):
-        self.disconnect()
-
-    def disconnect(self):
-        if self._client is not None:
-            self._client.disconnect()
-            self._client = None
-
-    def __len__(self):
-        """Save reads by caching len"""
-        if self._n is None:
-            self._n = len(self.array)
-        return self._n
-
-
-GB100 = (1024 ** 3) * 100
-
-
-class PlasmaStore:
-    def __init__(self, path=DEFAULT_PLASMA_PATH, nbytes: int = GB100):
-
-        self.server = self.start(path, nbytes)
-
-    def __del__(self):
-        self.server.kill()
-
-    @staticmethod
-    def start(path=DEFAULT_PLASMA_PATH, nbytes: int = GB100) -> subprocess.Popen:
-        if not PYARROW_AVAILABLE:
-            raise ImportError("please run pip install pyarrow to use --use_plasma_view")
-        # best practice is to allocate more space than we need. The limitation seems to be the size of /dev/shm
-        _server = subprocess.Popen(["plasma_store", "-m", str(nbytes), "-s", path])
-        plasma.connect(path, num_retries=200)  # If we can't connect we fail immediately
-        return _server
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/m2m_100/process_data/dedup_data.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/m2m_100/process_data/dedup_data.py
deleted file mode 100644
index 58d9ed1cd17b3ba70772a6d9adab709785495fd9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/m2m_100/process_data/dedup_data.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import argparse
-from collections import namedtuple
-import os
-
-DATADIR = "/path/to/train_data"
-DEDUP_FROM_DIR = "/path/to/eval/data"
-OUTPUT_DIR = "/path/to/output/data"
-
-
-def main(args):
-    languages = set()
-    for language_directory in os.listdir(DATADIR):
-        if "_" in language_directory:
-            src, tgt = language_directory.split("_")
-            languages.add(LanguagePair(src=src, tgt=tgt))
-
-    data = existing_data()
-    train_languages = sorted(languages)
-    for language_pair in train_languages[args.start_index:args.start_index + args.size]:
-        print(language_pair)
-        dedup(language_pair, data)
-
-
-LanguagePair = namedtuple("LanguagePair", ["src", "tgt"])
-
-
-def existing_data():
-    data = set()
-    for file in os.listdir(DEDUP_FROM_DIR):
-        with open(os.path.join(DEDUP_FROM_DIR, file)) as f:
-            data |= set(f.readlines())
-    return data
- 
-def dedup(language_pair, data, verbose=True, output=True):
-    train_filenames = LanguagePair(
-            src=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.src}",
-            tgt=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.tgt}",
-        )
-
-    output_filenames = LanguagePair(
-        src=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.src}",
-        tgt=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.tgt}"
-    )
-
-    # If output exists, skip this pair. It has already been done.
-    if (os.path.exists(output_filenames.src) and
-        os.path.exists(output_filenames.tgt)):
-        if verbose:
-            print(f"{language_pair.src}-{language_pair.tgt} already done.")
-        return
-
-    if verbose:
-        print(f"{language_pair.src}-{language_pair.tgt} ready, will check dups.")
-
-    # If there is no output, no need to actually do the loop.
-    if not output:
-        return
-
-    if os.path.exists(train_filenames.src) and os.path.exists(train_filenames.tgt):
-        with open(train_filenames.src) as f:
-            train_source = f.readlines()
-
-        with open(train_filenames.tgt) as f:
-            train_target = f.readlines()
-
-        # do dedup
-        new_train_source = []
-        new_train_target = []
-        for i, train_line in enumerate(train_source):
-            if train_line not in data and train_target[i] not in data:
-                new_train_source.append(train_line)
-                new_train_target.append(train_target[i])
-
-        assert len(train_source) == len(train_target)
-        assert len(new_train_source) == len(new_train_target)
-        assert len(new_train_source) <= len(train_source)
-
-        with open(output_filenames.src, "w") as o:
-            for line in new_train_source:
-                o.write(line)
-
-        with open(output_filenames.tgt, "w") as o:
-            for line in new_train_target:
-                o.write(line)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-s", "--start-index", required=True, type=int)
-    parser.add_argument("-n", "--size", required=True, type=int)
-    main(parser.parse_args())
diff --git a/spaces/OFA-Sys/expertllama/README.md b/spaces/OFA-Sys/expertllama/README.md
deleted file mode 100644
index 7eb0f603fcd823a840c555b4a90c76bc3a94bcf5..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/expertllama/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Expertllama
-emoji: 📊
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Omnibus/text-to-vid/app.py b/spaces/Omnibus/text-to-vid/app.py
deleted file mode 100644
index 30a600958a7c3a1249f7def6a38b1d172762eb28..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/text-to-vid/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-#model = gr.Interface.load("models/camenduru/text2_video_zero")
-# load pipeline
-pipe = DiffusionPipeline.from_pretrained("camenduru/text2-video-zero").to(device)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-#if device == "cuda":
-    # optimize for GPU memory
-#    pipe.enable_model_cpu_offload()
-#else:
-#    pass
-#pipe.enable_vae_slicing()
-    
-def ttv():
-    
-    # generate
-    prompt = "Spiderman is surfing. Darth Vader is also surfing and following Spiderman"
-    video_frames = model(prompt, num_inference_steps=25, num_frames=20)
-    
-    # convent to video
-    #video_path = export_to_video(video_frames)
-    return video_frames
-
-with gr.Blocks() as app:
-    inp = gr.Textbox()
-    btn = gr.Button()
-    outp = gr.Gallery()
-    btn.click(ttv,None,outp)
-app.launch()
\ No newline at end of file
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas.py
deleted file mode 100644
index 12fa1e2b5db6bc7898a8dc6488d9045ec29f0a27..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas.py
+++ /dev/null
@@ -1,507 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-
-MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [
-    {
-        "color": [165, 42, 42],
-        "instances": True,
-        "readable": "Bird",
-        "name": "animal--bird",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 192, 0],
-        "instances": True,
-        "readable": "Ground Animal",
-        "name": "animal--ground-animal",
-        "evaluate": True,
-    },
-    {
-        "color": [196, 196, 196],
-        "instances": False,
-        "readable": "Curb",
-        "name": "construction--barrier--curb",
-        "evaluate": True,
-    },
-    {
-        "color": [190, 153, 153],
-        "instances": False,
-        "readable": "Fence",
-        "name": "construction--barrier--fence",
-        "evaluate": True,
-    },
-    {
-        "color": [180, 165, 180],
-        "instances": False,
-        "readable": "Guard Rail",
-        "name": "construction--barrier--guard-rail",
-        "evaluate": True,
-    },
-    {
-        "color": [90, 120, 150],
-        "instances": False,
-        "readable": "Barrier",
-        "name": "construction--barrier--other-barrier",
-        "evaluate": True,
-    },
-    {
-        "color": [102, 102, 156],
-        "instances": False,
-        "readable": "Wall",
-        "name": "construction--barrier--wall",
-        "evaluate": True,
-    },
-    {
-        "color": [128, 64, 255],
-        "instances": False,
-        "readable": "Bike Lane",
-        "name": "construction--flat--bike-lane",
-        "evaluate": True,
-    },
-    {
-        "color": [140, 140, 200],
-        "instances": True,
-        "readable": "Crosswalk - Plain",
-        "name": "construction--flat--crosswalk-plain",
-        "evaluate": True,
-    },
-    {
-        "color": [170, 170, 170],
-        "instances": False,
-        "readable": "Curb Cut",
-        "name": "construction--flat--curb-cut",
-        "evaluate": True,
-    },
-    {
-        "color": [250, 170, 160],
-        "instances": False,
-        "readable": "Parking",
-        "name": "construction--flat--parking",
-        "evaluate": True,
-    },
-    {
-        "color": [96, 96, 96],
-        "instances": False,
-        "readable": "Pedestrian Area",
-        "name": "construction--flat--pedestrian-area",
-        "evaluate": True,
-    },
-    {
-        "color": [230, 150, 140],
-        "instances": False,
-        "readable": "Rail Track",
-        "name": "construction--flat--rail-track",
-        "evaluate": True,
-    },
-    {
-        "color": [128, 64, 128],
-        "instances": False,
-        "readable": "Road",
-        "name": "construction--flat--road",
-        "evaluate": True,
-    },
-    {
-        "color": [110, 110, 110],
-        "instances": False,
-        "readable": "Service Lane",
-        "name": "construction--flat--service-lane",
-        "evaluate": True,
-    },
-    {
-        "color": [244, 35, 232],
-        "instances": False,
-        "readable": "Sidewalk",
-        "name": "construction--flat--sidewalk",
-        "evaluate": True,
-    },
-    {
-        "color": [150, 100, 100],
-        "instances": False,
-        "readable": "Bridge",
-        "name": "construction--structure--bridge",
-        "evaluate": True,
-    },
-    {
-        "color": [70, 70, 70],
-        "instances": False,
-        "readable": "Building",
-        "name": "construction--structure--building",
-        "evaluate": True,
-    },
-    {
-        "color": [150, 120, 90],
-        "instances": False,
-        "readable": "Tunnel",
-        "name": "construction--structure--tunnel",
-        "evaluate": True,
-    },
-    {
-        "color": [220, 20, 60],
-        "instances": True,
-        "readable": "Person",
-        "name": "human--person",
-        "evaluate": True,
-    },
-    {
-        "color": [255, 0, 0],
-        "instances": True,
-        "readable": "Bicyclist",
-        "name": "human--rider--bicyclist",
-        "evaluate": True,
-    },
-    {
-        "color": [255, 0, 100],
-        "instances": True,
-        "readable": "Motorcyclist",
-        "name": "human--rider--motorcyclist",
-        "evaluate": True,
-    },
-    {
-        "color": [255, 0, 200],
-        "instances": True,
-        "readable": "Other Rider",
-        "name": "human--rider--other-rider",
-        "evaluate": True,
-    },
-    {
-        "color": [200, 128, 128],
-        "instances": True,
-        "readable": "Lane Marking - Crosswalk",
-        "name": "marking--crosswalk-zebra",
-        "evaluate": True,
-    },
-    {
-        "color": [255, 255, 255],
-        "instances": False,
-        "readable": "Lane Marking - General",
-        "name": "marking--general",
-        "evaluate": True,
-    },
-    {
-        "color": [64, 170, 64],
-        "instances": False,
-        "readable": "Mountain",
-        "name": "nature--mountain",
-        "evaluate": True,
-    },
-    {
-        "color": [230, 160, 50],
-        "instances": False,
-        "readable": "Sand",
-        "name": "nature--sand",
-        "evaluate": True,
-    },
-    {
-        "color": [70, 130, 180],
-        "instances": False,
-        "readable": "Sky",
-        "name": "nature--sky",
-        "evaluate": True,
-    },
-    {
-        "color": [190, 255, 255],
-        "instances": False,
-        "readable": "Snow",
-        "name": "nature--snow",
-        "evaluate": True,
-    },
-    {
-        "color": [152, 251, 152],
-        "instances": False,
-        "readable": "Terrain",
-        "name": "nature--terrain",
-        "evaluate": True,
-    },
-    {
-        "color": [107, 142, 35],
-        "instances": False,
-        "readable": "Vegetation",
-        "name": "nature--vegetation",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 170, 30],
-        "instances": False,
-        "readable": "Water",
-        "name": "nature--water",
-        "evaluate": True,
-    },
-    {
-        "color": [255, 255, 128],
-        "instances": True,
-        "readable": "Banner",
-        "name": "object--banner",
-        "evaluate": True,
-    },
-    {
-        "color": [250, 0, 30],
-        "instances": True,
-        "readable": "Bench",
-        "name": "object--bench",
-        "evaluate": True,
-    },
-    {
-        "color": [100, 140, 180],
-        "instances": True,
-        "readable": "Bike Rack",
-        "name": "object--bike-rack",
-        "evaluate": True,
-    },
-    {
-        "color": [220, 220, 220],
-        "instances": True,
-        "readable": "Billboard",
-        "name": "object--billboard",
-        "evaluate": True,
-    },
-    {
-        "color": [220, 128, 128],
-        "instances": True,
-        "readable": "Catch Basin",
-        "name": "object--catch-basin",
-        "evaluate": True,
-    },
-    {
-        "color": [222, 40, 40],
-        "instances": True,
-        "readable": "CCTV Camera",
-        "name": "object--cctv-camera",
-        "evaluate": True,
-    },
-    {
-        "color": [100, 170, 30],
-        "instances": True,
-        "readable": "Fire Hydrant",
-        "name": "object--fire-hydrant",
-        "evaluate": True,
-    },
-    {
-        "color": [40, 40, 40],
-        "instances": True,
-        "readable": "Junction Box",
-        "name": "object--junction-box",
-        "evaluate": True,
-    },
-    {
-        "color": [33, 33, 33],
-        "instances": True,
-        "readable": "Mailbox",
-        "name": "object--mailbox",
-        "evaluate": True,
-    },
-    {
-        "color": [100, 128, 160],
-        "instances": True,
-        "readable": "Manhole",
-        "name": "object--manhole",
-        "evaluate": True,
-    },
-    {
-        "color": [142, 0, 0],
-        "instances": True,
-        "readable": "Phone Booth",
-        "name": "object--phone-booth",
-        "evaluate": True,
-    },
-    {
-        "color": [70, 100, 150],
-        "instances": False,
-        "readable": "Pothole",
-        "name": "object--pothole",
-        "evaluate": True,
-    },
-    {
-        "color": [210, 170, 100],
-        "instances": True,
-        "readable": "Street Light",
-        "name": "object--street-light",
-        "evaluate": True,
-    },
-    {
-        "color": [153, 153, 153],
-        "instances": True,
-        "readable": "Pole",
-        "name": "object--support--pole",
-        "evaluate": True,
-    },
-    {
-        "color": [128, 128, 128],
-        "instances": True,
-        "readable": "Traffic Sign Frame",
-        "name": "object--support--traffic-sign-frame",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 80],
-        "instances": True,
-        "readable": "Utility Pole",
-        "name": "object--support--utility-pole",
-        "evaluate": True,
-    },
-    {
-        "color": [250, 170, 30],
-        "instances": True,
-        "readable": "Traffic Light",
-        "name": "object--traffic-light",
-        "evaluate": True,
-    },
-    {
-        "color": [192, 192, 192],
-        "instances": True,
-        "readable": "Traffic Sign (Back)",
-        "name": "object--traffic-sign--back",
-        "evaluate": True,
-    },
-    {
-        "color": [220, 220, 0],
-        "instances": True,
-        "readable": "Traffic Sign (Front)",
-        "name": "object--traffic-sign--front",
-        "evaluate": True,
-    },
-    {
-        "color": [140, 140, 20],
-        "instances": True,
-        "readable": "Trash Can",
-        "name": "object--trash-can",
-        "evaluate": True,
-    },
-    {
-        "color": [119, 11, 32],
-        "instances": True,
-        "readable": "Bicycle",
-        "name": "object--vehicle--bicycle",
-        "evaluate": True,
-    },
-    {
-        "color": [150, 0, 255],
-        "instances": True,
-        "readable": "Boat",
-        "name": "object--vehicle--boat",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 60, 100],
-        "instances": True,
-        "readable": "Bus",
-        "name": "object--vehicle--bus",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 142],
-        "instances": True,
-        "readable": "Car",
-        "name": "object--vehicle--car",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 90],
-        "instances": True,
-        "readable": "Caravan",
-        "name": "object--vehicle--caravan",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 230],
-        "instances": True,
-        "readable": "Motorcycle",
-        "name": "object--vehicle--motorcycle",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 80, 100],
-        "instances": False,
-        "readable": "On Rails",
-        "name": "object--vehicle--on-rails",
-        "evaluate": True,
-    },
-    {
-        "color": [128, 64, 64],
-        "instances": True,
-        "readable": "Other Vehicle",
-        "name": "object--vehicle--other-vehicle",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 110],
-        "instances": True,
-        "readable": "Trailer",
-        "name": "object--vehicle--trailer",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 70],
-        "instances": True,
-        "readable": "Truck",
-        "name": "object--vehicle--truck",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 192],
-        "instances": True,
-        "readable": "Wheeled Slow",
-        "name": "object--vehicle--wheeled-slow",
-        "evaluate": True,
-    },
-    {
-        "color": [32, 32, 32],
-        "instances": False,
-        "readable": "Car Mount",
-        "name": "void--car-mount",
-        "evaluate": True,
-    },
-    {
-        "color": [120, 10, 10],
-        "instances": False,
-        "readable": "Ego Vehicle",
-        "name": "void--ego-vehicle",
-        "evaluate": True,
-    },
-    {
-        "color": [0, 0, 0],
-        "instances": False,
-        "readable": "Unlabeled",
-        "name": "void--unlabeled",
-        "evaluate": False,
-    },
-]
-
-
-def _get_mapillary_vistas_meta():
-    stuff_classes = [k["readable"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]]
-    assert len(stuff_classes) == 65
-
-    stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]]
-    assert len(stuff_colors) == 65
-
-    ret = {
-        "stuff_classes": stuff_classes,
-        "stuff_colors": stuff_colors,
-    }
-    return ret
-
-
-def register_all_mapillary_vistas(root):
-    root = os.path.join(root, "mapillary_vistas")
-    meta = _get_mapillary_vistas_meta()
-    for name, dirname in [("train", "training"), ("val", "validation")]:
-        image_dir = os.path.join(root, dirname, "images")
-        gt_dir = os.path.join(root, dirname, "labels")
-        name = f"mapillary_vistas_sem_seg_{name}"
-        DatasetCatalog.register(
-            name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
-        )
-        MetadataCatalog.get(name).set(
-            image_root=image_dir,
-            sem_seg_root=gt_dir,
-            evaluator_type="sem_seg",
-            ignore_label=65,  # different from other datasets, Mapillary Vistas sets ignore_label to 65
-            **meta,
-        )
-
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_mapillary_vistas(_root)
\ No newline at end of file
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/tree-il/optimize.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/tree-il/optimize.go
deleted file mode 100644
index 8ce3e87d73df39e3cf8873594bc7927400b47751..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/tree-il/optimize.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/CarperAI-stable-vicuna-13b-delta/README.md b/spaces/PeepDaSlan9/CarperAI-stable-vicuna-13b-delta/README.md
deleted file mode 100644
index 8fdb2879dae3b99b6e1cc0aea08ec0bcf4aa5344..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/CarperAI-stable-vicuna-13b-delta/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CarperAI Stable Vicuna 13b Delta
-emoji: 🐢
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
-license: cc-by-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/PeepDaSlan9/Dup_Digital_India/README.md b/spaces/PeepDaSlan9/Dup_Digital_India/README.md
deleted file mode 100644
index ca4832a6727d4ad10ba4d1e722be2b8525d70fbe..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/Dup_Digital_India/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Digital India
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pie31415/control-animation/annotator/openpose/__init__.py b/spaces/Pie31415/control-animation/annotator/openpose/__init__.py
deleted file mode 100644
index 8c26f1b37dae854f51da938da2fa67a8ef48ce5a..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/openpose/__init__.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-import torch
-import numpy as np
-from . import util
-from .body import Body
-from .hand import Hand
-from annotator.util import annotator_ckpts_path
-
-
-body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
-hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth"
-
-
-class OpenposeDetector:
-    def __init__(self):
-        body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth")
-        hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth")
-
-        if not os.path.exists(hand_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
-            load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
-
-        self.body_estimation = Body(body_modelpath)
-        self.hand_estimation = Hand(hand_modelpath)
-
-    def __call__(self, oriImg, hand=False):
-        oriImg = oriImg[:, :, ::-1].copy()
-        with torch.no_grad():
-            candidate, subset = self.body_estimation(oriImg)
-            canvas = np.zeros_like(oriImg)
-            canvas = util.draw_bodypose(canvas, candidate, subset)
-            if hand:
-                hands_list = util.handDetect(candidate, subset, oriImg)
-                all_hand_peaks = []
-                for x, y, w, is_left in hands_list:
-                    peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :])
-                    peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
-                    peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
-                    all_hand_peaks.append(peaks)
-                canvas = util.draw_handpose(canvas, all_hand_peaks)
-            return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/parallel/data_parallel.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/parallel/data_parallel.py
deleted file mode 100644
index 79b5f69b654cf647dc7ae9174223781ab5c607d2..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/parallel/data_parallel.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from itertools import chain
-
-from torch.nn.parallel import DataParallel
-
-from .scatter_gather import scatter_kwargs
-
-
-class MMDataParallel(DataParallel):
-    """The DataParallel module that supports DataContainer.
-
-    MMDataParallel has two main differences with PyTorch DataParallel:
-
-    - It supports a custom type :class:`DataContainer` which allows more
-      flexible control of input data during both GPU and CPU inference.
-    - It implement two more APIs ``train_step()`` and ``val_step()``.
-
-    Args:
-        module (:class:`nn.Module`): Module to be encapsulated.
-        device_ids (list[int]): Device IDS of modules to be scattered to.
-            Defaults to None when GPU is not available.
-        output_device (str | int): Device ID for output. Defaults to None.
-        dim (int): Dimension used to scatter the data. Defaults to 0.
-    """
-
-    def __init__(self, *args, dim=0, **kwargs):
-        super(MMDataParallel, self).__init__(*args, dim=dim, **kwargs)
-        self.dim = dim
-
-    def forward(self, *inputs, **kwargs):
-        """Override the original forward function.
-
-        The main difference lies in the CPU inference where the data in
-        :class:`DataContainers` will still be gathered.
-        """
-        if not self.device_ids:
-            # We add the following line thus the module could gather and
-            # convert data containers as those in GPU inference
-            inputs, kwargs = self.scatter(inputs, kwargs, [-1])
-            return self.module(*inputs[0], **kwargs[0])
-        else:
-            return super().forward(*inputs, **kwargs)
-
-    def scatter(self, inputs, kwargs, device_ids):
-        return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
-
-    def train_step(self, *inputs, **kwargs):
-        if not self.device_ids:
-            # We add the following line thus the module could gather and
-            # convert data containers as those in GPU inference
-            inputs, kwargs = self.scatter(inputs, kwargs, [-1])
-            return self.module.train_step(*inputs[0], **kwargs[0])
-
-        assert len(self.device_ids) == 1, \
-            ('MMDataParallel only supports single GPU training, if you need to'
-             ' train with multiple GPUs, please use MMDistributedDataParallel'
-             'instead.')
-
-        for t in chain(self.module.parameters(), self.module.buffers()):
-            if t.device != self.src_device_obj:
-                raise RuntimeError(
-                    'module must have its parameters and buffers '
-                    f'on device {self.src_device_obj} (device_ids[0]) but '
-                    f'found one of them on device: {t.device}')
-
-        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
-        return self.module.train_step(*inputs[0], **kwargs[0])
-
-    def val_step(self, *inputs, **kwargs):
-        if not self.device_ids:
-            # We add the following line thus the module could gather and
-            # convert data containers as those in GPU inference
-            inputs, kwargs = self.scatter(inputs, kwargs, [-1])
-            return self.module.val_step(*inputs[0], **kwargs[0])
-
-        assert len(self.device_ids) == 1, \
-            ('MMDataParallel only supports single GPU training, if you need to'
-             ' train with multiple GPUs, please use MMDistributedDataParallel'
-             ' instead.')
-
-        for t in chain(self.module.parameters(), self.module.buffers()):
-            if t.device != self.src_device_obj:
-                raise RuntimeError(
-                    'module must have its parameters and buffers '
-                    f'on device {self.src_device_obj} (device_ids[0]) but '
-                    f'found one of them on device: {t.device}')
-
-        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
-        return self.module.val_step(*inputs[0], **kwargs[0])
diff --git a/spaces/PrabhuKiranKonda/fastapi-postgres-todo-api/services.py b/spaces/PrabhuKiranKonda/fastapi-postgres-todo-api/services.py
deleted file mode 100644
index 8923046097424d5592dd99b67362b7a2602d5556..0000000000000000000000000000000000000000
--- a/spaces/PrabhuKiranKonda/fastapi-postgres-todo-api/services.py
+++ /dev/null
@@ -1,151 +0,0 @@
-from copy import deepcopy
-import models
-from psql_database import engine, Base, SessionLocal
-import sqlalchemy as sa
-import schemas as schemas
-import passlib.hash as pwd_hash
-import re
-from fastapi import HTTPException, Depends, security
-import jwt
-
-
-OAUTH2_SCHEME = security.OAuth2PasswordBearer(tokenUrl="/api/token")
-
-JWT_SECRET = 'myjwtsecret'
-
-
-def validate_email(email):
-    pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
-    return bool(re.match(pattern, email))
-
-
-def create_database_tables():
-    return Base.metadata.create_all(bind=engine)
-
-
-def get_db():
-    db = SessionLocal()
-    try:
-        yield db
-    finally:
-        db.close()
-
-
-async def get_user_by_email(email: str, db: sa.orm.Session):
-    return db.query(models.User).filter(models.User.email == email).first()
-
-
-async def create_user(user: schemas.userCreate, db: sa.orm.Session):
-
-    if not validate_email(user.email):
-        raise HTTPException(status_code=400, detail="Invalid email")
-
-    user_obj = models.User(
-        first_name=user.first_name,
-        last_name=user.last_name,
-        email=user.email,
-        password=pwd_hash.bcrypt.hash(user.password)
-    )
-
-    db.add(user_obj)
-    db.commit()  # commit operation
-    db.refresh(user_obj)  # Await the refresh operation
-    return user_obj
-
-
-async def authenticate_user(email, password, db: sa.orm.Session):
-    user = await get_user_by_email(email=email, db=db)
-
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    return user if user.verify_password(password) else False
-
-
-async def create_token(user: models.User):
-    user_dict = {
-        "first_name": user.first_name,
-        "last_name": user.last_name,
-        "email": user.email,
-    }
-    token = jwt.encode(user_dict, JWT_SECRET)
-
-    return dict(access_token=token, token_type='bearer', status='success')
-
-
-async def get_current_user(db: sa.orm.Session = Depends(get_db), token: str = Depends(OAUTH2_SCHEME)):
-    try:
-        payload = jwt.decode(token, JWT_SECRET, algorithms=['HS256'])
-        print(payload)
-        user = db.query(models.User).filter(
-            models.User.email == payload['email']).first()
-        if not user:
-            raise HTTPException(status_code=404, detail="User not found")
-        return schemas.User(**user.__dict__)
-    except Exception as e:
-        raise HTTPException(status_code=401, detail="Invalid token") from e
-
-
-async def create_todo(user: schemas.User, todo: schemas.TodoCreate, db: sa.orm.Session = Depends(get_db)):
-    print(todo.dict())
-
-    todo_obj = models.Todo(
-        task_name=todo.task_name,
-        task_description=todo.task_description,
-        priority=todo.priority,
-        category=todo.category,
-        due_date=todo.due_date,
-        status=todo.status,
-        user_id=user.user_id,
-    )
-
-    db.add(todo_obj)
-    db.commit()
-    db.refresh(todo_obj)
-    return todo_obj
-
-
-async def get_todos(user: schemas.User, db: sa.orm.Session = Depends(get_db)):
-    return db.query(models.Todo).filter(models.Todo.user_id == user.user_id).all()
-
-
-async def delete_todo(todo_id: int, user: schemas.User, db: sa.orm.Session = Depends(get_db)):
-    todo = db.query(models.Todo).filter(models.Todo.user_id ==
-                                        user.user_id, models.Todo.todo_id == todo_id).first()
-    if not todo:
-        raise HTTPException(status_code=404, detail="Todo not found")
-    db.delete(todo)
-    db.commit()
-    return todo
-
-
-async def update_todo(todo_id: int, user: schemas.User, todo: schemas.TodoCreate, db: sa.orm.Session = Depends(get_db)):
-    todo_db = db.query(models.Todo).filter(
-        models.Todo.user_id == user.user_id, models.Todo.todo_id == todo_id).first()
-    if not todo_db:
-        raise HTTPException(status_code=404, detail="Todo not found")
-    todo_db.task_name = todo.task_name
-    todo_db.task_description = todo.task_description or todo.task_description
-    todo_db.priority = todo.priority
-    todo_db.category = todo.category
-    todo_db.due_date = todo.due_date
-    todo_db.status = todo.status
-
-    db.commit()
-    db.refresh(todo_db)
-    return todo_db
-
-
-async def get_today_todos(user: schemas.User, db: sa.orm.Session = Depends(get_db)):
-    return db.query(models.Todo).filter(models.Todo.user_id == user.user_id, sa.func.date(models.Todo.due_date) == sa.func.date(sa.func.now())).all()
-
-
-async def get_overdue_todos(user: schemas.User, db: sa.orm.Session = Depends(get_db)):
-    return db.query(models.Todo).filter(models.Todo.user_id == user.user_id, models.Todo.due_date < sa.func.now()).all()
-
-
-async def get_upcoming_todos(user: schemas.User, db: sa.orm.Session = Depends(get_db)):
-    return db.query(models.Todo).filter(models.Todo.user_id == user.user_id, models.Todo.due_date > sa.func.now()).all()
-
-
-async def get_completed_todos(user: schemas.User, db: sa.orm.Session = Depends(get_db)):
-    return db.query(models.Todo).filter(models.Todo.user_id == user.user_id, models.Todo.status == True).all()
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/solvers/base.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/solvers/base.py
deleted file mode 100644
index 0432e44a36838c5731711f9d54f81822b21f20bd..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/solvers/base.py
+++ /dev/null
@@ -1,631 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-from contextlib import contextmanager
-from pathlib import Path
-import typing as tp
-
-import flashy
-import omegaconf
-import torch
-from torch import nn
-
-from .. import optim
-from ..optim import fsdp
-from ..utils import checkpoint
-from ..utils.autocast import TorchAutocast
-from ..utils.best_state import BestStateDictManager
-from ..utils.deadlock import DeadlockDetect
-from ..utils.profiler import Profiler
-from ..utils.utils import copy_state, dict_from_config, model_hash, with_rank_rng
-
-
-class StandardSolver(ABC, flashy.BaseSolver):
-    """Standard solver for AudioCraft.
-
-    The standard solver implements a base training loop with the following stages:
-    train, valid, evaluate and generate that are expected to be all defined for
-    solvers in AudioCraft. It also provides a nice default management of Dora history replay,
-    checkpoint management across epoch, and logging configuration.
-
-    AudioCraft solvers must inherit from the StandardSolver and define the methods
-    associated to each stage as well as the show, build_model and build_dataloaders methods.
-    """
-    def __init__(self, cfg: omegaconf.DictConfig):
-        super().__init__()
-        self.logger.info(f"Instantiating solver {self.__class__.__name__} for XP {self.xp.sig}")
-        self.logger.info(f"All XP logs are stored in {self.xp.folder}")
-        self.cfg = cfg
-        self.device = cfg.device
-        self.model: nn.Module
-        self._continue_best_source_keys = ['best_state', 'fsdp_best_state']
-        self._fsdp_modules: tp.List[fsdp.FSDP] = []
-        self._ema_sources: nn.ModuleDict = nn.ModuleDict()
-        self.ema: tp.Optional[optim.ModuleDictEMA] = None
-        self.dataloaders: tp.Dict[str, torch.utils.data.DataLoader] = dict()
-        self._log_updates = self.cfg.logging.get('log_updates', 10)
-        if self.cfg.logging.log_tensorboard:
-            self.init_tensorboard(**self.cfg.get('tensorboard'))
-        if self.cfg.logging.log_wandb and self:
-            self.init_wandb(**self.cfg.get('wandb'))
-        # keep a copy of the best performing state for stateful objects
-        # used for evaluation and generation stages
-        dtype_best: tp.Optional[torch.dtype] = None
-        if self.cfg.fsdp.use:
-            dtype_best = getattr(torch, self.cfg.fsdp.param_dtype)  # type: ignore
-            assert isinstance(dtype_best, torch.dtype)
-        elif self.cfg.autocast:
-            dtype_best = getattr(torch, self.cfg.autocast_dtype)  # type: ignore
-            assert isinstance(dtype_best, torch.dtype)
-        self.best_state: BestStateDictManager = BestStateDictManager(dtype=dtype_best)
-        # Hacky support for keeping a copy of the full best state in rank0.
-        self.fsdp_best_state: tp.Dict[str, tp.Any] = {}
-        self.register_stateful('best_state', 'fsdp_best_state')  # register best_state object to keep it in state_dict
-        self._new_best_state: bool = False  # should save a new checkpoint
-        # instantiate datasets and appropriate number of updates per epoch
-        self.build_dataloaders()
-        if self.cfg.execute_only is None:
-            assert 'train' in self.dataloaders, "The train dataset split must be provided."
-            assert 'valid' in self.dataloaders, "The valid dataset split must be provided."
-        self.train_updates_per_epoch = len(self.dataloaders['train']) if 'train' in self.dataloaders else 0
-        if self.cfg.optim.updates_per_epoch:
-            self.train_updates_per_epoch = self.cfg.optim.updates_per_epoch
-        self.total_updates = self.train_updates_per_epoch * self.cfg.optim.epochs
-        # instantiate model & exponential moving average on the model
-        self.build_model()
-        self.logger.info("Model hash: %s", model_hash(self.model))
-        assert 'model' in self.stateful.sources, \
-            "Please register the model to stateful with self.register_stateful('model') in build_model."
-        self.profiler = Profiler(self.model, **self.cfg.profiler)
-        self.initialize_ema()
-        self.register_stateful('ema')
-        assert self.ema is None or 'ema' in self.stateful.sources, \
-            "Please register the ema to stateful with self.register_stateful('ema') in build_model."
-        self.deadlock_detect = DeadlockDetect(**self.cfg.deadlock)
-        # basic statistics on the trained model
-        model_size = sum(p.numel() for p in self.model.parameters() if p.requires_grad) / 1e6
-        # one copy of grad, one copy of momentum, one copy of denominator and model weights.
-        # and 4 bytes for each float!
-        mem_usage = model_size * 4 * 4 / 1000
-        self.logger.info("Model size: %.2f M params", model_size)
-        self.logger.info("Base memory usage, with model, grad and optim: %.2f GB", mem_usage)
-
-    @property
-    def autocast(self):
-        """Convenient autocast (or not) using the solver configuration."""
-        return TorchAutocast(enabled=self.cfg.autocast, device_type=self.device, dtype=self.autocast_dtype)
-
-    def _get_state_source(self, name) -> flashy.state.StateDictSource:
-        # Internal utility to get a state source from the solver
-        return self.stateful.sources[name]
-
-    @property
-    def best_metric_name(self) -> tp.Optional[str]:
-        """Metric name used to identify the best state. This metric should be stored in the metrics
-        used on the stage for best state identification (most likely, `valid`). If None, then
-        no best state is saved.
-        """
-        return None
-
-    def register_best_state(self, *args: str):
-        """Register state sources in `BestStateDictManager` to keep their best states along with their
-        latest states. The best state will be used at evaluation stages instead of the latest states.
-
-        Shortcut around `BestStateDictManager.register` method. You can pass any number of
-        attribute, included nested attributes and those will be included into the checkpoints
-        and automatically restored when `BaseSolver.restore` is called.
-        """
-        for name in args:
-            state_source = self._get_state_source(name)
-            assert name in self.stateful.sources, "Registered states in best should be registered in stateful first!"
-            self.best_state.register(name, state_source)
-
-    def register_ema(self, *args: str):
-        """Register state sources for exponential moving average.
-
-        The registered sources are used to instantiate a ModuleDictEMA instance.
-        The ModuleDictEMA keeps a `nn.ModuleDict` module that is updated when self.ema.step() is called
-        and swapped with the original state sources with self.swap_ema_state() method.
-
-        Usage:
-            self.register_ema('model')
-        """
-        assert self.ema is None, "Cannot register state source to already instantiated EMA."
-        for name in args:
-            self._ema_sources[name] = getattr(self, name)
-
-    def wrap_with_fsdp(self, model: torch.nn.Module, *args, **kwargs):
-        model = fsdp.wrap_with_fsdp(self.cfg.fsdp, model, *args, **kwargs)
-        if isinstance(model, fsdp.FSDP):
-            self._fsdp_modules.append(model)
-        return model
-
-    def update_best_state_from_stage(self, stage_name: str = 'valid'):
-        """Update latest best state based on pending metrics of a given stage. This method relies
-        on the `BestStateDictManager.update` method to update the best state_dict with latest weights
-        if the registered states happen to match to the best performing setup.
-        """
-        if self.best_metric_name is None:
-            # when no best metric is defined, the last state is always the best
-            self._new_best_state = True
-            self.logger.info("Updating best state with current state.")
-        else:
-            assert stage_name in self._pending_metrics, f"Metrics for stage {stage_name} not found."
-            assert self.best_metric_name in self._pending_metrics[stage_name], \
-                f"Best metric not found in {stage_name} metrics. Cannot register best state"
-            current_score = self._pending_metrics[stage_name][self.best_metric_name]
-            all_best_metric_scores = [
-                past_metrics[stage_name][self.best_metric_name]
-                for past_metrics in self.history
-            ]
-            all_best_metric_scores.append(current_score)
-            best_score = min(all_best_metric_scores)
-            self._new_best_state = current_score == best_score
-            if self._new_best_state:
-                old_best = min(all_best_metric_scores[:-1] + [float('inf')])
-                self.logger.info(
-                    f"New best state with {self.best_metric_name}={current_score:.3f} (was {old_best:.3f})")
-
-        if self._new_best_state:
-            if self.cfg.fsdp.use:
-                # this will give an empty state dict on all ranks but the rank 0
-                # which will have a copy in memory of the full model.
-                with fsdp.switch_to_full_state_dict(self._fsdp_modules):
-                    for name in self.best_state.states.keys():
-                        state_source = self._get_state_source(name)
-                        self.best_state.update(name, state_source)
-                    # we save to a different dict.
-                    self.fsdp_best_state.update(self.best_state.state_dict())
-                # We cannot efficiently load fsdp_best_state when using FSDP,
-                # so we have do do a second pass, with the local shards.
-            for name in self.best_state.states.keys():
-                state_source = self._get_state_source(name)
-                self.best_state.update(name, state_source)
-
-    def _load_new_state_dict(self, state_dict: dict) -> dict:
-        old_states = {}
-        for name, new_state in state_dict.items():
-            state_source = self._get_state_source(name)
-            old_states[name] = copy_state(state_source.state_dict())
-            state_source.load_state_dict(new_state)
-        return old_states
-
-    @contextmanager
-    def swap_best_state(self):
-        self.logger.debug(f"Swapping to best state for: {', '.join(self.best_state.state_dict().keys())}")
-        old_states = self._load_new_state_dict(self.best_state.state_dict())
-        try:
-            yield
-        finally:
-            self.logger.debug("Swapping back from best to original state")
-            for name, old_state in old_states.items():
-                state_source = self._get_state_source(name)
-                state_source.load_state_dict(old_state)
-
-    @contextmanager
-    def swap_ema_state(self):
-        if self.ema is None:
-            yield
-        else:
-            ema_state_dict = self.ema.state_dict()['state']
-            self.logger.debug(f"Swapping to EMA state for: {', '.join(ema_state_dict.keys())}")
-            old_states = self._load_new_state_dict(ema_state_dict)
-            try:
-                yield
-            finally:
-                self.logger.debug("Swapping back from EMA state to original state")
-                for name, old_state in old_states.items():
-                    state_source = self._get_state_source(name)
-                    state_source.load_state_dict(old_state)
-
-    @property
-    def is_training(self):
-        return self.current_stage == 'train'
-
-    def log_model_summary(self, model: nn.Module):
-        """Log model summary, architecture and size of the model."""
-        self.logger.info(model)
-        mb = sum(p.numel() for p in model.parameters()) * 4 / 2 ** 20
-        self.logger.info("Size: %.1f MB", mb)
-
-    @abstractmethod
-    def build_model(self):
-        """Method to implement to initialize model."""
-        ...
-
-    def initialize_ema(self):
-        """Initialize exponential moving average with the registered sources.
-        EMA object is created if the optim.ema.model.decay value is non-null.
-        """
-        from .builders import get_ema
-        self.ema = get_ema(self._ema_sources, self.cfg.optim.ema)
-        if self.ema is None:
-            self.logger.info('No EMA on the model.')
-        else:
-            assert self.cfg.optim.ema.updates > 0
-            self.logger.info(
-                f'Initializing EMA on the model with decay = {self.ema.decay}'
-                f' every {self.cfg.optim.ema.updates} updates'
-            )
-
-    @abstractmethod
-    def build_dataloaders(self):
-        """Method to implement to initialize dataloaders."""
-        ...
-
-    @abstractmethod
-    def show(self):
-        """Method to log any information without running the job."""
-        ...
-
-    @property
-    def log_updates(self):
-        # convenient access to log updates
-        return self._log_updates
-
-    def checkpoint_path(self, **kwargs):
-        kwargs.setdefault('use_fsdp', self.cfg.fsdp.use)
-        return self.folder / checkpoint.checkpoint_name(**kwargs)
-
-    def epoch_checkpoint_path(self, epoch: int, **kwargs):
-        kwargs.setdefault('use_fsdp', self.cfg.fsdp.use)
-        return self.folder / checkpoint.checkpoint_name(str(epoch), **kwargs)
-
-    def checkpoint_path_with_name(self, name: str, **kwargs):
-        kwargs.setdefault('use_fsdp', self.cfg.fsdp.use)
-        return self.folder / checkpoint.checkpoint_name(name=name, **kwargs)
-
-    def save_checkpoints(self):
-        """Save checkpoint, optionally keeping a copy for a given epoch."""
-        is_sharded = self.cfg.fsdp.use
-        if not flashy.distrib.is_rank_zero() and not is_sharded:
-            return
-        self.logger.info("Model hash: %s", model_hash(self.model))
-        state = self.state_dict()
-        epoch = self.epoch - 1  # pushing metrics will increase the epoch in Flashy, so we do -1 here
-
-        # save minimal state_dict as new checkpoint every X epoch
-        if self.cfg.checkpoint.save_every:
-            if epoch % self.cfg.checkpoint.save_every == 0:
-                minimal_state = state
-                if self.cfg.checkpoint.keep_every_states is not None and len(self.cfg.checkpoint.keep_every_states) > 0:
-                    minimal_state = {
-                        name: source for name, source in state.items()
-                        if name in self.cfg.checkpoint.keep_every_states
-                    }
-                epoch_checkpoint_path = self.epoch_checkpoint_path(epoch)
-                checkpoint.save_checkpoint(minimal_state, epoch_checkpoint_path, is_sharded)
-
-        # save checkpoint as latest checkpoint
-        if self.cfg.checkpoint.save_last:
-            last_checkpoint_path = self.checkpoint_path()
-            checkpoint.save_checkpoint(state, last_checkpoint_path, is_sharded)
-
-        # flush any stale checkpoint to reduce disk footprint
-        checkpoint.flush_stale_checkpoints(self.checkpoint_path())
-
-    def load_from_pretrained(self, name: str) -> dict:
-        raise NotImplementedError("Solver does not provide a way to load pretrained models.")
-
-    def load_checkpoints(self, load_best: bool = False, ignore_state_keys: tp.List[str] = []) -> tp.Optional[dict]:
-        """Load last checkpoint or the one specified in continue_from.
-
-        Args:
-            load_best (bool): Whether to load from best state dict or not.
-                Best state dict is always used when not loading the current xp.
-            ignore_state_keys (list of str): List of sources to ignore when loading the state, e.g. `optimizer`.
-        Returns:
-            state (dict, optional): The loaded state dictionary.
-        """
-        # load checkpoints from xp folder or cfg.continue_from
-        is_sharded = self.cfg.fsdp.use
-        load_from_path: tp.Optional[Path] = None
-        checkpoint_source: tp.Optional[checkpoint.CheckpointSource] = None
-
-        if load_best:
-            self.logger.info("Trying to load state_dict from best state.")
-
-        state: tp.Optional[dict] = None
-        rank0_checkpoint_path = self.checkpoint_path(use_fsdp=False)
-        current_checkpoint_path = self.checkpoint_path()
-        _pretrained_prefix = '//pretrained/'
-        continue_pretrained = (self.cfg.continue_from or '').startswith(_pretrained_prefix)
-        if rank0_checkpoint_path.exists():
-            self.logger.info(f"Loading existing checkpoint: {current_checkpoint_path}")
-            load_from_path = current_checkpoint_path
-            checkpoint.check_sharded_checkpoint(current_checkpoint_path, rank0_checkpoint_path)
-            checkpoint_source = checkpoint.CheckpointSource.CURRENT_XP
-        elif self.cfg.continue_from and not continue_pretrained:
-            self.logger.info(f"Continuing from provided checkpoint: {self.cfg.continue_from}")
-            # we're always continuing from consolidated checkpoints: self.cfg.use_fsdp and not continue_best
-            load_from_path = checkpoint.resolve_checkpoint_path(self.cfg.continue_from, use_fsdp=False)
-            if load_from_path is None:
-                self.logger.error('Could not resolve the continue_from checkpoint %s', self.cfg.continue_from)
-                raise RuntimeError(f'Could not resolve continue_from checkpoint {self.cfg.continue_from}')
-            checkpoint_source = checkpoint.CheckpointSource.OTHER
-
-        if load_from_path is not None:
-            state = checkpoint.load_checkpoint(load_from_path, is_sharded)
-        elif continue_pretrained:
-            self.logger.info("Loading a pretrained model. Ignoring 'load_best' and 'ignore_state_keys' params.")
-            state = self.load_from_pretrained(self.cfg.continue_from[len(_pretrained_prefix):])
-            checkpoint_source = checkpoint.CheckpointSource.PRETRAINED
-            load_best = True
-
-        # checkpoints are not from the current xp, we only retrieve the best state
-        if checkpoint_source is not None and checkpoint_source != checkpoint.CheckpointSource.CURRENT_XP:
-            assert state is not None
-            self.logger.info("Checkpoint source is not the current xp: Load state_dict from best state.")
-            load_best = True
-            state = {key: state[key] for key in self._continue_best_source_keys if key in state}
-            # loaded checkpoints are FSDP checkpoints: we're reading the best state
-            # from FSDP and we drop the regular best_state
-            if 'fsdp_best_state' in state and state['fsdp_best_state']:
-                state.pop('best_state', None)
-                self.logger.info("... Loaded checkpoint has FSDP best state")
-            # FSDP is enabled in the solver, if the loaded checkpoints do not have FSDP support
-            # then we're initializing FSDP best state with the regular best state
-            elif self.cfg.fsdp.use:
-                if 'fsdp_best_state' not in state or not state['fsdp_best_state']:
-                    # we swap non-FSDP checkpoints best_state to FSDP-compatible best state
-                    state['fsdp_best_state'] = state.pop('best_state')
-                    self.logger.info("... Loaded checkpoint does not have FSDP best state. Use regular best state")
-
-        if state is not None:
-            if load_best:
-                self.logger.info("Ignoring keys when loading best %r", ignore_state_keys)
-                for key in set(ignore_state_keys):
-                    if key in state:
-                        state.pop(key)
-                has_best_state = 'best_state' in state or 'fsdp_best_state' in state
-                assert has_best_state, ("Trying to load best state but neither 'best_state'",
-                                        " or 'fsdp_best_state' found in checkpoints.")
-            self.load_state_dict(state)
-
-        # for FSDP, let's make extra sure nothing bad happened with out of sync
-        # checkpoints across workers.
-        epoch = float(self.epoch)
-        avg_epoch = flashy.distrib.average_metrics({'epoch': epoch})['epoch']
-        if avg_epoch != epoch:
-            raise RuntimeError(
-                f"Inconsistent loading of checkpoints happened, our epoch is {epoch} "
-                f"but average of epochs is {avg_epoch}, at least one gpu must have a "
-                "different epoch number.")
-
-        # on load_best, properly reinitialize state_dict, best states and ema
-        # otherwise we load from the current xp and don't alter anything
-        if load_best:
-            self.logger.info("Loading state_dict from best state.")
-            if not self.cfg.fsdp.use and self.fsdp_best_state:
-                # loading from an FSDP checkpoint but with FSDP deactivated
-                self.logger.info("... Loading from FSDP best state dict.")
-                self.best_state.load_state_dict(self.fsdp_best_state)
-
-            # if load_best, we permanently override the regular state_dict with the best state
-            if self.cfg.fsdp.use:
-                self.logger.info("FSDP is used, loading from FSDP best state.")
-                with fsdp.switch_to_full_state_dict(self._fsdp_modules):
-                    # this might be really fragile but okay for now.
-                    self.load_state_dict(self.fsdp_best_state)
-            else:
-                # we permanently swap the stateful objects to their best state
-                self._load_new_state_dict(self.best_state.state_dict())
-
-            # the EMA modules should also be instantiated with best state.
-            # the easiest way to do so is to reinitialize a new EMA with best state loaded.
-            if self.ema is not None:
-                self.logger.info("Re-initializing EMA from best state")
-                self.initialize_ema()
-
-            if self.cfg.fsdp.use:
-                self.logger.info("Re-initializing best state after using FSDP best state.")
-                for name in self.best_state.states.keys():
-                    state_source = self._get_state_source(name)
-                    self.best_state.update(name, state_source)
-
-        return state
-
-    def restore(self, load_best: bool = False, replay_metrics: bool = False,
-                ignore_state_keys: tp.List[str] = []) -> bool:
-        """Restore the status of a solver for a given xp.
-
-        Args:
-            load_best (bool): if `True`, load the best state from the checkpoint.
-            replay_metrics (bool): if `True`, logs all the metrics from past epochs.
-            ignore_state_keys (list of str): list of sources to ignore when loading the state, e.g. `optimizer`.
-        """
-        self.logger.info("Restoring weights and history.")
-        restored_checkpoints = self.load_checkpoints(load_best, ignore_state_keys)
-
-        self.logger.info("Model hash: %s", model_hash(self.model))
-
-        if replay_metrics and len(self.history) > 0:
-            self.logger.info("Replaying past metrics...")
-            for epoch, stages in enumerate(self.history):
-                for stage_name, metrics in stages.items():
-                    # We manually log the metrics summary to the result logger
-                    # as we don't want to add them to the pending metrics
-                    self.result_logger._log_summary(stage_name, metrics, step=epoch + 1, step_name='epoch',
-                                                    formatter=self.get_formatter(stage_name))
-        return restored_checkpoints is not None
-
-    def commit(self, save_checkpoints: bool = True):
-        """Commit metrics to dora and save checkpoints at the end of an epoch."""
-        # we override commit to introduce more complex checkpoint saving behaviors
-        self.history.append(self._pending_metrics)  # This will increase self.epoch
-        if save_checkpoints:
-            self.save_checkpoints()
-        self._start_epoch()
-        if flashy.distrib.is_rank_zero():
-            self.xp.link.update_history(self.history)
-
-    def run_epoch(self):
-        """Run a single epoch with all stages.
-
-        Metrics for a given stage are stored in _pending_metrics and committed by the solver afterwards.
-        Children solvers can extend this method with custom behavior, e.g.:
-
-            def run_epoch(self):
-                ... # custom code
-                super().run_epoch()
-                ... # custom code
-        """
-        self.run_stage('train', self.train)
-        with torch.no_grad():
-            with self.swap_ema_state():
-                self.run_stage('valid', self.valid)
-                # the best state is updated with EMA states if available
-                self.update_best_state_from_stage('valid')
-            with self.swap_best_state():
-                if self.should_run_stage('evaluate'):
-                    self.run_stage('evaluate', self.evaluate)
-                if self.should_run_stage('generate'):
-                    self.run_stage('generate', with_rank_rng()(self.generate))
-
-    def run(self):
-        """Training loop."""
-        assert len(self.state_dict()) > 0
-        self.restore(replay_metrics=True)  # load checkpoint and replay history
-        self.log_hyperparams(dict_from_config(self.cfg))
-        for epoch in range(self.epoch, self.cfg.optim.epochs + 1):
-            if self.should_stop_training():
-                return
-            self.run_epoch()
-            # Commit will send the metrics to Dora and save checkpoints by default.
-            self.commit()
-
-    def should_stop_training(self) -> bool:
-        """Check whether we should stop training or not."""
-        return self.epoch > self.cfg.optim.epochs
-
-    def should_run_stage(self, stage_name) -> bool:
-        """Check whether we want to run the specified stages."""
-        stage_every = self.cfg[stage_name].get('every', None)
-        is_last_epoch = self.epoch == self.cfg.optim.epochs
-        is_epoch_every = (stage_every and self.epoch % stage_every == 0)
-        return is_last_epoch or is_epoch_every
-
-    @abstractmethod
-    def run_step(self, idx: int, batch: tp.Any, metrics: dict):
-        """Perform one training or valid step on a given batch."""
-        ...
-
-    def common_train_valid(self, dataset_split: str, **kwargs: tp.Any):
-        """Common logic for train and valid stages."""
-        self.model.train(self.is_training)
-
-        loader = self.dataloaders[dataset_split]
-        # get a different order for distributed training, otherwise this will get ignored
-        if flashy.distrib.world_size() > 1 \
-           and isinstance(loader.sampler, torch.utils.data.distributed.DistributedSampler):
-            loader.sampler.set_epoch(self.epoch)
-        updates_per_epoch = self.train_updates_per_epoch if self.is_training else len(loader)
-        if self.cfg.benchmark_no_load:
-            self.logger.warning("Fake loading for benchmarking: re-using first batch")
-            batch = next(iter(loader))
-            loader = [batch] * updates_per_epoch  # type: ignore
-        lp = self.log_progress(self.current_stage, loader, total=updates_per_epoch, updates=self.log_updates)
-        average = flashy.averager()  # epoch wise average
-        instant_average = flashy.averager()  # average between two logging
-        metrics: dict = {}
-
-        with self.profiler, self.deadlock_detect:  # profiler will only run for the first 20 updates.
-            for idx, batch in enumerate(lp):
-                self.deadlock_detect.update('batch')
-                if idx >= updates_per_epoch:
-                    break
-                metrics = {}
-                metrics = self.run_step(idx, batch, metrics)
-                self.deadlock_detect.update('step')
-                # run EMA step
-                if self.ema is not None and self.is_training and (idx + 1) % self.cfg.optim.ema.updates == 0:
-                    self.logger.debug("EMA model step")
-                    self.ema.step()
-                self.deadlock_detect.update('ema')
-                self.profiler.step()
-                instant_metrics = instant_average(metrics)
-                if lp.update(**instant_metrics):
-                    instant_average = flashy.averager()  # reset averager between two logging
-                metrics = average(metrics)  # epoch wise average
-                self.deadlock_detect.update('end_batch')
-
-        metrics = flashy.distrib.average_metrics(metrics, updates_per_epoch)
-        return metrics
-
-    def train(self):
-        """Train stage."""
-        return self.common_train_valid('train')
-
-    def valid(self):
-        """Valid stage."""
-        return self.common_train_valid('valid')
-
-    @abstractmethod
-    def evaluate(self):
-        """Evaluate stage."""
-        ...
-
-    @abstractmethod
-    def generate(self):
-        """Generate stage."""
-        ...
-
-    def run_one_stage(self, stage_name: str):
-        """Run only the specified stage.
-        This method is useful to only generate samples from a trained experiment
-        or rerun the validation or evaluation stages.
-        """
-        fn = {
-            'generate': with_rank_rng()(self.generate),
-            'evaluate': self.evaluate,
-            'valid': self.valid,
-        }
-        if stage_name not in fn:
-            raise ValueError(f'Trying to run stage {stage_name} is not supported.')
-        assert len(self.state_dict()) > 0
-        self._start_epoch()
-        with torch.no_grad(), self.swap_best_state():
-            self.run_stage(stage_name, fn[stage_name])
-        if not self.cfg.execute_inplace:
-            self.commit(save_checkpoints=False)
-
-    @staticmethod
-    def get_eval_solver_from_sig(sig: str, dtype: tp.Optional[str] = None,
-                                 device: tp.Optional[str] = None, autocast: bool = True,
-                                 batch_size: tp.Optional[int] = None,
-                                 override_cfg: tp.Optional[tp.Union[dict, omegaconf.DictConfig]] = None,
-                                 **kwargs):
-        """Mostly a convenience function around audiocraft.train.get_solver_from_sig,
-        populating all the proper param, deactivating EMA, FSDP, loading the best state,
-        basically all you need to get a solver ready to "play" with in single GPU mode
-        and with minimal memory overhead.
-
-        Args:
-            sig (str): signature to load.
-            dtype (str or None): potential dtype, as a string, i.e. 'float16'.
-            device (str or None): potential device, as a string, i.e. 'cuda'.
-            override_cfg (dict or omegaconf.DictConfig or None): potential device, as a string, i.e. 'cuda'.
-        """
-        from audiocraft import train
-        our_override_cfg: tp.Dict[str, tp.Any] = {'optim': {'ema': {'use': False}}}
-        our_override_cfg['autocast'] = autocast
-        if dtype is not None:
-            our_override_cfg['dtype'] = dtype
-        if device is not None:
-            our_override_cfg['device'] = device
-        if batch_size is not None:
-            our_override_cfg['dataset'] = {'batch_size': batch_size}
-        if override_cfg is None:
-            override_cfg = {}
-        override_cfg = omegaconf.OmegaConf.merge(
-            omegaconf.DictConfig(override_cfg), omegaconf.DictConfig(our_override_cfg))  # type: ignore
-        solver = train.get_solver_from_sig(
-            sig, override_cfg=override_cfg,
-            load_best=True, disable_fsdp=True,
-            ignore_state_keys=['optimizer', 'ema'], **kwargs)
-        solver.model.eval()
-        return solver
diff --git a/spaces/PrussianBlue/White-box-Cartoonization/app.py b/spaces/PrussianBlue/White-box-Cartoonization/app.py
deleted file mode 100644
index c55ced56bd87a85f59d1c8ef84b7eca87422720f..0000000000000000000000000000000000000000
--- a/spaces/PrussianBlue/White-box-Cartoonization/app.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-import argparse
-import functools
-import os
-import pathlib
-import sys
-from typing import Callable
-import uuid
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-
-from io import BytesIO
-from wbc.cartoonize import Cartoonize
-
-ORIGINAL_REPO_URL = 'https://github.com/SystemErrorWang/White-box-Cartoonization'
-TITLE = 'SystemErrorWang/White-box-Cartoonization'
-DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.
-
-"""
-ARTICLE = """
-
-"""
-
-SAFEHASH = [x for x in "0123456789-abcdefghijklmnopqrstuvwxyz_ABCDEFGHIJKLMNOPQRSTUVWXYZ"]
-def compress_UUID():
-    '''
-    根据http://www.ietf.org/rfc/rfc1738.txt,由uuid编码扩bai大字符域生成du串
-    包括:[0-9a-zA-Z\-_]共64个
-    长度:(32-2)/3*2=20
-    备注:可在地球上人zhi人都用,使用100年不重复(2^120)
-    :return:String
-    '''
-    row = str(uuid.uuid4()).replace('-', '')
-    safe_code = ''
-    for i in range(10):
-        enbin = "%012d" % int(bin(int(row[i * 3] + row[i * 3 + 1] + row[i * 3 + 2], 16))[2:], 10)
-        safe_code += (SAFEHASH[int(enbin[0:6], 2)] + SAFEHASH[int(enbin[6:12], 2)])
-    safe_code = safe_code.replace('-', '')
-    return safe_code
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    parser.add_argument('--allow-screenshot', action='store_true')
-    return parser.parse_args()
-
-def run(
-        image,
-        cartoonize : Cartoonize
-) -> tuple[PIL.Image.Image]:
-
-    out_path = compress_UUID()+'.png'
-    cartoonize.run_sigle(image.name, out_path)
-
-    return PIL.Image.open(out_path)
-
-
-def main():
-    gr.close_all()
-
-    args = parse_args()
-
-    cartoonize = Cartoonize(os.path.join(os.path.dirname(os.path.abspath(__file__)),'wbc/saved_models/'))
-
-    func = functools.partial(run, cartoonize=cartoonize)
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='file', label='Input Image'),
-        ],
-        [
-            gr.outputs.Image(
-                type='pil',
-                label='Result'),
-        ],
-        # examples=examples,
-        theme=args.theme,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        allow_screenshot=args.allow_screenshot,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/QuoQA-NLP/KoQuillBot/app.py b/spaces/QuoQA-NLP/KoQuillBot/app.py
deleted file mode 100644
index c77aa2f130619f6eb914ed5e813c5d53e44a8343..0000000000000000000000000000000000000000
--- a/spaces/QuoQA-NLP/KoQuillBot/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-
-st.set_page_config(
-    page_title="KoQuillBot", layout="wide", initial_sidebar_state="expanded"
-)
-
-@st.cache
-def load_model(model_name):
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    return model
-
-tokenizer = AutoTokenizer.from_pretrained("QuoQA-NLP/KE-T5-Ko2En-Base")
-ko2en_model = load_model("QuoQA-NLP/KE-T5-Ko2En-Base")
-en2ko_model = load_model("QuoQA-NLP/KE-T5-En2Ko-Base")
-
-
-st.title("🤖 KoQuillBot")
-
-
-default_value = "프로젝트 가치가 미화 1백만 달러 이상인 공공 파트너가 시작한 PPP 프로젝트에 대해 2단계 입찰이 실시됩니다. 입찰을 전자 방식으로 진행하는 것이 허용됩니다. (즉, 신청서 및 입찰 제안의 전자 제출). COVID-19 전염병과 그에 따른 여행 제한으로 인해 오늘날에는 일반적인 관행이 되었습니다."
-src_text = st.text_area(
-    "바꾸고 싶은 문장을 입력하세요:",
-    default_value,
-    height=300,
-    max_chars=200,
-)
-print(src_text)
-
-
-
-if src_text == "":
-    st.warning("Please **enter text** for translation")
-
-# translate into english sentence
-english_translation = ko2en_model.generate(
-    **tokenizer(
-        src_text,
-        return_tensors="pt",
-        padding="max_length",
-        truncation=True,
-        max_length=64,
-    ),
-    max_length=64,
-    num_beams=5,
-    repetition_penalty=1.3,
-    no_repeat_ngram_size=3,
-    num_return_sequences=1,
-)
-english_translation = tokenizer.decode(
-    english_translation[0],
-    clean_up_tokenization_spaces=True,
-    skip_special_tokens=True,
-)
-
-# translate back to korean
-korean_translation = en2ko_model.generate(
-    **tokenizer(
-        english_translation,
-        return_tensors="pt",
-        padding="max_length",
-        truncation=True,
-        max_length=64,
-    ),
-    max_length=64,
-    num_beams=5,
-    repetition_penalty=1.3,
-    no_repeat_ngram_size=3,
-    num_return_sequences=1,
-)
-
-korean_translation = tokenizer.decode(
-    korean_translation[0],
-    clean_up_tokenization_spaces=True,
-    skip_special_tokens=True,
-)
-print(f"{src_text} -> {english_translation} -> {korean_translation}")
-
-st.write(korean_translation)
-print(korean_translation)
diff --git a/spaces/RVVY/test01/README.md b/spaces/RVVY/test01/README.md
deleted file mode 100644
index 2862b624cae0b3e7b0446a011b8514067c7c5e1b..0000000000000000000000000000000000000000
--- a/spaces/RVVY/test01/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Test01
-emoji: 🏆
-colorFrom: gray
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/certifi/__init__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/certifi/__init__.py
deleted file mode 100644
index af4bcc1510f63fae52d504eb805e8497f2b11cde..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/certifi/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .core import contents, where
-
-__all__ = ["contents", "where"]
-__version__ = "2022.09.24"
diff --git a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/datasets/InvISP/config/config.py b/spaces/Realcat/image-matching-webui/third_party/DarkFeat/datasets/InvISP/config/config.py
deleted file mode 100644
index d0b041cd724db5d8edf629fd56dfba10b83ea6c0..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/datasets/InvISP/config/config.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import argparse
-
-BATCH_SIZE = 1
-
-DATA_PATH = "./data/"
-
-
-def get_arguments():
-    parser = argparse.ArgumentParser(description="training codes")
-
-    parser.add_argument("--task", type=str, help="Name of this training")
-    parser.add_argument(
-        "--data_path", type=str, default=DATA_PATH, help="Dataset root path."
-    )
-    parser.add_argument(
-        "--batch_size", type=int, default=BATCH_SIZE, help="Batch size for training. "
-    )
-    parser.add_argument(
-        "--debug_mode",
-        dest="debug_mode",
-        action="store_true",
-        help="If debug mode, load less data.",
-    )
-    parser.add_argument(
-        "--gamma",
-        dest="gamma",
-        action="store_true",
-        help="Use gamma compression for raw data.",
-    )
-    parser.add_argument(
-        "--camera",
-        type=str,
-        default="NIKON_D700",
-        choices=["NIKON_D700", "Canon_EOS_5D"],
-        help="Choose which camera to use. ",
-    )
-    parser.add_argument(
-        "--rgb_weight", type=float, default=1, help="Weight for rgb loss. "
-    )
-
-    return parser
diff --git a/spaces/Realcat/image-matching-webui/third_party/DeDoDe/demo/demo_match.py b/spaces/Realcat/image-matching-webui/third_party/DeDoDe/demo/demo_match.py
deleted file mode 100644
index 2ddecc453e1e3d0beb5e832819833209ad431048..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DeDoDe/demo/demo_match.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch
-from DeDoDe import dedode_detector_L, dedode_descriptor_B
-from DeDoDe.matchers.dual_softmax_matcher import DualSoftMaxMatcher
-from DeDoDe.utils import *
-from PIL import Image
-import cv2
-
-
-def draw_matches(im_A, kpts_A, im_B, kpts_B):
-    kpts_A = [cv2.KeyPoint(x, y, 1.0) for x, y in kpts_A.cpu().numpy()]
-    kpts_B = [cv2.KeyPoint(x, y, 1.0) for x, y in kpts_B.cpu().numpy()]
-    matches_A_to_B = [cv2.DMatch(idx, idx, 0.0) for idx in range(len(kpts_A))]
-    im_A, im_B = np.array(im_A), np.array(im_B)
-    ret = cv2.drawMatches(im_A, kpts_A, im_B, kpts_B, matches_A_to_B, None)
-    return ret
-
-
-detector = dedode_detector_L(weights=torch.load("dedode_detector_L.pth"))
-descriptor = dedode_descriptor_B(weights=torch.load("dedode_descriptor_B.pth"))
-matcher = DualSoftMaxMatcher()
-
-im_A_path = "assets/im_A.jpg"
-im_B_path = "assets/im_B.jpg"
-im_A = Image.open(im_A_path)
-im_B = Image.open(im_B_path)
-W_A, H_A = im_A.size
-W_B, H_B = im_B.size
-
-
-detections_A = detector.detect_from_path(im_A_path, num_keypoints=10_000)
-keypoints_A, P_A = detections_A["keypoints"], detections_A["confidence"]
-detections_B = detector.detect_from_path(im_B_path, num_keypoints=10_000)
-keypoints_B, P_B = detections_B["keypoints"], detections_B["confidence"]
-description_A = descriptor.describe_keypoints_from_path(im_A_path, keypoints_A)[
-    "descriptions"
-]
-description_B = descriptor.describe_keypoints_from_path(im_B_path, keypoints_B)[
-    "descriptions"
-]
-matches_A, matches_B, batch_ids = matcher.match(
-    keypoints_A,
-    description_A,
-    keypoints_B,
-    description_B,
-    P_A=P_A,
-    P_B=P_B,
-    normalize=True,
-    inv_temp=20,
-    threshold=0.1,
-)  # Increasing threshold -> fewer matches, fewer outliers
-
-matches_A, matches_B = matcher.to_pixel_coords(matches_A, matches_B, H_A, W_A, H_B, W_B)
-
-import cv2
-import numpy as np
-
-Image.fromarray(draw_matches(im_A, matches_A[::5], im_B, matches_B[::5])).save(
-    "demo/matches.png"
-)
diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/benchmarks/hpatches_sequences_homog_benchmark.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/benchmarks/hpatches_sequences_homog_benchmark.py
deleted file mode 100644
index 6417d4d54798360a027a0d11d50fc65cdfae015a..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/benchmarks/hpatches_sequences_homog_benchmark.py
+++ /dev/null
@@ -1,111 +0,0 @@
-from PIL import Image
-import numpy as np
-
-import os
-
-from tqdm import tqdm
-from roma.utils import pose_auc
-import cv2
-
-
-class HpatchesHomogBenchmark:
-    """Hpatches grid goes from [0,n-1] instead of [0.5,n-0.5]"""
-
-    def __init__(self, dataset_path) -> None:
-        seqs_dir = "hpatches-sequences-release"
-        self.seqs_path = os.path.join(dataset_path, seqs_dir)
-        self.seq_names = sorted(os.listdir(self.seqs_path))
-        # Ignore seqs is same as LoFTR.
-        self.ignore_seqs = set(
-            [
-                "i_contruction",
-                "i_crownnight",
-                "i_dc",
-                "i_pencils",
-                "i_whitebuilding",
-                "v_artisans",
-                "v_astronautis",
-                "v_talent",
-            ]
-        )
-
-    def convert_coordinates(self, im_A_coords, im_A_to_im_B, wq, hq, wsup, hsup):
-        offset = 0.5  # Hpatches assumes that the center of the top-left pixel is at [0,0] (I think)
-        im_A_coords = (
-            np.stack(
-                (
-                    wq * (im_A_coords[..., 0] + 1) / 2,
-                    hq * (im_A_coords[..., 1] + 1) / 2,
-                ),
-                axis=-1,
-            )
-            - offset
-        )
-        im_A_to_im_B = (
-            np.stack(
-                (
-                    wsup * (im_A_to_im_B[..., 0] + 1) / 2,
-                    hsup * (im_A_to_im_B[..., 1] + 1) / 2,
-                ),
-                axis=-1,
-            )
-            - offset
-        )
-        return im_A_coords, im_A_to_im_B
-
-    def benchmark(self, model, model_name=None):
-        n_matches = []
-        homog_dists = []
-        for seq_idx, seq_name in tqdm(
-            enumerate(self.seq_names), total=len(self.seq_names)
-        ):
-            im_A_path = os.path.join(self.seqs_path, seq_name, "1.ppm")
-            im_A = Image.open(im_A_path)
-            w1, h1 = im_A.size
-            for im_idx in range(2, 7):
-                im_B_path = os.path.join(self.seqs_path, seq_name, f"{im_idx}.ppm")
-                im_B = Image.open(im_B_path)
-                w2, h2 = im_B.size
-                H = np.loadtxt(
-                    os.path.join(self.seqs_path, seq_name, "H_1_" + str(im_idx))
-                )
-                dense_matches, dense_certainty = model.match(im_A_path, im_B_path)
-                good_matches, _ = model.sample(dense_matches, dense_certainty, 5000)
-                pos_a, pos_b = self.convert_coordinates(
-                    good_matches[:, :2], good_matches[:, 2:], w1, h1, w2, h2
-                )
-                try:
-                    H_pred, inliers = cv2.findHomography(
-                        pos_a,
-                        pos_b,
-                        method=cv2.RANSAC,
-                        confidence=0.99999,
-                        ransacReprojThreshold=3 * min(w2, h2) / 480,
-                    )
-                except:
-                    H_pred = None
-                if H_pred is None:
-                    H_pred = np.zeros((3, 3))
-                    H_pred[2, 2] = 1.0
-                corners = np.array(
-                    [[0, 0, 1], [0, h1 - 1, 1], [w1 - 1, 0, 1], [w1 - 1, h1 - 1, 1]]
-                )
-                real_warped_corners = np.dot(corners, np.transpose(H))
-                real_warped_corners = (
-                    real_warped_corners[:, :2] / real_warped_corners[:, 2:]
-                )
-                warped_corners = np.dot(corners, np.transpose(H_pred))
-                warped_corners = warped_corners[:, :2] / warped_corners[:, 2:]
-                mean_dist = np.mean(
-                    np.linalg.norm(real_warped_corners - warped_corners, axis=1)
-                ) / (min(w2, h2) / 480.0)
-                homog_dists.append(mean_dist)
-
-        n_matches = np.array(n_matches)
-        thresholds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-        auc = pose_auc(np.array(homog_dists), thresholds)
-        return {
-            "hpatches_homog_auc_3": auc[2],
-            "hpatches_homog_auc_5": auc[4],
-            "hpatches_homog_auc_10": auc[9],
-        }
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/iter_timer.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/iter_timer.py
deleted file mode 100644
index cfd5002fe85ffc6992155ac01003878064a1d9be..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/iter_timer.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import time
-
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class IterTimerHook(Hook):
-
-    def before_epoch(self, runner):
-        self.t = time.time()
-
-    def before_iter(self, runner):
-        runner.log_buffer.update({'data_time': time.time() - self.t})
-
-    def after_iter(self, runner):
-        runner.log_buffer.update({'time': time.time() - self.t})
-        self.t = time.time()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/logger/base.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/logger/base.py
deleted file mode 100644
index f845256729458ced821762a1b8ef881e17ff9955..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/logger/base.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numbers
-from abc import ABCMeta, abstractmethod
-
-import numpy as np
-import torch
-
-from ..hook import Hook
-
-
-class LoggerHook(Hook):
-    """Base class for logger hooks.
-
-    Args:
-        interval (int): Logging interval (every k iterations).
-        ignore_last (bool): Ignore the log of last iterations in each epoch
-            if less than `interval`.
-        reset_flag (bool): Whether to clear the output buffer after logging.
-        by_epoch (bool): Whether EpochBasedRunner is used.
-    """
-
-    __metaclass__ = ABCMeta
-
-    def __init__(self,
-                 interval=10,
-                 ignore_last=True,
-                 reset_flag=False,
-                 by_epoch=True):
-        self.interval = interval
-        self.ignore_last = ignore_last
-        self.reset_flag = reset_flag
-        self.by_epoch = by_epoch
-
-    @abstractmethod
-    def log(self, runner):
-        pass
-
-    @staticmethod
-    def is_scalar(val, include_np=True, include_torch=True):
-        """Tell the input variable is a scalar or not.
-
-        Args:
-            val: Input variable.
-            include_np (bool): Whether include 0-d np.ndarray as a scalar.
-            include_torch (bool): Whether include 0-d torch.Tensor as a scalar.
-
-        Returns:
-            bool: True or False.
-        """
-        if isinstance(val, numbers.Number):
-            return True
-        elif include_np and isinstance(val, np.ndarray) and val.ndim == 0:
-            return True
-        elif include_torch and isinstance(val, torch.Tensor) and len(val) == 1:
-            return True
-        else:
-            return False
-
-    def get_mode(self, runner):
-        if runner.mode == 'train':
-            if 'time' in runner.log_buffer.output:
-                mode = 'train'
-            else:
-                mode = 'val'
-        elif runner.mode == 'val':
-            mode = 'val'
-        else:
-            raise ValueError(f"runner mode should be 'train' or 'val', "
-                             f'but got {runner.mode}')
-        return mode
-
-    def get_epoch(self, runner):
-        if runner.mode == 'train':
-            epoch = runner.epoch + 1
-        elif runner.mode == 'val':
-            # normal val mode
-            # runner.epoch += 1 has been done before val workflow
-            epoch = runner.epoch
-        else:
-            raise ValueError(f"runner mode should be 'train' or 'val', "
-                             f'but got {runner.mode}')
-        return epoch
-
-    def get_iter(self, runner, inner_iter=False):
-        """Get the current training iteration step."""
-        if self.by_epoch and inner_iter:
-            current_iter = runner.inner_iter + 1
-        else:
-            current_iter = runner.iter + 1
-        return current_iter
-
-    def get_lr_tags(self, runner):
-        tags = {}
-        lrs = runner.current_lr()
-        if isinstance(lrs, dict):
-            for name, value in lrs.items():
-                tags[f'learning_rate/{name}'] = value[0]
-        else:
-            tags['learning_rate'] = lrs[0]
-        return tags
-
-    def get_momentum_tags(self, runner):
-        tags = {}
-        momentums = runner.current_momentum()
-        if isinstance(momentums, dict):
-            for name, value in momentums.items():
-                tags[f'momentum/{name}'] = value[0]
-        else:
-            tags['momentum'] = momentums[0]
-        return tags
-
-    def get_loggable_tags(self,
-                          runner,
-                          allow_scalar=True,
-                          allow_text=False,
-                          add_mode=True,
-                          tags_to_skip=('time', 'data_time')):
-        tags = {}
-        for var, val in runner.log_buffer.output.items():
-            if var in tags_to_skip:
-                continue
-            if self.is_scalar(val) and not allow_scalar:
-                continue
-            if isinstance(val, str) and not allow_text:
-                continue
-            if add_mode:
-                var = f'{self.get_mode(runner)}/{var}'
-            tags[var] = val
-        tags.update(self.get_lr_tags(runner))
-        tags.update(self.get_momentum_tags(runner))
-        return tags
-
-    def before_run(self, runner):
-        for hook in runner.hooks[::-1]:
-            if isinstance(hook, LoggerHook):
-                hook.reset_flag = True
-                break
-
-    def before_epoch(self, runner):
-        runner.log_buffer.clear()  # clear logs of last epoch
-
-    def after_train_iter(self, runner):
-        if self.by_epoch and self.every_n_inner_iters(runner, self.interval):
-            runner.log_buffer.average(self.interval)
-        elif not self.by_epoch and self.every_n_iters(runner, self.interval):
-            runner.log_buffer.average(self.interval)
-        elif self.end_of_epoch(runner) and not self.ignore_last:
-            # not precise but more stable
-            runner.log_buffer.average(self.interval)
-
-        if runner.log_buffer.ready:
-            self.log(runner)
-            if self.reset_flag:
-                runner.log_buffer.clear_output()
-
-    def after_train_epoch(self, runner):
-        if runner.log_buffer.ready:
-            self.log(runner)
-            if self.reset_flag:
-                runner.log_buffer.clear_output()
-
-    def after_val_epoch(self, runner):
-        runner.log_buffer.average()
-        self.log(runner)
-        if self.reset_flag:
-            runner.log_buffer.clear_output()
diff --git a/spaces/Romanian/Ok/README.md b/spaces/Romanian/Ok/README.md
deleted file mode 100644
index 7137c852b8631b114f9e6b3970b45005c3a5e513..0000000000000000000000000000000000000000
--- a/spaces/Romanian/Ok/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Ok
-emoji: 📉
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SHIBATAATSUSHI/aioccupationaltherapist2/template.md b/spaces/SHIBATAATSUSHI/aioccupationaltherapist2/template.md
deleted file mode 100644
index 8143d8982e72096b4b31571c27e44b62e59078d9..0000000000000000000000000000000000000000
--- a/spaces/SHIBATAATSUSHI/aioccupationaltherapist2/template.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### AI作業療法士（β）からの返信
-
-ここにユーザのメッセージに対する返信を記載する．
-併発されるような症状も推測して記載する．
-症状で大変であることを労う言葉をかける．
-
-### AI作業療法士（β）からの注意点
-
-ここに作業療法アシスタントとしての注意点を記載する．
-疾患や症状に対して，作業療法を行うことでのメリットとデメリットも記載する．
-
-### 作業療法プログラム内容
-
-ここに問題点を書く．問題点は，疾患の病期や程度に応じて，ICFにしたがって機能レベル，活動レベル，社会的レベル毎に予測して重要な順に列挙する．
-それぞれの問題点に対する対する作業療法をガイドラインや報告や教科書や解剖学や生理学や運動学や作業療法学や精神科作業療法学や高次脳機能障害や認知機能障害を踏まえてプログラムを立てて書く．
-作業療法ではないことは書かない．
-プログラムを立てる際に参考にした文献や考え方を考察として記載する．
-実践時の注意点を記載する．
-投薬治療が行われる場合はその治療方法で起こりうる副作用についても記載する．
-再発を予防することや、進行を緩徐にする方法があれば記載する．
-実行できるよう励ましの言葉を記載する．
-
-### 作業療法ゴール内容
-
-ここに，上記プログラムを実行することで見込まれる短期ゴール(2週間後)と長期ゴール(1ヶ月後)を記載する．
-短期ゴールは，機能的レベルと活動レベルの内容として到達目標とする．
-長期ゴールは，活動レベルと社会的レベルの内容として到達目標とする．
-
-なぜこのゴールにしたかの理由を考察として記載する
-考察は，ゴールとした社会的レベルを考慮し，なぜ活動レベルが改善されると達成できるかについての理由を記載する．
-さらに，それらの活動レベルが改善されると機能レベルが改善されると達成できるかについてのも理由を記載する．
-本プログラムでの限界についても記載する．
-
diff --git a/spaces/Salesforce/EDICT/my_diffusers/testing_utils.py b/spaces/Salesforce/EDICT/my_diffusers/testing_utils.py
deleted file mode 100644
index ff8b6aa9b41c45b0ab77f343904bffc53fa9e9cb..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/testing_utils.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-import random
-import unittest
-from distutils.util import strtobool
-
-import torch
-
-from packaging import version
-
-
-global_rng = random.Random()
-torch_device = "cuda" if torch.cuda.is_available() else "cpu"
-is_torch_higher_equal_than_1_12 = version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.12")
-
-if is_torch_higher_equal_than_1_12:
-    torch_device = "mps" if torch.backends.mps.is_available() else torch_device
-
-
-def parse_flag_from_env(key, default=False):
-    try:
-        value = os.environ[key]
-    except KeyError:
-        # KEY isn't set, default to `default`.
-        _value = default
-    else:
-        # KEY is set, convert it to True or False.
-        try:
-            _value = strtobool(value)
-        except ValueError:
-            # More values are supported, but let's keep the message simple.
-            raise ValueError(f"If set, {key} must be yes or no.")
-    return _value
-
-
-_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
-
-
-def floats_tensor(shape, scale=1.0, rng=None, name=None):
-    """Creates a random float32 tensor"""
-    if rng is None:
-        rng = global_rng
-
-    total_dims = 1
-    for dim in shape:
-        total_dims *= dim
-
-    values = []
-    for _ in range(total_dims):
-        values.append(rng.random() * scale)
-
-    return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
-
-
-def slow(test_case):
-    """
-    Decorator marking a test as slow.
-
-    Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.
-
-    """
-    return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
diff --git a/spaces/Southstar1/img-to-music/app.py b/spaces/Southstar1/img-to-music/app.py
deleted file mode 100644
index 53ba74a6bbbf3c20f5df8f7b3cabc7c84bc63fdd..0000000000000000000000000000000000000000
--- a/spaces/Southstar1/img-to-music/app.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import gradio as gr
-import os
-import requests
-import urllib
-
-from os import path
-from pydub import AudioSegment
-
-img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
-text_to_music = gr.Interface.load("spaces/fffiloni/text-2-music")
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-def get_prompts(uploaded_image):
-  
-  prompt = img_to_text(uploaded_image, fn_index=1)[0]
-  
-  music_result = get_music(prompt)
-  
-  return music_result
-
-def get_music(prompt):
-  
-  result = text_to_music(prompt, fn_index=0)
-  
-  print(f"""—————
-  NEW RESULTS
-  prompt : {prompt}
-  music : {result}
-  ———————
-  """)
-  
-  url = result
-  save_as = "file.mp3"
-  
-  data = urllib.request.urlopen(url)
-
-  f = open(save_as,'wb')
-  f.write(data.read())
-  f.close()
-  
-  wave_file="file.wav"
-  
-  sound = AudioSegment.from_mp3(save_as)
-  sound.export(wave_file, format="wav")
-  
-  return wave_file, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-css = """
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {
-    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-}
-#share-btn {
-    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-}
-#share-btn * {
-    all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
-    width: auto !important;
-    min-height: 0px !important;
-}
-#share-btn-container .wrap {
-    display: none !important;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-  with gr.Column(elem_id="col-container"):
-    gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div
-              style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-              "
-              >
-              <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                  Image to Music
-              </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-              Sends an image in to <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator" target="_blank">CLIP Interrogator</a>
-              to generate a text prompt which is then run through 
-              <a href="https://huggingface.co/Mubert" target="_blank">Mubert</a> text-to-music to generate music from the input image!
-              </p>
-          </div>""")
-    
-    
-    input_img = gr.Image(type="filepath", elem_id="input-img")
-    generate = gr.Button("Generate Music from Image")
-  
-    music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output")
-    
-    with gr.Group(elem_id="share-btn-container"):
-      community_icon = gr.HTML(community_icon_html, visible=False)
-      loading_icon = gr.HTML(loading_icon_html, visible=False)
-      share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-      
-  generate.click(get_prompts, inputs=[input_img], outputs=[music_output, share_button, community_icon, loading_icon], api_name="i2m")
-  share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=32, concurrency_count=20).launch()
\ No newline at end of file
diff --git a/spaces/SpacesExamples/llama-cpp-python-cuda-gradio/README.md b/spaces/SpacesExamples/llama-cpp-python-cuda-gradio/README.md
deleted file mode 100644
index d5bd1daec7e1f4c2868a62e8289b955500a070fb..0000000000000000000000000000000000000000
--- a/spaces/SpacesExamples/llama-cpp-python-cuda-gradio/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Llama Cpp Python Cuda
-emoji: 🦙
-colorFrom: pink
-colorTo: indigo
-sdk: docker
-pinned: false
-duplicated_from: radames/llama-cpp-python-cuda-gradio
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sreeja123/memristor-based-neural-search-optimization-GUI/SCNN.py b/spaces/Sreeja123/memristor-based-neural-search-optimization-GUI/SCNN.py
deleted file mode 100644
index 72a4f1a5a64dd73ad17f4853960aa2338b4d6f23..0000000000000000000000000000000000000000
--- a/spaces/Sreeja123/memristor-based-neural-search-optimization-GUI/SCNN.py
+++ /dev/null
@@ -1,315 +0,0 @@
-import tensorflow as tf
-import time as tm
-import sys
-# import numpy as np
-# import cudnn as cd
-# from tensorflow.keras import datasets, layers, models
-# import matplotlib.pyplot as plt
-# from tensorflow.python.client import device_lib
-
-class Integrator_layer(tf.keras.layers.Layer):
-    def __init__(self, n_steps=100, integration_window=100, time_constant=1.0, leakyness=0.0,
-                 V_m_threshold = 1.0, refractory_period=0, amplitude=1.0, V_m_min=0, V_cm=2.5, device='cuda', name='I&F'):
-        super(Integrator_layer, self).__init__(name=name)
-        # self.threshold = nn.Threshold(V_m_threshold, 0)
-        # self.zero = torch.tensor(0, dtype=torch.float, device=device)
-        self.Vm_threshold = V_m_threshold
-        self.integration_window = integration_window
-        self.refractory_period = refractory_period
-        self.time_constant = time_constant
-        # self.epsilon = 0.001
-        self.epsilon = tf.keras.backend.epsilon
-        self.amplitude = amplitude
-        # self.threshold = nn.Threshold(V_m_threshold - self.epsilon, 0) ### Thresholding function
-        # self.threshold = tf.nn.relu(V_m_threshold - self.epsilon, 0)  ### Thresholding function
-        self.V_m_min = V_m_min
-        self.device = device
-
-    @tf.function
-    def chunk_sizes(self, length, chunk_size):
-        chunks = [chunk_size for x in range(length//chunk_size)]
-        if length % chunk_size != 0:
-            chunks.append(length % chunk_size)
-        return chunks
-
-    def build(self, input_shape):
-        self.batch_size = input_shape[0]
-        self.timesteps = input_shape[1]
-        self.image_shape = input_shape[2:]
-        self.image_rank = len(input_shape)
-
-        # self.chunk_sizes = self.chunk_sizes(self.timesteps, self.integration_window)
-        self.chunk_sizes = self.chunk_sizes(input_shape[1], self.integration_window)
-        self.tensor_invariance = [None for i in range(self.image_rank)]
-        # self.list_of_indices = [[x, 0] for x in tf.range(input_shape[0])]
-        # self.list_of_indices = tf.range(input_shape[0])
-
-    @tf.function
-    def call(self, inputs):
-        ### List of indices - list of indices to replace very first timestep with zero after the roll operation
-        # list_of_indices = tf.pad(tf.expand_dims(tf.range(tf.shape(inputs)[0]), axis=1),
-        #                          paddings=[[0, 0], [0, 1]],
-        #                          mode="CONSTANT")
-        list_of_indices = tf.pad(tf.expand_dims(tf.range(tf.shape(inputs)[0]), axis=1),
-                                 paddings=[[0, 0], [0, 1]],
-                                 mode="CONSTANT")
-        roll_padding = tf.zeros([self.image_rank, 2], dtype=tf.int32)
-        roll_padding = tf.tensor_scatter_nd_update(roll_padding, indices=[[1, 0]], updates= [1])
-        images_chunks = tf.split(inputs, self.chunk_sizes, axis=1) ### Fragment current sample into multiple chunks with length equal to the integration window
-        first_chunk = True
-        # zero = torch.tensor(0, dtype=torch.float, device=self.device)
-        for chunk, n_timesteps in zip(images_chunks, self.chunk_sizes):
-            ### n_timesteps - the number of timesteps for current chunk of integration window
-            Spikes_out = tf.zeros([tf.shape(chunk)[0], *self.image_shape, n_timesteps + 1])
-            ### V_m_out - array for storing membrane potential
-            V_m_out = tf.zeros_like(chunk)
-            # V_m_temp = tf.zeros_like(chunk)
-            V_m_temp = tf.ones_like(chunk)
-            # V_m_temp = tf.tensor_scatter_nd_update(V_m_temp, indices=list_of_indices,
-            #                                        updates=tf.ones([1, *self.image_shape]))
-            while tf.math.count_nonzero(V_m_temp) != 0:
-                tf.autograph.experimental.set_loop_options(shape_invariants=[(V_m_temp, tf.TensorShape(self.tensor_invariance))])
-                ### V_m_chunk - cumulative summation (integration) along time dimension
-                V_m_chunk = tf.math.cumsum(tf.math.multiply(chunk, self.time_constant), axis=1)
-                ### Thresholding chunks, all values bellow threshold value are zeroed
-                V_m_temp = tf.nn.relu(V_m_chunk - self.Vm_threshold)
-                # V_m_temp = tf.print(V_m_temp, [V_m_temp], 'breaking')
-                if tf.math.count_nonzero(V_m_temp) == 0: ### if Vm did not cross threshold, break the cycle
-                    # V_m_out = V_m_out + V_m_chunk
-                    # V_m_out = tf.print(V_m_out, [V_m_out], 'breaking')
-                    break
-                ### Cumsum of the thresholded cumsum - to avoid any future threshold crossings (additional zeroes) that can occur after threshold is hit:
-                V_m_temp = tf.math.cumsum(V_m_temp, axis=1)
-                ### V_m_temp == 0 The amount of zero values before function crosses the threshold. Used to calculated how many timesteps it took for an integrator to fire an output spike
-                Spikes_out = Spikes_out + tf.one_hot(tf.reduce_sum(tf.cast((V_m_temp == 0), tf.int32), axis=1), depth=n_timesteps + 1)### One hotted zero counts
-                ### TF roll operation is used to shift the vector values by 1, other timestep which crossed threshold is not included:
-                V_m_temp = tf.pad(V_m_temp, paddings=roll_padding, mode="CONSTANT")
-                V_m_temp, _ = tf.split(V_m_temp, [n_timesteps, 1], axis=1)
-                # V_m_temp = tf.roll(V_m_temp, shift=1, axis=1)
-                ###Since roll operation will shift the last value to the first place, the first value should be 0'ed for a proper counting of 0 in the next code fragments.
-                # __, V_m_temp = tf.split(V_m_temp, [1, n_timesteps - 1], axis=1)
-                # V_m_temp = tf.concat((V_m_temp, tf.zeros_like(__)), axis=1)
-                # V_m_temp = tf.tensor_scatter_nd_update(V_m_temp, indices=list_of_indices,
-                #                                        updates=tf.zeros([tf.shape(chunk)[0], *self.image_shape]))
-                # V_m_out = tf.where(V_m_temp == 0, V_m_out + V_m_chunk, 0) ### Resets V_m to 0 after firing
-                if self.refractory_period!=0: ### Resets (=0) number of timesteps after output spike is fired
-                    V_m_temp = tf.roll(chunk, shift=self.refractory_period, axis=1)
-                    V_m_temp[:, 0:(self.refractory_period-1), :, :, :] = 0
-                chunk = tf.where(V_m_temp == 0.0, 0.0, chunk) ### Removes spikes before firing. So new V_m can be calculated for a next spike.
-            # Spikes_out = torch.narrow(Spikes_out, dim=-1, start=0, length= n_timesteps)
-            Spikes_out, _ = tf.split(Spikes_out, [n_timesteps, 1], axis=-1) ### Onehot operation adds back time dimension to the last place, so it must be popped out
-            if first_chunk:
-                # V_m_final = V_m_out
-                Spikes_out_final = Spikes_out
-                first_chunk = False
-            else:
-                V_m_final = tf.concat((V_m_final, V_m_out), axis=1)
-                Spikes_out_final = tf.concat((Spikes_out_final, Spikes_out), axis=-1)
-
-        ### Onehotting puts time as the last tensor dimension. 'movedim' moves time dimension to the 2nd place, after the batch number, as it was before.
-        Spikes_out_final = tf.experimental.numpy.moveaxis(Spikes_out_final, source=-1, destination=1)
-        # return V_m_final, Spikes_out_final
-        # print('LIF forward end:')
-        # print(f'{datetime.now().time().replace(microsecond=0)} --- ')
-        # print(Spikes_out.type())
-        if self.amplitude !=1.0:
-            Spikes_out_final = Spikes_out_final*self.amplitude
-        return Spikes_out_final
-
-def sparse_data_generator_non_spiking(input_images, input_labels, batch_size=32, nb_steps=100, shuffle=True, flatten= False):
-    """ This generator takes datasets in analog format and generates network input as constant currents.
-        If repeat=True, encoding is rate-based, otherwise it is a latency encoding
-    Args:
-        X: The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples
-        y: The labels
-    """
-    # data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=n_workers)
-    data_loader_original = tf.data.Dataset.from_tensor_slices((tf.cast(input_images, tf.float32), input_labels))
-    if shuffle:
-        data_loader_original = data_loader_original.shuffle(buffer_size=100)
-    data_loader = data_loader_original.batch(batch_size=batch_size, drop_remainder=False)
-
-    number_of_batches = input_labels.__len__() // batch_size
-    counter = 0
-    time = tm.time()
-
-    for X, y in data_loader:
-        if flatten:
-            X = X.reshape(X.shape[0], -1)
-        # sample_dims = np.array(X.shape[1:], dtype=int)
-        sample_dims = X.shape[1:]
-        # X = torch.unsqueeze(X, dim=1)
-        X = tf.expand_dims(X, axis=1)
-        X = tf.repeat(X, repeats=nb_steps, axis=1)
-        time_taken = tm.time() - time
-        time = tm.time()
-        ETA = time_taken * (number_of_batches - counter)
-        sys.stdout.write(
-            "\rBatch: {0}/{1}, Progress: {2:0.2f}%, Time to process last batch: {3:0.2f} seconds, Estimated time to finish epoch: {4:0.2f} seconds | {5}:{6} minutes".format(
-                counter, number_of_batches, (counter / number_of_batches) * 100, time_taken, ETA, int(ETA // 60),
-                int(ETA % 60)))
-        sys.stdout.flush()
-        # X_batch = torch.tensor(X, device=device, dtype=torch.float)
-        # yield X.expand(-1, nb_steps, *sample_dims).to(device), y.to(device)  ### Returns this values after each batch
-        counter += 1
-        yield X, y  ### Returns this values after each batch
-
-# return argument_free_generator()
-
-class Reduce_sum(tf.keras.layers.Layer):
-    def __init__(self, name=None):
-        super(Reduce_sum, self).__init__(name=name)
-
-
-    def call(self, inputs):
-        return tf.math.reduce_sum(inputs, axis=1, keepdims=False)
-
-# """
-# class Integrator_layer(tf.keras.layers.Layer):
-#     def __init__(self, n_steps=100, integration_window=100, time_constant=1.0, leakyness=0.0,
-#                  V_m_threshold = 2.0, refractory_period=0, amplitude=1.0, V_m_min=0, V_cm=2.5, device='cuda', name='I&F'):
-#         super(Integrator_layer, self).__init__(name=name)
-#         # self.threshold = nn.Threshold(V_m_threshold, 0)
-#         # self.zero = torch.tensor(0, dtype=torch.float, device=device)
-#         self.Vm_threshold = V_m_threshold
-#         self.integration_window = integration_window
-#         self.refractory_period = refractory_period
-#         self.time_constant = time_constant
-#         # self.epsilon = 0.001
-#         self.epsilon = tf.keras.backend.epsilon
-#         self.amplitude = amplitude
-#         # self.threshold = nn.Threshold(V_m_threshold - self.epsilon, 0) ### Thresholding function
-#         # self.threshold = tf.nn.relu(V_m_threshold - self.epsilon, 0)  ### Thresholding function
-#         self.V_m_min = V_m_min
-#         self.device = device
-#
-#     @tf.function
-#     def chunk_sizes(self, length, chunk_size):
-#         chunks = [chunk_size for x in range(length//chunk_size)]
-#         if length % chunk_size != 0:
-#             chunks.append(length % chunk_size)
-#         return chunks
-#
-#     def build(self, input_shape):
-#         self.batch_size = input_shape[0]
-#         self.timesteps = input_shape[1]
-#         self.image_shape = input_shape[2:]
-#         # self.chunk_sizes = self.chunk_sizes(self.timesteps, self.integration_window)
-#         self.chunk_sizes = self.chunk_sizes(input_shape[1], self.integration_window)
-#         ###
-#         ###
-#         ###
-#         # self.list_of_indices = [[x, 0] for x in tf.range(input_shape[0])]
-#         # self.list_of_indices = tf.range(input_shape[0])
-#
-#     @tf.function
-#     def call(self, inputs):
-#         ### List of indices - list of indices to replace very first timestep with zero after the roll operation
-#         list_of_indices = tf.pad(tf.expand_dims(tf.range(tf.shape(inputs)[0]), axis=1),
-#                                  paddings=[[0, 0], [0, 1]],
-#                                  mode="CONSTANT")
-#         images_chunks = tf.split(inputs, self.chunk_sizes, axis=1) ### Fragment current sample into multiple chunks with length equal to the integration window
-#         first_chunk = True
-#         # zero = torch.tensor(0, dtype=torch.float, device=self.device)
-#         for chunk, n_timesteps in zip(images_chunks, self.chunk_sizes):
-#             ### n_timesteps - the number of timesteps for current chunk of integration window
-#             Spikes_out = tf.zeros([tf.shape(chunk)[0], *self.image_shape, n_timesteps + 1])
-#             ### V_m_out - array for storing membrane potential
-#             V_m_out = tf.zeros_like(chunk)
-#             V_m_temp = tf.zeros_like(chunk)
-#             while tf.math.count_nonzero(V_m_temp) != 0:
-#                 ### V_m_chunk - cumulative summation (integration) along time dimension
-#                 V_m_chunk = tf.math.cumsum(tf.math.multiply(chunk, self.time_constant), axis=1)
-#                 ### Thresholding chunks, all values bellow threshold value are zeroed
-#                 V_m_temp = tf.nn.relu(V_m_chunk - self.Vm_threshold)
-#                 if tf.math.count_nonzero(V_m_temp) == 0: ### if Vm did not cross threshold, break the cycle
-#                     V_m_out = V_m_out + V_m_chunk
-#                     break
-#                 ### Cumsum of the thresholded cumsum - to avoid any future threshold crossings (additional zeroes) that can occur after threshold is hit:
-#                 V_m_temp = tf.math.cumsum(V_m_temp, axis=1)
-#                 ### V_m_temp == 0 The amount of zero values before function crosses the threshold. Used to calculated how many timesteps it took for an integrator to fire an output spike
-#                 Spikes_out = Spikes_out + tf.one_hot(tf.reduce_sum(tf.cast((V_m_temp == 0), tf.int32), axis=1), depth=n_timesteps + 1)### One hotted zero counts
-#                 ### TF roll operation is used to shift the vector values by 1, other timestep which crossed threshold is not included:
-#                 V_m_temp = tf.roll(V_m_temp, shift=1, axis=1)
-#                 ###Since roll operation will shift the last value to the first place, the first value should be 0'ed for a proper counting of 0 in the next code fragments.
-#                 V_m_temp = tf.tensor_scatter_nd_update(V_m_temp, indices=list_of_indices,
-#                                                        updates=tf.zeros([1, *self.image_shape]))
-#                 V_m_out = tf.where(V_m_temp == 0, V_m_out + V_m_chunk, 0) ### Resets V_m to 0 after firing
-#                 if self.refractory_period!=0: ### Resets (=0) number of timesteps after output spike is fired
-#                     V_m_temp = tf.roll(chunk, shift=self.refractory_period, axis=1)
-#                     V_m_temp[:, 0:(self.refractory_period-1), :, :, :] = 0
-#                 chunk = tf.where(V_m_temp == 0.0, 0.0, chunk) ### Removes spikes before firing. So new V_m can be calculated for a next spike.
-#             # Spikes_out = torch.narrow(Spikes_out, dim=-1, start=0, length= n_timesteps)
-#             Spikes_out, _ = tf.split(Spikes_out, [n_timesteps, 1], axis=-1) ### Onehot operation adds back time dimension to the last place, so it must be popped out
-#             if first_chunk:
-#                 V_m_final = V_m_out
-#                 Spikes_out_final = Spikes_out
-#                 first_chunk = False
-#             else:
-#                 V_m_final = tf.concat((V_m_final, V_m_out), axis=1)
-#                 Spikes_out_final = tf.concat((Spikes_out_final, Spikes_out), axis=-1)
-#
-#         # Spikes_out_final = torch.movedim(Spikes_out_final, source=-1, destination=1)
-#         Spikes_out_final = tf.experimental.numpy.swapaxes(Spikes_out_final, axis1=-1,
-#                                          axis2=1)  ### Onehotting puts time as the last tensor dimension. 'movedim' moves time dimension to the 2nd place, after the batch number, as it was before.
-#
-#         # return V_m_final, Spikes_out_final
-#         # print('LIF forward end:')
-#         # print(f'{datetime.now().time().replace(microsecond=0)} --- ')
-#         # print(Spikes_out.type())
-#         if self.amplitude !=1.0:
-#             Spikes_out_final = Spikes_out_final*self.amplitude
-#         return Spikes_out_final
-# """
-#
-# def sparse_data_generator_non_spiking(input_images, input_labels, batch_size=32, nb_steps=100, shuffle=True, flatten= False):
-#     """ This generator takes datasets in analog format and generates network input as constant currents.
-#         If repeat=True, encoding is rate-based, otherwise it is a latency encoding
-#     Args:
-#         X: The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples
-#         y: The labels
-#     """
-#
-# # def argument_free_generator():
-#     # data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=n_workers)
-#     data_loader_original = tf.data.Dataset.from_tensor_slices((tf.cast(input_images, tf.float32), input_labels))
-#     if shuffle:
-#         data_loader_original = data_loader_original.shuffle(buffer_size=100)
-#     data_loader = data_loader_original.batch(batch_size=batch_size, drop_remainder=False)
-#
-#     number_of_batches = input_labels.__len__() // batch_size
-#     counter = 0
-#     time = tm.time()
-#
-#     for X, y in data_loader:
-#         if flatten:
-#             X = X.reshape(X.shape[0], -1)
-#         # sample_dims = np.array(X.shape[1:], dtype=int)
-#         sample_dims = X.shape[1:]
-#         # X = torch.unsqueeze(X, dim=1)
-#         X = tf.expand_dims(X, axis=1)
-#         X = tf.repeat(X, repeats=nb_steps, axis=1)
-#         time_taken = tm.time() - time
-#         time = tm.time()
-#         ETA = time_taken * (number_of_batches - counter)
-#         sys.stdout.write(
-#             "\rBatch: {0}/{1}, Progress: {2:0.2f}%, Time to process last batch: {3:0.2f} seconds, Estimated time to finish epoch: {4:0.2f} seconds | {5}:{6} minutes".format(
-#                 counter, number_of_batches, (counter / number_of_batches) * 100, time_taken, ETA, int(ETA // 60),
-#                 int(ETA % 60)))
-#         sys.stdout.flush()
-#         # X_batch = torch.tensor(X, device=device, dtype=torch.float)
-#         # yield X.expand(-1, nb_steps, *sample_dims).to(device), y.to(device)  ### Returns this values after each batch
-#         counter += 1
-#         yield X, y  ### Returns this values after each batch
-#
-# # return argument_free_generator()
-#
-#
-# class Reduce_sum(tf.keras.layers.Layer):
-#     def __init__(self, name=None):
-#         super(Reduce_sum, self).__init__(name=name)
-#
-#     def call(self, inputs):
-#         return tf.math.reduce_sum(inputs, axis=1, keepdims=False)
-#
-#
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/utils/checkpoint.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/utils/checkpoint.py
deleted file mode 100644
index f6f871837e09c5cc7832b85b0d80b84f59e87ca0..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/utils/checkpoint.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from enum import Enum
-import logging
-from pathlib import Path
-import re
-import typing as tp
-
-import flashy
-import torch
-
-from ..environment import AudioCraftEnvironment
-
-
-logger = logging.getLogger(__name__)
-
-
-class CheckpointSource(Enum):
-    CURRENT_XP = "current_xp"
-    PRETRAINED = "pretrained"
-    OTHER = "other"
-
-
-def checkpoint_name(name: tp.Optional[str] = None, rank: tp.Optional[int] = None, use_fsdp: bool = False) -> str:
-    """Checkpoint name formatted for all use in AudioCraft codebase and has the following format:
-    `checkpoint_<name>.th(.<rank>)`. By convention, name is expected to be empty for last checkpoint,
-    'best' for the best checkpoint or the epoch number.
-
-    Args:
-        name (str, optional): Name suffix for the checkpoint file stem.
-        rank (optional, int): Rank for distributed processing, retrieved with flashy if not provided.
-        use_fsdp (bool): Whether the calling solver relies on FSDP.
-    Returns:
-        str: The checkpoint name.
-    """
-    suffix = ''
-    if rank is None:
-        rank = flashy.distrib.rank()
-    if rank > 0 and use_fsdp:
-        suffix = '.' + str(rank)
-    name_part = ''
-    if name is not None:
-        name_part = f'_{name}'
-    return f'checkpoint{name_part}.th{suffix}'
-
-
-def is_sharded_checkpoint(path: Path) -> bool:
-    """Whether the checkpoint at the given path corresponds to a sharded checkpoint across rank."""
-    return re.search(r'\.th\.\d+$', path.name) is not None
-
-
-def resolve_checkpoint_path(sig_or_path: tp.Union[Path, str], name: tp.Optional[str] = None,
-                            use_fsdp: bool = False) -> tp.Optional[Path]:
-    """Resolve a given checkpoint path for a provided dora sig or path.
-
-    Args:
-        sig_or_path (Path or str): Checkpoint path or dora signature.
-        name (str, optional): Name suffix for the checkpoint file stem.
-        rank (optional, int): Rank for distributed processing, retrieved with flashy if not provided.
-        use_fsdp (bool): Whether the calling solver relies on FSDP.
-    Returns:
-        Path, optional: Resolved checkpoint path, if it exists.
-    """
-    from audiocraft import train
-    xps_root = train.main.dora.dir / 'xps'
-    sig_or_path = str(sig_or_path)
-    if sig_or_path.startswith('//sig/'):
-        sig = sig_or_path[len('//sig/'):]
-        path = xps_root / sig
-    else:
-        path = Path(sig_or_path)
-        path = AudioCraftEnvironment.resolve_reference_path(path)
-
-    if path.is_dir():
-        path = path / checkpoint_name(name, use_fsdp=use_fsdp)
-
-    if path.exists():
-        return path
-    else:
-        return None
-
-
-def load_checkpoint(checkpoint_path: Path, is_sharded: bool = False) -> tp.Any:
-    """Load state from checkpoints at the specified checkpoint path."""
-    if is_sharded:
-        rank0_checkpoint_path = checkpoint_path.parent / checkpoint_name(use_fsdp=False)
-        if rank0_checkpoint_path.exists():
-            check_sharded_checkpoint(checkpoint_path, rank0_checkpoint_path)
-    state = torch.load(checkpoint_path, 'cpu')
-    logger.info("Checkpoint loaded from %s", checkpoint_path)
-    return state
-
-
-def save_checkpoint(state: tp.Any, checkpoint_path: Path, is_sharded: bool = False) -> None:
-    """Save state to disk to the specified checkpoint_path."""
-    _safe_save_checkpoint(state, checkpoint_path, is_sharded)
-    logger.info("Checkpoint saved to %s", checkpoint_path)
-
-
-def flush_stale_checkpoints(checkpoint_path: Path, keep_last: tp.Optional[int] = None) -> None:
-    """Flush checkpoints to only keep last N checkpoints."""
-    if keep_last is None or keep_last <= 0:
-        return
-    checkpoint_dir = checkpoint_path.parent
-    suffix = ''
-    if flashy.distrib.rank() > 0:
-        suffix = f'.{flashy.distrib.rank()}'
-    checkpoint_files_with_epoch = []
-    for path in Path(checkpoint_dir).glob(f'checkpoint_*.th{suffix}'):
-        epoch_part = path.name.split('.', 1)[0].split('_', 1)[1]
-        if epoch_part.isdigit():
-            checkpoint_files_with_epoch.append((path, int(epoch_part)))
-    checkpoint_files = [path for path, _ in list(sorted(checkpoint_files_with_epoch, key=lambda t: t[1]))]
-    total_to_flush = max(0, len(checkpoint_files) - keep_last)
-    files_to_flush = checkpoint_files[:total_to_flush]
-    for path in files_to_flush:
-        logger.debug("Removing checkpoint: %s", str(path))
-        path.unlink(missing_ok=True)
-
-
-def check_sharded_checkpoint(checkpoint_path: Path, rank0_checkpoint_path: Path) -> None:
-    """Check sharded checkpoint state, ensuring the checkpoints are not corrupted."""
-    # Finish the work of a previous run that got interrupted while dumping.
-    old_path = Path(str(checkpoint_path) + '.old')
-    if old_path.exists():
-        raise RuntimeError(
-            f"Old checkpoint {old_path} from previous version of this code exist, cannot safely proceed.")
-    token = Path(str(rank0_checkpoint_path) + '.tmp.done')
-    tmp_path = Path(str(checkpoint_path) + '.tmp')
-    if token.exists():
-        if tmp_path.exists():
-            tmp_path.rename(checkpoint_path)
-    flashy.distrib.barrier()
-    if flashy.distrib.is_rank_zero() and token.exists():
-        token.unlink()
-
-
-def _safe_save_checkpoint(state: tp.Any, checkpoint_path: Path, is_sharded: bool = False) -> None:
-    """Save checkpoints in a safe manner even with when sharded checkpoints across nodes."""
-    def _barrier_if_sharded():
-        if is_sharded:
-            flashy.distrib.barrier()
-
-    if flashy.distrib.is_rank_zero():
-        token = Path(str(checkpoint_path) + '.tmp.done')
-        if token.exists():
-            token.unlink()
-    _barrier_if_sharded()
-    with flashy.utils.write_and_rename(checkpoint_path) as f:
-        torch.save(state, f)
-        _barrier_if_sharded()
-        if flashy.distrib.is_rank_zero():
-            token.touch()
-        _barrier_if_sharded()
-    _barrier_if_sharded()
-    if flashy.distrib.rank() == 0:
-        token.unlink()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/api/models/Collection.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/api/models/Collection.py
deleted file mode 100644
index 334714bf9dd1c51b280d45c7ca43957b18c669be..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/api/models/Collection.py
+++ /dev/null
@@ -1,394 +0,0 @@
-from typing import TYPE_CHECKING, Optional, Tuple, cast, List
-from pydantic import BaseModel, PrivateAttr
-from uuid import UUID
-import chromadb.utils.embedding_functions as ef
-
-from chromadb.api.types import (
-    CollectionMetadata,
-    Embedding,
-    Include,
-    Metadata,
-    Document,
-    Where,
-    IDs,
-    EmbeddingFunction,
-    GetResult,
-    QueryResult,
-    ID,
-    OneOrMany,
-    WhereDocument,
-    maybe_cast_one_to_many,
-    validate_ids,
-    validate_include,
-    validate_metadatas,
-    validate_where,
-    validate_where_document,
-    validate_n_results,
-    validate_embeddings,
-)
-import logging
-
-logger = logging.getLogger(__name__)
-
-if TYPE_CHECKING:
-    from chromadb.api import API
-
-
-class Collection(BaseModel):
-    name: str
-    id: UUID
-    metadata: Optional[CollectionMetadata] = None
-    _client: "API" = PrivateAttr()
-    _embedding_function: Optional[EmbeddingFunction] = PrivateAttr()
-
-    def __init__(
-        self,
-        client: "API",
-        name: str,
-        id: UUID,
-        embedding_function: Optional[EmbeddingFunction] = ef.DefaultEmbeddingFunction(),
-        metadata: Optional[CollectionMetadata] = None,
-    ):
-        self._client = client
-        self._embedding_function = embedding_function
-        super().__init__(name=name, metadata=metadata, id=id)
-
-    def __repr__(self) -> str:
-        return f"Collection(name={self.name})"
-
-    def count(self) -> int:
-        """The total number of embeddings added to the database
-
-        Returns:
-            int: The total number of embeddings added to the database
-
-        """
-        return self._client._count(collection_id=self.id)
-
-    def add(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]] = None,
-        metadatas: Optional[OneOrMany[Metadata]] = None,
-        documents: Optional[OneOrMany[Document]] = None,
-        increment_index: bool = True,
-    ) -> None:
-        """Add embeddings to the data store.
-        Args:
-            ids: The ids of the embeddings you wish to add
-            embedding: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
-            metadata: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
-            documents: The documents to associate with the embeddings. Optional.
-            ids: The ids to associate with the embeddings. Optional.
-
-        Returns:
-            None
-
-        Raises:
-            ValueError: If you don't provide either embeddings or documents
-            ValueError: If the length of ids, embeddings, metadatas, or documents don't match
-            ValueError: If you don't provide an embedding function and don't provide embeddings
-            ValueError: If you provide both embeddings and documents
-            ValueError: If you provide an id that already exists
-
-        """
-
-        ids, embeddings, metadatas, documents = self._validate_embedding_set(
-            ids, embeddings, metadatas, documents
-        )
-
-        self._client._add(
-            ids, self.id, embeddings, metadatas, documents, increment_index
-        )
-
-    def get(
-        self,
-        ids: Optional[OneOrMany[ID]] = None,
-        where: Optional[Where] = None,
-        limit: Optional[int] = None,
-        offset: Optional[int] = None,
-        where_document: Optional[WhereDocument] = None,
-        include: Include = ["metadatas", "documents"],
-    ) -> GetResult:
-        """Get embeddings and their associate data from the data store. If no ids or where filter is provided returns
-        all embeddings up to limit starting at offset.
-
-        Args:
-            ids: The ids of the embeddings to get. Optional.
-            where: A Where type dict used to filter results by. E.g. `{"color" : "red", "price": 4.20}`. Optional.
-            limit: The number of documents to return. Optional.
-            offset: The offset to start returning results from. Useful for paging results with limit. Optional.
-            where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$contains: {"text": "hello"}}`. Optional.
-            include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional.
-
-        Returns:
-            GetResult: A GetResult object containing the results.
-
-        """
-        where = validate_where(where) if where else None
-        where_document = (
-            validate_where_document(where_document) if where_document else None
-        )
-        ids = validate_ids(maybe_cast_one_to_many(ids)) if ids else None
-        include = validate_include(include, allow_distances=False)
-        return self._client._get(
-            self.id,
-            ids,
-            where,
-            None,
-            limit,
-            offset,
-            where_document=where_document,
-            include=include,
-        )
-
-    def peek(self, limit: int = 10) -> GetResult:
-        """Get the first few results in the database up to limit
-
-        Args:
-            limit: The number of results to return.
-
-        Returns:
-            GetResult: A GetResult object containing the results.
-        """
-        return self._client._peek(self.id, limit)
-
-    def query(
-        self,
-        query_embeddings: Optional[OneOrMany[Embedding]] = None,
-        query_texts: Optional[OneOrMany[Document]] = None,
-        n_results: int = 10,
-        where: Optional[Where] = None,
-        where_document: Optional[WhereDocument] = None,
-        include: Include = ["metadatas", "documents", "distances"],
-    ) -> QueryResult:
-        """Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts.
-
-        Args:
-            query_embeddings: The embeddings to get the closes neighbors of. Optional.
-            query_texts: The document texts to get the closes neighbors of. Optional.
-            n_results: The number of neighbors to return for each query_embedding or query_texts. Optional.
-            where: A Where type dict used to filter results by. E.g. `{"color" : "red", "price": 4.20}`. Optional.
-            where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$contains: {"text": "hello"}}`. Optional.
-            include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional.
-
-        Returns:
-            QueryResult: A QueryResult object containing the results.
-
-        Raises:
-            ValueError: If you don't provide either query_embeddings or query_texts
-            ValueError: If you provide both query_embeddings and query_texts
-
-        """
-        where = validate_where(where) if where else None
-        where_document = (
-            validate_where_document(where_document) if where_document else None
-        )
-        query_embeddings = (
-            validate_embeddings(maybe_cast_one_to_many(query_embeddings))
-            if query_embeddings is not None
-            else None
-        )
-        query_texts = (
-            maybe_cast_one_to_many(query_texts) if query_texts is not None else None
-        )
-        include = validate_include(include, allow_distances=True)
-        n_results = validate_n_results(n_results)
-
-        # If neither query_embeddings nor query_texts are provided, or both are provided, raise an error
-        if (query_embeddings is None and query_texts is None) or (
-            query_embeddings is not None and query_texts is not None
-        ):
-            raise ValueError(
-                "You must provide either query embeddings or query texts, but not both"
-            )
-
-        # If query_embeddings are not provided, we need to compute them from the query_texts
-        if query_embeddings is None:
-            if self._embedding_function is None:
-                raise ValueError(
-                    "You must provide embeddings or a function to compute them"
-                )
-            # We know query texts is not None at this point, cast for the typechecker
-            query_embeddings = self._embedding_function(
-                cast(List[Document], query_texts)
-            )
-
-        if where is None:
-            where = {}
-
-        if where_document is None:
-            where_document = {}
-
-        return self._client._query(
-            collection_id=self.id,
-            query_embeddings=query_embeddings,
-            n_results=n_results,
-            where=where,
-            where_document=where_document,
-            include=include,
-        )
-
-    def modify(
-        self, name: Optional[str] = None, metadata: Optional[CollectionMetadata] = None
-    ) -> None:
-        """Modify the collection name or metadata
-
-        Args:
-            name: The updated name for the collection. Optional.
-            metadata: The updated metadata for the collection. Optional.
-
-        Returns:
-            None
-        """
-        self._client._modify(id=self.id, new_name=name, new_metadata=metadata)
-        if name:
-            self.name = name
-        if metadata:
-            self.metadata = metadata
-
-    def update(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]] = None,
-        metadatas: Optional[OneOrMany[Metadata]] = None,
-        documents: Optional[OneOrMany[Document]] = None,
-    ) -> None:
-        """Update the embeddings, metadatas or documents for provided ids.
-
-        Args:
-            ids: The ids of the embeddings to update
-            embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
-            metadatas:  The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
-            documents: The documents to associate with the embeddings. Optional.
-
-        Returns:
-            None
-        """
-
-        ids, embeddings, metadatas, documents = self._validate_embedding_set(
-            ids, embeddings, metadatas, documents, require_embeddings_or_documents=False
-        )
-
-        self._client._update(self.id, ids, embeddings, metadatas, documents)
-
-    def upsert(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]] = None,
-        metadatas: Optional[OneOrMany[Metadata]] = None,
-        documents: Optional[OneOrMany[Document]] = None,
-        increment_index: bool = True,
-    ) -> None:
-        """Update the embeddings, metadatas or documents for provided ids, or create them if they don't exist.
-
-        Args:
-            ids: The ids of the embeddings to update
-            embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
-            metadatas:  The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
-            documents: The documents to associate with the embeddings. Optional.
-
-        Returns:
-            None
-        """
-
-        ids, embeddings, metadatas, documents = self._validate_embedding_set(
-            ids, embeddings, metadatas, documents
-        )
-
-        self._client._upsert(
-            collection_id=self.id,
-            ids=ids,
-            embeddings=embeddings,
-            metadatas=metadatas,
-            documents=documents,
-            increment_index=increment_index,
-        )
-
-    def delete(
-        self,
-        ids: Optional[IDs] = None,
-        where: Optional[Where] = None,
-        where_document: Optional[WhereDocument] = None,
-    ) -> None:
-        """Delete the embeddings based on ids and/or a where filter
-
-        Args:
-            ids: The ids of the embeddings to delete
-            where: A Where type dict used to filter the delection by. E.g. `{"color" : "red", "price": 4.20}`. Optional.
-            where_document: A WhereDocument type dict used to filter the deletion by the document content. E.g. `{$contains: {"text": "hello"}}`. Optional.
-
-        Returns:
-            None
-        """
-        ids = validate_ids(maybe_cast_one_to_many(ids)) if ids else None
-        where = validate_where(where) if where else None
-        where_document = (
-            validate_where_document(where_document) if where_document else None
-        )
-        self._client._delete(self.id, ids, where, where_document)
-
-    def create_index(self) -> None:
-        self._client.create_index(self.name)
-
-    def _validate_embedding_set(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]],
-        metadatas: Optional[OneOrMany[Metadata]],
-        documents: Optional[OneOrMany[Document]],
-        require_embeddings_or_documents: bool = True,
-    ) -> Tuple[
-        IDs,
-        List[Embedding],
-        Optional[List[Metadata]],
-        Optional[List[Document]],
-    ]:
-        ids = validate_ids(maybe_cast_one_to_many(ids))
-        embeddings = (
-            validate_embeddings(maybe_cast_one_to_many(embeddings))
-            if embeddings is not None
-            else None
-        )
-        metadatas = (
-            validate_metadatas(maybe_cast_one_to_many(metadatas))
-            if metadatas is not None
-            else None
-        )
-        documents = maybe_cast_one_to_many(documents) if documents is not None else None
-
-        # Check that one of embeddings or documents is provided
-        if require_embeddings_or_documents:
-            if embeddings is None and documents is None:
-                raise ValueError(
-                    "You must provide either embeddings or documents, or both"
-                )
-
-        # Check that, if they're provided, the lengths of the arrays match the length of ids
-        if embeddings is not None and len(embeddings) != len(ids):
-            raise ValueError(
-                f"Number of embeddings {len(embeddings)} must match number of ids {len(ids)}"
-            )
-        if metadatas is not None and len(metadatas) != len(ids):
-            raise ValueError(
-                f"Number of metadatas {len(metadatas)} must match number of ids {len(ids)}"
-            )
-        if documents is not None and len(documents) != len(ids):
-            raise ValueError(
-                f"Number of documents {len(documents)} must match number of ids {len(ids)}"
-            )
-
-        # If document embeddings are not provided, we need to compute them
-        if embeddings is None and documents is not None:
-            if self._embedding_function is None:
-                raise ValueError(
-                    "You must provide embeddings or a function to compute them"
-                )
-            embeddings = self._embedding_function(documents)
-
-        # if embeddings is None:
-        #     raise ValueError(
-        #         "Something went wrong. Embeddings should be computed at this point"
-        #     )
-
-        return ids, embeddings, metadatas, documents  # type: ignore
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/clickhouse_connect/dbapi/connection.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/clickhouse_connect/dbapi/connection.py
deleted file mode 100644
index d1b3cb7f3cd49f30d470f1fcd62b8b13fe40e33d..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/clickhouse_connect/dbapi/connection.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from typing import Union
-
-from clickhouse_connect.dbapi.cursor import Cursor
-from clickhouse_connect.driver import create_client
-from clickhouse_connect.driver.query import QueryResult
-
-
-class Connection:
-    """
-    See :ref:`https://peps.python.org/pep-0249/`
-    """
-    # pylint: disable=too-many-arguments
-    def __init__(self,
-                 dsn: str = None,
-                 username: str = '',
-                 password: str = '',
-                 host: str = None,
-                 database: str = None,
-                 interface: str = None,
-                 port: int = 0,
-                 secure: Union[bool, str] = False,
-                 **kwargs):
-        self.client = create_client(host=host,
-                                    username=username,
-                                    password=password,
-                                    database=database,
-                                    interface=interface,
-                                    port=port,
-                                    secure=secure,
-                                    dsn=dsn,
-                                    generic_args=kwargs)
-        self.timezone = self.client.server_tz
-
-    def close(self):
-        self.client.close()
-
-    def commit(self):
-        pass
-
-    def rollback(self):
-        pass
-
-    def command(self, cmd: str):
-        return self.client.command(cmd)
-
-    def raw_query(self, query: str) -> QueryResult:
-        return self.client.query(query)
-
-    def cursor(self):
-        return Cursor(self.client)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/force_pydevd.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/force_pydevd.py
deleted file mode 100644
index cfd8927584cf11b73db0a6450fe26a748031e505..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/force_pydevd.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License. See LICENSE in the project root
-# for license information.
-
-from importlib import import_module
-import os
-import warnings
-
-from . import check_modules, prefix_matcher, preimport, vendored
-
-# Ensure that pydevd is our vendored copy.
-_unvendored, _ = check_modules('pydevd',
-                               prefix_matcher('pydev', '_pydev'))
-if _unvendored:
-    _unvendored = sorted(_unvendored.values())
-    msg = 'incompatible copy of pydevd already imported'
-    # raise ImportError(msg)
-    warnings.warn(msg + ':\n {}'.format('\n  '.join(_unvendored)))
-
-# If debugpy logging is enabled, enable it for pydevd as well
-if "DEBUGPY_LOG_DIR" in os.environ:
-    os.environ[str("PYDEVD_DEBUG")] = str("True")
-    os.environ[str("PYDEVD_DEBUG_FILE")] = os.environ["DEBUGPY_LOG_DIR"] + str("/debugpy.pydevd.log")
-
-# Disable pydevd frame-eval optimizations only if unset, to allow opt-in.
-if "PYDEVD_USE_FRAME_EVAL" not in os.environ:
-    os.environ[str("PYDEVD_USE_FRAME_EVAL")] = str("NO")
-
-# Constants must be set before importing any other pydevd module
-# # due to heavy use of "from" in them.
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", category=DeprecationWarning)
-    with vendored('pydevd'):
-        pydevd_constants = import_module('_pydevd_bundle.pydevd_constants')
-# We limit representation size in our representation provider when needed.
-pydevd_constants.MAXIMUM_VARIABLE_REPRESENTATION_SIZE = 2 ** 32
-
-# Now make sure all the top-level modules and packages in pydevd are
-# loaded.  Any pydevd modules that aren't loaded at this point, will
-# be loaded using their parent package's __path__ (i.e. one of the
-# following).
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", category=DeprecationWarning)
-    preimport('pydevd', [
-        '_pydev_bundle',
-        '_pydev_runfiles',
-        '_pydevd_bundle',
-        '_pydevd_frame_eval',
-        'pydev_ipython',
-        'pydevd_plugins',
-        'pydevd',
-    ])
-
-# When pydevd is imported it sets the breakpoint behavior, but it needs to be
-# overridden because by default pydevd will connect to the remote debugger using
-# its own custom protocol rather than DAP.
-import pydevd  # noqa
-import debugpy  # noqa
-
-
-def debugpy_breakpointhook():
-    debugpy.breakpoint()
-
-
-pydevd.install_breakpointhook(debugpy_breakpointhook)
-
-# Ensure that pydevd uses JSON protocol
-from _pydevd_bundle import pydevd_constants
-from _pydevd_bundle import pydevd_defaults
-pydevd_defaults.PydevdCustomization.DEFAULT_PROTOCOL = pydevd_constants.HTTP_JSON_PROTOCOL
-
-# Enable some defaults related to debugpy such as sending a single notification when
-# threads pause and stopping on any exception.
-pydevd_defaults.PydevdCustomization.DEBUG_MODE = 'debugpy-dap'
-
-# This is important when pydevd attaches automatically to a subprocess. In this case, we have to
-# make sure that debugpy is properly put back in the game for users to be able to use it.
-pydevd_defaults.PydevdCustomization.PREIMPORT = '%s;%s' % (
-    os.path.dirname(os.path.dirname(debugpy.__file__)), 
-    'debugpy._vendored.force_pydevd'
-)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/pydev_is_thread_alive.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/pydev_is_thread_alive.py
deleted file mode 100644
index d949ba25608dba8c357142f12b8bd1ff9759496b..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/pydev_is_thread_alive.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from _pydev_bundle._pydev_saved_modules import threading
-
-# Hack for https://www.brainwy.com/tracker/PyDev/363 (i.e.: calling is_alive() can throw AssertionError under some
-# circumstances).
-# It is required to debug threads started by start_new_thread in Python 3.4
-_temp = threading.Thread()
-if hasattr(_temp, '_is_stopped'):  # Python 3.x has this
-
-    def is_thread_alive(t):
-        return not t._is_stopped
-
-elif hasattr(_temp, '_Thread__stopped'):  # Python 2.x has this
-
-    def is_thread_alive(t):
-        return not t._Thread__stopped
-
-else:
-
-    # Jython wraps a native java thread and thus only obeys the public API.
-    def is_thread_alive(t):
-        return t.is_alive()
-
-del _temp
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/_debug_adapter/pydevd_schema.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/_debug_adapter/pydevd_schema.py
deleted file mode 100644
index 3159f63edd9fb4de9d567e7eaf55773f8beb7539..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/_debug_adapter/pydevd_schema.py
+++ /dev/null
@@ -1,19514 +0,0 @@
-# coding: utf-8
-# Automatically generated code.
-# Do not edit manually.
-# Generated by running: __main__pydevd_gen_debug_adapter_protocol.py
-from .pydevd_base_schema import BaseSchema, register, register_request, register_response, register_event
-
-
-@register
-class ProtocolMessage(BaseSchema):
-    """
-    Base class of requests, responses, and events.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "description": "Message type.",
-            "_enum": [
-                "request",
-                "response",
-                "event"
-            ]
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, type, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: Message type.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = type
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        seq = self.seq
-        dct = {
-            'type': type,
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Request(BaseSchema):
-    """
-    A client or debug adapter initiated request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "description": "The command to execute."
-        },
-        "arguments": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Object containing arguments for the command."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, command, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: The command to execute.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] arguments: Object containing arguments for the command.
-        """
-        self.type = 'request'
-        self.command = command
-        self.seq = seq
-        self.arguments = arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Event(BaseSchema):
-    """
-    A debug adapter initiated event.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "description": "Type of event."
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Event-specific information."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, event, seq=-1, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: Type of event.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Event-specific information.
-        """
-        self.type = 'event'
-        self.event = event
-        self.seq = seq
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        seq = self.seq
-        body = self.body
-        dct = {
-            'type': type,
-            'event': event,
-            'seq': seq,
-        }
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Response(BaseSchema):
-    """
-    Response for a request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('error')
-@register
-class ErrorResponse(BaseSchema):
-    """
-    On error (whenever 'success' is false), the body can provide more details.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "error": {
-                    "$ref": "#/definitions/Message",
-                    "description": "An optional, structured error message."
-                }
-            }
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param ErrorResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = ErrorResponseBody()
-        else:
-            self.body = ErrorResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ErrorResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('cancel')
-@register
-class CancelRequest(BaseSchema):
-    """
-    The 'cancel' request is used by the frontend in two situations:
-    
-    - to indicate that it is no longer interested in the result produced by a specific request issued
-    earlier
-    
-    - to cancel a progress sequence. Clients should only call this request if the capability
-    'supportsCancelRequest' is true.
-    
-    This request has a hint characteristic: a debug adapter can only be expected to make a 'best effort'
-    in honouring this request but there are no guarantees.
-    
-    The 'cancel' request may return an error if it could not cancel an operation but a frontend should
-    refrain from presenting this error to end users.
-    
-    A frontend client should only call this request if the capability 'supportsCancelRequest' is true.
-    
-    The request that got canceled still needs to send a response back. This can either be a normal
-    result ('success' attribute true)
-    
-    or an error response ('success' attribute false and the 'message' set to 'cancelled').
-    
-    Returning partial results from a cancelled request is possible but please note that a frontend
-    client has no generic way for detecting that a response is partial or not.
-    
-    The progress that got cancelled still needs to send a 'progressEnd' event back.
-    
-    A client should not assume that progress just got cancelled after sending the 'cancel' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "cancel"
-            ]
-        },
-        "arguments": {
-            "type": "CancelArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param CancelArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'cancel'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = CancelArguments()
-        else:
-            self.arguments = CancelArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  CancelArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class CancelArguments(BaseSchema):
-    """
-    Arguments for 'cancel' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "requestId": {
-            "type": "integer",
-            "description": "The ID (attribute 'seq') of the request to cancel. If missing no request is cancelled.\nBoth a 'requestId' and a 'progressId' can be specified in one request."
-        },
-        "progressId": {
-            "type": "string",
-            "description": "The ID (attribute 'progressId') of the progress to cancel. If missing no progress is cancelled.\nBoth a 'requestId' and a 'progressId' can be specified in one request."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, requestId=None, progressId=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer requestId: The ID (attribute 'seq') of the request to cancel. If missing no request is cancelled.
-        Both a 'requestId' and a 'progressId' can be specified in one request.
-        :param string progressId: The ID (attribute 'progressId') of the progress to cancel. If missing no progress is cancelled.
-        Both a 'requestId' and a 'progressId' can be specified in one request.
-        """
-        self.requestId = requestId
-        self.progressId = progressId
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        requestId = self.requestId
-        progressId = self.progressId
-        dct = {
-        }
-        if requestId is not None:
-            dct['requestId'] = requestId
-        if progressId is not None:
-            dct['progressId'] = progressId
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('cancel')
-@register
-class CancelResponse(BaseSchema):
-    """
-    Response to 'cancel' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('initialized')
-@register
-class InitializedEvent(BaseSchema):
-    """
-    This event indicates that the debug adapter is ready to accept configuration requests (e.g.
-    SetBreakpointsRequest, SetExceptionBreakpointsRequest).
-    
-    A debug adapter is expected to send this event when it is ready to accept configuration requests
-    (but not before the 'initialize' request has finished).
-    
-    The sequence of events/requests is as follows:
-    
-    - adapters sends 'initialized' event (after the 'initialize' request has returned)
-    
-    - frontend sends zero or more 'setBreakpoints' requests
-    
-    - frontend sends one 'setFunctionBreakpoints' request (if capability 'supportsFunctionBreakpoints'
-    is true)
-    
-    - frontend sends a 'setExceptionBreakpoints' request if one or more 'exceptionBreakpointFilters'
-    have been defined (or if 'supportsConfigurationDoneRequest' is not defined or false)
-    
-    - frontend sends other future configuration requests
-    
-    - frontend sends one 'configurationDone' request to indicate the end of the configuration.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "initialized"
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Event-specific information."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Event-specific information.
-        """
-        self.type = 'event'
-        self.event = 'initialized'
-        self.seq = seq
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        seq = self.seq
-        body = self.body
-        dct = {
-            'type': type,
-            'event': event,
-            'seq': seq,
-        }
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('stopped')
-@register
-class StoppedEvent(BaseSchema):
-    """
-    The event indicates that the execution of the debuggee has stopped due to some condition.
-    
-    This can be caused by a break point previously set, a stepping request has completed, by executing a
-    debugger statement etc.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "stopped"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "reason": {
-                    "type": "string",
-                    "description": "The reason for the event.\nFor backward compatibility this string is shown in the UI if the 'description' attribute is missing (but it must not be translated).",
-                    "_enum": [
-                        "step",
-                        "breakpoint",
-                        "exception",
-                        "pause",
-                        "entry",
-                        "goto",
-                        "function breakpoint",
-                        "data breakpoint",
-                        "instruction breakpoint"
-                    ]
-                },
-                "description": {
-                    "type": "string",
-                    "description": "The full reason for the event, e.g. 'Paused on exception'. This string is shown in the UI as is and must be translated."
-                },
-                "threadId": {
-                    "type": "integer",
-                    "description": "The thread which was stopped."
-                },
-                "preserveFocusHint": {
-                    "type": "boolean",
-                    "description": "A value of true hints to the frontend that this event should not change the focus."
-                },
-                "text": {
-                    "type": "string",
-                    "description": "Additional information. E.g. if reason is 'exception', text contains the exception name. This string is shown in the UI."
-                },
-                "allThreadsStopped": {
-                    "type": "boolean",
-                    "description": "If 'allThreadsStopped' is true, a debug adapter can announce that all threads have stopped.\n- The client should use this information to enable that all threads can be expanded to access their stacktraces.\n- If the attribute is missing or false, only the thread with the given threadId can be expanded."
-                },
-                "hitBreakpointIds": {
-                    "type": "array",
-                    "items": {
-                        "type": "integer"
-                    },
-                    "description": "Ids of the breakpoints that triggered the event. In most cases there will be only a single breakpoint but here are some examples for multiple breakpoints:\n- Different types of breakpoints map to the same location.\n- Multiple source breakpoints get collapsed to the same instruction by the compiler/runtime.\n- Multiple function breakpoints with different function names map to the same location."
-                }
-            },
-            "required": [
-                "reason"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param StoppedEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'stopped'
-        if body is None:
-            self.body = StoppedEventBody()
-        else:
-            self.body = StoppedEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  StoppedEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('continued')
-@register
-class ContinuedEvent(BaseSchema):
-    """
-    The event indicates that the execution of the debuggee has continued.
-    
-    Please note: a debug adapter is not expected to send this event in response to a request that
-    implies that execution continues, e.g. 'launch' or 'continue'.
-    
-    It is only necessary to send a 'continued' event if there was no previous request that implied this.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "continued"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "threadId": {
-                    "type": "integer",
-                    "description": "The thread which was continued."
-                },
-                "allThreadsContinued": {
-                    "type": "boolean",
-                    "description": "If 'allThreadsContinued' is true, a debug adapter can announce that all threads have continued."
-                }
-            },
-            "required": [
-                "threadId"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ContinuedEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'continued'
-        if body is None:
-            self.body = ContinuedEventBody()
-        else:
-            self.body = ContinuedEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ContinuedEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('exited')
-@register
-class ExitedEvent(BaseSchema):
-    """
-    The event indicates that the debuggee has exited and returns its exit code.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "exited"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "exitCode": {
-                    "type": "integer",
-                    "description": "The exit code returned from the debuggee."
-                }
-            },
-            "required": [
-                "exitCode"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ExitedEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'exited'
-        if body is None:
-            self.body = ExitedEventBody()
-        else:
-            self.body = ExitedEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ExitedEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('terminated')
-@register
-class TerminatedEvent(BaseSchema):
-    """
-    The event indicates that debugging of the debuggee has terminated. This does **not** mean that the
-    debuggee itself has exited.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "terminated"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "restart": {
-                    "type": [
-                        "array",
-                        "boolean",
-                        "integer",
-                        "null",
-                        "number",
-                        "object",
-                        "string"
-                    ],
-                    "description": "A debug adapter may set 'restart' to true (or to an arbitrary object) to request that the front end restarts the session.\nThe value is not interpreted by the client and passed unmodified as an attribute '__restart' to the 'launch' and 'attach' requests."
-                }
-            }
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param TerminatedEventBody body: 
-        """
-        self.type = 'event'
-        self.event = 'terminated'
-        self.seq = seq
-        if body is None:
-            self.body = TerminatedEventBody()
-        else:
-            self.body = TerminatedEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  TerminatedEventBody else body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        seq = self.seq
-        body = self.body
-        dct = {
-            'type': type,
-            'event': event,
-            'seq': seq,
-        }
-        if body is not None:
-            dct['body'] = body.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('thread')
-@register
-class ThreadEvent(BaseSchema):
-    """
-    The event indicates that a thread has started or exited.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "thread"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "reason": {
-                    "type": "string",
-                    "description": "The reason for the event.",
-                    "_enum": [
-                        "started",
-                        "exited"
-                    ]
-                },
-                "threadId": {
-                    "type": "integer",
-                    "description": "The identifier of the thread."
-                }
-            },
-            "required": [
-                "reason",
-                "threadId"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ThreadEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'thread'
-        if body is None:
-            self.body = ThreadEventBody()
-        else:
-            self.body = ThreadEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ThreadEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('output')
-@register
-class OutputEvent(BaseSchema):
-    """
-    The event indicates that the target has produced some output.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "output"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "category": {
-                    "type": "string",
-                    "description": "The output category. If not specified or if the category is not understand by the client, 'console' is assumed.",
-                    "_enum": [
-                        "console",
-                        "important",
-                        "stdout",
-                        "stderr",
-                        "telemetry"
-                    ],
-                    "enumDescriptions": [
-                        "Show the output in the client's default message UI, e.g. a 'debug console'. This category should only be used for informational output from the debugger (as opposed to the debuggee).",
-                        "A hint for the client to show the ouput in the client's UI for important and highly visible information, e.g. as a popup notification. This category should only be used for important messages from the debugger (as opposed to the debuggee). Since this category value is a hint, clients might ignore the hint and assume the 'console' category.",
-                        "Show the output as normal program output from the debuggee.",
-                        "Show the output as error program output from the debuggee.",
-                        "Send the output to telemetry instead of showing it to the user."
-                    ]
-                },
-                "output": {
-                    "type": "string",
-                    "description": "The output to report."
-                },
-                "group": {
-                    "type": "string",
-                    "description": "Support for keeping an output log organized by grouping related messages.",
-                    "enum": [
-                        "start",
-                        "startCollapsed",
-                        "end"
-                    ],
-                    "enumDescriptions": [
-                        "Start a new group in expanded mode. Subsequent output events are members of the group and should be shown indented.\nThe 'output' attribute becomes the name of the group and is not indented.",
-                        "Start a new group in collapsed mode. Subsequent output events are members of the group and should be shown indented (as soon as the group is expanded).\nThe 'output' attribute becomes the name of the group and is not indented.",
-                        "End the current group and decreases the indentation of subsequent output events.\nA non empty 'output' attribute is shown as the unindented end of the group."
-                    ]
-                },
-                "variablesReference": {
-                    "type": "integer",
-                    "description": "If an attribute 'variablesReference' exists and its value is > 0, the output contains objects which can be retrieved by passing 'variablesReference' to the 'variables' request. The value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "source": {
-                    "$ref": "#/definitions/Source",
-                    "description": "An optional source location where the output was produced."
-                },
-                "line": {
-                    "type": "integer",
-                    "description": "An optional source location line where the output was produced."
-                },
-                "column": {
-                    "type": "integer",
-                    "description": "An optional source location column where the output was produced."
-                },
-                "data": {
-                    "type": [
-                        "array",
-                        "boolean",
-                        "integer",
-                        "null",
-                        "number",
-                        "object",
-                        "string"
-                    ],
-                    "description": "Optional data to report. For the 'telemetry' category the data will be sent to telemetry, for the other categories the data is shown in JSON format."
-                }
-            },
-            "required": [
-                "output"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param OutputEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'output'
-        if body is None:
-            self.body = OutputEventBody()
-        else:
-            self.body = OutputEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  OutputEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('breakpoint')
-@register
-class BreakpointEvent(BaseSchema):
-    """
-    The event indicates that some information about a breakpoint has changed.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "breakpoint"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "reason": {
-                    "type": "string",
-                    "description": "The reason for the event.",
-                    "_enum": [
-                        "changed",
-                        "new",
-                        "removed"
-                    ]
-                },
-                "breakpoint": {
-                    "$ref": "#/definitions/Breakpoint",
-                    "description": "The 'id' attribute is used to find the target breakpoint and the other attributes are used as the new values."
-                }
-            },
-            "required": [
-                "reason",
-                "breakpoint"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param BreakpointEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'breakpoint'
-        if body is None:
-            self.body = BreakpointEventBody()
-        else:
-            self.body = BreakpointEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  BreakpointEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('module')
-@register
-class ModuleEvent(BaseSchema):
-    """
-    The event indicates that some information about a module has changed.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "module"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "reason": {
-                    "type": "string",
-                    "description": "The reason for the event.",
-                    "enum": [
-                        "new",
-                        "changed",
-                        "removed"
-                    ]
-                },
-                "module": {
-                    "$ref": "#/definitions/Module",
-                    "description": "The new, changed, or removed module. In case of 'removed' only the module id is used."
-                }
-            },
-            "required": [
-                "reason",
-                "module"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ModuleEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'module'
-        if body is None:
-            self.body = ModuleEventBody()
-        else:
-            self.body = ModuleEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ModuleEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('loadedSource')
-@register
-class LoadedSourceEvent(BaseSchema):
-    """
-    The event indicates that some source has been added, changed, or removed from the set of all loaded
-    sources.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "loadedSource"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "reason": {
-                    "type": "string",
-                    "description": "The reason for the event.",
-                    "enum": [
-                        "new",
-                        "changed",
-                        "removed"
-                    ]
-                },
-                "source": {
-                    "$ref": "#/definitions/Source",
-                    "description": "The new, changed, or removed source."
-                }
-            },
-            "required": [
-                "reason",
-                "source"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param LoadedSourceEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'loadedSource'
-        if body is None:
-            self.body = LoadedSourceEventBody()
-        else:
-            self.body = LoadedSourceEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  LoadedSourceEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('process')
-@register
-class ProcessEvent(BaseSchema):
-    """
-    The event indicates that the debugger has begun debugging a new process. Either one that it has
-    launched, or one that it has attached to.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "process"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "name": {
-                    "type": "string",
-                    "description": "The logical name of the process. This is usually the full path to process's executable file. Example: /home/example/myproj/program.js."
-                },
-                "systemProcessId": {
-                    "type": "integer",
-                    "description": "The system process id of the debugged process. This property will be missing for non-system processes."
-                },
-                "isLocalProcess": {
-                    "type": "boolean",
-                    "description": "If true, the process is running on the same computer as the debug adapter."
-                },
-                "startMethod": {
-                    "type": "string",
-                    "enum": [
-                        "launch",
-                        "attach",
-                        "attachForSuspendedLaunch"
-                    ],
-                    "description": "Describes how the debug engine started debugging this process.",
-                    "enumDescriptions": [
-                        "Process was launched under the debugger.",
-                        "Debugger attached to an existing process.",
-                        "A project launcher component has launched a new process in a suspended state and then asked the debugger to attach."
-                    ]
-                },
-                "pointerSize": {
-                    "type": "integer",
-                    "description": "The size of a pointer or address for this process, in bits. This value may be used by clients when formatting addresses for display."
-                }
-            },
-            "required": [
-                "name"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ProcessEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'process'
-        if body is None:
-            self.body = ProcessEventBody()
-        else:
-            self.body = ProcessEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ProcessEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('capabilities')
-@register
-class CapabilitiesEvent(BaseSchema):
-    """
-    The event indicates that one or more capabilities have changed.
-    
-    Since the capabilities are dependent on the frontend and its UI, it might not be possible to change
-    that at random times (or too late).
-    
-    Consequently this event has a hint characteristic: a frontend can only be expected to make a 'best
-    effort' in honouring individual capabilities but there are no guarantees.
-    
-    Only changed capabilities need to be included, all other capabilities keep their values.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "capabilities"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "capabilities": {
-                    "$ref": "#/definitions/Capabilities",
-                    "description": "The set of updated capabilities."
-                }
-            },
-            "required": [
-                "capabilities"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param CapabilitiesEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'capabilities'
-        if body is None:
-            self.body = CapabilitiesEventBody()
-        else:
-            self.body = CapabilitiesEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  CapabilitiesEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('progressStart')
-@register
-class ProgressStartEvent(BaseSchema):
-    """
-    The event signals that a long running operation is about to start and
-    
-    provides additional information for the client to set up a corresponding progress and cancellation
-    UI.
-    
-    The client is free to delay the showing of the UI in order to reduce flicker.
-    
-    This event should only be sent if the client has passed the value true for the
-    'supportsProgressReporting' capability of the 'initialize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "progressStart"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "progressId": {
-                    "type": "string",
-                    "description": "An ID that must be used in subsequent 'progressUpdate' and 'progressEnd' events to make them refer to the same progress reporting.\nIDs must be unique within a debug session."
-                },
-                "title": {
-                    "type": "string",
-                    "description": "Mandatory (short) title of the progress reporting. Shown in the UI to describe the long running operation."
-                },
-                "requestId": {
-                    "type": "integer",
-                    "description": "The request ID that this progress report is related to. If specified a debug adapter is expected to emit\nprogress events for the long running request until the request has been either completed or cancelled.\nIf the request ID is omitted, the progress report is assumed to be related to some general activity of the debug adapter."
-                },
-                "cancellable": {
-                    "type": "boolean",
-                    "description": "If true, the request that reports progress may be canceled with a 'cancel' request.\nSo this property basically controls whether the client should use UX that supports cancellation.\nClients that don't support cancellation are allowed to ignore the setting."
-                },
-                "message": {
-                    "type": "string",
-                    "description": "Optional, more detailed progress message."
-                },
-                "percentage": {
-                    "type": "number",
-                    "description": "Optional progress percentage to display (value range: 0 to 100). If omitted no percentage will be shown."
-                }
-            },
-            "required": [
-                "progressId",
-                "title"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ProgressStartEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'progressStart'
-        if body is None:
-            self.body = ProgressStartEventBody()
-        else:
-            self.body = ProgressStartEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ProgressStartEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('progressUpdate')
-@register
-class ProgressUpdateEvent(BaseSchema):
-    """
-    The event signals that the progress reporting needs to updated with a new message and/or percentage.
-    
-    The client does not have to update the UI immediately, but the clients needs to keep track of the
-    message and/or percentage values.
-    
-    This event should only be sent if the client has passed the value true for the
-    'supportsProgressReporting' capability of the 'initialize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "progressUpdate"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "progressId": {
-                    "type": "string",
-                    "description": "The ID that was introduced in the initial 'progressStart' event."
-                },
-                "message": {
-                    "type": "string",
-                    "description": "Optional, more detailed progress message. If omitted, the previous message (if any) is used."
-                },
-                "percentage": {
-                    "type": "number",
-                    "description": "Optional progress percentage to display (value range: 0 to 100). If omitted no percentage will be shown."
-                }
-            },
-            "required": [
-                "progressId"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ProgressUpdateEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'progressUpdate'
-        if body is None:
-            self.body = ProgressUpdateEventBody()
-        else:
-            self.body = ProgressUpdateEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ProgressUpdateEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('progressEnd')
-@register
-class ProgressEndEvent(BaseSchema):
-    """
-    The event signals the end of the progress reporting with an optional final message.
-    
-    This event should only be sent if the client has passed the value true for the
-    'supportsProgressReporting' capability of the 'initialize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "progressEnd"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "progressId": {
-                    "type": "string",
-                    "description": "The ID that was introduced in the initial 'ProgressStartEvent'."
-                },
-                "message": {
-                    "type": "string",
-                    "description": "Optional, more detailed progress message. If omitted, the previous message (if any) is used."
-                }
-            },
-            "required": [
-                "progressId"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param ProgressEndEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'progressEnd'
-        if body is None:
-            self.body = ProgressEndEventBody()
-        else:
-            self.body = ProgressEndEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ProgressEndEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('invalidated')
-@register
-class InvalidatedEvent(BaseSchema):
-    """
-    This event signals that some state in the debug adapter has changed and requires that the client
-    needs to re-render the data snapshot previously requested.
-    
-    Debug adapters do not have to emit this event for runtime changes like stopped or thread events
-    because in that case the client refetches the new state anyway. But the event can be used for
-    example to refresh the UI after rendering formatting has changed in the debug adapter.
-    
-    This event should only be sent if the debug adapter has received a value true for the
-    'supportsInvalidatedEvent' capability of the 'initialize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "invalidated"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "areas": {
-                    "type": "array",
-                    "description": "Optional set of logical areas that got invalidated. This property has a hint characteristic: a client can only be expected to make a 'best effort' in honouring the areas but there are no guarantees. If this property is missing, empty, or if values are not understand the client should assume a single value 'all'.",
-                    "items": {
-                        "$ref": "#/definitions/InvalidatedAreas"
-                    }
-                },
-                "threadId": {
-                    "type": "integer",
-                    "description": "If specified, the client only needs to refetch data related to this thread."
-                },
-                "stackFrameId": {
-                    "type": "integer",
-                    "description": "If specified, the client only needs to refetch data related to this stack frame (and the 'threadId' is ignored)."
-                }
-            }
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param InvalidatedEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'invalidated'
-        if body is None:
-            self.body = InvalidatedEventBody()
-        else:
-            self.body = InvalidatedEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  InvalidatedEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('memory')
-@register
-class MemoryEvent(BaseSchema):
-    """
-    This event indicates that some memory range has been updated. It should only be sent if the debug
-    adapter has received a value true for the `supportsMemoryEvent` capability of the `initialize`
-    request.
-    
-    Clients typically react to the event by re-issuing a `readMemory` request if they show the memory
-    identified by the `memoryReference` and if the updated memory range overlaps the displayed range.
-    Clients should not make assumptions how individual memory references relate to each other, so they
-    should not assume that they are part of a single continuous address range and might overlap.
-    
-    Debug adapters can use this event to indicate that the contents of a memory range has changed due to
-    some other DAP request like `setVariable` or `setExpression`. Debug adapters are not expected to
-    emit this event for each and every memory change of a running program, because that information is
-    typically not available from debuggers and it would flood clients with too many events.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "memory"
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "memoryReference": {
-                    "type": "string",
-                    "description": "Memory reference of a memory range that has been updated."
-                },
-                "offset": {
-                    "type": "integer",
-                    "description": "Starting offset in bytes where memory has been updated. Can be negative."
-                },
-                "count": {
-                    "type": "integer",
-                    "description": "Number of bytes updated."
-                }
-            },
-            "required": [
-                "memoryReference",
-                "offset",
-                "count"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, body, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param MemoryEventBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'event'
-        self.event = 'memory'
-        if body is None:
-            self.body = MemoryEventBody()
-        else:
-            self.body = MemoryEventBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  MemoryEventBody else body
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        body = self.body
-        seq = self.seq
-        dct = {
-            'type': type,
-            'event': event,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('runInTerminal')
-@register
-class RunInTerminalRequest(BaseSchema):
-    """
-    This optional request is sent from the debug adapter to the client to run a command in a terminal.
-    
-    This is typically used to launch the debuggee in a terminal provided by the client.
-    
-    This request should only be called if the client has passed the value true for the
-    'supportsRunInTerminalRequest' capability of the 'initialize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "runInTerminal"
-            ]
-        },
-        "arguments": {
-            "type": "RunInTerminalRequestArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param RunInTerminalRequestArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'runInTerminal'
-        if arguments is None:
-            self.arguments = RunInTerminalRequestArguments()
-        else:
-            self.arguments = RunInTerminalRequestArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  RunInTerminalRequestArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class RunInTerminalRequestArguments(BaseSchema):
-    """
-    Arguments for 'runInTerminal' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "kind": {
-            "type": "string",
-            "enum": [
-                "integrated",
-                "external"
-            ],
-            "description": "What kind of terminal to launch."
-        },
-        "title": {
-            "type": "string",
-            "description": "Optional title of the terminal."
-        },
-        "cwd": {
-            "type": "string",
-            "description": "Working directory for the command. For non-empty, valid paths this typically results in execution of a change directory command."
-        },
-        "args": {
-            "type": "array",
-            "items": {
-                "type": "string"
-            },
-            "description": "List of arguments. The first argument is the command to run."
-        },
-        "env": {
-            "type": "object",
-            "description": "Environment key-value pairs that are added to or removed from the default environment.",
-            "additionalProperties": {
-                "type": [
-                    "string",
-                    "null"
-                ],
-                "description": "Proper values must be strings. A value of 'null' removes the variable from the environment."
-            }
-        }
-    }
-    __refs__ = set(['env'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, cwd, args, kind=None, title=None, env=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string cwd: Working directory for the command. For non-empty, valid paths this typically results in execution of a change directory command.
-        :param array args: List of arguments. The first argument is the command to run.
-        :param string kind: What kind of terminal to launch.
-        :param string title: Optional title of the terminal.
-        :param RunInTerminalRequestArgumentsEnv env: Environment key-value pairs that are added to or removed from the default environment.
-        """
-        self.cwd = cwd
-        self.args = args
-        self.kind = kind
-        self.title = title
-        if env is None:
-            self.env = RunInTerminalRequestArgumentsEnv()
-        else:
-            self.env = RunInTerminalRequestArgumentsEnv(update_ids_from_dap=update_ids_from_dap, **env) if env.__class__ !=  RunInTerminalRequestArgumentsEnv else env
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        cwd = self.cwd
-        args = self.args
-        if args and hasattr(args[0], "to_dict"):
-            args = [x.to_dict() for x in args]
-        kind = self.kind
-        title = self.title
-        env = self.env
-        dct = {
-            'cwd': cwd,
-            'args': args,
-        }
-        if kind is not None:
-            dct['kind'] = kind
-        if title is not None:
-            dct['title'] = title
-        if env is not None:
-            dct['env'] = env.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('runInTerminal')
-@register
-class RunInTerminalResponse(BaseSchema):
-    """
-    Response to 'runInTerminal' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "processId": {
-                    "type": "integer",
-                    "description": "The process ID. The value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "shellProcessId": {
-                    "type": "integer",
-                    "description": "The process ID of the terminal shell. The value should be less than or equal to 2147483647 (2^31-1)."
-                }
-            }
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param RunInTerminalResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = RunInTerminalResponseBody()
-        else:
-            self.body = RunInTerminalResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  RunInTerminalResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('initialize')
-@register
-class InitializeRequest(BaseSchema):
-    """
-    The 'initialize' request is sent as the first request from the client to the debug adapter
-    
-    in order to configure it with client capabilities and to retrieve capabilities from the debug
-    adapter.
-    
-    Until the debug adapter has responded to with an 'initialize' response, the client must not send any
-    additional requests or events to the debug adapter.
-    
-    In addition the debug adapter is not allowed to send any requests or events to the client until it
-    has responded with an 'initialize' response.
-    
-    The 'initialize' request may only be sent once.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "initialize"
-            ]
-        },
-        "arguments": {
-            "type": "InitializeRequestArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param InitializeRequestArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'initialize'
-        if arguments is None:
-            self.arguments = InitializeRequestArguments()
-        else:
-            self.arguments = InitializeRequestArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  InitializeRequestArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class InitializeRequestArguments(BaseSchema):
-    """
-    Arguments for 'initialize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "clientID": {
-            "type": "string",
-            "description": "The ID of the (frontend) client using this adapter."
-        },
-        "clientName": {
-            "type": "string",
-            "description": "The human readable name of the (frontend) client using this adapter."
-        },
-        "adapterID": {
-            "type": "string",
-            "description": "The ID of the debug adapter."
-        },
-        "locale": {
-            "type": "string",
-            "description": "The ISO-639 locale of the (frontend) client using this adapter, e.g. en-US or de-CH."
-        },
-        "linesStartAt1": {
-            "type": "boolean",
-            "description": "If true all line numbers are 1-based (default)."
-        },
-        "columnsStartAt1": {
-            "type": "boolean",
-            "description": "If true all column numbers are 1-based (default)."
-        },
-        "pathFormat": {
-            "type": "string",
-            "_enum": [
-                "path",
-                "uri"
-            ],
-            "description": "Determines in what format paths are specified. The default is 'path', which is the native format."
-        },
-        "supportsVariableType": {
-            "type": "boolean",
-            "description": "Client supports the optional type attribute for variables."
-        },
-        "supportsVariablePaging": {
-            "type": "boolean",
-            "description": "Client supports the paging of variables."
-        },
-        "supportsRunInTerminalRequest": {
-            "type": "boolean",
-            "description": "Client supports the runInTerminal request."
-        },
-        "supportsMemoryReferences": {
-            "type": "boolean",
-            "description": "Client supports memory references."
-        },
-        "supportsProgressReporting": {
-            "type": "boolean",
-            "description": "Client supports progress reporting."
-        },
-        "supportsInvalidatedEvent": {
-            "type": "boolean",
-            "description": "Client supports the invalidated event."
-        },
-        "supportsMemoryEvent": {
-            "type": "boolean",
-            "description": "Client supports the memory event."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, adapterID, clientID=None, clientName=None, locale=None, linesStartAt1=None, columnsStartAt1=None, pathFormat=None, supportsVariableType=None, supportsVariablePaging=None, supportsRunInTerminalRequest=None, supportsMemoryReferences=None, supportsProgressReporting=None, supportsInvalidatedEvent=None, supportsMemoryEvent=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string adapterID: The ID of the debug adapter.
-        :param string clientID: The ID of the (frontend) client using this adapter.
-        :param string clientName: The human readable name of the (frontend) client using this adapter.
-        :param string locale: The ISO-639 locale of the (frontend) client using this adapter, e.g. en-US or de-CH.
-        :param boolean linesStartAt1: If true all line numbers are 1-based (default).
-        :param boolean columnsStartAt1: If true all column numbers are 1-based (default).
-        :param string pathFormat: Determines in what format paths are specified. The default is 'path', which is the native format.
-        :param boolean supportsVariableType: Client supports the optional type attribute for variables.
-        :param boolean supportsVariablePaging: Client supports the paging of variables.
-        :param boolean supportsRunInTerminalRequest: Client supports the runInTerminal request.
-        :param boolean supportsMemoryReferences: Client supports memory references.
-        :param boolean supportsProgressReporting: Client supports progress reporting.
-        :param boolean supportsInvalidatedEvent: Client supports the invalidated event.
-        :param boolean supportsMemoryEvent: Client supports the memory event.
-        """
-        self.adapterID = adapterID
-        self.clientID = clientID
-        self.clientName = clientName
-        self.locale = locale
-        self.linesStartAt1 = linesStartAt1
-        self.columnsStartAt1 = columnsStartAt1
-        self.pathFormat = pathFormat
-        self.supportsVariableType = supportsVariableType
-        self.supportsVariablePaging = supportsVariablePaging
-        self.supportsRunInTerminalRequest = supportsRunInTerminalRequest
-        self.supportsMemoryReferences = supportsMemoryReferences
-        self.supportsProgressReporting = supportsProgressReporting
-        self.supportsInvalidatedEvent = supportsInvalidatedEvent
-        self.supportsMemoryEvent = supportsMemoryEvent
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        adapterID = self.adapterID
-        clientID = self.clientID
-        clientName = self.clientName
-        locale = self.locale
-        linesStartAt1 = self.linesStartAt1
-        columnsStartAt1 = self.columnsStartAt1
-        pathFormat = self.pathFormat
-        supportsVariableType = self.supportsVariableType
-        supportsVariablePaging = self.supportsVariablePaging
-        supportsRunInTerminalRequest = self.supportsRunInTerminalRequest
-        supportsMemoryReferences = self.supportsMemoryReferences
-        supportsProgressReporting = self.supportsProgressReporting
-        supportsInvalidatedEvent = self.supportsInvalidatedEvent
-        supportsMemoryEvent = self.supportsMemoryEvent
-        dct = {
-            'adapterID': adapterID,
-        }
-        if clientID is not None:
-            dct['clientID'] = clientID
-        if clientName is not None:
-            dct['clientName'] = clientName
-        if locale is not None:
-            dct['locale'] = locale
-        if linesStartAt1 is not None:
-            dct['linesStartAt1'] = linesStartAt1
-        if columnsStartAt1 is not None:
-            dct['columnsStartAt1'] = columnsStartAt1
-        if pathFormat is not None:
-            dct['pathFormat'] = pathFormat
-        if supportsVariableType is not None:
-            dct['supportsVariableType'] = supportsVariableType
-        if supportsVariablePaging is not None:
-            dct['supportsVariablePaging'] = supportsVariablePaging
-        if supportsRunInTerminalRequest is not None:
-            dct['supportsRunInTerminalRequest'] = supportsRunInTerminalRequest
-        if supportsMemoryReferences is not None:
-            dct['supportsMemoryReferences'] = supportsMemoryReferences
-        if supportsProgressReporting is not None:
-            dct['supportsProgressReporting'] = supportsProgressReporting
-        if supportsInvalidatedEvent is not None:
-            dct['supportsInvalidatedEvent'] = supportsInvalidatedEvent
-        if supportsMemoryEvent is not None:
-            dct['supportsMemoryEvent'] = supportsMemoryEvent
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('initialize')
-@register
-class InitializeResponse(BaseSchema):
-    """
-    Response to 'initialize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "description": "The capabilities of this debug adapter.",
-            "type": "Capabilities"
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param Capabilities body: The capabilities of this debug adapter.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        if body is None:
-            self.body = Capabilities()
-        else:
-            self.body = Capabilities(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  Capabilities else body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('configurationDone')
-@register
-class ConfigurationDoneRequest(BaseSchema):
-    """
-    This optional request indicates that the client has finished initialization of the debug adapter.
-    
-    So it is the last request in the sequence of configuration requests (which was started by the
-    'initialized' event).
-    
-    Clients should only call this request if the capability 'supportsConfigurationDoneRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "configurationDone"
-            ]
-        },
-        "arguments": {
-            "type": "ConfigurationDoneArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param ConfigurationDoneArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'configurationDone'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = ConfigurationDoneArguments()
-        else:
-            self.arguments = ConfigurationDoneArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  ConfigurationDoneArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ConfigurationDoneArguments(BaseSchema):
-    """
-    Arguments for 'configurationDone' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('configurationDone')
-@register
-class ConfigurationDoneResponse(BaseSchema):
-    """
-    Response to 'configurationDone' request. This is just an acknowledgement, so no body field is
-    required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('launch')
-@register
-class LaunchRequest(BaseSchema):
-    """
-    This launch request is sent from the client to the debug adapter to start the debuggee with or
-    without debugging (if 'noDebug' is true).
-    
-    Since launching is debugger/runtime specific, the arguments for this request are not part of this
-    specification.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "launch"
-            ]
-        },
-        "arguments": {
-            "type": "LaunchRequestArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param LaunchRequestArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'launch'
-        if arguments is None:
-            self.arguments = LaunchRequestArguments()
-        else:
-            self.arguments = LaunchRequestArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  LaunchRequestArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class LaunchRequestArguments(BaseSchema):
-    """
-    Arguments for 'launch' request. Additional attributes are implementation specific.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "noDebug": {
-            "type": "boolean",
-            "description": "If noDebug is true the launch request should launch the program without enabling debugging."
-        },
-        "__restart": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Optional data from the previous, restarted session.\nThe data is sent as the 'restart' attribute of the 'terminated' event.\nThe client should leave the data intact."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, noDebug=None, __restart=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean noDebug: If noDebug is true the launch request should launch the program without enabling debugging.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] __restart: Optional data from the previous, restarted session.
-        The data is sent as the 'restart' attribute of the 'terminated' event.
-        The client should leave the data intact.
-        """
-        self.noDebug = noDebug
-        self.__restart = __restart
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        noDebug = self.noDebug
-        __restart = self.__restart
-        dct = {
-        }
-        if noDebug is not None:
-            dct['noDebug'] = noDebug
-        if __restart is not None:
-            dct['__restart'] = __restart
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('launch')
-@register
-class LaunchResponse(BaseSchema):
-    """
-    Response to 'launch' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('attach')
-@register
-class AttachRequest(BaseSchema):
-    """
-    The attach request is sent from the client to the debug adapter to attach to a debuggee that is
-    already running.
-    
-    Since attaching is debugger/runtime specific, the arguments for this request are not part of this
-    specification.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "attach"
-            ]
-        },
-        "arguments": {
-            "type": "AttachRequestArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param AttachRequestArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'attach'
-        if arguments is None:
-            self.arguments = AttachRequestArguments()
-        else:
-            self.arguments = AttachRequestArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  AttachRequestArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class AttachRequestArguments(BaseSchema):
-    """
-    Arguments for 'attach' request. Additional attributes are implementation specific.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "__restart": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Optional data from the previous, restarted session.\nThe data is sent as the 'restart' attribute of the 'terminated' event.\nThe client should leave the data intact."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, __restart=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] __restart: Optional data from the previous, restarted session.
-        The data is sent as the 'restart' attribute of the 'terminated' event.
-        The client should leave the data intact.
-        """
-        self.__restart = __restart
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        __restart = self.__restart
-        dct = {
-        }
-        if __restart is not None:
-            dct['__restart'] = __restart
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('attach')
-@register
-class AttachResponse(BaseSchema):
-    """
-    Response to 'attach' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('restart')
-@register
-class RestartRequest(BaseSchema):
-    """
-    Restarts a debug session. Clients should only call this request if the capability
-    'supportsRestartRequest' is true.
-    
-    If the capability is missing or has the value false, a typical client will emulate 'restart' by
-    terminating the debug adapter first and then launching it anew.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "restart"
-            ]
-        },
-        "arguments": {
-            "type": "RestartArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param RestartArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'restart'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = RestartArguments()
-        else:
-            self.arguments = RestartArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  RestartArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class RestartArguments(BaseSchema):
-    """
-    Arguments for 'restart' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "arguments": {
-            "oneOf": [
-                {
-                    "$ref": "#/definitions/LaunchRequestArguments"
-                },
-                {
-                    "$ref": "#/definitions/AttachRequestArguments"
-                }
-            ],
-            "description": "The latest version of the 'launch' or 'attach' configuration."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param TypeNA arguments: The latest version of the 'launch' or 'attach' configuration.
-        """
-        self.arguments = arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        arguments = self.arguments
-        dct = {
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('restart')
-@register
-class RestartResponse(BaseSchema):
-    """
-    Response to 'restart' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('disconnect')
-@register
-class DisconnectRequest(BaseSchema):
-    """
-    The 'disconnect' request is sent from the client to the debug adapter in order to stop debugging.
-    
-    It asks the debug adapter to disconnect from the debuggee and to terminate the debug adapter.
-    
-    If the debuggee has been started with the 'launch' request, the 'disconnect' request terminates the
-    debuggee.
-    
-    If the 'attach' request was used to connect to the debuggee, 'disconnect' does not terminate the
-    debuggee.
-    
-    This behavior can be controlled with the 'terminateDebuggee' argument (if supported by the debug
-    adapter).
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "disconnect"
-            ]
-        },
-        "arguments": {
-            "type": "DisconnectArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param DisconnectArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'disconnect'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = DisconnectArguments()
-        else:
-            self.arguments = DisconnectArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  DisconnectArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DisconnectArguments(BaseSchema):
-    """
-    Arguments for 'disconnect' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "restart": {
-            "type": "boolean",
-            "description": "A value of true indicates that this 'disconnect' request is part of a restart sequence."
-        },
-        "terminateDebuggee": {
-            "type": "boolean",
-            "description": "Indicates whether the debuggee should be terminated when the debugger is disconnected.\nIf unspecified, the debug adapter is free to do whatever it thinks is best.\nThe attribute is only honored by a debug adapter if the capability 'supportTerminateDebuggee' is true."
-        },
-        "suspendDebuggee": {
-            "type": "boolean",
-            "description": "Indicates whether the debuggee should stay suspended when the debugger is disconnected.\nIf unspecified, the debuggee should resume execution.\nThe attribute is only honored by a debug adapter if the capability 'supportSuspendDebuggee' is true."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, restart=None, terminateDebuggee=None, suspendDebuggee=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean restart: A value of true indicates that this 'disconnect' request is part of a restart sequence.
-        :param boolean terminateDebuggee: Indicates whether the debuggee should be terminated when the debugger is disconnected.
-        If unspecified, the debug adapter is free to do whatever it thinks is best.
-        The attribute is only honored by a debug adapter if the capability 'supportTerminateDebuggee' is true.
-        :param boolean suspendDebuggee: Indicates whether the debuggee should stay suspended when the debugger is disconnected.
-        If unspecified, the debuggee should resume execution.
-        The attribute is only honored by a debug adapter if the capability 'supportSuspendDebuggee' is true.
-        """
-        self.restart = restart
-        self.terminateDebuggee = terminateDebuggee
-        self.suspendDebuggee = suspendDebuggee
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        restart = self.restart
-        terminateDebuggee = self.terminateDebuggee
-        suspendDebuggee = self.suspendDebuggee
-        dct = {
-        }
-        if restart is not None:
-            dct['restart'] = restart
-        if terminateDebuggee is not None:
-            dct['terminateDebuggee'] = terminateDebuggee
-        if suspendDebuggee is not None:
-            dct['suspendDebuggee'] = suspendDebuggee
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('disconnect')
-@register
-class DisconnectResponse(BaseSchema):
-    """
-    Response to 'disconnect' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('terminate')
-@register
-class TerminateRequest(BaseSchema):
-    """
-    The 'terminate' request is sent from the client to the debug adapter in order to give the debuggee a
-    chance for terminating itself.
-    
-    Clients should only call this request if the capability 'supportsTerminateRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "terminate"
-            ]
-        },
-        "arguments": {
-            "type": "TerminateArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param TerminateArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'terminate'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = TerminateArguments()
-        else:
-            self.arguments = TerminateArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  TerminateArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class TerminateArguments(BaseSchema):
-    """
-    Arguments for 'terminate' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "restart": {
-            "type": "boolean",
-            "description": "A value of true indicates that this 'terminate' request is part of a restart sequence."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, restart=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean restart: A value of true indicates that this 'terminate' request is part of a restart sequence.
-        """
-        self.restart = restart
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        restart = self.restart
-        dct = {
-        }
-        if restart is not None:
-            dct['restart'] = restart
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('terminate')
-@register
-class TerminateResponse(BaseSchema):
-    """
-    Response to 'terminate' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('breakpointLocations')
-@register
-class BreakpointLocationsRequest(BaseSchema):
-    """
-    The 'breakpointLocations' request returns all possible locations for source breakpoints in a given
-    range.
-    
-    Clients should only call this request if the capability 'supportsBreakpointLocationsRequest' is
-    true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "breakpointLocations"
-            ]
-        },
-        "arguments": {
-            "type": "BreakpointLocationsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param BreakpointLocationsArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'breakpointLocations'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = BreakpointLocationsArguments()
-        else:
-            self.arguments = BreakpointLocationsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  BreakpointLocationsArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class BreakpointLocationsArguments(BaseSchema):
-    """
-    Arguments for 'breakpointLocations' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "source": {
-            "description": "The source location of the breakpoints; either 'source.path' or 'source.reference' must be specified.",
-            "type": "Source"
-        },
-        "line": {
-            "type": "integer",
-            "description": "Start line of range to search possible breakpoint locations in. If only the line is specified, the request returns all possible locations in that line."
-        },
-        "column": {
-            "type": "integer",
-            "description": "Optional start column of range to search possible breakpoint locations in. If no start column is given, the first column in the start line is assumed."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "Optional end line of range to search possible breakpoint locations in. If no end line is given, then the end line is assumed to be the start line."
-        },
-        "endColumn": {
-            "type": "integer",
-            "description": "Optional end column of range to search possible breakpoint locations in. If no end column is given, then it is assumed to be in the last column of the end line."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, source, line, column=None, endLine=None, endColumn=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param Source source: The source location of the breakpoints; either 'source.path' or 'source.reference' must be specified.
-        :param integer line: Start line of range to search possible breakpoint locations in. If only the line is specified, the request returns all possible locations in that line.
-        :param integer column: Optional start column of range to search possible breakpoint locations in. If no start column is given, the first column in the start line is assumed.
-        :param integer endLine: Optional end line of range to search possible breakpoint locations in. If no end line is given, then the end line is assumed to be the start line.
-        :param integer endColumn: Optional end column of range to search possible breakpoint locations in. If no end column is given, then it is assumed to be in the last column of the end line.
-        """
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.line = line
-        self.column = column
-        self.endLine = endLine
-        self.endColumn = endColumn
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        source = self.source
-        line = self.line
-        column = self.column
-        endLine = self.endLine
-        endColumn = self.endColumn
-        dct = {
-            'source': source.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'line': line,
-        }
-        if column is not None:
-            dct['column'] = column
-        if endLine is not None:
-            dct['endLine'] = endLine
-        if endColumn is not None:
-            dct['endColumn'] = endColumn
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('breakpointLocations')
-@register
-class BreakpointLocationsResponse(BaseSchema):
-    """
-    Response to 'breakpointLocations' request.
-    
-    Contains possible locations for source breakpoints.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "breakpoints": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/BreakpointLocation"
-                    },
-                    "description": "Sorted set of possible breakpoint locations."
-                }
-            },
-            "required": [
-                "breakpoints"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param BreakpointLocationsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = BreakpointLocationsResponseBody()
-        else:
-            self.body = BreakpointLocationsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  BreakpointLocationsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setBreakpoints')
-@register
-class SetBreakpointsRequest(BaseSchema):
-    """
-    Sets multiple breakpoints for a single source and clears all previous breakpoints in that source.
-    
-    To clear all breakpoint for a source, specify an empty array.
-    
-    When a breakpoint is hit, a 'stopped' event (with reason 'breakpoint') is generated.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setBreakpoints"
-            ]
-        },
-        "arguments": {
-            "type": "SetBreakpointsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetBreakpointsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setBreakpoints'
-        if arguments is None:
-            self.arguments = SetBreakpointsArguments()
-        else:
-            self.arguments = SetBreakpointsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetBreakpointsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetBreakpointsArguments(BaseSchema):
-    """
-    Arguments for 'setBreakpoints' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "source": {
-            "description": "The source location of the breakpoints; either 'source.path' or 'source.reference' must be specified.",
-            "type": "Source"
-        },
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/SourceBreakpoint"
-            },
-            "description": "The code locations of the breakpoints."
-        },
-        "lines": {
-            "type": "array",
-            "items": {
-                "type": "integer"
-            },
-            "description": "Deprecated: The code locations of the breakpoints."
-        },
-        "sourceModified": {
-            "type": "boolean",
-            "description": "A value of true indicates that the underlying source has been modified which results in new breakpoint locations."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, source, breakpoints=None, lines=None, sourceModified=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param Source source: The source location of the breakpoints; either 'source.path' or 'source.reference' must be specified.
-        :param array breakpoints: The code locations of the breakpoints.
-        :param array lines: Deprecated: The code locations of the breakpoints.
-        :param boolean sourceModified: A value of true indicates that the underlying source has been modified which results in new breakpoint locations.
-        """
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                SourceBreakpoint.update_dict_ids_from_dap(o)
-        self.lines = lines
-        self.sourceModified = sourceModified
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        source = self.source
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        lines = self.lines
-        if lines and hasattr(lines[0], "to_dict"):
-            lines = [x.to_dict() for x in lines]
-        sourceModified = self.sourceModified
-        dct = {
-            'source': source.to_dict(update_ids_to_dap=update_ids_to_dap),
-        }
-        if breakpoints is not None:
-            dct['breakpoints'] = [SourceBreakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints
-        if lines is not None:
-            dct['lines'] = lines
-        if sourceModified is not None:
-            dct['sourceModified'] = sourceModified
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('setBreakpoints')
-@register
-class SetBreakpointsResponse(BaseSchema):
-    """
-    Response to 'setBreakpoints' request.
-    
-    Returned is information about each breakpoint created by this request.
-    
-    This includes the actual code location and whether the breakpoint could be verified.
-    
-    The breakpoints returned are in the same order as the elements of the 'breakpoints'
-    
-    (or the deprecated 'lines') array in the arguments.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "breakpoints": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Breakpoint"
-                    },
-                    "description": "Information about the breakpoints.\nThe array elements are in the same order as the elements of the 'breakpoints' (or the deprecated 'lines') array in the arguments."
-                }
-            },
-            "required": [
-                "breakpoints"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param SetBreakpointsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = SetBreakpointsResponseBody()
-        else:
-            self.body = SetBreakpointsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SetBreakpointsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setFunctionBreakpoints')
-@register
-class SetFunctionBreakpointsRequest(BaseSchema):
-    """
-    Replaces all existing function breakpoints with new function breakpoints.
-    
-    To clear all function breakpoints, specify an empty array.
-    
-    When a function breakpoint is hit, a 'stopped' event (with reason 'function breakpoint') is
-    generated.
-    
-    Clients should only call this request if the capability 'supportsFunctionBreakpoints' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setFunctionBreakpoints"
-            ]
-        },
-        "arguments": {
-            "type": "SetFunctionBreakpointsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetFunctionBreakpointsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setFunctionBreakpoints'
-        if arguments is None:
-            self.arguments = SetFunctionBreakpointsArguments()
-        else:
-            self.arguments = SetFunctionBreakpointsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetFunctionBreakpointsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetFunctionBreakpointsArguments(BaseSchema):
-    """
-    Arguments for 'setFunctionBreakpoints' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/FunctionBreakpoint"
-            },
-            "description": "The function names of the breakpoints."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: The function names of the breakpoints.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                FunctionBreakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [FunctionBreakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('setFunctionBreakpoints')
-@register
-class SetFunctionBreakpointsResponse(BaseSchema):
-    """
-    Response to 'setFunctionBreakpoints' request.
-    
-    Returned is information about each breakpoint created by this request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "breakpoints": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Breakpoint"
-                    },
-                    "description": "Information about the breakpoints. The array elements correspond to the elements of the 'breakpoints' array."
-                }
-            },
-            "required": [
-                "breakpoints"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param SetFunctionBreakpointsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = SetFunctionBreakpointsResponseBody()
-        else:
-            self.body = SetFunctionBreakpointsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SetFunctionBreakpointsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setExceptionBreakpoints')
-@register
-class SetExceptionBreakpointsRequest(BaseSchema):
-    """
-    The request configures the debuggers response to thrown exceptions.
-    
-    If an exception is configured to break, a 'stopped' event is fired (with reason 'exception').
-    
-    Clients should only call this request if the capability 'exceptionBreakpointFilters' returns one or
-    more filters.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setExceptionBreakpoints"
-            ]
-        },
-        "arguments": {
-            "type": "SetExceptionBreakpointsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetExceptionBreakpointsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setExceptionBreakpoints'
-        if arguments is None:
-            self.arguments = SetExceptionBreakpointsArguments()
-        else:
-            self.arguments = SetExceptionBreakpointsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetExceptionBreakpointsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetExceptionBreakpointsArguments(BaseSchema):
-    """
-    Arguments for 'setExceptionBreakpoints' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "filters": {
-            "type": "array",
-            "items": {
-                "type": "string"
-            },
-            "description": "Set of exception filters specified by their ID. The set of all possible exception filters is defined by the 'exceptionBreakpointFilters' capability. The 'filter' and 'filterOptions' sets are additive."
-        },
-        "filterOptions": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ExceptionFilterOptions"
-            },
-            "description": "Set of exception filters and their options. The set of all possible exception filters is defined by the 'exceptionBreakpointFilters' capability. This attribute is only honored by a debug adapter if the capability 'supportsExceptionFilterOptions' is true. The 'filter' and 'filterOptions' sets are additive."
-        },
-        "exceptionOptions": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ExceptionOptions"
-            },
-            "description": "Configuration options for selected exceptions.\nThe attribute is only honored by a debug adapter if the capability 'supportsExceptionOptions' is true."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, filters, filterOptions=None, exceptionOptions=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array filters: Set of exception filters specified by their ID. The set of all possible exception filters is defined by the 'exceptionBreakpointFilters' capability. The 'filter' and 'filterOptions' sets are additive.
-        :param array filterOptions: Set of exception filters and their options. The set of all possible exception filters is defined by the 'exceptionBreakpointFilters' capability. This attribute is only honored by a debug adapter if the capability 'supportsExceptionFilterOptions' is true. The 'filter' and 'filterOptions' sets are additive.
-        :param array exceptionOptions: Configuration options for selected exceptions.
-        The attribute is only honored by a debug adapter if the capability 'supportsExceptionOptions' is true.
-        """
-        self.filters = filters
-        self.filterOptions = filterOptions
-        if update_ids_from_dap and self.filterOptions:
-            for o in self.filterOptions:
-                ExceptionFilterOptions.update_dict_ids_from_dap(o)
-        self.exceptionOptions = exceptionOptions
-        if update_ids_from_dap and self.exceptionOptions:
-            for o in self.exceptionOptions:
-                ExceptionOptions.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        filters = self.filters
-        if filters and hasattr(filters[0], "to_dict"):
-            filters = [x.to_dict() for x in filters]
-        filterOptions = self.filterOptions
-        if filterOptions and hasattr(filterOptions[0], "to_dict"):
-            filterOptions = [x.to_dict() for x in filterOptions]
-        exceptionOptions = self.exceptionOptions
-        if exceptionOptions and hasattr(exceptionOptions[0], "to_dict"):
-            exceptionOptions = [x.to_dict() for x in exceptionOptions]
-        dct = {
-            'filters': filters,
-        }
-        if filterOptions is not None:
-            dct['filterOptions'] = [ExceptionFilterOptions.update_dict_ids_to_dap(o) for o in filterOptions] if (update_ids_to_dap and filterOptions) else filterOptions
-        if exceptionOptions is not None:
-            dct['exceptionOptions'] = [ExceptionOptions.update_dict_ids_to_dap(o) for o in exceptionOptions] if (update_ids_to_dap and exceptionOptions) else exceptionOptions
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('setExceptionBreakpoints')
-@register
-class SetExceptionBreakpointsResponse(BaseSchema):
-    """
-    Response to 'setExceptionBreakpoints' request.
-    
-    The response contains an array of Breakpoint objects with information about each exception
-    breakpoint or filter. The Breakpoint objects are in the same order as the elements of the 'filters',
-    'filterOptions', 'exceptionOptions' arrays given as arguments. If both 'filters' and 'filterOptions'
-    are given, the returned array must start with 'filters' information first, followed by
-    'filterOptions' information.
-    
-    The mandatory 'verified' property of a Breakpoint object signals whether the exception breakpoint or
-    filter could be successfully created and whether the optional condition or hit count expressions are
-    valid. In case of an error the 'message' property explains the problem. An optional 'id' property
-    can be used to introduce a unique ID for the exception breakpoint or filter so that it can be
-    updated subsequently by sending breakpoint events.
-    
-    For backward compatibility both the 'breakpoints' array and the enclosing 'body' are optional. If
-    these elements are missing a client will not be able to show problems for individual exception
-    breakpoints or filters.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "breakpoints": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Breakpoint"
-                    },
-                    "description": "Information about the exception breakpoints or filters.\nThe breakpoints returned are in the same order as the elements of the 'filters', 'filterOptions', 'exceptionOptions' arrays in the arguments. If both 'filters' and 'filterOptions' are given, the returned array must start with 'filters' information first, followed by 'filterOptions' information."
-                }
-            }
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param SetExceptionBreakpointsResponseBody body: 
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        if body is None:
-            self.body = SetExceptionBreakpointsResponseBody()
-        else:
-            self.body = SetExceptionBreakpointsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SetExceptionBreakpointsResponseBody else body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('dataBreakpointInfo')
-@register
-class DataBreakpointInfoRequest(BaseSchema):
-    """
-    Obtains information on a possible data breakpoint that could be set on an expression or variable.
-    
-    Clients should only call this request if the capability 'supportsDataBreakpoints' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "dataBreakpointInfo"
-            ]
-        },
-        "arguments": {
-            "type": "DataBreakpointInfoArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param DataBreakpointInfoArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'dataBreakpointInfo'
-        if arguments is None:
-            self.arguments = DataBreakpointInfoArguments()
-        else:
-            self.arguments = DataBreakpointInfoArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  DataBreakpointInfoArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DataBreakpointInfoArguments(BaseSchema):
-    """
-    Arguments for 'dataBreakpointInfo' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "variablesReference": {
-            "type": "integer",
-            "description": "Reference to the Variable container if the data breakpoint is requested for a child of the container."
-        },
-        "name": {
-            "type": "string",
-            "description": "The name of the Variable's child to obtain data breakpoint information for.\nIf variablesReference isn't provided, this can be an expression."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name, variablesReference=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: The name of the Variable's child to obtain data breakpoint information for.
-        If variablesReference isn't provided, this can be an expression.
-        :param integer variablesReference: Reference to the Variable container if the data breakpoint is requested for a child of the container.
-        """
-        self.name = name
-        self.variablesReference = variablesReference
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        variablesReference = self.variablesReference
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'name': name,
-        }
-        if variablesReference is not None:
-            dct['variablesReference'] = variablesReference
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register_response('dataBreakpointInfo')
-@register
-class DataBreakpointInfoResponse(BaseSchema):
-    """
-    Response to 'dataBreakpointInfo' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "dataId": {
-                    "type": [
-                        "string",
-                        "null"
-                    ],
-                    "description": "An identifier for the data on which a data breakpoint can be registered with the setDataBreakpoints request or null if no data breakpoint is available."
-                },
-                "description": {
-                    "type": "string",
-                    "description": "UI string that describes on what data the breakpoint is set on or why a data breakpoint is not available."
-                },
-                "accessTypes": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/DataBreakpointAccessType"
-                    },
-                    "description": "Optional attribute listing the available access types for a potential data breakpoint. A UI frontend could surface this information."
-                },
-                "canPersist": {
-                    "type": "boolean",
-                    "description": "Optional attribute indicating that a potential data breakpoint could be persisted across sessions."
-                }
-            },
-            "required": [
-                "dataId",
-                "description"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param DataBreakpointInfoResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = DataBreakpointInfoResponseBody()
-        else:
-            self.body = DataBreakpointInfoResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  DataBreakpointInfoResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setDataBreakpoints')
-@register
-class SetDataBreakpointsRequest(BaseSchema):
-    """
-    Replaces all existing data breakpoints with new data breakpoints.
-    
-    To clear all data breakpoints, specify an empty array.
-    
-    When a data breakpoint is hit, a 'stopped' event (with reason 'data breakpoint') is generated.
-    
-    Clients should only call this request if the capability 'supportsDataBreakpoints' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setDataBreakpoints"
-            ]
-        },
-        "arguments": {
-            "type": "SetDataBreakpointsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetDataBreakpointsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setDataBreakpoints'
-        if arguments is None:
-            self.arguments = SetDataBreakpointsArguments()
-        else:
-            self.arguments = SetDataBreakpointsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetDataBreakpointsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetDataBreakpointsArguments(BaseSchema):
-    """
-    Arguments for 'setDataBreakpoints' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/DataBreakpoint"
-            },
-            "description": "The contents of this array replaces all existing data breakpoints. An empty array clears all data breakpoints."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: The contents of this array replaces all existing data breakpoints. An empty array clears all data breakpoints.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                DataBreakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [DataBreakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('setDataBreakpoints')
-@register
-class SetDataBreakpointsResponse(BaseSchema):
-    """
-    Response to 'setDataBreakpoints' request.
-    
-    Returned is information about each breakpoint created by this request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "breakpoints": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Breakpoint"
-                    },
-                    "description": "Information about the data breakpoints. The array elements correspond to the elements of the input argument 'breakpoints' array."
-                }
-            },
-            "required": [
-                "breakpoints"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param SetDataBreakpointsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = SetDataBreakpointsResponseBody()
-        else:
-            self.body = SetDataBreakpointsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SetDataBreakpointsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setInstructionBreakpoints')
-@register
-class SetInstructionBreakpointsRequest(BaseSchema):
-    """
-    Replaces all existing instruction breakpoints. Typically, instruction breakpoints would be set from
-    a diassembly window.
-    
-    To clear all instruction breakpoints, specify an empty array.
-    
-    When an instruction breakpoint is hit, a 'stopped' event (with reason 'instruction breakpoint') is
-    generated.
-    
-    Clients should only call this request if the capability 'supportsInstructionBreakpoints' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setInstructionBreakpoints"
-            ]
-        },
-        "arguments": {
-            "type": "SetInstructionBreakpointsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetInstructionBreakpointsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setInstructionBreakpoints'
-        if arguments is None:
-            self.arguments = SetInstructionBreakpointsArguments()
-        else:
-            self.arguments = SetInstructionBreakpointsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetInstructionBreakpointsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetInstructionBreakpointsArguments(BaseSchema):
-    """
-    Arguments for 'setInstructionBreakpoints' request
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/InstructionBreakpoint"
-            },
-            "description": "The instruction references of the breakpoints"
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: The instruction references of the breakpoints
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                InstructionBreakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [InstructionBreakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('setInstructionBreakpoints')
-@register
-class SetInstructionBreakpointsResponse(BaseSchema):
-    """
-    Response to 'setInstructionBreakpoints' request
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "breakpoints": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Breakpoint"
-                    },
-                    "description": "Information about the breakpoints. The array elements correspond to the elements of the 'breakpoints' array."
-                }
-            },
-            "required": [
-                "breakpoints"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param SetInstructionBreakpointsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = SetInstructionBreakpointsResponseBody()
-        else:
-            self.body = SetInstructionBreakpointsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SetInstructionBreakpointsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('continue')
-@register
-class ContinueRequest(BaseSchema):
-    """
-    The request resumes execution of all threads. If the debug adapter supports single thread execution
-    (see capability 'supportsSingleThreadExecutionRequests') setting the 'singleThread' argument to true
-    resumes only the specified thread. If not all threads were resumed, the 'allThreadsContinued'
-    attribute of the response must be set to false.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "continue"
-            ]
-        },
-        "arguments": {
-            "type": "ContinueArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param ContinueArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'continue'
-        if arguments is None:
-            self.arguments = ContinueArguments()
-        else:
-            self.arguments = ContinueArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  ContinueArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ContinueArguments(BaseSchema):
-    """
-    Arguments for 'continue' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Specifies the active thread. If the debug adapter supports single thread execution (see 'supportsSingleThreadExecutionRequests') and the optional argument 'singleThread' is true, only the thread with this ID is resumed."
-        },
-        "singleThread": {
-            "type": "boolean",
-            "description": "If this optional flag is true, execution is resumed only for the thread with given 'threadId'."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, singleThread=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Specifies the active thread. If the debug adapter supports single thread execution (see 'supportsSingleThreadExecutionRequests') and the optional argument 'singleThread' is true, only the thread with this ID is resumed.
-        :param boolean singleThread: If this optional flag is true, execution is resumed only for the thread with given 'threadId'.
-        """
-        self.threadId = threadId
-        self.singleThread = singleThread
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        singleThread = self.singleThread
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if singleThread is not None:
-            dct['singleThread'] = singleThread
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('continue')
-@register
-class ContinueResponse(BaseSchema):
-    """
-    Response to 'continue' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "allThreadsContinued": {
-                    "type": "boolean",
-                    "description": "The value true (or a missing property) signals to the client that all threads have been resumed. The value false must be returned if not all threads were resumed."
-                }
-            }
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param ContinueResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = ContinueResponseBody()
-        else:
-            self.body = ContinueResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ContinueResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('next')
-@register
-class NextRequest(BaseSchema):
-    """
-    The request executes one step (in the given granularity) for the specified thread and allows all
-    other threads to run freely by resuming them.
-    
-    If the debug adapter supports single thread execution (see capability
-    'supportsSingleThreadExecutionRequests') setting the 'singleThread' argument to true prevents other
-    suspended threads from resuming.
-    
-    The debug adapter first sends the response and then a 'stopped' event (with reason 'step') after the
-    step has completed.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "next"
-            ]
-        },
-        "arguments": {
-            "type": "NextArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param NextArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'next'
-        if arguments is None:
-            self.arguments = NextArguments()
-        else:
-            self.arguments = NextArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  NextArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class NextArguments(BaseSchema):
-    """
-    Arguments for 'next' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Specifies the thread for which to resume execution for one step (of the given granularity)."
-        },
-        "singleThread": {
-            "type": "boolean",
-            "description": "If this optional flag is true, all other suspended threads are not resumed."
-        },
-        "granularity": {
-            "description": "Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.",
-            "type": "SteppingGranularity"
-        }
-    }
-    __refs__ = set(['granularity'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, singleThread=None, granularity=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Specifies the thread for which to resume execution for one step (of the given granularity).
-        :param boolean singleThread: If this optional flag is true, all other suspended threads are not resumed.
-        :param SteppingGranularity granularity: Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.
-        """
-        self.threadId = threadId
-        self.singleThread = singleThread
-        if granularity is not None:
-            assert granularity in SteppingGranularity.VALID_VALUES
-        self.granularity = granularity
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        singleThread = self.singleThread
-        granularity = self.granularity
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if singleThread is not None:
-            dct['singleThread'] = singleThread
-        if granularity is not None:
-            dct['granularity'] = granularity
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('next')
-@register
-class NextResponse(BaseSchema):
-    """
-    Response to 'next' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('stepIn')
-@register
-class StepInRequest(BaseSchema):
-    """
-    The request resumes the given thread to step into a function/method and allows all other threads to
-    run freely by resuming them.
-    
-    If the debug adapter supports single thread execution (see capability
-    'supportsSingleThreadExecutionRequests') setting the 'singleThread' argument to true prevents other
-    suspended threads from resuming.
-    
-    If the request cannot step into a target, 'stepIn' behaves like the 'next' request.
-    
-    The debug adapter first sends the response and then a 'stopped' event (with reason 'step') after the
-    step has completed.
-    
-    If there are multiple function/method calls (or other targets) on the source line,
-    
-    the optional argument 'targetId' can be used to control into which target the 'stepIn' should occur.
-    
-    The list of possible targets for a given source line can be retrieved via the 'stepInTargets'
-    request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "stepIn"
-            ]
-        },
-        "arguments": {
-            "type": "StepInArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param StepInArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'stepIn'
-        if arguments is None:
-            self.arguments = StepInArguments()
-        else:
-            self.arguments = StepInArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  StepInArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StepInArguments(BaseSchema):
-    """
-    Arguments for 'stepIn' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Specifies the thread for which to resume execution for one step-into (of the given granularity)."
-        },
-        "singleThread": {
-            "type": "boolean",
-            "description": "If this optional flag is true, all other suspended threads are not resumed."
-        },
-        "targetId": {
-            "type": "integer",
-            "description": "Optional id of the target to step into."
-        },
-        "granularity": {
-            "description": "Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.",
-            "type": "SteppingGranularity"
-        }
-    }
-    __refs__ = set(['granularity'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, singleThread=None, targetId=None, granularity=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Specifies the thread for which to resume execution for one step-into (of the given granularity).
-        :param boolean singleThread: If this optional flag is true, all other suspended threads are not resumed.
-        :param integer targetId: Optional id of the target to step into.
-        :param SteppingGranularity granularity: Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.
-        """
-        self.threadId = threadId
-        self.singleThread = singleThread
-        self.targetId = targetId
-        if granularity is not None:
-            assert granularity in SteppingGranularity.VALID_VALUES
-        self.granularity = granularity
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        singleThread = self.singleThread
-        targetId = self.targetId
-        granularity = self.granularity
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if singleThread is not None:
-            dct['singleThread'] = singleThread
-        if targetId is not None:
-            dct['targetId'] = targetId
-        if granularity is not None:
-            dct['granularity'] = granularity
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('stepIn')
-@register
-class StepInResponse(BaseSchema):
-    """
-    Response to 'stepIn' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('stepOut')
-@register
-class StepOutRequest(BaseSchema):
-    """
-    The request resumes the given thread to step out (return) from a function/method and allows all
-    other threads to run freely by resuming them.
-    
-    If the debug adapter supports single thread execution (see capability
-    'supportsSingleThreadExecutionRequests') setting the 'singleThread' argument to true prevents other
-    suspended threads from resuming.
-    
-    The debug adapter first sends the response and then a 'stopped' event (with reason 'step') after the
-    step has completed.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "stepOut"
-            ]
-        },
-        "arguments": {
-            "type": "StepOutArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param StepOutArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'stepOut'
-        if arguments is None:
-            self.arguments = StepOutArguments()
-        else:
-            self.arguments = StepOutArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  StepOutArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StepOutArguments(BaseSchema):
-    """
-    Arguments for 'stepOut' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Specifies the thread for which to resume execution for one step-out (of the given granularity)."
-        },
-        "singleThread": {
-            "type": "boolean",
-            "description": "If this optional flag is true, all other suspended threads are not resumed."
-        },
-        "granularity": {
-            "description": "Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.",
-            "type": "SteppingGranularity"
-        }
-    }
-    __refs__ = set(['granularity'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, singleThread=None, granularity=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Specifies the thread for which to resume execution for one step-out (of the given granularity).
-        :param boolean singleThread: If this optional flag is true, all other suspended threads are not resumed.
-        :param SteppingGranularity granularity: Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.
-        """
-        self.threadId = threadId
-        self.singleThread = singleThread
-        if granularity is not None:
-            assert granularity in SteppingGranularity.VALID_VALUES
-        self.granularity = granularity
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        singleThread = self.singleThread
-        granularity = self.granularity
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if singleThread is not None:
-            dct['singleThread'] = singleThread
-        if granularity is not None:
-            dct['granularity'] = granularity
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('stepOut')
-@register
-class StepOutResponse(BaseSchema):
-    """
-    Response to 'stepOut' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('stepBack')
-@register
-class StepBackRequest(BaseSchema):
-    """
-    The request executes one backward step (in the given granularity) for the specified thread and
-    allows all other threads to run backward freely by resuming them.
-    
-    If the debug adapter supports single thread execution (see capability
-    'supportsSingleThreadExecutionRequests') setting the 'singleThread' argument to true prevents other
-    suspended threads from resuming.
-    
-    The debug adapter first sends the response and then a 'stopped' event (with reason 'step') after the
-    step has completed.
-    
-    Clients should only call this request if the capability 'supportsStepBack' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "stepBack"
-            ]
-        },
-        "arguments": {
-            "type": "StepBackArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param StepBackArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'stepBack'
-        if arguments is None:
-            self.arguments = StepBackArguments()
-        else:
-            self.arguments = StepBackArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  StepBackArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StepBackArguments(BaseSchema):
-    """
-    Arguments for 'stepBack' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Specifies the thread for which to resume execution for one step backwards (of the given granularity)."
-        },
-        "singleThread": {
-            "type": "boolean",
-            "description": "If this optional flag is true, all other suspended threads are not resumed."
-        },
-        "granularity": {
-            "description": "Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.",
-            "type": "SteppingGranularity"
-        }
-    }
-    __refs__ = set(['granularity'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, singleThread=None, granularity=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Specifies the thread for which to resume execution for one step backwards (of the given granularity).
-        :param boolean singleThread: If this optional flag is true, all other suspended threads are not resumed.
-        :param SteppingGranularity granularity: Optional granularity to step. If no granularity is specified, a granularity of 'statement' is assumed.
-        """
-        self.threadId = threadId
-        self.singleThread = singleThread
-        if granularity is not None:
-            assert granularity in SteppingGranularity.VALID_VALUES
-        self.granularity = granularity
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        singleThread = self.singleThread
-        granularity = self.granularity
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if singleThread is not None:
-            dct['singleThread'] = singleThread
-        if granularity is not None:
-            dct['granularity'] = granularity
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('stepBack')
-@register
-class StepBackResponse(BaseSchema):
-    """
-    Response to 'stepBack' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('reverseContinue')
-@register
-class ReverseContinueRequest(BaseSchema):
-    """
-    The request resumes backward execution of all threads. If the debug adapter supports single thread
-    execution (see capability 'supportsSingleThreadExecutionRequests') setting the 'singleThread'
-    argument to true resumes only the specified thread. If not all threads were resumed, the
-    'allThreadsContinued' attribute of the response must be set to false.
-    
-    Clients should only call this request if the capability 'supportsStepBack' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "reverseContinue"
-            ]
-        },
-        "arguments": {
-            "type": "ReverseContinueArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param ReverseContinueArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'reverseContinue'
-        if arguments is None:
-            self.arguments = ReverseContinueArguments()
-        else:
-            self.arguments = ReverseContinueArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  ReverseContinueArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ReverseContinueArguments(BaseSchema):
-    """
-    Arguments for 'reverseContinue' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Specifies the active thread. If the debug adapter supports single thread execution (see 'supportsSingleThreadExecutionRequests') and the optional argument 'singleThread' is true, only the thread with this ID is resumed."
-        },
-        "singleThread": {
-            "type": "boolean",
-            "description": "If this optional flag is true, backward execution is resumed only for the thread with given 'threadId'."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, singleThread=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Specifies the active thread. If the debug adapter supports single thread execution (see 'supportsSingleThreadExecutionRequests') and the optional argument 'singleThread' is true, only the thread with this ID is resumed.
-        :param boolean singleThread: If this optional flag is true, backward execution is resumed only for the thread with given 'threadId'.
-        """
-        self.threadId = threadId
-        self.singleThread = singleThread
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        singleThread = self.singleThread
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if singleThread is not None:
-            dct['singleThread'] = singleThread
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('reverseContinue')
-@register
-class ReverseContinueResponse(BaseSchema):
-    """
-    Response to 'reverseContinue' request. This is just an acknowledgement, so no body field is
-    required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('restartFrame')
-@register
-class RestartFrameRequest(BaseSchema):
-    """
-    The request restarts execution of the specified stackframe.
-    
-    The debug adapter first sends the response and then a 'stopped' event (with reason 'restart') after
-    the restart has completed.
-    
-    Clients should only call this request if the capability 'supportsRestartFrame' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "restartFrame"
-            ]
-        },
-        "arguments": {
-            "type": "RestartFrameArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param RestartFrameArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'restartFrame'
-        if arguments is None:
-            self.arguments = RestartFrameArguments()
-        else:
-            self.arguments = RestartFrameArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  RestartFrameArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class RestartFrameArguments(BaseSchema):
-    """
-    Arguments for 'restartFrame' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "frameId": {
-            "type": "integer",
-            "description": "Restart this stackframe."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, frameId, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer frameId: Restart this stackframe.
-        """
-        self.frameId = frameId
-        if update_ids_from_dap:
-            self.frameId = self._translate_id_from_dap(self.frameId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_from_dap(dct['frameId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        frameId = self.frameId
-        if update_ids_to_dap:
-            if frameId is not None:
-                frameId = self._translate_id_to_dap(frameId)
-        dct = {
-            'frameId': frameId,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_to_dap(dct['frameId'])
-        return dct
-
-
-@register_response('restartFrame')
-@register
-class RestartFrameResponse(BaseSchema):
-    """
-    Response to 'restartFrame' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('goto')
-@register
-class GotoRequest(BaseSchema):
-    """
-    The request sets the location where the debuggee will continue to run.
-    
-    This makes it possible to skip the execution of code or to executed code again.
-    
-    The code between the current location and the goto target is not executed but skipped.
-    
-    The debug adapter first sends the response and then a 'stopped' event with reason 'goto'.
-    
-    Clients should only call this request if the capability 'supportsGotoTargetsRequest' is true
-    (because only then goto targets exist that can be passed as arguments).
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "goto"
-            ]
-        },
-        "arguments": {
-            "type": "GotoArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param GotoArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'goto'
-        if arguments is None:
-            self.arguments = GotoArguments()
-        else:
-            self.arguments = GotoArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  GotoArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class GotoArguments(BaseSchema):
-    """
-    Arguments for 'goto' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Set the goto target for this thread."
-        },
-        "targetId": {
-            "type": "integer",
-            "description": "The location where the debuggee will continue to run."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, targetId, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Set the goto target for this thread.
-        :param integer targetId: The location where the debuggee will continue to run.
-        """
-        self.threadId = threadId
-        self.targetId = targetId
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        targetId = self.targetId
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-            'targetId': targetId,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('goto')
-@register
-class GotoResponse(BaseSchema):
-    """
-    Response to 'goto' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('pause')
-@register
-class PauseRequest(BaseSchema):
-    """
-    The request suspends the debuggee.
-    
-    The debug adapter first sends the response and then a 'stopped' event (with reason 'pause') after
-    the thread has been paused successfully.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "pause"
-            ]
-        },
-        "arguments": {
-            "type": "PauseArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param PauseArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'pause'
-        if arguments is None:
-            self.arguments = PauseArguments()
-        else:
-            self.arguments = PauseArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  PauseArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PauseArguments(BaseSchema):
-    """
-    Arguments for 'pause' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Pause execution for this thread."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Pause execution for this thread.
-        """
-        self.threadId = threadId
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('pause')
-@register
-class PauseResponse(BaseSchema):
-    """
-    Response to 'pause' request. This is just an acknowledgement, so no body field is required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('stackTrace')
-@register
-class StackTraceRequest(BaseSchema):
-    """
-    The request returns a stacktrace from the current execution state of a given thread.
-    
-    A client can request all stack frames by omitting the startFrame and levels arguments. For
-    performance conscious clients and if the debug adapter's 'supportsDelayedStackTraceLoading'
-    capability is true, stack frames can be retrieved in a piecemeal way with the startFrame and levels
-    arguments. The response of the stackTrace request may contain a totalFrames property that hints at
-    the total number of frames in the stack. If a client needs this total number upfront, it can issue a
-    request for a single (first) frame and depending on the value of totalFrames decide how to proceed.
-    In any case a client should be prepared to receive less frames than requested, which is an
-    indication that the end of the stack has been reached.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "stackTrace"
-            ]
-        },
-        "arguments": {
-            "type": "StackTraceArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param StackTraceArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'stackTrace'
-        if arguments is None:
-            self.arguments = StackTraceArguments()
-        else:
-            self.arguments = StackTraceArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  StackTraceArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StackTraceArguments(BaseSchema):
-    """
-    Arguments for 'stackTrace' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Retrieve the stacktrace for this thread."
-        },
-        "startFrame": {
-            "type": "integer",
-            "description": "The index of the first frame to return; if omitted frames start at 0."
-        },
-        "levels": {
-            "type": "integer",
-            "description": "The maximum number of frames to return. If levels is not specified or 0, all frames are returned."
-        },
-        "format": {
-            "description": "Specifies details on how to format the stack frames.\nThe attribute is only honored by a debug adapter if the capability 'supportsValueFormattingOptions' is true.",
-            "type": "StackFrameFormat"
-        }
-    }
-    __refs__ = set(['format'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, startFrame=None, levels=None, format=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Retrieve the stacktrace for this thread.
-        :param integer startFrame: The index of the first frame to return; if omitted frames start at 0.
-        :param integer levels: The maximum number of frames to return. If levels is not specified or 0, all frames are returned.
-        :param StackFrameFormat format: Specifies details on how to format the stack frames.
-        The attribute is only honored by a debug adapter if the capability 'supportsValueFormattingOptions' is true.
-        """
-        self.threadId = threadId
-        self.startFrame = startFrame
-        self.levels = levels
-        if format is None:
-            self.format = StackFrameFormat()
-        else:
-            self.format = StackFrameFormat(update_ids_from_dap=update_ids_from_dap, **format) if format.__class__ !=  StackFrameFormat else format
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        startFrame = self.startFrame
-        levels = self.levels
-        format = self.format  # noqa (assign to builtin)
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if startFrame is not None:
-            dct['startFrame'] = startFrame
-        if levels is not None:
-            dct['levels'] = levels
-        if format is not None:
-            dct['format'] = format.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('stackTrace')
-@register
-class StackTraceResponse(BaseSchema):
-    """
-    Response to 'stackTrace' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "stackFrames": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/StackFrame"
-                    },
-                    "description": "The frames of the stackframe. If the array has length zero, there are no stackframes available.\nThis means that there is no location information available."
-                },
-                "totalFrames": {
-                    "type": "integer",
-                    "description": "The total number of frames available in the stack. If omitted or if totalFrames is larger than the available frames, a client is expected to request frames until a request returns less frames than requested (which indicates the end of the stack). Returning monotonically increasing totalFrames values for subsequent requests can be used to enforce paging in the client."
-                }
-            },
-            "required": [
-                "stackFrames"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param StackTraceResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = StackTraceResponseBody()
-        else:
-            self.body = StackTraceResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  StackTraceResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('scopes')
-@register
-class ScopesRequest(BaseSchema):
-    """
-    The request returns the variable scopes for a given stackframe ID.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "scopes"
-            ]
-        },
-        "arguments": {
-            "type": "ScopesArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param ScopesArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'scopes'
-        if arguments is None:
-            self.arguments = ScopesArguments()
-        else:
-            self.arguments = ScopesArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  ScopesArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ScopesArguments(BaseSchema):
-    """
-    Arguments for 'scopes' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "frameId": {
-            "type": "integer",
-            "description": "Retrieve the scopes for this stackframe."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, frameId, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer frameId: Retrieve the scopes for this stackframe.
-        """
-        self.frameId = frameId
-        if update_ids_from_dap:
-            self.frameId = self._translate_id_from_dap(self.frameId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_from_dap(dct['frameId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        frameId = self.frameId
-        if update_ids_to_dap:
-            if frameId is not None:
-                frameId = self._translate_id_to_dap(frameId)
-        dct = {
-            'frameId': frameId,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_to_dap(dct['frameId'])
-        return dct
-
-
-@register_response('scopes')
-@register
-class ScopesResponse(BaseSchema):
-    """
-    Response to 'scopes' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "scopes": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Scope"
-                    },
-                    "description": "The scopes of the stackframe. If the array has length zero, there are no scopes available."
-                }
-            },
-            "required": [
-                "scopes"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param ScopesResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = ScopesResponseBody()
-        else:
-            self.body = ScopesResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ScopesResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('variables')
-@register
-class VariablesRequest(BaseSchema):
-    """
-    Retrieves all child variables for the given variable reference.
-    
-    An optional filter can be used to limit the fetched children to either named or indexed children.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "variables"
-            ]
-        },
-        "arguments": {
-            "type": "VariablesArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param VariablesArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'variables'
-        if arguments is None:
-            self.arguments = VariablesArguments()
-        else:
-            self.arguments = VariablesArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  VariablesArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class VariablesArguments(BaseSchema):
-    """
-    Arguments for 'variables' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "variablesReference": {
-            "type": "integer",
-            "description": "The Variable reference."
-        },
-        "filter": {
-            "type": "string",
-            "enum": [
-                "indexed",
-                "named"
-            ],
-            "description": "Optional filter to limit the child variables to either named or indexed. If omitted, both types are fetched."
-        },
-        "start": {
-            "type": "integer",
-            "description": "The index of the first variable to return; if omitted children start at 0."
-        },
-        "count": {
-            "type": "integer",
-            "description": "The number of variables to return. If count is missing or 0, all variables are returned."
-        },
-        "format": {
-            "description": "Specifies details on how to format the Variable values.\nThe attribute is only honored by a debug adapter if the capability 'supportsValueFormattingOptions' is true.",
-            "type": "ValueFormat"
-        }
-    }
-    __refs__ = set(['format'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, variablesReference, filter=None, start=None, count=None, format=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer variablesReference: The Variable reference.
-        :param string filter: Optional filter to limit the child variables to either named or indexed. If omitted, both types are fetched.
-        :param integer start: The index of the first variable to return; if omitted children start at 0.
-        :param integer count: The number of variables to return. If count is missing or 0, all variables are returned.
-        :param ValueFormat format: Specifies details on how to format the Variable values.
-        The attribute is only honored by a debug adapter if the capability 'supportsValueFormattingOptions' is true.
-        """
-        self.variablesReference = variablesReference
-        self.filter = filter
-        self.start = start
-        self.count = count
-        if format is None:
-            self.format = ValueFormat()
-        else:
-            self.format = ValueFormat(update_ids_from_dap=update_ids_from_dap, **format) if format.__class__ !=  ValueFormat else format
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        variablesReference = self.variablesReference
-        filter = self.filter  # noqa (assign to builtin)
-        start = self.start
-        count = self.count
-        format = self.format  # noqa (assign to builtin)
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'variablesReference': variablesReference,
-        }
-        if filter is not None:
-            dct['filter'] = filter
-        if start is not None:
-            dct['start'] = start
-        if count is not None:
-            dct['count'] = count
-        if format is not None:
-            dct['format'] = format.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register_response('variables')
-@register
-class VariablesResponse(BaseSchema):
-    """
-    Response to 'variables' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "variables": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Variable"
-                    },
-                    "description": "All (or a range) of variables for the given variable reference."
-                }
-            },
-            "required": [
-                "variables"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param VariablesResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = VariablesResponseBody()
-        else:
-            self.body = VariablesResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  VariablesResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setVariable')
-@register
-class SetVariableRequest(BaseSchema):
-    """
-    Set the variable with the given name in the variable container to a new value. Clients should only
-    call this request if the capability 'supportsSetVariable' is true.
-    
-    If a debug adapter implements both setVariable and setExpression, a client will only use
-    setExpression if the variable has an evaluateName property.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setVariable"
-            ]
-        },
-        "arguments": {
-            "type": "SetVariableArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetVariableArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setVariable'
-        if arguments is None:
-            self.arguments = SetVariableArguments()
-        else:
-            self.arguments = SetVariableArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetVariableArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetVariableArguments(BaseSchema):
-    """
-    Arguments for 'setVariable' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "variablesReference": {
-            "type": "integer",
-            "description": "The reference of the variable container."
-        },
-        "name": {
-            "type": "string",
-            "description": "The name of the variable in the container."
-        },
-        "value": {
-            "type": "string",
-            "description": "The value of the variable."
-        },
-        "format": {
-            "description": "Specifies details on how to format the response value.",
-            "type": "ValueFormat"
-        }
-    }
-    __refs__ = set(['format'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, variablesReference, name, value, format=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer variablesReference: The reference of the variable container.
-        :param string name: The name of the variable in the container.
-        :param string value: The value of the variable.
-        :param ValueFormat format: Specifies details on how to format the response value.
-        """
-        self.variablesReference = variablesReference
-        self.name = name
-        self.value = value
-        if format is None:
-            self.format = ValueFormat()
-        else:
-            self.format = ValueFormat(update_ids_from_dap=update_ids_from_dap, **format) if format.__class__ !=  ValueFormat else format
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        variablesReference = self.variablesReference
-        name = self.name
-        value = self.value
-        format = self.format  # noqa (assign to builtin)
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'variablesReference': variablesReference,
-            'name': name,
-            'value': value,
-        }
-        if format is not None:
-            dct['format'] = format.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register_response('setVariable')
-@register
-class SetVariableResponse(BaseSchema):
-    """
-    Response to 'setVariable' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "value": {
-                    "type": "string",
-                    "description": "The new value of the variable."
-                },
-                "type": {
-                    "type": "string",
-                    "description": "The type of the new value. Typically shown in the UI when hovering over the value."
-                },
-                "variablesReference": {
-                    "type": "integer",
-                    "description": "If variablesReference is > 0, the new value is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "namedVariables": {
-                    "type": "integer",
-                    "description": "The number of named child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "indexedVariables": {
-                    "type": "integer",
-                    "description": "The number of indexed child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                }
-            },
-            "required": [
-                "value"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param SetVariableResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = SetVariableResponseBody()
-        else:
-            self.body = SetVariableResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SetVariableResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('source')
-@register
-class SourceRequest(BaseSchema):
-    """
-    The request retrieves the source code for a given source reference.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "source"
-            ]
-        },
-        "arguments": {
-            "type": "SourceArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SourceArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'source'
-        if arguments is None:
-            self.arguments = SourceArguments()
-        else:
-            self.arguments = SourceArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SourceArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SourceArguments(BaseSchema):
-    """
-    Arguments for 'source' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "source": {
-            "description": "Specifies the source content to load. Either source.path or source.sourceReference must be specified.",
-            "type": "Source"
-        },
-        "sourceReference": {
-            "type": "integer",
-            "description": "The reference to the source. This is the same as source.sourceReference.\nThis is provided for backward compatibility since old backends do not understand the 'source' attribute."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, sourceReference, source=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer sourceReference: The reference to the source. This is the same as source.sourceReference.
-        This is provided for backward compatibility since old backends do not understand the 'source' attribute.
-        :param Source source: Specifies the source content to load. Either source.path or source.sourceReference must be specified.
-        """
-        self.sourceReference = sourceReference
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        sourceReference = self.sourceReference
-        source = self.source
-        dct = {
-            'sourceReference': sourceReference,
-        }
-        if source is not None:
-            dct['source'] = source.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('source')
-@register
-class SourceResponse(BaseSchema):
-    """
-    Response to 'source' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "content": {
-                    "type": "string",
-                    "description": "Content of the source reference."
-                },
-                "mimeType": {
-                    "type": "string",
-                    "description": "Optional content type (mime type) of the source."
-                }
-            },
-            "required": [
-                "content"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param SourceResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = SourceResponseBody()
-        else:
-            self.body = SourceResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SourceResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('threads')
-@register
-class ThreadsRequest(BaseSchema):
-    """
-    The request retrieves a list of all threads.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "threads"
-            ]
-        },
-        "arguments": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Object containing arguments for the command."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] arguments: Object containing arguments for the command.
-        """
-        self.type = 'request'
-        self.command = 'threads'
-        self.seq = seq
-        self.arguments = arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('threads')
-@register
-class ThreadsResponse(BaseSchema):
-    """
-    Response to 'threads' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "threads": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Thread"
-                    },
-                    "description": "All threads."
-                }
-            },
-            "required": [
-                "threads"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param ThreadsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = ThreadsResponseBody()
-        else:
-            self.body = ThreadsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ThreadsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('terminateThreads')
-@register
-class TerminateThreadsRequest(BaseSchema):
-    """
-    The request terminates the threads with the given ids.
-    
-    Clients should only call this request if the capability 'supportsTerminateThreadsRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "terminateThreads"
-            ]
-        },
-        "arguments": {
-            "type": "TerminateThreadsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param TerminateThreadsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'terminateThreads'
-        if arguments is None:
-            self.arguments = TerminateThreadsArguments()
-        else:
-            self.arguments = TerminateThreadsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  TerminateThreadsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class TerminateThreadsArguments(BaseSchema):
-    """
-    Arguments for 'terminateThreads' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadIds": {
-            "type": "array",
-            "items": {
-                "type": "integer"
-            },
-            "description": "Ids of threads to be terminated."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadIds=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array threadIds: Ids of threads to be terminated.
-        """
-        self.threadIds = threadIds
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadIds = self.threadIds
-        if threadIds and hasattr(threadIds[0], "to_dict"):
-            threadIds = [x.to_dict() for x in threadIds]
-        dct = {
-        }
-        if threadIds is not None:
-            dct['threadIds'] = threadIds
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('terminateThreads')
-@register
-class TerminateThreadsResponse(BaseSchema):
-    """
-    Response to 'terminateThreads' request. This is just an acknowledgement, so no body field is
-    required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('modules')
-@register
-class ModulesRequest(BaseSchema):
-    """
-    Modules can be retrieved from the debug adapter with this request which can either return all
-    modules or a range of modules to support paging.
-    
-    Clients should only call this request if the capability 'supportsModulesRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "modules"
-            ]
-        },
-        "arguments": {
-            "type": "ModulesArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param ModulesArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'modules'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = ModulesArguments()
-        else:
-            self.arguments = ModulesArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  ModulesArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ModulesArguments(BaseSchema):
-    """
-    Arguments for 'modules' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "startModule": {
-            "type": "integer",
-            "description": "The index of the first module to return; if omitted modules start at 0."
-        },
-        "moduleCount": {
-            "type": "integer",
-            "description": "The number of modules to return. If moduleCount is not specified or 0, all modules are returned."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, startModule=None, moduleCount=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer startModule: The index of the first module to return; if omitted modules start at 0.
-        :param integer moduleCount: The number of modules to return. If moduleCount is not specified or 0, all modules are returned.
-        """
-        self.startModule = startModule
-        self.moduleCount = moduleCount
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        startModule = self.startModule
-        moduleCount = self.moduleCount
-        dct = {
-        }
-        if startModule is not None:
-            dct['startModule'] = startModule
-        if moduleCount is not None:
-            dct['moduleCount'] = moduleCount
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('modules')
-@register
-class ModulesResponse(BaseSchema):
-    """
-    Response to 'modules' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "modules": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Module"
-                    },
-                    "description": "All modules or range of modules."
-                },
-                "totalModules": {
-                    "type": "integer",
-                    "description": "The total number of modules available."
-                }
-            },
-            "required": [
-                "modules"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param ModulesResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = ModulesResponseBody()
-        else:
-            self.body = ModulesResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ModulesResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('loadedSources')
-@register
-class LoadedSourcesRequest(BaseSchema):
-    """
-    Retrieves the set of all sources currently loaded by the debugged process.
-    
-    Clients should only call this request if the capability 'supportsLoadedSourcesRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "loadedSources"
-            ]
-        },
-        "arguments": {
-            "type": "LoadedSourcesArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param LoadedSourcesArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'loadedSources'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = LoadedSourcesArguments()
-        else:
-            self.arguments = LoadedSourcesArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  LoadedSourcesArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class LoadedSourcesArguments(BaseSchema):
-    """
-    Arguments for 'loadedSources' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('loadedSources')
-@register
-class LoadedSourcesResponse(BaseSchema):
-    """
-    Response to 'loadedSources' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "sources": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/Source"
-                    },
-                    "description": "Set of loaded sources."
-                }
-            },
-            "required": [
-                "sources"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param LoadedSourcesResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = LoadedSourcesResponseBody()
-        else:
-            self.body = LoadedSourcesResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  LoadedSourcesResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('evaluate')
-@register
-class EvaluateRequest(BaseSchema):
-    """
-    Evaluates the given expression in the context of the top most stack frame.
-    
-    The expression has access to any variables and arguments that are in scope.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "evaluate"
-            ]
-        },
-        "arguments": {
-            "type": "EvaluateArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param EvaluateArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'evaluate'
-        if arguments is None:
-            self.arguments = EvaluateArguments()
-        else:
-            self.arguments = EvaluateArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  EvaluateArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class EvaluateArguments(BaseSchema):
-    """
-    Arguments for 'evaluate' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "expression": {
-            "type": "string",
-            "description": "The expression to evaluate."
-        },
-        "frameId": {
-            "type": "integer",
-            "description": "Evaluate the expression in the scope of this stack frame. If not specified, the expression is evaluated in the global scope."
-        },
-        "context": {
-            "type": "string",
-            "_enum": [
-                "watch",
-                "repl",
-                "hover",
-                "clipboard"
-            ],
-            "enumDescriptions": [
-                "evaluate is run in a watch.",
-                "evaluate is run from REPL console.",
-                "evaluate is run from a data hover.",
-                "evaluate is run to generate the value that will be stored in the clipboard.\nThe attribute is only honored by a debug adapter if the capability 'supportsClipboardContext' is true."
-            ],
-            "description": "The context in which the evaluate request is run."
-        },
-        "format": {
-            "description": "Specifies details on how to format the Evaluate result.\nThe attribute is only honored by a debug adapter if the capability 'supportsValueFormattingOptions' is true.",
-            "type": "ValueFormat"
-        }
-    }
-    __refs__ = set(['format'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, expression, frameId=None, context=None, format=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string expression: The expression to evaluate.
-        :param integer frameId: Evaluate the expression in the scope of this stack frame. If not specified, the expression is evaluated in the global scope.
-        :param string context: The context in which the evaluate request is run.
-        :param ValueFormat format: Specifies details on how to format the Evaluate result.
-        The attribute is only honored by a debug adapter if the capability 'supportsValueFormattingOptions' is true.
-        """
-        self.expression = expression
-        self.frameId = frameId
-        self.context = context
-        if format is None:
-            self.format = ValueFormat()
-        else:
-            self.format = ValueFormat(update_ids_from_dap=update_ids_from_dap, **format) if format.__class__ !=  ValueFormat else format
-        if update_ids_from_dap:
-            self.frameId = self._translate_id_from_dap(self.frameId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_from_dap(dct['frameId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        expression = self.expression
-        frameId = self.frameId
-        context = self.context
-        format = self.format  # noqa (assign to builtin)
-        if update_ids_to_dap:
-            if frameId is not None:
-                frameId = self._translate_id_to_dap(frameId)
-        dct = {
-            'expression': expression,
-        }
-        if frameId is not None:
-            dct['frameId'] = frameId
-        if context is not None:
-            dct['context'] = context
-        if format is not None:
-            dct['format'] = format.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_to_dap(dct['frameId'])
-        return dct
-
-
-@register_response('evaluate')
-@register
-class EvaluateResponse(BaseSchema):
-    """
-    Response to 'evaluate' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "result": {
-                    "type": "string",
-                    "description": "The result of the evaluate request."
-                },
-                "type": {
-                    "type": "string",
-                    "description": "The optional type of the evaluate result.\nThis attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request."
-                },
-                "presentationHint": {
-                    "$ref": "#/definitions/VariablePresentationHint",
-                    "description": "Properties of a evaluate result that can be used to determine how to render the result in the UI."
-                },
-                "variablesReference": {
-                    "type": "integer",
-                    "description": "If variablesReference is > 0, the evaluate result is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "namedVariables": {
-                    "type": "integer",
-                    "description": "The number of named child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "indexedVariables": {
-                    "type": "integer",
-                    "description": "The number of indexed child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "memoryReference": {
-                    "type": "string",
-                    "description": "Optional memory reference to a location appropriate for this result.\nFor pointer type eval results, this is generally a reference to the memory address contained in the pointer.\nThis attribute should be returned by a debug adapter if the client has passed the value true for the 'supportsMemoryReferences' capability of the 'initialize' request."
-                }
-            },
-            "required": [
-                "result",
-                "variablesReference"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param EvaluateResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = EvaluateResponseBody()
-        else:
-            self.body = EvaluateResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  EvaluateResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setExpression')
-@register
-class SetExpressionRequest(BaseSchema):
-    """
-    Evaluates the given 'value' expression and assigns it to the 'expression' which must be a modifiable
-    l-value.
-    
-    The expressions have access to any variables and arguments that are in scope of the specified frame.
-    
-    Clients should only call this request if the capability 'supportsSetExpression' is true.
-    
-    If a debug adapter implements both setExpression and setVariable, a client will only use
-    setExpression if the variable has an evaluateName property.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setExpression"
-            ]
-        },
-        "arguments": {
-            "type": "SetExpressionArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetExpressionArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setExpression'
-        if arguments is None:
-            self.arguments = SetExpressionArguments()
-        else:
-            self.arguments = SetExpressionArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetExpressionArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetExpressionArguments(BaseSchema):
-    """
-    Arguments for 'setExpression' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "expression": {
-            "type": "string",
-            "description": "The l-value expression to assign to."
-        },
-        "value": {
-            "type": "string",
-            "description": "The value expression to assign to the l-value expression."
-        },
-        "frameId": {
-            "type": "integer",
-            "description": "Evaluate the expressions in the scope of this stack frame. If not specified, the expressions are evaluated in the global scope."
-        },
-        "format": {
-            "description": "Specifies how the resulting value should be formatted.",
-            "type": "ValueFormat"
-        }
-    }
-    __refs__ = set(['format'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, expression, value, frameId=None, format=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string expression: The l-value expression to assign to.
-        :param string value: The value expression to assign to the l-value expression.
-        :param integer frameId: Evaluate the expressions in the scope of this stack frame. If not specified, the expressions are evaluated in the global scope.
-        :param ValueFormat format: Specifies how the resulting value should be formatted.
-        """
-        self.expression = expression
-        self.value = value
-        self.frameId = frameId
-        if format is None:
-            self.format = ValueFormat()
-        else:
-            self.format = ValueFormat(update_ids_from_dap=update_ids_from_dap, **format) if format.__class__ !=  ValueFormat else format
-        if update_ids_from_dap:
-            self.frameId = self._translate_id_from_dap(self.frameId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_from_dap(dct['frameId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        expression = self.expression
-        value = self.value
-        frameId = self.frameId
-        format = self.format  # noqa (assign to builtin)
-        if update_ids_to_dap:
-            if frameId is not None:
-                frameId = self._translate_id_to_dap(frameId)
-        dct = {
-            'expression': expression,
-            'value': value,
-        }
-        if frameId is not None:
-            dct['frameId'] = frameId
-        if format is not None:
-            dct['format'] = format.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_to_dap(dct['frameId'])
-        return dct
-
-
-@register_response('setExpression')
-@register
-class SetExpressionResponse(BaseSchema):
-    """
-    Response to 'setExpression' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "value": {
-                    "type": "string",
-                    "description": "The new value of the expression."
-                },
-                "type": {
-                    "type": "string",
-                    "description": "The optional type of the value.\nThis attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request."
-                },
-                "presentationHint": {
-                    "$ref": "#/definitions/VariablePresentationHint",
-                    "description": "Properties of a value that can be used to determine how to render the result in the UI."
-                },
-                "variablesReference": {
-                    "type": "integer",
-                    "description": "If variablesReference is > 0, the value is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "namedVariables": {
-                    "type": "integer",
-                    "description": "The number of named child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                },
-                "indexedVariables": {
-                    "type": "integer",
-                    "description": "The number of indexed child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-                }
-            },
-            "required": [
-                "value"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param SetExpressionResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = SetExpressionResponseBody()
-        else:
-            self.body = SetExpressionResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  SetExpressionResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('stepInTargets')
-@register
-class StepInTargetsRequest(BaseSchema):
-    """
-    This request retrieves the possible stepIn targets for the specified stack frame.
-    
-    These targets can be used in the 'stepIn' request.
-    
-    The StepInTargets may only be called if the 'supportsStepInTargetsRequest' capability exists and is
-    true.
-    
-    Clients should only call this request if the capability 'supportsStepInTargetsRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "stepInTargets"
-            ]
-        },
-        "arguments": {
-            "type": "StepInTargetsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param StepInTargetsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'stepInTargets'
-        if arguments is None:
-            self.arguments = StepInTargetsArguments()
-        else:
-            self.arguments = StepInTargetsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  StepInTargetsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StepInTargetsArguments(BaseSchema):
-    """
-    Arguments for 'stepInTargets' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "frameId": {
-            "type": "integer",
-            "description": "The stack frame for which to retrieve the possible stepIn targets."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, frameId, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer frameId: The stack frame for which to retrieve the possible stepIn targets.
-        """
-        self.frameId = frameId
-        if update_ids_from_dap:
-            self.frameId = self._translate_id_from_dap(self.frameId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_from_dap(dct['frameId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        frameId = self.frameId
-        if update_ids_to_dap:
-            if frameId is not None:
-                frameId = self._translate_id_to_dap(frameId)
-        dct = {
-            'frameId': frameId,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_to_dap(dct['frameId'])
-        return dct
-
-
-@register_response('stepInTargets')
-@register
-class StepInTargetsResponse(BaseSchema):
-    """
-    Response to 'stepInTargets' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "targets": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/StepInTarget"
-                    },
-                    "description": "The possible stepIn targets of the specified source location."
-                }
-            },
-            "required": [
-                "targets"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param StepInTargetsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = StepInTargetsResponseBody()
-        else:
-            self.body = StepInTargetsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  StepInTargetsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('gotoTargets')
-@register
-class GotoTargetsRequest(BaseSchema):
-    """
-    This request retrieves the possible goto targets for the specified source location.
-    
-    These targets can be used in the 'goto' request.
-    
-    Clients should only call this request if the capability 'supportsGotoTargetsRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "gotoTargets"
-            ]
-        },
-        "arguments": {
-            "type": "GotoTargetsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param GotoTargetsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'gotoTargets'
-        if arguments is None:
-            self.arguments = GotoTargetsArguments()
-        else:
-            self.arguments = GotoTargetsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  GotoTargetsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class GotoTargetsArguments(BaseSchema):
-    """
-    Arguments for 'gotoTargets' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "source": {
-            "description": "The source location for which the goto targets are determined.",
-            "type": "Source"
-        },
-        "line": {
-            "type": "integer",
-            "description": "The line location for which the goto targets are determined."
-        },
-        "column": {
-            "type": "integer",
-            "description": "An optional column location for which the goto targets are determined."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, source, line, column=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param Source source: The source location for which the goto targets are determined.
-        :param integer line: The line location for which the goto targets are determined.
-        :param integer column: An optional column location for which the goto targets are determined.
-        """
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.line = line
-        self.column = column
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        source = self.source
-        line = self.line
-        column = self.column
-        dct = {
-            'source': source.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'line': line,
-        }
-        if column is not None:
-            dct['column'] = column
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('gotoTargets')
-@register
-class GotoTargetsResponse(BaseSchema):
-    """
-    Response to 'gotoTargets' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "targets": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/GotoTarget"
-                    },
-                    "description": "The possible goto targets of the specified location."
-                }
-            },
-            "required": [
-                "targets"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param GotoTargetsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = GotoTargetsResponseBody()
-        else:
-            self.body = GotoTargetsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  GotoTargetsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('completions')
-@register
-class CompletionsRequest(BaseSchema):
-    """
-    Returns a list of possible completions for a given caret position and text.
-    
-    Clients should only call this request if the capability 'supportsCompletionsRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "completions"
-            ]
-        },
-        "arguments": {
-            "type": "CompletionsArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param CompletionsArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'completions'
-        if arguments is None:
-            self.arguments = CompletionsArguments()
-        else:
-            self.arguments = CompletionsArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  CompletionsArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class CompletionsArguments(BaseSchema):
-    """
-    Arguments for 'completions' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "frameId": {
-            "type": "integer",
-            "description": "Returns completions in the scope of this stack frame. If not specified, the completions are returned for the global scope."
-        },
-        "text": {
-            "type": "string",
-            "description": "One or more source lines. Typically this is the text a user has typed into the debug console before he asked for completion."
-        },
-        "column": {
-            "type": "integer",
-            "description": "The character position for which to determine the completion proposals."
-        },
-        "line": {
-            "type": "integer",
-            "description": "An optional line for which to determine the completion proposals. If missing the first line of the text is assumed."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, text, column, frameId=None, line=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string text: One or more source lines. Typically this is the text a user has typed into the debug console before he asked for completion.
-        :param integer column: The character position for which to determine the completion proposals.
-        :param integer frameId: Returns completions in the scope of this stack frame. If not specified, the completions are returned for the global scope.
-        :param integer line: An optional line for which to determine the completion proposals. If missing the first line of the text is assumed.
-        """
-        self.text = text
-        self.column = column
-        self.frameId = frameId
-        self.line = line
-        if update_ids_from_dap:
-            self.frameId = self._translate_id_from_dap(self.frameId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_from_dap(dct['frameId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        text = self.text
-        column = self.column
-        frameId = self.frameId
-        line = self.line
-        if update_ids_to_dap:
-            if frameId is not None:
-                frameId = self._translate_id_to_dap(frameId)
-        dct = {
-            'text': text,
-            'column': column,
-        }
-        if frameId is not None:
-            dct['frameId'] = frameId
-        if line is not None:
-            dct['line'] = line
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'frameId' in dct:
-            dct['frameId'] = cls._translate_id_to_dap(dct['frameId'])
-        return dct
-
-
-@register_response('completions')
-@register
-class CompletionsResponse(BaseSchema):
-    """
-    Response to 'completions' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "targets": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/CompletionItem"
-                    },
-                    "description": "The possible completions for ."
-                }
-            },
-            "required": [
-                "targets"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param CompletionsResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = CompletionsResponseBody()
-        else:
-            self.body = CompletionsResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  CompletionsResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('exceptionInfo')
-@register
-class ExceptionInfoRequest(BaseSchema):
-    """
-    Retrieves the details of the exception that caused this event to be raised.
-    
-    Clients should only call this request if the capability 'supportsExceptionInfoRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "exceptionInfo"
-            ]
-        },
-        "arguments": {
-            "type": "ExceptionInfoArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param ExceptionInfoArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'exceptionInfo'
-        if arguments is None:
-            self.arguments = ExceptionInfoArguments()
-        else:
-            self.arguments = ExceptionInfoArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  ExceptionInfoArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionInfoArguments(BaseSchema):
-    """
-    Arguments for 'exceptionInfo' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "Thread for which exception information should be retrieved."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: Thread for which exception information should be retrieved.
-        """
-        self.threadId = threadId
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register_response('exceptionInfo')
-@register
-class ExceptionInfoResponse(BaseSchema):
-    """
-    Response to 'exceptionInfo' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "exceptionId": {
-                    "type": "string",
-                    "description": "ID of the exception that was thrown."
-                },
-                "description": {
-                    "type": "string",
-                    "description": "Descriptive text for the exception provided by the debug adapter."
-                },
-                "breakMode": {
-                    "$ref": "#/definitions/ExceptionBreakMode",
-                    "description": "Mode that caused the exception notification to be raised."
-                },
-                "details": {
-                    "$ref": "#/definitions/ExceptionDetails",
-                    "description": "Detailed information about the exception."
-                }
-            },
-            "required": [
-                "exceptionId",
-                "breakMode"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param ExceptionInfoResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = ExceptionInfoResponseBody()
-        else:
-            self.body = ExceptionInfoResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ExceptionInfoResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('readMemory')
-@register
-class ReadMemoryRequest(BaseSchema):
-    """
-    Reads bytes from memory at the provided location.
-    
-    Clients should only call this request if the capability 'supportsReadMemoryRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "readMemory"
-            ]
-        },
-        "arguments": {
-            "type": "ReadMemoryArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param ReadMemoryArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'readMemory'
-        if arguments is None:
-            self.arguments = ReadMemoryArguments()
-        else:
-            self.arguments = ReadMemoryArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  ReadMemoryArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ReadMemoryArguments(BaseSchema):
-    """
-    Arguments for 'readMemory' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "memoryReference": {
-            "type": "string",
-            "description": "Memory reference to the base location from which data should be read."
-        },
-        "offset": {
-            "type": "integer",
-            "description": "Optional offset (in bytes) to be applied to the reference location before reading data. Can be negative."
-        },
-        "count": {
-            "type": "integer",
-            "description": "Number of bytes to read at the specified location and offset."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, memoryReference, count, offset=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string memoryReference: Memory reference to the base location from which data should be read.
-        :param integer count: Number of bytes to read at the specified location and offset.
-        :param integer offset: Optional offset (in bytes) to be applied to the reference location before reading data. Can be negative.
-        """
-        self.memoryReference = memoryReference
-        self.count = count
-        self.offset = offset
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        memoryReference = self.memoryReference
-        count = self.count
-        offset = self.offset
-        dct = {
-            'memoryReference': memoryReference,
-            'count': count,
-        }
-        if offset is not None:
-            dct['offset'] = offset
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('readMemory')
-@register
-class ReadMemoryResponse(BaseSchema):
-    """
-    Response to 'readMemory' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "address": {
-                    "type": "string",
-                    "description": "The address of the first byte of data returned.\nTreated as a hex value if prefixed with '0x', or as a decimal value otherwise."
-                },
-                "unreadableBytes": {
-                    "type": "integer",
-                    "description": "The number of unreadable bytes encountered after the last successfully read byte.\nThis can be used to determine the number of bytes that must be skipped before a subsequent 'readMemory' request will succeed."
-                },
-                "data": {
-                    "type": "string",
-                    "description": "The bytes read from memory, encoded using base64."
-                }
-            },
-            "required": [
-                "address"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ReadMemoryResponseBody body: 
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        if body is None:
-            self.body = ReadMemoryResponseBody()
-        else:
-            self.body = ReadMemoryResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  ReadMemoryResponseBody else body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('writeMemory')
-@register
-class WriteMemoryRequest(BaseSchema):
-    """
-    Writes bytes to memory at the provided location.
-    
-    Clients should only call this request if the capability 'supportsWriteMemoryRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "writeMemory"
-            ]
-        },
-        "arguments": {
-            "type": "WriteMemoryArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param WriteMemoryArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'writeMemory'
-        if arguments is None:
-            self.arguments = WriteMemoryArguments()
-        else:
-            self.arguments = WriteMemoryArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  WriteMemoryArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class WriteMemoryArguments(BaseSchema):
-    """
-    Arguments for 'writeMemory' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "memoryReference": {
-            "type": "string",
-            "description": "Memory reference to the base location to which data should be written."
-        },
-        "offset": {
-            "type": "integer",
-            "description": "Optional offset (in bytes) to be applied to the reference location before writing data. Can be negative."
-        },
-        "allowPartial": {
-            "type": "boolean",
-            "description": "Optional property to control partial writes. If true, the debug adapter should attempt to write memory even if the entire memory region is not writable. In such a case the debug adapter should stop after hitting the first byte of memory that cannot be written and return the number of bytes written in the response via the 'offset' and 'bytesWritten' properties.\nIf false or missing, a debug adapter should attempt to verify the region is writable before writing, and fail the response if it is not."
-        },
-        "data": {
-            "type": "string",
-            "description": "Bytes to write, encoded using base64."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, memoryReference, data, offset=None, allowPartial=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string memoryReference: Memory reference to the base location to which data should be written.
-        :param string data: Bytes to write, encoded using base64.
-        :param integer offset: Optional offset (in bytes) to be applied to the reference location before writing data. Can be negative.
-        :param boolean allowPartial: Optional property to control partial writes. If true, the debug adapter should attempt to write memory even if the entire memory region is not writable. In such a case the debug adapter should stop after hitting the first byte of memory that cannot be written and return the number of bytes written in the response via the 'offset' and 'bytesWritten' properties.
-        If false or missing, a debug adapter should attempt to verify the region is writable before writing, and fail the response if it is not.
-        """
-        self.memoryReference = memoryReference
-        self.data = data
-        self.offset = offset
-        self.allowPartial = allowPartial
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        memoryReference = self.memoryReference
-        data = self.data
-        offset = self.offset
-        allowPartial = self.allowPartial
-        dct = {
-            'memoryReference': memoryReference,
-            'data': data,
-        }
-        if offset is not None:
-            dct['offset'] = offset
-        if allowPartial is not None:
-            dct['allowPartial'] = allowPartial
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('writeMemory')
-@register
-class WriteMemoryResponse(BaseSchema):
-    """
-    Response to 'writeMemory' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "offset": {
-                    "type": "integer",
-                    "description": "Optional property that should be returned when 'allowPartial' is true to indicate the offset of the first byte of data successfully written. Can be negative."
-                },
-                "bytesWritten": {
-                    "type": "integer",
-                    "description": "Optional property that should be returned when 'allowPartial' is true to indicate the number of bytes starting from address that were successfully written."
-                }
-            }
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param WriteMemoryResponseBody body: 
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        if body is None:
-            self.body = WriteMemoryResponseBody()
-        else:
-            self.body = WriteMemoryResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  WriteMemoryResponseBody else body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('disassemble')
-@register
-class DisassembleRequest(BaseSchema):
-    """
-    Disassembles code stored at the provided location.
-    
-    Clients should only call this request if the capability 'supportsDisassembleRequest' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "disassemble"
-            ]
-        },
-        "arguments": {
-            "type": "DisassembleArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param DisassembleArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'disassemble'
-        if arguments is None:
-            self.arguments = DisassembleArguments()
-        else:
-            self.arguments = DisassembleArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  DisassembleArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DisassembleArguments(BaseSchema):
-    """
-    Arguments for 'disassemble' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "memoryReference": {
-            "type": "string",
-            "description": "Memory reference to the base location containing the instructions to disassemble."
-        },
-        "offset": {
-            "type": "integer",
-            "description": "Optional offset (in bytes) to be applied to the reference location before disassembling. Can be negative."
-        },
-        "instructionOffset": {
-            "type": "integer",
-            "description": "Optional offset (in instructions) to be applied after the byte offset (if any) before disassembling. Can be negative."
-        },
-        "instructionCount": {
-            "type": "integer",
-            "description": "Number of instructions to disassemble starting at the specified location and offset.\nAn adapter must return exactly this number of instructions - any unavailable instructions should be replaced with an implementation-defined 'invalid instruction' value."
-        },
-        "resolveSymbols": {
-            "type": "boolean",
-            "description": "If true, the adapter should attempt to resolve memory addresses and other values to symbolic names."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, memoryReference, instructionCount, offset=None, instructionOffset=None, resolveSymbols=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string memoryReference: Memory reference to the base location containing the instructions to disassemble.
-        :param integer instructionCount: Number of instructions to disassemble starting at the specified location and offset.
-        An adapter must return exactly this number of instructions - any unavailable instructions should be replaced with an implementation-defined 'invalid instruction' value.
-        :param integer offset: Optional offset (in bytes) to be applied to the reference location before disassembling. Can be negative.
-        :param integer instructionOffset: Optional offset (in instructions) to be applied after the byte offset (if any) before disassembling. Can be negative.
-        :param boolean resolveSymbols: If true, the adapter should attempt to resolve memory addresses and other values to symbolic names.
-        """
-        self.memoryReference = memoryReference
-        self.instructionCount = instructionCount
-        self.offset = offset
-        self.instructionOffset = instructionOffset
-        self.resolveSymbols = resolveSymbols
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        memoryReference = self.memoryReference
-        instructionCount = self.instructionCount
-        offset = self.offset
-        instructionOffset = self.instructionOffset
-        resolveSymbols = self.resolveSymbols
-        dct = {
-            'memoryReference': memoryReference,
-            'instructionCount': instructionCount,
-        }
-        if offset is not None:
-            dct['offset'] = offset
-        if instructionOffset is not None:
-            dct['instructionOffset'] = instructionOffset
-        if resolveSymbols is not None:
-            dct['resolveSymbols'] = resolveSymbols
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('disassemble')
-@register
-class DisassembleResponse(BaseSchema):
-    """
-    Response to 'disassemble' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "instructions": {
-                    "type": "array",
-                    "items": {
-                        "$ref": "#/definitions/DisassembledInstruction"
-                    },
-                    "description": "The list of disassembled instructions."
-                }
-            },
-            "required": [
-                "instructions"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param DisassembleResponseBody body: 
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        if body is None:
-            self.body = DisassembleResponseBody()
-        else:
-            self.body = DisassembleResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  DisassembleResponseBody else body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Capabilities(BaseSchema):
-    """
-    Information about the capabilities of a debug adapter.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "supportsConfigurationDoneRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'configurationDone' request."
-        },
-        "supportsFunctionBreakpoints": {
-            "type": "boolean",
-            "description": "The debug adapter supports function breakpoints."
-        },
-        "supportsConditionalBreakpoints": {
-            "type": "boolean",
-            "description": "The debug adapter supports conditional breakpoints."
-        },
-        "supportsHitConditionalBreakpoints": {
-            "type": "boolean",
-            "description": "The debug adapter supports breakpoints that break execution after a specified number of hits."
-        },
-        "supportsEvaluateForHovers": {
-            "type": "boolean",
-            "description": "The debug adapter supports a (side effect free) evaluate request for data hovers."
-        },
-        "exceptionBreakpointFilters": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ExceptionBreakpointsFilter"
-            },
-            "description": "Available exception filter options for the 'setExceptionBreakpoints' request."
-        },
-        "supportsStepBack": {
-            "type": "boolean",
-            "description": "The debug adapter supports stepping back via the 'stepBack' and 'reverseContinue' requests."
-        },
-        "supportsSetVariable": {
-            "type": "boolean",
-            "description": "The debug adapter supports setting a variable to a value."
-        },
-        "supportsRestartFrame": {
-            "type": "boolean",
-            "description": "The debug adapter supports restarting a frame."
-        },
-        "supportsGotoTargetsRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'gotoTargets' request."
-        },
-        "supportsStepInTargetsRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'stepInTargets' request."
-        },
-        "supportsCompletionsRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'completions' request."
-        },
-        "completionTriggerCharacters": {
-            "type": "array",
-            "items": {
-                "type": "string"
-            },
-            "description": "The set of characters that should trigger completion in a REPL. If not specified, the UI should assume the '.' character."
-        },
-        "supportsModulesRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'modules' request."
-        },
-        "additionalModuleColumns": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ColumnDescriptor"
-            },
-            "description": "The set of additional module information exposed by the debug adapter."
-        },
-        "supportedChecksumAlgorithms": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ChecksumAlgorithm"
-            },
-            "description": "Checksum algorithms supported by the debug adapter."
-        },
-        "supportsRestartRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'restart' request. In this case a client should not implement 'restart' by terminating and relaunching the adapter but by calling the RestartRequest."
-        },
-        "supportsExceptionOptions": {
-            "type": "boolean",
-            "description": "The debug adapter supports 'exceptionOptions' on the setExceptionBreakpoints request."
-        },
-        "supportsValueFormattingOptions": {
-            "type": "boolean",
-            "description": "The debug adapter supports a 'format' attribute on the stackTraceRequest, variablesRequest, and evaluateRequest."
-        },
-        "supportsExceptionInfoRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'exceptionInfo' request."
-        },
-        "supportTerminateDebuggee": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'terminateDebuggee' attribute on the 'disconnect' request."
-        },
-        "supportSuspendDebuggee": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'suspendDebuggee' attribute on the 'disconnect' request."
-        },
-        "supportsDelayedStackTraceLoading": {
-            "type": "boolean",
-            "description": "The debug adapter supports the delayed loading of parts of the stack, which requires that both the 'startFrame' and 'levels' arguments and an optional 'totalFrames' result of the 'StackTrace' request are supported."
-        },
-        "supportsLoadedSourcesRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'loadedSources' request."
-        },
-        "supportsLogPoints": {
-            "type": "boolean",
-            "description": "The debug adapter supports logpoints by interpreting the 'logMessage' attribute of the SourceBreakpoint."
-        },
-        "supportsTerminateThreadsRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'terminateThreads' request."
-        },
-        "supportsSetExpression": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'setExpression' request."
-        },
-        "supportsTerminateRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'terminate' request."
-        },
-        "supportsDataBreakpoints": {
-            "type": "boolean",
-            "description": "The debug adapter supports data breakpoints."
-        },
-        "supportsReadMemoryRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'readMemory' request."
-        },
-        "supportsWriteMemoryRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'writeMemory' request."
-        },
-        "supportsDisassembleRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'disassemble' request."
-        },
-        "supportsCancelRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'cancel' request."
-        },
-        "supportsBreakpointLocationsRequest": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'breakpointLocations' request."
-        },
-        "supportsClipboardContext": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'clipboard' context value in the 'evaluate' request."
-        },
-        "supportsSteppingGranularity": {
-            "type": "boolean",
-            "description": "The debug adapter supports stepping granularities (argument 'granularity') for the stepping requests."
-        },
-        "supportsInstructionBreakpoints": {
-            "type": "boolean",
-            "description": "The debug adapter supports adding breakpoints based on instruction references."
-        },
-        "supportsExceptionFilterOptions": {
-            "type": "boolean",
-            "description": "The debug adapter supports 'filterOptions' as an argument on the 'setExceptionBreakpoints' request."
-        },
-        "supportsSingleThreadExecutionRequests": {
-            "type": "boolean",
-            "description": "The debug adapter supports the 'singleThread' property on the execution requests ('continue', 'next', 'stepIn', 'stepOut', 'reverseContinue', 'stepBack')."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, supportsConfigurationDoneRequest=None, supportsFunctionBreakpoints=None, supportsConditionalBreakpoints=None, supportsHitConditionalBreakpoints=None, supportsEvaluateForHovers=None, exceptionBreakpointFilters=None, supportsStepBack=None, supportsSetVariable=None, supportsRestartFrame=None, supportsGotoTargetsRequest=None, supportsStepInTargetsRequest=None, supportsCompletionsRequest=None, completionTriggerCharacters=None, supportsModulesRequest=None, additionalModuleColumns=None, supportedChecksumAlgorithms=None, supportsRestartRequest=None, supportsExceptionOptions=None, supportsValueFormattingOptions=None, supportsExceptionInfoRequest=None, supportTerminateDebuggee=None, supportSuspendDebuggee=None, supportsDelayedStackTraceLoading=None, supportsLoadedSourcesRequest=None, supportsLogPoints=None, supportsTerminateThreadsRequest=None, supportsSetExpression=None, supportsTerminateRequest=None, supportsDataBreakpoints=None, supportsReadMemoryRequest=None, supportsWriteMemoryRequest=None, supportsDisassembleRequest=None, supportsCancelRequest=None, supportsBreakpointLocationsRequest=None, supportsClipboardContext=None, supportsSteppingGranularity=None, supportsInstructionBreakpoints=None, supportsExceptionFilterOptions=None, supportsSingleThreadExecutionRequests=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean supportsConfigurationDoneRequest: The debug adapter supports the 'configurationDone' request.
-        :param boolean supportsFunctionBreakpoints: The debug adapter supports function breakpoints.
-        :param boolean supportsConditionalBreakpoints: The debug adapter supports conditional breakpoints.
-        :param boolean supportsHitConditionalBreakpoints: The debug adapter supports breakpoints that break execution after a specified number of hits.
-        :param boolean supportsEvaluateForHovers: The debug adapter supports a (side effect free) evaluate request for data hovers.
-        :param array exceptionBreakpointFilters: Available exception filter options for the 'setExceptionBreakpoints' request.
-        :param boolean supportsStepBack: The debug adapter supports stepping back via the 'stepBack' and 'reverseContinue' requests.
-        :param boolean supportsSetVariable: The debug adapter supports setting a variable to a value.
-        :param boolean supportsRestartFrame: The debug adapter supports restarting a frame.
-        :param boolean supportsGotoTargetsRequest: The debug adapter supports the 'gotoTargets' request.
-        :param boolean supportsStepInTargetsRequest: The debug adapter supports the 'stepInTargets' request.
-        :param boolean supportsCompletionsRequest: The debug adapter supports the 'completions' request.
-        :param array completionTriggerCharacters: The set of characters that should trigger completion in a REPL. If not specified, the UI should assume the '.' character.
-        :param boolean supportsModulesRequest: The debug adapter supports the 'modules' request.
-        :param array additionalModuleColumns: The set of additional module information exposed by the debug adapter.
-        :param array supportedChecksumAlgorithms: Checksum algorithms supported by the debug adapter.
-        :param boolean supportsRestartRequest: The debug adapter supports the 'restart' request. In this case a client should not implement 'restart' by terminating and relaunching the adapter but by calling the RestartRequest.
-        :param boolean supportsExceptionOptions: The debug adapter supports 'exceptionOptions' on the setExceptionBreakpoints request.
-        :param boolean supportsValueFormattingOptions: The debug adapter supports a 'format' attribute on the stackTraceRequest, variablesRequest, and evaluateRequest.
-        :param boolean supportsExceptionInfoRequest: The debug adapter supports the 'exceptionInfo' request.
-        :param boolean supportTerminateDebuggee: The debug adapter supports the 'terminateDebuggee' attribute on the 'disconnect' request.
-        :param boolean supportSuspendDebuggee: The debug adapter supports the 'suspendDebuggee' attribute on the 'disconnect' request.
-        :param boolean supportsDelayedStackTraceLoading: The debug adapter supports the delayed loading of parts of the stack, which requires that both the 'startFrame' and 'levels' arguments and an optional 'totalFrames' result of the 'StackTrace' request are supported.
-        :param boolean supportsLoadedSourcesRequest: The debug adapter supports the 'loadedSources' request.
-        :param boolean supportsLogPoints: The debug adapter supports logpoints by interpreting the 'logMessage' attribute of the SourceBreakpoint.
-        :param boolean supportsTerminateThreadsRequest: The debug adapter supports the 'terminateThreads' request.
-        :param boolean supportsSetExpression: The debug adapter supports the 'setExpression' request.
-        :param boolean supportsTerminateRequest: The debug adapter supports the 'terminate' request.
-        :param boolean supportsDataBreakpoints: The debug adapter supports data breakpoints.
-        :param boolean supportsReadMemoryRequest: The debug adapter supports the 'readMemory' request.
-        :param boolean supportsWriteMemoryRequest: The debug adapter supports the 'writeMemory' request.
-        :param boolean supportsDisassembleRequest: The debug adapter supports the 'disassemble' request.
-        :param boolean supportsCancelRequest: The debug adapter supports the 'cancel' request.
-        :param boolean supportsBreakpointLocationsRequest: The debug adapter supports the 'breakpointLocations' request.
-        :param boolean supportsClipboardContext: The debug adapter supports the 'clipboard' context value in the 'evaluate' request.
-        :param boolean supportsSteppingGranularity: The debug adapter supports stepping granularities (argument 'granularity') for the stepping requests.
-        :param boolean supportsInstructionBreakpoints: The debug adapter supports adding breakpoints based on instruction references.
-        :param boolean supportsExceptionFilterOptions: The debug adapter supports 'filterOptions' as an argument on the 'setExceptionBreakpoints' request.
-        :param boolean supportsSingleThreadExecutionRequests: The debug adapter supports the 'singleThread' property on the execution requests ('continue', 'next', 'stepIn', 'stepOut', 'reverseContinue', 'stepBack').
-        """
-        self.supportsConfigurationDoneRequest = supportsConfigurationDoneRequest
-        self.supportsFunctionBreakpoints = supportsFunctionBreakpoints
-        self.supportsConditionalBreakpoints = supportsConditionalBreakpoints
-        self.supportsHitConditionalBreakpoints = supportsHitConditionalBreakpoints
-        self.supportsEvaluateForHovers = supportsEvaluateForHovers
-        self.exceptionBreakpointFilters = exceptionBreakpointFilters
-        if update_ids_from_dap and self.exceptionBreakpointFilters:
-            for o in self.exceptionBreakpointFilters:
-                ExceptionBreakpointsFilter.update_dict_ids_from_dap(o)
-        self.supportsStepBack = supportsStepBack
-        self.supportsSetVariable = supportsSetVariable
-        self.supportsRestartFrame = supportsRestartFrame
-        self.supportsGotoTargetsRequest = supportsGotoTargetsRequest
-        self.supportsStepInTargetsRequest = supportsStepInTargetsRequest
-        self.supportsCompletionsRequest = supportsCompletionsRequest
-        self.completionTriggerCharacters = completionTriggerCharacters
-        self.supportsModulesRequest = supportsModulesRequest
-        self.additionalModuleColumns = additionalModuleColumns
-        if update_ids_from_dap and self.additionalModuleColumns:
-            for o in self.additionalModuleColumns:
-                ColumnDescriptor.update_dict_ids_from_dap(o)
-        self.supportedChecksumAlgorithms = supportedChecksumAlgorithms
-        if update_ids_from_dap and self.supportedChecksumAlgorithms:
-            for o in self.supportedChecksumAlgorithms:
-                ChecksumAlgorithm.update_dict_ids_from_dap(o)
-        self.supportsRestartRequest = supportsRestartRequest
-        self.supportsExceptionOptions = supportsExceptionOptions
-        self.supportsValueFormattingOptions = supportsValueFormattingOptions
-        self.supportsExceptionInfoRequest = supportsExceptionInfoRequest
-        self.supportTerminateDebuggee = supportTerminateDebuggee
-        self.supportSuspendDebuggee = supportSuspendDebuggee
-        self.supportsDelayedStackTraceLoading = supportsDelayedStackTraceLoading
-        self.supportsLoadedSourcesRequest = supportsLoadedSourcesRequest
-        self.supportsLogPoints = supportsLogPoints
-        self.supportsTerminateThreadsRequest = supportsTerminateThreadsRequest
-        self.supportsSetExpression = supportsSetExpression
-        self.supportsTerminateRequest = supportsTerminateRequest
-        self.supportsDataBreakpoints = supportsDataBreakpoints
-        self.supportsReadMemoryRequest = supportsReadMemoryRequest
-        self.supportsWriteMemoryRequest = supportsWriteMemoryRequest
-        self.supportsDisassembleRequest = supportsDisassembleRequest
-        self.supportsCancelRequest = supportsCancelRequest
-        self.supportsBreakpointLocationsRequest = supportsBreakpointLocationsRequest
-        self.supportsClipboardContext = supportsClipboardContext
-        self.supportsSteppingGranularity = supportsSteppingGranularity
-        self.supportsInstructionBreakpoints = supportsInstructionBreakpoints
-        self.supportsExceptionFilterOptions = supportsExceptionFilterOptions
-        self.supportsSingleThreadExecutionRequests = supportsSingleThreadExecutionRequests
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        supportsConfigurationDoneRequest = self.supportsConfigurationDoneRequest
-        supportsFunctionBreakpoints = self.supportsFunctionBreakpoints
-        supportsConditionalBreakpoints = self.supportsConditionalBreakpoints
-        supportsHitConditionalBreakpoints = self.supportsHitConditionalBreakpoints
-        supportsEvaluateForHovers = self.supportsEvaluateForHovers
-        exceptionBreakpointFilters = self.exceptionBreakpointFilters
-        if exceptionBreakpointFilters and hasattr(exceptionBreakpointFilters[0], "to_dict"):
-            exceptionBreakpointFilters = [x.to_dict() for x in exceptionBreakpointFilters]
-        supportsStepBack = self.supportsStepBack
-        supportsSetVariable = self.supportsSetVariable
-        supportsRestartFrame = self.supportsRestartFrame
-        supportsGotoTargetsRequest = self.supportsGotoTargetsRequest
-        supportsStepInTargetsRequest = self.supportsStepInTargetsRequest
-        supportsCompletionsRequest = self.supportsCompletionsRequest
-        completionTriggerCharacters = self.completionTriggerCharacters
-        if completionTriggerCharacters and hasattr(completionTriggerCharacters[0], "to_dict"):
-            completionTriggerCharacters = [x.to_dict() for x in completionTriggerCharacters]
-        supportsModulesRequest = self.supportsModulesRequest
-        additionalModuleColumns = self.additionalModuleColumns
-        if additionalModuleColumns and hasattr(additionalModuleColumns[0], "to_dict"):
-            additionalModuleColumns = [x.to_dict() for x in additionalModuleColumns]
-        supportedChecksumAlgorithms = self.supportedChecksumAlgorithms
-        if supportedChecksumAlgorithms and hasattr(supportedChecksumAlgorithms[0], "to_dict"):
-            supportedChecksumAlgorithms = [x.to_dict() for x in supportedChecksumAlgorithms]
-        supportsRestartRequest = self.supportsRestartRequest
-        supportsExceptionOptions = self.supportsExceptionOptions
-        supportsValueFormattingOptions = self.supportsValueFormattingOptions
-        supportsExceptionInfoRequest = self.supportsExceptionInfoRequest
-        supportTerminateDebuggee = self.supportTerminateDebuggee
-        supportSuspendDebuggee = self.supportSuspendDebuggee
-        supportsDelayedStackTraceLoading = self.supportsDelayedStackTraceLoading
-        supportsLoadedSourcesRequest = self.supportsLoadedSourcesRequest
-        supportsLogPoints = self.supportsLogPoints
-        supportsTerminateThreadsRequest = self.supportsTerminateThreadsRequest
-        supportsSetExpression = self.supportsSetExpression
-        supportsTerminateRequest = self.supportsTerminateRequest
-        supportsDataBreakpoints = self.supportsDataBreakpoints
-        supportsReadMemoryRequest = self.supportsReadMemoryRequest
-        supportsWriteMemoryRequest = self.supportsWriteMemoryRequest
-        supportsDisassembleRequest = self.supportsDisassembleRequest
-        supportsCancelRequest = self.supportsCancelRequest
-        supportsBreakpointLocationsRequest = self.supportsBreakpointLocationsRequest
-        supportsClipboardContext = self.supportsClipboardContext
-        supportsSteppingGranularity = self.supportsSteppingGranularity
-        supportsInstructionBreakpoints = self.supportsInstructionBreakpoints
-        supportsExceptionFilterOptions = self.supportsExceptionFilterOptions
-        supportsSingleThreadExecutionRequests = self.supportsSingleThreadExecutionRequests
-        dct = {
-        }
-        if supportsConfigurationDoneRequest is not None:
-            dct['supportsConfigurationDoneRequest'] = supportsConfigurationDoneRequest
-        if supportsFunctionBreakpoints is not None:
-            dct['supportsFunctionBreakpoints'] = supportsFunctionBreakpoints
-        if supportsConditionalBreakpoints is not None:
-            dct['supportsConditionalBreakpoints'] = supportsConditionalBreakpoints
-        if supportsHitConditionalBreakpoints is not None:
-            dct['supportsHitConditionalBreakpoints'] = supportsHitConditionalBreakpoints
-        if supportsEvaluateForHovers is not None:
-            dct['supportsEvaluateForHovers'] = supportsEvaluateForHovers
-        if exceptionBreakpointFilters is not None:
-            dct['exceptionBreakpointFilters'] = [ExceptionBreakpointsFilter.update_dict_ids_to_dap(o) for o in exceptionBreakpointFilters] if (update_ids_to_dap and exceptionBreakpointFilters) else exceptionBreakpointFilters
-        if supportsStepBack is not None:
-            dct['supportsStepBack'] = supportsStepBack
-        if supportsSetVariable is not None:
-            dct['supportsSetVariable'] = supportsSetVariable
-        if supportsRestartFrame is not None:
-            dct['supportsRestartFrame'] = supportsRestartFrame
-        if supportsGotoTargetsRequest is not None:
-            dct['supportsGotoTargetsRequest'] = supportsGotoTargetsRequest
-        if supportsStepInTargetsRequest is not None:
-            dct['supportsStepInTargetsRequest'] = supportsStepInTargetsRequest
-        if supportsCompletionsRequest is not None:
-            dct['supportsCompletionsRequest'] = supportsCompletionsRequest
-        if completionTriggerCharacters is not None:
-            dct['completionTriggerCharacters'] = completionTriggerCharacters
-        if supportsModulesRequest is not None:
-            dct['supportsModulesRequest'] = supportsModulesRequest
-        if additionalModuleColumns is not None:
-            dct['additionalModuleColumns'] = [ColumnDescriptor.update_dict_ids_to_dap(o) for o in additionalModuleColumns] if (update_ids_to_dap and additionalModuleColumns) else additionalModuleColumns
-        if supportedChecksumAlgorithms is not None:
-            dct['supportedChecksumAlgorithms'] = [ChecksumAlgorithm.update_dict_ids_to_dap(o) for o in supportedChecksumAlgorithms] if (update_ids_to_dap and supportedChecksumAlgorithms) else supportedChecksumAlgorithms
-        if supportsRestartRequest is not None:
-            dct['supportsRestartRequest'] = supportsRestartRequest
-        if supportsExceptionOptions is not None:
-            dct['supportsExceptionOptions'] = supportsExceptionOptions
-        if supportsValueFormattingOptions is not None:
-            dct['supportsValueFormattingOptions'] = supportsValueFormattingOptions
-        if supportsExceptionInfoRequest is not None:
-            dct['supportsExceptionInfoRequest'] = supportsExceptionInfoRequest
-        if supportTerminateDebuggee is not None:
-            dct['supportTerminateDebuggee'] = supportTerminateDebuggee
-        if supportSuspendDebuggee is not None:
-            dct['supportSuspendDebuggee'] = supportSuspendDebuggee
-        if supportsDelayedStackTraceLoading is not None:
-            dct['supportsDelayedStackTraceLoading'] = supportsDelayedStackTraceLoading
-        if supportsLoadedSourcesRequest is not None:
-            dct['supportsLoadedSourcesRequest'] = supportsLoadedSourcesRequest
-        if supportsLogPoints is not None:
-            dct['supportsLogPoints'] = supportsLogPoints
-        if supportsTerminateThreadsRequest is not None:
-            dct['supportsTerminateThreadsRequest'] = supportsTerminateThreadsRequest
-        if supportsSetExpression is not None:
-            dct['supportsSetExpression'] = supportsSetExpression
-        if supportsTerminateRequest is not None:
-            dct['supportsTerminateRequest'] = supportsTerminateRequest
-        if supportsDataBreakpoints is not None:
-            dct['supportsDataBreakpoints'] = supportsDataBreakpoints
-        if supportsReadMemoryRequest is not None:
-            dct['supportsReadMemoryRequest'] = supportsReadMemoryRequest
-        if supportsWriteMemoryRequest is not None:
-            dct['supportsWriteMemoryRequest'] = supportsWriteMemoryRequest
-        if supportsDisassembleRequest is not None:
-            dct['supportsDisassembleRequest'] = supportsDisassembleRequest
-        if supportsCancelRequest is not None:
-            dct['supportsCancelRequest'] = supportsCancelRequest
-        if supportsBreakpointLocationsRequest is not None:
-            dct['supportsBreakpointLocationsRequest'] = supportsBreakpointLocationsRequest
-        if supportsClipboardContext is not None:
-            dct['supportsClipboardContext'] = supportsClipboardContext
-        if supportsSteppingGranularity is not None:
-            dct['supportsSteppingGranularity'] = supportsSteppingGranularity
-        if supportsInstructionBreakpoints is not None:
-            dct['supportsInstructionBreakpoints'] = supportsInstructionBreakpoints
-        if supportsExceptionFilterOptions is not None:
-            dct['supportsExceptionFilterOptions'] = supportsExceptionFilterOptions
-        if supportsSingleThreadExecutionRequests is not None:
-            dct['supportsSingleThreadExecutionRequests'] = supportsSingleThreadExecutionRequests
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionBreakpointsFilter(BaseSchema):
-    """
-    An ExceptionBreakpointsFilter is shown in the UI as an filter option for configuring how exceptions
-    are dealt with.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "filter": {
-            "type": "string",
-            "description": "The internal ID of the filter option. This value is passed to the 'setExceptionBreakpoints' request."
-        },
-        "label": {
-            "type": "string",
-            "description": "The name of the filter option. This will be shown in the UI."
-        },
-        "description": {
-            "type": "string",
-            "description": "An optional help text providing additional information about the exception filter. This string is typically shown as a hover and must be translated."
-        },
-        "default": {
-            "type": "boolean",
-            "description": "Initial value of the filter option. If not specified a value 'false' is assumed."
-        },
-        "supportsCondition": {
-            "type": "boolean",
-            "description": "Controls whether a condition can be specified for this filter option. If false or missing, a condition can not be set."
-        },
-        "conditionDescription": {
-            "type": "string",
-            "description": "An optional help text providing information about the condition. This string is shown as the placeholder text for a text box and must be translated."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, filter, label, description=None, default=None, supportsCondition=None, conditionDescription=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string filter: The internal ID of the filter option. This value is passed to the 'setExceptionBreakpoints' request.
-        :param string label: The name of the filter option. This will be shown in the UI.
-        :param string description: An optional help text providing additional information about the exception filter. This string is typically shown as a hover and must be translated.
-        :param boolean default: Initial value of the filter option. If not specified a value 'false' is assumed.
-        :param boolean supportsCondition: Controls whether a condition can be specified for this filter option. If false or missing, a condition can not be set.
-        :param string conditionDescription: An optional help text providing information about the condition. This string is shown as the placeholder text for a text box and must be translated.
-        """
-        self.filter = filter
-        self.label = label
-        self.description = description
-        self.default = default
-        self.supportsCondition = supportsCondition
-        self.conditionDescription = conditionDescription
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        filter = self.filter  # noqa (assign to builtin)
-        label = self.label
-        description = self.description
-        default = self.default
-        supportsCondition = self.supportsCondition
-        conditionDescription = self.conditionDescription
-        dct = {
-            'filter': filter,
-            'label': label,
-        }
-        if description is not None:
-            dct['description'] = description
-        if default is not None:
-            dct['default'] = default
-        if supportsCondition is not None:
-            dct['supportsCondition'] = supportsCondition
-        if conditionDescription is not None:
-            dct['conditionDescription'] = conditionDescription
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Message(BaseSchema):
-    """
-    A structured message object. Used to return errors from requests.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "id": {
-            "type": "integer",
-            "description": "Unique identifier for the message."
-        },
-        "format": {
-            "type": "string",
-            "description": "A format string for the message. Embedded variables have the form '{name}'.\nIf variable name starts with an underscore character, the variable does not contain user data (PII) and can be safely used for telemetry purposes."
-        },
-        "variables": {
-            "type": "object",
-            "description": "An object used as a dictionary for looking up the variables in the format string.",
-            "additionalProperties": {
-                "type": "string",
-                "description": "Values must be strings."
-            }
-        },
-        "sendTelemetry": {
-            "type": "boolean",
-            "description": "If true send to telemetry."
-        },
-        "showUser": {
-            "type": "boolean",
-            "description": "If true show user."
-        },
-        "url": {
-            "type": "string",
-            "description": "An optional url where additional information about this message can be found."
-        },
-        "urlLabel": {
-            "type": "string",
-            "description": "An optional label that is presented to the user as the UI for opening the url."
-        }
-    }
-    __refs__ = set(['variables'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, id, format, variables=None, sendTelemetry=None, showUser=None, url=None, urlLabel=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer id: Unique identifier for the message.
-        :param string format: A format string for the message. Embedded variables have the form '{name}'.
-        If variable name starts with an underscore character, the variable does not contain user data (PII) and can be safely used for telemetry purposes.
-        :param MessageVariables variables: An object used as a dictionary for looking up the variables in the format string.
-        :param boolean sendTelemetry: If true send to telemetry.
-        :param boolean showUser: If true show user.
-        :param string url: An optional url where additional information about this message can be found.
-        :param string urlLabel: An optional label that is presented to the user as the UI for opening the url.
-        """
-        self.id = id
-        self.format = format
-        if variables is None:
-            self.variables = MessageVariables()
-        else:
-            self.variables = MessageVariables(update_ids_from_dap=update_ids_from_dap, **variables) if variables.__class__ !=  MessageVariables else variables
-        self.sendTelemetry = sendTelemetry
-        self.showUser = showUser
-        self.url = url
-        self.urlLabel = urlLabel
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        id = self.id  # noqa (assign to builtin)
-        format = self.format  # noqa (assign to builtin)
-        variables = self.variables
-        sendTelemetry = self.sendTelemetry
-        showUser = self.showUser
-        url = self.url
-        urlLabel = self.urlLabel
-        dct = {
-            'id': id,
-            'format': format,
-        }
-        if variables is not None:
-            dct['variables'] = variables.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if sendTelemetry is not None:
-            dct['sendTelemetry'] = sendTelemetry
-        if showUser is not None:
-            dct['showUser'] = showUser
-        if url is not None:
-            dct['url'] = url
-        if urlLabel is not None:
-            dct['urlLabel'] = urlLabel
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Module(BaseSchema):
-    """
-    A Module object represents a row in the modules view.
-    
-    Two attributes are mandatory: an id identifies a module in the modules view and is used in a
-    ModuleEvent for identifying a module for adding, updating or deleting.
-    
-    The name is used to minimally render the module in the UI.
-    
-    
-    Additional attributes can be added to the module. They will show up in the module View if they have
-    a corresponding ColumnDescriptor.
-    
-    
-    To avoid an unnecessary proliferation of additional attributes with similar semantics but different
-    names
-    
-    we recommend to re-use attributes from the 'recommended' list below first, and only introduce new
-    attributes if nothing appropriate could be found.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "id": {
-            "type": [
-                "integer",
-                "string"
-            ],
-            "description": "Unique identifier for the module."
-        },
-        "name": {
-            "type": "string",
-            "description": "A name of the module."
-        },
-        "path": {
-            "type": "string",
-            "description": "optional but recommended attributes.\nalways try to use these first before introducing additional attributes.\n\nLogical full path to the module. The exact definition is implementation defined, but usually this would be a full path to the on-disk file for the module."
-        },
-        "isOptimized": {
-            "type": "boolean",
-            "description": "True if the module is optimized."
-        },
-        "isUserCode": {
-            "type": "boolean",
-            "description": "True if the module is considered 'user code' by a debugger that supports 'Just My Code'."
-        },
-        "version": {
-            "type": "string",
-            "description": "Version of Module."
-        },
-        "symbolStatus": {
-            "type": "string",
-            "description": "User understandable description of if symbols were found for the module (ex: 'Symbols Loaded', 'Symbols not found', etc."
-        },
-        "symbolFilePath": {
-            "type": "string",
-            "description": "Logical full path to the symbol file. The exact definition is implementation defined."
-        },
-        "dateTimeStamp": {
-            "type": "string",
-            "description": "Module created or modified."
-        },
-        "addressRange": {
-            "type": "string",
-            "description": "Address range covered by this module."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, id, name, path=None, isOptimized=None, isUserCode=None, version=None, symbolStatus=None, symbolFilePath=None, dateTimeStamp=None, addressRange=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param ['integer', 'string'] id: Unique identifier for the module.
-        :param string name: A name of the module.
-        :param string path: optional but recommended attributes.
-        always try to use these first before introducing additional attributes.
-        
-        Logical full path to the module. The exact definition is implementation defined, but usually this would be a full path to the on-disk file for the module.
-        :param boolean isOptimized: True if the module is optimized.
-        :param boolean isUserCode: True if the module is considered 'user code' by a debugger that supports 'Just My Code'.
-        :param string version: Version of Module.
-        :param string symbolStatus: User understandable description of if symbols were found for the module (ex: 'Symbols Loaded', 'Symbols not found', etc.
-        :param string symbolFilePath: Logical full path to the symbol file. The exact definition is implementation defined.
-        :param string dateTimeStamp: Module created or modified.
-        :param string addressRange: Address range covered by this module.
-        """
-        self.id = id
-        self.name = name
-        self.path = path
-        self.isOptimized = isOptimized
-        self.isUserCode = isUserCode
-        self.version = version
-        self.symbolStatus = symbolStatus
-        self.symbolFilePath = symbolFilePath
-        self.dateTimeStamp = dateTimeStamp
-        self.addressRange = addressRange
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        id = self.id  # noqa (assign to builtin)
-        name = self.name
-        path = self.path
-        isOptimized = self.isOptimized
-        isUserCode = self.isUserCode
-        version = self.version
-        symbolStatus = self.symbolStatus
-        symbolFilePath = self.symbolFilePath
-        dateTimeStamp = self.dateTimeStamp
-        addressRange = self.addressRange
-        dct = {
-            'id': id,
-            'name': name,
-        }
-        if path is not None:
-            dct['path'] = path
-        if isOptimized is not None:
-            dct['isOptimized'] = isOptimized
-        if isUserCode is not None:
-            dct['isUserCode'] = isUserCode
-        if version is not None:
-            dct['version'] = version
-        if symbolStatus is not None:
-            dct['symbolStatus'] = symbolStatus
-        if symbolFilePath is not None:
-            dct['symbolFilePath'] = symbolFilePath
-        if dateTimeStamp is not None:
-            dct['dateTimeStamp'] = dateTimeStamp
-        if addressRange is not None:
-            dct['addressRange'] = addressRange
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ColumnDescriptor(BaseSchema):
-    """
-    A ColumnDescriptor specifies what module attribute to show in a column of the ModulesView, how to
-    format it,
-    
-    and what the column's label should be.
-    
-    It is only used if the underlying UI actually supports this level of customization.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "attributeName": {
-            "type": "string",
-            "description": "Name of the attribute rendered in this column."
-        },
-        "label": {
-            "type": "string",
-            "description": "Header UI label of column."
-        },
-        "format": {
-            "type": "string",
-            "description": "Format to use for the rendered values in this column. TBD how the format strings looks like."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "string",
-                "number",
-                "boolean",
-                "unixTimestampUTC"
-            ],
-            "description": "Datatype of values in this column.  Defaults to 'string' if not specified."
-        },
-        "width": {
-            "type": "integer",
-            "description": "Width of this column in characters (hint only)."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, attributeName, label, format=None, type=None, width=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string attributeName: Name of the attribute rendered in this column.
-        :param string label: Header UI label of column.
-        :param string format: Format to use for the rendered values in this column. TBD how the format strings looks like.
-        :param string type: Datatype of values in this column.  Defaults to 'string' if not specified.
-        :param integer width: Width of this column in characters (hint only).
-        """
-        self.attributeName = attributeName
-        self.label = label
-        self.format = format
-        self.type = type
-        self.width = width
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        attributeName = self.attributeName
-        label = self.label
-        format = self.format  # noqa (assign to builtin)
-        type = self.type  # noqa (assign to builtin)
-        width = self.width
-        dct = {
-            'attributeName': attributeName,
-            'label': label,
-        }
-        if format is not None:
-            dct['format'] = format
-        if type is not None:
-            dct['type'] = type
-        if width is not None:
-            dct['width'] = width
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ModulesViewDescriptor(BaseSchema):
-    """
-    The ModulesViewDescriptor is the container for all declarative configuration options of a
-    ModuleView.
-    
-    For now it only specifies the columns to be shown in the modules view.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "columns": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ColumnDescriptor"
-            }
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, columns, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array columns: 
-        """
-        self.columns = columns
-        if update_ids_from_dap and self.columns:
-            for o in self.columns:
-                ColumnDescriptor.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        columns = self.columns
-        if columns and hasattr(columns[0], "to_dict"):
-            columns = [x.to_dict() for x in columns]
-        dct = {
-            'columns': [ColumnDescriptor.update_dict_ids_to_dap(o) for o in columns] if (update_ids_to_dap and columns) else columns,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Thread(BaseSchema):
-    """
-    A Thread
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "id": {
-            "type": "integer",
-            "description": "Unique identifier for the thread."
-        },
-        "name": {
-            "type": "string",
-            "description": "A name of the thread."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, id, name, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer id: Unique identifier for the thread.
-        :param string name: A name of the thread.
-        """
-        self.id = id
-        self.name = name
-        if update_ids_from_dap:
-            self.id = self._translate_id_from_dap(self.id)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'id' in dct:
-            dct['id'] = cls._translate_id_from_dap(dct['id'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        id = self.id  # noqa (assign to builtin)
-        name = self.name
-        if update_ids_to_dap:
-            if id is not None:
-                id = self._translate_id_to_dap(id)  # noqa (assign to builtin)
-        dct = {
-            'id': id,
-            'name': name,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'id' in dct:
-            dct['id'] = cls._translate_id_to_dap(dct['id'])
-        return dct
-
-
-@register
-class Source(BaseSchema):
-    """
-    A Source is a descriptor for source code.
-    
-    It is returned from the debug adapter as part of a StackFrame and it is used by clients when
-    specifying breakpoints.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "name": {
-            "type": "string",
-            "description": "The short name of the source. Every source returned from the debug adapter has a name.\nWhen sending a source to the debug adapter this name is optional."
-        },
-        "path": {
-            "type": "string",
-            "description": "The path of the source to be shown in the UI.\nIt is only used to locate and load the content of the source if no sourceReference is specified (or its value is 0)."
-        },
-        "sourceReference": {
-            "type": "integer",
-            "description": "If sourceReference > 0 the contents of the source must be retrieved through the SourceRequest (even if a path is specified).\nA sourceReference is only valid for a session, so it must not be used to persist a source.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "presentationHint": {
-            "type": "string",
-            "description": "An optional hint for how to present the source in the UI.\nA value of 'deemphasize' can be used to indicate that the source is not available or that it is skipped on stepping.",
-            "enum": [
-                "normal",
-                "emphasize",
-                "deemphasize"
-            ]
-        },
-        "origin": {
-            "type": "string",
-            "description": "The (optional) origin of this source: possible values 'internal module', 'inlined content from source map', etc."
-        },
-        "sources": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Source"
-            },
-            "description": "An optional list of sources that are related to this source. These may be the source that generated this source."
-        },
-        "adapterData": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Optional data that a debug adapter might want to loop through the client.\nThe client should leave the data intact and persist it across sessions. The client should not interpret the data."
-        },
-        "checksums": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Checksum"
-            },
-            "description": "The checksums associated with this file."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name=None, path=None, sourceReference=None, presentationHint=None, origin=None, sources=None, adapterData=None, checksums=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: The short name of the source. Every source returned from the debug adapter has a name.
-        When sending a source to the debug adapter this name is optional.
-        :param string path: The path of the source to be shown in the UI.
-        It is only used to locate and load the content of the source if no sourceReference is specified (or its value is 0).
-        :param integer sourceReference: If sourceReference > 0 the contents of the source must be retrieved through the SourceRequest (even if a path is specified).
-        A sourceReference is only valid for a session, so it must not be used to persist a source.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param string presentationHint: An optional hint for how to present the source in the UI.
-        A value of 'deemphasize' can be used to indicate that the source is not available or that it is skipped on stepping.
-        :param string origin: The (optional) origin of this source: possible values 'internal module', 'inlined content from source map', etc.
-        :param array sources: An optional list of sources that are related to this source. These may be the source that generated this source.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] adapterData: Optional data that a debug adapter might want to loop through the client.
-        The client should leave the data intact and persist it across sessions. The client should not interpret the data.
-        :param array checksums: The checksums associated with this file.
-        """
-        self.name = name
-        self.path = path
-        self.sourceReference = sourceReference
-        self.presentationHint = presentationHint
-        self.origin = origin
-        self.sources = sources
-        if update_ids_from_dap and self.sources:
-            for o in self.sources:
-                Source.update_dict_ids_from_dap(o)
-        self.adapterData = adapterData
-        self.checksums = checksums
-        if update_ids_from_dap and self.checksums:
-            for o in self.checksums:
-                Checksum.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        path = self.path
-        sourceReference = self.sourceReference
-        presentationHint = self.presentationHint
-        origin = self.origin
-        sources = self.sources
-        if sources and hasattr(sources[0], "to_dict"):
-            sources = [x.to_dict() for x in sources]
-        adapterData = self.adapterData
-        checksums = self.checksums
-        if checksums and hasattr(checksums[0], "to_dict"):
-            checksums = [x.to_dict() for x in checksums]
-        dct = {
-        }
-        if name is not None:
-            dct['name'] = name
-        if path is not None:
-            dct['path'] = path
-        if sourceReference is not None:
-            dct['sourceReference'] = sourceReference
-        if presentationHint is not None:
-            dct['presentationHint'] = presentationHint
-        if origin is not None:
-            dct['origin'] = origin
-        if sources is not None:
-            dct['sources'] = [Source.update_dict_ids_to_dap(o) for o in sources] if (update_ids_to_dap and sources) else sources
-        if adapterData is not None:
-            dct['adapterData'] = adapterData
-        if checksums is not None:
-            dct['checksums'] = [Checksum.update_dict_ids_to_dap(o) for o in checksums] if (update_ids_to_dap and checksums) else checksums
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StackFrame(BaseSchema):
-    """
-    A Stackframe contains the source location.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "id": {
-            "type": "integer",
-            "description": "An identifier for the stack frame. It must be unique across all threads.\nThis id can be used to retrieve the scopes of the frame with the 'scopesRequest' or to restart the execution of a stackframe."
-        },
-        "name": {
-            "type": "string",
-            "description": "The name of the stack frame, typically a method name."
-        },
-        "source": {
-            "description": "The optional source of the frame.",
-            "type": "Source"
-        },
-        "line": {
-            "type": "integer",
-            "description": "The line within the file of the frame. If source is null or doesn't exist, line is 0 and must be ignored."
-        },
-        "column": {
-            "type": "integer",
-            "description": "The column within the line. If source is null or doesn't exist, column is 0 and must be ignored."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "An optional end line of the range covered by the stack frame."
-        },
-        "endColumn": {
-            "type": "integer",
-            "description": "An optional end column of the range covered by the stack frame."
-        },
-        "canRestart": {
-            "type": "boolean",
-            "description": "Indicates whether this frame can be restarted with the 'restart' request. Clients should only use this if the debug adapter supports the 'restart' request (capability 'supportsRestartRequest' is true)."
-        },
-        "instructionPointerReference": {
-            "type": "string",
-            "description": "Optional memory reference for the current instruction pointer in this frame."
-        },
-        "moduleId": {
-            "type": [
-                "integer",
-                "string"
-            ],
-            "description": "The module associated with this frame, if any."
-        },
-        "presentationHint": {
-            "type": "string",
-            "enum": [
-                "normal",
-                "label",
-                "subtle"
-            ],
-            "description": "An optional hint for how to present this frame in the UI.\nA value of 'label' can be used to indicate that the frame is an artificial frame that is used as a visual label or separator. A value of 'subtle' can be used to change the appearance of a frame in a 'subtle' way."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, id, name, line, column, source=None, endLine=None, endColumn=None, canRestart=None, instructionPointerReference=None, moduleId=None, presentationHint=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer id: An identifier for the stack frame. It must be unique across all threads.
-        This id can be used to retrieve the scopes of the frame with the 'scopesRequest' or to restart the execution of a stackframe.
-        :param string name: The name of the stack frame, typically a method name.
-        :param integer line: The line within the file of the frame. If source is null or doesn't exist, line is 0 and must be ignored.
-        :param integer column: The column within the line. If source is null or doesn't exist, column is 0 and must be ignored.
-        :param Source source: The optional source of the frame.
-        :param integer endLine: An optional end line of the range covered by the stack frame.
-        :param integer endColumn: An optional end column of the range covered by the stack frame.
-        :param boolean canRestart: Indicates whether this frame can be restarted with the 'restart' request. Clients should only use this if the debug adapter supports the 'restart' request (capability 'supportsRestartRequest' is true).
-        :param string instructionPointerReference: Optional memory reference for the current instruction pointer in this frame.
-        :param ['integer', 'string'] moduleId: The module associated with this frame, if any.
-        :param string presentationHint: An optional hint for how to present this frame in the UI.
-        A value of 'label' can be used to indicate that the frame is an artificial frame that is used as a visual label or separator. A value of 'subtle' can be used to change the appearance of a frame in a 'subtle' way.
-        """
-        self.id = id
-        self.name = name
-        self.line = line
-        self.column = column
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.endLine = endLine
-        self.endColumn = endColumn
-        self.canRestart = canRestart
-        self.instructionPointerReference = instructionPointerReference
-        self.moduleId = moduleId
-        self.presentationHint = presentationHint
-        if update_ids_from_dap:
-            self.id = self._translate_id_from_dap(self.id)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'id' in dct:
-            dct['id'] = cls._translate_id_from_dap(dct['id'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        id = self.id  # noqa (assign to builtin)
-        name = self.name
-        line = self.line
-        column = self.column
-        source = self.source
-        endLine = self.endLine
-        endColumn = self.endColumn
-        canRestart = self.canRestart
-        instructionPointerReference = self.instructionPointerReference
-        moduleId = self.moduleId
-        presentationHint = self.presentationHint
-        if update_ids_to_dap:
-            if id is not None:
-                id = self._translate_id_to_dap(id)  # noqa (assign to builtin)
-        dct = {
-            'id': id,
-            'name': name,
-            'line': line,
-            'column': column,
-        }
-        if source is not None:
-            dct['source'] = source.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if endLine is not None:
-            dct['endLine'] = endLine
-        if endColumn is not None:
-            dct['endColumn'] = endColumn
-        if canRestart is not None:
-            dct['canRestart'] = canRestart
-        if instructionPointerReference is not None:
-            dct['instructionPointerReference'] = instructionPointerReference
-        if moduleId is not None:
-            dct['moduleId'] = moduleId
-        if presentationHint is not None:
-            dct['presentationHint'] = presentationHint
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'id' in dct:
-            dct['id'] = cls._translate_id_to_dap(dct['id'])
-        return dct
-
-
-@register
-class Scope(BaseSchema):
-    """
-    A Scope is a named container for variables. Optionally a scope can map to a source or a range within
-    a source.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "name": {
-            "type": "string",
-            "description": "Name of the scope such as 'Arguments', 'Locals', or 'Registers'. This string is shown in the UI as is and can be translated."
-        },
-        "presentationHint": {
-            "type": "string",
-            "description": "An optional hint for how to present this scope in the UI. If this attribute is missing, the scope is shown with a generic UI.",
-            "_enum": [
-                "arguments",
-                "locals",
-                "registers"
-            ],
-            "enumDescriptions": [
-                "Scope contains method arguments.",
-                "Scope contains local variables.",
-                "Scope contains registers. Only a single 'registers' scope should be returned from a 'scopes' request."
-            ]
-        },
-        "variablesReference": {
-            "type": "integer",
-            "description": "The variables of this scope can be retrieved by passing the value of variablesReference to the VariablesRequest."
-        },
-        "namedVariables": {
-            "type": "integer",
-            "description": "The number of named variables in this scope.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks."
-        },
-        "indexedVariables": {
-            "type": "integer",
-            "description": "The number of indexed variables in this scope.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks."
-        },
-        "expensive": {
-            "type": "boolean",
-            "description": "If true, the number of variables in this scope is large or expensive to retrieve."
-        },
-        "source": {
-            "description": "Optional source for this scope.",
-            "type": "Source"
-        },
-        "line": {
-            "type": "integer",
-            "description": "Optional start line of the range covered by this scope."
-        },
-        "column": {
-            "type": "integer",
-            "description": "Optional start column of the range covered by this scope."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "Optional end line of the range covered by this scope."
-        },
-        "endColumn": {
-            "type": "integer",
-            "description": "Optional end column of the range covered by this scope."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name, variablesReference, expensive, presentationHint=None, namedVariables=None, indexedVariables=None, source=None, line=None, column=None, endLine=None, endColumn=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: Name of the scope such as 'Arguments', 'Locals', or 'Registers'. This string is shown in the UI as is and can be translated.
-        :param integer variablesReference: The variables of this scope can be retrieved by passing the value of variablesReference to the VariablesRequest.
-        :param boolean expensive: If true, the number of variables in this scope is large or expensive to retrieve.
-        :param string presentationHint: An optional hint for how to present this scope in the UI. If this attribute is missing, the scope is shown with a generic UI.
-        :param integer namedVariables: The number of named variables in this scope.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        :param integer indexedVariables: The number of indexed variables in this scope.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        :param Source source: Optional source for this scope.
-        :param integer line: Optional start line of the range covered by this scope.
-        :param integer column: Optional start column of the range covered by this scope.
-        :param integer endLine: Optional end line of the range covered by this scope.
-        :param integer endColumn: Optional end column of the range covered by this scope.
-        """
-        self.name = name
-        self.variablesReference = variablesReference
-        self.expensive = expensive
-        self.presentationHint = presentationHint
-        self.namedVariables = namedVariables
-        self.indexedVariables = indexedVariables
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.line = line
-        self.column = column
-        self.endLine = endLine
-        self.endColumn = endColumn
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        variablesReference = self.variablesReference
-        expensive = self.expensive
-        presentationHint = self.presentationHint
-        namedVariables = self.namedVariables
-        indexedVariables = self.indexedVariables
-        source = self.source
-        line = self.line
-        column = self.column
-        endLine = self.endLine
-        endColumn = self.endColumn
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'name': name,
-            'variablesReference': variablesReference,
-            'expensive': expensive,
-        }
-        if presentationHint is not None:
-            dct['presentationHint'] = presentationHint
-        if namedVariables is not None:
-            dct['namedVariables'] = namedVariables
-        if indexedVariables is not None:
-            dct['indexedVariables'] = indexedVariables
-        if source is not None:
-            dct['source'] = source.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if line is not None:
-            dct['line'] = line
-        if column is not None:
-            dct['column'] = column
-        if endLine is not None:
-            dct['endLine'] = endLine
-        if endColumn is not None:
-            dct['endColumn'] = endColumn
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register
-class Variable(BaseSchema):
-    """
-    A Variable is a name/value pair.
-    
-    Optionally a variable can have a 'type' that is shown if space permits or when hovering over the
-    variable's name.
-    
-    An optional 'kind' is used to render additional properties of the variable, e.g. different icons can
-    be used to indicate that a variable is public or private.
-    
-    If the value is structured (has children), a handle is provided to retrieve the children with the
-    VariablesRequest.
-    
-    If the number of named or indexed children is large, the numbers should be returned via the optional
-    'namedVariables' and 'indexedVariables' attributes.
-    
-    The client can use this optional information to present the children in a paged UI and fetch them in
-    chunks.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "name": {
-            "type": "string",
-            "description": "The variable's name."
-        },
-        "value": {
-            "type": "string",
-            "description": "The variable's value. This can be a multi-line text, e.g. for a function the body of a function."
-        },
-        "type": {
-            "type": "string",
-            "description": "The type of the variable's value. Typically shown in the UI when hovering over the value.\nThis attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request."
-        },
-        "presentationHint": {
-            "description": "Properties of a variable that can be used to determine how to render the variable in the UI.",
-            "type": "VariablePresentationHint"
-        },
-        "evaluateName": {
-            "type": "string",
-            "description": "Optional evaluatable name of this variable which can be passed to the 'EvaluateRequest' to fetch the variable's value."
-        },
-        "variablesReference": {
-            "type": "integer",
-            "description": "If variablesReference is > 0, the variable is structured and its children can be retrieved by passing variablesReference to the VariablesRequest."
-        },
-        "namedVariables": {
-            "type": "integer",
-            "description": "The number of named child variables.\nThe client can use this optional information to present the children in a paged UI and fetch them in chunks."
-        },
-        "indexedVariables": {
-            "type": "integer",
-            "description": "The number of indexed child variables.\nThe client can use this optional information to present the children in a paged UI and fetch them in chunks."
-        },
-        "memoryReference": {
-            "type": "string",
-            "description": "Optional memory reference for the variable if the variable represents executable code, such as a function pointer.\nThis attribute is only required if the client has passed the value true for the 'supportsMemoryReferences' capability of the 'initialize' request."
-        }
-    }
-    __refs__ = set(['presentationHint'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name, value, variablesReference, type=None, presentationHint=None, evaluateName=None, namedVariables=None, indexedVariables=None, memoryReference=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: The variable's name.
-        :param string value: The variable's value. This can be a multi-line text, e.g. for a function the body of a function.
-        :param integer variablesReference: If variablesReference is > 0, the variable is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.
-        :param string type: The type of the variable's value. Typically shown in the UI when hovering over the value.
-        This attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request.
-        :param VariablePresentationHint presentationHint: Properties of a variable that can be used to determine how to render the variable in the UI.
-        :param string evaluateName: Optional evaluatable name of this variable which can be passed to the 'EvaluateRequest' to fetch the variable's value.
-        :param integer namedVariables: The number of named child variables.
-        The client can use this optional information to present the children in a paged UI and fetch them in chunks.
-        :param integer indexedVariables: The number of indexed child variables.
-        The client can use this optional information to present the children in a paged UI and fetch them in chunks.
-        :param string memoryReference: Optional memory reference for the variable if the variable represents executable code, such as a function pointer.
-        This attribute is only required if the client has passed the value true for the 'supportsMemoryReferences' capability of the 'initialize' request.
-        """
-        self.name = name
-        self.value = value
-        self.variablesReference = variablesReference
-        self.type = type
-        if presentationHint is None:
-            self.presentationHint = VariablePresentationHint()
-        else:
-            self.presentationHint = VariablePresentationHint(update_ids_from_dap=update_ids_from_dap, **presentationHint) if presentationHint.__class__ !=  VariablePresentationHint else presentationHint
-        self.evaluateName = evaluateName
-        self.namedVariables = namedVariables
-        self.indexedVariables = indexedVariables
-        self.memoryReference = memoryReference
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        value = self.value
-        variablesReference = self.variablesReference
-        type = self.type  # noqa (assign to builtin)
-        presentationHint = self.presentationHint
-        evaluateName = self.evaluateName
-        namedVariables = self.namedVariables
-        indexedVariables = self.indexedVariables
-        memoryReference = self.memoryReference
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'name': name,
-            'value': value,
-            'variablesReference': variablesReference,
-        }
-        if type is not None:
-            dct['type'] = type
-        if presentationHint is not None:
-            dct['presentationHint'] = presentationHint.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if evaluateName is not None:
-            dct['evaluateName'] = evaluateName
-        if namedVariables is not None:
-            dct['namedVariables'] = namedVariables
-        if indexedVariables is not None:
-            dct['indexedVariables'] = indexedVariables
-        if memoryReference is not None:
-            dct['memoryReference'] = memoryReference
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register
-class VariablePresentationHint(BaseSchema):
-    """
-    Optional properties of a variable that can be used to determine how to render the variable in the
-    UI.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "kind": {
-            "description": "The kind of variable. Before introducing additional values, try to use the listed values.",
-            "type": "string",
-            "_enum": [
-                "property",
-                "method",
-                "class",
-                "data",
-                "event",
-                "baseClass",
-                "innerClass",
-                "interface",
-                "mostDerivedClass",
-                "virtual",
-                "dataBreakpoint"
-            ],
-            "enumDescriptions": [
-                "Indicates that the object is a property.",
-                "Indicates that the object is a method.",
-                "Indicates that the object is a class.",
-                "Indicates that the object is data.",
-                "Indicates that the object is an event.",
-                "Indicates that the object is a base class.",
-                "Indicates that the object is an inner class.",
-                "Indicates that the object is an interface.",
-                "Indicates that the object is the most derived class.",
-                "Indicates that the object is virtual, that means it is a synthetic object introducedby the\nadapter for rendering purposes, e.g. an index range for large arrays.",
-                "Deprecated: Indicates that a data breakpoint is registered for the object. The 'hasDataBreakpoint' attribute should generally be used instead."
-            ]
-        },
-        "attributes": {
-            "description": "Set of attributes represented as an array of strings. Before introducing additional values, try to use the listed values.",
-            "type": "array",
-            "items": {
-                "type": "string",
-                "_enum": [
-                    "static",
-                    "constant",
-                    "readOnly",
-                    "rawString",
-                    "hasObjectId",
-                    "canHaveObjectId",
-                    "hasSideEffects",
-                    "hasDataBreakpoint"
-                ],
-                "enumDescriptions": [
-                    "Indicates that the object is static.",
-                    "Indicates that the object is a constant.",
-                    "Indicates that the object is read only.",
-                    "Indicates that the object is a raw string.",
-                    "Indicates that the object can have an Object ID created for it.",
-                    "Indicates that the object has an Object ID associated with it.",
-                    "Indicates that the evaluation had side effects.",
-                    "Indicates that the object has its value tracked by a data breakpoint."
-                ]
-            }
-        },
-        "visibility": {
-            "description": "Visibility of variable. Before introducing additional values, try to use the listed values.",
-            "type": "string",
-            "_enum": [
-                "public",
-                "private",
-                "protected",
-                "internal",
-                "final"
-            ]
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, kind=None, attributes=None, visibility=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string kind: The kind of variable. Before introducing additional values, try to use the listed values.
-        :param array attributes: Set of attributes represented as an array of strings. Before introducing additional values, try to use the listed values.
-        :param string visibility: Visibility of variable. Before introducing additional values, try to use the listed values.
-        """
-        self.kind = kind
-        self.attributes = attributes
-        self.visibility = visibility
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        kind = self.kind
-        attributes = self.attributes
-        if attributes and hasattr(attributes[0], "to_dict"):
-            attributes = [x.to_dict() for x in attributes]
-        visibility = self.visibility
-        dct = {
-        }
-        if kind is not None:
-            dct['kind'] = kind
-        if attributes is not None:
-            dct['attributes'] = attributes
-        if visibility is not None:
-            dct['visibility'] = visibility
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class BreakpointLocation(BaseSchema):
-    """
-    Properties of a breakpoint location returned from the 'breakpointLocations' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "line": {
-            "type": "integer",
-            "description": "Start line of breakpoint location."
-        },
-        "column": {
-            "type": "integer",
-            "description": "Optional start column of breakpoint location."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "Optional end line of breakpoint location if the location covers a range."
-        },
-        "endColumn": {
-            "type": "integer",
-            "description": "Optional end column of breakpoint location if the location covers a range."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, line, column=None, endLine=None, endColumn=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer line: Start line of breakpoint location.
-        :param integer column: Optional start column of breakpoint location.
-        :param integer endLine: Optional end line of breakpoint location if the location covers a range.
-        :param integer endColumn: Optional end column of breakpoint location if the location covers a range.
-        """
-        self.line = line
-        self.column = column
-        self.endLine = endLine
-        self.endColumn = endColumn
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        line = self.line
-        column = self.column
-        endLine = self.endLine
-        endColumn = self.endColumn
-        dct = {
-            'line': line,
-        }
-        if column is not None:
-            dct['column'] = column
-        if endLine is not None:
-            dct['endLine'] = endLine
-        if endColumn is not None:
-            dct['endColumn'] = endColumn
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SourceBreakpoint(BaseSchema):
-    """
-    Properties of a breakpoint or logpoint passed to the setBreakpoints request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "line": {
-            "type": "integer",
-            "description": "The source line of the breakpoint or logpoint."
-        },
-        "column": {
-            "type": "integer",
-            "description": "An optional source column of the breakpoint."
-        },
-        "condition": {
-            "type": "string",
-            "description": "An optional expression for conditional breakpoints.\nIt is only honored by a debug adapter if the capability 'supportsConditionalBreakpoints' is true."
-        },
-        "hitCondition": {
-            "type": "string",
-            "description": "An optional expression that controls how many hits of the breakpoint are ignored.\nThe backend is expected to interpret the expression as needed.\nThe attribute is only honored by a debug adapter if the capability 'supportsHitConditionalBreakpoints' is true."
-        },
-        "logMessage": {
-            "type": "string",
-            "description": "If this attribute exists and is non-empty, the backend must not 'break' (stop)\nbut log the message instead. Expressions within {} are interpolated.\nThe attribute is only honored by a debug adapter if the capability 'supportsLogPoints' is true."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, line, column=None, condition=None, hitCondition=None, logMessage=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer line: The source line of the breakpoint or logpoint.
-        :param integer column: An optional source column of the breakpoint.
-        :param string condition: An optional expression for conditional breakpoints.
-        It is only honored by a debug adapter if the capability 'supportsConditionalBreakpoints' is true.
-        :param string hitCondition: An optional expression that controls how many hits of the breakpoint are ignored.
-        The backend is expected to interpret the expression as needed.
-        The attribute is only honored by a debug adapter if the capability 'supportsHitConditionalBreakpoints' is true.
-        :param string logMessage: If this attribute exists and is non-empty, the backend must not 'break' (stop)
-        but log the message instead. Expressions within {} are interpolated.
-        The attribute is only honored by a debug adapter if the capability 'supportsLogPoints' is true.
-        """
-        self.line = line
-        self.column = column
-        self.condition = condition
-        self.hitCondition = hitCondition
-        self.logMessage = logMessage
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        line = self.line
-        column = self.column
-        condition = self.condition
-        hitCondition = self.hitCondition
-        logMessage = self.logMessage
-        dct = {
-            'line': line,
-        }
-        if column is not None:
-            dct['column'] = column
-        if condition is not None:
-            dct['condition'] = condition
-        if hitCondition is not None:
-            dct['hitCondition'] = hitCondition
-        if logMessage is not None:
-            dct['logMessage'] = logMessage
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class FunctionBreakpoint(BaseSchema):
-    """
-    Properties of a breakpoint passed to the setFunctionBreakpoints request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "name": {
-            "type": "string",
-            "description": "The name of the function."
-        },
-        "condition": {
-            "type": "string",
-            "description": "An optional expression for conditional breakpoints.\nIt is only honored by a debug adapter if the capability 'supportsConditionalBreakpoints' is true."
-        },
-        "hitCondition": {
-            "type": "string",
-            "description": "An optional expression that controls how many hits of the breakpoint are ignored.\nThe backend is expected to interpret the expression as needed.\nThe attribute is only honored by a debug adapter if the capability 'supportsHitConditionalBreakpoints' is true."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name, condition=None, hitCondition=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: The name of the function.
-        :param string condition: An optional expression for conditional breakpoints.
-        It is only honored by a debug adapter if the capability 'supportsConditionalBreakpoints' is true.
-        :param string hitCondition: An optional expression that controls how many hits of the breakpoint are ignored.
-        The backend is expected to interpret the expression as needed.
-        The attribute is only honored by a debug adapter if the capability 'supportsHitConditionalBreakpoints' is true.
-        """
-        self.name = name
-        self.condition = condition
-        self.hitCondition = hitCondition
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        condition = self.condition
-        hitCondition = self.hitCondition
-        dct = {
-            'name': name,
-        }
-        if condition is not None:
-            dct['condition'] = condition
-        if hitCondition is not None:
-            dct['hitCondition'] = hitCondition
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DataBreakpointAccessType(BaseSchema):
-    """
-    This enumeration defines all possible access types for data breakpoints.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    READ = 'read'
-    WRITE = 'write'
-    READWRITE = 'readWrite'
-
-    VALID_VALUES = set(['read', 'write', 'readWrite'])
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DataBreakpoint(BaseSchema):
-    """
-    Properties of a data breakpoint passed to the setDataBreakpoints request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "dataId": {
-            "type": "string",
-            "description": "An id representing the data. This id is returned from the dataBreakpointInfo request."
-        },
-        "accessType": {
-            "description": "The access type of the data.",
-            "type": "DataBreakpointAccessType"
-        },
-        "condition": {
-            "type": "string",
-            "description": "An optional expression for conditional breakpoints."
-        },
-        "hitCondition": {
-            "type": "string",
-            "description": "An optional expression that controls how many hits of the breakpoint are ignored.\nThe backend is expected to interpret the expression as needed."
-        }
-    }
-    __refs__ = set(['accessType'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, dataId, accessType=None, condition=None, hitCondition=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string dataId: An id representing the data. This id is returned from the dataBreakpointInfo request.
-        :param DataBreakpointAccessType accessType: The access type of the data.
-        :param string condition: An optional expression for conditional breakpoints.
-        :param string hitCondition: An optional expression that controls how many hits of the breakpoint are ignored.
-        The backend is expected to interpret the expression as needed.
-        """
-        self.dataId = dataId
-        if accessType is not None:
-            assert accessType in DataBreakpointAccessType.VALID_VALUES
-        self.accessType = accessType
-        self.condition = condition
-        self.hitCondition = hitCondition
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dataId = self.dataId
-        accessType = self.accessType
-        condition = self.condition
-        hitCondition = self.hitCondition
-        dct = {
-            'dataId': dataId,
-        }
-        if accessType is not None:
-            dct['accessType'] = accessType
-        if condition is not None:
-            dct['condition'] = condition
-        if hitCondition is not None:
-            dct['hitCondition'] = hitCondition
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class InstructionBreakpoint(BaseSchema):
-    """
-    Properties of a breakpoint passed to the setInstructionBreakpoints request
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "instructionReference": {
-            "type": "string",
-            "description": "The instruction reference of the breakpoint.\nThis should be a memory or instruction pointer reference from an EvaluateResponse, Variable, StackFrame, GotoTarget, or Breakpoint."
-        },
-        "offset": {
-            "type": "integer",
-            "description": "An optional offset from the instruction reference.\nThis can be negative."
-        },
-        "condition": {
-            "type": "string",
-            "description": "An optional expression for conditional breakpoints.\nIt is only honored by a debug adapter if the capability 'supportsConditionalBreakpoints' is true."
-        },
-        "hitCondition": {
-            "type": "string",
-            "description": "An optional expression that controls how many hits of the breakpoint are ignored.\nThe backend is expected to interpret the expression as needed.\nThe attribute is only honored by a debug adapter if the capability 'supportsHitConditionalBreakpoints' is true."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, instructionReference, offset=None, condition=None, hitCondition=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string instructionReference: The instruction reference of the breakpoint.
-        This should be a memory or instruction pointer reference from an EvaluateResponse, Variable, StackFrame, GotoTarget, or Breakpoint.
-        :param integer offset: An optional offset from the instruction reference.
-        This can be negative.
-        :param string condition: An optional expression for conditional breakpoints.
-        It is only honored by a debug adapter if the capability 'supportsConditionalBreakpoints' is true.
-        :param string hitCondition: An optional expression that controls how many hits of the breakpoint are ignored.
-        The backend is expected to interpret the expression as needed.
-        The attribute is only honored by a debug adapter if the capability 'supportsHitConditionalBreakpoints' is true.
-        """
-        self.instructionReference = instructionReference
-        self.offset = offset
-        self.condition = condition
-        self.hitCondition = hitCondition
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        instructionReference = self.instructionReference
-        offset = self.offset
-        condition = self.condition
-        hitCondition = self.hitCondition
-        dct = {
-            'instructionReference': instructionReference,
-        }
-        if offset is not None:
-            dct['offset'] = offset
-        if condition is not None:
-            dct['condition'] = condition
-        if hitCondition is not None:
-            dct['hitCondition'] = hitCondition
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Breakpoint(BaseSchema):
-    """
-    Information about a Breakpoint created in setBreakpoints, setFunctionBreakpoints,
-    setInstructionBreakpoints, or setDataBreakpoints.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "id": {
-            "type": "integer",
-            "description": "An optional identifier for the breakpoint. It is needed if breakpoint events are used to update or remove breakpoints."
-        },
-        "verified": {
-            "type": "boolean",
-            "description": "If true breakpoint could be set (but not necessarily at the desired location)."
-        },
-        "message": {
-            "type": "string",
-            "description": "An optional message about the state of the breakpoint.\nThis is shown to the user and can be used to explain why a breakpoint could not be verified."
-        },
-        "source": {
-            "description": "The source where the breakpoint is located.",
-            "type": "Source"
-        },
-        "line": {
-            "type": "integer",
-            "description": "The start line of the actual range covered by the breakpoint."
-        },
-        "column": {
-            "type": "integer",
-            "description": "An optional start column of the actual range covered by the breakpoint."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "An optional end line of the actual range covered by the breakpoint."
-        },
-        "endColumn": {
-            "type": "integer",
-            "description": "An optional end column of the actual range covered by the breakpoint.\nIf no end line is given, then the end column is assumed to be in the start line."
-        },
-        "instructionReference": {
-            "type": "string",
-            "description": "An optional memory reference to where the breakpoint is set."
-        },
-        "offset": {
-            "type": "integer",
-            "description": "An optional offset from the instruction reference.\nThis can be negative."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, verified, id=None, message=None, source=None, line=None, column=None, endLine=None, endColumn=None, instructionReference=None, offset=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean verified: If true breakpoint could be set (but not necessarily at the desired location).
-        :param integer id: An optional identifier for the breakpoint. It is needed if breakpoint events are used to update or remove breakpoints.
-        :param string message: An optional message about the state of the breakpoint.
-        This is shown to the user and can be used to explain why a breakpoint could not be verified.
-        :param Source source: The source where the breakpoint is located.
-        :param integer line: The start line of the actual range covered by the breakpoint.
-        :param integer column: An optional start column of the actual range covered by the breakpoint.
-        :param integer endLine: An optional end line of the actual range covered by the breakpoint.
-        :param integer endColumn: An optional end column of the actual range covered by the breakpoint.
-        If no end line is given, then the end column is assumed to be in the start line.
-        :param string instructionReference: An optional memory reference to where the breakpoint is set.
-        :param integer offset: An optional offset from the instruction reference.
-        This can be negative.
-        """
-        self.verified = verified
-        self.id = id
-        self.message = message
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.line = line
-        self.column = column
-        self.endLine = endLine
-        self.endColumn = endColumn
-        self.instructionReference = instructionReference
-        self.offset = offset
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        verified = self.verified
-        id = self.id  # noqa (assign to builtin)
-        message = self.message
-        source = self.source
-        line = self.line
-        column = self.column
-        endLine = self.endLine
-        endColumn = self.endColumn
-        instructionReference = self.instructionReference
-        offset = self.offset
-        dct = {
-            'verified': verified,
-        }
-        if id is not None:
-            dct['id'] = id
-        if message is not None:
-            dct['message'] = message
-        if source is not None:
-            dct['source'] = source.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if line is not None:
-            dct['line'] = line
-        if column is not None:
-            dct['column'] = column
-        if endLine is not None:
-            dct['endLine'] = endLine
-        if endColumn is not None:
-            dct['endColumn'] = endColumn
-        if instructionReference is not None:
-            dct['instructionReference'] = instructionReference
-        if offset is not None:
-            dct['offset'] = offset
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SteppingGranularity(BaseSchema):
-    """
-    The granularity of one 'step' in the stepping requests 'next', 'stepIn', 'stepOut', and 'stepBack'.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    STATEMENT = 'statement'
-    LINE = 'line'
-    INSTRUCTION = 'instruction'
-
-    VALID_VALUES = set(['statement', 'line', 'instruction'])
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StepInTarget(BaseSchema):
-    """
-    A StepInTarget can be used in the 'stepIn' request and determines into which single target the
-    stepIn request should step.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "id": {
-            "type": "integer",
-            "description": "Unique identifier for a stepIn target."
-        },
-        "label": {
-            "type": "string",
-            "description": "The name of the stepIn target (shown in the UI)."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, id, label, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer id: Unique identifier for a stepIn target.
-        :param string label: The name of the stepIn target (shown in the UI).
-        """
-        self.id = id
-        self.label = label
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        id = self.id  # noqa (assign to builtin)
-        label = self.label
-        dct = {
-            'id': id,
-            'label': label,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class GotoTarget(BaseSchema):
-    """
-    A GotoTarget describes a code location that can be used as a target in the 'goto' request.
-    
-    The possible goto targets can be determined via the 'gotoTargets' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "id": {
-            "type": "integer",
-            "description": "Unique identifier for a goto target. This is used in the goto request."
-        },
-        "label": {
-            "type": "string",
-            "description": "The name of the goto target (shown in the UI)."
-        },
-        "line": {
-            "type": "integer",
-            "description": "The line of the goto target."
-        },
-        "column": {
-            "type": "integer",
-            "description": "An optional column of the goto target."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "An optional end line of the range covered by the goto target."
-        },
-        "endColumn": {
-            "type": "integer",
-            "description": "An optional end column of the range covered by the goto target."
-        },
-        "instructionPointerReference": {
-            "type": "string",
-            "description": "Optional memory reference for the instruction pointer value represented by this target."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, id, label, line, column=None, endLine=None, endColumn=None, instructionPointerReference=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer id: Unique identifier for a goto target. This is used in the goto request.
-        :param string label: The name of the goto target (shown in the UI).
-        :param integer line: The line of the goto target.
-        :param integer column: An optional column of the goto target.
-        :param integer endLine: An optional end line of the range covered by the goto target.
-        :param integer endColumn: An optional end column of the range covered by the goto target.
-        :param string instructionPointerReference: Optional memory reference for the instruction pointer value represented by this target.
-        """
-        self.id = id
-        self.label = label
-        self.line = line
-        self.column = column
-        self.endLine = endLine
-        self.endColumn = endColumn
-        self.instructionPointerReference = instructionPointerReference
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        id = self.id  # noqa (assign to builtin)
-        label = self.label
-        line = self.line
-        column = self.column
-        endLine = self.endLine
-        endColumn = self.endColumn
-        instructionPointerReference = self.instructionPointerReference
-        dct = {
-            'id': id,
-            'label': label,
-            'line': line,
-        }
-        if column is not None:
-            dct['column'] = column
-        if endLine is not None:
-            dct['endLine'] = endLine
-        if endColumn is not None:
-            dct['endColumn'] = endColumn
-        if instructionPointerReference is not None:
-            dct['instructionPointerReference'] = instructionPointerReference
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class CompletionItem(BaseSchema):
-    """
-    CompletionItems are the suggestions returned from the CompletionsRequest.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "label": {
-            "type": "string",
-            "description": "The label of this completion item. By default this is also the text that is inserted when selecting this completion."
-        },
-        "text": {
-            "type": "string",
-            "description": "If text is not falsy then it is inserted instead of the label."
-        },
-        "sortText": {
-            "type": "string",
-            "description": "A string that should be used when comparing this item with other items. When `falsy` the label is used."
-        },
-        "type": {
-            "description": "The item's type. Typically the client uses this information to render the item in the UI with an icon.",
-            "type": "CompletionItemType"
-        },
-        "start": {
-            "type": "integer",
-            "description": "This value determines the location (in the CompletionsRequest's 'text' attribute) where the completion text is added.\nIf missing the text is added at the location specified by the CompletionsRequest's 'column' attribute."
-        },
-        "length": {
-            "type": "integer",
-            "description": "This value determines how many characters are overwritten by the completion text.\nIf missing the value 0 is assumed which results in the completion text being inserted."
-        },
-        "selectionStart": {
-            "type": "integer",
-            "description": "Determines the start of the new selection after the text has been inserted (or replaced).\nThe start position must in the range 0 and length of the completion text.\nIf omitted the selection starts at the end of the completion text."
-        },
-        "selectionLength": {
-            "type": "integer",
-            "description": "Determines the length of the new selection after the text has been inserted (or replaced).\nThe selection can not extend beyond the bounds of the completion text.\nIf omitted the length is assumed to be 0."
-        }
-    }
-    __refs__ = set(['type'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, label, text=None, sortText=None, type=None, start=None, length=None, selectionStart=None, selectionLength=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string label: The label of this completion item. By default this is also the text that is inserted when selecting this completion.
-        :param string text: If text is not falsy then it is inserted instead of the label.
-        :param string sortText: A string that should be used when comparing this item with other items. When `falsy` the label is used.
-        :param CompletionItemType type: The item's type. Typically the client uses this information to render the item in the UI with an icon.
-        :param integer start: This value determines the location (in the CompletionsRequest's 'text' attribute) where the completion text is added.
-        If missing the text is added at the location specified by the CompletionsRequest's 'column' attribute.
-        :param integer length: This value determines how many characters are overwritten by the completion text.
-        If missing the value 0 is assumed which results in the completion text being inserted.
-        :param integer selectionStart: Determines the start of the new selection after the text has been inserted (or replaced).
-        The start position must in the range 0 and length of the completion text.
-        If omitted the selection starts at the end of the completion text.
-        :param integer selectionLength: Determines the length of the new selection after the text has been inserted (or replaced).
-        The selection can not extend beyond the bounds of the completion text.
-        If omitted the length is assumed to be 0.
-        """
-        self.label = label
-        self.text = text
-        self.sortText = sortText
-        if type is not None:
-            assert type in CompletionItemType.VALID_VALUES
-        self.type = type
-        self.start = start
-        self.length = length
-        self.selectionStart = selectionStart
-        self.selectionLength = selectionLength
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        label = self.label
-        text = self.text
-        sortText = self.sortText
-        type = self.type  # noqa (assign to builtin)
-        start = self.start
-        length = self.length
-        selectionStart = self.selectionStart
-        selectionLength = self.selectionLength
-        dct = {
-            'label': label,
-        }
-        if text is not None:
-            dct['text'] = text
-        if sortText is not None:
-            dct['sortText'] = sortText
-        if type is not None:
-            dct['type'] = type
-        if start is not None:
-            dct['start'] = start
-        if length is not None:
-            dct['length'] = length
-        if selectionStart is not None:
-            dct['selectionStart'] = selectionStart
-        if selectionLength is not None:
-            dct['selectionLength'] = selectionLength
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class CompletionItemType(BaseSchema):
-    """
-    Some predefined types for the CompletionItem. Please note that not all clients have specific icons
-    for all of them.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    METHOD = 'method'
-    FUNCTION = 'function'
-    CONSTRUCTOR = 'constructor'
-    FIELD = 'field'
-    VARIABLE = 'variable'
-    CLASS = 'class'
-    INTERFACE = 'interface'
-    MODULE = 'module'
-    PROPERTY = 'property'
-    UNIT = 'unit'
-    VALUE = 'value'
-    ENUM = 'enum'
-    KEYWORD = 'keyword'
-    SNIPPET = 'snippet'
-    TEXT = 'text'
-    COLOR = 'color'
-    FILE = 'file'
-    REFERENCE = 'reference'
-    CUSTOMCOLOR = 'customcolor'
-
-    VALID_VALUES = set(['method', 'function', 'constructor', 'field', 'variable', 'class', 'interface', 'module', 'property', 'unit', 'value', 'enum', 'keyword', 'snippet', 'text', 'color', 'file', 'reference', 'customcolor'])
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ChecksumAlgorithm(BaseSchema):
-    """
-    Names of checksum algorithms that may be supported by a debug adapter.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    MD5 = 'MD5'
-    SHA1 = 'SHA1'
-    SHA256 = 'SHA256'
-    TIMESTAMP = 'timestamp'
-
-    VALID_VALUES = set(['MD5', 'SHA1', 'SHA256', 'timestamp'])
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class Checksum(BaseSchema):
-    """
-    The checksum of an item calculated by the specified algorithm.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "algorithm": {
-            "description": "The algorithm used to calculate this checksum.",
-            "type": "ChecksumAlgorithm"
-        },
-        "checksum": {
-            "type": "string",
-            "description": "Value of the checksum."
-        }
-    }
-    __refs__ = set(['algorithm'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, algorithm, checksum, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param ChecksumAlgorithm algorithm: The algorithm used to calculate this checksum.
-        :param string checksum: Value of the checksum.
-        """
-        if algorithm is not None:
-            assert algorithm in ChecksumAlgorithm.VALID_VALUES
-        self.algorithm = algorithm
-        self.checksum = checksum
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        algorithm = self.algorithm
-        checksum = self.checksum
-        dct = {
-            'algorithm': algorithm,
-            'checksum': checksum,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ValueFormat(BaseSchema):
-    """
-    Provides formatting information for a value.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "hex": {
-            "type": "boolean",
-            "description": "Display the value in hex."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, hex=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean hex: Display the value in hex.
-        """
-        self.hex = hex
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        hex = self.hex  # noqa (assign to builtin)
-        dct = {
-        }
-        if hex is not None:
-            dct['hex'] = hex
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StackFrameFormat(BaseSchema):
-    """
-    Provides formatting information for a stack frame.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "hex": {
-            "type": "boolean",
-            "description": "Display the value in hex."
-        },
-        "parameters": {
-            "type": "boolean",
-            "description": "Displays parameters for the stack frame."
-        },
-        "parameterTypes": {
-            "type": "boolean",
-            "description": "Displays the types of parameters for the stack frame."
-        },
-        "parameterNames": {
-            "type": "boolean",
-            "description": "Displays the names of parameters for the stack frame."
-        },
-        "parameterValues": {
-            "type": "boolean",
-            "description": "Displays the values of parameters for the stack frame."
-        },
-        "line": {
-            "type": "boolean",
-            "description": "Displays the line number of the stack frame."
-        },
-        "module": {
-            "type": "boolean",
-            "description": "Displays the module of the stack frame."
-        },
-        "includeAll": {
-            "type": "boolean",
-            "description": "Includes all stack frames, including those the debug adapter might otherwise hide."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, hex=None, parameters=None, parameterTypes=None, parameterNames=None, parameterValues=None, line=None, module=None, includeAll=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean hex: Display the value in hex.
-        :param boolean parameters: Displays parameters for the stack frame.
-        :param boolean parameterTypes: Displays the types of parameters for the stack frame.
-        :param boolean parameterNames: Displays the names of parameters for the stack frame.
-        :param boolean parameterValues: Displays the values of parameters for the stack frame.
-        :param boolean line: Displays the line number of the stack frame.
-        :param boolean module: Displays the module of the stack frame.
-        :param boolean includeAll: Includes all stack frames, including those the debug adapter might otherwise hide.
-        """
-        self.hex = hex
-        self.parameters = parameters
-        self.parameterTypes = parameterTypes
-        self.parameterNames = parameterNames
-        self.parameterValues = parameterValues
-        self.line = line
-        self.module = module
-        self.includeAll = includeAll
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        hex = self.hex  # noqa (assign to builtin)
-        parameters = self.parameters
-        parameterTypes = self.parameterTypes
-        parameterNames = self.parameterNames
-        parameterValues = self.parameterValues
-        line = self.line
-        module = self.module
-        includeAll = self.includeAll
-        dct = {
-        }
-        if hex is not None:
-            dct['hex'] = hex
-        if parameters is not None:
-            dct['parameters'] = parameters
-        if parameterTypes is not None:
-            dct['parameterTypes'] = parameterTypes
-        if parameterNames is not None:
-            dct['parameterNames'] = parameterNames
-        if parameterValues is not None:
-            dct['parameterValues'] = parameterValues
-        if line is not None:
-            dct['line'] = line
-        if module is not None:
-            dct['module'] = module
-        if includeAll is not None:
-            dct['includeAll'] = includeAll
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionFilterOptions(BaseSchema):
-    """
-    An ExceptionFilterOptions is used to specify an exception filter together with a condition for the
-    setExceptionsFilter request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "filterId": {
-            "type": "string",
-            "description": "ID of an exception filter returned by the 'exceptionBreakpointFilters' capability."
-        },
-        "condition": {
-            "type": "string",
-            "description": "An optional expression for conditional exceptions.\nThe exception will break into the debugger if the result of the condition is true."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, filterId, condition=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string filterId: ID of an exception filter returned by the 'exceptionBreakpointFilters' capability.
-        :param string condition: An optional expression for conditional exceptions.
-        The exception will break into the debugger if the result of the condition is true.
-        """
-        self.filterId = filterId
-        self.condition = condition
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        filterId = self.filterId
-        condition = self.condition
-        dct = {
-            'filterId': filterId,
-        }
-        if condition is not None:
-            dct['condition'] = condition
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionOptions(BaseSchema):
-    """
-    An ExceptionOptions assigns configuration options to a set of exceptions.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "path": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ExceptionPathSegment"
-            },
-            "description": "A path that selects a single or multiple exceptions in a tree. If 'path' is missing, the whole tree is selected.\nBy convention the first segment of the path is a category that is used to group exceptions in the UI."
-        },
-        "breakMode": {
-            "description": "Condition when a thrown exception should result in a break.",
-            "type": "ExceptionBreakMode"
-        }
-    }
-    __refs__ = set(['breakMode'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakMode, path=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param ExceptionBreakMode breakMode: Condition when a thrown exception should result in a break.
-        :param array path: A path that selects a single or multiple exceptions in a tree. If 'path' is missing, the whole tree is selected.
-        By convention the first segment of the path is a category that is used to group exceptions in the UI.
-        """
-        if breakMode is not None:
-            assert breakMode in ExceptionBreakMode.VALID_VALUES
-        self.breakMode = breakMode
-        self.path = path
-        if update_ids_from_dap and self.path:
-            for o in self.path:
-                ExceptionPathSegment.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakMode = self.breakMode
-        path = self.path
-        if path and hasattr(path[0], "to_dict"):
-            path = [x.to_dict() for x in path]
-        dct = {
-            'breakMode': breakMode,
-        }
-        if path is not None:
-            dct['path'] = [ExceptionPathSegment.update_dict_ids_to_dap(o) for o in path] if (update_ids_to_dap and path) else path
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionBreakMode(BaseSchema):
-    """
-    This enumeration defines all possible conditions when a thrown exception should result in a break.
-    
-    never: never breaks,
-    
-    always: always breaks,
-    
-    unhandled: breaks when exception unhandled,
-    
-    userUnhandled: breaks if the exception is not handled by user code.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    NEVER = 'never'
-    ALWAYS = 'always'
-    UNHANDLED = 'unhandled'
-    USERUNHANDLED = 'userUnhandled'
-
-    VALID_VALUES = set(['never', 'always', 'unhandled', 'userUnhandled'])
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionPathSegment(BaseSchema):
-    """
-    An ExceptionPathSegment represents a segment in a path that is used to match leafs or nodes in a
-    tree of exceptions.
-    
-    If a segment consists of more than one name, it matches the names provided if 'negate' is false or
-    missing or
-    
-    it matches anything except the names provided if 'negate' is true.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "negate": {
-            "type": "boolean",
-            "description": "If false or missing this segment matches the names provided, otherwise it matches anything except the names provided."
-        },
-        "names": {
-            "type": "array",
-            "items": {
-                "type": "string"
-            },
-            "description": "Depending on the value of 'negate' the names that should match or not match."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, names, negate=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array names: Depending on the value of 'negate' the names that should match or not match.
-        :param boolean negate: If false or missing this segment matches the names provided, otherwise it matches anything except the names provided.
-        """
-        self.names = names
-        self.negate = negate
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        names = self.names
-        if names and hasattr(names[0], "to_dict"):
-            names = [x.to_dict() for x in names]
-        negate = self.negate
-        dct = {
-            'names': names,
-        }
-        if negate is not None:
-            dct['negate'] = negate
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionDetails(BaseSchema):
-    """
-    Detailed information about an exception that has occurred.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "message": {
-            "type": "string",
-            "description": "Message contained in the exception."
-        },
-        "typeName": {
-            "type": "string",
-            "description": "Short type name of the exception object."
-        },
-        "fullTypeName": {
-            "type": "string",
-            "description": "Fully-qualified type name of the exception object."
-        },
-        "evaluateName": {
-            "type": "string",
-            "description": "Optional expression that can be evaluated in the current scope to obtain the exception object."
-        },
-        "stackTrace": {
-            "type": "string",
-            "description": "Stack trace at the time the exception was thrown."
-        },
-        "innerException": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/ExceptionDetails"
-            },
-            "description": "Details of the exception contained by this exception, if any."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, message=None, typeName=None, fullTypeName=None, evaluateName=None, stackTrace=None, innerException=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string message: Message contained in the exception.
-        :param string typeName: Short type name of the exception object.
-        :param string fullTypeName: Fully-qualified type name of the exception object.
-        :param string evaluateName: Optional expression that can be evaluated in the current scope to obtain the exception object.
-        :param string stackTrace: Stack trace at the time the exception was thrown.
-        :param array innerException: Details of the exception contained by this exception, if any.
-        """
-        self.message = message
-        self.typeName = typeName
-        self.fullTypeName = fullTypeName
-        self.evaluateName = evaluateName
-        self.stackTrace = stackTrace
-        self.innerException = innerException
-        if update_ids_from_dap and self.innerException:
-            for o in self.innerException:
-                ExceptionDetails.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        message = self.message
-        typeName = self.typeName
-        fullTypeName = self.fullTypeName
-        evaluateName = self.evaluateName
-        stackTrace = self.stackTrace
-        innerException = self.innerException
-        if innerException and hasattr(innerException[0], "to_dict"):
-            innerException = [x.to_dict() for x in innerException]
-        dct = {
-        }
-        if message is not None:
-            dct['message'] = message
-        if typeName is not None:
-            dct['typeName'] = typeName
-        if fullTypeName is not None:
-            dct['fullTypeName'] = fullTypeName
-        if evaluateName is not None:
-            dct['evaluateName'] = evaluateName
-        if stackTrace is not None:
-            dct['stackTrace'] = stackTrace
-        if innerException is not None:
-            dct['innerException'] = [ExceptionDetails.update_dict_ids_to_dap(o) for o in innerException] if (update_ids_to_dap and innerException) else innerException
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DisassembledInstruction(BaseSchema):
-    """
-    Represents a single disassembled instruction.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "address": {
-            "type": "string",
-            "description": "The address of the instruction. Treated as a hex value if prefixed with '0x', or as a decimal value otherwise."
-        },
-        "instructionBytes": {
-            "type": "string",
-            "description": "Optional raw bytes representing the instruction and its operands, in an implementation-defined format."
-        },
-        "instruction": {
-            "type": "string",
-            "description": "Text representing the instruction and its operands, in an implementation-defined format."
-        },
-        "symbol": {
-            "type": "string",
-            "description": "Name of the symbol that corresponds with the location of this instruction, if any."
-        },
-        "location": {
-            "description": "Source location that corresponds to this instruction, if any.\nShould always be set (if available) on the first instruction returned,\nbut can be omitted afterwards if this instruction maps to the same source file as the previous instruction.",
-            "type": "Source"
-        },
-        "line": {
-            "type": "integer",
-            "description": "The line within the source location that corresponds to this instruction, if any."
-        },
-        "column": {
-            "type": "integer",
-            "description": "The column within the line that corresponds to this instruction, if any."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "The end line of the range that corresponds to this instruction, if any."
-        },
-        "endColumn": {
-            "type": "integer",
-            "description": "The end column of the range that corresponds to this instruction, if any."
-        }
-    }
-    __refs__ = set(['location'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, address, instruction, instructionBytes=None, symbol=None, location=None, line=None, column=None, endLine=None, endColumn=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string address: The address of the instruction. Treated as a hex value if prefixed with '0x', or as a decimal value otherwise.
-        :param string instruction: Text representing the instruction and its operands, in an implementation-defined format.
-        :param string instructionBytes: Optional raw bytes representing the instruction and its operands, in an implementation-defined format.
-        :param string symbol: Name of the symbol that corresponds with the location of this instruction, if any.
-        :param Source location: Source location that corresponds to this instruction, if any.
-        Should always be set (if available) on the first instruction returned,
-        but can be omitted afterwards if this instruction maps to the same source file as the previous instruction.
-        :param integer line: The line within the source location that corresponds to this instruction, if any.
-        :param integer column: The column within the line that corresponds to this instruction, if any.
-        :param integer endLine: The end line of the range that corresponds to this instruction, if any.
-        :param integer endColumn: The end column of the range that corresponds to this instruction, if any.
-        """
-        self.address = address
-        self.instruction = instruction
-        self.instructionBytes = instructionBytes
-        self.symbol = symbol
-        if location is None:
-            self.location = Source()
-        else:
-            self.location = Source(update_ids_from_dap=update_ids_from_dap, **location) if location.__class__ !=  Source else location
-        self.line = line
-        self.column = column
-        self.endLine = endLine
-        self.endColumn = endColumn
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        address = self.address
-        instruction = self.instruction
-        instructionBytes = self.instructionBytes
-        symbol = self.symbol
-        location = self.location
-        line = self.line
-        column = self.column
-        endLine = self.endLine
-        endColumn = self.endColumn
-        dct = {
-            'address': address,
-            'instruction': instruction,
-        }
-        if instructionBytes is not None:
-            dct['instructionBytes'] = instructionBytes
-        if symbol is not None:
-            dct['symbol'] = symbol
-        if location is not None:
-            dct['location'] = location.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if line is not None:
-            dct['line'] = line
-        if column is not None:
-            dct['column'] = column
-        if endLine is not None:
-            dct['endLine'] = endLine
-        if endColumn is not None:
-            dct['endColumn'] = endColumn
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class InvalidatedAreas(BaseSchema):
-    """
-    Logical areas that can be invalidated by the 'invalidated' event.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setDebuggerProperty')
-@register
-class SetDebuggerPropertyRequest(BaseSchema):
-    """
-    The request can be used to enable or disable debugger features.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setDebuggerProperty"
-            ]
-        },
-        "arguments": {
-            "type": "SetDebuggerPropertyArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetDebuggerPropertyArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setDebuggerProperty'
-        if arguments is None:
-            self.arguments = SetDebuggerPropertyArguments()
-        else:
-            self.arguments = SetDebuggerPropertyArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetDebuggerPropertyArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetDebuggerPropertyArguments(BaseSchema):
-    """
-    Arguments for 'setDebuggerProperty' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "ideOS": {
-            "type": [
-                "string"
-            ],
-            "description": "OS where the ide is running. Supported values [Windows, Linux]"
-        },
-        "dontTraceStartPatterns": {
-            "type": [
-                "array"
-            ],
-            "description": "Patterns to match with the start of the file paths. Matching paths will be added to a list of file where trace is ignored."
-        },
-        "dontTraceEndPatterns": {
-            "type": [
-                "array"
-            ],
-            "description": "Patterns to match with the end of the file paths. Matching paths will be added to a list of file where trace is ignored."
-        },
-        "skipSuspendOnBreakpointException": {
-            "type": [
-                "array"
-            ],
-            "description": "List of exceptions that should be skipped when doing condition evaluations."
-        },
-        "skipPrintBreakpointException": {
-            "type": [
-                "array"
-            ],
-            "description": "List of exceptions that should skip printing to stderr when doing condition evaluations."
-        },
-        "multiThreadsSingleNotification": {
-            "type": [
-                "boolean"
-            ],
-            "description": "If false then a notification is generated for each thread event. If true a single event is gnenerated, and all threads follow that behavior."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, ideOS=None, dontTraceStartPatterns=None, dontTraceEndPatterns=None, skipSuspendOnBreakpointException=None, skipPrintBreakpointException=None, multiThreadsSingleNotification=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param ['string'] ideOS: OS where the ide is running. Supported values [Windows, Linux]
-        :param ['array'] dontTraceStartPatterns: Patterns to match with the start of the file paths. Matching paths will be added to a list of file where trace is ignored.
-        :param ['array'] dontTraceEndPatterns: Patterns to match with the end of the file paths. Matching paths will be added to a list of file where trace is ignored.
-        :param ['array'] skipSuspendOnBreakpointException: List of exceptions that should be skipped when doing condition evaluations.
-        :param ['array'] skipPrintBreakpointException: List of exceptions that should skip printing to stderr when doing condition evaluations.
-        :param ['boolean'] multiThreadsSingleNotification: If false then a notification is generated for each thread event. If true a single event is gnenerated, and all threads follow that behavior.
-        """
-        self.ideOS = ideOS
-        self.dontTraceStartPatterns = dontTraceStartPatterns
-        self.dontTraceEndPatterns = dontTraceEndPatterns
-        self.skipSuspendOnBreakpointException = skipSuspendOnBreakpointException
-        self.skipPrintBreakpointException = skipPrintBreakpointException
-        self.multiThreadsSingleNotification = multiThreadsSingleNotification
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        ideOS = self.ideOS
-        dontTraceStartPatterns = self.dontTraceStartPatterns
-        dontTraceEndPatterns = self.dontTraceEndPatterns
-        skipSuspendOnBreakpointException = self.skipSuspendOnBreakpointException
-        skipPrintBreakpointException = self.skipPrintBreakpointException
-        multiThreadsSingleNotification = self.multiThreadsSingleNotification
-        dct = {
-        }
-        if ideOS is not None:
-            dct['ideOS'] = ideOS
-        if dontTraceStartPatterns is not None:
-            dct['dontTraceStartPatterns'] = dontTraceStartPatterns
-        if dontTraceEndPatterns is not None:
-            dct['dontTraceEndPatterns'] = dontTraceEndPatterns
-        if skipSuspendOnBreakpointException is not None:
-            dct['skipSuspendOnBreakpointException'] = skipSuspendOnBreakpointException
-        if skipPrintBreakpointException is not None:
-            dct['skipPrintBreakpointException'] = skipPrintBreakpointException
-        if multiThreadsSingleNotification is not None:
-            dct['multiThreadsSingleNotification'] = multiThreadsSingleNotification
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('setDebuggerProperty')
-@register
-class SetDebuggerPropertyResponse(BaseSchema):
-    """
-    Response to 'setDebuggerProperty' request. This is just an acknowledgement, so no body field is
-    required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_event('pydevdInputRequested')
-@register
-class PydevdInputRequestedEvent(BaseSchema):
-    """
-    The event indicates input was requested by debuggee.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "event"
-            ]
-        },
-        "event": {
-            "type": "string",
-            "enum": [
-                "pydevdInputRequested"
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Event-specific information."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string event: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Event-specific information.
-        """
-        self.type = 'event'
-        self.event = 'pydevdInputRequested'
-        self.seq = seq
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        event = self.event
-        seq = self.seq
-        body = self.body
-        dct = {
-            'type': type,
-            'event': event,
-            'seq': seq,
-        }
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('setPydevdSourceMap')
-@register
-class SetPydevdSourceMapRequest(BaseSchema):
-    """
-    Sets multiple PydevdSourceMap for a single source and clears all previous PydevdSourceMap in that
-    source.
-    
-    i.e.: Maps paths and lines in a 1:N mapping (use case: map a single file in the IDE to multiple
-    IPython cells).
-    
-    To clear all PydevdSourceMap for a source, specify an empty array.
-    
-    Interaction with breakpoints: When a new mapping is sent, breakpoints that match the source (or
-    previously matched a source) are reapplied.
-    
-    Interaction with launch pathMapping: both mappings are independent. This mapping is applied after
-    the launch pathMapping.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "setPydevdSourceMap"
-            ]
-        },
-        "arguments": {
-            "type": "SetPydevdSourceMapArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param SetPydevdSourceMapArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'setPydevdSourceMap'
-        if arguments is None:
-            self.arguments = SetPydevdSourceMapArguments()
-        else:
-            self.arguments = SetPydevdSourceMapArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  SetPydevdSourceMapArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetPydevdSourceMapArguments(BaseSchema):
-    """
-    Arguments for 'setPydevdSourceMap' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "source": {
-            "description": "The source location of the PydevdSourceMap; 'source.path' must be specified (e.g.: for an ipython notebook this could be something as /home/notebook/note.py).",
-            "type": "Source"
-        },
-        "pydevdSourceMaps": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/PydevdSourceMap"
-            },
-            "description": "The PydevdSourceMaps to be set to the given source (provide an empty array to clear the source mappings for a given path)."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, source, pydevdSourceMaps=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param Source source: The source location of the PydevdSourceMap; 'source.path' must be specified (e.g.: for an ipython notebook this could be something as /home/notebook/note.py).
-        :param array pydevdSourceMaps: The PydevdSourceMaps to be set to the given source (provide an empty array to clear the source mappings for a given path).
-        """
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.pydevdSourceMaps = pydevdSourceMaps
-        if update_ids_from_dap and self.pydevdSourceMaps:
-            for o in self.pydevdSourceMaps:
-                PydevdSourceMap.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        source = self.source
-        pydevdSourceMaps = self.pydevdSourceMaps
-        if pydevdSourceMaps and hasattr(pydevdSourceMaps[0], "to_dict"):
-            pydevdSourceMaps = [x.to_dict() for x in pydevdSourceMaps]
-        dct = {
-            'source': source.to_dict(update_ids_to_dap=update_ids_to_dap),
-        }
-        if pydevdSourceMaps is not None:
-            dct['pydevdSourceMaps'] = [PydevdSourceMap.update_dict_ids_to_dap(o) for o in pydevdSourceMaps] if (update_ids_to_dap and pydevdSourceMaps) else pydevdSourceMaps
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('setPydevdSourceMap')
-@register
-class SetPydevdSourceMapResponse(BaseSchema):
-    """
-    Response to 'setPydevdSourceMap' request. This is just an acknowledgement, so no body field is
-    required.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Contains request result if success is true and optional error details if success is false."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, seq=-1, message=None, body=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] body: Contains request result if success is true and optional error details if success is false.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        self.seq = seq
-        self.message = message
-        self.body = body
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        seq = self.seq
-        message = self.message
-        body = self.body
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        if body is not None:
-            dct['body'] = body
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdSourceMap(BaseSchema):
-    """
-    Information that allows mapping a local line to a remote source/line.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "line": {
-            "type": "integer",
-            "description": "The local line to which the mapping should map to (e.g.: for an ipython notebook this would be the first line of the cell in the file)."
-        },
-        "endLine": {
-            "type": "integer",
-            "description": "The end line."
-        },
-        "runtimeSource": {
-            "description": "The path that the user has remotely -- 'source.path' must be specified (e.g.: for an ipython notebook this could be something as '<ipython-input-1-4561234>')",
-            "type": "Source"
-        },
-        "runtimeLine": {
-            "type": "integer",
-            "description": "The remote line to which the mapping should map to (e.g.: for an ipython notebook this would be always 1 as it'd map the start of the cell)."
-        }
-    }
-    __refs__ = set(['runtimeSource'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, line, endLine, runtimeSource, runtimeLine, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer line: The local line to which the mapping should map to (e.g.: for an ipython notebook this would be the first line of the cell in the file).
-        :param integer endLine: The end line.
-        :param Source runtimeSource: The path that the user has remotely -- 'source.path' must be specified (e.g.: for an ipython notebook this could be something as '<ipython-input-1-4561234>')
-        :param integer runtimeLine: The remote line to which the mapping should map to (e.g.: for an ipython notebook this would be always 1 as it'd map the start of the cell).
-        """
-        self.line = line
-        self.endLine = endLine
-        if runtimeSource is None:
-            self.runtimeSource = Source()
-        else:
-            self.runtimeSource = Source(update_ids_from_dap=update_ids_from_dap, **runtimeSource) if runtimeSource.__class__ !=  Source else runtimeSource
-        self.runtimeLine = runtimeLine
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        line = self.line
-        endLine = self.endLine
-        runtimeSource = self.runtimeSource
-        runtimeLine = self.runtimeLine
-        dct = {
-            'line': line,
-            'endLine': endLine,
-            'runtimeSource': runtimeSource.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'runtimeLine': runtimeLine,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('pydevdSystemInfo')
-@register
-class PydevdSystemInfoRequest(BaseSchema):
-    """
-    The request can be used retrieve system information, python version, etc.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "pydevdSystemInfo"
-            ]
-        },
-        "arguments": {
-            "type": "PydevdSystemInfoArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, seq=-1, arguments=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param PydevdSystemInfoArguments arguments: 
-        """
-        self.type = 'request'
-        self.command = 'pydevdSystemInfo'
-        self.seq = seq
-        if arguments is None:
-            self.arguments = PydevdSystemInfoArguments()
-        else:
-            self.arguments = PydevdSystemInfoArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  PydevdSystemInfoArguments else arguments
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        seq = self.seq
-        arguments = self.arguments
-        dct = {
-            'type': type,
-            'command': command,
-            'seq': seq,
-        }
-        if arguments is not None:
-            dct['arguments'] = arguments.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdSystemInfoArguments(BaseSchema):
-    """
-    Arguments for 'pydevdSystemInfo' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('pydevdSystemInfo')
-@register
-class PydevdSystemInfoResponse(BaseSchema):
-    """
-    Response to 'pydevdSystemInfo' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "python": {
-                    "$ref": "#/definitions/PydevdPythonInfo",
-                    "description": "Information about the python version running in the current process."
-                },
-                "platform": {
-                    "$ref": "#/definitions/PydevdPlatformInfo",
-                    "description": "Information about the plarforn on which the current process is running."
-                },
-                "process": {
-                    "$ref": "#/definitions/PydevdProcessInfo",
-                    "description": "Information about the current process."
-                },
-                "pydevd": {
-                    "$ref": "#/definitions/PydevdInfo",
-                    "description": "Information about pydevd."
-                }
-            },
-            "required": [
-                "python",
-                "platform",
-                "process",
-                "pydevd"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param PydevdSystemInfoResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = PydevdSystemInfoResponseBody()
-        else:
-            self.body = PydevdSystemInfoResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  PydevdSystemInfoResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdPythonInfo(BaseSchema):
-    """
-    This object contains python version and implementation details.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "version": {
-            "type": "string",
-            "description": "Python version as a string in semver format: <major>.<minor>.<micro><releaselevel><serial>."
-        },
-        "implementation": {
-            "description": "Python version as a string in this format <major>.<minor>.<micro><releaselevel><serial>.",
-            "type": "PydevdPythonImplementationInfo"
-        }
-    }
-    __refs__ = set(['implementation'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, version=None, implementation=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string version: Python version as a string in semver format: <major>.<minor>.<micro><releaselevel><serial>.
-        :param PydevdPythonImplementationInfo implementation: Python version as a string in this format <major>.<minor>.<micro><releaselevel><serial>.
-        """
-        self.version = version
-        if implementation is None:
-            self.implementation = PydevdPythonImplementationInfo()
-        else:
-            self.implementation = PydevdPythonImplementationInfo(update_ids_from_dap=update_ids_from_dap, **implementation) if implementation.__class__ !=  PydevdPythonImplementationInfo else implementation
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        version = self.version
-        implementation = self.implementation
-        dct = {
-        }
-        if version is not None:
-            dct['version'] = version
-        if implementation is not None:
-            dct['implementation'] = implementation.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdPythonImplementationInfo(BaseSchema):
-    """
-    This object contains python implementation details.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "name": {
-            "type": "string",
-            "description": "Python implementation name."
-        },
-        "version": {
-            "type": "string",
-            "description": "Python version as a string in semver format: <major>.<minor>.<micro><releaselevel><serial>."
-        },
-        "description": {
-            "type": "string",
-            "description": "Optional description for this python implementation."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name=None, version=None, description=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: Python implementation name.
-        :param string version: Python version as a string in semver format: <major>.<minor>.<micro><releaselevel><serial>.
-        :param string description: Optional description for this python implementation.
-        """
-        self.name = name
-        self.version = version
-        self.description = description
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        version = self.version
-        description = self.description
-        dct = {
-        }
-        if name is not None:
-            dct['name'] = name
-        if version is not None:
-            dct['version'] = version
-        if description is not None:
-            dct['description'] = description
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdPlatformInfo(BaseSchema):
-    """
-    This object contains python version and implementation details.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "name": {
-            "type": "string",
-            "description": "Name of the platform as returned by 'sys.platform'."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: Name of the platform as returned by 'sys.platform'.
-        """
-        self.name = name
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        dct = {
-        }
-        if name is not None:
-            dct['name'] = name
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdProcessInfo(BaseSchema):
-    """
-    This object contains python process details.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "pid": {
-            "type": "integer",
-            "description": "Process ID for the current process."
-        },
-        "ppid": {
-            "type": "integer",
-            "description": "Parent Process ID for the current process."
-        },
-        "executable": {
-            "type": "string",
-            "description": "Path to the executable as returned by 'sys.executable'."
-        },
-        "bitness": {
-            "type": "integer",
-            "description": "Integer value indicating the bitness of the current process."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, pid=None, ppid=None, executable=None, bitness=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer pid: Process ID for the current process.
-        :param integer ppid: Parent Process ID for the current process.
-        :param string executable: Path to the executable as returned by 'sys.executable'.
-        :param integer bitness: Integer value indicating the bitness of the current process.
-        """
-        self.pid = pid
-        self.ppid = ppid
-        self.executable = executable
-        self.bitness = bitness
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        pid = self.pid
-        ppid = self.ppid
-        executable = self.executable
-        bitness = self.bitness
-        dct = {
-        }
-        if pid is not None:
-            dct['pid'] = pid
-        if ppid is not None:
-            dct['ppid'] = ppid
-        if executable is not None:
-            dct['executable'] = executable
-        if bitness is not None:
-            dct['bitness'] = bitness
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdInfo(BaseSchema):
-    """
-    This object contains details on pydevd.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "usingCython": {
-            "type": "boolean",
-            "description": "Specifies whether the cython native module is being used."
-        },
-        "usingFrameEval": {
-            "type": "boolean",
-            "description": "Specifies whether the frame eval native module is being used."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, usingCython=None, usingFrameEval=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean usingCython: Specifies whether the cython native module is being used.
-        :param boolean usingFrameEval: Specifies whether the frame eval native module is being used.
-        """
-        self.usingCython = usingCython
-        self.usingFrameEval = usingFrameEval
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        usingCython = self.usingCython
-        usingFrameEval = self.usingFrameEval
-        dct = {
-        }
-        if usingCython is not None:
-            dct['usingCython'] = usingCython
-        if usingFrameEval is not None:
-            dct['usingFrameEval'] = usingFrameEval
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_request('pydevdAuthorize')
-@register
-class PydevdAuthorizeRequest(BaseSchema):
-    """
-    A request to authorize the ide to start accepting commands.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "request"
-            ]
-        },
-        "command": {
-            "type": "string",
-            "enum": [
-                "pydevdAuthorize"
-            ]
-        },
-        "arguments": {
-            "type": "PydevdAuthorizeArguments"
-        }
-    }
-    __refs__ = set(['arguments'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, arguments, seq=-1, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param string command: 
-        :param PydevdAuthorizeArguments arguments: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        """
-        self.type = 'request'
-        self.command = 'pydevdAuthorize'
-        if arguments is None:
-            self.arguments = PydevdAuthorizeArguments()
-        else:
-            self.arguments = PydevdAuthorizeArguments(update_ids_from_dap=update_ids_from_dap, **arguments) if arguments.__class__ !=  PydevdAuthorizeArguments else arguments
-        self.seq = seq
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        command = self.command
-        arguments = self.arguments
-        seq = self.seq
-        dct = {
-            'type': type,
-            'command': command,
-            'arguments': arguments.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdAuthorizeArguments(BaseSchema):
-    """
-    Arguments for 'pydevdAuthorize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "debugServerAccessToken": {
-            "type": "string",
-            "description": "The access token to access the debug server."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, debugServerAccessToken=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string debugServerAccessToken: The access token to access the debug server.
-        """
-        self.debugServerAccessToken = debugServerAccessToken
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        debugServerAccessToken = self.debugServerAccessToken
-        dct = {
-        }
-        if debugServerAccessToken is not None:
-            dct['debugServerAccessToken'] = debugServerAccessToken
-        dct.update(self.kwargs)
-        return dct
-
-
-@register_response('pydevdAuthorize')
-@register
-class PydevdAuthorizeResponse(BaseSchema):
-    """
-    Response to 'pydevdAuthorize' request.
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "seq": {
-            "type": "integer",
-            "description": "Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request."
-        },
-        "type": {
-            "type": "string",
-            "enum": [
-                "response"
-            ]
-        },
-        "request_seq": {
-            "type": "integer",
-            "description": "Sequence number of the corresponding request."
-        },
-        "success": {
-            "type": "boolean",
-            "description": "Outcome of the request.\nIf true, the request was successful and the 'body' attribute may contain the result of the request.\nIf the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error')."
-        },
-        "command": {
-            "type": "string",
-            "description": "The command requested."
-        },
-        "message": {
-            "type": "string",
-            "description": "Contains the raw error in short form if 'success' is false.\nThis raw error might be interpreted by the frontend and is not shown in the UI.\nSome predefined values exist.",
-            "_enum": [
-                "cancelled"
-            ],
-            "enumDescriptions": [
-                "request was cancelled."
-            ]
-        },
-        "body": {
-            "type": "object",
-            "properties": {
-                "clientAccessToken": {
-                    "type": "string",
-                    "description": "The access token to access the client (i.e.: usually the IDE)."
-                }
-            },
-            "required": [
-                "clientAccessToken"
-            ]
-        }
-    }
-    __refs__ = set(['body'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, request_seq, success, command, body, seq=-1, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string type: 
-        :param integer request_seq: Sequence number of the corresponding request.
-        :param boolean success: Outcome of the request.
-        If true, the request was successful and the 'body' attribute may contain the result of the request.
-        If the value is false, the attribute 'message' contains the error in short form and the 'body' may contain additional information (see 'ErrorResponse.body.error').
-        :param string command: The command requested.
-        :param PydevdAuthorizeResponseBody body: 
-        :param integer seq: Sequence number (also known as message ID). For protocol messages of type 'request' this ID can be used to cancel the request.
-        :param string message: Contains the raw error in short form if 'success' is false.
-        This raw error might be interpreted by the frontend and is not shown in the UI.
-        Some predefined values exist.
-        """
-        self.type = 'response'
-        self.request_seq = request_seq
-        self.success = success
-        self.command = command
-        if body is None:
-            self.body = PydevdAuthorizeResponseBody()
-        else:
-            self.body = PydevdAuthorizeResponseBody(update_ids_from_dap=update_ids_from_dap, **body) if body.__class__ !=  PydevdAuthorizeResponseBody else body
-        self.seq = seq
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        type = self.type  # noqa (assign to builtin)
-        request_seq = self.request_seq
-        success = self.success
-        command = self.command
-        body = self.body
-        seq = self.seq
-        message = self.message
-        dct = {
-            'type': type,
-            'request_seq': request_seq,
-            'success': success,
-            'command': command,
-            'body': body.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'seq': seq,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ErrorResponseBody(BaseSchema):
-    """
-    "body" of ErrorResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "error": {
-            "description": "An optional, structured error message.",
-            "type": "Message"
-        }
-    }
-    __refs__ = set(['error'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, error=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param Message error: An optional, structured error message.
-        """
-        if error is None:
-            self.error = Message()
-        else:
-            self.error = Message(update_ids_from_dap=update_ids_from_dap, **error) if error.__class__ !=  Message else error
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        error = self.error
-        dct = {
-        }
-        if error is not None:
-            dct['error'] = error.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StoppedEventBody(BaseSchema):
-    """
-    "body" of StoppedEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "reason": {
-            "type": "string",
-            "description": "The reason for the event.\nFor backward compatibility this string is shown in the UI if the 'description' attribute is missing (but it must not be translated).",
-            "_enum": [
-                "step",
-                "breakpoint",
-                "exception",
-                "pause",
-                "entry",
-                "goto",
-                "function breakpoint",
-                "data breakpoint",
-                "instruction breakpoint"
-            ]
-        },
-        "description": {
-            "type": "string",
-            "description": "The full reason for the event, e.g. 'Paused on exception'. This string is shown in the UI as is and must be translated."
-        },
-        "threadId": {
-            "type": "integer",
-            "description": "The thread which was stopped."
-        },
-        "preserveFocusHint": {
-            "type": "boolean",
-            "description": "A value of true hints to the frontend that this event should not change the focus."
-        },
-        "text": {
-            "type": "string",
-            "description": "Additional information. E.g. if reason is 'exception', text contains the exception name. This string is shown in the UI."
-        },
-        "allThreadsStopped": {
-            "type": "boolean",
-            "description": "If 'allThreadsStopped' is true, a debug adapter can announce that all threads have stopped.\n- The client should use this information to enable that all threads can be expanded to access their stacktraces.\n- If the attribute is missing or false, only the thread with the given threadId can be expanded."
-        },
-        "hitBreakpointIds": {
-            "type": "array",
-            "items": {
-                "type": "integer"
-            },
-            "description": "Ids of the breakpoints that triggered the event. In most cases there will be only a single breakpoint but here are some examples for multiple breakpoints:\n- Different types of breakpoints map to the same location.\n- Multiple source breakpoints get collapsed to the same instruction by the compiler/runtime.\n- Multiple function breakpoints with different function names map to the same location."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, reason, description=None, threadId=None, preserveFocusHint=None, text=None, allThreadsStopped=None, hitBreakpointIds=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string reason: The reason for the event.
-        For backward compatibility this string is shown in the UI if the 'description' attribute is missing (but it must not be translated).
-        :param string description: The full reason for the event, e.g. 'Paused on exception'. This string is shown in the UI as is and must be translated.
-        :param integer threadId: The thread which was stopped.
-        :param boolean preserveFocusHint: A value of true hints to the frontend that this event should not change the focus.
-        :param string text: Additional information. E.g. if reason is 'exception', text contains the exception name. This string is shown in the UI.
-        :param boolean allThreadsStopped: If 'allThreadsStopped' is true, a debug adapter can announce that all threads have stopped.
-        - The client should use this information to enable that all threads can be expanded to access their stacktraces.
-        - If the attribute is missing or false, only the thread with the given threadId can be expanded.
-        :param array hitBreakpointIds: Ids of the breakpoints that triggered the event. In most cases there will be only a single breakpoint but here are some examples for multiple breakpoints:
-        - Different types of breakpoints map to the same location.
-        - Multiple source breakpoints get collapsed to the same instruction by the compiler/runtime.
-        - Multiple function breakpoints with different function names map to the same location.
-        """
-        self.reason = reason
-        self.description = description
-        self.threadId = threadId
-        self.preserveFocusHint = preserveFocusHint
-        self.text = text
-        self.allThreadsStopped = allThreadsStopped
-        self.hitBreakpointIds = hitBreakpointIds
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        reason = self.reason
-        description = self.description
-        threadId = self.threadId
-        preserveFocusHint = self.preserveFocusHint
-        text = self.text
-        allThreadsStopped = self.allThreadsStopped
-        hitBreakpointIds = self.hitBreakpointIds
-        if hitBreakpointIds and hasattr(hitBreakpointIds[0], "to_dict"):
-            hitBreakpointIds = [x.to_dict() for x in hitBreakpointIds]
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'reason': reason,
-        }
-        if description is not None:
-            dct['description'] = description
-        if threadId is not None:
-            dct['threadId'] = threadId
-        if preserveFocusHint is not None:
-            dct['preserveFocusHint'] = preserveFocusHint
-        if text is not None:
-            dct['text'] = text
-        if allThreadsStopped is not None:
-            dct['allThreadsStopped'] = allThreadsStopped
-        if hitBreakpointIds is not None:
-            dct['hitBreakpointIds'] = hitBreakpointIds
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register
-class ContinuedEventBody(BaseSchema):
-    """
-    "body" of ContinuedEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threadId": {
-            "type": "integer",
-            "description": "The thread which was continued."
-        },
-        "allThreadsContinued": {
-            "type": "boolean",
-            "description": "If 'allThreadsContinued' is true, a debug adapter can announce that all threads have continued."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threadId, allThreadsContinued=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer threadId: The thread which was continued.
-        :param boolean allThreadsContinued: If 'allThreadsContinued' is true, a debug adapter can announce that all threads have continued.
-        """
-        self.threadId = threadId
-        self.allThreadsContinued = allThreadsContinued
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threadId = self.threadId
-        allThreadsContinued = self.allThreadsContinued
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'threadId': threadId,
-        }
-        if allThreadsContinued is not None:
-            dct['allThreadsContinued'] = allThreadsContinued
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register
-class ExitedEventBody(BaseSchema):
-    """
-    "body" of ExitedEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "exitCode": {
-            "type": "integer",
-            "description": "The exit code returned from the debuggee."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, exitCode, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer exitCode: The exit code returned from the debuggee.
-        """
-        self.exitCode = exitCode
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        exitCode = self.exitCode
-        dct = {
-            'exitCode': exitCode,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class TerminatedEventBody(BaseSchema):
-    """
-    "body" of TerminatedEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "restart": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "A debug adapter may set 'restart' to true (or to an arbitrary object) to request that the front end restarts the session.\nThe value is not interpreted by the client and passed unmodified as an attribute '__restart' to the 'launch' and 'attach' requests."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, restart=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] restart: A debug adapter may set 'restart' to true (or to an arbitrary object) to request that the front end restarts the session.
-        The value is not interpreted by the client and passed unmodified as an attribute '__restart' to the 'launch' and 'attach' requests.
-        """
-        self.restart = restart
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        restart = self.restart
-        dct = {
-        }
-        if restart is not None:
-            dct['restart'] = restart
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ThreadEventBody(BaseSchema):
-    """
-    "body" of ThreadEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "reason": {
-            "type": "string",
-            "description": "The reason for the event.",
-            "_enum": [
-                "started",
-                "exited"
-            ]
-        },
-        "threadId": {
-            "type": "integer",
-            "description": "The identifier of the thread."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, reason, threadId, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string reason: The reason for the event.
-        :param integer threadId: The identifier of the thread.
-        """
-        self.reason = reason
-        self.threadId = threadId
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        reason = self.reason
-        threadId = self.threadId
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-            'reason': reason,
-            'threadId': threadId,
-        }
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register
-class OutputEventBody(BaseSchema):
-    """
-    "body" of OutputEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "category": {
-            "type": "string",
-            "description": "The output category. If not specified or if the category is not understand by the client, 'console' is assumed.",
-            "_enum": [
-                "console",
-                "important",
-                "stdout",
-                "stderr",
-                "telemetry"
-            ],
-            "enumDescriptions": [
-                "Show the output in the client's default message UI, e.g. a 'debug console'. This category should only be used for informational output from the debugger (as opposed to the debuggee).",
-                "A hint for the client to show the ouput in the client's UI for important and highly visible information, e.g. as a popup notification. This category should only be used for important messages from the debugger (as opposed to the debuggee). Since this category value is a hint, clients might ignore the hint and assume the 'console' category.",
-                "Show the output as normal program output from the debuggee.",
-                "Show the output as error program output from the debuggee.",
-                "Send the output to telemetry instead of showing it to the user."
-            ]
-        },
-        "output": {
-            "type": "string",
-            "description": "The output to report."
-        },
-        "group": {
-            "type": "string",
-            "description": "Support for keeping an output log organized by grouping related messages.",
-            "enum": [
-                "start",
-                "startCollapsed",
-                "end"
-            ],
-            "enumDescriptions": [
-                "Start a new group in expanded mode. Subsequent output events are members of the group and should be shown indented.\nThe 'output' attribute becomes the name of the group and is not indented.",
-                "Start a new group in collapsed mode. Subsequent output events are members of the group and should be shown indented (as soon as the group is expanded).\nThe 'output' attribute becomes the name of the group and is not indented.",
-                "End the current group and decreases the indentation of subsequent output events.\nA non empty 'output' attribute is shown as the unindented end of the group."
-            ]
-        },
-        "variablesReference": {
-            "type": "integer",
-            "description": "If an attribute 'variablesReference' exists and its value is > 0, the output contains objects which can be retrieved by passing 'variablesReference' to the 'variables' request. The value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "source": {
-            "description": "An optional source location where the output was produced.",
-            "type": "Source"
-        },
-        "line": {
-            "type": "integer",
-            "description": "An optional source location line where the output was produced."
-        },
-        "column": {
-            "type": "integer",
-            "description": "An optional source location column where the output was produced."
-        },
-        "data": {
-            "type": [
-                "array",
-                "boolean",
-                "integer",
-                "null",
-                "number",
-                "object",
-                "string"
-            ],
-            "description": "Optional data to report. For the 'telemetry' category the data will be sent to telemetry, for the other categories the data is shown in JSON format."
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, output, category=None, group=None, variablesReference=None, source=None, line=None, column=None, data=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string output: The output to report.
-        :param string category: The output category. If not specified or if the category is not understand by the client, 'console' is assumed.
-        :param string group: Support for keeping an output log organized by grouping related messages.
-        :param integer variablesReference: If an attribute 'variablesReference' exists and its value is > 0, the output contains objects which can be retrieved by passing 'variablesReference' to the 'variables' request. The value should be less than or equal to 2147483647 (2^31-1).
-        :param Source source: An optional source location where the output was produced.
-        :param integer line: An optional source location line where the output was produced.
-        :param integer column: An optional source location column where the output was produced.
-        :param ['array', 'boolean', 'integer', 'null', 'number', 'object', 'string'] data: Optional data to report. For the 'telemetry' category the data will be sent to telemetry, for the other categories the data is shown in JSON format.
-        """
-        self.output = output
-        self.category = category
-        self.group = group
-        self.variablesReference = variablesReference
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.line = line
-        self.column = column
-        self.data = data
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        output = self.output
-        category = self.category
-        group = self.group
-        variablesReference = self.variablesReference
-        source = self.source
-        line = self.line
-        column = self.column
-        data = self.data
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'output': output,
-        }
-        if category is not None:
-            dct['category'] = category
-        if group is not None:
-            dct['group'] = group
-        if variablesReference is not None:
-            dct['variablesReference'] = variablesReference
-        if source is not None:
-            dct['source'] = source.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if line is not None:
-            dct['line'] = line
-        if column is not None:
-            dct['column'] = column
-        if data is not None:
-            dct['data'] = data
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register
-class BreakpointEventBody(BaseSchema):
-    """
-    "body" of BreakpointEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "reason": {
-            "type": "string",
-            "description": "The reason for the event.",
-            "_enum": [
-                "changed",
-                "new",
-                "removed"
-            ]
-        },
-        "breakpoint": {
-            "description": "The 'id' attribute is used to find the target breakpoint and the other attributes are used as the new values.",
-            "type": "Breakpoint"
-        }
-    }
-    __refs__ = set(['breakpoint'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, reason, breakpoint, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string reason: The reason for the event.
-        :param Breakpoint breakpoint: The 'id' attribute is used to find the target breakpoint and the other attributes are used as the new values.
-        """
-        self.reason = reason
-        if breakpoint is None:
-            self.breakpoint = Breakpoint()
-        else:
-            self.breakpoint = Breakpoint(update_ids_from_dap=update_ids_from_dap, **breakpoint) if breakpoint.__class__ !=  Breakpoint else breakpoint
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        reason = self.reason
-        breakpoint = self.breakpoint  # noqa (assign to builtin)
-        dct = {
-            'reason': reason,
-            'breakpoint': breakpoint.to_dict(update_ids_to_dap=update_ids_to_dap),
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ModuleEventBody(BaseSchema):
-    """
-    "body" of ModuleEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "reason": {
-            "type": "string",
-            "description": "The reason for the event.",
-            "enum": [
-                "new",
-                "changed",
-                "removed"
-            ]
-        },
-        "module": {
-            "description": "The new, changed, or removed module. In case of 'removed' only the module id is used.",
-            "type": "Module"
-        }
-    }
-    __refs__ = set(['module'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, reason, module, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string reason: The reason for the event.
-        :param Module module: The new, changed, or removed module. In case of 'removed' only the module id is used.
-        """
-        self.reason = reason
-        if module is None:
-            self.module = Module()
-        else:
-            self.module = Module(update_ids_from_dap=update_ids_from_dap, **module) if module.__class__ !=  Module else module
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        reason = self.reason
-        module = self.module
-        dct = {
-            'reason': reason,
-            'module': module.to_dict(update_ids_to_dap=update_ids_to_dap),
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class LoadedSourceEventBody(BaseSchema):
-    """
-    "body" of LoadedSourceEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "reason": {
-            "type": "string",
-            "description": "The reason for the event.",
-            "enum": [
-                "new",
-                "changed",
-                "removed"
-            ]
-        },
-        "source": {
-            "description": "The new, changed, or removed source.",
-            "type": "Source"
-        }
-    }
-    __refs__ = set(['source'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, reason, source, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string reason: The reason for the event.
-        :param Source source: The new, changed, or removed source.
-        """
-        self.reason = reason
-        if source is None:
-            self.source = Source()
-        else:
-            self.source = Source(update_ids_from_dap=update_ids_from_dap, **source) if source.__class__ !=  Source else source
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        reason = self.reason
-        source = self.source
-        dct = {
-            'reason': reason,
-            'source': source.to_dict(update_ids_to_dap=update_ids_to_dap),
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ProcessEventBody(BaseSchema):
-    """
-    "body" of ProcessEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "name": {
-            "type": "string",
-            "description": "The logical name of the process. This is usually the full path to process's executable file. Example: /home/example/myproj/program.js."
-        },
-        "systemProcessId": {
-            "type": "integer",
-            "description": "The system process id of the debugged process. This property will be missing for non-system processes."
-        },
-        "isLocalProcess": {
-            "type": "boolean",
-            "description": "If true, the process is running on the same computer as the debug adapter."
-        },
-        "startMethod": {
-            "type": "string",
-            "enum": [
-                "launch",
-                "attach",
-                "attachForSuspendedLaunch"
-            ],
-            "description": "Describes how the debug engine started debugging this process.",
-            "enumDescriptions": [
-                "Process was launched under the debugger.",
-                "Debugger attached to an existing process.",
-                "A project launcher component has launched a new process in a suspended state and then asked the debugger to attach."
-            ]
-        },
-        "pointerSize": {
-            "type": "integer",
-            "description": "The size of a pointer or address for this process, in bits. This value may be used by clients when formatting addresses for display."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, name, systemProcessId=None, isLocalProcess=None, startMethod=None, pointerSize=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string name: The logical name of the process. This is usually the full path to process's executable file. Example: /home/example/myproj/program.js.
-        :param integer systemProcessId: The system process id of the debugged process. This property will be missing for non-system processes.
-        :param boolean isLocalProcess: If true, the process is running on the same computer as the debug adapter.
-        :param string startMethod: Describes how the debug engine started debugging this process.
-        :param integer pointerSize: The size of a pointer or address for this process, in bits. This value may be used by clients when formatting addresses for display.
-        """
-        self.name = name
-        self.systemProcessId = systemProcessId
-        self.isLocalProcess = isLocalProcess
-        self.startMethod = startMethod
-        self.pointerSize = pointerSize
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        name = self.name
-        systemProcessId = self.systemProcessId
-        isLocalProcess = self.isLocalProcess
-        startMethod = self.startMethod
-        pointerSize = self.pointerSize
-        dct = {
-            'name': name,
-        }
-        if systemProcessId is not None:
-            dct['systemProcessId'] = systemProcessId
-        if isLocalProcess is not None:
-            dct['isLocalProcess'] = isLocalProcess
-        if startMethod is not None:
-            dct['startMethod'] = startMethod
-        if pointerSize is not None:
-            dct['pointerSize'] = pointerSize
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class CapabilitiesEventBody(BaseSchema):
-    """
-    "body" of CapabilitiesEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "capabilities": {
-            "description": "The set of updated capabilities.",
-            "type": "Capabilities"
-        }
-    }
-    __refs__ = set(['capabilities'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, capabilities, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param Capabilities capabilities: The set of updated capabilities.
-        """
-        if capabilities is None:
-            self.capabilities = Capabilities()
-        else:
-            self.capabilities = Capabilities(update_ids_from_dap=update_ids_from_dap, **capabilities) if capabilities.__class__ !=  Capabilities else capabilities
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        capabilities = self.capabilities
-        dct = {
-            'capabilities': capabilities.to_dict(update_ids_to_dap=update_ids_to_dap),
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ProgressStartEventBody(BaseSchema):
-    """
-    "body" of ProgressStartEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "progressId": {
-            "type": "string",
-            "description": "An ID that must be used in subsequent 'progressUpdate' and 'progressEnd' events to make them refer to the same progress reporting.\nIDs must be unique within a debug session."
-        },
-        "title": {
-            "type": "string",
-            "description": "Mandatory (short) title of the progress reporting. Shown in the UI to describe the long running operation."
-        },
-        "requestId": {
-            "type": "integer",
-            "description": "The request ID that this progress report is related to. If specified a debug adapter is expected to emit\nprogress events for the long running request until the request has been either completed or cancelled.\nIf the request ID is omitted, the progress report is assumed to be related to some general activity of the debug adapter."
-        },
-        "cancellable": {
-            "type": "boolean",
-            "description": "If true, the request that reports progress may be canceled with a 'cancel' request.\nSo this property basically controls whether the client should use UX that supports cancellation.\nClients that don't support cancellation are allowed to ignore the setting."
-        },
-        "message": {
-            "type": "string",
-            "description": "Optional, more detailed progress message."
-        },
-        "percentage": {
-            "type": "number",
-            "description": "Optional progress percentage to display (value range: 0 to 100). If omitted no percentage will be shown."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, progressId, title, requestId=None, cancellable=None, message=None, percentage=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string progressId: An ID that must be used in subsequent 'progressUpdate' and 'progressEnd' events to make them refer to the same progress reporting.
-        IDs must be unique within a debug session.
-        :param string title: Mandatory (short) title of the progress reporting. Shown in the UI to describe the long running operation.
-        :param integer requestId: The request ID that this progress report is related to. If specified a debug adapter is expected to emit
-        progress events for the long running request until the request has been either completed or cancelled.
-        If the request ID is omitted, the progress report is assumed to be related to some general activity of the debug adapter.
-        :param boolean cancellable: If true, the request that reports progress may be canceled with a 'cancel' request.
-        So this property basically controls whether the client should use UX that supports cancellation.
-        Clients that don't support cancellation are allowed to ignore the setting.
-        :param string message: Optional, more detailed progress message.
-        :param number percentage: Optional progress percentage to display (value range: 0 to 100). If omitted no percentage will be shown.
-        """
-        self.progressId = progressId
-        self.title = title
-        self.requestId = requestId
-        self.cancellable = cancellable
-        self.message = message
-        self.percentage = percentage
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        progressId = self.progressId
-        title = self.title
-        requestId = self.requestId
-        cancellable = self.cancellable
-        message = self.message
-        percentage = self.percentage
-        dct = {
-            'progressId': progressId,
-            'title': title,
-        }
-        if requestId is not None:
-            dct['requestId'] = requestId
-        if cancellable is not None:
-            dct['cancellable'] = cancellable
-        if message is not None:
-            dct['message'] = message
-        if percentage is not None:
-            dct['percentage'] = percentage
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ProgressUpdateEventBody(BaseSchema):
-    """
-    "body" of ProgressUpdateEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "progressId": {
-            "type": "string",
-            "description": "The ID that was introduced in the initial 'progressStart' event."
-        },
-        "message": {
-            "type": "string",
-            "description": "Optional, more detailed progress message. If omitted, the previous message (if any) is used."
-        },
-        "percentage": {
-            "type": "number",
-            "description": "Optional progress percentage to display (value range: 0 to 100). If omitted no percentage will be shown."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, progressId, message=None, percentage=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string progressId: The ID that was introduced in the initial 'progressStart' event.
-        :param string message: Optional, more detailed progress message. If omitted, the previous message (if any) is used.
-        :param number percentage: Optional progress percentage to display (value range: 0 to 100). If omitted no percentage will be shown.
-        """
-        self.progressId = progressId
-        self.message = message
-        self.percentage = percentage
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        progressId = self.progressId
-        message = self.message
-        percentage = self.percentage
-        dct = {
-            'progressId': progressId,
-        }
-        if message is not None:
-            dct['message'] = message
-        if percentage is not None:
-            dct['percentage'] = percentage
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ProgressEndEventBody(BaseSchema):
-    """
-    "body" of ProgressEndEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "progressId": {
-            "type": "string",
-            "description": "The ID that was introduced in the initial 'ProgressStartEvent'."
-        },
-        "message": {
-            "type": "string",
-            "description": "Optional, more detailed progress message. If omitted, the previous message (if any) is used."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, progressId, message=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string progressId: The ID that was introduced in the initial 'ProgressStartEvent'.
-        :param string message: Optional, more detailed progress message. If omitted, the previous message (if any) is used.
-        """
-        self.progressId = progressId
-        self.message = message
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        progressId = self.progressId
-        message = self.message
-        dct = {
-            'progressId': progressId,
-        }
-        if message is not None:
-            dct['message'] = message
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class InvalidatedEventBody(BaseSchema):
-    """
-    "body" of InvalidatedEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "areas": {
-            "type": "array",
-            "description": "Optional set of logical areas that got invalidated. This property has a hint characteristic: a client can only be expected to make a 'best effort' in honouring the areas but there are no guarantees. If this property is missing, empty, or if values are not understand the client should assume a single value 'all'.",
-            "items": {
-                "$ref": "#/definitions/InvalidatedAreas"
-            }
-        },
-        "threadId": {
-            "type": "integer",
-            "description": "If specified, the client only needs to refetch data related to this thread."
-        },
-        "stackFrameId": {
-            "type": "integer",
-            "description": "If specified, the client only needs to refetch data related to this stack frame (and the 'threadId' is ignored)."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, areas=None, threadId=None, stackFrameId=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array areas: Optional set of logical areas that got invalidated. This property has a hint characteristic: a client can only be expected to make a 'best effort' in honouring the areas but there are no guarantees. If this property is missing, empty, or if values are not understand the client should assume a single value 'all'.
-        :param integer threadId: If specified, the client only needs to refetch data related to this thread.
-        :param integer stackFrameId: If specified, the client only needs to refetch data related to this stack frame (and the 'threadId' is ignored).
-        """
-        self.areas = areas
-        if update_ids_from_dap and self.areas:
-            for o in self.areas:
-                InvalidatedAreas.update_dict_ids_from_dap(o)
-        self.threadId = threadId
-        self.stackFrameId = stackFrameId
-        if update_ids_from_dap:
-            self.threadId = self._translate_id_from_dap(self.threadId)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_from_dap(dct['threadId'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        areas = self.areas
-        if areas and hasattr(areas[0], "to_dict"):
-            areas = [x.to_dict() for x in areas]
-        threadId = self.threadId
-        stackFrameId = self.stackFrameId
-        if update_ids_to_dap:
-            if threadId is not None:
-                threadId = self._translate_id_to_dap(threadId)
-        dct = {
-        }
-        if areas is not None:
-            dct['areas'] = [InvalidatedAreas.update_dict_ids_to_dap(o) for o in areas] if (update_ids_to_dap and areas) else areas
-        if threadId is not None:
-            dct['threadId'] = threadId
-        if stackFrameId is not None:
-            dct['stackFrameId'] = stackFrameId
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'threadId' in dct:
-            dct['threadId'] = cls._translate_id_to_dap(dct['threadId'])
-        return dct
-
-
-@register
-class MemoryEventBody(BaseSchema):
-    """
-    "body" of MemoryEvent
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "memoryReference": {
-            "type": "string",
-            "description": "Memory reference of a memory range that has been updated."
-        },
-        "offset": {
-            "type": "integer",
-            "description": "Starting offset in bytes where memory has been updated. Can be negative."
-        },
-        "count": {
-            "type": "integer",
-            "description": "Number of bytes updated."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, memoryReference, offset, count, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string memoryReference: Memory reference of a memory range that has been updated.
-        :param integer offset: Starting offset in bytes where memory has been updated. Can be negative.
-        :param integer count: Number of bytes updated.
-        """
-        self.memoryReference = memoryReference
-        self.offset = offset
-        self.count = count
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        memoryReference = self.memoryReference
-        offset = self.offset
-        count = self.count
-        dct = {
-            'memoryReference': memoryReference,
-            'offset': offset,
-            'count': count,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class RunInTerminalRequestArgumentsEnv(BaseSchema):
-    """
-    "env" of RunInTerminalRequestArguments
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class RunInTerminalResponseBody(BaseSchema):
-    """
-    "body" of RunInTerminalResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "processId": {
-            "type": "integer",
-            "description": "The process ID. The value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "shellProcessId": {
-            "type": "integer",
-            "description": "The process ID of the terminal shell. The value should be less than or equal to 2147483647 (2^31-1)."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, processId=None, shellProcessId=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer processId: The process ID. The value should be less than or equal to 2147483647 (2^31-1).
-        :param integer shellProcessId: The process ID of the terminal shell. The value should be less than or equal to 2147483647 (2^31-1).
-        """
-        self.processId = processId
-        self.shellProcessId = shellProcessId
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        processId = self.processId
-        shellProcessId = self.shellProcessId
-        dct = {
-        }
-        if processId is not None:
-            dct['processId'] = processId
-        if shellProcessId is not None:
-            dct['shellProcessId'] = shellProcessId
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class BreakpointLocationsResponseBody(BaseSchema):
-    """
-    "body" of BreakpointLocationsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/BreakpointLocation"
-            },
-            "description": "Sorted set of possible breakpoint locations."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: Sorted set of possible breakpoint locations.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                BreakpointLocation.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [BreakpointLocation.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetBreakpointsResponseBody(BaseSchema):
-    """
-    "body" of SetBreakpointsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Breakpoint"
-            },
-            "description": "Information about the breakpoints.\nThe array elements are in the same order as the elements of the 'breakpoints' (or the deprecated 'lines') array in the arguments."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: Information about the breakpoints.
-        The array elements are in the same order as the elements of the 'breakpoints' (or the deprecated 'lines') array in the arguments.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                Breakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [Breakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetFunctionBreakpointsResponseBody(BaseSchema):
-    """
-    "body" of SetFunctionBreakpointsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Breakpoint"
-            },
-            "description": "Information about the breakpoints. The array elements correspond to the elements of the 'breakpoints' array."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: Information about the breakpoints. The array elements correspond to the elements of the 'breakpoints' array.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                Breakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [Breakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetExceptionBreakpointsResponseBody(BaseSchema):
-    """
-    "body" of SetExceptionBreakpointsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Breakpoint"
-            },
-            "description": "Information about the exception breakpoints or filters.\nThe breakpoints returned are in the same order as the elements of the 'filters', 'filterOptions', 'exceptionOptions' arrays in the arguments. If both 'filters' and 'filterOptions' are given, the returned array must start with 'filters' information first, followed by 'filterOptions' information."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: Information about the exception breakpoints or filters.
-        The breakpoints returned are in the same order as the elements of the 'filters', 'filterOptions', 'exceptionOptions' arrays in the arguments. If both 'filters' and 'filterOptions' are given, the returned array must start with 'filters' information first, followed by 'filterOptions' information.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                Breakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-        }
-        if breakpoints is not None:
-            dct['breakpoints'] = [Breakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DataBreakpointInfoResponseBody(BaseSchema):
-    """
-    "body" of DataBreakpointInfoResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "dataId": {
-            "type": [
-                "string",
-                "null"
-            ],
-            "description": "An identifier for the data on which a data breakpoint can be registered with the setDataBreakpoints request or null if no data breakpoint is available."
-        },
-        "description": {
-            "type": "string",
-            "description": "UI string that describes on what data the breakpoint is set on or why a data breakpoint is not available."
-        },
-        "accessTypes": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/DataBreakpointAccessType"
-            },
-            "description": "Optional attribute listing the available access types for a potential data breakpoint. A UI frontend could surface this information."
-        },
-        "canPersist": {
-            "type": "boolean",
-            "description": "Optional attribute indicating that a potential data breakpoint could be persisted across sessions."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, dataId, description, accessTypes=None, canPersist=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param ['string', 'null'] dataId: An identifier for the data on which a data breakpoint can be registered with the setDataBreakpoints request or null if no data breakpoint is available.
-        :param string description: UI string that describes on what data the breakpoint is set on or why a data breakpoint is not available.
-        :param array accessTypes: Optional attribute listing the available access types for a potential data breakpoint. A UI frontend could surface this information.
-        :param boolean canPersist: Optional attribute indicating that a potential data breakpoint could be persisted across sessions.
-        """
-        self.dataId = dataId
-        self.description = description
-        self.accessTypes = accessTypes
-        if update_ids_from_dap and self.accessTypes:
-            for o in self.accessTypes:
-                DataBreakpointAccessType.update_dict_ids_from_dap(o)
-        self.canPersist = canPersist
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dataId = self.dataId
-        description = self.description
-        accessTypes = self.accessTypes
-        if accessTypes and hasattr(accessTypes[0], "to_dict"):
-            accessTypes = [x.to_dict() for x in accessTypes]
-        canPersist = self.canPersist
-        dct = {
-            'dataId': dataId,
-            'description': description,
-        }
-        if accessTypes is not None:
-            dct['accessTypes'] = [DataBreakpointAccessType.update_dict_ids_to_dap(o) for o in accessTypes] if (update_ids_to_dap and accessTypes) else accessTypes
-        if canPersist is not None:
-            dct['canPersist'] = canPersist
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetDataBreakpointsResponseBody(BaseSchema):
-    """
-    "body" of SetDataBreakpointsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Breakpoint"
-            },
-            "description": "Information about the data breakpoints. The array elements correspond to the elements of the input argument 'breakpoints' array."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: Information about the data breakpoints. The array elements correspond to the elements of the input argument 'breakpoints' array.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                Breakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [Breakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetInstructionBreakpointsResponseBody(BaseSchema):
-    """
-    "body" of SetInstructionBreakpointsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "breakpoints": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Breakpoint"
-            },
-            "description": "Information about the breakpoints. The array elements correspond to the elements of the 'breakpoints' array."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, breakpoints, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array breakpoints: Information about the breakpoints. The array elements correspond to the elements of the 'breakpoints' array.
-        """
-        self.breakpoints = breakpoints
-        if update_ids_from_dap and self.breakpoints:
-            for o in self.breakpoints:
-                Breakpoint.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        breakpoints = self.breakpoints
-        if breakpoints and hasattr(breakpoints[0], "to_dict"):
-            breakpoints = [x.to_dict() for x in breakpoints]
-        dct = {
-            'breakpoints': [Breakpoint.update_dict_ids_to_dap(o) for o in breakpoints] if (update_ids_to_dap and breakpoints) else breakpoints,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ContinueResponseBody(BaseSchema):
-    """
-    "body" of ContinueResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "allThreadsContinued": {
-            "type": "boolean",
-            "description": "The value true (or a missing property) signals to the client that all threads have been resumed. The value false must be returned if not all threads were resumed."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, allThreadsContinued=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param boolean allThreadsContinued: The value true (or a missing property) signals to the client that all threads have been resumed. The value false must be returned if not all threads were resumed.
-        """
-        self.allThreadsContinued = allThreadsContinued
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        allThreadsContinued = self.allThreadsContinued
-        dct = {
-        }
-        if allThreadsContinued is not None:
-            dct['allThreadsContinued'] = allThreadsContinued
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class StackTraceResponseBody(BaseSchema):
-    """
-    "body" of StackTraceResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "stackFrames": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/StackFrame"
-            },
-            "description": "The frames of the stackframe. If the array has length zero, there are no stackframes available.\nThis means that there is no location information available."
-        },
-        "totalFrames": {
-            "type": "integer",
-            "description": "The total number of frames available in the stack. If omitted or if totalFrames is larger than the available frames, a client is expected to request frames until a request returns less frames than requested (which indicates the end of the stack). Returning monotonically increasing totalFrames values for subsequent requests can be used to enforce paging in the client."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, stackFrames, totalFrames=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array stackFrames: The frames of the stackframe. If the array has length zero, there are no stackframes available.
-        This means that there is no location information available.
-        :param integer totalFrames: The total number of frames available in the stack. If omitted or if totalFrames is larger than the available frames, a client is expected to request frames until a request returns less frames than requested (which indicates the end of the stack). Returning monotonically increasing totalFrames values for subsequent requests can be used to enforce paging in the client.
-        """
-        self.stackFrames = stackFrames
-        if update_ids_from_dap and self.stackFrames:
-            for o in self.stackFrames:
-                StackFrame.update_dict_ids_from_dap(o)
-        self.totalFrames = totalFrames
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        stackFrames = self.stackFrames
-        if stackFrames and hasattr(stackFrames[0], "to_dict"):
-            stackFrames = [x.to_dict() for x in stackFrames]
-        totalFrames = self.totalFrames
-        dct = {
-            'stackFrames': [StackFrame.update_dict_ids_to_dap(o) for o in stackFrames] if (update_ids_to_dap and stackFrames) else stackFrames,
-        }
-        if totalFrames is not None:
-            dct['totalFrames'] = totalFrames
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ScopesResponseBody(BaseSchema):
-    """
-    "body" of ScopesResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "scopes": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Scope"
-            },
-            "description": "The scopes of the stackframe. If the array has length zero, there are no scopes available."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, scopes, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array scopes: The scopes of the stackframe. If the array has length zero, there are no scopes available.
-        """
-        self.scopes = scopes
-        if update_ids_from_dap and self.scopes:
-            for o in self.scopes:
-                Scope.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        scopes = self.scopes
-        if scopes and hasattr(scopes[0], "to_dict"):
-            scopes = [x.to_dict() for x in scopes]
-        dct = {
-            'scopes': [Scope.update_dict_ids_to_dap(o) for o in scopes] if (update_ids_to_dap and scopes) else scopes,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class VariablesResponseBody(BaseSchema):
-    """
-    "body" of VariablesResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "variables": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Variable"
-            },
-            "description": "All (or a range) of variables for the given variable reference."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, variables, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array variables: All (or a range) of variables for the given variable reference.
-        """
-        self.variables = variables
-        if update_ids_from_dap and self.variables:
-            for o in self.variables:
-                Variable.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        variables = self.variables
-        if variables and hasattr(variables[0], "to_dict"):
-            variables = [x.to_dict() for x in variables]
-        dct = {
-            'variables': [Variable.update_dict_ids_to_dap(o) for o in variables] if (update_ids_to_dap and variables) else variables,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class SetVariableResponseBody(BaseSchema):
-    """
-    "body" of SetVariableResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "value": {
-            "type": "string",
-            "description": "The new value of the variable."
-        },
-        "type": {
-            "type": "string",
-            "description": "The type of the new value. Typically shown in the UI when hovering over the value."
-        },
-        "variablesReference": {
-            "type": "integer",
-            "description": "If variablesReference is > 0, the new value is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "namedVariables": {
-            "type": "integer",
-            "description": "The number of named child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "indexedVariables": {
-            "type": "integer",
-            "description": "The number of indexed child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, value, type=None, variablesReference=None, namedVariables=None, indexedVariables=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string value: The new value of the variable.
-        :param string type: The type of the new value. Typically shown in the UI when hovering over the value.
-        :param integer variablesReference: If variablesReference is > 0, the new value is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param integer namedVariables: The number of named child variables.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param integer indexedVariables: The number of indexed child variables.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        """
-        self.value = value
-        self.type = type
-        self.variablesReference = variablesReference
-        self.namedVariables = namedVariables
-        self.indexedVariables = indexedVariables
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        value = self.value
-        type = self.type  # noqa (assign to builtin)
-        variablesReference = self.variablesReference
-        namedVariables = self.namedVariables
-        indexedVariables = self.indexedVariables
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'value': value,
-        }
-        if type is not None:
-            dct['type'] = type
-        if variablesReference is not None:
-            dct['variablesReference'] = variablesReference
-        if namedVariables is not None:
-            dct['namedVariables'] = namedVariables
-        if indexedVariables is not None:
-            dct['indexedVariables'] = indexedVariables
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register
-class SourceResponseBody(BaseSchema):
-    """
-    "body" of SourceResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "content": {
-            "type": "string",
-            "description": "Content of the source reference."
-        },
-        "mimeType": {
-            "type": "string",
-            "description": "Optional content type (mime type) of the source."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, content, mimeType=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string content: Content of the source reference.
-        :param string mimeType: Optional content type (mime type) of the source.
-        """
-        self.content = content
-        self.mimeType = mimeType
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        content = self.content
-        mimeType = self.mimeType
-        dct = {
-            'content': content,
-        }
-        if mimeType is not None:
-            dct['mimeType'] = mimeType
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ThreadsResponseBody(BaseSchema):
-    """
-    "body" of ThreadsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "threads": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Thread"
-            },
-            "description": "All threads."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, threads, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array threads: All threads.
-        """
-        self.threads = threads
-        if update_ids_from_dap and self.threads:
-            for o in self.threads:
-                Thread.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        threads = self.threads
-        if threads and hasattr(threads[0], "to_dict"):
-            threads = [x.to_dict() for x in threads]
-        dct = {
-            'threads': [Thread.update_dict_ids_to_dap(o) for o in threads] if (update_ids_to_dap and threads) else threads,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ModulesResponseBody(BaseSchema):
-    """
-    "body" of ModulesResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "modules": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Module"
-            },
-            "description": "All modules or range of modules."
-        },
-        "totalModules": {
-            "type": "integer",
-            "description": "The total number of modules available."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, modules, totalModules=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array modules: All modules or range of modules.
-        :param integer totalModules: The total number of modules available.
-        """
-        self.modules = modules
-        if update_ids_from_dap and self.modules:
-            for o in self.modules:
-                Module.update_dict_ids_from_dap(o)
-        self.totalModules = totalModules
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        modules = self.modules
-        if modules and hasattr(modules[0], "to_dict"):
-            modules = [x.to_dict() for x in modules]
-        totalModules = self.totalModules
-        dct = {
-            'modules': [Module.update_dict_ids_to_dap(o) for o in modules] if (update_ids_to_dap and modules) else modules,
-        }
-        if totalModules is not None:
-            dct['totalModules'] = totalModules
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class LoadedSourcesResponseBody(BaseSchema):
-    """
-    "body" of LoadedSourcesResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "sources": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/Source"
-            },
-            "description": "Set of loaded sources."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, sources, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array sources: Set of loaded sources.
-        """
-        self.sources = sources
-        if update_ids_from_dap and self.sources:
-            for o in self.sources:
-                Source.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        sources = self.sources
-        if sources and hasattr(sources[0], "to_dict"):
-            sources = [x.to_dict() for x in sources]
-        dct = {
-            'sources': [Source.update_dict_ids_to_dap(o) for o in sources] if (update_ids_to_dap and sources) else sources,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class EvaluateResponseBody(BaseSchema):
-    """
-    "body" of EvaluateResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "result": {
-            "type": "string",
-            "description": "The result of the evaluate request."
-        },
-        "type": {
-            "type": "string",
-            "description": "The optional type of the evaluate result.\nThis attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request."
-        },
-        "presentationHint": {
-            "description": "Properties of a evaluate result that can be used to determine how to render the result in the UI.",
-            "type": "VariablePresentationHint"
-        },
-        "variablesReference": {
-            "type": "integer",
-            "description": "If variablesReference is > 0, the evaluate result is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "namedVariables": {
-            "type": "integer",
-            "description": "The number of named child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "indexedVariables": {
-            "type": "integer",
-            "description": "The number of indexed child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "memoryReference": {
-            "type": "string",
-            "description": "Optional memory reference to a location appropriate for this result.\nFor pointer type eval results, this is generally a reference to the memory address contained in the pointer.\nThis attribute should be returned by a debug adapter if the client has passed the value true for the 'supportsMemoryReferences' capability of the 'initialize' request."
-        }
-    }
-    __refs__ = set(['presentationHint'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, result, variablesReference, type=None, presentationHint=None, namedVariables=None, indexedVariables=None, memoryReference=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string result: The result of the evaluate request.
-        :param integer variablesReference: If variablesReference is > 0, the evaluate result is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param string type: The optional type of the evaluate result.
-        This attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request.
-        :param VariablePresentationHint presentationHint: Properties of a evaluate result that can be used to determine how to render the result in the UI.
-        :param integer namedVariables: The number of named child variables.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param integer indexedVariables: The number of indexed child variables.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param string memoryReference: Optional memory reference to a location appropriate for this result.
-        For pointer type eval results, this is generally a reference to the memory address contained in the pointer.
-        This attribute should be returned by a debug adapter if the client has passed the value true for the 'supportsMemoryReferences' capability of the 'initialize' request.
-        """
-        self.result = result
-        self.variablesReference = variablesReference
-        self.type = type
-        if presentationHint is None:
-            self.presentationHint = VariablePresentationHint()
-        else:
-            self.presentationHint = VariablePresentationHint(update_ids_from_dap=update_ids_from_dap, **presentationHint) if presentationHint.__class__ !=  VariablePresentationHint else presentationHint
-        self.namedVariables = namedVariables
-        self.indexedVariables = indexedVariables
-        self.memoryReference = memoryReference
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        result = self.result
-        variablesReference = self.variablesReference
-        type = self.type  # noqa (assign to builtin)
-        presentationHint = self.presentationHint
-        namedVariables = self.namedVariables
-        indexedVariables = self.indexedVariables
-        memoryReference = self.memoryReference
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'result': result,
-            'variablesReference': variablesReference,
-        }
-        if type is not None:
-            dct['type'] = type
-        if presentationHint is not None:
-            dct['presentationHint'] = presentationHint.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if namedVariables is not None:
-            dct['namedVariables'] = namedVariables
-        if indexedVariables is not None:
-            dct['indexedVariables'] = indexedVariables
-        if memoryReference is not None:
-            dct['memoryReference'] = memoryReference
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register
-class SetExpressionResponseBody(BaseSchema):
-    """
-    "body" of SetExpressionResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "value": {
-            "type": "string",
-            "description": "The new value of the expression."
-        },
-        "type": {
-            "type": "string",
-            "description": "The optional type of the value.\nThis attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request."
-        },
-        "presentationHint": {
-            "description": "Properties of a value that can be used to determine how to render the result in the UI.",
-            "type": "VariablePresentationHint"
-        },
-        "variablesReference": {
-            "type": "integer",
-            "description": "If variablesReference is > 0, the value is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "namedVariables": {
-            "type": "integer",
-            "description": "The number of named child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        },
-        "indexedVariables": {
-            "type": "integer",
-            "description": "The number of indexed child variables.\nThe client can use this optional information to present the variables in a paged UI and fetch them in chunks.\nThe value should be less than or equal to 2147483647 (2^31-1)."
-        }
-    }
-    __refs__ = set(['presentationHint'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, value, type=None, presentationHint=None, variablesReference=None, namedVariables=None, indexedVariables=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string value: The new value of the expression.
-        :param string type: The optional type of the value.
-        This attribute should only be returned by a debug adapter if the client has passed the value true for the 'supportsVariableType' capability of the 'initialize' request.
-        :param VariablePresentationHint presentationHint: Properties of a value that can be used to determine how to render the result in the UI.
-        :param integer variablesReference: If variablesReference is > 0, the value is structured and its children can be retrieved by passing variablesReference to the VariablesRequest.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param integer namedVariables: The number of named child variables.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        :param integer indexedVariables: The number of indexed child variables.
-        The client can use this optional information to present the variables in a paged UI and fetch them in chunks.
-        The value should be less than or equal to 2147483647 (2^31-1).
-        """
-        self.value = value
-        self.type = type
-        if presentationHint is None:
-            self.presentationHint = VariablePresentationHint()
-        else:
-            self.presentationHint = VariablePresentationHint(update_ids_from_dap=update_ids_from_dap, **presentationHint) if presentationHint.__class__ !=  VariablePresentationHint else presentationHint
-        self.variablesReference = variablesReference
-        self.namedVariables = namedVariables
-        self.indexedVariables = indexedVariables
-        if update_ids_from_dap:
-            self.variablesReference = self._translate_id_from_dap(self.variablesReference)
-        self.kwargs = kwargs
-    
-    
-    @classmethod
-    def update_dict_ids_from_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_from_dap(dct['variablesReference'])
-        return dct
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        value = self.value
-        type = self.type  # noqa (assign to builtin)
-        presentationHint = self.presentationHint
-        variablesReference = self.variablesReference
-        namedVariables = self.namedVariables
-        indexedVariables = self.indexedVariables
-        if update_ids_to_dap:
-            if variablesReference is not None:
-                variablesReference = self._translate_id_to_dap(variablesReference)
-        dct = {
-            'value': value,
-        }
-        if type is not None:
-            dct['type'] = type
-        if presentationHint is not None:
-            dct['presentationHint'] = presentationHint.to_dict(update_ids_to_dap=update_ids_to_dap)
-        if variablesReference is not None:
-            dct['variablesReference'] = variablesReference
-        if namedVariables is not None:
-            dct['namedVariables'] = namedVariables
-        if indexedVariables is not None:
-            dct['indexedVariables'] = indexedVariables
-        dct.update(self.kwargs)
-        return dct    
-    
-    @classmethod
-    def update_dict_ids_to_dap(cls, dct):
-        if 'variablesReference' in dct:
-            dct['variablesReference'] = cls._translate_id_to_dap(dct['variablesReference'])
-        return dct
-
-
-@register
-class StepInTargetsResponseBody(BaseSchema):
-    """
-    "body" of StepInTargetsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "targets": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/StepInTarget"
-            },
-            "description": "The possible stepIn targets of the specified source location."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, targets, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array targets: The possible stepIn targets of the specified source location.
-        """
-        self.targets = targets
-        if update_ids_from_dap and self.targets:
-            for o in self.targets:
-                StepInTarget.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        targets = self.targets
-        if targets and hasattr(targets[0], "to_dict"):
-            targets = [x.to_dict() for x in targets]
-        dct = {
-            'targets': [StepInTarget.update_dict_ids_to_dap(o) for o in targets] if (update_ids_to_dap and targets) else targets,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class GotoTargetsResponseBody(BaseSchema):
-    """
-    "body" of GotoTargetsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "targets": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/GotoTarget"
-            },
-            "description": "The possible goto targets of the specified location."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, targets, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array targets: The possible goto targets of the specified location.
-        """
-        self.targets = targets
-        if update_ids_from_dap and self.targets:
-            for o in self.targets:
-                GotoTarget.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        targets = self.targets
-        if targets and hasattr(targets[0], "to_dict"):
-            targets = [x.to_dict() for x in targets]
-        dct = {
-            'targets': [GotoTarget.update_dict_ids_to_dap(o) for o in targets] if (update_ids_to_dap and targets) else targets,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class CompletionsResponseBody(BaseSchema):
-    """
-    "body" of CompletionsResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "targets": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/CompletionItem"
-            },
-            "description": "The possible completions for ."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, targets, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array targets: The possible completions for .
-        """
-        self.targets = targets
-        if update_ids_from_dap and self.targets:
-            for o in self.targets:
-                CompletionItem.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        targets = self.targets
-        if targets and hasattr(targets[0], "to_dict"):
-            targets = [x.to_dict() for x in targets]
-        dct = {
-            'targets': [CompletionItem.update_dict_ids_to_dap(o) for o in targets] if (update_ids_to_dap and targets) else targets,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ExceptionInfoResponseBody(BaseSchema):
-    """
-    "body" of ExceptionInfoResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "exceptionId": {
-            "type": "string",
-            "description": "ID of the exception that was thrown."
-        },
-        "description": {
-            "type": "string",
-            "description": "Descriptive text for the exception provided by the debug adapter."
-        },
-        "breakMode": {
-            "description": "Mode that caused the exception notification to be raised.",
-            "type": "ExceptionBreakMode"
-        },
-        "details": {
-            "description": "Detailed information about the exception.",
-            "type": "ExceptionDetails"
-        }
-    }
-    __refs__ = set(['breakMode', 'details'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, exceptionId, breakMode, description=None, details=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string exceptionId: ID of the exception that was thrown.
-        :param ExceptionBreakMode breakMode: Mode that caused the exception notification to be raised.
-        :param string description: Descriptive text for the exception provided by the debug adapter.
-        :param ExceptionDetails details: Detailed information about the exception.
-        """
-        self.exceptionId = exceptionId
-        if breakMode is not None:
-            assert breakMode in ExceptionBreakMode.VALID_VALUES
-        self.breakMode = breakMode
-        self.description = description
-        if details is None:
-            self.details = ExceptionDetails()
-        else:
-            self.details = ExceptionDetails(update_ids_from_dap=update_ids_from_dap, **details) if details.__class__ !=  ExceptionDetails else details
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        exceptionId = self.exceptionId
-        breakMode = self.breakMode
-        description = self.description
-        details = self.details
-        dct = {
-            'exceptionId': exceptionId,
-            'breakMode': breakMode,
-        }
-        if description is not None:
-            dct['description'] = description
-        if details is not None:
-            dct['details'] = details.to_dict(update_ids_to_dap=update_ids_to_dap)
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class ReadMemoryResponseBody(BaseSchema):
-    """
-    "body" of ReadMemoryResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "address": {
-            "type": "string",
-            "description": "The address of the first byte of data returned.\nTreated as a hex value if prefixed with '0x', or as a decimal value otherwise."
-        },
-        "unreadableBytes": {
-            "type": "integer",
-            "description": "The number of unreadable bytes encountered after the last successfully read byte.\nThis can be used to determine the number of bytes that must be skipped before a subsequent 'readMemory' request will succeed."
-        },
-        "data": {
-            "type": "string",
-            "description": "The bytes read from memory, encoded using base64."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, address, unreadableBytes=None, data=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string address: The address of the first byte of data returned.
-        Treated as a hex value if prefixed with '0x', or as a decimal value otherwise.
-        :param integer unreadableBytes: The number of unreadable bytes encountered after the last successfully read byte.
-        This can be used to determine the number of bytes that must be skipped before a subsequent 'readMemory' request will succeed.
-        :param string data: The bytes read from memory, encoded using base64.
-        """
-        self.address = address
-        self.unreadableBytes = unreadableBytes
-        self.data = data
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        address = self.address
-        unreadableBytes = self.unreadableBytes
-        data = self.data
-        dct = {
-            'address': address,
-        }
-        if unreadableBytes is not None:
-            dct['unreadableBytes'] = unreadableBytes
-        if data is not None:
-            dct['data'] = data
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class WriteMemoryResponseBody(BaseSchema):
-    """
-    "body" of WriteMemoryResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "offset": {
-            "type": "integer",
-            "description": "Optional property that should be returned when 'allowPartial' is true to indicate the offset of the first byte of data successfully written. Can be negative."
-        },
-        "bytesWritten": {
-            "type": "integer",
-            "description": "Optional property that should be returned when 'allowPartial' is true to indicate the number of bytes starting from address that were successfully written."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, offset=None, bytesWritten=None, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param integer offset: Optional property that should be returned when 'allowPartial' is true to indicate the offset of the first byte of data successfully written. Can be negative.
-        :param integer bytesWritten: Optional property that should be returned when 'allowPartial' is true to indicate the number of bytes starting from address that were successfully written.
-        """
-        self.offset = offset
-        self.bytesWritten = bytesWritten
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        offset = self.offset
-        bytesWritten = self.bytesWritten
-        dct = {
-        }
-        if offset is not None:
-            dct['offset'] = offset
-        if bytesWritten is not None:
-            dct['bytesWritten'] = bytesWritten
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class DisassembleResponseBody(BaseSchema):
-    """
-    "body" of DisassembleResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "instructions": {
-            "type": "array",
-            "items": {
-                "$ref": "#/definitions/DisassembledInstruction"
-            },
-            "description": "The list of disassembled instructions."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, instructions, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param array instructions: The list of disassembled instructions.
-        """
-        self.instructions = instructions
-        if update_ids_from_dap and self.instructions:
-            for o in self.instructions:
-                DisassembledInstruction.update_dict_ids_from_dap(o)
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        instructions = self.instructions
-        if instructions and hasattr(instructions[0], "to_dict"):
-            instructions = [x.to_dict() for x in instructions]
-        dct = {
-            'instructions': [DisassembledInstruction.update_dict_ids_to_dap(o) for o in instructions] if (update_ids_to_dap and instructions) else instructions,
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class MessageVariables(BaseSchema):
-    """
-    "variables" of Message
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {}
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-    
-        """
-    
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        dct = {
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdSystemInfoResponseBody(BaseSchema):
-    """
-    "body" of PydevdSystemInfoResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "python": {
-            "description": "Information about the python version running in the current process.",
-            "type": "PydevdPythonInfo"
-        },
-        "platform": {
-            "description": "Information about the plarforn on which the current process is running.",
-            "type": "PydevdPlatformInfo"
-        },
-        "process": {
-            "description": "Information about the current process.",
-            "type": "PydevdProcessInfo"
-        },
-        "pydevd": {
-            "description": "Information about pydevd.",
-            "type": "PydevdInfo"
-        }
-    }
-    __refs__ = set(['python', 'platform', 'process', 'pydevd'])
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, python, platform, process, pydevd, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param PydevdPythonInfo python: Information about the python version running in the current process.
-        :param PydevdPlatformInfo platform: Information about the plarforn on which the current process is running.
-        :param PydevdProcessInfo process: Information about the current process.
-        :param PydevdInfo pydevd: Information about pydevd.
-        """
-        if python is None:
-            self.python = PydevdPythonInfo()
-        else:
-            self.python = PydevdPythonInfo(update_ids_from_dap=update_ids_from_dap, **python) if python.__class__ !=  PydevdPythonInfo else python
-        if platform is None:
-            self.platform = PydevdPlatformInfo()
-        else:
-            self.platform = PydevdPlatformInfo(update_ids_from_dap=update_ids_from_dap, **platform) if platform.__class__ !=  PydevdPlatformInfo else platform
-        if process is None:
-            self.process = PydevdProcessInfo()
-        else:
-            self.process = PydevdProcessInfo(update_ids_from_dap=update_ids_from_dap, **process) if process.__class__ !=  PydevdProcessInfo else process
-        if pydevd is None:
-            self.pydevd = PydevdInfo()
-        else:
-            self.pydevd = PydevdInfo(update_ids_from_dap=update_ids_from_dap, **pydevd) if pydevd.__class__ !=  PydevdInfo else pydevd
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        python = self.python
-        platform = self.platform
-        process = self.process
-        pydevd = self.pydevd
-        dct = {
-            'python': python.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'platform': platform.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'process': process.to_dict(update_ids_to_dap=update_ids_to_dap),
-            'pydevd': pydevd.to_dict(update_ids_to_dap=update_ids_to_dap),
-        }
-        dct.update(self.kwargs)
-        return dct
-
-
-@register
-class PydevdAuthorizeResponseBody(BaseSchema):
-    """
-    "body" of PydevdAuthorizeResponse
-
-    Note: automatically generated code. Do not edit manually.
-    """
-
-    __props__ = {
-        "clientAccessToken": {
-            "type": "string",
-            "description": "The access token to access the client (i.e.: usually the IDE)."
-        }
-    }
-    __refs__ = set()
-
-    __slots__ = list(__props__.keys()) + ['kwargs']
-
-    def __init__(self, clientAccessToken, update_ids_from_dap=False, **kwargs):  # noqa (update_ids_from_dap may be unused)
-        """
-        :param string clientAccessToken: The access token to access the client (i.e.: usually the IDE).
-        """
-        self.clientAccessToken = clientAccessToken
-        self.kwargs = kwargs
-
-
-    def to_dict(self, update_ids_to_dap=False):  # noqa (update_ids_to_dap may be unused)
-        clientAccessToken = self.clientAccessToken
-        dct = {
-            'clientAccessToken': clientAccessToken,
-        }
-        dct.update(self.kwargs)
-        return dct
diff --git a/spaces/Sygil/INE-dataset-explorer/src/textdir2sql/loading.py b/spaces/Sygil/INE-dataset-explorer/src/textdir2sql/loading.py
deleted file mode 100644
index c53dbbb9ece5981c3769ff4c32e0cfda14f8adc6..0000000000000000000000000000000000000000
--- a/spaces/Sygil/INE-dataset-explorer/src/textdir2sql/loading.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import sqlite3
-from functools import partial
-from itertools import islice
-from pathlib import Path
-
-import click
-
-BATCH_SIZE=1024
-
-
-@click.command()
-@click.argument('input_dir', type=click.Path(exists=True, file_okay=False, path_type=Path))
-@click.argument('output', type=click.Path(dir_okay=False, writable=True, path_type=Path))
-@click.option('--image-host', help="base URL of images")
-@click.option('--explicit/--no-explicit', default=True)
-def main(input_dir: Path, output: Path, image_host: str, explicit:bool):
-    connection = sqlite3.connect(output)
-    try:
-        _main_with_connection(input_dir, connection, image_host, explicit)
-    finally:
-        connection.close()
-
-def _main_with_connection(input_dir: Path, connection: sqlite3.Connection, image_host: str=None, explicit=True):
-    connection.execute("CREATE TABLE IF NOT EXISTS "
-                       "    captions(image_key text PRIMARY KEY, caption text NOT NULL);")
-
-    if image_host:
-        connection.execute(f"""
-        CREATE VIEW IF NOT EXISTS images AS
-            SELECT {sql_quote(connection, image_host)} || image_key || '.jpg' AS image,
-            caption, 
-            rowid
-        FROM captions
-        """)
-
-    text_files = input_dir.glob("*.txt")
-
-    with click.progressbar(chunked(text_files, BATCH_SIZE)) as progress:
-        for batch in progress:
-            text_file: Path
-            pairs = ((text_file.stem, text_file.read_text())
-                for text_file in batch)
-            with connection:
-                connection.executemany("INSERT INTO captions(image_key, caption) "
-                                       "VALUES(?, ?) ", pairs)
-
-    if not explicit:
-        ratings = ["rating:unsafe", "rating:explicit", "rating:mature", "meta:nsfw",
-                   "subreddit:%nsfw"]
-        for rating in ratings:
-            with connection:
-                c = connection.execute("DELETE FROM captions WHERE caption LIKE ?",
-                                       (f"%{rating}%",))
-                print(f"Removed {c.rowcount} {rating} rows")
-
-    with connection:
-        # Add full-text search index
-        connection.execute("""CREATE VIRTUAL TABLE
-           captions_fts USING 
-           fts5(caption, image_key UNINDEXED, content=captions)
-        """)
-        connection.execute("""
-            INSERT INTO "captions_fts" (rowid, image_key, caption)
-            SELECT rowid, image_key, caption
-            FROM captions
-        """)
-
-
-def chunked(iterable, n):
-    return iter(partial(take, n, iter(iterable)), [])
-
-def take(n, iterable):
-    return list(islice(iterable, n))
-
-def sql_quote(connection, value: str) -> str:
-    """
-    Apply SQLite string quoting to a value, including wrapping it in single quotes.
-    :param value: String to quote
-    """
-    # Normally we would use .execute(sql, [params]) for escaping, but
-    # occasionally that isn't available - most notable when we need
-    # to include a "... DEFAULT 'value'" in a column definition.
-    return connection.execute(
-        # Use SQLite itself to correctly escape this string:
-        "SELECT quote(:value)",
-        {"value": value},
-    ).fetchone()[0]
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/TNR-5/libt/README.md b/spaces/TNR-5/libt/README.md
deleted file mode 100644
index 76f30ec49f3078a2e765272b46cdbece21f65e37..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/libt/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: TNR LIBRERY
-emoji: ♾️
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-app_file: app.py
-pinned: true
-duplicated_from: nouamanetazi/hf-search
----
-# Configuration
-
-`title`: _string_
-Display title for the Space.
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/TempoFunk/makeavid-sd-jax/makeavid_sd/flax_impl/train.py b/spaces/TempoFunk/makeavid-sd-jax/makeavid_sd/flax_impl/train.py
deleted file mode 100644
index daa9765c0f5f9b8bcd36c673bf5b736d3983840e..0000000000000000000000000000000000000000
--- a/spaces/TempoFunk/makeavid-sd-jax/makeavid_sd/flax_impl/train.py
+++ /dev/null
@@ -1,143 +0,0 @@
-
-import jax
-_ = jax.device_count() # ugly hack to prevent tpu comms to lock/race or smth smh
-
-from typing import Tuple, Optional
-import os
-from argparse import ArgumentParser
-
-from flax_trainer import FlaxTrainerUNetPseudo3D
-from dataset import load_dataset
-
-def train(
-        dataset_path: str,
-        model_path: str,
-        output_dir: str,
-        dataset_cache_dir: Optional[str] = None,
-        from_pt: bool = True,
-        convert2d: bool = False,
-        only_temporal: bool = True,
-        sample_size: Tuple[int, int] = (64, 64),
-        lr: float = 5e-5,
-        batch_size: int = 1,
-        num_frames: int = 24,
-        epochs: int = 10,
-        warmup: float = 0.1,
-        decay: float = 0.0,
-        weight_decay: float = 1e-2,
-        log_every_step: int = 50,
-        save_every_epoch: int = 1,
-        sample_every_epoch: int = 1,
-        seed: int = 0,
-        dtype: str = 'bfloat16',
-        param_dtype: str = 'float32',
-        use_memory_efficient_attention: bool = True,
-        verbose: bool = True,
-        use_wandb: bool = False
-) -> None:
-    log = lambda x: print(x) if verbose else None
-    log('\n----------------')
-    log('Init trainer')
-    trainer = FlaxTrainerUNetPseudo3D(
-            model_path = model_path,
-            from_pt = from_pt,
-            convert2d = convert2d,
-            sample_size = sample_size,
-            seed = seed,
-            dtype = dtype,
-            param_dtype = param_dtype,
-            use_memory_efficient_attention = use_memory_efficient_attention,
-            verbose = verbose,
-            only_temporal = only_temporal
-    )
-    log('\n----------------')
-    log('Init dataset')
-    dataloader = load_dataset(
-            dataset_path = dataset_path,
-            model_path = model_path,
-            cache_dir = dataset_cache_dir,
-            batch_size = batch_size * trainer.num_devices,
-            num_frames = num_frames,
-            num_workers = min(trainer.num_devices * 2, os.cpu_count() - 1),
-            as_numpy = True,
-            shuffle = True
-    )
-    log('\n----------------')
-    log('Train')
-    if use_wandb:
-        trainer.enable_wandb()
-    trainer.train(
-            dataloader = dataloader,
-            epochs = epochs,
-            num_frames = num_frames,
-            log_every_step = log_every_step,
-            save_every_epoch = save_every_epoch,
-            sample_every_epoch = sample_every_epoch,
-            lr = lr,
-            warmup = warmup,
-            decay = decay,
-            weight_decay = weight_decay,
-            output_dir = output_dir
-    )
-    log('\n----------------')
-    log('Done')
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser()
-    bool_type = lambda x: x.lower() in ['true', '1', 'yes']
-    parser.add_argument('-v', '--verbose', type = bool_type, default = True)
-    parser.add_argument('-d', '--dataset_path', required = True)
-    parser.add_argument('-m', '--model_path', required = True)
-    parser.add_argument('-o', '--output_dir', required = True)
-    parser.add_argument('-b', '--batch_size', type = int, default = 1)
-    parser.add_argument('-f', '--num_frames', type = int, default = 24)
-    parser.add_argument('-e', '--epochs', type = int, default = 2)
-    parser.add_argument('--only_temporal', type = bool_type, default = True)
-    parser.add_argument('--dataset_cache_dir', type = str, default = None)
-    parser.add_argument('--from_pt', type = bool_type, default = True)
-    parser.add_argument('--convert2d', type = bool_type, default = False)
-    parser.add_argument('--lr', type = float, default = 1e-4)
-    parser.add_argument('--warmup', type = float, default = 0.1)
-    parser.add_argument('--decay', type = float, default = 0.0)
-    parser.add_argument('--weight_decay', type = float, default = 1e-2)
-    parser.add_argument('--sample_size', type = int, nargs = 2, default = [64, 64])
-    parser.add_argument('--log_every_step', type = int, default = 250)
-    parser.add_argument('--save_every_epoch', type = int, default = 1)
-    parser.add_argument('--sample_every_epoch', type = int, default = 1)
-    parser.add_argument('--seed', type = int, default = 0)
-    parser.add_argument('--use_memory_efficient_attention', type = bool_type, default = True)
-    parser.add_argument('--dtype', choices = ['float32', 'bfloat16', 'float16'], default = 'bfloat16')
-    parser.add_argument('--param_dtype', choices = ['float32', 'bfloat16', 'float16'], default = 'float32')
-    parser.add_argument('--wandb', type = bool_type, default = False)
-    args = parser.parse_args()
-    args.sample_size = tuple(args.sample_size)
-    if args.verbose:
-        print(args)
-    train(
-            dataset_path = args.dataset_path,
-            model_path = args.model_path,
-            from_pt = args.from_pt,
-            convert2d = args.convert2d,
-            only_temporal = args.only_temporal,
-            output_dir = args.output_dir,
-            dataset_cache_dir = args.dataset_cache_dir,
-            batch_size = args.batch_size,
-            num_frames = args.num_frames,
-            epochs = args.epochs,
-            lr = args.lr,
-            warmup = args.warmup,
-            decay = args.decay,
-            weight_decay = args.weight_decay,
-            sample_size = args.sample_size,
-            seed = args.seed,
-            dtype = args.dtype,
-            param_dtype = args.param_dtype,
-            use_memory_efficient_attention = args.use_memory_efficient_attention,
-            log_every_step = args.log_every_step,
-            save_every_epoch = args.save_every_epoch,
-            sample_every_epoch = args.sample_every_epoch,
-            verbose = args.verbose,
-            use_wandb = args.wandb
-    )
-
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py
deleted file mode 100644
index b867cc865e5ac4d7b70221da141894efd7cbd75c..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-
-train.max_iter *= 2  # 100ep -> 200ep
-
-lr_multiplier.scheduler.milestones = [
-    milestone * 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
diff --git a/spaces/TuringAgency/anic_gui/README.md b/spaces/TuringAgency/anic_gui/README.md
deleted file mode 100644
index 5eda717140e9e0dba379309f6c46719e74a79ad8..0000000000000000000000000000000000000000
--- a/spaces/TuringAgency/anic_gui/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: ANIC GUI
-emoji: 👩🏽‍🎤
-colorFrom: blue
-colorTo: red
-sdk: static
-app_file: index.html
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/Unggi/title_extraction_bart_logical/README.md b/spaces/Unggi/title_extraction_bart_logical/README.md
deleted file mode 100644
index 4157c58d1255bee5cd2bbabb06570894c11fce5c..0000000000000000000000000000000000000000
--- a/spaces/Unggi/title_extraction_bart_logical/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Title Extraction Bart Logical
-emoji: 🚀
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/UtkMal/Classifying-snake-breeds/app.py b/spaces/UtkMal/Classifying-snake-breeds/app.py
deleted file mode 100644
index 04167cea8276f1f2fcb9882c5853d086dfa53362..0000000000000000000000000000000000000000
--- a/spaces/UtkMal/Classifying-snake-breeds/app.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-import timm
-
-# Importing the model
-model = load_learner('model.pkl')
-
-categories = model.dls.vocab
-
-def predict_snake_type(img):
-    snake_type, idx, probs = model.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-# Gradio Interface
-input_img = gr.Image()
-outputs = gr.Label(num_top_classes=5)
-
-gr.Interface(
-    fn=predict_snake_type, 
-    inputs=[input_img], outputs=outputs,
-    examples=[
-        'examples/agkistrodon-contortrix.jpg', 
-        'examples/haldea-striatula.jpg', 
-        'examples/masticophis-flagellum.jpg',
-        'examples/storeria-occipitomaculata.jpg'
-        ],
-    title='Classifying Different Breeds of snakes',
-    description='''
-This is a multi-classification model that identifies different breeds of snakes.
-This model is `74%` accurate, and can identify the breeds of snakes present in this file: https://huggingface.co/spaces/UtkMal/Classifying-snake-breeds/blob/main/app.py'''
-).launch()
\ No newline at end of file
diff --git a/spaces/Vipitis/shadermatch/run_tests.py b/spaces/Vipitis/shadermatch/run_tests.py
deleted file mode 100644
index 07ca67a288e125926faaac8c87ef3d78a457f2e0..0000000000000000000000000000000000000000
--- a/spaces/Vipitis/shadermatch/run_tests.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from evaluate import load
-from tests import test_cases
-
-shadermatch = load("./shadermatch")
-
-for case in test_cases:
-    res = shadermatch.compute(predictions=case["predictions"], references=case["references"])
-    print(case["result"])
-    print(res)
-    print(res == case["result"])
\ No newline at end of file
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/processors/base_processor.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/processors/base_processor.py
deleted file mode 100644
index 39b33cdf8fcd97cfd3e4a5fbece6593357af9d41..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/processors/base_processor.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from omegaconf import OmegaConf
-
-
-class BaseProcessor:
-    def __init__(self):
-        self.transform = lambda x: x
-        return
-
-    def __call__(self, item):
-        return self.transform(item)
-
-    @classmethod
-    def from_config(cls, cfg=None):
-        return cls()
-
-    def build(self, **kwargs):
-        cfg = OmegaConf.create(kwargs)
-
-        return self.from_config(cfg)
diff --git a/spaces/Wazzzabeee/image-video-colorization/models/deep_colorization/colorizers/util.py b/spaces/Wazzzabeee/image-video-colorization/models/deep_colorization/colorizers/util.py
deleted file mode 100644
index 79968ba6b960a8c10047f1ce52400b6bfe766b9c..0000000000000000000000000000000000000000
--- a/spaces/Wazzzabeee/image-video-colorization/models/deep_colorization/colorizers/util.py
+++ /dev/null
@@ -1,47 +0,0 @@
-
-from PIL import Image
-import numpy as np
-from skimage import color
-import torch
-import torch.nn.functional as F
-from IPython import embed
-
-def load_img(img_path):
-	out_np = np.asarray(Image.open(img_path))
-	if(out_np.ndim==2):
-		out_np = np.tile(out_np[:,:,None],3)
-	return out_np
-
-def resize_img(img, HW=(256,256), resample=3):
-	return np.asarray(Image.fromarray(img).resize((HW[1],HW[0]), resample=resample))
-
-def preprocess_img(img_rgb_orig, HW=(256,256), resample=3):
-	# return original size L and resized L as torch Tensors
-	img_rgb_rs = resize_img(img_rgb_orig, HW=HW, resample=resample)
-	
-	img_lab_orig = color.rgb2lab(img_rgb_orig)
-	img_lab_rs = color.rgb2lab(img_rgb_rs)
-
-	img_l_orig = img_lab_orig[:,:,0]
-	img_l_rs = img_lab_rs[:,:,0]
-
-	tens_orig_l = torch.Tensor(img_l_orig)[None,None,:,:]
-	tens_rs_l = torch.Tensor(img_l_rs)[None,None,:,:]
-
-	return (tens_orig_l, tens_rs_l)
-
-def postprocess_tens(tens_orig_l, out_ab, mode='bilinear'):
-	# tens_orig_l 	1 x 1 x H_orig x W_orig
-	# out_ab 		1 x 2 x H x W
-
-	HW_orig = tens_orig_l.shape[2:]
-	HW = out_ab.shape[2:]
-
-	# call resize function if needed
-	if(HW_orig[0]!=HW[0] or HW_orig[1]!=HW[1]):
-		out_ab_orig = F.interpolate(out_ab, size=HW_orig, mode='bilinear')
-	else:
-		out_ab_orig = out_ab
-
-	out_lab_orig = torch.cat((tens_orig_l, out_ab_orig), dim=1)
-	return color.lab2rgb(out_lab_orig.data.cpu().numpy()[0,...].transpose((1,2,0)))
diff --git a/spaces/WindVChen/INR-Harmon/inference_for_arbitrary_resolution_image.py b/spaces/WindVChen/INR-Harmon/inference_for_arbitrary_resolution_image.py
deleted file mode 100644
index fcfdf5bacfdf7abca278c1997cf46484591168e1..0000000000000000000000000000000000000000
--- a/spaces/WindVChen/INR-Harmon/inference_for_arbitrary_resolution_image.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import argparse
-
-import torch.backends.cudnn as cudnn
-import torchvision.transforms as transforms
-from torch.utils.data import DataLoader
-
-from model.build_model import build_model
-
-import torch
-import cv2
-import numpy as np
-import torchvision
-import os
-import tqdm
-import time
-
-from utils.misc import prepare_cooridinate_input, customRandomCrop
-
-from datasets.build_INR_dataset import Implicit2DGenerator
-import albumentations
-from albumentations import Resize
-from torch.utils.data import DataLoader
-from utils.misc import normalize
-
-import math
-
-global_state = [1]  # For Gradio Stop Button.
-
-class single_image_dataset(torch.utils.data.Dataset):
-    def __init__(self, opt, composite_image=None, mask=None):
-        super().__init__()
-
-        self.opt = opt
-
-        if composite_image is None:
-            composite_image = cv2.imread(opt.composite_image)
-            composite_image = cv2.cvtColor(composite_image, cv2.COLOR_BGR2RGB)
-        self.composite_image = composite_image
-
-        if mask is None:
-            mask = cv2.imread(opt.mask)
-        mask = mask[:, :, 0].astype(np.float32) / 255.
-        self.mask = mask
-
-        self.torch_transforms = transforms.Compose([transforms.ToTensor(),
-                                                    transforms.Normalize([.5, .5, .5], [.5, .5, .5])])
-        self.INR_dataset = Implicit2DGenerator(opt, 'Val')
-
-        self.split_width_resolution = composite_image.shape[1] // opt.split_num
-        self.split_height_resolution = composite_image.shape[0] // opt.split_num
-
-        self.split_width_resolution = self.split_height_resolution = min(self.split_width_resolution,
-                                                                         self.split_height_resolution)
-
-        if self.split_width_resolution % 4 != 0:
-            self.split_width_resolution = self.split_width_resolution + (4 - self.split_width_resolution % 4)
-
-        if self.split_height_resolution % 4 != 0:
-            self.split_height_resolution = self.split_height_resolution + (4 - self.split_height_resolution % 4)
-
-        self.num_w = math.ceil(composite_image.shape[1] / self.split_width_resolution)
-        self.num_h = math.ceil(composite_image.shape[0] / self.split_height_resolution)
-
-        self.split_start_point = []
-
-        "Split the image into several parts."
-        for i in range(self.num_h):
-            for j in range(self.num_w):
-                if i == composite_image.shape[0] // self.split_height_resolution:
-                    if j == composite_image.shape[1] // self.split_width_resolution:
-                        self.split_start_point.append((composite_image.shape[0] - self.split_height_resolution,
-                                                       composite_image.shape[1] - self.split_width_resolution))
-                    else:
-                        self.split_start_point.append(
-                            (composite_image.shape[0] - self.split_height_resolution, j * self.split_width_resolution))
-                else:
-                    if j == composite_image.shape[1] // self.split_width_resolution:
-                        self.split_start_point.append(
-                            (i * self.split_height_resolution, composite_image.shape[1] - self.split_width_resolution))
-                    else:
-                        self.split_start_point.append(
-                            (i * self.split_height_resolution, j * self.split_width_resolution))
-
-        assert len(self.split_start_point) == self.num_w * self.num_h
-
-        print(
-            f"The image will be split into {self.num_h} pieces in height, and {self.num_w} pieces in width. Totally {self.num_h * self.num_w} patches.")
-        print(f"The final resolution of each patch is {self.split_height_resolution} x {self.split_width_resolution}")
-
-    def __len__(self):
-        return self.num_w * self.num_h
-
-    def __getitem__(self, idx):
-        composite_image = self.composite_image
-
-        mask = self.mask
-
-        full_coord = prepare_cooridinate_input(mask).transpose(1, 2, 0)
-
-        tmp_transform = albumentations.Compose([Resize(self.opt.base_size, self.opt.base_size)],
-                                               additional_targets={'object_mask': 'image'})
-        transform_out = tmp_transform(image=composite_image, object_mask=mask)
-        compos_list = [self.torch_transforms(transform_out['image'])]
-        mask_list = [
-            torchvision.transforms.ToTensor()(transform_out['object_mask'][..., np.newaxis].astype(np.float32))]
-        coord_map_list = []
-
-        if composite_image.shape[0] != self.split_height_resolution:
-            c_h = self.split_start_point[idx][0] / (composite_image.shape[0] - self.split_height_resolution)
-        else:
-            c_h = 0
-        if composite_image.shape[1] != self.split_width_resolution:
-            c_w = self.split_start_point[idx][1] / (composite_image.shape[1] - self.split_width_resolution)
-        else:
-            c_w = 0
-        transform_out, c_h, c_w = customRandomCrop([composite_image, mask, full_coord],
-                                                   self.split_height_resolution, self.split_width_resolution, c_h, c_w)
-
-        compos_list.append(self.torch_transforms(transform_out[0]))
-        mask_list.append(
-            torchvision.transforms.ToTensor()(transform_out[1][..., np.newaxis].astype(np.float32)))
-        coord_map_list.append(torchvision.transforms.ToTensor()(transform_out[2]))
-        coord_map_list.append(torchvision.transforms.ToTensor()(transform_out[2]))
-        for n in range(2):
-            tmp_comp = cv2.resize(composite_image, (
-                composite_image.shape[1] // 2 ** (n + 1), composite_image.shape[0] // 2 ** (n + 1)))
-            tmp_mask = cv2.resize(mask, (mask.shape[1] // 2 ** (n + 1), mask.shape[0] // 2 ** (n + 1)))
-            tmp_coord = prepare_cooridinate_input(tmp_mask).transpose(1, 2, 0)
-
-            transform_out, c_h, c_w = customRandomCrop([tmp_comp, tmp_mask, tmp_coord],
-                                                       self.split_height_resolution // 2 ** (n + 1),
-                                                       self.split_width_resolution // 2 ** (n + 1), c_h, c_w)
-            compos_list.append(self.torch_transforms(transform_out[0]))
-            mask_list.append(
-                torchvision.transforms.ToTensor()(transform_out[1][..., np.newaxis].astype(np.float32)))
-            coord_map_list.append(torchvision.transforms.ToTensor()(transform_out[2]))
-        out_comp = compos_list
-        out_mask = mask_list
-        out_coord = coord_map_list
-
-        fg_INR_coordinates, bg_INR_coordinates, fg_INR_RGB, fg_transfer_INR_RGB, bg_INR_RGB = self.INR_dataset.generator(
-            self.torch_transforms, transform_out[0], transform_out[0], mask)
-
-        return {
-            'composite_image': out_comp,
-            'mask': out_mask,
-            'coordinate_map': out_coord,
-            'composite_image0': out_comp[0],
-            'mask0': out_mask[0],
-            'coordinate_map0': out_coord[0],
-            'composite_image1': out_comp[1],
-            'mask1': out_mask[1],
-            'coordinate_map1': out_coord[1],
-            'composite_image2': out_comp[2],
-            'mask2': out_mask[2],
-            'coordinate_map2': out_coord[2],
-            'composite_image3': out_comp[3],
-            'mask3': out_mask[3],
-            'coordinate_map3': out_coord[3],
-            'fg_INR_coordinates': fg_INR_coordinates,
-            'bg_INR_coordinates': bg_INR_coordinates,
-            'fg_INR_RGB': fg_INR_RGB,
-            'fg_transfer_INR_RGB': fg_transfer_INR_RGB,
-            'bg_INR_RGB': bg_INR_RGB,
-            'start_point': self.split_start_point[idx],
-        }
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--split_num', type=int, default=4,
-                        help='How many pieces do you want to split an image width / height.')
-
-    parser.add_argument('--composite_image', type=str, default=r'./demo/demo_2k_composite.jpg',
-                        help='composite image path')
-
-    parser.add_argument('--mask', type=str, default=r'./demo/demo_2k_mask.jpg',
-                        help='mask path')
-
-    parser.add_argument('--save_path', type=str, default=r'./demo/',
-                        help='save path')
-
-    parser.add_argument('--workers', type=int, default=8,
-                        metavar='N', help='Dataloader threads.')
-
-    parser.add_argument('--batch_size', type=int, default=1,
-                        help='You can override model batch size by specify positive number.')
-
-    parser.add_argument('--device', type=str, default='cuda',
-                        help="Whether use cuda, 'cuda' or 'cpu'.")
-
-    parser.add_argument('--base_size', type=int, default=256,
-                        help='Base size. Resolution of the image input into the Encoder')
-
-    parser.add_argument('--input_size', type=int, default=256,
-                        help='Input size. Resolution of the image that want to be generated by the Decoder')
-
-    parser.add_argument('--INR_input_size', type=int, default=256,
-                        help='INR input size. Resolution of the image that want to be generated by the Decoder. '
-                             'Should be the same as `input_size`')
-
-    parser.add_argument('--INR_MLP_dim', type=int, default=32,
-                        help='Number of channels for INR linear layer.')
-
-    parser.add_argument('--LUT_dim', type=int, default=7,
-                        help='Dim of the output LUT. Refer to https://ieeexplore.ieee.org/abstract/document/9206076')
-
-    parser.add_argument('--activation', type=str, default='leakyrelu_pe',
-                        help='INR activation layer type: leakyrelu_pe, sine')
-
-    parser.add_argument('--pretrained', type=str,
-                        default=r'.\pretrained_models\Resolution_RAW_iHarmony4.pth',
-                        help='Pretrained weight path')
-
-    parser.add_argument('--param_factorize_dim', type=int,
-                        default=10,
-                        help='The intermediate dimensions of the factorization of the predicted MLP parameters. '
-                             'Refer to https://arxiv.org/abs/2011.12026')
-
-    parser.add_argument('--embedding_type', type=str,
-                        default="CIPS_embed",
-                        help='Which embedding_type to use.')
-
-    parser.add_argument('--INRDecode', action="store_false",
-                        help='Whether INR decoder. Set it to False if you want to test the baseline '
-                             '(https://github.com/SamsungLabs/image_harmonization)')
-
-    parser.add_argument('--isMoreINRInput', action="store_false",
-                        help='Whether to cat RGB and mask. See Section 3.4 in the paper.')
-
-    parser.add_argument('--hr_train', action="store_false",
-                        help='Whether use hr_train. See section 3.4 in the paper.')
-
-    parser.add_argument('--isFullRes', action="store_true",
-                        help='Whether for original resolution. See section 3.4 in the paper.')
-
-    opt = parser.parse_args()
-
-    return opt
-
-@torch.no_grad()
-def inference(model, opt, composite_image=None, mask=None):
-    model.eval()
-
-    "dataset here is actually consisted of several patches of a single image."
-    singledataset = single_image_dataset(opt, composite_image, mask)
-
-    single_data_loader = DataLoader(singledataset, opt.batch_size, shuffle=False, drop_last=False, pin_memory=True,
-                                    num_workers=opt.workers, persistent_workers=False if composite_image is not None else True)
-
-    "Init a pure black image with the same size as the input image."
-    init_img = np.zeros_like(singledataset.composite_image)
-
-    time_all = 0
-
-    for step, batch in tqdm.tqdm(enumerate(single_data_loader)):
-        composite_image = [batch[f'composite_image{name}'].to(opt.device) for name in range(4)]
-        mask = [batch[f'mask{name}'].to(opt.device) for name in range(4)]
-        coordinate_map = [batch[f'coordinate_map{name}'].to(opt.device) for name in range(4)]
-        start_points = batch['start_point']
-
-        if opt.batch_size == 1:
-            start_points = [torch.cat(start_points)]
-
-        fg_INR_coordinates = coordinate_map[1:]
-
-        try:
-            if global_state[0] == 0:
-                print("Stop Harmonizing...!")
-                break
-
-            if step == 0:  # This is for CUDA Kernel Warm-up, or the first inference step will be quite slow.
-                fg_content_bg_appearance_construct, _, lut_transform_image = model(
-                    composite_image,
-                    mask,
-                    fg_INR_coordinates,
-                )
-                print("Ready for harmonization...")
-
-            if opt.device == "cuda":
-                torch.cuda.reset_max_memory_allocated()
-                torch.cuda.reset_max_memory_cached()
-                start_time = time.time()
-                torch.cuda.synchronize()
-            fg_content_bg_appearance_construct, _, lut_transform_image = model(
-                composite_image,
-                mask,
-                fg_INR_coordinates,
-            )
-            if opt.device == "cuda":
-                torch.cuda.synchronize()
-                end_time = time.time()
-
-                end_max_memory = torch.cuda.max_memory_allocated() // 1024 ** 2
-                end_memory = torch.cuda.memory_allocated() // 1024 ** 2
-
-                print(f'GPU max memory usage: {end_max_memory} MB')
-                print(f'GPU memory usage: {end_memory} MB')
-                time_all += (end_time - start_time)
-            print(f'progress: {step} / {len(single_data_loader)}')
-        except:
-            raise Exception(
-                f'The image resolution is large. Please increase the `split_num` value. Your current set is {opt.split_num}')
-
-        "Assemble the every patch's harmonized result into the final whole image."
-        for id in range(len(fg_INR_coordinates[0])):
-            pred_fg_image = fg_content_bg_appearance_construct[-1][id]
-            pred_harmonized_image = pred_fg_image * (mask[1][id] > 100 / 255.) + composite_image[1][id] * (
-                ~(mask[1][id] > 100 / 255.))
-
-            pred_harmonized_tmp = cv2.cvtColor(
-                normalize(pred_harmonized_image.unsqueeze(0), opt, 'inv')[0].permute(1, 2, 0).cpu().mul_(255.).clamp_(
-                    0., 255.).numpy().astype(np.uint8), cv2.COLOR_RGB2BGR)
-
-            init_img[start_points[id][0]:start_points[id][0] + singledataset.split_height_resolution,
-            start_points[id][1]:start_points[id][1] + singledataset.split_width_resolution] = pred_harmonized_tmp
-
-    if opt.device == "cuda":
-        print(f'Inference time: {time_all}')
-    if opt.save_path is not None:
-        os.makedirs(opt.save_path, exist_ok=True)
-        cv2.imwrite(os.path.join(opt.save_path, "pred_harmonized_image.jpg"), init_img)
-    return init_img
-
-
-def main_process(opt, composite_image=None, mask=None):
-    cudnn.benchmark = True
-
-    print("Preparing model...")
-    model = build_model(opt).to(opt.device)
-
-    load_dict = torch.load(opt.pretrained, map_location='cpu')['model']
-
-    model.load_state_dict(load_dict, strict=False)
-
-    return inference(model, opt, composite_image, mask)
-
-
-if __name__ == '__main__':
-    opt = parse_args()
-    opt.transform_mean = [.5, .5, .5]
-    opt.transform_var = [.5, .5, .5]
-    main_process(opt)
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fid/inception.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fid/inception.py
deleted file mode 100644
index ff81b7ac0552e5fdc22b7e10877eac2f0e2d014b..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fid/inception.py
+++ /dev/null
@@ -1,315 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision import models
-
-try:
-    from torchvision.models.utils import load_state_dict_from_url
-except ImportError:
-    from torch.utils.model_zoo import load_url as load_state_dict_from_url
-
-# Inception weights ported to Pytorch from
-# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
-
-
-class InceptionV3(nn.Module):
-    """Pretrained InceptionV3 network returning feature maps"""
-
-    # Index of default block of inception to return,
-    # corresponds to output of final average pooling
-    DEFAULT_BLOCK_INDEX = 3
-
-    # Maps feature dimensionality to their output blocks indices
-    BLOCK_INDEX_BY_DIM = {
-        64: 0,  # First max pooling features
-        192: 1,  # Second max pooling featurs
-        768: 2,  # Pre-aux classifier features
-        2048: 3,  # Final average pooling features
-    }
-
-    def __init__(
-        self,
-        output_blocks=[DEFAULT_BLOCK_INDEX],
-        resize_input=True,
-        normalize_input=True,
-        requires_grad=False,
-        use_fid_inception=True,
-    ):
-        """Build pretrained InceptionV3
-
-        Parameters
-        ----------
-        output_blocks : list of int
-            Indices of blocks to return features of. Possible values are:
-                - 0: corresponds to output of first max pooling
-                - 1: corresponds to output of second max pooling
-                - 2: corresponds to output which is fed to aux classifier
-                - 3: corresponds to output of final average pooling
-        resize_input : bool
-            If true, bilinearly resizes input to width and height 299 before
-            feeding input to model. As the network without fully connected
-            layers is fully convolutional, it should be able to handle inputs
-            of arbitrary size, so resizing might not be strictly needed
-        normalize_input : bool
-            If true, scales the input from range (0, 1) to the range the
-            pretrained Inception network expects, namely (-1, 1)
-        requires_grad : bool
-            If true, parameters of the model require gradients. Possibly useful
-            for finetuning the network
-        use_fid_inception : bool
-            If true, uses the pretrained Inception model used in Tensorflow's
-            FID implementation. If false, uses the pretrained Inception model
-            available in torchvision. The FID Inception model has different
-            weights and a slightly different structure from torchvision's
-            Inception model. If you want to compute FID scores, you are
-            strongly advised to set this parameter to true to get comparable
-            results.
-        """
-        super(InceptionV3, self).__init__()
-
-        self.resize_input = resize_input
-        self.normalize_input = normalize_input
-        self.output_blocks = sorted(output_blocks)
-        self.last_needed_block = max(output_blocks)
-
-        assert self.last_needed_block <= 3, 'Last possible output block index is 3'
-
-        self.blocks = nn.ModuleList()
-
-        if use_fid_inception:
-            inception = fid_inception_v3()
-        else:
-            inception = models.inception_v3(pretrained=True)
-
-        # Block 0: input to maxpool1
-        block0 = [
-            inception.Conv2d_1a_3x3,
-            inception.Conv2d_2a_3x3,
-            inception.Conv2d_2b_3x3,
-            nn.MaxPool2d(kernel_size=3, stride=2),
-        ]
-        self.blocks.append(nn.Sequential(*block0))
-
-        # Block 1: maxpool1 to maxpool2
-        if self.last_needed_block >= 1:
-            block1 = [
-                inception.Conv2d_3b_1x1,
-                inception.Conv2d_4a_3x3,
-                nn.MaxPool2d(kernel_size=3, stride=2),
-            ]
-            self.blocks.append(nn.Sequential(*block1))
-
-        # Block 2: maxpool2 to aux classifier
-        if self.last_needed_block >= 2:
-            block2 = [
-                inception.Mixed_5b,
-                inception.Mixed_5c,
-                inception.Mixed_5d,
-                inception.Mixed_6a,
-                inception.Mixed_6b,
-                inception.Mixed_6c,
-                inception.Mixed_6d,
-                inception.Mixed_6e,
-            ]
-            self.blocks.append(nn.Sequential(*block2))
-
-        # Block 3: aux classifier to final avgpool
-        if self.last_needed_block >= 3:
-            block3 = [
-                inception.Mixed_7a,
-                inception.Mixed_7b,
-                inception.Mixed_7c,
-                nn.AdaptiveAvgPool2d(output_size=(1, 1)),
-            ]
-            self.blocks.append(nn.Sequential(*block3))
-
-        for param in self.parameters():
-            param.requires_grad = requires_grad
-
-    def forward(self, inp):
-        """Get Inception feature maps
-
-        Parameters
-        ----------
-        inp : torch.autograd.Variable
-            Input tensor of shape Bx3xHxW. Values are expected to be in
-            range (0, 1)
-
-        Returns
-        -------
-        List of torch.autograd.Variable, corresponding to the selected output
-        block, sorted ascending by index
-        """
-        outp = []
-        x = inp
-
-        if self.resize_input:
-            x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False)
-
-        if self.normalize_input:
-            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
-
-        for idx, block in enumerate(self.blocks):
-            x = block(x)
-            if idx in self.output_blocks:
-                outp.append(x)
-
-            if idx == self.last_needed_block:
-                break
-
-        return outp
-
-
-def fid_inception_v3():
-    """Build pretrained Inception model for FID computation
-
-    The Inception model for FID computation uses a different set of weights
-    and has a slightly different structure than torchvision's Inception.
-
-    This method first constructs torchvision's Inception and then patches the
-    necessary parts that are different in the FID Inception model.
-    """
-    inception = models.inception_v3(
-        num_classes=1008, aux_logits=False, pretrained=False
-    )
-    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
-    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
-    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
-    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
-    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
-    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
-    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
-    inception.Mixed_7b = FIDInceptionE_1(1280)
-    inception.Mixed_7c = FIDInceptionE_2(2048)
-
-    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
-    inception.load_state_dict(state_dict)
-    return inception
-
-
-class FIDInceptionA(models.inception.InceptionA):
-    """InceptionA block patched for FID computation"""
-
-    def __init__(self, in_channels, pool_features):
-        super(FIDInceptionA, self).__init__(in_channels, pool_features)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch5x5 = self.branch5x5_1(x)
-        branch5x5 = self.branch5x5_2(branch5x5)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(
-            x, kernel_size=3, stride=1, padding=1, count_include_pad=False
-        )
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionC(models.inception.InceptionC):
-    """InceptionC block patched for FID computation"""
-
-    def __init__(self, in_channels, channels_7x7):
-        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch7x7 = self.branch7x7_1(x)
-        branch7x7 = self.branch7x7_2(branch7x7)
-        branch7x7 = self.branch7x7_3(branch7x7)
-
-        branch7x7dbl = self.branch7x7dbl_1(x)
-        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(
-            x, kernel_size=3, stride=1, padding=1, count_include_pad=False
-        )
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_1(models.inception.InceptionE):
-    """First InceptionE block patched for FID computation"""
-
-    def __init__(self, in_channels):
-        super(FIDInceptionE_1, self).__init__(in_channels)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch3x3 = self.branch3x3_1(x)
-        branch3x3 = [
-            self.branch3x3_2a(branch3x3),
-            self.branch3x3_2b(branch3x3),
-        ]
-        branch3x3 = torch.cat(branch3x3, 1)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = [
-            self.branch3x3dbl_3a(branch3x3dbl),
-            self.branch3x3dbl_3b(branch3x3dbl),
-        ]
-        branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(
-            x, kernel_size=3, stride=1, padding=1, count_include_pad=False
-        )
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_2(models.inception.InceptionE):
-    """Second InceptionE block patched for FID computation"""
-
-    def __init__(self, in_channels):
-        super(FIDInceptionE_2, self).__init__(in_channels)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch3x3 = self.branch3x3_1(x)
-        branch3x3 = [
-            self.branch3x3_2a(branch3x3),
-            self.branch3x3_2b(branch3x3),
-        ]
-        branch3x3 = torch.cat(branch3x3, 1)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = [
-            self.branch3x3dbl_3a(branch3x3dbl),
-            self.branch3x3dbl_3b(branch3x3dbl),
-        ]
-        branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
-        # Patch: The FID Inception model uses max pooling instead of average
-        # pooling. This is likely an error in this specific Inception
-        # implementation, as other Inception models use average pooling here
-        # (which matches the description in the paper).
-        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
diff --git a/spaces/Xenova/distil-whisper-web/assets/worker-437c363b.js b/spaces/Xenova/distil-whisper-web/assets/worker-437c363b.js
deleted file mode 100644
index 4b889d5e1638a64b3b9eab0ccc8176b2842839b5..0000000000000000000000000000000000000000
--- a/spaces/Xenova/distil-whisper-web/assets/worker-437c363b.js
+++ /dev/null
@@ -1,1790 +0,0 @@
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ERR: ${o.map(u=>`[${u.name}] ${u.err}`).join(", ")}`)};class EnvImpl{constructor(){this.wasm={},this.webgl={},this.logLevelInternal="warning"}set logLevel(n){if(n!==void 0){if(typeof n!="string"||["verbose","info","warning","error","fatal"].indexOf(n)===-1)throw new Error(`Unsupported logging level: ${n}`);this.logLevelInternal=n}}get logLevel(){return this.logLevelInternal}}const env$1=new EnvImpl,isBigInt64ArrayAvailable=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",isBigUint64ArrayAvailable=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function",NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);isBigInt64ArrayAvailable&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("int64",BigInt64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array,"int64")),isBigUint64ArrayAvailable&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("uint64",BigUint64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array,"uint64"));const calculateSize=_=>{let n=1;for(let o=0;o<_.length;o++){const u=_[o];if(typeof u!="number"||!Number.isSafeInteger(u))throw new TypeError(`dims[${o}] must be an integer, got: ${u}`);if(u<0)throw new RangeError(`dims[${o}] must be a non-negative integer, got: ${u}`);n*=u}return n};let Tensor$2=class ut{constructor(n,o,u){let l,d,s;if(typeof n=="string")if(l=n,s=u,n==="string"){if(!Array.isArray(o))throw new TypeError("A string tensor's data must be a string array.");d=o}else{const 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bufferToTensor(n,o){if(n===void 0)throw new Error("Image buffer must be defined");if(o.height===void 0||o.width===void 0)throw new Error("Image height and width must be defined");const{height:u,width:l}=o,d=o.norm;let s,p;d===void 0||d.mean===void 0?s=255:s=d.mean,d===void 0||d.bias===void 0?p=0:p=d.bias;const f=o.bitmapFormat!==void 0?o.bitmapFormat:"RGBA",c=o.tensorFormat!==void 0&&o.tensorFormat!==void 0?o.tensorFormat:"RGB",a=u*l,t=c==="RGBA"?new Float32Array(a*4):new Float32Array(a*3);let e=4,r=0,i=1,h=2,g=3,m=0,b=a,y=a*2,w=-1;f==="RGB"&&(e=3,r=0,i=1,h=2,g=-1),c==="RGBA"?w=a*3:c==="RBG"?(m=0,y=a,b=a*2):c==="BGR"&&(y=0,b=a,m=a*2);for(let S=0;S<a;S++,r+=e,h+=e,i+=e,g+=e)t[m++]=(n[r]+p)/s,t[b++]=(n[i]+p)/s,t[y++]=(n[h]+p)/s,w!==-1&&g!==-1&&(t[w++]=(n[g]+p)/s);return c==="RGBA"?new ut("float32",t,[1,4,u,l]):new ut("float32",t,[1,3,u,l])}static async fromImage(n,o){const u=typeof HTMLImageElement<"u"&&n instanceof HTMLImageElement,l=typeof ImageData<"u"&&n instanceof ImageData,d=typeof 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i=0;i<e.floats.length;++i)r.float(e.floats[i]);r.ldelim()}if(e.ints!=null&&e.ints.length){for(r.uint32(66).fork(),i=0;i<e.ints.length;++i)r.int64(e.ints[i]);r.ldelim()}if(e.strings!=null&&e.strings.length)for(i=0;i<e.strings.length;++i)r.uint32(74).bytes(e.strings[i]);if(e.tensors!=null&&e.tensors.length)for(i=0;i<e.tensors.length;++i)a.onnx.TensorProto.encode(e.tensors[i],r.uint32(82).fork()).ldelim();if(e.graphs!=null&&e.graphs.length)for(i=0;i<e.graphs.length;++i)a.onnx.GraphProto.encode(e.graphs[i],r.uint32(90).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(106).string(e.docString),e.type!=null&&e.hasOwnProperty("type")&&r.uint32(160).int32(e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&r.uint32(170).string(e.refAttrName),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.AttributeProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.name=e.string();break;case 21:h.refAttrName=e.string();break;case 13:h.docString=e.string();break;case 20:h.type=e.int32();break;case 2:h.f=e.float();break;case 3:h.i=e.int64();break;case 4:h.s=e.bytes();break;case 5:h.t=a.onnx.TensorProto.decode(e,e.uint32());break;case 6:h.g=a.onnx.GraphProto.decode(e,e.uint32());break;case 7:if(h.floats&&h.floats.length||(h.floats=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)h.floats.push(e.float());else h.floats.push(e.float());break;case 8:if(h.ints&&h.ints.length||(h.ints=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.ints.push(e.int64());else h.ints.push(e.int64());break;case 9:h.strings&&h.strings.length||(h.strings=[]),h.strings.push(e.bytes());break;case 10:h.tensors&&h.tensors.length||(h.tensors=[]),h.tensors.push(a.onnx.TensorProto.decode(e,e.uint32()));break;case 11:h.graphs&&h.graphs.length||(h.graphs=[]),h.graphs.push(a.onnx.GraphProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!c.isString(e.name))return"name: string expected";if(e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&!c.isString(e.refAttrName))return"refAttrName: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString))return"docString: string expected";if(e.type!=null&&e.hasOwnProperty("type"))switch(e.type){default:return"type: enum value expected";case 0:case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:}if(e.f!=null&&e.hasOwnProperty("f")&&typeof e.f!="number")return"f: number expected";if(e.i!=null&&e.hasOwnProperty("i")&&!(c.isInteger(e.i)||e.i&&c.isInteger(e.i.low)&&c.isInteger(e.i.high)))return"i: integer|Long expected";if(e.s!=null&&e.hasOwnProperty("s")&&!(e.s&&typeof e.s.length=="number"||c.isString(e.s)))return"s: buffer expected";if(e.t!=null&&e.hasOwnProperty("t")&&(i=a.onnx.TensorProto.verify(e.t)))return"t."+i;if(e.g!=null&&e.hasOwnProperty("g")&&(i=a.onnx.GraphProto.verify(e.g)))return"g."+i;if(e.floats!=null&&e.hasOwnProperty("floats")){if(!Array.isArray(e.floats))return"floats: array expected";for(var r=0;r<e.floats.length;++r)if(typeof e.floats[r]!="number")return"floats: number[] expected"}if(e.ints!=null&&e.hasOwnProperty("ints")){if(!Array.isArray(e.ints))return"ints: array expected";for(r=0;r<e.ints.length;++r)if(!(c.isInteger(e.ints[r])||e.ints[r]&&c.isInteger(e.ints[r].low)&&c.isInteger(e.ints[r].high)))return"ints: integer|Long[] expected"}if(e.strings!=null&&e.hasOwnProperty("strings")){if(!Array.isArray(e.strings))return"strings: array expected";for(r=0;r<e.strings.length;++r)if(!(e.strings[r]&&typeof e.strings[r].length=="number"||c.isString(e.strings[r])))return"strings: buffer[] expected"}if(e.tensors!=null&&e.hasOwnProperty("tensors")){if(!Array.isArray(e.tensors))return"tensors: array expected";for(r=0;r<e.tensors.length;++r)if(i=a.onnx.TensorProto.verify(e.tensors[r]))return"tensors."+i}if(e.graphs!=null&&e.hasOwnProperty("graphs")){if(!Array.isArray(e.graphs))return"graphs: array expected";for(r=0;r<e.graphs.length;++r){var i;if(i=a.onnx.GraphProto.verify(e.graphs[r]))return"graphs."+i}}return null},t.fromObject=function(e){if(e instanceof a.onnx.AttributeProto)return e;var r=new a.onnx.AttributeProto;switch(e.name!=null&&(r.name=String(e.name)),e.refAttrName!=null&&(r.refAttrName=String(e.refAttrName)),e.docString!=null&&(r.docString=String(e.docString)),e.type){case"UNDEFINED":case 0:r.type=0;break;case"FLOAT":case 1:r.type=1;break;case"INT":case 2:r.type=2;break;case"STRING":case 3:r.type=3;break;case"TENSOR":case 4:r.type=4;break;case"GRAPH":case 5:r.type=5;break;case"FLOATS":case 6:r.type=6;break;case"INTS":case 7:r.type=7;break;case"STRINGS":case 8:r.type=8;break;case"TENSORS":case 9:r.type=9;break;case"GRAPHS":case 10:r.type=10}if(e.f!=null&&(r.f=Number(e.f)),e.i!=null&&(c.Long?(r.i=c.Long.fromValue(e.i)).unsigned=!1:typeof e.i=="string"?r.i=parseInt(e.i,10):typeof e.i=="number"?r.i=e.i:typeof e.i=="object"&&(r.i=new c.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber())),e.s!=null&&(typeof e.s=="string"?c.base64.decode(e.s,r.s=c.newBuffer(c.base64.length(e.s)),0):e.s.length&&(r.s=e.s)),e.t!=null){if(typeof e.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");r.t=a.onnx.TensorProto.fromObject(e.t)}if(e.g!=null){if(typeof e.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");r.g=a.onnx.GraphProto.fromObject(e.g)}if(e.floats){if(!Array.isArray(e.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");r.floats=[];for(var i=0;i<e.floats.length;++i)r.floats[i]=Number(e.floats[i])}if(e.ints){if(!Array.isArray(e.ints))throw TypeError(".onnx.AttributeProto.ints: array expected");for(r.ints=[],i=0;i<e.ints.length;++i)c.Long?(r.ints[i]=c.Long.fromValue(e.ints[i])).unsigned=!1:typeof e.ints[i]=="string"?r.ints[i]=parseInt(e.ints[i],10):typeof e.ints[i]=="number"?r.ints[i]=e.ints[i]:typeof e.ints[i]=="object"&&(r.ints[i]=new c.LongBits(e.ints[i].low>>>0,e.ints[i].high>>>0).toNumber())}if(e.strings){if(!Array.isArray(e.strings))throw TypeError(".onnx.AttributeProto.strings: array expected");for(r.strings=[],i=0;i<e.strings.length;++i)typeof e.strings[i]=="string"?c.base64.decode(e.strings[i],r.strings[i]=c.newBuffer(c.base64.length(e.strings[i])),0):e.strings[i].length&&(r.strings[i]=e.strings[i])}if(e.tensors){if(!Array.isArray(e.tensors))throw TypeError(".onnx.AttributeProto.tensors: array expected");for(r.tensors=[],i=0;i<e.tensors.length;++i){if(typeof e.tensors[i]!="object")throw TypeError(".onnx.AttributeProto.tensors: object expected");r.tensors[i]=a.onnx.TensorProto.fromObject(e.tensors[i])}}if(e.graphs){if(!Array.isArray(e.graphs))throw TypeError(".onnx.AttributeProto.graphs: array expected");for(r.graphs=[],i=0;i<e.graphs.length;++i){if(typeof e.graphs[i]!="object")throw TypeError(".onnx.AttributeProto.graphs: object expected");r.graphs[i]=a.onnx.GraphProto.fromObject(e.graphs[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.floats=[],i.ints=[],i.strings=[],i.tensors=[],i.graphs=[]),r.defaults){if(i.name="",i.f=0,c.Long){var h=new c.Long(0,0,!1);i.i=r.longs===String?h.toString():r.longs===Number?h.toNumber():h}else i.i=r.longs===String?"0":0;r.bytes===String?i.s="":(i.s=[],r.bytes!==Array&&(i.s=c.newBuffer(i.s))),i.t=null,i.g=null,i.docString="",i.type=r.enums===String?"UNDEFINED":0,i.refAttrName=""}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.f!=null&&e.hasOwnProperty("f")&&(i.f=r.json&&!isFinite(e.f)?String(e.f):e.f),e.i!=null&&e.hasOwnProperty("i")&&(typeof e.i=="number"?i.i=r.longs===String?String(e.i):e.i:i.i=r.longs===String?c.Long.prototype.toString.call(e.i):r.longs===Number?new c.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber():e.i),e.s!=null&&e.hasOwnProperty("s")&&(i.s=r.bytes===String?c.base64.encode(e.s,0,e.s.length):r.bytes===Array?Array.prototype.slice.call(e.s):e.s),e.t!=null&&e.hasOwnProperty("t")&&(i.t=a.onnx.TensorProto.toObject(e.t,r)),e.g!=null&&e.hasOwnProperty("g")&&(i.g=a.onnx.GraphProto.toObject(e.g,r)),e.floats&&e.floats.length){i.floats=[];for(var g=0;g<e.floats.length;++g)i.floats[g]=r.json&&!isFinite(e.floats[g])?String(e.floats[g]):e.floats[g]}if(e.ints&&e.ints.length)for(i.ints=[],g=0;g<e.ints.length;++g)typeof e.ints[g]=="number"?i.ints[g]=r.longs===String?String(e.ints[g]):e.ints[g]:i.ints[g]=r.longs===String?c.Long.prototype.toString.call(e.ints[g]):r.longs===Number?new c.LongBits(e.ints[g].low>>>0,e.ints[g].high>>>0).toNumber():e.ints[g];if(e.strings&&e.strings.length)for(i.strings=[],g=0;g<e.strings.length;++g)i.strings[g]=r.bytes===String?c.base64.encode(e.strings[g],0,e.strings[g].length):r.bytes===Array?Array.prototype.slice.call(e.strings[g]):e.strings[g];if(e.tensors&&e.tensors.length)for(i.tensors=[],g=0;g<e.tensors.length;++g)i.tensors[g]=a.onnx.TensorProto.toObject(e.tensors[g],r);if(e.graphs&&e.graphs.length)for(i.graphs=[],g=0;g<e.graphs.length;++g)i.graphs[g]=a.onnx.GraphProto.toObject(e.graphs[g],r);return e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.type!=null&&e.hasOwnProperty("type")&&(i.type=r.enums===String?a.onnx.AttributeProto.AttributeType[e.type]:e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&(i.refAttrName=e.refAttrName),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.AttributeType=function(){var e={},r=Object.create(e);return r[e[0]="UNDEFINED"]=0,r[e[1]="FLOAT"]=1,r[e[2]="INT"]=2,r[e[3]="STRING"]=3,r[e[4]="TENSOR"]=4,r[e[5]="GRAPH"]=5,r[e[6]="FLOATS"]=6,r[e[7]="INTS"]=7,r[e[8]="STRINGS"]=8,r[e[9]="TENSORS"]=9,r[e[10]="GRAPHS"]=10,r}(),t}(),d.ValueInfoProto=function(){function t(e){if(e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.name="",t.prototype.type=null,t.prototype.docString="",t.create=function(e){return new t(e)},t.encode=function(e,r){return r||(r=f.create()),e.name!=null&&e.hasOwnProperty("name")&&r.uint32(10).string(e.name),e.type!=null&&e.hasOwnProperty("type")&&a.onnx.TypeProto.encode(e.type,r.uint32(18).fork()).ldelim(),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(26).string(e.docString),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.ValueInfoProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.name=e.string();break;case 2:h.type=a.onnx.TypeProto.decode(e,e.uint32());break;case 3:h.docString=e.string();break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!c.isString(e.name))return"name: string expected";if(e.type!=null&&e.hasOwnProperty("type")){var r=a.onnx.TypeProto.verify(e.type);if(r)return"type."+r}return e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof a.onnx.ValueInfoProto)return e;var r=new a.onnx.ValueInfoProto;if(e.name!=null&&(r.name=String(e.name)),e.type!=null){if(typeof e.type!="object")throw TypeError(".onnx.ValueInfoProto.type: object expected");r.type=a.onnx.TypeProto.fromObject(e.type)}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.name="",i.type=null,i.docString=""),e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.type!=null&&e.hasOwnProperty("type")&&(i.type=a.onnx.TypeProto.toObject(e.type,r)),e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),d.NodeProto=function(){function t(e){if(this.input=[],this.output=[],this.attribute=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.input=c.emptyArray,t.prototype.output=c.emptyArray,t.prototype.name="",t.prototype.opType="",t.prototype.domain="",t.prototype.attribute=c.emptyArray,t.prototype.docString="",t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.input!=null&&e.input.length)for(var i=0;i<e.input.length;++i)r.uint32(10).string(e.input[i]);if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)r.uint32(18).string(e.output[i]);if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(26).string(e.name),e.opType!=null&&e.hasOwnProperty("opType")&&r.uint32(34).string(e.opType),e.attribute!=null&&e.attribute.length)for(i=0;i<e.attribute.length;++i)a.onnx.AttributeProto.encode(e.attribute[i],r.uint32(42).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(58).string(e.domain),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.NodeProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.input&&h.input.length||(h.input=[]),h.input.push(e.string());break;case 2:h.output&&h.output.length||(h.output=[]),h.output.push(e.string());break;case 3:h.name=e.string();break;case 4:h.opType=e.string();break;case 7:h.domain=e.string();break;case 5:h.attribute&&h.attribute.length||(h.attribute=[]),h.attribute.push(a.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:h.docString=e.string();break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var r=0;r<e.input.length;++r)if(!c.isString(e.input[r]))return"input: string[] expected"}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(r=0;r<e.output.length;++r)if(!c.isString(e.output[r]))return"output: string[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!c.isString(e.name))return"name: string expected";if(e.opType!=null&&e.hasOwnProperty("opType")&&!c.isString(e.opType))return"opType: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!c.isString(e.domain))return"domain: string expected";if(e.attribute!=null&&e.hasOwnProperty("attribute")){if(!Array.isArray(e.attribute))return"attribute: array expected";for(r=0;r<e.attribute.length;++r){var i=a.onnx.AttributeProto.verify(e.attribute[r]);if(i)return"attribute."+i}}return e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof a.onnx.NodeProto)return e;var r=new a.onnx.NodeProto;if(e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.NodeProto.input: array expected");r.input=[];for(var i=0;i<e.input.length;++i)r.input[i]=String(e.input[i])}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.NodeProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i)r.output[i]=String(e.output[i])}if(e.name!=null&&(r.name=String(e.name)),e.opType!=null&&(r.opType=String(e.opType)),e.domain!=null&&(r.domain=String(e.domain)),e.attribute){if(!Array.isArray(e.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");for(r.attribute=[],i=0;i<e.attribute.length;++i){if(typeof e.attribute[i]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");r.attribute[i]=a.onnx.AttributeProto.fromObject(e.attribute[i])}}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.input=[],i.output=[],i.attribute=[]),r.defaults&&(i.name="",i.opType="",i.docString="",i.domain=""),e.input&&e.input.length){i.input=[];for(var h=0;h<e.input.length;++h)i.input[h]=e.input[h]}if(e.output&&e.output.length)for(i.output=[],h=0;h<e.output.length;++h)i.output[h]=e.output[h];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.opType!=null&&e.hasOwnProperty("opType")&&(i.opType=e.opType),e.attribute&&e.attribute.length)for(i.attribute=[],h=0;h<e.attribute.length;++h)i.attribute[h]=a.onnx.AttributeProto.toObject(e.attribute[h],r);return e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&(i.domain=e.domain),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),d.ModelProto=function(){function t(e){if(this.opsetImport=[],this.metadataProps=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.irVersion=c.Long?c.Long.fromBits(0,0,!1):0,t.prototype.opsetImport=c.emptyArray,t.prototype.producerName="",t.prototype.producerVersion="",t.prototype.domain="",t.prototype.modelVersion=c.Long?c.Long.fromBits(0,0,!1):0,t.prototype.docString="",t.prototype.graph=null,t.prototype.metadataProps=c.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.irVersion!=null&&e.hasOwnProperty("irVersion")&&r.uint32(8).int64(e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&r.uint32(18).string(e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&r.uint32(26).string(e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(34).string(e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&r.uint32(40).int64(e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&a.onnx.GraphProto.encode(e.graph,r.uint32(58).fork()).ldelim(),e.opsetImport!=null&&e.opsetImport.length)for(var i=0;i<e.opsetImport.length;++i)a.onnx.OperatorSetIdProto.encode(e.opsetImport[i],r.uint32(66).fork()).ldelim();if(e.metadataProps!=null&&e.metadataProps.length)for(i=0;i<e.metadataProps.length;++i)a.onnx.StringStringEntryProto.encode(e.metadataProps[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.ModelProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.irVersion=e.int64();break;case 8:h.opsetImport&&h.opsetImport.length||(h.opsetImport=[]),h.opsetImport.push(a.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:h.producerName=e.string();break;case 3:h.producerVersion=e.string();break;case 4:h.domain=e.string();break;case 5:h.modelVersion=e.int64();break;case 6:h.docString=e.string();break;case 7:h.graph=a.onnx.GraphProto.decode(e,e.uint32());break;case 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a.onnx.ModelProto)return e;var r=new a.onnx.ModelProto;if(e.irVersion!=null&&(c.Long?(r.irVersion=c.Long.fromValue(e.irVersion)).unsigned=!1:typeof e.irVersion=="string"?r.irVersion=parseInt(e.irVersion,10):typeof e.irVersion=="number"?r.irVersion=e.irVersion:typeof e.irVersion=="object"&&(r.irVersion=new c.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber())),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");r.opsetImport=[];for(var i=0;i<e.opsetImport.length;++i){if(typeof e.opsetImport[i]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");r.opsetImport[i]=a.onnx.OperatorSetIdProto.fromObject(e.opsetImport[i])}}if(e.producerName!=null&&(r.producerName=String(e.producerName)),e.producerVersion!=null&&(r.producerVersion=String(e.producerVersion)),e.domain!=null&&(r.domain=String(e.domain)),e.modelVersion!=null&&(c.Long?(r.modelVersion=c.Long.fromValue(e.modelVersion)).unsigned=!1:typeof 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r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.key="",t.prototype.value="",t.create=function(e){return new t(e)},t.encode=function(e,r){return r||(r=f.create()),e.key!=null&&e.hasOwnProperty("key")&&r.uint32(10).string(e.key),e.value!=null&&e.hasOwnProperty("value")&&r.uint32(18).string(e.value),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.StringStringEntryProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.key=e.string();break;case 2:h.value=e.string();break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){return typeof e!="object"||e===null?"object expected":e.key!=null&&e.hasOwnProperty("key")&&!c.isString(e.key)?"key: string expected":e.value!=null&&e.hasOwnProperty("value")&&!c.isString(e.value)?"value: string expected":null},t.fromObject=function(e){if(e instanceof a.onnx.StringStringEntryProto)return e;var r=new a.onnx.StringStringEntryProto;return e.key!=null&&(r.key=String(e.key)),e.value!=null&&(r.value=String(e.value)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.key="",i.value=""),e.key!=null&&e.hasOwnProperty("key")&&(i.key=e.key),e.value!=null&&e.hasOwnProperty("value")&&(i.value=e.value),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),d.TensorAnnotation=function(){function t(e){if(this.quantParameterTensorNames=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.tensorName="",t.prototype.quantParameterTensorNames=c.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&r.uint32(10).string(e.tensorName),e.quantParameterTensorNames!=null&&e.quantParameterTensorNames.length)for(var i=0;i<e.quantParameterTensorNames.length;++i)a.onnx.StringStringEntryProto.encode(e.quantParameterTensorNames[i],r.uint32(18).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.TensorAnnotation;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.tensorName=e.string();break;case 2:h.quantParameterTensorNames&&h.quantParameterTensorNames.length||(h.quantParameterTensorNames=[]),h.quantParameterTensorNames.push(a.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!c.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var r=0;r<e.quantParameterTensorNames.length;++r){var i=a.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[r]);if(i)return"quantParameterTensorNames."+i}}return null},t.fromObject=function(e){if(e instanceof a.onnx.TensorAnnotation)return e;var r=new a.onnx.TensorAnnotation;if(e.tensorName!=null&&(r.tensorName=String(e.tensorName)),e.quantParameterTensorNames){if(!Array.isArray(e.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");r.quantParameterTensorNames=[];for(var i=0;i<e.quantParameterTensorNames.length;++i){if(typeof e.quantParameterTensorNames[i]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");r.quantParameterTensorNames[i]=a.onnx.StringStringEntryProto.fromObject(e.quantParameterTensorNames[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.quantParameterTensorNames=[]),r.defaults&&(i.tensorName=""),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&(i.tensorName=e.tensorName),e.quantParameterTensorNames&&e.quantParameterTensorNames.length){i.quantParameterTensorNames=[];for(var h=0;h<e.quantParameterTensorNames.length;++h)i.quantParameterTensorNames[h]=a.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[h],r)}return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),d.GraphProto=function(){function t(e){if(this.node=[],this.initializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.node=c.emptyArray,t.prototype.name="",t.prototype.initializer=c.emptyArray,t.prototype.docString="",t.prototype.input=c.emptyArray,t.prototype.output=c.emptyArray,t.prototype.valueInfo=c.emptyArray,t.prototype.quantizationAnnotation=c.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.node!=null&&e.node.length)for(var i=0;i<e.node.length;++i)a.onnx.NodeProto.encode(e.node[i],r.uint32(10).fork()).ldelim();if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(18).string(e.name),e.initializer!=null&&e.initializer.length)for(i=0;i<e.initializer.length;++i)a.onnx.TensorProto.encode(e.initializer[i],r.uint32(42).fork()).ldelim();if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(82).string(e.docString),e.input!=null&&e.input.length)for(i=0;i<e.input.length;++i)a.onnx.ValueInfoProto.encode(e.input[i],r.uint32(90).fork()).ldelim();if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)a.onnx.ValueInfoProto.encode(e.output[i],r.uint32(98).fork()).ldelim();if(e.valueInfo!=null&&e.valueInfo.length)for(i=0;i<e.valueInfo.length;++i)a.onnx.ValueInfoProto.encode(e.valueInfo[i],r.uint32(106).fork()).ldelim();if(e.quantizationAnnotation!=null&&e.quantizationAnnotation.length)for(i=0;i<e.quantizationAnnotation.length;++i)a.onnx.TensorAnnotation.encode(e.quantizationAnnotation[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.GraphProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.node&&h.node.length||(h.node=[]),h.node.push(a.onnx.NodeProto.decode(e,e.uint32()));break;case 2:h.name=e.string();break;case 5:h.initializer&&h.initializer.length||(h.initializer=[]),h.initializer.push(a.onnx.TensorProto.decode(e,e.uint32()));break;case 10:h.docString=e.string();break;case 11:h.input&&h.input.length||(h.input=[]),h.input.push(a.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:h.output&&h.output.length||(h.output=[]),h.output.push(a.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:h.valueInfo&&h.valueInfo.length||(h.valueInfo=[]),h.valueInfo.push(a.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:h.quantizationAnnotation&&h.quantizationAnnotation.length||(h.quantizationAnnotation=[]),h.quantizationAnnotation.push(a.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.node!=null&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var r=0;r<e.node.length;++r)if(i=a.onnx.NodeProto.verify(e.node[r]))return"node."+i}if(e.name!=null&&e.hasOwnProperty("name")&&!c.isString(e.name))return"name: string expected";if(e.initializer!=null&&e.hasOwnProperty("initializer")){if(!Array.isArray(e.initializer))return"initializer: array expected";for(r=0;r<e.initializer.length;++r)if(i=a.onnx.TensorProto.verify(e.initializer[r]))return"initializer."+i}if(e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString))return"docString: string expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(r=0;r<e.input.length;++r)if(i=a.onnx.ValueInfoProto.verify(e.input[r]))return"input."+i}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(r=0;r<e.output.length;++r)if(i=a.onnx.ValueInfoProto.verify(e.output[r]))return"output."+i}if(e.valueInfo!=null&&e.hasOwnProperty("valueInfo")){if(!Array.isArray(e.valueInfo))return"valueInfo: array expected";for(r=0;r<e.valueInfo.length;++r)if(i=a.onnx.ValueInfoProto.verify(e.valueInfo[r]))return"valueInfo."+i}if(e.quantizationAnnotation!=null&&e.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(e.quantizationAnnotation))return"quantizationAnnotation: array expected";for(r=0;r<e.quantizationAnnotation.length;++r){var i;if(i=a.onnx.TensorAnnotation.verify(e.quantizationAnnotation[r]))return"quantizationAnnotation."+i}}return null},t.fromObject=function(e){if(e instanceof a.onnx.GraphProto)return e;var r=new a.onnx.GraphProto;if(e.node){if(!Array.isArray(e.node))throw TypeError(".onnx.GraphProto.node: array expected");r.node=[];for(var i=0;i<e.node.length;++i){if(typeof e.node[i]!="object")throw TypeError(".onnx.GraphProto.node: object expected");r.node[i]=a.onnx.NodeProto.fromObject(e.node[i])}}if(e.name!=null&&(r.name=String(e.name)),e.initializer){if(!Array.isArray(e.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");for(r.initializer=[],i=0;i<e.initializer.length;++i){if(typeof e.initializer[i]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");r.initializer[i]=a.onnx.TensorProto.fromObject(e.initializer[i])}}if(e.docString!=null&&(r.docString=String(e.docString)),e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.GraphProto.input: array expected");for(r.input=[],i=0;i<e.input.length;++i){if(typeof e.input[i]!="object")throw TypeError(".onnx.GraphProto.input: object expected");r.input[i]=a.onnx.ValueInfoProto.fromObject(e.input[i])}}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.GraphProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i){if(typeof e.output[i]!="object")throw TypeError(".onnx.GraphProto.output: object expected");r.output[i]=a.onnx.ValueInfoProto.fromObject(e.output[i])}}if(e.valueInfo){if(!Array.isArray(e.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");for(r.valueInfo=[],i=0;i<e.valueInfo.length;++i){if(typeof e.valueInfo[i]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");r.valueInfo[i]=a.onnx.ValueInfoProto.fromObject(e.valueInfo[i])}}if(e.quantizationAnnotation){if(!Array.isArray(e.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");for(r.quantizationAnnotation=[],i=0;i<e.quantizationAnnotation.length;++i){if(typeof e.quantizationAnnotation[i]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");r.quantizationAnnotation[i]=a.onnx.TensorAnnotation.fromObject(e.quantizationAnnotation[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.node=[],i.initializer=[],i.input=[],i.output=[],i.valueInfo=[],i.quantizationAnnotation=[]),r.defaults&&(i.name="",i.docString=""),e.node&&e.node.length){i.node=[];for(var h=0;h<e.node.length;++h)i.node[h]=a.onnx.NodeProto.toObject(e.node[h],r)}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.initializer&&e.initializer.length)for(i.initializer=[],h=0;h<e.initializer.length;++h)i.initializer[h]=a.onnx.TensorProto.toObject(e.initializer[h],r);if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.input&&e.input.length)for(i.input=[],h=0;h<e.input.length;++h)i.input[h]=a.onnx.ValueInfoProto.toObject(e.input[h],r);if(e.output&&e.output.length)for(i.output=[],h=0;h<e.output.length;++h)i.output[h]=a.onnx.ValueInfoProto.toObject(e.output[h],r);if(e.valueInfo&&e.valueInfo.length)for(i.valueInfo=[],h=0;h<e.valueInfo.length;++h)i.valueInfo[h]=a.onnx.ValueInfoProto.toObject(e.valueInfo[h],r);if(e.quantizationAnnotation&&e.quantizationAnnotation.length)for(i.quantizationAnnotation=[],h=0;h<e.quantizationAnnotation.length;++h)i.quantizationAnnotation[h]=a.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[h],r);return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),d.TensorProto=function(){function t(e){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.dims=c.emptyArray,t.prototype.dataType=0,t.prototype.segment=null,t.prototype.floatData=c.emptyArray,t.prototype.int32Data=c.emptyArray,t.prototype.stringData=c.emptyArray,t.prototype.int64Data=c.emptyArray,t.prototype.name="",t.prototype.docString="",t.prototype.rawData=c.newBuffer([]),t.prototype.externalData=c.emptyArray,t.prototype.dataLocation=0,t.prototype.doubleData=c.emptyArray,t.prototype.uint64Data=c.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.dims!=null&&e.dims.length){r.uint32(10).fork();for(var i=0;i<e.dims.length;++i)r.int64(e.dims[i]);r.ldelim()}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&r.uint32(16).int32(e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&a.onnx.TensorProto.Segment.encode(e.segment,r.uint32(26).fork()).ldelim(),e.floatData!=null&&e.floatData.length){for(r.uint32(34).fork(),i=0;i<e.floatData.length;++i)r.float(e.floatData[i]);r.ldelim()}if(e.int32Data!=null&&e.int32Data.length){for(r.uint32(42).fork(),i=0;i<e.int32Data.length;++i)r.int32(e.int32Data[i]);r.ldelim()}if(e.stringData!=null&&e.stringData.length)for(i=0;i<e.stringData.length;++i)r.uint32(50).bytes(e.stringData[i]);if(e.int64Data!=null&&e.int64Data.length){for(r.uint32(58).fork(),i=0;i<e.int64Data.length;++i)r.int64(e.int64Data[i]);r.ldelim()}if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(66).string(e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&r.uint32(74).bytes(e.rawData),e.doubleData!=null&&e.doubleData.length){for(r.uint32(82).fork(),i=0;i<e.doubleData.length;++i)r.double(e.doubleData[i]);r.ldelim()}if(e.uint64Data!=null&&e.uint64Data.length){for(r.uint32(90).fork(),i=0;i<e.uint64Data.length;++i)r.uint64(e.uint64Data[i]);r.ldelim()}if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(98).string(e.docString),e.externalData!=null&&e.externalData.length)for(i=0;i<e.externalData.length;++i)a.onnx.StringStringEntryProto.encode(e.externalData[i],r.uint32(106).fork()).ldelim();return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&r.uint32(112).int32(e.dataLocation),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.TensorProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:if(h.dims&&h.dims.length||(h.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)h.dims.push(e.int64());else h.dims.push(e.int64());break;case 2:h.dataType=e.int32();break;case 3:h.segment=a.onnx.TensorProto.Segment.decode(e,e.uint32());break;case 4:if(h.floatData&&h.floatData.length||(h.floatData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.floatData.push(e.float());else h.floatData.push(e.float());break;case 5:if(h.int32Data&&h.int32Data.length||(h.int32Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.int32Data.push(e.int32());else h.int32Data.push(e.int32());break;case 6:h.stringData&&h.stringData.length||(h.stringData=[]),h.stringData.push(e.bytes());break;case 7:if(h.int64Data&&h.int64Data.length||(h.int64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.int64Data.push(e.int64());else h.int64Data.push(e.int64());break;case 8:h.name=e.string();break;case 12:h.docString=e.string();break;case 9:h.rawData=e.bytes();break;case 13:h.externalData&&h.externalData.length||(h.externalData=[]),h.externalData.push(a.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 14:h.dataLocation=e.int32();break;case 10:if(h.doubleData&&h.doubleData.length||(h.doubleData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.doubleData.push(e.double());else h.doubleData.push(e.double());break;case 11:if(h.uint64Data&&h.uint64Data.length||(h.uint64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.uint64Data.push(e.uint64());else h.uint64Data.push(e.uint64());break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.dims!=null&&e.hasOwnProperty("dims")){if(!Array.isArray(e.dims))return"dims: array expected";for(var r=0;r<e.dims.length;++r)if(!(c.isInteger(e.dims[r])||e.dims[r]&&c.isInteger(e.dims[r].low)&&c.isInteger(e.dims[r].high)))return"dims: integer|Long[] expected"}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&!c.isInteger(e.dataType))return"dataType: integer expected";if(e.segment!=null&&e.hasOwnProperty("segment")&&(i=a.onnx.TensorProto.Segment.verify(e.segment)))return"segment."+i;if(e.floatData!=null&&e.hasOwnProperty("floatData")){if(!Array.isArray(e.floatData))return"floatData: array expected";for(r=0;r<e.floatData.length;++r)if(typeof e.floatData[r]!="number")return"floatData: number[] expected"}if(e.int32Data!=null&&e.hasOwnProperty("int32Data")){if(!Array.isArray(e.int32Data))return"int32Data: array expected";for(r=0;r<e.int32Data.length;++r)if(!c.isInteger(e.int32Data[r]))return"int32Data: integer[] expected"}if(e.stringData!=null&&e.hasOwnProperty("stringData")){if(!Array.isArray(e.stringData))return"stringData: array expected";for(r=0;r<e.stringData.length;++r)if(!(e.stringData[r]&&typeof e.stringData[r].length=="number"||c.isString(e.stringData[r])))return"stringData: buffer[] expected"}if(e.int64Data!=null&&e.hasOwnProperty("int64Data")){if(!Array.isArray(e.int64Data))return"int64Data: array expected";for(r=0;r<e.int64Data.length;++r)if(!(c.isInteger(e.int64Data[r])||e.int64Data[r]&&c.isInteger(e.int64Data[r].low)&&c.isInteger(e.int64Data[r].high)))return"int64Data: integer|Long[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!c.isString(e.name))return"name: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString))return"docString: string expected";if(e.rawData!=null&&e.hasOwnProperty("rawData")&&!(e.rawData&&typeof e.rawData.length=="number"||c.isString(e.rawData)))return"rawData: buffer expected";if(e.externalData!=null&&e.hasOwnProperty("externalData")){if(!Array.isArray(e.externalData))return"externalData: array expected";for(r=0;r<e.externalData.length;++r){var i;if(i=a.onnx.StringStringEntryProto.verify(e.externalData[r]))return"externalData."+i}}if(e.dataLocation!=null&&e.hasOwnProperty("dataLocation"))switch(e.dataLocation){default:return"dataLocation: enum value expected";case 0:case 1:}if(e.doubleData!=null&&e.hasOwnProperty("doubleData")){if(!Array.isArray(e.doubleData))return"doubleData: array expected";for(r=0;r<e.doubleData.length;++r)if(typeof e.doubleData[r]!="number")return"doubleData: number[] expected"}if(e.uint64Data!=null&&e.hasOwnProperty("uint64Data")){if(!Array.isArray(e.uint64Data))return"uint64Data: array expected";for(r=0;r<e.uint64Data.length;++r)if(!(c.isInteger(e.uint64Data[r])||e.uint64Data[r]&&c.isInteger(e.uint64Data[r].low)&&c.isInteger(e.uint64Data[r].high)))return"uint64Data: integer|Long[] expected"}return null},t.fromObject=function(e){if(e instanceof a.onnx.TensorProto)return e;var r=new a.onnx.TensorProto;if(e.dims){if(!Array.isArray(e.dims))throw TypeError(".onnx.TensorProto.dims: array expected");r.dims=[];for(var i=0;i<e.dims.length;++i)c.Long?(r.dims[i]=c.Long.fromValue(e.dims[i])).unsigned=!1:typeof e.dims[i]=="string"?r.dims[i]=parseInt(e.dims[i],10):typeof e.dims[i]=="number"?r.dims[i]=e.dims[i]:typeof e.dims[i]=="object"&&(r.dims[i]=new c.LongBits(e.dims[i].low>>>0,e.dims[i].high>>>0).toNumber())}if(e.dataType!=null&&(r.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");r.segment=a.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(r.floatData=[],i=0;i<e.floatData.length;++i)r.floatData[i]=Number(e.floatData[i])}if(e.int32Data){if(!Array.isArray(e.int32Data))throw TypeError(".onnx.TensorProto.int32Data: array expected");for(r.int32Data=[],i=0;i<e.int32Data.length;++i)r.int32Data[i]=0|e.int32Data[i]}if(e.stringData){if(!Array.isArray(e.stringData))throw TypeError(".onnx.TensorProto.stringData: array expected");for(r.stringData=[],i=0;i<e.stringData.length;++i)typeof e.stringData[i]=="string"?c.base64.decode(e.stringData[i],r.stringData[i]=c.newBuffer(c.base64.length(e.stringData[i])),0):e.stringData[i].length&&(r.stringData[i]=e.stringData[i])}if(e.int64Data){if(!Array.isArray(e.int64Data))throw TypeError(".onnx.TensorProto.int64Data: array expected");for(r.int64Data=[],i=0;i<e.int64Data.length;++i)c.Long?(r.int64Data[i]=c.Long.fromValue(e.int64Data[i])).unsigned=!1:typeof e.int64Data[i]=="string"?r.int64Data[i]=parseInt(e.int64Data[i],10):typeof e.int64Data[i]=="number"?r.int64Data[i]=e.int64Data[i]:typeof e.int64Data[i]=="object"&&(r.int64Data[i]=new c.LongBits(e.int64Data[i].low>>>0,e.int64Data[i].high>>>0).toNumber())}if(e.name!=null&&(r.name=String(e.name)),e.docString!=null&&(r.docString=String(e.docString)),e.rawData!=null&&(typeof e.rawData=="string"?c.base64.decode(e.rawData,r.rawData=c.newBuffer(c.base64.length(e.rawData)),0):e.rawData.length&&(r.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(r.externalData=[],i=0;i<e.externalData.length;++i){if(typeof e.externalData[i]!="object")throw TypeError(".onnx.TensorProto.externalData: object expected");r.externalData[i]=a.onnx.StringStringEntryProto.fromObject(e.externalData[i])}}switch(e.dataLocation){case"DEFAULT":case 0:r.dataLocation=0;break;case"EXTERNAL":case 1:r.dataLocation=1}if(e.doubleData){if(!Array.isArray(e.doubleData))throw TypeError(".onnx.TensorProto.doubleData: array 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i={};if((r.arrays||r.defaults)&&(i.dims=[],i.floatData=[],i.int32Data=[],i.stringData=[],i.int64Data=[],i.doubleData=[],i.uint64Data=[],i.externalData=[]),r.defaults&&(i.dataType=0,i.segment=null,i.name="",r.bytes===String?i.rawData="":(i.rawData=[],r.bytes!==Array&&(i.rawData=c.newBuffer(i.rawData))),i.docString="",i.dataLocation=r.enums===String?"DEFAULT":0),e.dims&&e.dims.length){i.dims=[];for(var h=0;h<e.dims.length;++h)typeof e.dims[h]=="number"?i.dims[h]=r.longs===String?String(e.dims[h]):e.dims[h]:i.dims[h]=r.longs===String?c.Long.prototype.toString.call(e.dims[h]):r.longs===Number?new c.LongBits(e.dims[h].low>>>0,e.dims[h].high>>>0).toNumber():e.dims[h]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(i.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(i.segment=a.onnx.TensorProto.Segment.toObject(e.segment,r)),e.floatData&&e.floatData.length)for(i.floatData=[],h=0;h<e.floatData.length;++h)i.floatData[h]=r.json&&!isFinite(e.floatData[h])?String(e.floatData[h]):e.floatData[h];if(e.int32Data&&e.int32Data.length)for(i.int32Data=[],h=0;h<e.int32Data.length;++h)i.int32Data[h]=e.int32Data[h];if(e.stringData&&e.stringData.length)for(i.stringData=[],h=0;h<e.stringData.length;++h)i.stringData[h]=r.bytes===String?c.base64.encode(e.stringData[h],0,e.stringData[h].length):r.bytes===Array?Array.prototype.slice.call(e.stringData[h]):e.stringData[h];if(e.int64Data&&e.int64Data.length)for(i.int64Data=[],h=0;h<e.int64Data.length;++h)typeof e.int64Data[h]=="number"?i.int64Data[h]=r.longs===String?String(e.int64Data[h]):e.int64Data[h]:i.int64Data[h]=r.longs===String?c.Long.prototype.toString.call(e.int64Data[h]):r.longs===Number?new c.LongBits(e.int64Data[h].low>>>0,e.int64Data[h].high>>>0).toNumber():e.int64Data[h];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&(i.rawData=r.bytes===String?c.base64.encode(e.rawData,0,e.rawData.length):r.bytes===Array?Array.prototype.slice.call(e.rawData):e.rawData),e.doubleData&&e.doubleData.length)for(i.doubleData=[],h=0;h<e.doubleData.length;++h)i.doubleData[h]=r.json&&!isFinite(e.doubleData[h])?String(e.doubleData[h]):e.doubleData[h];if(e.uint64Data&&e.uint64Data.length)for(i.uint64Data=[],h=0;h<e.uint64Data.length;++h)typeof e.uint64Data[h]=="number"?i.uint64Data[h]=r.longs===String?String(e.uint64Data[h]):e.uint64Data[h]:i.uint64Data[h]=r.longs===String?c.Long.prototype.toString.call(e.uint64Data[h]):r.longs===Number?new c.LongBits(e.uint64Data[h].low>>>0,e.uint64Data[h].high>>>0).toNumber(!0):e.uint64Data[h];if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.externalData&&e.externalData.length)for(i.externalData=[],h=0;h<e.externalData.length;++h)i.externalData[h]=a.onnx.StringStringEntryProto.toObject(e.externalData[h],r);return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&(i.dataLocation=r.enums===String?a.onnx.TensorProto.DataLocation[e.dataLocation]:e.dataLocation),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.DataType=function(){var e={},r=Object.create(e);return r[e[0]="UNDEFINED"]=0,r[e[1]="FLOAT"]=1,r[e[2]="UINT8"]=2,r[e[3]="INT8"]=3,r[e[4]="UINT16"]=4,r[e[5]="INT16"]=5,r[e[6]="INT32"]=6,r[e[7]="INT64"]=7,r[e[8]="STRING"]=8,r[e[9]="BOOL"]=9,r[e[10]="FLOAT16"]=10,r[e[11]="DOUBLE"]=11,r[e[12]="UINT32"]=12,r[e[13]="UINT64"]=13,r[e[14]="COMPLEX64"]=14,r[e[15]="COMPLEX128"]=15,r[e[16]="BFLOAT16"]=16,r}(),t.Segment=function(){function e(r){if(r)for(var i=Object.keys(r),h=0;h<i.length;++h)r[i[h]]!=null&&(this[i[h]]=r[i[h]])}return e.prototype.begin=c.Long?c.Long.fromBits(0,0,!1):0,e.prototype.end=c.Long?c.Long.fromBits(0,0,!1):0,e.create=function(r){return new e(r)},e.encode=function(r,i){return i||(i=f.create()),r.begin!=null&&r.hasOwnProperty("begin")&&i.uint32(8).int64(r.begin),r.end!=null&&r.hasOwnProperty("end")&&i.uint32(16).int64(r.end),i},e.encodeDelimited=function(r,i){return this.encode(r,i).ldelim()},e.decode=function(r,i){r instanceof p||(r=p.create(r));for(var h=i===void 0?r.len:r.pos+i,g=new a.onnx.TensorProto.Segment;r.pos<h;){var m=r.uint32();switch(m>>>3){case 1:g.begin=r.int64();break;case 2:g.end=r.int64();break;default:r.skipType(7&m)}}return g},e.decodeDelimited=function(r){return r instanceof p||(r=new p(r)),this.decode(r,r.uint32())},e.verify=function(r){return typeof r!="object"||r===null?"object expected":r.begin!=null&&r.hasOwnProperty("begin")&&!(c.isInteger(r.begin)||r.begin&&c.isInteger(r.begin.low)&&c.isInteger(r.begin.high))?"begin: integer|Long expected":r.end!=null&&r.hasOwnProperty("end")&&!(c.isInteger(r.end)||r.end&&c.isInteger(r.end.low)&&c.isInteger(r.end.high))?"end: integer|Long expected":null},e.fromObject=function(r){if(r instanceof a.onnx.TensorProto.Segment)return r;var i=new a.onnx.TensorProto.Segment;return r.begin!=null&&(c.Long?(i.begin=c.Long.fromValue(r.begin)).unsigned=!1:typeof r.begin=="string"?i.begin=parseInt(r.begin,10):typeof r.begin=="number"?i.begin=r.begin:typeof r.begin=="object"&&(i.begin=new 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-        return offset;
-      }
-      `)}}getOutputSamplingSnippet(){const a=this.context.outputTextureLayout;return a.isPacked?this.getPackedOutputSamplingSnippet(a):this.getUnpackedOutputSamplingSnippet(a)}getPackedOutputSamplingSnippet(a){const t=a.unpackedShape,e=[a.width,a.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,e);break;case 2:r[i]=this.getOutputPacked2DCoords(t,e);break;case 3:r[i]=this.getOutputPacked3DCoords(t,e);break;default:r[i]=this.getOutputPackedNDCoords(t,e)}const h=`
-      void setOutput(vec4 val) {
-        ${(0,d.getGlsl)(this.context.glContext.version).output} = val;
-      }
-    `;return r.floatTextureSetRGBA=new l.GlslLibRoutine(h),r}getUnpackedOutputSamplingSnippet(a){const t=a.unpackedShape,e=[a.width,a.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,e);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,e);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,e);break;case 4:r[i]=this.getOutputUnpacked4DCoords(t,e);break;case 5:r[i]=this.getOutputUnpacked5DCoords(t,e);break;case 6:r[i]=this.getOutputUnpacked6DCoords(t,e);break;default:throw new Error(`Unsupported output dimensionality: ${t.length}`)}const h=`
-        void setOutput(float val) {
-          ${(0,d.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
-        }
-    `;return r.floatTextureSetR=new l.GlslLibRoutine(h),r}getOutputScalarCoords(){return new l.GlslLibRoutine(`
-      int getOutputCoords() {
-        return 0;
-      }
-    `)}getOutputPacked1DCoords(a,t){const e=t;let r="";return e[0]===1?(r=`
-          int getOutputCoords() {
-            return 2 * int(TexCoords.y * ${e[1]}.0);
-          }
-        `,new l.GlslLibRoutine(r)):e[1]===1?(r=`
-          int getOutputCoords() {
-            return 2 * int(TexCoords.x * ${e[0]}.0);
-          }
-        `,new l.GlslLibRoutine(r)):(r=`
-        int getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                 vec2(${e[0]}, ${e[1]}));
-          return 2 * (resTexRC.y * ${e[0]} + resTexRC.x);
-        }
-      `,new l.GlslLibRoutine(r))}getOutputPacked2DCoords(a,t){let e="";if(u.ArrayUtil.arraysEqual(a,t))return e=`
-        ivec2 getOutputCoords() {
-          return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));
-        }
-      `,new l.GlslLibRoutine(e);const r=t,i=Math.ceil(a[1]/2);return e=`
-        ivec2 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${r[0]}, ${r[1]}));
-
-          int index = resTexRC.y * ${r[0]} + resTexRC.x;
-
-          // reverse r and c order for packed texture
-          int r = imod(index, ${i}) * 2;
-          int c = 2 * (index / ${i});
-
-          return ivec2(r, c);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputPacked3DCoords(a,t){const e=[t[0],t[1]],r=Math.ceil(a[2]/2),i=r*Math.ceil(a[1]/2),h=`
-        ivec3 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${e[0]}, ${e[1]}));
-          int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
-          int b = index / ${i};
-          index -= b * ${i};
-
-          // reverse r and c order for packed texture
-          int r = imod(index, ${r}) * 2;
-          int c = 2 * (index / ${r});
-
-          return ivec3(b, r, c);
-        }
-      `;return new l.GlslLibRoutine(h)}getOutputPackedNDCoords(a,t){const e=[t[0],t[1]],r=Math.ceil(a[a.length-1]/2),i=r*Math.ceil(a[a.length-2]/2);let h=i,g="",m="b, r, c";for(let y=2;y<a.length-1;y++)h*=a[a.length-y-1],g=`
-      int b${y} = index / ${h};
-      index -= b${y} * ${h};
-    `+g,m=`b${y}, `+m;const b=`
-      ivec${a.length} getOutputCoords() {
-        ivec2 resTexRC = ivec2(TexCoords.xy *
-                              vec2(${e[0]}, ${e[1]}));
-        int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
-        ${g}
-
-        int b = index / ${i};
-        index -= b * ${i};
-
-        // reverse r and c order for packed texture
-        int r = imod(index, ${r}) * 2;
-        int c = 2 * (index / ${r});
-
-        return ivec${a.length}(${m});
-      }
-    `;return new l.GlslLibRoutine(b)}getOutputUnpacked1DCoords(a,t){const e=`
-        int getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          return resTexRC.y * ${t[0]} + resTexRC.x;
-        }
-      `;return new l.GlslLibRoutine(e)}getOutputUnpacked2DCoords(a,t){const e=`
-        ivec2 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          int r = index / ${a[1]};
-          int c = index - r * ${a[1]};
-          return ivec2(r, c);
-        }
-      `;return new l.GlslLibRoutine(e)}getOutputUnpacked3DCoords(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[b]} * ${m}`};`).join("");return e=`
-        ivec3 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          ${g}
-          return ivec3(r, c, d);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputUnpacked4DCoords(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d","d2"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[b]} * ${m}`};`).join("");return e=`
-      ivec4 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          ${g}
-          return ivec4(r, c, d, d2);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputUnpacked5DCoords(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d","d2","d3"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[b]} * ${m}`};`).join("");return e=`
-      ivec5 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          ${g}
-          return ivec5(r, c, d, d2, d3);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputUnpacked6DCoords(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d","d2","d3","d4"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[b]} * ${m}`};`).join("");return e=`
-     ivec6 getOutputCoords() {
-         ivec2 resTexRC = ivec2(TexCoords.xy *
-                               vec2(${t[0]}, ${t[1]}));
-         int index = resTexRC.y * ${t[0]} + resTexRC.x;
-         ${g}
-         return ivec6(r, c, d, d2, d3, d4);
-       }
-     `,new l.GlslLibRoutine(e)}getCommonUtilFuncs(){const a={};let t="uvFromFlat";a[t]=new l.GlslLibRoutine(`
-    vec2 uvFromFlat(int texNumR, int texNumC, int index) {
-      int texC = index / texNumR;
-      int texR = index - texC * texNumR;
-      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
-      //       v.
-      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
-    }
-    `),t="packedUVfrom1D",a[t]=new l.GlslLibRoutine(`
-      vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
-        int texelIndex = index / 2;
-        int texR = texelIndex / texNumC;
-        int texC = texelIndex - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),t="packedUVfrom2D",a[t]=new l.GlslLibRoutine(`
-      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
-        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
-        int texR = texelIndex / texNumC;
-        int texC = texelIndex - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),t="packedUVfrom3D",a[t]=new l.GlslLibRoutine(`
-      vec2 packedUVfrom3D(int texNumR, int texNumC,
-          int texelsInBatch, int texelsInLogicalRow, int b,
-          int row, int col) {
-        int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
-        int texR = index / texNumC;
-        int texC = index - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),t="sampleTexture";const e=(0,d.getGlsl)(this.context.glContext.version);return a[t]=new l.GlslLibRoutine(`
-        float sampleTexture(sampler2D textureSampler, vec2 uv) {
-            return ${e.texture2D}(textureSampler, uv).r;
-        }`),a}getInputsSamplingSnippets(){const a={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((e,r)=>{const i=this.context.inputTextureLayouts[r],h=(0,p.generateShaderFuncNameFromInputSamplerName)(e);i.isPacked?a[h]=this.getPackedSamplerFromInput(h,e,i):a[h]=this.getUnpackedSamplerFromInput(h,e,i);const g=(0,p.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);i.unpackedShape.length<=t.unpackedShape.length&&(i.isPacked?a[g]=this.getPackedSamplerAtOutputCoords(g,i,t,e):a[g]=this.getUnpackedSamplerAtOutputCoords(g,i,t,e))}),a}getPackedSamplerAtOutputCoords(a,t,e,r){const i=t.unpackedShape,h=e.unpackedShape,g=r,m=(0,p.generateShaderFuncNameFromInputSamplerName)(g),b=i.length,y=h.length,w=u.BroadcastUtil.getBroadcastDims(i,h),T=(0,p.getCoordsDataType)(y),S=y-b;let A;const O=(0,p.getGlChannels)();A=b===0?"":y<2&&w.length>=1?"coords = 0;":w.map(N=>`coords.${O[N+S]} = 0;`).join(`
-`);let v="";v=y<2&&b>0?"coords":i.map((N,G)=>`coords.${O[G+S]}`).join(", ");let M="return outputValue;";const F=u.ShapeUtil.size(i)===1,R=u.ShapeUtil.size(h)===1;if(b!==1||F||R){if(F&&!R)M=y===1?`
-          return vec4(outputValue.x, outputValue.x, 0., 0.);
-        `:`
-          return vec4(outputValue.x);
-        `;else if(w.length){const N=b-2,G=b-1;w.indexOf(N)>-1&&w.indexOf(G)>-1?M="return vec4(outputValue.x);":w.indexOf(N)>-1?M="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":w.indexOf(G)>-1&&(M="return vec4(outputValue.xx, outputValue.zz);")}}else M=`
-        return vec4(outputValue.xy, outputValue.xy);
-      `;const $=`
-      vec4 ${a}() {
-        ${T} coords = getOutputCoords();
-        
-        int lastDim = coords.${O[y-1]};
-        coords.${O[y-1]} = coords.${O[y-2]};
-        coords.${O[y-2]} = lastDim;
-      
-        ${A}
-        vec4 outputValue = ${m}(${v});
-        ${M}
-      }
-    `;return new l.GlslLibRoutine($,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(a,t,e,r){const i=[e.width,e.height],h=[t.width,t.height],g=t.unpackedShape.length,m=e.unpackedShape.length,b=t.unpackedShape,y=e.unpackedShape,w=(0,p.generateShaderFuncNameFromInputSamplerName)(r);if(g===m&&u.ArrayUtil.arraysEqual(h,i)){const R=`
-          float ${a}() {
-            return sampleTexture(${r}, TexCoords);
-          }
-        `;return new l.GlslLibRoutine(R,["coordinates.sampleTexture"])}const T=(0,p.getCoordsDataType)(m),S=u.BroadcastUtil.getBroadcastDims(b,y),A=m-g;let O;const v=(0,p.getGlChannels)();O=g===0?"":m<2&&S.length>=1?"coords = 0;":S.map(R=>`coords.${v[R+A]} = 0;`).join(`
-`);let M="";M=m<2&&g>0?"coords":t.unpackedShape.map((R,$)=>`coords.${v[$+A]}`).join(", ");const F=`
-        float ${a}() {
-          ${T} coords = getOutputCoords();
-          ${O}
-          return ${w}(${M});
-        }
-      `;return new l.GlslLibRoutine(F,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(a,t,e){switch(e.unpackedShape.length){case 0:return this.getPackedSamplerScalar(a,t);case 1:return this.getPackedSampler1D(a,t,e);case 2:return this.getPackedSampler2D(a,t,e);case 3:return this.getPackedSampler3D(a,t,e);default:return this.getPackedSamplerND(a,t,e)}}getUnpackedSamplerFromInput(a,t,e){const r=e.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(a,t,e);case 1:return this.getUnpackedSampler1D(a,t,e);case 2:return this.getUnpackedSampler2D(a,t,e);case 3:return this.getUnpackedSampler3D(a,t,e);case 4:return this.getUnpackedSampler4D(a,t,e);case 5:return this.getUnpackedSampler5D(a,t,e);case 6:return this.getUnpackedSampler6D(a,t,e);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(a,t){const e=`
-          vec4 ${a}() {
-            return ${(0,d.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR);
-          }
-        `;return new l.GlslLibRoutine(e)}getPackedSampler1D(a,t,e){const r=[e.width,e.height],i=[r[1],r[0]],h=(0,d.getGlsl)(this.context.glContext.version),g=`vec4 ${a}(int index) {
-      vec2 uv = packedUVfrom1D(
-      ${i[0]}, ${i[1]}, index);
-      return ${h.texture2D}(${t}, uv);
-    }`;return new l.GlslLibRoutine(g,["coordinates.packedUVfrom1D"])}getPackedSampler2D(a,t,e){const r=e.unpackedShape,i=[e.width,e.height],h=(0,d.getGlsl)(this.context.glContext.version),g=i[0],m=i[1];if(i!=null&&u.ArrayUtil.arraysEqual(r,i)){const T=`vec4 ${a}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${g}.0);
-        return ${h.texture2D}(${t}, uv);
-      }`;return new l.GlslLibRoutine(T)}const b=i,y=Math.ceil(r[1]/2),w=`vec4 ${a}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${b[1]}, ${b[0]}, ${y}, row, col);
-      return ${h.texture2D}(${t}, uv);
-    }`;return new l.GlslLibRoutine(w,["coordinates.packedUVfrom2D"])}getPackedSampler3D(a,t,e){const r=e.unpackedShape,i=[e.width,e.height],h=[i[0],i[1]],g=(0,d.getGlsl)(this.context.glContext.version);if(r[0]===1){const T=r.slice(1),S=[1,2],A=(0,p.squeezeInputShape)(r,T),O=["b","row","col"],v=JSON.parse(JSON.stringify(e));v.unpackedShape=A;const M=this.getPackedSamplerFromInput(a,t,v),F=`${M.routineBody}
-      vec4 ${a}(int b, int row, int col) {
-        return ${a}(${(0,p.getSqueezedParams)(O,S)});
-      } `;return new l.GlslLibRoutine(F,M.dependencies)}const m=h[0],b=h[1],y=Math.ceil(r[2]/2),w=`vec4 ${a}(int b, int row, int col) {
-      vec2 uv = packedUVfrom3D(
-        ${b}, ${m}, ${y*Math.ceil(r[1]/2)}, ${y}, b, row, col);
-      return ${g.texture2D}(${t}, uv);}`;return new l.GlslLibRoutine(w,["coordinates.packedUVfrom3D"])}getPackedSamplerND(a,t,e){const r=e.unpackedShape,i=r.length,h=[e.width,e.height],g=(0,d.getGlsl)(this.context.glContext.version),m=[h[0],h[1]],b=m[1],y=m[0],w=Math.ceil(r[i-1]/2);let T=w*Math.ceil(r[i-2]/2),S="int b, int row, int col",A=`b * ${T} + (row / 2) * ${w} + (col / 2)`;for(let v=2;v<i-1;v++)S=`int b${v}, `+S,T*=r[i-v-1],A=`b${v} * ${T} + `+A;const O=`vec4 ${a}(${S}) {
-      int index = ${A};
-      int texR = index / ${y};
-      int texC = index - texR * ${y};
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}, ${b});
-      return ${g.texture2D}(${t}, uv);
-    }`;return new l.GlslLibRoutine(O)}getUnpackedSamplerScalar(a,t,e){const[r,i]=[e.width,e.height];if(r===1&&i===1){const g=`
-          float ${a}() {
-            return sampleTexture(${t}, halfCR);
-          }
-        `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}const h=`
-        float ${a}() {
-          int offset_${t} = coordsToOffset(TexCoords, ${r}, ${i});
-          vec2 uv = uvFromFlat(${r}, ${i}, offset_${t});
-          return sampleTexture(${t}, uv);
-        }
-      `;return new l.GlslLibRoutine(h,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler1D(a,t,e){const r=e.width,i=e.height;if(i===1&&r===1){const g=`
-        float ${a}(int index) {
-          return sampleTexture(${t}, halfCR);
-        }
-      `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}if(i===1){const g=`
-          float ${a}(int index) {
-            vec2 uv = vec2((float(index) + 0.5) / ${r}.0, 0.5);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}if(r===1){const g=`
-          float ${a}(int index) {
-            vec2 uv = vec2(0.5, (float(index) + 0.5) / ${i}.0);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}const h=`
-        float ${a}(int index) {
-          vec2 uv = uvFromFlat(${r}, ${i}, index);
-          return sampleTexture(${t}, uv);
-        }
-      `;return new l.GlslLibRoutine(h,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler2D(a,t,e){const r=e.unpackedShape,i=[e.height,e.width];if(i!=null&&u.ArrayUtil.arraysEqual(r,i)){const T=`
-          float ${a}(int row, int col) {
-            vec2 uv = (vec2(row, col) + halfCR) / vec2(${i[1]}.0, ${i[0]}.0);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(T,["coordinates.sampleTexture"])}const{newShape:h,keptDims:g}=(0,s.squeezeShape)(r),m=h;if(m.length<r.length){const T=(0,p.squeezeInputShape)(r,m),S=JSON.parse(JSON.stringify(e));S.unpackedShape=T;const A=["col","row"],O=`
-          ${this.getUnpackedSamplerFromInput(a,t,S).routineBody}
-          float ${a}(int row, int col) {
-            return ${a}(${(0,p.getSqueezedParams)(A,g)});
-          }
-        `;return new l.GlslLibRoutine(O,["coordinates.sampleTexture"])}const b=i[1],y=i[0];if(y===1){const T=`
-          float ${a}(int row, int col) {
-            int offset_${t} = coordsToOffset(TexCoords, ${b}, ${y});
-            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
-            vec2 uv = vec2(0.5, (index + 0.5) / ${b}.0);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(T,["coordinates.sampleTexture","coordinates.coordsToOffset"])}if(b===1){const T=`
-          float ${a}(int row, int col) {
-            int offset_${t} = coordsToOffset(TexCoords, ${b}, ${y});
-            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
-            vec2 uv = vec2((index + 0.5) / ${y}.0, 0.5);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(T,["coordinates.sampleTexture","coordinates.coordsToOffset"])}const w=`
-        float ${a}(int row, int col) {
-          int index = col * ${r[1]} + row;
-          vec2 uv = uvFromFlat(${b}, ${y}, index);
-          return sampleTexture(${t}, uv);
-        }
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-        rgba[3] = floor(exp2(23.0)*mod(Mantissa,exp2(-15.0)));
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-          highp float Sign = 1.0 - step(128.0,rgba[0])*2.0;
-          highp float Exponent = 2.0 * mod(rgba[0],128.0) + step(128.0,rgba[1]) - 127.0;
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-          `;const h=`
-        void ${r}(int bcastedIndices[${s}], out int realIndices[${t}]) {
-          ${i}
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-        }
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-      int ${s}(int indices[${p}]) {
-        int offset = 0;
-        ${c}
-        return offset;
-      }
-      `}offsetToIndices(){const s={};return this.context.programInfo.inputNames.forEach((p,f)=>{const c=this.context.inputTextureLayouts[f].shape,a=this.context.inputTextureLayouts[f].strides,t=c.length;let e=`offsetToIndices_${p}`;s[e]=new u.GlslLibRoutine(l.offsetToIndicesSingle(e,t,a)),e=`offsetToIndices_${p}_T`,s[e]=new u.GlslLibRoutine(l.offsetToIndicesSingle(e,t,a.slice().reverse()))}),s}static offsetToIndicesSingle(s,p,f){const c=[];for(let a=0;a<p-1;++a)c.push(`
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-      void ${s}(int offset, out int indices[${p}]) {
-        ${c.join("")}
-      }
-      `}incrementIndices(){const s={};return this.context.programInfo.inputNames.forEach((p,f)=>{const c=this.context.inputTextureLayouts[f].shape,a=c.length,t=`incrementIndices_${p}`;let e="";for(let i=0;i<a;++i)e+=`
-        shape[${i}] = ${c[i]};`;const r=`
-        void ${t}(int axis, out int indices[${a}]) {
-          int shape[${a}];
-          ${e};
-          for(int i = ${a} -1 ; i >= 0; --i) {
-            if(i > axis) continue;
-            indices[i] += 1;
-            if(indices[i] < shape[i]) {
-              break;
-            }
-            indices[i] = 0;
-          }
-        }
-        `;s[t]=new u.GlslLibRoutine(r)}),s}}n.ShapeUtilsGlslLib=l},5060:(_,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.getDefaultFragShaderMain=n.getFragShaderPreamble=n.getVertexShaderSource=n.getGlsl=void 0;const o={version:"",attribute:"attribute",varyingVertex:"varying",varyingFrag:"varying",texture2D:"texture2D",output:"gl_FragColor",outputDeclaration:""},u={version:"#version 300 es",attribute:"in",varyingVertex:"out",varyingFrag:"in",texture2D:"texture",output:"outputColor",outputDeclaration:"out vec4 outputColor;"};function l(d){return d===1?o:u}n.getGlsl=l,n.getVertexShaderSource=function(d){const s=l(d);return`${s.version}
-      precision highp float;
-      ${s.attribute} vec3 position;
-      ${s.attribute} vec2 textureCoord;
-
-      ${s.varyingVertex} vec2 TexCoords;
-
-      void main()
-      {
-          gl_Position = vec4(position, 1.0);
-          TexCoords = textureCoord;
-      }`},n.getFragShaderPreamble=function(d){const s=l(d);return`${s.version}
-    precision highp float;
-    precision highp int;
-    precision highp sampler2D;
-    ${s.varyingFrag} vec2 TexCoords;
-    ${s.outputDeclaration}
-    const vec2 halfCR = vec2(0.5, 0.5);
-
-    // Custom vector types to handle higher dimenalities.
-    struct ivec5
-    {
-      int x;
-      int y;
-      int z;
-      int w;
-      int u;
-    };
-
-    struct ivec6
-    {
-      int x;
-      int y;
-      int z;
-      int w;
-      int u;
-      int v;
-    };
-
-    int imod(int x, int y) {
-      return x - y * (x / y);
-    }
-
-    `},n.getDefaultFragShaderMain=function(d,s){return`
-  void main() {
-    int indices[${s}];
-    toVec(TexCoords, indices);
-    vec4 result = vec4(process(indices));
-    ${l(d).output} = result;
-  }
-  `}},3891:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.VecGlslLib=void 0;const u=o(8520);class l extends u.GlslLib{constructor(s){super(s)}getCustomTypes(){return{}}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign({},this.binaryVecFunctions()),this.copyVec()),this.setVecItem()),this.getVecItem())}binaryVecFunctions(){const s=this.context.outputTextureLayout.shape.length,p={add:"+=",sub:"-=",mul:"*=",div:"/="},f={};for(const c in p){const a=`${c}Vec`;let t="";for(let r=0;r<s;++r)t+=`
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-            index =${s} + index;
-        if (index == 0)
-            m[0] = value;
-        `;for(let c=1;c<s-1;++c)p+=`
-        else if (index == ${c})
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-            `;p+=`
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-      void setVecItem(out int m[${s}], int index, int value) {
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-            index = ${s} + index;
-        if (index == 0)
-            return m[0];
-      `;for(let c=1;c<s-1;++c)p+=`
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-    `;return{name:A.name,inputNames:["A","B"],inputTypes:[v,v],output:{dims:S[0].dims,type:O,textureType:v},shaderSource:N,hasMain:!0}};n.add=(T,S)=>[T.run(y(T,S,p()),S)],n.and=(T,S)=>[T.run(y(T,S,i(),"bool"),S)],n.div=(T,S)=>[T.run(y(T,S,f()),S)],n.equal=(T,S)=>[T.run(y(T,S,t(),"bool"),S)],n.greater=(T,S)=>[T.run(y(T,S,e(),"bool"),S)],n.less=(T,S)=>[T.run(y(T,S,r(),"bool"),S)],n.mul=(T,S)=>[T.run(y(T,S,c()),S)],n.or=(T,S)=>[T.run(y(T,S,h(),"bool"),S)],n.pow=(T,S)=>[T.run(y(T,S,m()),S)],n.pRelu=(T,S)=>[T.run(y(T,S,b()),S)],n.sub=(T,S)=>[T.run(y(T,S,a()),S)],n.xor=(T,S)=>[T.run(y(T,S,g(),"bool"),S)]},4196:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseCastAttributes=n.cast=void 0;const u=o(2517);n.cast=(d,s,p)=>(l(s),[d.cast(s[0],p)]),n.parseCastAttributes=d=>u.ProtoUtil.tensorDataTypeFromProto(d.attributes.getInt("to"));const l=d=>{if(!d||d.length!==1)throw new Error("Cast requires 1 input.");if(d[0].type==="string")throw new Error("Invalid input type.")}},1163:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedConcatProgramInfoLoader=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827);n.createPackedConcatProgramInfoLoader=(f,c,a)=>{const t=(e=c.length,r=a.cacheKey,{name:"Concat (packed)",inputNames:Array.from({length:e},(i,h)=>`X${h}`),inputTypes:Array(e).fill(l.TextureType.packed),cacheHint:r});var e,r;return Object.assign(Object.assign({},t),{get:()=>((i,h,g,m)=>{const b=g[0].dims.slice();if(m>=b.length||m<-1*b.length)throw new Error("axis specified for concat doesn't match input dimensionality");m<0&&(m=b.length+m);const y=b.slice(0);for(let X=1;X<g.length;X++){const Z=g[X].dims.slice();for(let ee=0;ee<b.length;ee++)if(ee===m)y[m]+=Z[ee];else if(b[ee]!==Z[ee])throw new Error("non concat dimensions must match")}const w=y.length,T=(0,s.getChannels)("coords",w),S=(0,d.getCoordsDataType)(w),A=(0,s.unpackFromChannel)(),O=g.map(X=>X.dims),v=(0,d.getGlChannels)(w),M=new Array(O.length-1);M[0]=O[0][m];for(let X=1;X<M.length;X++)M[X]=M[X-1]+O[X][m];const F=v[m],R=v.slice(-2),$=v.join();let N=`if (${F} < ${M[0]}) {
-        return getChannel(
-            getX0(${$}), vec2(${R.join()}));
-        }`;for(let X=1;X<M.length;X++){const Z=M[X-1];N+=`
-            if (${F} < ${M[X]}  && ${F} >= ${M[X-1]}) {
-              return getChannel(
-                getX${X}(${p(v,F,Z)}),
-                vec2(${p(R,F,Z)}));
-            }`}const G=M.length,D=M[M.length-1];N+=`
-            return getChannel(
-              getX${G}(${p(v,F,D)}),
-              vec2(${p(R,F,D)}));`;const j=(0,u.getGlsl)(i.session.backend.glContext.version),Q=`
-          ${A}
-          float getValue(${v.map(X=>"int "+X)}) {
-            ${N}
-          }
-
-          void main() {
-            ${S} coords = getOutputCoords();
-            int lastDim = coords.${v[w-1]};
-            coords.${v[w-1]} = coords.${v[w-2]};
-            coords.${v[w-2]} = lastDim;
-
-            vec4 result = vec4(getValue(${T}), 0., 0., 0.);
-
-            ${T[w-1]} = ${T[w-1]} + 1;
-            if (${T[w-1]} < ${y[w-1]}) {
-              result.g = getValue(${T});
-            }
-
-            ${T[w-2]} = ${T[w-2]} + 1;
-            if (${T[w-2]} < ${y[w-2]}) {
-              result.a = getValue(${T});
-            }
-
-            ${T[w-1]} = ${T[w-1]} - 1;
-            if (${T[w-2]} < ${y[w-2]} &&
-                ${T[w-1]} < ${y[w-1]}) {
-              result.b = getValue(${T});
-            }
-            ${j.output} = result;
-          }
-        `;return Object.assign(Object.assign({},h),{output:{dims:y,type:g[0].type,textureType:l.TextureType.packed},shaderSource:Q,hasMain:!0})})(f,t,c,a.axis)})};const p=(f,c,a)=>{const t=f.indexOf(c);return f.map((e,r)=>r===t?`${e} - ${a}`:e).join()}},2069:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConcatAttributes=n.concat=void 0;const u=o(246),l=o(2039),d=o(1163);n.concat=(e,r,i)=>(t(r),e.session.pack&&r[0].dims.length>1?[e.run((0,d.createPackedConcatProgramInfoLoader)(e,r,i),r)]:[e.run(s(e,r,i),r)]);const s=(e,r,i)=>{const h=(g=r.length,m=i.cacheKey,{name:"Concat",inputNames:Array.from({length:g},(b,y)=>`X${y}`),inputTypes:Array(g).fill(l.TextureType.unpacked),cacheHint:m});var g,m;return Object.assign(Object.assign({},h),{get:()=>((b,y,w,T)=>{const S=w[0].dims.slice();if(T>=S.length||T<-1*S.length)throw new Error("axis specified for concat doesn't match input dimensionality");T<0&&(T=S.length+T);const A=S.slice(0);for(let $=1;$<w.length;$++){const N=w[$].dims.slice();for(let G=0;G<S.length;G++)if(G===T)A[T]+=N[G];else if(S[G]!==N[G])throw new Error("non concat dimensions must match")}const O=A.length,v=new Array(w.length);let M=0;for(let $=0;$<v.length;++$)M+=w[$].dims[T],v[$]=M;let F="";F=w.length<5?p(v):f(v);const R=`
-        ${c(w.length,O)}
-        ${a(v)}
-        ${F}
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-
-          if(textureIndex != 0) {
-            indices[${T}] = indices[${T}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));
-          }
-
-          return fetchDataFromCorrectTexture(textureIndex, indices);
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-`).join("")}
-    }`,f=e=>p(e),c=(e,r)=>{const i=[`float fetchDataFromCorrectTexture(int textureIndex, int indices[${r}]) {`];for(let h=0;h<e;++h)h===0?i.push(`	if (textureIndex == ${h}) { return _X${h}(indices); }`):h===e-1?i.push(`	else { return _X${h}(indices); }`):i.push(`	else if (textureIndex == ${h}) { return _X${h}(indices); }`);return i.push("	}"),i.join(`
-`)},a=e=>{const r=["int getSizeInConcatAxisValueFromIndex(int index) {"];for(let i=0;i<e.length;++i)i===0?r.push(`	if (index == ${i}) { return ${e[i]}; }`):i===e.length-1?r.push(`	else { return ${e[i]}; }`):r.push(`	else if (index == ${i}) { return ${e[i]}; }`);return r.push("	}"),r.join(`
-`)};n.parseConcatAttributes=e=>(0,u.createAttributeWithCacheKey)({axis:e.attributes.getInt("axis")});const t=e=>{if(!e||e.length<1)throw new Error("too few inputs");const r=e[0].type,i=e[0].dims.length;if(r==="string")throw new Error("string tensor is not supported yet");for(const h of e){if(h.type!==r)throw new Error("input tensors should be one type");if(h.dims.length!==i)throw new Error("input tensors should have the same shape")}}},4770:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createUnpackedGroupedConvProgramInfoLoader=void 0;const u=o(6231),l=o(5060),d=o(2039),s=o(8138),p=o(2823);n.createUnpackedGroupedConvProgramInfoLoader=(f,c,a)=>{const t=(e=c.length>2,r=a.cacheKey,{name:"GroupedConv",inputNames:e?["X","W","Bias"]:["X","W"],inputTypes:e?[d.TextureType.unpacked,d.TextureType.unpacked,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.unpacked],cacheHint:r});var e,r;return Object.assign(Object.assign({},t),{get:()=>((i,h,g,m)=>{const b=h.length>2?"value += getBias(output_channel);":"",y=h[0].dims.slice(),w=h[1].dims.slice(),T=w[0]/m.group;u.Logger.verbose("GroupedConv",`autpPad:${m.autoPad}, dilations:${m.dilations}, group:${m.group}, kernelShape:${m.kernelShape}, pads:${m.pads}, strides:${m.strides}`);const S=(0,s.calculateOutputShape)(y,w,m.dilations,m.pads,m.strides),A=(0,l.getGlsl)(i.session.backend.glContext.version),{activationFunction:O,applyActivation:v}=(0,p.getActivationSnippet)(m),M=`
-  const ivec2 strides = ivec2(${m.strides[0]}, ${m.strides[1]});
-  const ivec2 pads = ivec2(${m.pads[0]}, ${m.pads[1]});
-  ${O}
-  void main() {
-    ivec4 coords = getOutputCoords();
-    int batch = coords.x;
-    int output_channel = coords.y;
-    ivec2 xRCCorner = coords.zw * strides - pads;
-    int group_id = output_channel / ${T};
-
-    float value = 0.0;
-    for (int wInChannel = 0; wInChannel < ${w[1]}; wInChannel++) {
-      int input_channel = group_id * ${w[1]} + wInChannel;
-      for (int wHeight = 0; wHeight < ${w[2]}; wHeight++) {
-        int xHeight = xRCCorner.x + wHeight * ${m.dilations[0]};
-
-        if (xHeight < 0 || xHeight >= ${y[2]}) {
-          continue;
-        }
-
-        for (int wWidth = 0; wWidth < ${w[3]}; wWidth++) {
-          int xWidth = xRCCorner.y + wWidth * ${m.dilations[1]};
-          if (xWidth < 0 || xWidth >= ${y[3]}) {
-            continue;
-          }
-
-          float xVal = getX(batch, input_channel, xWidth, xHeight);
-          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
-          value += xVal*wVal;
-        }
-      }
-    }
-    ${b}
-    ${v}
-    ${A.output} = vec4(value, .0, .0, .0);
-  }
-`;return Object.assign(Object.assign({},g),{output:{dims:S,type:h[0].type,textureType:d.TextureType.unpacked},shaderSource:M,hasMain:!0})})(f,c,t,a)})}},1386:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.conv2DPacked=n.conv2DPackedPointwise=void 0;const u=o(8138),l=o(8555),d=o(708);n.conv2DPackedPointwise=(s,p,f)=>{const c=p[0].dims,a=p[1].dims,t=(0,u.calculateOutputShape)(c,a,f.dilations,f.pads,f.strides),e=s.reshapePacked(p[0],[c[1],c[2]*c[3]]),r=s.reshapePacked(p[1],[a[0],a[1]]),i=p.length>2?[r,e,p[2]]:[r,e],h=s.run((0,d.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(h,t)},n.conv2DPacked=(s,p,f)=>{const c=p[0].dims,a=p[1].dims,t=(0,u.calculateOutputShape)(c,a,f.dilations,f.pads,f.strides),e=s.run((0,l.createPackedIm2ColProgramInfoLoader)(s,p[0],p[1],t,f),[p[0]]),r=s.reshapePacked(p[1],[a[0],a[1]*a[2]*a[3]]),i=p.length===3?[r,e,p[2]]:[r,e],h=s.run((0,d.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(h,t)}},9663:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvTransposeAttributes=n.convTranspose=void 0;const u=o(246),l=o(5060),d=o(2039),s=o(2823),p=(r,i,h,g,m,b)=>(r-1)*i+h+(g-1)*m+1-b,f=(r,i,h,g,m)=>{const b=Math.floor(r/2);i==="SAME_UPPER"?(h[g]=b,h[m]=r-b):i==="SAME_LOWER"&&(h[g]=r-b,h[m]=b)};n.convTranspose=(r,i,h)=>(e(i,h),c(r,i,h));const c=(r,i,h)=>{const g=t(h,i);return[a(r,i,g)]},a=(r,i,h)=>r.run(((g,m,b)=>{const y=(w=m.length>2,T=b.cacheKey,{name:"ConvTranspose",inputNames:w?["X","W","B"]:["X","W"],inputTypes:w?[d.TextureType.unpacked,d.TextureType.unpacked,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.unpacked],cacheHint:T});var w,T;return Object.assign(Object.assign({},y),{get:()=>((S,A,O,v)=>{const M=A.length>2?"getB(output_channel)":"0.0",F=A[0].dims,R=A[1].dims,$=R[1],N=R[0]/v.group,G=[A[0].dims[0],A[1].dims[1]*v.group,...v.outputShape],D=(0,l.getGlsl)(S.session.backend.glContext.version),{activationFunction:j,applyActivation:Q}=(0,s.getActivationSnippet)(v),X=`
-  const ivec2 strides = ivec2(${v.strides[0]}, ${v.strides[1]});
-  const ivec2 pads = ivec2(${v.pads[0]}, ${v.pads[1]});
-  ${j}
-  void main() {
-    ivec4 coords = getOutputCoords();
-    int batch = coords.x;
-    int output_channel = coords.y;
-
-    ivec2 loc = coords.zw + pads;
-
-    int group_id = output_channel / ${$};
-    int wOutChannel = output_channel - group_id * ${$};
-
-    float value = ${M};
-    for (int inChannelOffset = 0; inChannelOffset < ${N}; inChannelOffset++) {
-      int input_channel = group_id * ${N} + inChannelOffset;
-      for (int wWOff = 0; wWOff < ${R[2]}; wWOff++) {
-        for (int wHOff = 0; wHOff < ${R[3]}; wHOff++) {
-          ivec2 wOff = ivec2(wWOff * ${v.dilations[0]}, wHOff * ${v.dilations[1]});
-          ivec2 wLoc = loc - wOff;
-          ivec2 wLocIn = wLoc / strides;
-          if (
-            wLocIn * strides == wLoc &&
-            wLocIn.x >= 0 && wLocIn.x < ${F[2]} &&
-            wLocIn.y >= 0 && wLocIn.y < ${F[3]}
-          ) {
-            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
-            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
-            value += xVal * wVal;
-          }
-        }
-      }
-    }
-    ${Q}
-    ${D.output} = vec4(value, .0, .0, .0);
-  }
-`;return Object.assign(Object.assign({},O),{output:{dims:G,type:A[0].type,textureType:d.TextureType.unpacked},shaderSource:X,hasMain:!0})})(g,m,y,b)})})(r,i,h),i),t=(r,i)=>{const h=r.kernelShape.slice();if(r.kernelShape.length===0)for(let y=2;y<i[1].dims.length;++y)h.push(i[1].dims[y]);const g=r.pads.slice(),m=r.outputShape.slice();((y,w,T,S,A,O,v,M)=>{const F=y.length-2,R=M.length===0;for(let $=0;$<F;++$){const N=R?y[$+2]*O[$]:M[$],G=p(y[$+2],O[$],A[$],w[$],T[$],N);f(G,S,A,$,$+F),R&&M.push(O[$]*(y[$+2]-1)+v[$]+(w[$]-1)*T[$]+1-A[$]-A[$+F])}})(i[0].dims,h,r.dilations,r.autoPad,g,r.strides,r.outputPadding,m);const b=Object.assign({},r);return Object.assign(b,{kernelShape:h,pads:g,outputShape:m,cacheKey:r.cacheKey}),b};n.parseConvTransposeAttributes=r=>{const i=r.attributes,h=(0,s.parseInternalActivationAttributes)(i),g=i.getString("auto_pad","NOTSET"),m=i.getInts("dilations",[1,1]),b=i.getInt("group",1),y=i.getInts("kernel_shape",[]),w=i.getInts("output_padding",[0,0]),T=i.getInts("output_shape",[]),S=i.getInts("pads",[0,0,0,0]),A=i.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:g,dilations:m,group:b,kernelShape:y,outputPadding:w,outputShape:T,pads:S,strides:A},h))};const e=(r,i)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4||r[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(r[0].dims[1]!==r[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");const h=r[1].dims[1]*i.group;if(r.length===3&&(r[2].dims.length!==1||r[2].dims[0]!==h))throw new Error("invalid bias");const g=r[0].dims.length-2;if(i.dilations.length!==g)throw new Error(`dilations should be ${g}D`);if(i.strides.length!==g)throw new Error(`strides should be ${g}D`);if(i.pads.length!==2*g)throw new Error(`pads should be ${2*g}D`);if(i.outputPadding.length!==g)throw new Error(`output_padding should be ${g}D`);if(i.kernelShape.length!==0&&i.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape");if(i.outputShape.length!==0&&i.outputShape.length!==r[0].dims.length-2)throw new Error("invalid output shape");if(r[0].type!=="float32"||r[1].type!=="float32")throw new Error("ConvTranspose input(X,W) should be float tensor");if(r.length===3&&r[2].type!=="float32")throw new Error("ConvTranspose input(bias) should be float tensor")}},8138:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvAttributes=n.conv=n.calculateOutputShape=void 0;const u=o(246),l=o(2517),d=o(4770),s=o(1386),p=o(9828),f=o(2823),c=o(3248),a=o(5623);n.calculateOutputShape=(g,m,b,y,w)=>{const T=g[0],S=g.slice(2),A=S.length,O=m[0],v=m.slice(2).map((F,R)=>F+(F-1)*(b[R]-1)),M=S.map((F,R)=>F+y[R]+y[R+A]).map((F,R)=>Math.floor((F-v[R]+w[R])/w[R]));return[T,O].concat(...M)},n.conv=(g,m,b)=>(h(m,b),t(g,m,b));const t=(g,m,b)=>{const y=i(b,m),w=g.session.pack,T=y.kernelShape[0]===1&&y.kernelShape[1]===1;return y.group>1?[g.run((0,d.createUnpackedGroupedConvProgramInfoLoader)(g,m,y),m)]:T&&w?[e(g,m,y)]:w&&m[0].dims.length===4&&m[0].dims[0]===1&&!T?[(0,s.conv2DPacked)(g,m,y)]:[r(g,m,y)]},e=(g,m,b)=>{const y=m[0].dims,w=m[1].dims,T=(0,n.calculateOutputShape)(y,w,b.dilations,b.pads,b.strides),S=g.reshapeUnpacked(m[0],[y[1],y[2]*y[3]]),A=g.reshapeUnpacked(m[1],[w[0],w[1]]),O=m.length>2?[A,S,m[2]]:[A,S],v=g.run((0,a.createMatmulProgramInfoLoader)(O,b),O);return g.reshapeUnpacked(v,T)},r=(g,m,b)=>{const y=m[0].dims,w=m[1].dims,T=(0,n.calculateOutputShape)(y,w,b.dilations,b.pads,b.strides),S=g.run((0,c.createIm2ColProgramInfoLoader)(g,m[0],m[1],T,b),[m[0]]),A=m.length===3?[S,m[1],m[2]]:[S,m[1]];return g.run((0,p.createDotProductProgramInfoLoader)(g,m,T,b),A)},i=(g,m)=>{const b=g.kernelShape.slice();if(g.kernelShape.length===0)for(let T=2;T<m[1].dims.length;++T)b.push(m[1].dims[T]);const y=g.pads.slice();l.PoolConvUtil.adjustPadsBasedOnAutoPad(m[0].dims,g.strides,g.dilations,b,y,g.autoPad);const w=Object.assign({},g);return Object.assign(w,{kernelShape:b,pads:y,cacheKey:g.cacheKey}),w};n.parseConvAttributes=g=>{const m=g.attributes,b=(0,f.parseInternalActivationAttributes)(m),y=m.getString("auto_pad","NOTSET"),w=m.getInts("dilations",[1,1]),T=m.getInt("group",1),S=m.getInts("kernel_shape",[]),A=m.getInts("pads",[0,0,0,0]),O=m.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:y,dilations:w,group:T,kernelShape:S,pads:A,strides:O},b))};const h=(g,m)=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(g[0].dims.length!==4||g[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(g[0].dims[1]!==g[1].dims[1]*m.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(g.length===3&&(g[2].dims.length!==1||g[1].dims[0]!==g[2].dims[0]))throw new Error("invalid bias");const b=g[0].dims.length-2;if(m.dilations.length!==b)throw new Error(`dilations should be ${b}D`);if(m.strides.length!==b)throw new Error(`strides should be ${b}D`);if(m.pads.length!==2*b)throw new Error(`pads should be ${2*b}D`);if(m.kernelShape.length!==0&&m.kernelShape.length!==g[1].dims.length-2)throw new Error("invalid kernel shape");if(g[0].type!=="float32"||g[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(g.length===3&&g[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}},5193:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseDepthToSpaceAttributes=n.depthToSpace=void 0;const u=o(3738);n.depthToSpace=(d,s,p)=>{l(s);const f=p.blocksize,c=f*f,a=p.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],t=p.mode==="DCR"?[s[0].dims[0],f,f,s[0].dims[1]/c,s[0].dims[2],s[0].dims[3]]:[s[0].dims[0],s[0].dims[1]/c,f,f,s[0].dims[2],s[0].dims[3]],e=d.reshapeUnpacked(s[0],t),r={perm:a,cacheKey:`${a}`},[i]=(0,u.transpose)(d,[e],r),h=[s[0].dims[0],s[0].dims[1]/c,s[0].dims[2]*f,s[0].dims[3]*f];return[d.reshapeUnpacked(i,h)]},n.parseDepthToSpaceAttributes=d=>{const s=d.attributes.getInt("blocksize");if(s<1)throw new Error(`blocksize must be >= 1, but got : ${s} for DepthToSpace`);const p=d.attributes.getString("mode","DCR");if(p!=="DCR"&&p!=="CRD")throw new Error(`unrecognized mode: ${p} for DepthToSpace`);return{mode:p,blocksize:s}};const l=d=>{if(d.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${d.length}`);if(d[0].type==="string"||d[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createDotProductProgramInfoLoader=void 0;const u=o(2517),l=o(5060),d=o(2039),s=o(2823),p=o(3248);n.createDotProductProgramInfoLoader=(f,c,a,t)=>{const e=((r,i)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[d.TextureType.unpacked,d.TextureType.packedLastDimension,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.packedLastDimension],cacheKey:i.activationCacheKey}))(c.length>2,t);return Object.assign(Object.assign({},e),{get:()=>((r,i,h,g,m)=>{const b=h[0].dims,y=h[1].dims,w=[y[0],Math.ceil(b[1]*y[2]*y[3]/4)],T=(0,p.calculateIm2ColDims)(b,y,g),[S,A]=r.calculateTextureWidthAndHeight(w,d.TextureType.packedLastDimension),O=u.ShapeUtil.computeStrides(T),[v,M]=r.calculateTextureWidthAndHeight(T,d.TextureType.packedLastDimension),F=g.length,R=h.length<3?"0.0":"_B(b)",$=Math.ceil(b[1]*y[2]*y[3]/4),{activationFunction:N,applyActivation:G}=(0,s.getActivationSnippet)(m),D=(0,l.getGlsl)(r.session.backend.glContext.version),j=`
-${N}
-float process(int indices[${F}]) {
-  int b[1];
-  b[0] = indices[1];
-  int im2col[4];
-  im2col[0] = indices[0];
-  im2col[1] = indices[2];
-  im2col[2] = indices[3];
-  int im2colOffset = im2col[0] * ${O[0]} + im2col[1] * ${O[1]} + im2col[2] * ${O[2]};
-  int kernelOffset = indices[1] * ${w[1]};
-  float value = ${R};
-  for (int i = 0; i < ${$}; ++i) {
-    vec2 im2colCoords = offsetToCoords(im2colOffset, ${v}, ${M});
-    vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${A});
-    value += dot(${D.texture2D}(Im2Col, im2colCoords), ${D.texture2D}(K, kernelCoords));
-    ++im2colOffset;
-    ++kernelOffset;
-  }
-  ${G}
-  return value;
-}`;return Object.assign(Object.assign({},i),{output:{dims:g,type:h[0].type,textureType:d.TextureType.unpacked},shaderSource:j})})(f,e,c,a,t)})}},7992:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseFlattenAttributes=n.flatten=void 0;const u=o(2517);n.flatten=(d,s,p)=>{l(s,p);const f=u.ShapeUtil.flattenShape(s[0].dims,p);return[d.reshapeUnpacked(s[0],f)]},n.parseFlattenAttributes=d=>d.attributes.getInt("axis",1);const l=(d,s)=>{if(!d||d.length!==1)throw new Error("Flatten requires 1 input.");const p=d[0].dims.length;if(p===0)throw new Error("scalar tensor is not supported.");if(s<-p||s>p)throw new Error("Invalid axis");if(d[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInternalActivationAttributes=n.getActivationSnippet=void 0;const u=o(2517),l=o(4909);n.getActivationSnippet=function(d){let s;switch(d.activation){case"Relu":s=(0,l.glslRelu)();break;case"Sigmoid":s=(0,l.glslSigmoid)();break;case"Clip":s=(0,l.glslClip)(d.clipMin,d.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const p=s.name;return{activationFunction:s.body,applyActivation:`value = ${p}_(value);`}},n.parseInternalActivationAttributes=d=>{const s=d.getString("activation","");if(s==="Clip"){const[p,f]=d.getFloats("activation_params",[u.MIN_CLIP,u.MAX_CLIP]);return{activation:s,clipMax:f,clipMin:p,activationCacheKey:`${s}:${p},${f}`}}return{activation:s,activationCacheKey:s}}},1253:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGatherAttributes=n.gather=void 0;const u=o(246),l=o(782),d=o(2517),s=o(2039);n.gather=(a,t,e)=>(c(t,e.axis),[a.run(f(a,t,e),t)]),n.parseGatherAttributes=a=>(0,u.createAttributeWithCacheKey)({axis:a.attributes.getInt("axis",0)});const p={name:"Gather",inputNames:["A","B"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},f=(a,t,e)=>{const r=Object.assign(Object.assign({},p),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((i,h,g,m)=>{const b=g[0].dims.slice(),y=g[1].dims.slice(),w=new Array(b.length+y.length-1);m=d.ShapeUtil.normalizeAxis(m,b.length);const T=[];for(let A=0;A<w.length;A++)A<m?(w[A]=b[A],T.push(`inputIdx[${A}] = outputIdx[${A}];`)):A<m+y.length?(w[A]=y[A-m],T.push(`indexDataIdx[${A-m}] = outputIdx[${A}];`)):(w[A]=b[A-y.length+1],T.push(`inputIdx[${A-y.length+1}] = outputIdx[${A}];`));const S=`
-      float process(int outputIdx[${w.length||1}]) {
-        int inputIdx[${b.length}];
-        int indexDataIdx[${y.length||1}];
-        indexDataIdx[0] = 0;
-        ${T.join(`
-        `)}
-        int idx = int(_B(indexDataIdx));
-        inputIdx[${m}] = idx < 0 ? idx + ${b[m]} : idx;
-        return _A(inputIdx);
-      }`;return Object.assign(Object.assign({},h),{output:{dims:w,type:g[0].type,textureType:s.TextureType.unpacked},shaderSource:S})})(0,r,t,e.axis)})},c=(a,t)=>{if(!a||a.length!==2)throw new Error("Gather requires 2 inputs.");const e=a[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(l.NUMBER_TYPES.indexOf(a[0].type)===-1)throw new Error("Invaid input type.");if(a[1].type!=="int32"&&a[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const u=o(246),l=o(2517),d=o(2039);n.gemm=(a,t,e)=>(c(t,e),[a.run(p(t,e),t)]);const s=(a,t)=>{const e=a.attributes.getInt("transA",0)!==0,r=a.attributes.getInt("transB",0)!==0,i=a.attributes.getFloat("alpha",1),h=a.attributes.getFloat("beta",1);return(0,u.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:h,isOptionalC:t})};n.parseGemmAttributesV7=a=>s(a,!1),n.parseGemmAttributesV11=a=>s(a,!0);const p=(a,t)=>{const e={name:"Gemm",inputNames:a.length===3?["A","B","C"]:["A","B"],inputTypes:a.length===3?[d.TextureType.unpacked,d.TextureType.unpacked,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>f(e,a,t)})},f=(a,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[h,g]=l.GemmUtil.getShapeOfGemmResult(r,e.transA,i,e.transB,t.length===3?t[2].dims:void 0),m=[h,g];if(!m)throw new Error("Can't use gemm on the given tensors");let b=r[r.length-1],y="";e.transA&&(b=r[0]),e.transA&&e.transB?y="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?y="value += _A_T(a) * _B(b);":!e.transA&&e.transB?y="value += _A(a) * _B_T(b);":e.transA||e.transB||(y="value += _A(a) * _B(b);");const w=m.length,T=`
-      float process(int indices[${w}]) {
-          int a[${w}];
-          int b[${w}];
-          ${t.length===3?`int c[${t[2].dims.length}];`:""}
-
-          copyVec(indices, a);
-          copyVec(indices, b);
-          ${t.length===3?"bcastIndices_C(indices, c);":""}
-
-          float value = 0.0;
-          for (int k=0; k<${b}; ++k) {
-              a[${w-1}] = k;
-              b[${w-2}] = k;
-              ${y}
-          }
-
-          value = value * alpha;
-          ${t.length===3?"value += beta * _C(c);":""}
-          return value;
-      }`;return Object.assign(Object.assign({},a),{output:{dims:m,type:t[0].type,textureType:d.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:e.alpha},{name:"beta",type:"float",data:e.beta}],shaderSource:T})},c=(a,t)=>{if(!a)throw new Error("Input is missing");if(t.isOptionalC&&(a.length<2||a.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&a.length!==3)throw new Error("Gemm requires 3 inputs");if(a.length===3&&a[2].dims.length!==1&&a[2].dims.length!==2)throw new Error("Invalid input shape of C");if(a[0].type!=="float32"&&a[0].type!=="float64"||a[1].type!=="float32"&&a[1].type!=="float64"||a.length===3&&a[2].type!=="float32"&&a[2].type!=="float64")throw new Error("Invalid input type.");if(a[0].type!==a[1].type||a.length===3&&a[0].type!==a[2].type)throw new Error("Input types are mismatched")}},8555:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedIm2ColProgramInfoLoader=void 0;const u=o(5060),l=o(2039),d=o(2827);n.createPackedIm2ColProgramInfoLoader=(s,p,f,c,a)=>{const t=(e=a.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[l.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},t),{get:()=>((r,i,h,g,m,b)=>{const y=h.dims,w=g.dims,T=m.length,S=[w[1]*w[2]*w[3],m[2]*m[3]],A=w[2]*w[3],O=(0,d.unpackFromChannel)(),v=(0,u.getGlsl)(r.session.backend.glContext.version);let M="";for(let R=0;R<=1;R++)for(let $=0;$<=1;$++)M+=`
-            blockIndex = rc.x + ${$};
-            pos = rc.y + ${R};
-
-            if(blockIndex < ${S[1]} && pos < ${S[0]}) {
-              offsetY = int(blockIndex / (${m[T-1]})) * ${b.strides[0]} -
-                ${b.pads[0]};
-              d0 = offsetY + ${b.dilations[0]} * (imod(pos, ${A}) / ${w[2]});
-
-              if(d0 < ${y[2]} && d0 >= 0) {
-                offsetX = imod(blockIndex, ${m[T-1]}) * ${b.strides[1]} -
-                  ${b.pads[1]};
-                d1 = offsetX + ${b.dilations[1]} * imod(imod(pos, ${A}), ${w[2]});
-
-                if(d1 < ${y[3]} && d1 >= 0) {
-
-                  ch = int(float(pos)/ ${A}.);
-                    innerDims = vec2(d0, d1);
-                    result[${2*R+$}] = getChannel(
-                      getA(0, ch, int(innerDims.x),
-                      int(innerDims.y)), innerDims);
-                }
-              }
-            }
-
-          `;const F=`
-      ${O}
-
-      void main() {
-        ivec2 rc = getOutputCoords();
-          vec4 result = vec4(0.0);
-          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
-          vec2 innerDims;
-          ${M}
-          ${v.output} = result;
-      }
-            `;return Object.assign(Object.assign({},i),{output:{dims:S,type:h.type,textureType:l.TextureType.packed},shaderSource:F,hasMain:!0})})(s,t,p,f,c,a)})}},3248:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.calculateIm2ColDims=n.createIm2ColProgramInfoLoader=void 0;const u=o(2039);n.createIm2ColProgramInfoLoader=(l,d,s,p,f)=>{const c=(a=f.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[u.TextureType.unpacked],cacheHint:a});var a;return Object.assign(Object.assign({},c),{get:()=>((t,e,r,i,h,g)=>{const m=r.dims,b=i.dims,y=h.length,w=(0,n.calculateIm2ColDims)(m,b,h,4),T=`
-        const int XC = ${m[1]};
-        const int XH = ${m[2]};
-        const int XW = ${m[3]};
-        const int KH = ${g.kernelShape[0]};
-        const int KW = ${g.kernelShape[1]};
-        const int dilationH = ${g.dilations[0]};
-        const int dilationW = ${g.dilations[1]};
-        const int strideH = ${g.strides[0]};
-        const int strideW = ${g.strides[1]};
-        const int padH = ${g.pads[0]};
-        const int padW = ${g.pads[1]};
-        const int KHKW = KH*KW;
-        const int XCKHKW = XC * KHKW;
-        const int outputChannels = 4;
-        vec4 process(int indices[${y}]) {
-          int b  = indices[0]; // batch size
-          int oh = indices[1] * strideH - padH; //output height
-          int ow = indices[2] * strideW - padW; //output width
-          int p = indices[3] * outputChannels; //patch
-          vec4 value = vec4(0.0);
-          for(int i=0; i < outputChannels; ++i) {
-            if(p < XCKHKW) {
-              int patchC = p / KHKW;
-              int patchH = (p - patchC*KHKW) / KW;
-              int patchW = (p - patchC*KHKW) - patchH * KW;
-              int xh2 = oh + patchH * dilationH;
-              int xw2 = ow + patchW * dilationW;
-              int x[${m.length}];
-              x[0] = b;
-              x[1] = patchC;
-              x[2] = xh2;
-              x[3] = xw2;
-              if(xh2 >= 0 &&
-                  xh2 < XH &&
-                  xw2 >= 0 &&
-                  xw2 < XW) {
-                value[i] = _X(x);
-              }
-            }
-            ++p;
-          }
-          return value;
-        }
-        `;return Object.assign(Object.assign({},e),{output:{dims:w,type:r.type,textureType:u.TextureType.packedLastDimension},shaderSource:T})})(0,c,d,s,p,f)})},n.calculateIm2ColDims=(l,d,s,p=4)=>[s[0],s[2],s[3],Math.ceil(l[1]*d[2]*d[3]/p)]},6572:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseImageScalerAttributes=n.imageScaler=void 0;const u=o(246),l=o(2039);n.imageScaler=(c,a,t)=>(f(a),[c.run(s(c,a,t),a)]),n.parseImageScalerAttributes=c=>{const a=c.attributes.getFloat("scale"),t=c.attributes.getFloats("bias");return(0,u.createAttributeWithCacheKey)({scale:a,bias:t})};const d={name:"ImageScaler",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=(c,a,t)=>{const e=Object.assign(Object.assign({},d),{cacheHint:t.cacheKey});return Object.assign(Object.assign({},e),{get:()=>((r,i,h,g)=>{const m=h[0].dims.slice(),b=m.length,y=`
-      ${p(g.bias.length)}
-      float process(int indices[${b}]) {
-        return _X(indices) * scale + getBias(bias, indices[1]);
-      }`;return Object.assign(Object.assign({},i),{output:{dims:m,type:h[0].type,textureType:l.TextureType.unpacked},variables:[{name:"bias",type:"float",arrayLength:g.bias.length,data:g.bias},{name:"scale",type:"float",data:g.scale}],shaderSource:y})})(0,e,a,t)})},p=c=>{const a=[`float getBias(float bias[${c}], int channel) {`];for(let t=0;t<c;++t)t===0?a.push(`	if (channel == ${t}) { return bias[${t}]; }`):t===c-1?a.push(`	else { return bias[${t}]; }`):a.push(`	else if (channel == ${t}) { return bias[${t}]; }`);return a.push("	}"),a.join(`
-`)},f=c=>{if(!c||c.length!==1)throw new Error("ImageScaler requires 1 input.");if(c[0].dims.length!==4)throw new Error("Invalid input shape.");if(c[0].type!=="float32"&&c[0].type!=="float64")throw new Error("Invalid input type.")}},3346:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInstanceNormalizationAttributes=n.instanceNormalization=void 0;const u=o(5060),l=o(2039);n.instanceNormalization=(a,t,e)=>{c(t);const r=a.run(s(t[0]),t);return[a.run(f(a,t[0],e,r.dims),[t[0],r,t[1],t[2]])]},n.parseInstanceNormalizationAttributes=a=>a.attributes.getFloat("epsilon",1e-5);const d={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=a=>Object.assign(Object.assign({},d),{get:()=>((t,e)=>{const r=e.dims.slice(),i=r[1],h=r[2]*r[3],g=[r[0],i],m=`
-      vec4 process(int[2] indices) {
-        vec4 v = vec4(0.0);
-        int a[4];
-        a[0] = indices[0];
-        a[1] = indices[1];
-        float temp = 0.0;
-        for(int a2=0; a2<${r[2]}; a2++) {
-          a[2] = a2;
-          for(int a3=0; a3<${r[3]}; a3++) {
-            a[3] = a3;
-            float x = _X(a);
-            temp += x;
-          }
-        }
-        float mean = temp / float(${h});
-        temp = 0.0;
-        for(int a2=0; a2<${r[2]}; a2++) {
-          a[2] = a2;
-          for(int a3=0; a3<${r[3]}; a3++) {
-            a[3] = a3;
-            float x = _X(a);
-            temp += (x - mean) * (x - mean);
-          }
-        }
-        v.r = mean;
-        v.g = temp / float(${h});
-
-        return v;
-      }`;return Object.assign(Object.assign({},t),{output:{dims:g,type:e.type,textureType:l.TextureType.packedLastDimension},shaderSource:m})})(d,a)}),p={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[l.TextureType.unpacked,l.TextureType.packedLastDimension,l.TextureType.unpacked,l.TextureType.unpacked]},f=(a,t,e,r)=>{const i=Object.assign(Object.assign({},p),{cacheHint:`${e}`});return Object.assign(Object.assign({},i),{get:()=>((h,g,m,b,y)=>{const w=(0,u.getGlsl)(h.session.backend.glContext.version),[T,S]=h.calculateTextureWidthAndHeight(y,l.TextureType.packedLastDimension),[A,O]=[T/4,S],v=`
-      vec4 get_MeanAndVariance(int[2] mv) {
-        int offset = indicesToOffset_MeanAndVariance(mv);
-        vec2 coords = offsetToCoords(offset, ${A}, ${O});
-        return ${w.texture2D}(MeanAndVariance, coords);
-      }
-
-      float process(int[4] indices) {
-        int mv[2];
-        mv[0] = indices[0];
-        mv[1] = indices[1];
-        vec4 mean_and_variance = get_MeanAndVariance(mv);
-        float mean = mean_and_variance.r;
-        float variance = mean_and_variance.g;
-
-        int sb[1];
-        sb[0] = indices[1];
-        float scale = _Scale(sb);
-        float b = _B(sb);
-
-        return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;
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-  ${Ue}
-  ${Qe}
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-vec4 getBAtOutCoordsMatmul(int i) {
-  ${ve} coords = getOutputCoords();
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-  ${qe}
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-            ${X}
-            ${j}
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-      `}
-
-              vec4 value = vec4(0);
-              for (int i = 0; i < ${M}; i++) {
-                vec4 a = ${ee};
-                vec4 b = ${ue};
-
-                value += (a.rrbb * b.rgrg);
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-              ${w}
-              ${Q}
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-        bcastMatmulIndices_B(indices, b);
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-        float value;
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-            b[${G-2}] = k;
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-`);let T="vec4(outputValue.xx, outputValue.yy)";return u.ShapeUtil.size(r)===1&&(T="vec4(outputValue.x)"),h?`
-vec4 getBiasForMatmul() {
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-  ${w}
-  vec4 outputValue = getBias(${g});
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-float getBiasForMatmul() {
-  ${t} coords = getOutputCoords();
-  ${w}
-  return getBias(coords.x);
-}`}n.getBiasForMatmul=a},2403:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackProgramInfoLoader=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827),p={name:"pack",inputNames:["A"],inputTypes:[l.TextureType.unpackedReversed]};n.createPackProgramInfoLoader=(f,c)=>Object.assign(Object.assign({},p),{get:()=>((a,t)=>{const e=(0,u.getGlsl)(a.session.backend.glContext.version),r=t.dims,i=r.length,h=t.dims.length,g=(0,d.getCoordsDataType)(h),m=(0,s.getChannels)("rc",h),b=(y=h,w=m,T=r[r.length-2],S=r[r.length-1],y===0||y===1?"":`
-    int r = ${w[y-2]};
-    int c = ${w[y-1]};
-    int rp1 = ${w[y-2]} + 1;
-    int cp1 = ${w[y-1]} + 1;
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-    bool cEdge = cp1 >= ${T};
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-            rc + 1 >= ${F[0]} ? 0. : getA(rc + 1),
-            0, 0`;let N="";if($>2)for(let G=0;G<$-2;++G)N+=`${R[G]},`;return`getA(${N}r, c),
-          rEdge ? 0. : getA(${N}rp1, c),
-          cEdge ? 0. : getA(${N}r, cp1),
-          rEdge || cEdge ? 0. : getA(${N}rp1, cp1)`}(r,m),M=`
-        void main() {
-          ${g} rc = getOutputCoords();
-
-          if(${O}) {
-            ${e.output} = vec4(0);
-          } else {
-            ${b}
-
-            ${e.output} = vec4(${v});
-          }
-        }
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-    float getChannel(vec4 frag, int dim) {
-      int modCoord = imod(dim, 2);
-      return modCoord == 0 ? frag.r : frag.g;
-    }
-
-    float getChannel(vec4 frag, vec2 innerDims) {
-      vec2 modCoord = mod(innerDims, 2.);
-      return modCoord.x == 0. ?
-        (modCoord.y == 0. ? frag.r : frag.g) :
-        (modCoord.y == 0. ? frag.b : frag.a);
-    }
-  `}},2870:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const u=o(246),l=o(2517),d=o(5060),s=o(2039),p={name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.padV2=(g,m,b)=>(a(m),[g.run(Object.assign(Object.assign({},p),{cacheHint:b.cacheKey,get:()=>c(g,m[0],b)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),b=g.attributes.getFloat("value",0),y=g.attributes.getInts("pads");return(0,u.createAttributeWithCacheKey)({mode:m,value:b,pads:y})},n.padV11=(g,m,b)=>{t(m);const y=f(g,m,b);return(0,n.padV2)(g,[m[0]],y)},n.parsePadAttributesV11=g=>g.attributes.getString("mode","constant");const f=(g,m,b)=>{if(!g.session.isInitializer(m[1].dataId)||m.length>=3&&!g.session.isInitializer(m[2].dataId))throw new Error("dynamic pad attributes are not allowed");const y=Array.from(m[1].integerData),w=m.length>=3?m[2].floatData[0]:0;return(0,u.createAttributeWithCacheKey)({mode:b,pads:y,value:w})},c=(g,m,b)=>{const y=l.ShapeUtil.padShape(m.dims.slice(),b.pads),w=y.length,T=`
-      ${e(g,m,b)}
-      float process(int[${w}] indices) {
-          return padA(indices);
-      }`;return{name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked],output:{dims:y,type:m.type,textureType:s.TextureType.unpacked},shaderSource:T}},a=g=>{if(!g||g.length!==1)throw new Error("Pad requires 1 input");if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type.")},t=g=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Pad requires 2 or 3 inputs");if(g[1].type!=="int32")throw new Error("Invalid input type.");if(g.length>=3&&g[2].type==="string")throw new Error("Invalid input type.")},e=(g,m,b)=>{const y=(0,d.getGlsl)(g.session.backend.glContext.version),[w,T]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),S=l.ShapeUtil.computeStrides(m.dims);switch(b.mode){case"constant":return r(y,m.dims,S,w,T,b.pads,b.value);case"reflect":return i(y,m.dims,S,w,T,b.pads);case"edge":return h(y,m.dims,S,w,T,b.pads);default:throw new Error("Invalid mode")}},r=(g,m,b,y,w,T,S)=>{const A=m.length;let O="";for(let v=A-1;v>=0;--v)O+=`
-        k = m[${v}] - ${T[v]};
-        if (k < 0)  return constant;
-        if (k >= ${m[v]}) return constant;
-        offset += k * ${b[v]};
-        `;return`
-      float padA(int m[${A}]) {
-        const float constant = float(${S});
-        int offset = 0;
-        int k = 0;
-        ${O}
-        vec2 coords = offsetToCoords(offset, ${y}, ${w});
-        float value = getColorAsFloat(${g.texture2D}(A, coords));
-        return value;
-      }
-      `},i=(g,m,b,y,w,T)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=`
-        k = m[${O}] - ${T[O]};
-        if (k < 0) { k = -k; }
-        {
-          const int _2n_1 = ${2*(m[O]-1)};
-          k = int( mod( float(k), float(_2n_1) ) ) ;
-          if(k >= ${m[O]}) { k = _2n_1 - k; }
-        }
-        offset += k * ${b[O]};
-        `;return`
-      float padA(int m[${S}]) {
-        int offset = 0;
-        int k = 0;
-        ${A}
-        vec2 coords = offsetToCoords(offset, ${y}, ${w});
-        float value = getColorAsFloat(${g.texture2D}(A, coords));
-        return value;
-      }
-      `},h=(g,m,b,y,w,T)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=`
-        k = m[${O}] - ${T[O]};
-        if (k < 0)  k = 0;
-        if (k >= ${m[O]}) k = ${m[O]-1};
-        offset += k * ${b[O]};
-      `;return`
-      float padA(int m[${S}]) {
-        int offset = 0;
-        int k = 0;
-        ${A}
-        vec2 coords = offsetToCoords(offset, ${y}, ${w});
-        float value = getColorAsFloat(${g.texture2D}(A, coords));
-        return value;
-      }
-      `}},2143:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.globalMaxPool=n.parseMaxPoolAttributes=n.maxPool=n.parseGlobalAveragePoolAttributes=n.globalAveragePool=n.parseAveragePoolAttributes=n.averagePool=void 0;const u=o(246),l=o(2517),d=o(2039);n.averagePool=(h,g,m)=>{t(g);const b={name:"AveragePool",inputNames:["X"],inputTypes:[d.TextureType.unpacked],cacheHint:m.cacheKey};return[h.run(Object.assign(Object.assign({},b),{get:()=>s(g,b,!1,m)}),g)]},n.parseAveragePoolAttributes=h=>{const g=h.attributes.getString("auto_pad","NOTSET"),m=h.attributes.getInt("ceil_mode",0),b=h.attributes.getInt("count_include_pad",0)!==0,y=h.attributes.getInts("kernel_shape"),w=h.attributes.getInts("strides",[]),T=h.attributes.getInts("pads",[]);if(m!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return(0,u.createAttributeWithCacheKey)({autoPad:g,ceilMode:m,countIncludePad:b,kernelShape:y,strides:w,pads:T})};const s=(h,g,m,b)=>{const[y,w]=f(h,b,m),T=l.ShapeUtil.size(y.kernelShape);let S="";y.countIncludePad?S+=`value /= float(${T});`:S+=`value /= float(${T} - pad);`;const A=`
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-      ${e(h[0].dims,y,`
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-    `;return Object.assign(Object.assign({},g),{output:{dims:w,type:h[0].type,textureType:d.TextureType.unpacked},shaderSource:T})},f=(h,g,m)=>{const b=h[0].dims.slice(),y=Object.hasOwnProperty.call(g,"dilations"),w=g.kernelShape.slice(),T=g.strides.slice(),S=y?g.dilations.slice():[],A=g.pads.slice();l.PoolConvUtil.adjustPoolAttributes(m,b,w,T,S,A);const O=l.PoolConvUtil.computePoolOutputShape(m,b,T,S,w,A,g.autoPad),v=Object.assign({},g);return y?Object.assign(v,{kernelShape:w,strides:T,pads:A,dilations:S,cacheKey:g.cacheKey}):Object.assign(v,{kernelShape:w,strides:T,pads:A,cacheKey:g.cacheKey}),[v,O]},c={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},a={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[d.TextureType.unpacked]};n.globalMaxPool=(h,g)=>(t(g),[h.run(Object.assign(Object.assign({},a),{get:()=>p(g,a,!0,c)}),g)]);const t=h=>{if(!h||h.length!==1)throw new Error("Pool ops requires 1 input.");if(h[0].type!=="float32"&&h[0].type!=="float64")throw new Error("Invalid input type.")},e=(h,g,m,b,y)=>{const w=h.length;if(g.kernelShape.length<=2){const T=g.kernelShape[g.kernelShape.length-1],S=g.strides[g.strides.length-1],A=g.pads[g.pads.length/2-1],O=g.pads[g.pads.length-1],v=h[w-1];let M="",F="",R="";if(M=A+O!==0?`
-          for (int i = 0; i < ${T}; i++) {
-            x[${w} - 1] = indices[${w} - 1] * ${S} - ${A} + i;
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-              continue;
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-            ${m}
-          }`:`
-          for (int i = 0; i < ${T}; i++) {
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-            ${m}
-          }`,g.kernelShape.length===2){const $=g.kernelShape[g.kernelShape.length-2],N=g.strides[g.strides.length-2],G=g.pads[g.pads.length/2-2],D=g.pads[g.pads.length-2],j=h[w-2];F=G+D!==0?`
-            for (int j = 0; j < ${$}; j++) {
-              x[${w} - 2] = indices[${w} - 2] * ${N} - ${G} + j;
-              if (x[${w} - 2] < 0 || x[${w} - 2] >= ${j}) {
-                pad+= ${T};
-                continue;
-              }
-          `:`
-            for (int j = 0; j < ${$}; j++) {
-              x[${w} - 2] = indices[${w} - 2] * ${N} - ${G} + j;
-            `,R=`
-          }
-        `}return`
-        float process(int indices[${w}]) {
-          int x[${w}];
-          copyVec(indices, x);
-
-          float value = ${y};
-          int pad = 0;
-          ${F}
-          ${M}
-          ${R}
-          ${b}
-          return value;
-        }
-      `}{const T=l.ShapeUtil.size(g.kernelShape),S=l.ShapeUtil.computeStrides(g.kernelShape),A=S.length,O=g.pads.length,v=i(A),M=r(h,"inputDims"),F=r(g.pads,"pads"),R=r(S,"kernelStrides"),$=r(g.strides,"strides");let N="";return N=g.pads.reduce((G,D)=>G+D)?`
-            if (x[j] >= inputDims[j] || x[j] < 0) {
-              pad++;
-              isPad = true;
-              break;
-            }
-          }
-          if (!isPad) {
-            ${m}
-          }`:`
-          }
-          ${m}
-        `,`
-        ${v}
-        float process(int indices[${w}]) {
-          int x[${w}];
-          copyVec(indices, x);
-          int offset[${A}];
-          int pads[${O}];
-          int inputDims[${w}];
-          int kernelStrides[${A}];
-          int strides[${A}];
-          ${F}
-          ${M}
-          ${$}
-          ${R}
-
-          float value = ${y};
-          int pad = 0;
-          bool isPad = false;
-          for (int i = 0; i < ${T}; i++) {
-            offsetToIndices(i, kernelStrides, offset);
-            isPad = false;
-            for (int j = ${w} - ${A}; j < ${w}; j++) {
-              x[j] = indices[j] * strides[j - ${w} + ${A}]
-                + offset[j - ${w} + ${A}] - pads[j - 2];
-              ${N}
-          }
-          ${b}
-
-          return value;
-        }
-      `}},r=(h,g)=>{let m="";for(let b=0;b<h.length;b++)m+=`
-      ${g}[${b}] = ${h[b]};
-    `;return m},i=h=>`
-  void offsetToIndices(int offset, int[${h}] strides, out int[${h}] indices) {
-    if (${h} == 0) {
-      return;
-    }
-    for (int i = 0; i < ${h} - 1; ++i) {
-      indices[i] = offset / strides[i];
-      offset -= indices[i] * strides[i];
-    }
-    indices[${h} - 1] = offset;
-  }`},4939:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reduceLogSumSquare=n.reduceLogSum=n.reduceProd=n.reduceMin=n.reduceMax=n.reduceMean=n.reduceSum=n.parseReduceAttributes=void 0;const u=o(246),l=o(782),d=o(2517),s=o(2039),p=(a,t,e,r,i)=>{c(t);const h={name:r,inputNames:["A"],inputTypes:[s.TextureType.unpacked]};return[a.run(Object.assign(Object.assign({},h),{cacheHint:e.cacheKey,get:()=>f(a,t,e,r,i,h)}),t)]};n.parseReduceAttributes=a=>{const t=a.attributes.getInts("axes",[]),e=a.attributes.getInt("keepdims",1)===1;return(0,u.createAttributeWithCacheKey)({axes:t,keepDims:e})};const f=(a,t,e,r,i,h)=>{const g=[],m=t[0].dims.length||1,b=[],y=d.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),w=i(t,y);let T=w[1];for(let A=0;A<t[0].dims.length;A++)y.indexOf(A)>=0||y.length===0?(e.keepDims&&g.push(1),T=`
-          for(int j${A} = 0; j${A} < ${t[0].dims[A]}; j${A}++) {
-            inputIdx[${A}] = j${A};
-            ${T}
-          }`):(b.push(`inputIdx[${A}] = outputIdx[${g.length}];`),g.push(t[0].dims[A]));const S=`
-      float process(int outputIdx[${g.length||1}]) {
-        float value;                 // final result
-        int inputIdx[${m}];      // addressing input data
-        ${b.join(`
-`)}
-        ${w[0]}       // init ops for reduce max/min
-        ${T}
-        ${w[2]}       // final computation for reduce mean
-        return value;
-      }`;return Object.assign(Object.assign({},h),{output:{dims:g,type:t[0].type,textureType:s.TextureType.unpacked},shaderSource:S})},c=a=>{if(!a||a.length!==1)throw new Error("Reduce op requires 1 input.");if(l.NUMBER_TYPES.indexOf(a[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(a,t,e)=>p(a,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(a,t,e)=>p(a,t,e,"ReduceMean",(r,i)=>{let h=1;for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&(h*=r[0].dims[g]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${h}.;`]}),n.reduceMax=(a,t,e)=>p(a,t,e,"ReduceMax",(r,i)=>{const h=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&h.push(`inputIdx[${g}] = 0;`);return[`${h.join(`
-`)}
-value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),n.reduceMin=(a,t,e)=>p(a,t,e,"ReduceMin",(r,i)=>{const h=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&h.push(`inputIdx[${g}] = 0;`);return[`${h.join(`
-`)}
-value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),n.reduceProd=(a,t,e)=>p(a,t,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),n.reduceLogSum=(a,t,e)=>p(a,t,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),n.reduceLogSumSquare=(a,t,e)=>p(a,t,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.isReshapeCheap=n.processDims3D=n.createPackedReshape3DProgramInfoLoader=void 0;const u=o(2517),l=o(5060),d=o(2039),s=o(2827);n.createPackedReshape3DProgramInfoLoader=(p,f,c)=>{const a=(t=>({name:"Reshape (packed)",inputTypes:[d.TextureType.packed],inputNames:["A"],cacheHint:`${t}`}))(c);return Object.assign(Object.assign({},a),{get:()=>((t,e,r,i)=>{const h=e.dims,g=i;let m="";for(let w=0;w<4;w++){let T="";switch(w){case 0:T="outputCoords = rc;";break;case 1:T="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:T="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:T="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}m+=`
-        ${T}
-        ${w>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
-          int flattenedIndex = getFlattenedIndex(outputCoords);
-
-          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
-          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));
-
-          result[${w}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);
-
-        ${w>0?"}":""}
-      `}const b=(0,l.getGlsl)(t.session.backend.glContext.version),y=`
-      ${function(w){const T=u.ShapeUtil.computeStrides(w),S=["b","r","c"],A="index";return`
-    ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${T.map((O,v)=>`int ${S[v]} = ${A} / ${O}; ${v===T.length-1?`int ${S[v+1]} = ${A} - ${S[v]} * ${O}`:`index -= ${S[v]} * ${O}`};`).join("")}
-      return ivec3(b, r, c);
-    }
-  `}(h)}
-      ${function(w){const T=u.ShapeUtil.computeStrides(w);return`
-  int getFlattenedIndex(ivec3 coords) {
-    // reverse y, z order
-    return coords.x * ${T[0]} + coords.z * ${T[1]} + coords.y;
-  }
-`}(g)}
-      ${(0,s.unpackFromChannel)()}
-
-      void main() {
-        ivec3 rc = getOutputCoords();
-
-        vec4 result = vec4(0.0);
-
-        ivec3 outputCoords;
-        int rows = ${g[2]};
-        int cols = ${g[1]};
-
-        ${m}
-        ${b.output} = result;
-      }
-    `;return Object.assign(Object.assign({},r),{output:{dims:g,type:e.type,textureType:d.TextureType.packed},shaderSource:y,hasMain:!0})})(p,f,a,c)})},n.processDims3D=function(p){if(p.length===0)return[1,1,1];let f=1;for(let c=0;c<p.length-2;++c)f*=p[c];return[f,p.length>1?p[p.length-2]:1,p[p.length-1]]},n.isReshapeCheap=function(p,f){let c=!1;return c=p.length===0||f.length===0||(p.length<2||f.length<2?p[p.length-1]===f[f.length-1]:p[p.length-1]===f[f.length-1]&&p[p.length-2]===f[f.length-2]),c}},718:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const u=o(2517);n.reshape=(l,d)=>{const s=u.ShapeUtil.calculateReshapedDims(d[0].dims,d[1].integerData);return l.session.pack?[l.reshapePacked(d[0],s)]:[l.reshapeUnpacked(d[0],s)]}},2268:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827),p=o(9793),f={name:"Resize",inputNames:["A"],inputTypes:[l.TextureType.packed]};n.resize=(r,i,h)=>((0,p.validateInputs)(i,h),[r.run(Object.assign(Object.assign({},f),{cacheHint:h.cacheKey,get:()=>c(r,i,h)}),i)]),n.parseResizeAttributesV10=r=>(0,p.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,p.parseUpsampleAttributes)(r,11);const c=(r,i,h)=>{const g=(0,u.getGlsl)(r.session.backend.glContext.version),[m,b]=a(i,h);if(m.every(N=>N===1)&&h.coordinateTransformMode!=="tf_crop_and_resize")return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:l.TextureType.packed},hasMain:!0,shaderSource:`void main() {
-                    vec4 v = ${g.texture2D}(X, TexCoords);
-                    ${g.output} = v;
-                }`});const y=b.length;if(y<2)throw new Error(`output dimension should be at least 2, but got ${y}`);const w=b[y-2],T=b[y-1],S=i[0].dims;if(y!==S.length)throw new Error(`output dimension should match input ${S.length}, but got ${y}`);const A=S[y-2],O=S[y-1],v=m[y-2],M=m[y-1];let F="";if(h.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${h.mode}'`);switch(h.coordinateTransformMode){case"asymmetric":F=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        return vec4(coords) / scaleWHWH;
-                    }
-                `;break;case"half_pixel":F=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
-                    }
-                `;break;case"pytorch_half_pixel":F=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        vec4 fcoords = vec4(coords);
-                        return vec4(
-                            ${T}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
-                            ${w}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
-                            ${T}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
-                            ${w}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
-                          );
-                    }
-                `;break;case"align_corners":F=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        vec4 resized = vec4(${T}.0 - 1.0, ${w}.0 - 1.0, ${T}.0 - 1.0,
-                            ${w}.0 - 1.0);
-                        vec4 original = vec4(${O}.0 - 1.0, ${A}.0 - 1.0, ${O}.0 - 1.0,
-                            ${A}.0 - 1.0);
-                        vec4 new_scale = original / resized;
-                        return vec4(coords) * new_scale;
-                    }
-                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${h.coordinateTransformMode}'`)}const R=(0,d.getCoordsDataType)(y),$=`
-            const vec2 inputWH = vec2(${A}.0, ${O}.0);
-            const vec4 scaleWHWH = vec4(float(${v}), float(${M}), float(${v}), float(${M}));
-            ${(0,s.unpackFromChannel)()}
-            ${F}
-            float getAValue(int x10, int r, int c, int d) {
-                return getChannel(getA(x10, r, c, d), vec2(c, d));
-            }
-            void main() {
-                ${R} rc = getOutputCoords();
-
-                int batch = rc[0];
-                int depth = rc[1];
-
-                // retrieve the 4 coordinates that is used in the 4 packed output values.
-                ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);
-
-                // calculate the source index in fraction
-                vec4 sourceFrac = getSourceFracIndex(coords);
-
-                // get the lower and upper bound of the 4 values that will be packed into one texel.
-                ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));
-                ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));
-                ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));
-                ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));
-
-                bool hasNextRow = rc.w < ${w-1};
-                bool hasNextCol = rc.z < ${T-1};
-
-                // pack x00, x01, x10, x11's top-left corner into one vec4 structure
-                vec4 topLeft = vec4(
-                    getAValue(batch, depth, x00.x, x00.y),
-                    hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,
-                    hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,
-                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);
-
-                // pack x00, x01, x10, x11's top-right corner into one vec4 structure
-                vec4 topRight = vec4(
-                    getAValue(batch, depth, x00.x, x00.w),
-                    hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,
-                    hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,
-                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);
-
-                // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure
-                vec4 bottomLeft = vec4(
-                    getAValue(batch, depth, x00.z, x00.y),
-                    hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,
-                    hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,
-                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);
-
-                // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure
-                vec4 bottomRight = vec4(
-                    getAValue(batch, depth, x00.z, x00.w),
-                    hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,
-                    hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,
-                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);
-
-                // calculate the interpolation fraction on u and v direction
-                vec4 frac = vec4(sourceFrac) - floor(sourceFrac);
-                vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));
-
-                vec4 top = mix(topLeft, topRight, clampFrac.ywyw);
-                vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);
-                vec4 newValue = mix(top, bottom, clampFrac.xxzz);
-
-                ${g.output} = vec4(newValue);
-            }
-        `;return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:l.TextureType.packed},hasMain:!0,shaderSource:$})},a=(r,i)=>{const h=r[0].dims;let g,m=i.scales;if(m.length===0){const y=r[i.scalesInputIdx];if(y&&y.size!==0){if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");m=t(y,i.mode,i.isResize)}else{const w=r[i.sizesInputIdx];if(!w||w.size===0)throw new Error("Either scales or sizes MUST be provided as input.");g=Array.from(w.integerData),m=e(g,h,i.mode,i.isResize)}}else if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const b=g||h.map((y,w)=>Math.floor(y*m[w]));return[m,b]},t=(r,i,h)=>{const g=Array.from(r.floatData);return(0,p.scalesValidation)(g,i,h),g},e=(r,i,h,g)=>{const m=i.length,b=new Array(m);for(let y=0,w=m;y<w;y++)if(i[y]===0){if(r[y]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");b[y]=1}else b[y]=r[y]/i[y];return(0,p.scalesValidation)(b,h,g),b}},8117:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const u=o(9162);n.shape=(d,s)=>(l(s),[new u.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const l=d=>{if(!d||d.length!==1)throw new Error("Shape requires 1 input.")}},2278:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const u=o(246),l=o(782),d=o(2517),s=o(2039),p={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(c(r),[e.run(Object.assign(Object.assign({},p),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),h=e.attributes.getInts("axes",[]);return(0,u.createAttributeWithCacheKey)({starts:r,ends:i,axes:h})};const f=(e,r,i)=>{const h=i.axes.length===0?r.dims.slice(0).map((S,A)=>A):i.axes,g=d.ShapeUtil.normalizeAxes(h,r.dims.length),m=i.starts.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:d.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),b=i.ends.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:d.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),y=r.dims.slice(),w=[];for(let S=0;S<g.length;S++)y[g[S]]=b[S]-m[S],m[S]>0&&w.push(`outputIdx[${g[S]}] += ${m[S]};`);const T=`
-      float process(int outputIdx[${y.length}]) {
-        ${w.join(`
-      `)}
-        return _A(outputIdx);
-      }`;return Object.assign(Object.assign({},p),{output:{dims:y,type:r.type,textureType:s.TextureType.unpacked},shaderSource:T})},c=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(l.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};n.sliceV10=(e,r)=>{t(r);const i=a(e,r);return[e.run(Object.assign(Object.assign({},p),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),[r[0]])]};const a=(e,r)=>{if(!e.session.isInitializer(r[1].dataId)||!e.session.isInitializer(r[2].dataId)||r.length>=4&&!e.session.isInitializer(r[3].dataId)||r.length>=5&&!e.session.isInitializer(r[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(r.length>=5&&r[4].integerData.some(m=>m!==1))throw new Error("currently non-1 steps is not supported for Slice");const i=Array.from(r[1].integerData),h=Array.from(r[2].integerData),g=r.length>=4?Array.from(r[3].integerData):[];return{starts:i,ends:h,axes:g,cacheKey:`${g};${i};${h}`}},t=e=>{if(!e||e.length<3||e.length>5)throw new Error("Invalid input number.");if(e[1].type!=="int32"||e[1].dims.length!==1)throw new Error("Invalid input type.");if(e[2].type!=="int32"||e[2].dims.length!==1)throw new Error("Invalid input type.");if(e.length>=4&&(e[3].type!=="int32"||e[3].dims.length!==1))throw new Error("Invalid input type.");if(e.length>=5&&(e[4].type!=="int32"||e[4].dims.length!==1))throw new Error("Invalid input type.")}},5524:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.softmaxV13=n.parseSoftmaxAttributesV13=n.parseSoftmaxAttributes=n.softmax=void 0;const u=o(246),l=o(2517),d=o(5060),s=o(2039),p=o(3738),f={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[s.TextureType.unpacked]},c={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},a={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]};n.softmax=(g,m,b)=>{h(m);const y=m[0].dims.slice(),w=l.ShapeUtil.normalizeAxis(b.axis,y.length),T=l.ShapeUtil.sizeToDimension(y,w),S=l.ShapeUtil.sizeFromDimension(y,w);return t(g,m,b,T,S)},n.parseSoftmaxAttributes=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",1)}),n.parseSoftmaxAttributesV13=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",-1)}),n.softmaxV13=(g,m,b)=>{h(m);const y=m[0].dims.slice(),w=l.ShapeUtil.normalizeAxis(b.axis,y.length),T=y.length,S=w!==T-1,A=[];let O,v=[],M=[];S&&(v=Array.from({length:T}).map((N,G)=>G),v[w]=T-1,v[T-1]=w,v.map(N=>A.push(y[N])),O=(0,u.createAttributeWithCacheKey)({perm:v}),M=(0,p.transpose)(g,m,O));const F=S?l.ShapeUtil.sizeToDimension(A,T-1):l.ShapeUtil.sizeToDimension(y,T-1),R=S?l.ShapeUtil.sizeFromDimension(A,T-1):l.ShapeUtil.sizeFromDimension(y,T-1),$=t(g,S?M:m,b,F,R);return S?(0,p.transpose)(g,$,O):$};const t=(g,m,b,y,w)=>{const T=e(g,m[0],y,w,[y]),S=g.run(Object.assign(Object.assign({},f),{cacheHint:b.cacheKey,get:()=>T}),m),A=r(g,m[0],y,w,T.output.dims,[y]),O=g.run(Object.assign(Object.assign({},c),{cacheHint:b.cacheKey,get:()=>A}),[m[0],S]),v=i(g,m[0],y,w,T.output.dims,A.output.dims);return[g.run(Object.assign(Object.assign({},a),{cacheHint:b.cacheKey,get:()=>v}),[m[0],S,O])]},e=(g,m,b,y,w)=>{const[T,S]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),A=w.length;if(b<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1)throw new Error("Dimensionality of the output should be 1");if(w[0]!==b)throw new Error("Shape of the output should be equal to logical row count");const O=(0,d.getGlsl)(g.session.backend.glContext.version),v=`
-      float process(int[${A}] indices) {
-        int logical_row_start_offset = indices[0] * ${y};
-
-        float max = getColorAsFloat(${O.texture2D}(A, offsetToCoords(logical_row_start_offset, ${T},
-        ${S} )));
-        for(int i=1; i<${y}; ++i)
-        {
-          float current = getColorAsFloat(${O.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
-            ${T}, ${S})));
-          if(current > max)
-          max = current;
-        }
-
-        return max;
-      }`;return Object.assign(Object.assign({},f),{output:{dims:w,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},r=(g,m,b,y,w,T)=>{const[S,A]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=T.length;if(b<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(T.length!==1)throw new Error("Dimensionality of the output should be 1");if(T[0]!==b)throw new Error("Shape of the output should be equal to logical row count");if(w.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(w[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const v=`
-      float process(int[${O}] indices) {
-        int logical_row_start_offset = indices[0] * ${y};
-
-        float norm_factor = 0.0;
-        float max = _Max(indices);
-        for(int i=0; i<${y}; ++i)
-        {
-          norm_factor += exp(getColorAsFloat(${(0,d.getGlsl)(g.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
-            ${S}, ${A}))) - max);
-        }
-
-        return norm_factor;
-      }`;return Object.assign(Object.assign({},c),{output:{dims:T,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},i=(g,m,b,y,w,T)=>{const[S,A]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=m.dims.length;if(b<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1||T.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(w[0]!==b||T[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const v=`
-      float process(int[${O}] indices) {
-
-      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
-      int offset = coordsToOffset(TexCoords, ${S}, ${A});
-
-      //determine the logical row for this index
-      int logical_row_index[1];
-      logical_row_index[0] = offset / ${y};
-
-      float norm_factor = _Norm(logical_row_index);
-
-      // avoid possible division by 0
-      // if norm_facor is 0, all elements are zero
-      // if so, return 0
-      if(norm_factor == 0.0)
-        return 0.0;
-
-      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
-    }`;return Object.assign(Object.assign({},a),{output:{dims:m.dims,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},h=g=>{if(!g||g.length!==1)throw new Error("Softmax requires 1 input.");if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type")}},5975:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSplitAttributes=n.split=void 0;const u=o(246),l=o(2517),d=o(2039),s={name:"Split",inputNames:["A"],inputTypes:[d.TextureType.unpacked]};n.split=(a,t,e)=>{c(t);const r=l.ShapeUtil.normalizeAxis(e.axis,t[0].dims.length),i=p(a,t,r,e),h=[];for(let g=0;g<i;++g)h.push(a.run(Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey};${g}`,get:()=>f(a,t[0],e,r,g)}),t));return h},n.parseSplitAttributes=a=>{const t=a.attributes.getInt("axis",0),e=a.attributes.getInts("split",[]),r=a.outputs.length;return(0,u.createAttributeWithCacheKey)({axis:t,split:e,numOutputs:r})};const p=(a,t,e,r)=>{const[,i]=l.SplitUtil.splitShape(t[0].dims,e,r.split,r.numOutputs);return i.length},f=(a,t,e,r,i)=>{const[h,g]=l.SplitUtil.splitShape(t.dims,r,e.split,e.numOutputs),m=g[i],b=h[i],y=`
-      float process(int indices[${b.length}]) {
-        indices[${r}] += ${m};
-        return _A(indices);
-      }
-    `;return Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey}:${i}`,output:{dims:b,type:t.type,textureType:d.TextureType.unpacked},shaderSource:y})},c=a=>{if(!a||a.length!==1)throw new Error("Split requires one input.");if(a[0].type!=="int8"&&a[0].type!=="uint8"&&a[0].type!=="int16"&&a[0].type!=="uint16"&&a[0].type!=="int32"&&a[0].type!=="uint32"&&a[0].type!=="float32"&&a[0].type!=="float64"&&a[0].type!=="bool")throw new Error("Invalid input type.")}},3933:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSqueezeAttributes=n.squeezeV13=n.squeeze=void 0;const u=o(2517);n.squeeze=(s,p,f)=>{l(p);const c=u.ShapeUtil.squeezeShape(p[0].dims,f);return[s.reshapeUnpacked(p[0],c)]},n.squeezeV13=(s,p)=>(d(p),(0,n.squeeze)(s,[p[0]],Array.from(p[1].integerData))),n.parseSqueezeAttributes=s=>s.attributes.getInts("axes");const l=s=>{if(!s||s.length!==1)throw new Error("Squeeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},d=s=>{if(!s||s.length!==2)throw new Error("Squeeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},6558:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sum=void 0;const u=o(5060),l=o(2039);n.sum=(p,f)=>{s(f);const c={name:"Sum",inputNames:f.map((a,t)=>`X${t}`),inputTypes:new Array(f.length).fill(l.TextureType.unpacked)};return[p.run(Object.assign(Object.assign({},c),{get:()=>d(p,f,c)}),f)]};const d=(p,f,c)=>{const a=(0,u.getGlsl)(p.session.backend.glContext.version),t=f[0].dims.slice(),e=`
-      void main() {
-        vec4 result = ${f.map((r,i)=>`${a.texture2D}(X${i},TexCoords)`).join(" + ")};
-        ${a.output} = result;
-      }
-    `;return Object.assign(Object.assign({},c),{output:{dims:t,type:f[0].type,textureType:l.TextureType.unpacked},hasMain:!0,shaderSource:e})},s=p=>{if(!p||p.length===0)throw new Error("Sum requires inputs.");const f=p[0].dims.length;for(let c=1;c<p.length;c++){if(f!==p[c].dims.length)throw new Error("Input shapes are mismatched.");for(let a=0;a<f;a++)if(p[0].dims[a]!==p[c].dims[a])throw new Error("Input shapes are not matched.")}if(p[0].type!=="float32"&&p[0].type!=="float64")throw new Error("Invalid input type.");for(let c=1;c<p.length;c++)if(p[0].type!==p[c].type)throw new Error("Input types are not matched.")}},5723:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.tile=void 0;const u=o(782),l=o(2039);n.tile=(p,f)=>{s(f);const c={name:"Tile",inputNames:["A"],inputTypes:[l.TextureType.unpacked]};return[p.run(Object.assign(Object.assign({},c),{get:()=>d(p,f,c)}),f)]};const d=(p,f,c)=>{const a=f[0].dims.slice(),t=new Array(a.length),e=[];for(let h=0;h<a.length;h++)t[h]=a[h]*f[1].numberData[h],e.push(`inputIdx[${h}] = int(mod(float(outputIdx[${h}]), ${a[h]}.));`);const r=t.length,i=`
-      float process(int outputIdx[${r}]) {
-        int inputIdx[${r}];
-        ${e.join(`
-`)}
-        return _A(inputIdx);
-      }
-    `;return Object.assign(Object.assign({},c),{output:{dims:t,type:f[0].type,textureType:l.TextureType.unpacked},shaderSource:i})},s=p=>{if(!p||p.length!==2)throw new Error("Tile requires 2 input.");if(p[1].dims.length!==1)throw new Error("The second input shape must 1 dimension.");if(p[1].dims[0]!==p[0].dims.length)throw new Error("Invalid input shape.");if(u.NUMBER_TYPES.indexOf(p[0].type)===-1)throw new Error("Invalid input type.");if(p[1].type!=="int32"&&p[1].type!=="int16")throw new Error("Invalid repeat type.")}},3738:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseTransposeAttributes=n.transpose=void 0;const u=o(246),l=o(2517),d=o(2039),s={name:"Transpose",inputNames:["A"],inputTypes:[d.TextureType.unpacked]};n.transpose=(e,r,i)=>(t(r),[e.run(Object.assign(Object.assign({},s),{cacheHint:i.cacheKey,get:()=>p(e,r[0],i.perm)}),r)]),n.parseTransposeAttributes=e=>(0,u.createAttributeWithCacheKey)({perm:e.attributes.getInts("perm",[])});const p=(e,r,i)=>{const h=r.dims;i=f(h,i);const g=c(h,i),m=h.length,b=`
-      ${a("perm",i,m)}
-      float process(int indices[${m}]) {
-        int a[${m}];
-        perm(a, indices);
-        return _A(a);
-      }`;return Object.assign(Object.assign({},s),{output:{dims:g,type:r.type,textureType:d.TextureType.unpacked},shaderSource:b})},f=(e,r)=>(r&&r.length!==e.length&&(r=[...e.keys()].reverse()),r),c=(e,r)=>(r=f(e,r),l.ShapeUtil.sortBasedOnPerm(e,r)),a=(e,r,i)=>{const h=[];h.push(`void ${e}(out int a[${i}], int src[${i}]) {`);for(let g=0;g<i;++g)h.push(`	a[${r[g]}]=src[${g}];`);return h.push("	}"),h.join(`
-`)},t=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(e[0].type!=="float32"&&e[0].type!=="float64")throw new Error("input should be float tensor")}},8710:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.encodeAsUint8=void 0;const u=o(5060),l=o(2039);n.encodeAsUint8=(d,s)=>{const p=s.shape,f=(0,u.getGlsl)(d.session.backend.glContext.version),c=`
-    const float FLOAT_MAX = 1.70141184e38;
-    const float FLOAT_MIN = 1.17549435e-38;
-
-    bool isNaN(float val) {
-      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;
-    }
-
-    highp vec4 encodeAsUint8(highp float v) {
-      if (isNaN(v)) {
-        return vec4(255, 255, 255, 255);
-      }
-
-      highp float av = abs(v);
-
-      if(av < FLOAT_MIN) {
-        return vec4(0.0, 0.0, 0.0, 0.0);
-      } else if(v > FLOAT_MAX) {
-        return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
-      } else if(v < -FLOAT_MAX) {
-        return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
-      }
-
-      highp vec4 c = vec4(0,0,0,0);
-
-      highp float e = floor(log2(av));
-      highp float m = exp2(fract(log2(av))) - 1.0;
-
-      c[2] = floor(128.0 * m);
-      m -= c[2] / 128.0;
-      c[1] = floor(32768.0 * m);
-      m -= c[1] / 32768.0;
-      c[0] = floor(8388608.0 * m);
-
-      highp float ebias = e + 127.0;
-      c[3] = floor(ebias / 2.0);
-      ebias -= c[3] * 2.0;
-      c[2] += floor(ebias) * 128.0;
-
-      c[3] += 128.0 * step(0.0, -v);
-
-      return c / 255.0;
-    }
-
-    void main() {
-      float value = ${f.texture2D}(X,TexCoords).r;
-      ${f.output} = encodeAsUint8(value);
-    }`,a={name:"Uint8Encode",inputTypes:[l.TextureType.unpacked],inputNames:["X"],output:{dims:p,type:s.tensor.type,textureType:l.TextureType.downloadUint8AsFloat},shaderSource:c,hasMain:!0};return d.executeProgram(a,[s.tensor])}},4909:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.tanh=n.tan=n.sqrt=n.sin=n.sigmoid=n.relu=n.not=n.neg=n.log=n.parseLeakyReluAttributes=n.leakyRelu=n.identity=n.floor=n.exp=n.parseEluAttributes=n.elu=n.cos=n.ceil=n.clipV11=n.parseClipAttributes=n.clip=n.atan=n.asin=n.acos=n.abs=n.glslTanh=n.glslTan=n.glslSqrt=n.glslSigmoid=n.glslRelu=n.glslSin=n.glslNot=n.glslNeg=n.glslLog=n.glslLeakyRelu=n.glslIdentity=n.glslClip=n.glslFloor=n.glslExp=n.glslElu=n.glslCos=n.glslCeil=n.glslAtan=n.glslAsin=n.glslAcos=n.glslAbs=void 0;const u=o(246),l=o(2517),d=o(8520),s=o(5060),p=o(2039);function f(){return $("abs")}function c(){return $("acos")}function a(){return $("asin")}function t(){return $("atan")}function e(){return $("ceil")}function r(){return $("cos")}function i(D){const j="elu";return{body:`
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-
-  float ${j}_(float a) {
-    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
-  }
-  vec4 ${j}_(vec4 v) {
-    return vec4(${j}_(v.x), ${j}_(v.y), ${j}_(v.z), ${j}_(v.w));
-  }
-  `,name:j,type:d.FunctionType.ValueBased}}function h(){return $("exp")}function g(){return $("floor")}function m(D,j){const Q="clip";return{body:`
-  const float min = float(${D});
-  const float max = float(${j});
-
-  float ${Q}_(float a) {
-    return clamp(a, min, max);
-  }
-  vec4 ${Q}_(vec4 v) {
-    return clamp(v, min, max);
-  }
-  `,name:Q,type:d.FunctionType.ValueBased}}function b(){const D="indentity";return{body:`
-  float ${D}_(float a) {
-    return a;
-  }
-  vec4 ${D}_(vec4 v) {
-    return v;
-  }
-  `,name:D,type:d.FunctionType.ValueBased}}function y(D){const j="leakyRelu";return{body:`
-  const float alpha = float(${D});
-
-  float ${j}_(float a) {
-    return a < 0.0 ? a * alpha : a;
-  }
-  vec4 ${j}_(vec4 v) {
-    return vec4(${j}_(v.x), ${j}_(v.y), ${j}_(v.z), ${j}_(v.w));
-  }
-  `,name:j,type:d.FunctionType.ValueBased}}function w(){return $("log")}function T(){const D="neg";return{body:`
-  float ${D}_(float a) {
-    return -a;
-  }
-  vec4 ${D}_(vec4 v) {
-    return -v;
-  }
-  `,name:D,type:d.FunctionType.ValueBased}}function S(){const D="not";return{body:`
-  float ${D}_(float a) {
-    return float( ! bool(a) );
-  }
-  bool ${D}_(bool a) {
-    return !a;
-  }
-  vec4 ${D}_(vec4 v) {
-    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
-  }
-  bvec4 ${D}_(bvec4 v) {
-    return bvec4(!v.x, !v.y, !v.z, !v.w);
-  }
-  `,name:D,type:d.FunctionType.ValueBased}}function A(){return $("sin")}function O(){const D="relu";return{body:`
-  float ${D}_(float a) {
-    return max( a, 0.0 );
-  }
-  vec4 ${D}_(vec4 v) {
-    return max( v, 0.0 );
-  }
-  `,name:D,type:d.FunctionType.ValueBased}}function v(){const D="sigmoid";return{body:`
-  float ${D}_(float a) {
-    return 1.0 / (1.0 + exp(-a));
-  }
-  vec4 ${D}_(vec4 v) {
-    return 1.0 / (1.0 + exp(-v));
-  }
-  `,name:D,type:d.FunctionType.ValueBased}}function M(){return $("sqrt")}function F(){return $("tan")}function R(){const D="tanh";return{body:`
-  float ${D}_(float a) {
-    a = clamp(a, -10., 10.);
-    a = exp(2.*a);
-    return (a - 1.) / (a + 1.);
-  }
-  vec4 ${D}_(vec4 v) {
-    v = clamp(v, -10., 10.);
-    v = exp(2.*v);
-    return (v - 1.) / (v + 1.);
-  }
-  `,name:D,type:d.FunctionType.ValueBased}}function $(D){return{body:`
-  float ${D}_(float a) {
-    return ${D}(a);
-  }
-  vec4 ${D}_(vec4 v) {
-    return ${D}(v);
-  }
-  `,name:D,type:d.FunctionType.ValueBased}}n.glslAbs=f,n.glslAcos=c,n.glslAsin=a,n.glslAtan=t,n.glslCeil=e,n.glslCos=r,n.glslElu=i,n.glslExp=h,n.glslFloor=g,n.glslClip=m,n.glslIdentity=b,n.glslLeakyRelu=y,n.glslLog=w,n.glslNeg=T,n.glslNot=S,n.glslSin=A,n.glslRelu=O,n.glslSigmoid=v,n.glslSqrt=M,n.glslTan=F,n.glslTanh=R;const N=(D,j,Q,X)=>{const Z=D.session.pack?p.TextureType.packed:p.TextureType.unpacked,ee={name:Q.name,inputTypes:[Z],inputNames:["A"],cacheHint:X};return Object.assign(Object.assign({},ee),{get:()=>((ue,Ae,ve,oe)=>{const we=ue.session.pack?p.TextureType.packed:p.TextureType.unpacked,be=(0,s.getGlsl)(ue.session.backend.glContext.version);return Object.assign(Object.assign({},Ae),{output:{dims:ve.dims,type:ve.type,textureType:we},shaderSource:`
-     ${oe.body}
-     void main() {
-       vec4 v = ${be.texture2D}(A, TexCoords);
-       v = ${oe.name}_(v);
-       ${be.output} = v;
-     }
-     `,hasMain:!0})})(D,ee,j,Q)})};n.abs=(D,j)=>[D.run(N(D,j[0],f()),j)],n.acos=(D,j)=>[D.run(N(D,j[0],c()),j)],n.asin=(D,j)=>[D.run(N(D,j[0],a()),j)],n.atan=(D,j)=>[D.run(N(D,j[0],t()),j)],n.clip=(D,j,Q)=>[D.run(N(D,j[0],m(Q.min,Q.max),Q.cacheKey),j)],n.parseClipAttributes=D=>(0,u.createAttributeWithCacheKey)({min:D.attributes.getFloat("min",l.MIN_CLIP),max:D.attributes.getFloat("max",l.MAX_CLIP)}),n.clipV11=(D,j)=>{const Q=G(D,j);return(0,n.clip)(D,[j[0]],Q)};const G=(D,j)=>{if(j.length>=3&&(!D.session.isInitializer(j[1].dataId)||!D.session.isInitializer(j[2].dataId)))throw new Error("dynamic clip attributes are not allowed");const Q=j.length>=3?j[1].numberData[0]:l.MIN_CLIP,X=j.length>=3?j[2].numberData[0]:l.MAX_CLIP;return(0,u.createAttributeWithCacheKey)({min:Q,max:X})};n.ceil=(D,j)=>[D.run(N(D,j[0],e()),j)],n.cos=(D,j)=>[D.run(N(D,j[0],r()),j)],n.elu=(D,j,Q)=>[D.run(N(D,j[0],i(Q.alpha),Q.cacheKey),j)],n.parseEluAttributes=D=>(0,u.createAttributeWithCacheKey)({alpha:D.attributes.getFloat("alpha",1)}),n.exp=(D,j)=>[D.run(N(D,j[0],h()),j)],n.floor=(D,j)=>[D.run(N(D,j[0],g()),j)],n.identity=(D,j)=>[D.run(N(D,j[0],b()),j)],n.leakyRelu=(D,j,Q)=>[D.run(N(D,j[0],y(Q.alpha),Q.cacheKey),j)],n.parseLeakyReluAttributes=D=>(0,u.createAttributeWithCacheKey)({alpha:D.attributes.getFloat("alpha",.01)}),n.log=(D,j)=>[D.run(N(D,j[0],w()),j)],n.neg=(D,j)=>[D.run(N(D,j[0],T()),j)],n.not=(D,j)=>[D.run(N(D,j[0],S()),j)],n.relu=(D,j)=>[D.run(N(D,j[0],O()),j)],n.sigmoid=(D,j)=>[D.run(N(D,j[0],v()),j)],n.sin=(D,j)=>[D.run(N(D,j[0],A()),j)],n.sqrt=(D,j)=>[D.run(N(D,j[0],M()),j)],n.tan=(D,j)=>[D.run(N(D,j[0],F()),j)],n.tanh=(D,j)=>[D.run(N(D,j[0],R()),j)]},5611:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createUnpackProgramInfoLoader=n.createUnpackProgramInfo=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827),p={name:"unpack",inputNames:["A"],inputTypes:[l.TextureType.packed]};n.createUnpackProgramInfo=(f,c)=>{const a=c.dims.length,t=(0,s.getChannels)("rc",a),e=t.slice(-2),r=(0,d.getCoordsDataType)(a),i=(0,s.unpackFromChannel)(),h=c.dims.length===0?"":function(b,y){if(b===1)return"rc";let w="";for(let T=0;T<b;T++)w+=y[T],T<b-1&&(w+=",");return w}(a,t),g=a<=1?"rc":`vec2(${e.join(",")})`,m=`
-    ${i}
-    void main() {
-      ${r} rc = getOutputCoords();
-
-       // Sample the texture with the coords to get the rgba channel value.
-       vec4 packedInput = getA(${h});
-
-       ${(0,u.getGlsl)(f.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${g}), 0, 0, 0);
-     }
-   `;return Object.assign(Object.assign({},p),{hasMain:!0,output:{dims:c.dims,type:c.type,textureType:l.TextureType.unpacked},shaderSource:m})},n.createUnpackProgramInfoLoader=(f,c)=>Object.assign(Object.assign({},p),{get:()=>(0,n.createUnpackProgramInfo)(f,c)})},8428:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseUnsqueezeAttributes=n.unsqueezeV13=n.unsqueeze=void 0;const u=o(2517);n.unsqueeze=(s,p,f)=>{l(p);const c=u.ShapeUtil.unsqueezeShape(p[0].dims,f);return[s.reshapeUnpacked(p[0],c)]},n.unsqueezeV13=(s,p)=>(d(p),(0,n.unsqueeze)(s,[p[0]],Array.from(p[1].integerData))),n.parseUnsqueezeAttributes=s=>s.attributes.getInts("axes");const l=s=>{if(!s||s.length!==1)throw new Error("Unsqueeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},d=s=>{if(!s||s.length!==2)throw new Error("Unsqueeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},9793:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.scalesValidation=n.validateInputs=n.parseUpsampleAttributes=n.parseUpsampleAttributesV9=n.parseUpsampleAttributesV7=n.upsample=void 0;const u=o(246),l=o(5060),d=o(2039),s={name:"Upsample",inputNames:["X"],inputTypes:[d.TextureType.unpacked]};n.upsample=(f,c,a)=>((0,n.validateInputs)(c,a),[f.run(Object.assign(Object.assign({},s),{cacheHint:a.cacheKey,get:()=>p(f,c,a)}),c)]),n.parseUpsampleAttributesV7=f=>(0,n.parseUpsampleAttributes)(f,7),n.parseUpsampleAttributesV9=f=>(0,n.parseUpsampleAttributes)(f,9),n.parseUpsampleAttributes=(f,c)=>{const a=c>=10,t=f.attributes.getString("mode","nearest");if(t!=="nearest"&&t!=="linear"&&(c<11||t!=="cubic"))throw new Error(`unrecognized mode: ${t}`);let e=[];c<9&&(e=f.attributes.getFloats("scales"),(0,n.scalesValidation)(e,t,a));const r=f.attributes.getFloat("extrapolation_value",0),i=c>10?f.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(i)===-1)throw new Error(`coordinate_transform_mode '${i}' is not supported`);const h=i==="tf_crop_and_resize",g=h,m=t==="nearest"&&c>=11?f.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(m)===-1)throw new Error(`nearest_mode '${m}' is not supported`);const b=f.attributes.getFloat("cubic_coeff_a",-.75),y=f.attributes.getInt("exclude_outside",0)!==0;if(y&&t!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");const w=c<11||t==="nearest"&&i==="asymmetric"&&m==="floor";let T=0,S=0,A=0;return c>10?f.inputs.length>2?(T=1,S=2,A=3):(S=1,A=2):c===9&&(S=1),(0,u.createAttributeWithCacheKey)({opset:c,isResize:a,mode:t,scales:e,extrapolationValue:r,coordinateTransformMode:i,useExtrapolation:g,needRoiInput:h,nearestMode:m,cubicCoefficientA:b,excludeOutside:y,useNearest2xOptimization:w,roiInputIdx:T,scalesInputIdx:S,sizesInputIdx:A})};const p=(f,c,a)=>{const t=(0,l.getGlsl)(f.session.backend.glContext.version),[e,r]=f.calculateTextureWidthAndHeight(c[0].dims,d.TextureType.unpacked),i=c[0].dims.map((A,O)=>Math.floor(A*a.scales[O])),[h,g]=f.calculateTextureWidthAndHeight(i,d.TextureType.unpacked),m=i.length,b=new Array(m),y=new Array(m);let w=`
-      int output_pitches[${m}];
-      int input_pitches[${m}];
-      `;for(let A=m-1;A>=0;A--)b[A]=A===m-1?1:b[A+1]*i[A+1],y[A]=A===m-1?1:y[A+1]*c[0].dims[A+1],w+=`
-        output_pitches[${A}] = ${b[A]};
-        input_pitches[${A}] = ${y[A]};
-        `;const T=`
-      float getInputFloat(int index) {
-        vec2 coords = offsetToCoords(index, ${e}, ${r});
-        float value = getColorAsFloat(${t.texture2D}(X, coords));
-        return value;
-      }
-      `,S=a.mode==="nearest"?`
-    ${T}
-    float process(int indices[${m}]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${h}, ${g});
-
-      ${w}
-
-      int d, m;
-      for (int dim = 0; dim < ${m}; ++dim) {
-        d = output_index / output_pitches[dim];
-        m = output_index - d * output_pitches[dim];
-        output_index = m;
-
-        if (scales[dim] != 1 && d > 0) {
-          int d2 = d / scales[dim];
-          m = d - d2 * scales[dim];
-          d = d2;
-        }
-        input_index += input_pitches[dim] * d;
-      }
-
-      return getInputFloat(input_index);
-    }`:m===4?`
-    ${T}
-    float process(int indices[4]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${h}, ${g});
-
-      ${w}
-
-      int m;
-      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
-      index_of_dim0 = output_index / output_pitches[0];
-      m = output_index - index_of_dim0 * output_pitches[0];
-      index_of_dim1 = m / output_pitches[1];
-      m = m - index_of_dim1 * output_pitches[1];
-      index_of_dim2 = m / output_pitches[2];
-      m = m - index_of_dim2 * output_pitches[2];
-      index_of_dim3 = m;
-
-      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
-      index_of_input_dim2 = index_of_dim2 / scales[2];
-      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
-      index_of_input_dim3 = index_of_dim3 / scales[3];
-      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
-
-      input_index = index_of_dim0 * input_pitches[0] +
-            index_of_dim1 * input_pitches[1] +
-            index_of_input_dim2 * input_pitches[2] +
-            index_of_input_dim3;
-
-      float x00 = getInputFloat(input_index);
-      float x10, x01, x11;
-
-      bool end_of_dim2 = false;
-      if (index_of_input_dim2 == (${c[0].dims[2]} - 1)) {
-        // It's the end in dimension 2
-        x01 = x00;
-        end_of_dim2 = true;
-      } else {
-        x01 = getInputFloat(input_index + input_pitches[2]);
-      }
-
-      if (index_of_input_dim3 == (input_pitches[2] - 1)) {
-        // It's the end in dimension 3
-        x10 = x00;
-        x11 = x01;
-      }
-      else {
-        x10 = getInputFloat(input_index + 1);
-        x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
-      }
-
-      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
-      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
-      return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
-    }`:`
-    ${T}
-    float process(int indices[2]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${h}, ${g});
-
-      ${w}
-
-      int m;
-      int index_of_dim0, index_of_dim1;
-      index_of_dim0 = output_index / output_pitches[0];
-      m = output_index - index_of_dim0 * output_pitches[0];
-      index_of_dim1 = m;
-
-      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
-      index_of_input_dim0 = index_of_dim0 / scales[0];
-      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
-      index_of_input_dim1 = index_of_dim1 / scales[1];
-      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
-
-      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
-
-      float x00 = getInputFloat(input_index);
-      float x10, x01, x11;
-
-      bool end_of_dim0 = false;
-      if (index_of_input_dim0 == (${c[0].dims[0]} - 1)) {
-        // It's the end in dimension 0
-        x01 = x00;
-        end_of_dim0 = true;
-      } else {
-        x01 = getInputFloat(input_index + input_pitches[0]);
-      }
-
-      if (index_of_input_dim1 == (input_pitches[0] - 1)) {
-        // It's the end in dimension 1
-        x10 = x00;
-        x11 = x01;
-      }
-      else {
-        x10 = getInputFloat(input_index + 1);
-        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
-      }
-
-      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
-      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
-      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
-    }`;return Object.assign(Object.assign({},s),{output:{dims:i,type:c[0].type,textureType:d.TextureType.unpacked},shaderSource:S,variables:[{name:"scales",type:"int",arrayLength:a.scales.length,data:a.scales.map(A=>Math.ceil(A))}]})};n.validateInputs=(f,c)=>{if(!f||c.opset<9&&f.length!==1||c.opset>=9&&c.opset<11&&f.length!==2||c.opset>=11&&f.length<2)throw new Error("invalid inputs.");if(c.scales.length>0&&f[0].dims.length!==c.scales.length)throw new Error("Invalid input shape.");if(f[0].type==="string")throw new Error("Invalid input tensor types.")},n.scalesValidation=(f,c,a)=>{if(a){for(const t of f)if(t<=0)throw new Error("Scale value should be greater than 0.")}else for(const t of f)if(t<1)throw new Error("Scale value should be greater than or equal to 1.");if(!(c!=="linear"&&c!=="cubic"||f.length===2||f.length===4&&f[0]===1&&f[1]===1))throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic')         or 4-D inputs with the corresponding outermost 2 scale values being 1         in the ${a?"Resize":"Upsample"} opeartor.`)}},1958:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ProgramManager=void 0;const u=o(1670),l=o(6231),d=o(8879),s=o(5060);n.ProgramManager=class{constructor(p,f,c){this.profiler=p,this.glContext=f,this.textureLayoutStrategy=c,this.repo=new Map,this.attributesBound=!1}getArtifact(p){return this.repo.get(p)}setArtifact(p,f){this.repo.set(p,f)}run(p,f,c){var a;this.profiler.event("op",`ProgramManager.run ${(a=p.programInfo.name)!==null&&a!==void 0?a:"unknown kernel"}`,()=>{var t;const e=this.glContext.gl,r=p.program;e.useProgram(r);try{this.bindOutput(c),this.attributesBound||this.bindAttributes(p.attribLocations),this.bindUniforms(p.uniformLocations,(t=p.programInfo.variables)!==null&&t!==void 0?t:[],f)}catch(i){throw l.Logger.error("ProgramManager",p.programInfo.shaderSource),i}this.profiler.event("backend","GlContext.draw()",()=>{this.glContext.draw()})},this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach(p=>this.glContext.deleteProgram(p.program))}build(p,f,c){return this.profiler.event("backend","ProgramManager.build",()=>{const a=new d.GlslPreprocessor(this.glContext,p,f,c),t=a.preprocess(),e=this.compile(t);return{programInfo:p,program:e,uniformLocations:this.getUniformLocations(e,a.context.programInfo.inputNames,a.context.programInfo.variables),attribLocations:this.getAttribLocations(e)}})}compile(p){if(!this.vertexShader){l.Logger.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");const a=(0,s.getVertexShaderSource)(this.glContext.version);this.vertexShader=this.glContext.compileShader(a,this.glContext.gl.VERTEX_SHADER)}u.env.debug&&l.Logger.verbose("ProrgramManager",`FragShader:
-${p}
-`);const f=this.glContext.compileShader(p,this.glContext.gl.FRAGMENT_SHADER),c=this.glContext.createProgram(this.vertexShader,f);return this.glContext.deleteShader(f),c}bindOutput(p){const f=p.width,c=p.height;l.Logger.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${f}/${c}, shape=${p.shape}, type=${p.tensor.type}`),this.glContext.attachFramebuffer(p.texture,f,c)}bindAttributes(p){const f=p.position,c=p.textureCoord;this.glContext.setVertexAttributes(f,c),this.attributesBound=!0}bindUniforms(p,f,c){var a;const t=this.glContext.gl;let e=0;for(const{name:r,type:i,location:h,arrayLength:g}of p){const m=(a=f.find(b=>b.name===r))===null||a===void 0?void 0:a.data;if(i!=="sampler2D"&&!m)throw new Error(`variable '${r}' does not have data defined in program info`);switch(i){case"sampler2D":this.bindTexture(c[e],h,e),e++;break;case"float":g?t.uniform1fv(h,m):t.uniform1f(h,m);break;case"int":g?t.uniform1iv(h,m):t.uniform1i(h,m);break;default:throw new Error(`Uniform not implemented: ${i}`)}}}bindTexture(p,f,c){this.glContext.bindTextureToUniform(p.texture,c,f)}getAttribLocations(p){return{position:this.getAttribLocation(p,"position"),textureCoord:this.getAttribLocation(p,"textureCoord")}}getUniformLocations(p,f,c){const a=[];if(f)for(const t of f)a.push({name:t,type:"sampler2D",location:this.getUniformLocation(p,t)});if(c)for(const t of c)a.push(Object.assign(Object.assign({},t),{location:this.getUniformLocation(p,t.name)}));return a}getUniformLocation(p,f){const c=this.glContext.gl.getUniformLocation(p,f);if(c===null)throw new Error(`Uniform ${f} not found.`);return c}getAttribLocation(p,f){return this.glContext.gl.getAttribLocation(p,f)}}},6416:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.WebGLSessionHandler=void 0;const u=o(6231),l=o(1047),d=o(8316),s=o(1640),p=o(1958),f=o(7859),c=o(5702);n.WebGLSessionHandler=class{constructor(a,t){this.backend=a,this.context=t,this.layoutStrategy=new f.PreferLogicalStrategy(a.glContext.maxTextureSize),this.programManager=new p.ProgramManager(this.context.profiler,a.glContext,this.layoutStrategy),this.textureManager=new c.TextureManager(a.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:a.textureCacheMode==="full"}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=a.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new d.WebGLInferenceHandler(this)}onGraphInitialized(a){const t=a.getValues().filter(e=>e.from===-1&&e.tensor).map(e=>e.tensor.dataId);this.initializers=new Set(t)}isInitializer(a){return!!this.initializers&&this.initializers.has(a)}addInitializer(a){this.initializers.add(a)}getTextureData(a,t){return t?this.packedTextureDataCache.get(a):this.unpackedTextureDataCache.get(a)}setTextureData(a,t,e=!1){u.Logger.verbose("WebGLSessionHandler","Storing Texture data in cache"),e?this.packedTextureDataCache.set(a,t):this.unpackedTextureDataCache.set(a,t)}dispose(){this.programManager.dispose(),this.textureManager.clearActiveTextures(),this.packedTextureDataCache.forEach(a=>this.textureManager.releaseTexture(a,!0)),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache.forEach(a=>this.textureManager.releaseTexture(a,!0)),this.unpackedTextureDataCache=new Map}resolve(a,t,e){const r=(0,l.resolveOperator)(a,t,s.WEBGL_OP_RESOLVE_RULES);return{impl:r.opImpl,context:r.opInit?r.opInit(a,e):a}}}},7769:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Uint8DataEncoder=n.RGBAFloatDataEncoder=n.RedFloat32DataEncoder=void 0;const u=o(6231);n.RedFloat32DataEncoder=class{constructor(l,d=1){if(d===1)this.internalFormat=l.R32F,this.format=l.RED,this.textureType=l.FLOAT,this.channelSize=d;else{if(d!==4)throw new Error(`Invalid number of channels: ${d}`);this.internalFormat=l.RGBA32F,this.format=l.RGBA,this.textureType=l.FLOAT,this.channelSize=d}}encode(l,d){let s,p;return l.constructor!==Float32Array&&(u.Logger.warning("Encoder","data was not of type Float32; creating new Float32Array"),p=new Float32Array(l)),d*this.channelSize>l.length?(u.Logger.warning("Encoder","Source data too small. Allocating larger array"),p=l,s=this.allocate(d*this.channelSize),p.forEach((f,c)=>s[c]=f)):(p=l,s=p),s}allocate(l){return new Float32Array(4*l)}decode(l,d){return this.channelSize===1?l.filter((s,p)=>p%4==0).subarray(0,d):l.subarray(0,d)}},n.RGBAFloatDataEncoder=class{constructor(l,d=1,s){if(d!==1&&d!==4)throw new Error(`Invalid number of channels: ${d}`);this.internalFormat=l.RGBA,this.format=l.RGBA,this.channelSize=d,this.textureType=s||l.FLOAT}encode(l,d){let s=l;return this.channelSize===1&&(u.Logger.verbose("Encoder","Exploding into a larger array"),s=this.allocate(d),l.forEach((p,f)=>s[4*f]=p)),s}allocate(l){return new Float32Array(4*l)}decode(l,d){return this.channelSize===1?l.filter((s,p)=>p%4==0).subarray(0,d):l.subarray(0,d)}},n.Uint8DataEncoder=class{constructor(l,d=1){if(this.channelSize=4,d===1)this.internalFormat=l.ALPHA,this.format=l.ALPHA,this.textureType=l.UNSIGNED_BYTE,this.channelSize=d;else{if(d!==4)throw new Error(`Invalid number of channels: ${d}`);this.internalFormat=l.RGBA,this.format=l.RGBA,this.textureType=l.UNSIGNED_BYTE,this.channelSize=d}}encode(l,d){return new Uint8Array(l.buffer,l.byteOffset,l.byteLength)}allocate(l){return new Uint8Array(l*this.channelSize)}decode(l,d){if(l instanceof Uint8Array)return l.subarray(0,d);throw new Error(`Invalid array type: ${l.constructor}`)}}},7859:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.getBatchDim=n.sizeToSquarishShape=n.getRowsCols=n.sizeFromShape=n.isInt=n.parseAxisParam=n.squeezeShape=n.PreferLogicalStrategy=n.AlwaysKeepOriginalSizeStrategy=void 0;const u=o(6231),l=o(2517);function d(a,t){const e=[],r=[],i=t!=null&&Array.isArray(t)&&t.length===0,h=t==null||i?null:s(t,a).sort();let g=0;for(let m=0;m<a.length;++m){if(h!=null){if(h[g]===m&&a[m]!==1)throw new Error(`Can't squeeze axis ${m} since its dim '${a[m]}' is not 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a.bindBuffer(a.ARRAY_BUFFER,t),a.bufferData(a.ARRAY_BUFFER,e,a.STATIC_DRAW),this.checkError(),t}createFramebuffer(){const a=this.gl.createFramebuffer();if(!a)throw new Error("createFramebuffer returned null");return a}queryVitalParameters(){const a=this.gl;if(this.isFloatTextureAttachableToFrameBuffer=this.checkFloatTextureAttachableToFrameBuffer(),this.isRenderFloat32Supported=this.checkRenderFloat32(),this.isFloat32DownloadSupported=this.checkFloat32Download(),this.version===1&&!this.textureHalfFloatExtension&&!this.isRenderFloat32Supported)throw new Error("both float32 and float16 TextureType are not supported");this.isBlendSupported=!this.isRenderFloat32Supported||this.checkFloat32Blend(),this.maxTextureSize=a.getParameter(a.MAX_TEXTURE_SIZE),this.maxTextureImageUnits=a.getParameter(a.MAX_TEXTURE_IMAGE_UNITS),this.version}getExtensions(){this.version===2?(this.colorBufferFloatExtension=this.gl.getExtension("EXT_color_buffer_float"),this.disjointTimerQueryWebgl2Extension=this.gl.getExtension("EXT_disjoint_timer_query_webgl2")):(this.textureFloatExtension=this.gl.getExtension("OES_texture_float"),this.textureHalfFloatExtension=this.gl.getExtension("OES_texture_half_float"))}checkFloatTextureAttachableToFrameBuffer(){const a=this.gl,t=a.createTexture();a.bindTexture(a.TEXTURE_2D,t);const e=this.version===2?a.RGBA32F:a.RGBA;a.texImage2D(a.TEXTURE_2D,0,e,1,1,0,a.RGBA,a.FLOAT,null);const r=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,r),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,t,0);const i=a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE;return a.bindTexture(a.TEXTURE_2D,null),a.bindFramebuffer(a.FRAMEBUFFER,null),a.deleteTexture(t),a.deleteFramebuffer(r),i}checkRenderFloat32(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension)return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Download(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension||!this.gl.getExtension("WEBGL_color_buffer_float"))return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Blend(){const a=this.gl;let t,e,r,i,h;try{t=a.createTexture(),e=a.createFramebuffer(),a.bindTexture(a.TEXTURE_2D,t);const g=this.version===2?a.RGBA32F:a.RGBA;return a.texImage2D(a.TEXTURE_2D,0,g,1,1,0,a.RGBA,a.FLOAT,null),a.bindFramebuffer(a.FRAMEBUFFER,e),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,t,0),a.enable(a.BLEND),r=a.createShader(a.VERTEX_SHADER),!!r&&(a.shaderSource(r,"void main(){}"),a.compileShader(r),i=a.createShader(a.FRAGMENT_SHADER),!!i&&(a.shaderSource(i,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),a.compileShader(i),h=a.createProgram(),!!h&&(a.attachShader(h,r),a.attachShader(h,i),a.linkProgram(h),a.useProgram(h),a.drawArrays(a.POINTS,0,1),a.getError()===a.NO_ERROR)))}finally{a.disable(a.BLEND),h&&a.deleteProgram(h),r&&a.deleteShader(r),i&&a.deleteShader(i),e&&(a.bindFramebuffer(a.FRAMEBUFFER,null),a.deleteFramebuffer(e)),t&&(a.bindTexture(a.TEXTURE_2D,null),a.deleteTexture(t))}}beginTimer(){if(this.version===2&&this.disjointTimerQueryWebgl2Extension){const a=this.gl,t=this.disjointTimerQueryWebgl2Extension,e=a.createQuery();return a.beginQuery(t.TIME_ELAPSED_EXT,e),e}throw new Error("WebGL1 profiling currently not supported.")}endTimer(){if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const a=this.gl,t=this.disjointTimerQueryWebgl2Extension;a.endQuery(t.TIME_ELAPSED_EXT)}}isTimerResultAvailable(a){let t=!1,e=!1;if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const r=this.gl,i=this.disjointTimerQueryWebgl2Extension;t=r.getQueryParameter(a,r.QUERY_RESULT_AVAILABLE),e=r.getParameter(i.GPU_DISJOINT_EXT)}return t&&!e}getTimerResult(a){let t=0;if(this.version!==2)throw new Error("WebGL1 profiling currently not supported");{const e=this.gl;t=e.getQueryParameter(a,e.QUERY_RESULT),e.deleteQuery(a)}return t/1e6}async waitForQueryAndGetTime(a){return await(0,f.repeatedTry)(()=>this.isTimerResultAvailable(a)),this.getTimerResult(a)}async createAndWaitForFence(){const a=this.createFence(this.gl);return this.pollFence(a)}createFence(a){let t;const e=a,r=e.fenceSync(e.SYNC_GPU_COMMANDS_COMPLETE,0);return a.flush(),t=r===null?()=>!0:()=>{const i=e.clientWaitSync(r,0,0);return i===e.ALREADY_SIGNALED||i===e.CONDITION_SATISFIED},{query:r,isFencePassed:t}}async pollFence(a){return new Promise(t=>{this.addItemToPoll(()=>a.isFencePassed(),()=>t())})}pollItems(){const a=c(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=a;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(a+1)}async addItemToPoll(a,t){this.itemsToPoll.push({isDoneFn:a,resolveFn:t}),this.itemsToPoll.length>1||await(0,f.repeatedTry)(()=>(this.pollItems(),this.itemsToPoll.length===0))}}},1036:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ExecutionPlan=void 0;const u=o(6231);class l{constructor(s,p){this.op=s,this.node=p}}n.ExecutionPlan=class{constructor(d,s,p){this.graph=d,this.profiler=p,this.initialize(s)}initialize(d){this.profiler.event("session","ExecutionPlan.initialize",()=>{const s=this.graph.getNodes();if(s.length!==d.length)throw new Error("The size of nodes and OPs do not match.");this._ops=d.map((p,f)=>new l(p,s[f])),this.reset(),this._starter=[],this._ops.forEach((p,f)=>{let c=!0;for(const a of p.node.inputs)if(!this._values[a]&&this.graph.getInputIndices().indexOf(a)===-1){c=!1;break}c&&this._starter.push(f)})})}reset(){this._values=this.graph.getValues().map(d=>d.tensor)}async execute(d,s){return this.profiler.event("session","ExecutionPlan.execute",async()=>{this.reset();const p=d.createInferenceHandler(),f=this.graph.getInputIndices();if(s.length!==f.length)throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${s.length} expected: ${f.length}`);s.forEach((i,h)=>{const g=f[h];this._values[g]=i});const c=this._starter.slice(0),a=this.graph.getValues(),t=this.graph.getNodes();let e=0;for(;e<c.length;){const i=c[e++],h=this._ops[i],g=h.node.inputs.map(w=>this._values[w]);if(g.indexOf(void 0)!==-1)throw new Error(`unresolved input detected: op: ${h.node}`);const m=g;u.Logger.verbose("ExecPlan",`Runing op:${h.node.name} (${m.map((w,T)=>`'${h.node.inputs[T]}': ${w.type}[${w.dims.join(",")}]`).join(", ")})`);const b=await this.profiler.event("node",h.node.name,async()=>h.op.impl(p,m,h.op.context));if(b.length!==h.node.outputs.length)throw new Error("the size of output does not match model definition.");b.forEach((w,T)=>{const S=h.node.outputs[T];if(this._values[S])throw new Error(`output [${S}] already has value: op:${h.node.name}`);this._values[S]=w});const y=new Set;b.forEach((w,T)=>{const S=h.node.outputs[T];for(const A of a[S].to){const O=t[A];let v=!0;for(const M of O.inputs)if(!this._values[M]){v=!1;break}v&&y.add(A)}}),c.push(...y)}const r=[];for(let i=0;i<this.graph.getOutputIndices().length;i++){const h=this.graph.getOutputIndices()[i],g=this._values[h];if(g===void 0)throw new Error(`required output [${h}] does not have value`);h===0?await g.getData():g.data,r.push(g)}return u.Logger.verbose("ExecPlan","disposing of inferenceHandler"),p.dispose(),r})}}},7070:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Graph=void 0;const u=o(1446),l=o(7778),d=o(9395),s=o(9162),p=o(2517);var f=d.onnxruntime.experimental.fbs;n.Graph={from:(e,r)=>new t(e,r)};class c{constructor(r){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,r&&(this.type=p.ProtoUtil.tensorValueTypeFromProto(r.type.tensorType))}get from(){return this._from}get to(){return this._to}}class a{constructor(r,i){r instanceof u.onnx.NodeProto?(this.name=r.name,this.opType=r.opType,this.attributes=new l.Attribute(r.attribute)):r instanceof f.Node&&(this.name=i??r.name(),this.opType=r.opType(),this.attributes=new l.Attribute(p.ProtoUtil.tensorAttributesFromORTFormat(r))),this.inputs=[],this.outputs=[],this.executeNode=!0}}class t{constructor(r,i){if(!r)throw new TypeError("graph is empty");this.buildGraph(r),this.transformGraph(i),this.checkIsAcyclic()}getInputIndices(){return this._allInputIndices}getInputNames(){return this._allInputNames}getOutputIndices(){return this._allOutputIndices}getOutputNames(){return this._allOutputNames}getValues(){return this._allData}getNodes(){return this._nodes}buildGraph(r){if(r instanceof u.onnx.GraphProto)this.buildGraphFromOnnxFormat(r);else{if(!(r instanceof f.Graph))throw new TypeError("Graph type is not supported.");this.buildGraphFromOrtFormat(r)}}buildGraphFromOnnxFormat(r){const i=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const h=new Map;if(!r.input)throw new Error("missing information in graph: input");const g=[];for(const m of r.input){if(i.has(m.name))throw new Error(`duplicated input name: ${m.name}`);const b=this._allData.push(new c(m))-1;i.set(m.name,b),g.push(m.name)}if(!r.initializer)throw new Error("missing information in graph: initializer");for(const m of r.initializer){let b=i.get(m.name);if(b===void 0){const y=new c;y.type={shape:{dims:p.ProtoUtil.tensorDimsFromProto(m.dims)},tensorType:p.ProtoUtil.tensorDataTypeFromProto(m.dataType)},b=this._allData.push(y)-1,i.set(m.name,b)}this._allData[b]._from=-1,this._allData[b].tensor=s.Tensor.fromProto(m)}for(let m=0;m<this._allData.length;m++)this._allData[m].tensor||(this._allInputIndices.push(m),this._allInputNames.push(g[m]));if(!r.output)throw new Error("missing information in graph: output");for(const m of r.output){if(i.has(m.name))throw new Error(`duplicated output name: ${m.name}`);const b=this._allData.push(new c(m))-1;i.set(m.name,b),this._allOutputIndices.push(b),this._allOutputNames.push(m.name)}if(!r.node)throw new Error("missing information in graph: node");for(const m of r.node){if(!m.name)for(let y=0;;y++){const w=`unnamed_${m.opType}_${y}`;if(!h.has(w)){m.name=w;break}}if(h.has(m.name))throw new Error(`duplicated node name: ${m.name}`);const b=this._nodes.push(new a(m))-1;h.set(m.name,b)}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],y=r.node[m];if(!y.output)throw new Error(`missing output for node: ${y.name}`);for(const w of y.output){let T=i.get(w);if(T===void 0&&(T=this._allData.push(new c)-1,i.set(w,T)),b.outputs.push(T),this._allData[T]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${T}`);if(this._allData[T]._from=m,y.opType==="Constant"){if(!y.attribute||y.attribute.length!==1||!y.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!y.output||y.output.length!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");b.outputs.pop(),b.executeNode=!1,this._allData[T]._from=-1,this._allData[T].tensor=s.Tensor.fromProto(y.attribute[0].t)}}}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],y=r.node[m];if(!y.input)throw new Error(`missing input for node: ${y.name}`);for(const w of y.input){const T=i.get(w);if(T===void 0){if(w===""&&y.input.length===3&&y.opType==="Resize")continue;throw new Error(`unrecognized input '${w}' for node: ${y.name}`)}b.inputs.push(T),this._allData[T]._to.push(m)}}return!0}buildGraphFromOrtFormat(r){var i,h,g;const m=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const b=new Map,y=[];for(let w=0;w<r.inputsLength();w++){const T=r.inputs(w);if(m.has(T))throw new Error(`duplicated input name: ${T}`);for(let S=0;S<r.nodeArgsLength();S++)if(((i=r.nodeArgs(S))===null||i===void 0?void 0:i.name())===T){const A=new c;if(((g=(h=r.nodeArgs(S))===null||h===void 0?void 0:h.type())===null||g===void 0?void 0:g.valueType())!==f.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");const O=r.nodeArgs(S).type().value(new f.TensorTypeAndShape),v=p.ProtoUtil.tensorDataTypeFromProto(O.elemType()),M=O.shape(),F=[];for(let $=0;$<M.dimLength();$++)F.push(p.LongUtil.longToNumber(M.dim($).value().dimValue()));A.type={shape:{dims:F},tensorType:v};const R=this._allData.push(A)-1;m.set(T,R),y.push(T)}}for(let w=0;w<r.initializersLength();w++){const T=r.initializers(w);let S=m.get(T.name());if(S===void 0){const A=new c,O=p.ProtoUtil.tensorDimsFromORTFormat(T),v=p.ProtoUtil.tensorDataTypeFromProto(T.dataType());A.type={shape:{dims:O},tensorType:v},S=this._allData.push(A)-1,m.set(T.name(),S)}this._allData[S]._from=-1,this._allData[S].tensor=s.Tensor.fromOrtTensor(T)}for(let w=0;w<this._allData.length;w++)this._allData[w].tensor||(this._allInputIndices.push(w),this._allInputNames.push(y[w]));for(let w=0;w<r.outputsLength();w++){const T=r.outputs(w);if(m.has(T))throw new Error(`duplicated output name: ${T}`);const S=this._allData.push(new c)-1;m.set(T,S),this._allOutputIndices.push(S),this._allOutputNames.push(T)}if(!r.nodes)throw new Error("missing information in graph: node");for(let w=0;w<r.nodesLength();w++){const T=r.nodes(w);let S=T.name();if(!S)for(let O=0;S=`unnamed_${T.opType()}_${O}`,b.has(S);O++);if(b.has(S))throw new Error(`duplicated node name: ${S}`);const A=this._nodes.push(new a(T,S))-1;b.set(S,A)}for(let w=0;w<this._nodes.length;w++){const T=this._nodes[w],S=r.nodes(w);if(S==null)throw new Error(`No node exists at index ${w}`);if((S==null?void 0:S.outputsLength())===0)throw new Error(`missing output for node: ${S.name}`);for(let A=0;A<(S==null?void 0:S.outputsLength());A++){const O=S==null?void 0:S.outputs(A);let v=m.get(O);if(v===void 0&&(v=this._allData.push(new c)-1,m.set(O,v)),T.outputs.push(v),this._allData[v]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${v}`);if(this._allData[v]._from=w,S.opType()==="Constant"){if(S.attributesLength()!==1||!S.attributes(0).t())throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(S.outputsLength()!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");T.outputs.pop(),T.executeNode=!1,this._allData[v]._from=-1,this._allData[v].tensor=s.Tensor.fromOrtTensor(S.attributes(0).t())}}}for(let w=0;w<this._nodes.length;w++){const T=this._nodes[w],S=r.nodes(w);if(S.inputsLength()===0)throw new Error(`missing input for node: ${S.name}`);for(let A=0;A<S.inputsLength();A++){const O=S.inputs(A),v=m.get(O);if(v===void 0)throw new Error(`unrecognized input '${O}' for node: ${S.name()}`);T.inputs.push(v),this._allData[v]._to.push(w)}}}checkIsAcyclic(){const r=new Set;this._allInputIndices.forEach(g=>{this._allData[g]._to.forEach(m=>{r.add(m)})});const i=Array.from(r),h=new Array(this._nodes.length).fill("white");for(;i.length>0;){const g=i.pop();h[g]==="gray"?h[g]="black":(i.push(g),h[g]="gray",this._nodes[g].outputs.forEach(m=>{const b=this._allData[m];if(b.tensor!==void 0)throw new Error("node outputs should not be initialized");if(b._from!==g)throw new Error("from property of the Value object doesn't match index of Node being processed");b._to.forEach(y=>{if(h[y]==="gray")throw new Error("model graph is cyclic");h[y]==="white"&&i.push(y)})}))}}transformGraph(r){this.removeAllIdentityNodes(),this.removeAllDropoutNodes(),this.fuseConvActivationNodes(),r&&r.transformGraph(this),this.finalizeGraph()}finalizeGraph(){let r=0;for(let i=0;i<this._nodes.length;i++)this._nodes[i].executeNode?r>0&&(this._nodes[i].inputs.forEach(h=>{const g=this._allData[h]._to.indexOf(i+r);g!==-1&&(this._allData[h]._to[g]=i)}),this._nodes[i].outputs.forEach(h=>{this._allData[h]._from&&this._allData[h]._from===i+r&&(this._allData[h]._from=i)})):(r++,this._nodes[i].outputs.forEach(h=>{this._allData[h]._from=-2}),this._nodes.splice(i,1),i--);r=0;for(let i=0;i<this._allData.length;i++)if(this._allData[i].from!==-2||this._allOutputIndices.indexOf(i+r)!==-1){if(r>0){let h=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(h=this._nodes[this._allData[i].from].outputs.indexOf(i+r),h!==-1&&(this._nodes[this._allData[i].from].outputs[h]=i)):(h=this._allInputIndices.indexOf(i+r),h!==-1&&(this._allInputIndices[h]=i)),this._allData[i].to.forEach(g=>{h=this._nodes[g].inputs.indexOf(i+r),h!==-1&&(this._nodes[g].inputs[h]=i)}),this._allData[i].to.length===0&&(h=this._allOutputIndices.indexOf(i+r),h!==-1&&(this._allOutputIndices[h]=i))}}else r++,this._allData.splice(i,1),i--}deleteNode(r){const i=this._nodes[r];if(i.outputs.length>1){for(let w=1;w<i.outputs.length;w++)if(this._allData[i.outputs[w]].to.length>0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")}i.executeNode=!1;const h=i.inputs[0],g=i.outputs[0],m=this._allData[g].to,b=this._allData[h].to.indexOf(r);if(b===-1)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[h].to.splice(b,1),this._allData[g]._to=[];const y=this._allOutputIndices.indexOf(g);if(y!==-1&&(this._allOutputIndices[y]=h),m&&m.length>0)for(const w of m){const T=this._nodes[w].inputs.indexOf(g);if(T===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[w].inputs[T]=h,this._allData[h].to.push(w)}}removeAllDropoutNodes(){let r=0;for(const i of this._nodes){if(i.opType==="Dropout"){if(i.inputs.length!==1)throw new Error("Dropout nodes should only contain one input. ");if(i.outputs.length!==1&&i.outputs.length!==2)throw new Error("Dropout nodes should contain either 1 or 2 output(s)");if(i.outputs.length===2&&this._allData[i.outputs[1]]._to.length!==0)throw new Error("Dropout nodes's second output should not be referenced by other nodes");this.deleteNode(r)}r++}}removeAllIdentityNodes(){let r=0;for(const i of this._nodes)i.opType==="Identity"&&this.deleteNode(r),r++}isActivation(r){switch(r.opType){case"Relu":case"Sigmoid":case"Clip":return!0;default:return!1}}fuseConvActivationNodes(){for(const r of this._nodes)if(r.opType==="Conv"){const i=this._allData[r.outputs[0]]._to;if(i.length===1&&this.isActivation(this._nodes[i[0]])){const h=this._nodes[i[0]];if(h.opType==="Clip")if(h.inputs.length===1)try{r.attributes.set("activation_params","floats",[h.attributes.getFloat("min"),h.attributes.getFloat("max")])}catch{r.attributes.set("activation_params","floats",[p.MIN_CLIP,p.MAX_CLIP])}else{if(!(h.inputs.length>=3&&this._allData[h.inputs[1]].tensor!==void 0&&this._allData[h.inputs[2]].tensor!==void 0))continue;r.attributes.set("activation_params","floats",[this._allData[h.inputs[1]].tensor.floatData[0],this._allData[h.inputs[2]].tensor.floatData[0]])}r.attributes.set("activation","string",h.opType),this.deleteNode(i[0])}}}}},6231:(_,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.now=n.Profiler=n.Logger=void 0;const o={verbose:1e3,info:2e3,warning:4e3,error:5e3,fatal:6e3},u={none:new class{log(a,t,e){}},console:new class{log(a,t,e){console.log(`${this.color(a)} ${e?"\x1B[35m"+e+"\x1B[0m 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r=this.bb.__offset(this.bb_pos,14);return r?this.bb.__string(this.bb.__vector(this.bb_pos+r)+4*t,e):null}inputsLength(){let t=this.bb.__offset(this.bb_pos,14);return t?this.bb.__vector_len(this.bb_pos+t):0}outputs(t,e){let r=this.bb.__offset(this.bb_pos,16);return r?this.bb.__string(this.bb.__vector(this.bb_pos+r)+4*t,e):null}outputsLength(){let t=this.bb.__offset(this.bb_pos,16);return t?this.bb.__vector_len(this.bb_pos+t):0}sparseInitializers(t,e){let r=this.bb.__offset(this.bb_pos,18);return r?(e||new s.experimental.fbs.SparseTensor).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}sparseInitializersLength(){let t=this.bb.__offset(this.bb_pos,18);return t?this.bb.__vector_len(this.bb_pos+t):0}static startGraph(t){t.startObject(8)}static addInitializers(t,e){t.addFieldOffset(0,e,0)}static createInitializersVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static 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startInputsVector(t,e){t.startVector(4,e,4)}static addOutputs(t,e){t.addFieldOffset(6,e,0)}static createOutputsVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOutputsVector(t,e){t.startVector(4,e,4)}static addSparseInitializers(t,e){t.addFieldOffset(7,e,0)}static createSparseInitializersVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startSparseInitializersVector(t,e){t.startVector(4,e,4)}static endGraph(t){return t.endObject()}static createGraph(t,e,r,i,h,g,m,b,y){return c.startGraph(t),c.addInitializers(t,e),c.addNodeArgs(t,r),c.addNodes(t,i),c.addMaxNodeIndex(t,h),c.addNodeEdges(t,g),c.addInputs(t,m),c.addOutputs(t,b),c.addSparseInitializers(t,y),c.endGraph(t)}}f.Graph=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsModel(t,e){return(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsModel(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}irVersion(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}opsetImport(t,e){let r=this.bb.__offset(this.bb_pos,6);return r?(e||new s.experimental.fbs.OperatorSetId).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}opsetImportLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}producerName(t){let e=this.bb.__offset(this.bb_pos,8);return e?this.bb.__string(this.bb_pos+e,t):null}producerVersion(t){let e=this.bb.__offset(this.bb_pos,10);return e?this.bb.__string(this.bb_pos+e,t):null}domain(t){let e=this.bb.__offset(this.bb_pos,12);return e?this.bb.__string(this.bb_pos+e,t):null}modelVersion(){let t=this.bb.__offset(this.bb_pos,14);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}docString(t){let e=this.bb.__offset(this.bb_pos,16);return e?this.bb.__string(this.bb_pos+e,t):null}graph(t){let e=this.bb.__offset(this.bb_pos,18);return e?(t||new s.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}graphDocString(t){let e=this.bb.__offset(this.bb_pos,20);return e?this.bb.__string(this.bb_pos+e,t):null}static startModel(t){t.startObject(9)}static addIrVersion(t,e){t.addFieldInt64(0,e,t.createLong(0,0))}static addOpsetImport(t,e){t.addFieldOffset(1,e,0)}static createOpsetImportVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOpsetImportVector(t,e){t.startVector(4,e,4)}static addProducerName(t,e){t.addFieldOffset(2,e,0)}static addProducerVersion(t,e){t.addFieldOffset(3,e,0)}static addDomain(t,e){t.addFieldOffset(4,e,0)}static addModelVersion(t,e){t.addFieldInt64(5,e,t.createLong(0,0))}static addDocString(t,e){t.addFieldOffset(6,e,0)}static addGraph(t,e){t.addFieldOffset(7,e,0)}static addGraphDocString(t,e){t.addFieldOffset(8,e,0)}static endModel(t){return t.endObject()}static createModel(t,e,r,i,h,g,m,b,y,w){return c.startModel(t),c.addIrVersion(t,e),c.addOpsetImport(t,r),c.addProducerName(t,i),c.addProducerVersion(t,h),c.addDomain(t,g),c.addModelVersion(t,m),c.addDocString(t,b),c.addGraph(t,y),c.addGraphDocString(t,w),c.endModel(t)}}f.Model=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsKernelCreateInfos(t,e){return(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}nodeIndices(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.readUint32(this.bb.__vector(this.bb_pos+e)+4*t):0}nodeIndicesLength(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.__vector_len(this.bb_pos+t):0}nodeIndicesArray(){let t=this.bb.__offset(this.bb_pos,4);return t?new Uint32Array(this.bb.bytes().buffer,this.bb.bytes().byteOffset+this.bb.__vector(this.bb_pos+t),this.bb.__vector_len(this.bb_pos+t)):null}kernelDefHashes(t){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.readUint64(this.bb.__vector(this.bb_pos+e)+8*t):this.bb.createLong(0,0)}kernelDefHashesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startKernelCreateInfos(t){t.startObject(2)}static addNodeIndices(t,e){t.addFieldOffset(0,e,0)}static createNodeIndicesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addInt32(e[r]);return t.endVector()}static startNodeIndicesVector(t,e){t.startVector(4,e,4)}static addKernelDefHashes(t,e){t.addFieldOffset(1,e,0)}static createKernelDefHashesVector(t,e){t.startVector(8,e.length,8);for(let r=e.length-1;r>=0;r--)t.addInt64(e[r]);return t.endVector()}static startKernelDefHashesVector(t,e){t.startVector(8,e,8)}static endKernelCreateInfos(t){return t.endObject()}static createKernelCreateInfos(t,e,r){return c.startKernelCreateInfos(t),c.addNodeIndices(t,e),c.addKernelDefHashes(t,r),c.endKernelCreateInfos(t)}}f.KernelCreateInfos=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsSubGraphSessionState(t,e){return(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}graphId(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startSubGraphSessionState(t){t.startObject(2)}static addGraphId(t,e){t.addFieldOffset(0,e,0)}static addSessionState(t,e){t.addFieldOffset(1,e,0)}static endSubGraphSessionState(t){let e=t.endObject();return t.requiredField(e,4),e}static createSubGraphSessionState(t,e,r){return c.startSubGraphSessionState(t),c.addGraphId(t,e),c.addSessionState(t,r),c.endSubGraphSessionState(t)}}f.SubGraphSessionState=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class 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createSubGraphSessionStatesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startSubGraphSessionStatesVector(t,e){t.startVector(4,e,4)}static endSessionState(t){return t.endObject()}static createSessionState(t,e,r){return c.startSessionState(t),c.addKernels(t,e),c.addSubGraphSessionStates(t,r),c.endSessionState(t)}}f.SessionState=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsInferenceSession(t,e){return(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsInferenceSession(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static bufferHasIdentifier(t){return t.__has_identifier("ORTM")}ortVersion(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}model(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,8);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startInferenceSession(t){t.startObject(3)}static addOrtVersion(t,e){t.addFieldOffset(0,e,0)}static addModel(t,e){t.addFieldOffset(1,e,0)}static addSessionState(t,e){t.addFieldOffset(2,e,0)}static endInferenceSession(t){return t.endObject()}static finishInferenceSessionBuffer(t,e){t.finish(e,"ORTM")}static finishSizePrefixedInferenceSessionBuffer(t,e){t.finish(e,"ORTM",!0)}static createInferenceSession(t,e,r,i){return c.startInferenceSession(t),c.addOrtVersion(t,e),c.addModel(t,r),c.addSessionState(t,i),c.endInferenceSession(t)}}f.InferenceSession=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={}))},7448:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.OnnxjsSessionHandler=void 0;const u=o(1670),l=o(9162);n.OnnxjsSessionHandler=class{constructor(d){this.session=d,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(d,s,p){const f=new Map;for(const t in d)if(Object.hasOwnProperty.call(d,t)){const e=d[t];f.set(t,new l.Tensor(e.dims,e.type,void 0,void 0,e.data))}const c=await this.session.run(f),a={};return c.forEach((t,e)=>{a[e]=new u.Tensor(t.type,t.data,t.dims)}),a}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Session=void 0;const u=o(7067),l=o(1296),d=o(7091),s=o(1036),p=o(6231),f=o(2644);n.Session=class{constructor(c={}){this._initialized=!1,this.backendHint=c.backendHint,this.profiler=p.Profiler.create(c.profiler),this.context={profiler:this.profiler,graphInputTypes:[],graphInputDims:[]}}get inputNames(){return this._model.graph.getInputNames()}get outputNames(){return this._model.graph.getOutputNames()}startProfiling(){this.profiler.start()}endProfiling(){this.profiler.stop()}async loadModel(c,a,t){await this.profiler.event("session","Session.loadModel",async()=>{const e=await(0,d.resolveBackend)(this.backendHint);if(this.sessionHandler=e.createSessionHandler(this.context),this._model=new f.Model,typeof c=="string"){const r=c.endsWith(".ort");if(typeof fetch>"u"){const i=await(0,l.promisify)(u.readFile)(c);this.initialize(i,r)}else{const i=await fetch(c),h=await i.arrayBuffer();this.initialize(new Uint8Array(h),r)}}else if(ArrayBuffer.isView(c))this.initialize(c);else{const r=new Uint8Array(c,a||0,t||c.byteLength);this.initialize(r)}})}initialize(c,a){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",()=>{const t=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(c,t,a),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new s.ExecutionPlan(this._model.graph,this._ops,this.profiler)}),this._initialized=!0}async run(c){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{const a=this.normalizeAndValidateInputs(c),t=await this._executionPlan.execute(this.sessionHandler,a);return this.createOutput(t)})}normalizeAndValidateInputs(c){const a=this._model.graph.getInputNames();if(Array.isArray(c)){if(c.length!==a.length)throw new Error(`incorrect input array length: expected ${a.length} but got ${c.length}`)}else{if(c.size!==a.length)throw new Error(`incorrect input map size: expected ${a.length} but got ${c.size}`);const t=new Array(c.size);let e=0;for(let r=0;r<a.length;++r){const i=c.get(a[r]);if(!i)throw new Error(`missing input tensor for: '${name}'`);t[e++]=i}c=t}if(this.context.graphInputTypes&&this.context.graphInputTypes.length!==0&&this.context.graphInputDims&&this.context.graphInputDims.length!==0)this.validateInputTensorDims(this.context.graphInputDims,c,!1);else{const t=this._model.graph.getInputIndices(),e=this._model.graph.getValues(),r=new Array(t.length);for(let i=0;i<t.length;++i){const h=e[t[i]];r[i]=h.type.shape.dims,this.context.graphInputTypes.push(h.type.tensorType),this.context.graphInputDims.push(c[i].dims)}this.validateInputTensorDims(r,c,!0)}return this.validateInputTensorTypes(this.context.graphInputTypes,c),c}validateInputTensorTypes(c,a){for(let t=0;t<a.length;t++){const e=c[t],r=a[t].type;if(e!==r)throw new Error(`input tensor[${t}] check failed: expected type '${e}' but got ${r}`)}}validateInputTensorDims(c,a,t){for(let e=0;e<a.length;e++){const r=c[e],i=a[e].dims;if(!this.compareTensorDims(r,i,t))throw new Error(`input tensor[${e}] check failed: expected shape '[${r.join(",")}]' but got [${i.join(",")}]`)}}compareTensorDims(c,a,t){if(c.length!==a.length)return!1;for(let e=0;e<c.length;++e)if(c[e]!==a[e]&&(!t||c[e]!==0))return!1;return!0}createOutput(c){const a=this._model.graph.getOutputNames();if(c.length!==a.length)throw new Error("expected number of outputs do not match number of generated outputs");const t=new Map;for(let e=0;e<a.length;++e)t.set(a[e],c[e]);return t}initializeOps(c){const a=c.getNodes();this._ops=new Array(a.length);for(let t=0;t<a.length;t++)this._ops[t]=this.sessionHandler.resolve(a[t],this._model.opsets,c)}}},9162:function(_,n,o){var u=this&&this.__importDefault||function(h){return h&&h.__esModule?h:{default:h}};Object.defineProperty(n,"__esModule",{value:!0}),n.Tensor=void 0;const l=o(3442),d=u(o(3720)),s=o(1446),p=o(9395),f=o(2517);var c=p.onnxruntime.experimental.fbs;class a{get data(){if(this.cache===void 0){const g=this.dataProvider(this.dataId);if(g.length!==this.size)throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");this.cache=g}return this.cache}get stringData(){if(this.type!=="string")throw new TypeError("data type is not string");return this.data}get integerData(){switch(this.type){case"uint8":case"int8":case"uint16":case"int16":case"int32":case"uint32":case"bool":return this.data;default:throw new TypeError("data type is not integer (uint8, int8, uint16, int16, int32, uint32, bool)")}}get floatData(){switch(this.type){case"float32":case"float64":return this.data;default:throw new TypeError("data type is not float (float32, float64)")}}get numberData(){if(this.type!=="string")return this.data;throw new TypeError("type cannot be non-number (string)")}get(g){return this.data[f.ShapeUtil.indicesToOffset(g,this.strides)]}set(g,m){this.data[f.ShapeUtil.indicesToOffset(g,this.strides)]=m}async getData(){return this.cache===void 0&&(this.cache=await this.asyncDataProvider(this.dataId)),this.cache}get strides(){return this._strides||(this._strides=f.ShapeUtil.computeStrides(this.dims)),this._strides}constructor(g,m,b,y,w,T=l.Guid.create()){this.dims=g,this.type=m,this.dataProvider=b,this.asyncDataProvider=y,this.cache=w,this.dataId=T,this.size=f.ShapeUtil.validateDimsAndCalcSize(g);const S=this.size,A=b===void 0&&y===void 0&&w===void 0;if(w!==void 0&&w.length!==S)throw new RangeError("Input dims doesn't match data length.");if(m==="string"){if(!(w===void 0||Array.isArray(w)&&w.every(O=>typeof O=="string")))throw new TypeError("cache should be a string array");A&&(this.cache=new Array(S))}else{if(w!==void 0){const O=e(m);if(!(w instanceof O))throw new TypeError(`cache should be type ${O.name}`)}if(A){const O=new ArrayBuffer(S*function(v){switch(v){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${v}`)}}(m));this.cache=function(v,M){return new(e(M))(v)}(O,m)}}}static fromProto(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDataTypeFromProto(g.dataType),b=f.ProtoUtil.tensorDimsFromProto(g.dims),y=new a(b,m);if(m==="string")g.stringData.forEach((w,T)=>{y.data[T]=(0,f.decodeUtf8String)(w)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const w=y.data,T=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),A=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(w.length!==A)throw new Error("buffer length mismatch");for(let O=0;O<A;O++){const v=i(T,g.dataType,O*S);w[O]=v}}else{let w;switch(g.dataType){case s.onnx.TensorProto.DataType.FLOAT:w=g.floatData;break;case s.onnx.TensorProto.DataType.INT32:case s.onnx.TensorProto.DataType.INT16:case s.onnx.TensorProto.DataType.UINT16:case s.onnx.TensorProto.DataType.INT8:case s.onnx.TensorProto.DataType.UINT8:case s.onnx.TensorProto.DataType.BOOL:w=g.int32Data;break;case s.onnx.TensorProto.DataType.INT64:w=g.int64Data;break;case s.onnx.TensorProto.DataType.DOUBLE:w=g.doubleData;break;case s.onnx.TensorProto.DataType.UINT32:case s.onnx.TensorProto.DataType.UINT64:w=g.uint64Data;break;default:throw new Error("unspecific error")}if(w==null)throw new Error("failed to populate data from a tensorproto value");const T=y.data;if(T.length!==w.length)throw new Error("array length mismatch");for(let S=0;S<w.length;S++){const A=w[S];d.default.isLong(A)?T[S]=r(A,g.dataType):T[S]=A}}return y}static fromData(g,m,b){return new a(m,b,void 0,void 0,g)}static fromOrtTensor(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDimsFromORTFormat(g),b=f.ProtoUtil.tensorDataTypeFromProto(g.dataType()),y=new a(m,b);if(b==="string")for(let w=0;w<g.stringDataLength();w++)y.data[w]=g.stringData(w);else if(g.rawDataArray()&&typeof g.rawDataLength()=="number"&&g.rawDataLength()>0){const w=y.data,T=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),A=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(w.length!==A)throw new Error("buffer length mismatch");for(let O=0;O<A;O++){const v=i(T,g.dataType(),O*S);w[O]=v}}return y}}function t(h){switch(h){case s.onnx.TensorProto.DataType.UINT8:case s.onnx.TensorProto.DataType.INT8:case s.onnx.TensorProto.DataType.BOOL:return 1;case s.onnx.TensorProto.DataType.UINT16:case s.onnx.TensorProto.DataType.INT16:return 2;case s.onnx.TensorProto.DataType.FLOAT:case s.onnx.TensorProto.DataType.INT32:case s.onnx.TensorProto.DataType.UINT32:return 4;case s.onnx.TensorProto.DataType.INT64:case s.onnx.TensorProto.DataType.DOUBLE:case s.onnx.TensorProto.DataType.UINT64:return 8;default:throw new Error(`cannot calculate sizeof() on type ${s.onnx.TensorProto.DataType[h]}`)}}function e(h){switch(h){case"bool":case"uint8":return Uint8Array;case"int8":return Int8Array;case"int16":return Int16Array;case"uint16":return Uint16Array;case"int32":return Int32Array;case"uint32":return Uint32Array;case"float32":return Float32Array;case"float64":return Float64Array;default:throw new Error("unspecified error")}}function r(h,g){if(g===s.onnx.TensorProto.DataType.INT64||g===c.TensorDataType.INT64){if(h.greaterThanOrEqual(2147483648)||h.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else{if(g!==s.onnx.TensorProto.DataType.UINT32&&g!==c.TensorDataType.UINT32&&g!==s.onnx.TensorProto.DataType.UINT64&&g!==c.TensorDataType.UINT64)throw new TypeError(`not a LONG type: ${s.onnx.TensorProto.DataType[g]}`);if(h.greaterThanOrEqual(4294967296)||h.lessThan(0))throw new TypeError("uint64 is not supported")}return h.toNumber()}function i(h,g,m){switch(g){case s.onnx.TensorProto.DataType.BOOL:case s.onnx.TensorProto.DataType.UINT8:return h.getUint8(m);case s.onnx.TensorProto.DataType.INT8:return h.getInt8(m);case s.onnx.TensorProto.DataType.UINT16:return h.getUint16(m,!0);case s.onnx.TensorProto.DataType.INT16:return h.getInt16(m,!0);case s.onnx.TensorProto.DataType.FLOAT:return h.getFloat32(m,!0);case s.onnx.TensorProto.DataType.INT32:return h.getInt32(m,!0);case s.onnx.TensorProto.DataType.UINT32:return h.getUint32(m,!0);case s.onnx.TensorProto.DataType.INT64:return r(d.default.fromBits(h.getUint32(m,!0),h.getUint32(m+4,!0),!1),g);case s.onnx.TensorProto.DataType.DOUBLE:return h.getFloat64(m,!0);case s.onnx.TensorProto.DataType.UINT64:return r(d.default.fromBits(h.getUint32(m,!0),h.getUint32(m+4,!0),!0),g);default:throw new Error(`cannot read from DataView for type ${s.onnx.TensorProto.DataType[g]}`)}}n.Tensor=a},2517:function(_,n,o){var u=this&&this.__importDefault||function(g){return g&&g.__esModule?g:{default:g}};Object.defineProperty(n,"__esModule",{value:!0}),n.decodeUtf8String=n.MAX_CLIP=n.MIN_CLIP=n.PoolConvUtil=n.ReduceUtil=n.SplitUtil=n.MathUtil=n.ShapeUtil=n.LongUtil=n.ProtoUtil=n.GemmUtil=n.arrayCopyHelper=n.BroadcastUtil=n.MatMulUtil=n.ArrayUtil=n.assert=n.checkInputsShape=void 0;const l=o(5686),d=u(o(3720)),s=o(1446),p=o(9162);n.checkInputsShape=function(g,...m){if(!g||g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(!g[b].dims||g[b].dims.length!==m[b])return!1;return!0},n.assert=function(g,m){if(!g)throw new Error(typeof m=="string"?m:m())},n.ArrayUtil=class{static arraysEqual(g,m){if(g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(g[b]!==m[b])return!1;return!0}};class f{static preprocessInputShapes(m,b){return[m.length===1?[1,m[0]]:m,b.length===1?[b[0],1]:b]}static postprocessOutputShape(m,b,y){b===1&&m.splice(m.length-2,1),y===1&&m.pop()}static calcMatMulShape(m,b){return m[1]!==b[0]?void 0:[m[0],b[1]]}}n.MatMulUtil=f;class c{static calcShape(m,b,y=!1){const w=m.length,T=b.length;if(w===0)return b;if(T===0)return m;const S=Math.max(m.length,b.length),A=new Array(S);if(y){if(w<2||T<2)return;const O=f.calcMatMulShape([m[w-2],m[w-1]],[b[T-2],b[T-1]]);if(O===void 0)return;[A[S-2],A[S-1]]=O}for(let O=y?3:1;O<=S;O++){const v=w-O<0?1:m[w-O],M=T-O<0?1:b[T-O];if(v!==M&&v>1&&M>1)return;A[S-O]=Math.max(v,M)}return A}static index(m,b){const y=new Array(b.length);return c.fillIndex(m,b,y),y}static fillIndex(m,b,y){const w=m.length-b.length;for(let T=0;T<b.length;T++)y[T]=m[w+T]%b[T]}static calc(m,b,y,w,T){const S=c.calcShape(m.dims,b.dims);if(S){if(w&&!e.areEqual(S,m.dims))return;const A=e.size(S),O=w?m:new p.Tensor(S,T||m.type);if(S.length===0)O.set([],y(m.get([]),b.get([])));else{const v=new Array(S.length),M=new Array(m.dims.length),F=new Array(b.dims.length);let R,$=0,N=0,G=!1,D=!1;m.dims.length===0&&($=m.get([]),G=!0),b.dims.length===0&&(N=b.get([]),D=!0);for(let j=0;j<A;j++){R=j;for(let Q=S.length-1;Q>=0;Q--)v[Q]=R%S[Q],R=Math.floor(R/S[Q]);G||(c.fillIndex(v,m.dims,M),$=m.get(M)),D||(c.fillIndex(v,b.dims,F),N=b.get(F)),O.set(v,y($,N))}}return O}}static isValidBroadcast(m,b){const y=m.length,w=b.length;if(y>w)return!1;for(let T=1;T<=y;T++)if(m[y-T]!==1&&m[y-T]!==b[w-T])return!1;return!0}static getBroadcastDims(m,b){const y=m.length,w=[];for(let T=0;T<y;T++){const S=y-1-T,A=m[S]||1;(b[b.length-1-T]||1)>1&&A===1&&w.unshift(S)}return w}}n.BroadcastUtil=c,n.arrayCopyHelper=function(g,m,b,y,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let T=0;T<w;T++)g[b+T]=m[y+T]},n.GemmUtil=class{static getShapeOfGemmResult(g,m,b,y,w){if(g.length!==2||b.length!==2)throw new Error("shape need to be of size 2");let T,S,A;m?(T=g[1],S=g[0]):(T=g[0],S=g[1]);let O=-1;if(y?(A=b[0],O=1):(A=b[1],O=0),b[O]!==S)throw new Error("dimension mismatch");if(T<=0||A<=0||S<=0)throw new Error("invalid shape specified");if(w&&!c.isValidBroadcast(w,[T,A]))throw new Error("gemm: invalid bias shape for broadcast");return[T,A,S]}};class a{static tensorDataTypeFromProto(m){switch(m){case s.onnx.TensorProto.DataType.INT8:return"int8";case s.onnx.TensorProto.DataType.UINT8:return"uint8";case s.onnx.TensorProto.DataType.BOOL:return"bool";case s.onnx.TensorProto.DataType.INT16:return"int16";case s.onnx.TensorProto.DataType.UINT16:return"uint16";case s.onnx.TensorProto.DataType.INT32:return"int32";case s.onnx.TensorProto.DataType.UINT32:return"uint32";case s.onnx.TensorProto.DataType.FLOAT:return"float32";case s.onnx.TensorProto.DataType.DOUBLE:return"float64";case s.onnx.TensorProto.DataType.STRING:return"string";case s.onnx.TensorProto.DataType.INT64:return"int32";case s.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${s.onnx.TensorProto.DataType[m]}`)}}static tensorDataTypeStringToEnum(m){switch(m){case"int8":return s.onnx.TensorProto.DataType.INT8;case"uint8":return s.onnx.TensorProto.DataType.UINT8;case"bool":return s.onnx.TensorProto.DataType.BOOL;case"int16":return s.onnx.TensorProto.DataType.INT16;case"uint16":return s.onnx.TensorProto.DataType.UINT16;case"int32":return s.onnx.TensorProto.DataType.INT32;case"uint32":return s.onnx.TensorProto.DataType.UINT32;case"float32":return s.onnx.TensorProto.DataType.FLOAT;case"float64":return s.onnx.TensorProto.DataType.DOUBLE;case"string":return s.onnx.TensorProto.DataType.STRING;case"int64":return s.onnx.TensorProto.DataType.INT64;case"uint64":return s.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${m}`)}}static tensorDimsFromProto(m){return m.map(b=>d.default.isLong(b)?b.toNumber():b)}static tensorValueTypeFromProto(m){return{tensorType:a.tensorDataTypeFromProto(m.elemType),shape:{dims:a.tensorDimsFromProto(m.shape.dim.map(b=>b.dimValue))}}}static tensorDimsFromORTFormat(m){const b=[];for(let y=0;y<m.dimsLength();y++)b.push(t.longToNumber(m.dims(y)));return b}static tensorAttributesFromORTFormat(m){const b=[];for(let y=0;y<m.attributesLength();y++)b.push(m.attributes(y));return b}}n.ProtoUtil=a;class t{static longToNumber(m,b){return d.default.isLong(m)?m.toNumber():m instanceof l.flatbuffers.Long?d.default.fromValue({low:m.low,high:m.high,unsigned:b!=null&&b}).toNumber():m}static isLong(m){return d.default.isLong(m)||m instanceof l.flatbuffers.Long}}n.LongUtil=t;class e{static size(m){return e.getSizeFromDimensionRange(m,0,m.length)}static sizeFromDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeFromDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,b,m.length)}static sizeToDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeToDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,b)}static getSizeFromDimensionRange(m,b,y){let w=1;for(let T=b;T<y;T++){if(m[T]<=0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");w*=m[T]}return w}static computeStrides(m){const b=m.length;if(b===0)return[];if(b===1)return[1];const y=new Array(b);y[b-1]=1,y[b-2]=m[b-1];for(let w=b-3;w>=0;--w)y[w]=y[w+1]*m[w+1];return y}static transpose(m){return m.slice().reverse()}static indicesToOffset(m,b,y){y===void 0&&(y=m.length);let w=0;for(let T=0;T<y;++T)w+=b[T]*m[T];return w}static offsetToIndices(m,b){const y=b.length;if(y===0)return[];if(y===1)return[m*b[0]];const w=new Array(b.length);for(let T=0;T<w.length-1;++T)w[T]=Math.floor(m/b[T]),m-=w[T]*b[T];return w[w.length-1]=m,w}static normalizeAxis(m,b){if(m<-b&&m>=b)throw new Error("unsupported axis for this operation.");return m<0?m+b:m}static normalizeAxes(m,b){return m.map(y=>this.normalizeAxis(y,b))}static incrementIndex(m,b,y){if(b.length===0||m.length===0)throw new Error("Index incrementing unsupported for scalar Tensor");if(y===void 0)y=b.length;else if(y<=0||y>b.length)throw new Error("Incorrect axis to increment on");for(let w=y-1;w>=0&&(m[w]++,!(m[w]<b[w]));--w)m[w]=0}static calculateReshapedDims(m,b){if(b.length===0){if(m.length===0||e.size(m)===1)return[];throw new Error("cannot reshape to a scalar Tensor")}const y=b.length,w=new Array(y);let T=-1,S=1;for(let O=0;O<y;O++){if(b[O]<-1)throw new Error("a dimension in shape hints cannot be less than -1");if(b[O]===-1){if(T!==-1)throw new Error("at most one dimension in shape hints can be -1");T=O}else{if(b[O]===0){if(O>=m.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");w[O]=m[O]}else w[O]=b[O];S*=w[O]}}const A=e.size(m);if(T!==-1){if(A%S!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${m}] Output shape: [${b}]`);w[T]=A/S}else if(S!==A)throw new Error("reshapedDims and originalDims don't have matching sizes");return w}static sortBasedOnPerm(m,b){return b?b.map(y=>m[y]):m.slice().reverse()}static padShape(m,b){const y=m.length;return m.map((w,T)=>w+b[T]+b[T+y])}static areEqual(m,b){return m.length===b.length&&m.every((y,w)=>y===b[w])}static validateDimsAndCalcSize(m){if(m.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let b=1;for(const y of m){if(!Number.isInteger(y))throw new TypeError(`Invalid shape: ${y} is not an integer`);if(y<0||y>2147483647)throw new TypeError(`Invalid shape: length ${y} is not allowed`);b*=y}return b}static flattenShape(m,b){b<0&&(b+=m.length);const y=m.reduce((T,S)=>T*S,1),w=m.slice(b).reduce((T,S)=>T*S,1);return[y/w,w]}static squeezeShape(m,b){const y=new Array;b=e.normalizeAxes(b,m.length);for(let w=0;w<m.length;w++){const T=b.indexOf(w)>=0;if(T&&m[w]!==1)throw new Error("squeeze an axis of size different than 1");(b.length===0&&m[w]>1||b.length>0&&!T)&&y.push(m[w])}return y}static unsqueezeShape(m,b){const y=new Array(m.length+b.length);y.fill(0);for(let T=0;T<b.length;T++){const S=e.normalizeAxis(b[T],y.length);if(S>=y.length)throw new Error("'axes' has an out of range axis");if(y[S]!==0)throw new Error("'axes' has a duplicate axis");y[S]=1}let w=0;for(let T=0;T<y.length;T++)y[T]===0&&(y[T]=m[w++]);if(w!==m.length)throw new Error("the unsqueezed dimension could not be established");return y}}n.ShapeUtil=e,n.MathUtil=class{static sqr(g,m,b,y,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let T=0;T<w;T++)g[b+T]+=Math.pow(m[y+T],2)}static axpy(g,m,b,y,w,T){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<w;S++)g[b+S]+=T*m[y+S]}static powx(g,m,b,y,w,T){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<w;S++)g[b+S]=Math.pow(m[y+S],T)}static mul(g,m,b,y,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let T=0;T<w;T++)g[b+T]=m[y+T]*g[b+T]}};class r{static splitShape(m,b,y,w){if(y.length===0){if(!w)throw new Error("need to know number of outputs when the 'split' attribute is not specified");r.determineSplit(m[b],w,y)}const T=[],S=[0];for(let A=0;A<y.length;++A){A!==0&&S.push(S[A-1]+y[A-1]);const O=m.slice();O[b]=y[A],T.push(O)}return[T,S]}static determineSplit(m,b,y){if(m%b!=0)throw new Error("cannot split tensor to equal sized parts");for(let w=0;w<b;++w)y.push(m/b)}}n.SplitUtil=r;class i{static calcReduce(m,b,y,w,T){const S=m.dims.slice(0);b.length===0&&S.forEach(($,N)=>b.push(N));const A=i.calcReduceShape(S,b,!0),O=e.size(A),v=new p.Tensor(A,m.type),M=e.computeStrides(A),F=e.computeStrides(S),R=new Array(S.length);for(let $=0;$<O;$++){const N=e.offsetToIndices($,M);c.fillIndex(N,S,R),v.set(N,i.calcReduceByAxis(m.numberData,b,S,0,e.indicesToOffset(R,F),w,T))}return y?v:new p.Tensor(i.calcReduceShape(S,b,y),v.type,void 0,void 0,v.data,v.dataId)}static calcReduceByAxis(m,b,y,w,T,S,A){let O=0;if(w>=b.length)return S(m[T]);const v=b[w],M=v>=y.length?1:e.size(y.slice(v+1));for(let F=0;F<y[v];F++)O=F===0?i.calcReduceByAxis(m,b,y,w+1,T,S,A):A(O,i.calcReduceByAxis(m,b,y,w+1,T,S,A)),T+=M;return O}static calcReduceShape(m,b,y){const w=m.slice();for(let T=0;T<b.length;T++)w[b[T]]=y?1:0;return w.filter(T=>T!==0)}}n.ReduceUtil=i;class h{static adjustPoolAttributes(m,b,y,w,T,S){if(!m&&y.length!==b.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(m)for(let A=0;A<b.length-2;A++)A>=y.length?y.push(b[A+2]):y[A]=b[A+2];for(let A=0;A<y.length;A++)if(A<w.length){if(w[A]<0)throw new Error("strides should be greater than or equal to 1")}else w.push(1);for(let A=0;A<y.length;A++)if(A<T.length){if(T[A]<0)throw new Error("dilations should be greater than or equal to 1")}else T.push(1);for(let A=0;A<2*y.length;A++)if(A<S.length){if(S[A]<0)throw new Error("pad should be greater than or equal to 1")}else S.push(0);for(let A=0;A<y.length;A++){if(y[A]<=0)throw new 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h=r,g=h+t,m=g+t,b=m+t,y=n[h],w=n[h+1],T=n[g],S=n[g+1],A=n[m],O=n[m+1],v=n[b],M=n[b+1],F=this.table[i],R=u*this.table[i+1],$=T*F-S*R,N=T*R+S*F,G=this.table[2*i],D=u*this.table[2*i+1],j=A*G-O*D,Q=A*D+O*G,X=this.table[3*i],Z=u*this.table[3*i+1],ee=v*X-M*Z,ue=v*Z+M*X,Ae=y+j,ve=w+Q,oe=y-j,we=w-Q,be=$+ee,ke=N+ue,Le=u*($-ee),xe=u*(N-ue);n[h]=Ae+be,n[h+1]=ve+ke,n[g]=oe+xe,n[g+1]=we-Le,n[m]=Ae-be,n[m+1]=ve-ke,n[b]=oe-xe,n[b+1]=we+Le}}}}_singleTransform2(n,o,u,l,d){const s=n[l],p=n[l+1],f=n[l+d],c=n[l+d+1];o[u]=s+f,o[u+1]=p+c,o[u+2]=s-f,o[u+3]=p-c}_singleTransform4(n,o,u,l,d,s){const p=d*2,f=d*3,c=n[l],a=n[l+1],t=n[l+d],e=n[l+d+1],r=n[l+p],i=n[l+p+1],h=n[l+f],g=n[l+f+1],m=c+r,b=a+i,y=c-r,w=a-i,T=t+h,S=e+g,A=s*(t-h),O=s*(e-g);o[u]=m+T,o[u+1]=b+S,o[u+2]=y+O,o[u+3]=w-A,o[u+4]=m-T,o[u+5]=b-S,o[u+6]=y-O,o[u+7]=w+A}_realTransform4(n,o,u){const l=this._csize;let s=1<<this._width,p=l/s<<1;var f,c,a=this._bitrev;if(p===4)for(f=0,c=0;f<l;f+=p,++c){const 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c=="number")c=safeIndex(c,this.dims[f],f),u.push([c,c+1]);else if(Array.isArray(c)&&c.length===2){if(c[0]>c[1])throw new Error(`Invalid slice: ${c}`);let a=[Math.max(c[0],0),Math.min(c[1],this.dims[f])];u.push(a),o.push(a[1]-a[0])}else throw new Error(`Invalid slice: ${c}`)}let l=u.map(([f,c])=>c-f),d=l.reduce((f,c)=>f*c),s=new this.data.constructor(d);const p=this.stride();for(let f=0;f<d;++f){let c=0;for(let a=l.length-1,t=f;a>=0;--a){const e=l[a];c+=(t%e+u[a][0])*p[a],t=Math.floor(t/e)}s[f]=this.data[c]}return new Tensor(this.type,s,o)}transpose(...n){return transpose(this,n)}sum(n=null,o=!1){return this.norm(1,n,o)}norm(n="fro",o=null,u=!1){if(n==="fro")n=2;else if(typeof n=="string")throw Error(`Unsupported norm: ${n}`);if(o===null){let s=this.data.reduce((p,f)=>p+f**n,0)**(1/n);return new Tensor(this.type,[s],[])}o=safeIndex(o,this.dims.length);const l=this.dims.slice();l[o]=1;const d=new this.data.constructor(this.data.length/this.dims[o]);for(let s=0;s<this.data.length;++s){let p=0;for(let f=this.dims.length-1,c=s,a=1;f>=0;--f){const t=this.dims[f];if(f!==o){const e=c%t;p+=e*a,a*=l[f]}c=Math.floor(c/t)}d[p]+=this.data[s]**n}if(n!==1)for(let s=0;s<d.length;++s)d[s]=d[s]**(1/n);return u||l.splice(o,1),new Tensor(this.type,d,l)}normalize_(n=2,o=1){o=safeIndex(o,this.dims.length);const u=this.norm(n,o,!0);for(let l=0;l<this.data.length;++l){let d=0;for(let s=this.dims.length-1,p=l,f=1;s>=0;--s){const c=this.dims[s];if(s!==o){const a=p%c;d+=a*f,f*=this.dims[s]}p=Math.floor(p/c)}this.data[l]/=u.data[d]}return this}normalize(n=2,o=1){return this.clone().normalize_(n,o)}stride(){return dimsToStride(this.dims)}squeeze(n=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,n))}squeeze_(n=null){return this.dims=calc_squeeze_dims(this.dims,n),this}unsqueeze(n=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,n))}unsqueeze_(n=null){return this.dims=calc_unsqueeze_dims(this.dims,n),this}flatten_(n=0,o=-1){o=(o+this.dims.length)%this.dims.length;let u=this.dims.slice(0,n),l=this.dims.slice(n,o+1),d=this.dims.slice(o+1);return this.dims=[...u,l.reduce((s,p)=>s*p,1),...d],this}flatten(n=0,o=-1){return this.clone().flatten_(n,o)}view(...n){let o=-1;for(let u=0;u<n.length;++u)if(n[u]===-1){if(o!==-1)throw new Error("Only one dimension can be inferred");o=u}if(o!==-1){const u=n.reduce((l,d,s)=>s!==o?l*d:l,1);n[o]=this.data.length/u}return new Tensor(this.type,this.data,n)}neg_(){for(let n=0;n<this.data.length;++n)this.data[n]=-this.data[n];return this}neg(){return this.clone().neg_()}}function reshape(_,n){const o=_.length,u=n.reduce((d,s)=>d*s);if(o!==u)throw Error(`cannot reshape array of size ${o} into shape (${n})`);let l=_;for(let d=n.length-1;d>=0;d--)l=l.reduce((s,p)=>{let f=s[s.length-1];return f.length<n[d]?f.push(p):s.push([p]),s},[[]]);return l[0]}function transpose(_,n){const[o,u]=transpose_data(_.data,_.dims,n);return new Tensor(_.type,o,u)}function interpolate(_,[n,o],u="bilinear",l=!1){const d=_.dims.at(-3)??1,s=_.dims.at(-2),p=_.dims.at(-1);let f=interpolate_data(_.data,[d,s,p],[n,o],u,l);return new Tensor(_.type,f,[d,n,o])}function mean_pooling(_,n){let o=[_.dims[0],_.dims[2]],u=new _.data.constructor(o[0]*o[1]),[l,d,s]=_.dims,p=0;for(let f=0;f<l;++f){let c=f*s*d;for(let a=0;a<s;++a){let t=0,e=0,r=f*d,i=c+a;for(let g=0;g<d;++g){let m=Number(n.data[r+g]);e+=m,t+=_.data[i+g*s]*m}let h=t/e;u[p++]=h}}return new Tensor(_.type,u,o)}function calc_squeeze_dims(_,n){return _=_.slice(),n===null?_=_.filter(o=>o!==1):typeof n=="number"?_[n]===1&&_.splice(n,1):Array.isArray(n)&&(_=_.filter((o,u)=>o!==1||!n.includes(u))),_}function calc_unsqueeze_dims(_,n){return n=safeIndex(n,_.length+1),_=_.slice(),_.splice(n,0,1),_}function safeIndex(_,n,o=null){if(_<-n||_>=n)throw new Error(`IndexError: index ${_} is out of bounds for dimension${o===null?"":" "+o} with size ${n}`);return _<0&&(_=(_%n+n)%n),_}function cat(_,n=0){n=safeIndex(n,_[0].dims.length);const o=_[0].dims.slice();o[n]=_.reduce((s,p)=>s+p.dims[n],0);const u=o.reduce((s,p)=>s*p,1),l=new _[0].data.constructor(u),d=_[0].type;if(n===0){let s=0;for(let p of _)l.set(p.data,s),s+=p.data.length}else{let s=0;for(let p=0;p<_.length;++p){let f=_[p];for(let c=0;c<f.data.length;++c){let a=0;for(let t=f.dims.length-1,e=c,r=1;t>=0;--t){const i=f.dims[t];let h=e%i;t===n&&(h+=s),a+=h*r,r*=o[t],e=Math.floor(e/i)}l[a]=f.data[c]}s+=f.dims[n]}}return new Tensor(d,l,o)}function stack(_,n=0){return cat(_.map(o=>o.unsqueeze(n)),n)}function std_mean(_,n=null,o=1,u=!1){if(n===null){const c=_.data.reduce((r,i)=>r+i,0)/_.data.length,a=Math.sqrt(_.data.reduce((r,i)=>r+(i-c)**2,0)/(_.data.length-o)),t=new Tensor(_.type,[c],[]);return[new Tensor(_.type,[a],[]),t]}n=safeIndex(n,_.dims.length);const l=mean(_,n,u),d=_.dims.slice();d[n]=1;const s=new _.data.constructor(_.data.length/_.dims[n]);for(let f=0;f<_.data.length;++f){let c=0;for(let a=_.dims.length-1,t=f,e=1;a>=0;--a){const r=_.dims[a];if(a!==n){const i=t%r;c+=i*e,e*=d[a]}t=Math.floor(t/r)}s[c]+=(_.data[f]-l.data[c])**2}for(let f=0;f<s.length;++f)s[f]=Math.sqrt(s[f]/(_.dims[n]-o));return u||d.splice(n,1),[new Tensor(_.type,s,d),l]}function mean(_,n=null,o=!1){if(n===null){let d=_.data.reduce((s,p)=>s+p,0);return new Tensor(_.type,[d/_.data.length],[])}n=safeIndex(n,_.dims.length);const u=_.dims.slice();u[n]=1;const l=new _.data.constructor(_.data.length/_.dims[n]);for(let d=0;d<_.data.length;++d){let s=0;for(let p=_.dims.length-1,f=d,c=1;p>=0;--p){const a=_.dims[p];if(p!==n){const t=f%a;s+=t*c,c*=u[p]}f=Math.floor(f/a)}l[s]+=_.data[d]}if(_.dims[n]!==1)for(let d=0;d<l.length;++d)l[d]=l[d]/_.dims[n];return o||u.splice(n,1),new Tensor(_.type,l,u)}function dynamicTimeWarping(_){const[n,o]=_.dims,u=[n+1,o+1],l=new Tensor("float32",new Float32Array(u[0]*u[1]).fill(1/0),u),d=new Tensor("float32",new Float32Array(u[0]*u[1]).fill(-1),u);l[0].data[0]=0;for(let a=1;a<o+1;++a)for(let t=1;t<n+1;++t){const e=l[t-1][a-1].item(),r=l[t-1][a].item(),i=l[t][a-1].item();let h,g;e<r&&e<i?(h=e,g=0):r<e&&r<i?(h=r,g=1):(h=i,g=2),l[t].data[a]=_[t-1][a-1].item()+h,d[t].data[a]=g}let s=n,p=o;d.data.fill(2,0,u[1]);for(let a=0;a<u[0];++a)d[a].data[0]=1;let f=[],c=[];for(;s>0||p>0;)switch(f.push(s-1),c.push(p-1),d[s][p].item()){case 0:--s,--p;break;case 1:--s;break;case 2:--p;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${p}]. Please file a bug report.`)}return f.reverse(),c.reverse(),[f,c]}function dimsToStride(_){const n=new Array(_.length);for(let o=_.length-1,u=1;o>=0;--o)n[o]=u,u*=_[o];return n}function ones(_){const n=_.reduce((o,u)=>o*u,1);return new Tensor("int64",new BigInt64Array(n).fill(1n),_)}function ones_like(_){return ones(_.dims)}class PriorityQueue{constructor(n=(o,u)=>o>u){this._heap=[],this._comparator=n}get size(){return this._heap.length}isEmpty(){return this.size===0}peek(){return this._heap[0]}push(...n){return this.extend(n)}extend(n){for(const o of n)this._heap.push(o),this._siftUp();return this.size}pop(){const n=this.peek(),o=this.size-1;return o>0&&this._swap(0,o),this._heap.pop(),this._siftDown(),n}replace(n){const o=this.peek();return this._heap[0]=n,this._siftDown(),o}_parent(n){return(n+1>>>1)-1}_left(n){return(n<<1)+1}_right(n){return n+1<<1}_greater(n,o){return this._comparator(this._heap[n],this._heap[o])}_swap(n,o){const u=this._heap[n];this._heap[n]=this._heap[o],this._heap[o]=u}_siftUp(){let n=this.size-1;for(;n>0&&this._greater(n,this._parent(n));)this._swap(n,this._parent(n)),n=this._parent(n)}_siftDown(){let n=0;for(;this._left(n)<this.size&&this._greater(this._left(n),n)||this._right(n)<this.size&&this._greater(this._right(n),n);){const o=this._right(n)<this.size&&this._greater(this._right(n),this._left(n))?this._right(n):this._left(n);this._swap(n,o),n=o}}}class CharTrie{constructor(){this.root=CharTrieNode.default()}extend(n){for(let o of n)this.push(o)}push(n){let o=this.root;for(let u of n){let l=o.children.get(u);l===void 0&&(l=CharTrieNode.default(),o.children.set(u,l)),o=l}o.isLeaf=!0}*commonPrefixSearch(n){let o=this.root,u="";for(let l=0;l<n.length&&o!==void 0;++l){const d=n[l];u+=d,o=o.children.get(d),o!==void 0&&o.isLeaf&&(yield u)}}}class CharTrieNode{constructor(n,o){this.isLeaf=n,this.children=o}static default(){return new CharTrieNode(!1,new Map)}}class TokenLattice{constructor(n,o,u){this.sentence=n,this.len=n.length,this.bosTokenId=o,this.eosTokenId=u,this.nodes=[],this.beginNodes=Array.from({length:this.len+1},()=>[]),this.endNodes=Array.from({length:this.len+1},()=>[]);const l=new TokenLatticeNode(this.bosTokenId,0,0,0,0),d=new TokenLatticeNode(this.eosTokenId,1,this.len,0,0);this.nodes.push(l.clone()),this.nodes.push(d.clone()),this.beginNodes[this.len].push(d),this.endNodes[0].push(l)}insert(n,o,u,l){const d=this.nodes.length,s=new TokenLatticeNode(l,d,n,o,u);this.beginNodes[n].push(s),this.endNodes[n+o].push(s),this.nodes.push(s)}viterbi(){const n=this.len;let o=0;for(;o<=n;){if(this.beginNodes[o].length==0)return[];for(let p of this.beginNodes[o]){p.prev=null;let f=0,c=null;for(let a of this.endNodes[o]){const t=a.backtraceScore+p.score;(c===null||t>f)&&(c=a.clone(),f=t)}if(c!==null)p.prev=c,p.backtraceScore=f;else return[]}++o}const u=[],d=this.beginNodes[n][0].prev;if(d===null)return[];let s=d.clone();for(;s.prev!==null;)u.push(s.clone()),s=s.clone().prev.clone();return u.reverse(),u}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(o=>this.piece(o))}tokenIds(){return this.viterbi().map(o=>o.tokenId)}}class TokenLatticeNode{constructor(n,o,u,l,d){this.tokenId=n,this.nodeId=o,this.pos=u,this.length=l,this.score=d,this.prev=null,this.backtraceScore=0}clone(){const n=new TokenLatticeNode(this.tokenId,this.nodeId,this.pos,this.length,this.score);return n.prev=this.prev,n.backtraceScore=this.backtraceScore,n}}async function loadTokenizer(_,n){return await Promise.all([getModelJSON(_,"tokenizer.json",!0,n),getModelJSON(_,"tokenizer_config.json",!0,n)])}function createPattern(_,n=!0){return _.Regex!==void 0?new RegExp(n?_.Regex:`(${_.Regex})`,"gu"):_.String!==void 0?_.String:(console.warn("Unknown pattern type:",_),null)}function objectToMap(_){return new Map(Object.entries(_))}function clean_up_tokenization(_){return _.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function remove_accents(_){return _.replace(/[\u0300-\u036f]/g,"")}function lowercase_and_remove_accent(_){return remove_accents(_.toLowerCase())}function fuse(_,n){let o=[],u=0;for(;u<_.length;){if(o.push(_[u]),_[u]!==n){++u;continue}for(;u<_.length&&_[u]===n;)++u}return o}function whitespace_split(_){return _.match(/\S+/g)||[]}const PUNCTUATION_REGEX="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E";class TokenizerModel extends Callable{constructor(n){super(),this.config=n,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(n,...o){switch(n.type){case"WordPiece":return new WordPieceTokenizer(n);case"Unigram":return new Unigram(n,...o);case"BPE":return new BPE(n);default:if(n.vocab)return new LegacyTokenizerModel(n,...o);throw new Error(`Unknown TokenizerModel type: ${n.type}`)}}_call(n){return this.encode(n)}encode(n){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(n){let o=n.map(u=>this.tokens_to_ids.get(u)??this.unk_token_id);return this.fuse_unk&&(o=fuse(o,this.unk_token_id)),o}convert_ids_to_tokens(n){return n.map(o=>this.vocab[o]??this.unk_token)}}class WordPieceTokenizer extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=objectToMap(n.vocab),this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[o,u]of this.tokens_to_ids)this.vocab[u]=o}encode(n){let o=[];for(let u of n){let l=[...u],d=!1,s=0,p=[];for(;s<l.length;){let f=l.length,c=null;for(;s<f;){let a=l.slice(s,f).join("");if(s>0&&(a=this.config.continuing_subword_prefix+a),this.tokens_to_ids.has(a)){c=a;break}--f}if(c===null){d=!0;break}p.push(c),s=f}d?o.push(this.unk_token):o.push(...p)}return o}}class Unigram extends TokenizerModel{constructor(n,o){super(n);const u=n.vocab.length;this.vocab=new Array(u),this.scores=new Array(u);for(let l=0;l<u;++l){const d=n.vocab[l];this.vocab[l]=d[0],this.scores[l]=d[1]}this.unk_token_id=n.unk_id,this.unk_token=this.vocab[n.unk_id],this.tokens_to_ids=new Map(this.vocab.map((l,d)=>[l,d])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=o.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=min(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(n){const o=n.sentence,u=o.length;let l=0;for(;l<u;){let s=!1;for(let p of this.trie.commonPrefixSearch(o.slice(l))){const f=this.tokens_to_ids.get(p),c=this.scores[f],a=p.length;n.insert(l,a,c,f),!s&&a===1&&(s=!0)}s||n.insert(l,1,this.unkScore,this.unk_token_id),l+=1}}tokenize(n){const o=new TokenLattice(n,this.bosTokenId,this.eosTokenId);return this.populateNodes(o),o.tokens()}encode(n){let o=[];for(let u of n){const l=this.tokenize(u);o.push(...l)}return o}}const BYTES_TO_UNICODE=(()=>{const _=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(l,d)=>d+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(l,d)=>d+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(l,d)=>d+"®".charCodeAt(0))];let n=_.slice(),o=0;for(let l=0;l<256;++l)_.includes(l)||(_.push(l),n.push(256+o),o+=1);let u=n.map(l=>String.fromCharCode(l));return Object.fromEntries(_.map((l,d)=>[l,u[d]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=objectToMap(n.vocab),this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[o,u]of this.tokens_to_ids)this.vocab[u]=o;this.bpe_ranks=new Map(n.merges.map((o,u)=>[o,u])),this.merges=n.merges.map(o=>o.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=n.end_of_word_suffix,this.continuing_subword_suffix=n.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(n){if(n.length===0)return[];const o=this.cache.get(n);if(o!==void 0)return o;const u=Array.from(n);this.end_of_word_suffix&&(u[u.length-1]+=this.end_of_word_suffix);let l=[];if(u.length>1){const d=new PriorityQueue((f,c)=>f.score<c.score);let s={token:u[0],bias:0,prev:null,next:null},p=s;for(let f=1;f<u.length;++f){const c={bias:f/u.length,token:u[f],prev:p,next:null};p.next=c,this._add_node(d,p),p=c}for(;!d.isEmpty();){const f=d.pop();if(f.deleted||!f.next||f.next.deleted)continue;if(f.deleted=!0,f.next.deleted=!0,f.prev){const a={...f.prev};f.prev.deleted=!0,f.prev=a,a.prev?a.prev.next=a:s=a}const c={token:f.token+f.next.token,bias:f.bias,prev:f.prev,next:f.next.next};c.prev?(c.prev.next=c,this._add_node(d,c.prev)):s=c,c.next&&(c.next.prev=c,this._add_node(d,c))}for(let f=s;f!==null;f=f.next)l.push(f.token)}else l=u;if(this.continuing_subword_suffix)for(let d=0;d<l.length-1;++d)l[d]+=this.continuing_subword_suffix;return this.cache.set(n,l),l}_add_node(n,o){const u=this.bpe_ranks.get(o.token+this.BPE_SPLIT_TOKEN+o.next.token);u!==void 0&&(o.score=u+o.bias,n.push(o))}encode(n){let o=[];for(let u of n){let l=this.bpe(u);for(let d of l)this.tokens_to_ids.has(d)?o.push(d):this.byte_fallback?o.push(...Array.from(this.text_encoder.encode(d)).map(s=>`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):o.push(this.unk_token)}return o}}class LegacyTokenizerModel extends TokenizerModel{constructor(n,o){super(n),this.tokens_to_ids=objectToMap(o.target_lang?n.vocab[o.target_lang]:n.vocab),this.bos_token=o.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=o.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=o.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=o.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[u,l]of this.tokens_to_ids)this.vocab[l]=u}encode(n){return n}}class Normalizer extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"BertNormalizer":return new BertNormalizer(n);case"Precompiled":return new Precompiled(n);case"Sequence":return new NormalizerSequence(n);case"Replace":return new Replace(n);case"NFC":return new NFC(n);case"NFKD":return new NFKD(n);case"Strip":return new StripNormalizer(n);case"StripAccents":return new StripAccents(n);case"Lowercase":return new Lowercase(n);case"Prepend":return new Prepend(n);default:throw new Error(`Unknown Normalizer type: ${n.type}`)}}normalize(n){throw Error("normalize should be implemented in subclass.")}_call(n){return this.normalize(n)}}class Replace extends Normalizer{normalize(n){let o=createPattern(this.config.pattern);return o===null||(n=n.replaceAll(o,this.config.content)),n}}class NFC extends Normalizer{normalize(n){return n=n.normalize("NFC"),n}}class NFKD extends Normalizer{normalize(n){return n=n.normalize("NFKD"),n}}class StripNormalizer extends Normalizer{normalize(n){return this.config.strip_left&&this.config.strip_right?n=n.trim():(this.config.strip_left&&(n=n.trimStart()),this.config.strip_right&&(n=n.trimEnd())),n}}class StripAccents extends Normalizer{normalize(n){return n=remove_accents(n),n}}class Lowercase extends Normalizer{normalize(n){return n=n.toLowerCase(),n}}class Prepend extends Normalizer{normalize(n){return n=this.config.prepend+n,n}}class NormalizerSequence extends Normalizer{constructor(n){super(n),this.normalizers=n.normalizers.map(o=>Normalizer.fromConfig(o))}normalize(n){return this.normalizers.reduce((o,u)=>u.normalize(o),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let o=[];for(let u=0;u<n.length;++u){let l=n[u],d=l.charCodeAt(0);this._is_chinese_char(d)?(o.push(" "),o.push(l),o.push(" ")):o.push(l)}return o.join("")}_is_chinese_char(n){return n>=19968&&n<=40959||n>=13312&&n<=19903||n>=131072&&n<=173791||n>=173824&&n<=177983||n>=177984&&n<=178207||n>=178208&&n<=183983||n>=63744&&n<=64255||n>=194560&&n<=195103}stripAccents(n){return n.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}normalize(n){return this.config.handle_chinese_chars&&(n=this._tokenize_chinese_chars(n)),this.config.lowercase?(n=n.toLowerCase(),this.config.strip_accents!==!1&&(n=this.stripAccents(n))):this.config.strip_accents&&(n=this.stripAccents(n)),n}}class PreTokenizer extends Callable{static fromConfig(n){if(n===null)return null;switch(n.type){case"BertPreTokenizer":return new BertPreTokenizer(n);case"Sequence":return new PreTokenizerSequence(n);case"WhitespaceSplit":return new WhitespaceSplit(n);case"Metaspace":return new MetaspacePreTokenizer(n);case"ByteLevel":return new ByteLevelPreTokenizer(n);case"Split":return new SplitPreTokenizer(n);case"Punctuation":return new PunctuationPreTokenizer(n);case"Digits":return new DigitsPreTokenizer(n);case"Replace":return new ReplacePreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let o=[];return Array.isArray(n)?o=n.map(u=>this.pre_tokenize_text(u)):o=this.pre_tokenize_text(n),o.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super(),this.pattern=new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return this.add_prefix_space&&!n.startsWith(" ")&&(n=" "+n),(this.use_regex?n.match(this.pattern)||[]:[n]).map(u=>Array.from(this.text_encoder.encode(u),l=>this.byte_encoder[l]).join(""))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern,this.config.invert)}pre_tokenize_text(n){return this.pattern===null?[]:this.config.invert?n.match(this.pattern)||[]:n.split(this.pattern).filter(o=>o)}}class PunctuationPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class DigitsPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n;const o=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(o,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class PostProcessor extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"TemplateProcessing":return new TemplateProcessing(n);case"ByteLevel":return new ByteLevelPostProcessor(n);case"RobertaProcessing":return new RobertaProcessing(n);case"BertProcessing":return new BertProcessing(n);default:throw new Error(`Unknown PostProcessor type: ${n.type}`)}}post_process(n,...o){throw Error("post_process should be implemented in subclass.")}_call(n,...o){return this.post_process(n,...o)}}class BertProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,o=null){return n=mergeArrays([this.cls],n,[this.sep]),o!==null&&(n=mergeArrays(n,[this.sep],o,[this.sep])),n}}class RobertaProcessing extends BertProcessing{}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,o=null){let u=o===null?this.single:this.pair,l=[];for(let d of u)"SpecialToken"in d?l.push(d.SpecialToken.id):"Sequence"in d&&(d.Sequence.id==="A"?l=mergeArrays(l,n):d.Sequence.id==="B"&&(l=mergeArrays(l,o)));return l}}class ByteLevelPostProcessor extends PostProcessor{post_process(n){return n}}class Decoder extends Callable{constructor(n){super(),this.config=n,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=n.trim_offsets}static fromConfig(n){switch(n.type){case"WordPiece":return new WordPieceDecoder(n);case"Metaspace":return new MetaspaceDecoder(n);case"ByteLevel":return new ByteLevelDecoder(n);case"Replace":return new ReplaceDecoder(n);case"ByteFallback":return new ByteFallback(n);case"Fuse":return new FuseDecoder(n);case"Strip":return new StripDecoder(n);case"Sequence":return new DecoderSequence(n);case"CTC":return new CTCDecoder(n);case"BPEDecoder":return new BPEDecoder(n);default:throw new Error(`Unknown Decoder type: ${n.type}`)}}_call(n){return this.decode(n)}decode(n){return this.decode_chain(n).join("")}decode_chain(n){throw Error("`decode_chain` should be implemented in subclass.")}}class ReplaceDecoder extends Decoder{decode_chain(n){let o=createPattern(this.config.pattern);return o===null?n:n.map(u=>u.replaceAll(o,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let o=[],u=[];for(let l of n){let d=null;if(l.length===6&&l.startsWith("<0x")&&l.endsWith(">")){let s=parseInt(l.slice(3,5),16);isNaN(s)||(d=s)}if(d!==null)u.push(d);else{if(u.length>0){let s=this.text_decoder.decode(Uint8Array.from(u));o.push(s),u=[]}o.push(l)}}if(u.length>0){let l=this.text_decoder.decode(Uint8Array.from(u));o.push(l),u=[]}return o}}class FuseDecoder extends Decoder{decode_chain(n){return[n.join("")]}}class StripDecoder extends Decoder{constructor(n){super(n),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(n){return n.map(o=>{let u=0;for(let d=0;d<this.start&&o[d]===this.content;++d){u=d+1;continue}let l=o.length;for(let d=0;d<this.stop;++d){const s=o.length-d-1;if(o[s]===this.content){l=s;continue}else break}return o.slice(u,l)})}}class WordPieceDecoder extends Decoder{constructor(n){super(n),this.cleanup=n.cleanup}decode_chain(n){return n.map((o,u)=>(u!==0&&(o.startsWith(this.config.prefix)?o=o.replace(this.config.prefix,""):o=" "+o),this.cleanup&&(o=clean_up_tokenization(o)),o))}}class ByteLevelDecoder extends Decoder{constructor(n){super(n),this.byte_decoder=UNICODE_TO_BYTES,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(n){let o=n.join(""),u=new Uint8Array([...o].map(d=>this.byte_decoder[d]));return this.text_decoder.decode(u)}decode_chain(n){let o=[],u=[];for(let l of n)this.added_tokens.includes(l)?(u.length>0&&(o.push(this.convert_tokens_to_string(u)),u=[]),o.push(l)):u.push(l);return u.length>0&&o.push(this.convert_tokens_to_string(u)),o}}class CTCDecoder extends Decoder{constructor(n){super(n),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(n){if(n.length===0)return"";let o=[n[0]];for(let d=1;d<n.length;++d)n[d]!==o.at(-1)&&o.push(n[d]);let l=o.filter(d=>d!==this.pad_token).join("");return this.cleanup&&(l=clean_up_tokenization(l).replaceAll(this.word_delimiter_token," ").trim()),l}decode_chain(n){return[this.convert_tokens_to_string(n)]}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(o=>Decoder.fromConfig(o))}decode_chain(n){return this.decoders.reduce((o,u)=>u.decode_chain(o),n)}}class BPEDecoder extends Decoder{constructor(n){super(n),this.suffix=this.config.suffix}decode_chain(n){return n.map((o,u)=>o.replaceAll(this.suffix,u===n.length-1?"":" "))}}class MetaspacePreTokenizer extends PreTokenizer{constructor(n){super(),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement,this.strRep=n.str_rep||this.replacement}pre_tokenize(n){typeof n=="string"&&(n=n.trimStart().split(/\s+/));const o=[];for(let u of n){let l=u.replaceAll(" ",this.strRep);this.addPrefixSpace&&!l.startsWith(this.replacement)&&(l=this.strRep+l),o.push(l)}return o}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let o=[];for(let u=0;u<n.length;++u){let l=n[u].replaceAll(this.replacement," ");this.addPrefixSpace&&u==0&&l.startsWith(" ")&&(l=l.substring(1)),o.push(l)}return o}}class Precompiled extends Normalizer{constructor(n){super(n),this.charsmap=n.precompiled_charsmap}normalize(n){return n=n.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),n=n.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),n.includes("～")?n=n.split("～").map(u=>u.normalize("NFKC")).join("～"):n=n.normalize("NFKC"),n}}class PreTokenizerSequence extends PreTokenizer{constructor(n){super(),this.tokenizers=n.pretokenizers.map(o=>PreTokenizer.fromConfig(o))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((o,u)=>u.pre_tokenize(o),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class ReplacePreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern),this.content=this.config.content}pre_tokenize_text(n){return this.pattern===null?[n]:[n.replaceAll(this.pattern,this.config.content)]}}class PreTrainedTokenizer extends Callable{constructor(n,o){super(),this.normalizer=Normalizer.fromConfig(n.normalizer),this.pre_tokenizer=PreTokenizer.fromConfig(n.pre_tokenizer),this.model=TokenizerModel.fromConfig(n.model,o),this.post_processor=PostProcessor.fromConfig(n.post_processor),this.decoder=Decoder.fromConfig(n.decoder),this.decoder.end_of_word_suffix=this.model.end_of_word_suffix,this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(let u of n.added_tokens){let l=u.id,d=u.content;this.added_tokens.push(d),this.model.tokens_to_ids.set(d,l),this.model.vocab[l]=d,u.special&&(this.special_tokens.push(d),this.all_special_ids.push(l))}this.special_tokens.push(...o.additional_special_tokens??[]),this.special_tokens=[...new Set(this.special_tokens)],this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=this.added_tokens.length>0?new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"):null,this.mask_token=this.getToken(o,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(o,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(o,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=o.model_max_length,this.remove_space=o.remove_space,this.clean_up_tokenization_spaces=o.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=o.do_lowercase_and_remove_accent??!1,this.padding_side="right"}getToken(n,...o){for(let u of o){let l=n[u];if(l)if(typeof l=="object"){if(l.__type==="AddedToken")return l.content;throw Error(`Unknown token: ${l}`)}else return l}return null}static async from_pretrained(n,{progress_callback:o=null,config:u=null,cache_dir:l=null,local_files_only:d=!1,revision:s="main"}={}){let p=await loadTokenizer(n,{progress_callback:o,config:u,cache_dir:l,local_files_only:d,revision:s});return new this(...p)}prepare_model_inputs(n){return n}_call(n,{text_pair:o=null,add_special_tokens:u=!0,padding:l=!1,truncation:d=null,max_length:s=null,return_tensor:p=!0}={}){let f;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(o!==null){if(Array.isArray(o)){if(n.length!==o.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");f=n.map((e,r)=>this.encode(e,o[r],{add_special_tokens:u}))}else f=n.map(e=>this.encode(e,null,{add_special_tokens:u}))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(o))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");f=[this.encode(n,o,{add_special_tokens:u})]}let c=max(f.map(e=>e.length))[0];s===null&&(s=c),s=Math.min(s,this.model_max_length);let a=[];if(l||d)for(let e=0;e<f.length;++e)if(f[e].length===s){a.push(new Array(f[e].length).fill(1));continue}else if(f[e].length>s)d&&(f[e]=f[e].slice(0,s)),a.push(new Array(f[e].length).fill(1));else if(l){let r=s-f[e].length;this.padding_side==="right"?(a.push(new Array(f[e].length).fill(1).concat(new Array(r).fill(0))),f[e].push(...new Array(r).fill(this.pad_token_id))):(a.push(new Array(r).fill(0).concat(new Array(f[e].length).fill(1))),f[e].unshift(...new Array(r).fill(this.pad_token_id)))}else a.push(new Array(f[e].length).fill(1));else a=f.map(e=>new Array(e.length).fill(1));if(p){if(!(l&&d)&&f.some(r=>r.length!==f[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");let e=[f.length,f[0].length];f=new Tensor("int64",BigInt64Array.from(f.flat().map(BigInt)),e),a=new Tensor("int64",BigInt64Array.from(a.flat().map(BigInt)),e)}else Array.isArray(n)||(f=f[0],a=a[0]);let t={input_ids:f,attention_mask:a};return t=this.prepare_model_inputs(t),t}_encode_text(n){return n===null?null:(this.added_tokens_regex?n.split(this.added_tokens_regex).filter(l=>l):[n]).map(l=>{if(this.added_tokens.includes(l))return l;{this.remove_space===!0&&(l=l.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(l=lowercase_and_remove_accent(l)),this.normalizer!==null&&(l=this.normalizer(l));let d=this.pre_tokenizer!==null?this.pre_tokenizer(l):[l];return this.model(d)}}).flat()}encode(n,o=null,{add_special_tokens:u=!0}={}){let l=this._encode_text(n),d=this._encode_text(o),s=this.post_processor!==null&&u?this.post_processor(l,d):mergeArrays(l??[],d??[]);return this.model.convert_tokens_to_ids(s)}batch_decode(n,o={}){return n.map(u=>this.decode(u,o))}decode(n,o={}){if(!Array.isArray(n)||n.length===0||!isIntegralNumber(n[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(n,o)}decode_single(n,{skip_special_tokens:o=!1,clean_up_tokenization_spaces:u=null}){let l=this.model.convert_ids_to_tokens(n);o&&(l=l.filter(s=>!this.special_tokens.includes(s)));let d=this.decoder(l);return this.decoder.end_of_word_suffix&&(d=d.replaceAll(this.decoder.end_of_word_suffix," "),o&&(d=d.trim())),(u??this.clean_up_tokenization_spaces)&&(d=clean_up_tokenization(d)),d}}function add_token_types(_){if(_.input_ids instanceof Tensor)_.token_type_ids=new Tensor("int64",new BigInt64Array(_.input_ids.data.length),_.input_ids.dims);else if(Array.isArray(_.input_ids))Array.isArray(_.input_ids[0])?_.token_type_ids=_.input_ids.map(n=>new Array(n.length).fill(0)):_.token_type_ids=new Array(_.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return _}class BertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class AlbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class MobileBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class SqueezeBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DebertaTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DebertaV2Tokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class HerbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DistilBertTokenizer extends PreTrainedTokenizer{}class CamembertTokenizer extends PreTrainedTokenizer{}class XLMTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}prepare_model_inputs(n){return add_token_types(n)}}class T5Tokenizer extends PreTrainedTokenizer{}class GPT2Tokenizer extends PreTrainedTokenizer{}class BartTokenizer extends PreTrainedTokenizer{}class MBartTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)),this.lang_to_token=u=>u}_build_translation_inputs(n,o,u){return _build_translation_inputs(this,n,o,u)}}class MBart50Tokenizer extends MBartTokenizer{}class RobertaTokenizer extends PreTrainedTokenizer{}class BloomTokenizer extends PreTrainedTokenizer{constructor(n,o){var d,s;const u=".,!?…。，、।۔،",l=(s=(d=n.pre_tokenizer)==null?void 0:d.pretokenizers[0])==null?void 0:s.pattern;l&&l.Regex===` ?[^(\\s|[${u}])]+`&&(l.Regex=` ?[^\\s${u}]+`),super(n,o)}}class LlamaTokenizer extends PreTrainedTokenizer{}class CodeLlamaTokenizer extends PreTrainedTokenizer{}class XLMRobertaTokenizer extends PreTrainedTokenizer{}class MPNetTokenizer extends PreTrainedTokenizer{}class FalconTokenizer extends PreTrainedTokenizer{}class GPTNeoXTokenizer extends PreTrainedTokenizer{}function _build_translation_inputs(_,n,o,u){if(!("language_codes"in _)||!Array.isArray(_.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in _)||!(_.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in _)||typeof _.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const l=u.src_lang,d=u.tgt_lang;if(!_.language_codes.includes(d))throw new Error(`Target language code "${d}" is not valid. Must be one of: {${_.language_codes.join(", ")}}`);if(l!==void 0){if(!_.language_codes.includes(l))throw new Error(`Source language code "${l}" is not valid. Must be one of: {${_.language_codes.join(", ")}}`);for(let s of _.post_processor.config.single)if("SpecialToken"in s&&_.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=_.lang_to_token(l);break}}return u.forced_bos_token_id=_.model.convert_tokens_to_ids([_.lang_to_token(d)])[0],_._call(n,o)}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)),this.lang_to_token=u=>u}_build_translation_inputs(n,o,u){return _build_translation_inputs(this,n,o,u)}}class M2M100Tokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)).map(u=>u.slice(2,-2)),this.lang_to_token=u=>`__${u}__`}_build_translation_inputs(n,o,u){return _build_translation_inputs(this,n,o,u)}}const WHISPER_LANGUAGES=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],WHISPER_LANGUAGE_MAPPING=new Map(WHISPER_LANGUAGES),WHISPER_TO_LANGUAGE_CODE_MAPPING=new Map([...WHISPER_LANGUAGES.map(([_,n])=>[n,_]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class WhisperTokenizer extends PreTrainedTokenizer{_decode_asr(n,{return_timestamps:o=!1,return_language:u=!1,time_precision:l=null,force_full_sequences:d=!0}={}){if(l===null)throw Error("Must specify time_precision");let s=null;const p=o==="word";function f(){return{language:s,timestamp:[null,null],text:""}}const c=[];let a=f(),t=0;const e=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let r=[],i=[],h=!1,g=null;const m=new Set(this.all_special_ids);for(let w of n){const T=w.tokens,S=p?w.token_timestamps:null;let A=null,O=e;if("stride"in w){const[F,R,$]=w.stride;if(t-=R,g=F-$,R&&(O=R/l+e),$)for(let N=T.length-1;N>=0;--N){const G=T[N];if(G>=e){if(A!==null&&(G-e)*l<g)break;A=G}}}let v=[],M=[];for(let F=0;F<T.length;++F){const R=T[F];if(m.has(R)){const $=this.decode([R]),N=WHISPER_LANGUAGE_MAPPING.get($.slice(2,-2));if(N!==void 0){if(s!==null&&N!==s&&!o){r.push(v);const G=this.findLongestCommonSequence(r)[0],D=this.decode(G);a.text=D,c.push(a),r=[],v=[],a=f()}s=a.language=N}}else if(R>=e){const $=(R-e)*l+t,N=round($,2);if(A!==null&&R>=A)h=!0;else if(h||r.length>0&&R<O)h=!1;else if(a.timestamp[0]===null)a.timestamp[0]=N;else if(N!==a.timestamp[0]){a.timestamp[1]=N,r.push(v),p&&i.push(M);const[G,D]=this.findLongestCommonSequence(r,i),j=this.decode(G);a.text=j,p&&(a.words=this.collateWordTimestamps(G,D,s)),c.push(a),r=[],v=[],i=[],M=[],a=f()}}else if(v.push(R),p){let $=round(S[F]+t,2),N;F+1<S.length?N=round(S[F+1]+t,2):N=null,M.push([$,N])}}if("stride"in w){const[F,R,$]=w.stride;t+=F-$}v.length>0?(r.push(v),p&&i.push(M)):r.every(F=>F.length===0)&&(a=f(),r=[],v=[],i=[],M=[])}if(r.length>0){if(d&&o)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[w,T]=this.findLongestCommonSequence(r,i),S=this.decode(w);a.text=S,p&&(a.words=this.collateWordTimestamps(w,T,s)),c.push(a)}let b=Object.create(null);const y=c.map(w=>w.text).join("");if(o||u){for(let w=0;w<c.length;++w){const T=c[w];o||delete T.timestamp,u||delete T.language}if(p){let w=[];for(let T of c)for(let S of T.words)w.push(S);b={chunks:w}}else b={chunks:c}}return[y,b]}findLongestCommonSequence(n,o=null){let u=n[0],l=u.length,d=[];const s=Array.isArray(o)&&o.length>0;let p=s?[]:null,f=s?o[0]:null;for(let c=1;c<n.length;++c){const a=n[c];let t=0,e=[l,l,0,0];const r=a.length;for(let w=1;w<l+r;++w){const T=w/1e4,S=Math.max(0,l-w),A=Math.min(l,l+r-w),O=u.slice(S,A),v=Math.max(0,w-l),M=Math.min(r,w),F=a.slice(v,M);if(O.length!==F.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const R=O.filter((N,G)=>N===F[G]).length,$=R/w+T;R>1&&$>t&&(t=$,e=[S,A,v,M])}const[i,h,g,m]=e,b=Math.floor((h+i)/2),y=Math.floor((m+g)/2);d.push(...u.slice(0,b)),u=a.slice(y),l=u.length,s&&(p.push(...f.slice(0,b)),f=o[c].slice(y))}return d.push(...u),s?(p.push(...f),[d,p]):[d,[]]}collateWordTimestamps(n,o,u){let[l,d,s]=this.combineTokensIntoWords(n,u),p=[];for(let f=0;f<l.length;++f){const c=s[f];p.push({text:l[f],timestamp:[o[c.at(0)][0],o[c.at(-1)][1]]})}return p}combineTokensIntoWords(n,o,u=`"'“¡¿([{-`,l=`"'.。,，!！?？:：”)]}、`){o=o??"english";let d,s,p;return["chinese","japanese","thai","lao","myanmar"].includes(o)?[d,s,p]=this.splitTokensOnUnicode(n):[d,s,p]=this.splitTokensOnSpaces(n),this.mergePunctuations(d,s,p,u,l)}decode(n,o){let u;return o&&o.decode_with_timestamps?u=this.decodeWithTimestamps(n,o):u=super.decode(n,o),u}decodeWithTimestamps(n,o){const u=(o==null?void 0:o.time_precision)??.02,l=Array.from(this.all_special_ids).at(-1)+1;let d=[[]];for(let s of n)if(s>=l){let p=(s-l)*u;p=round(p,2),d.push(`<|${p}|>`),d.push([])}else d[d.length-1].push(s);return d=d.map(s=>typeof s=="string"?s:super.decode(s,o)),d.join("")}splitTokensOnUnicode(n){const o=this.decode(n,{decode_with_timestamps:!0}),u="�";let l=[],d=[],s=[],p=[],f=[],c=0;for(let a=0;a<n.length;++a){const t=n[a];p.push(t),f.push(a);const e=this.decode(p,{decode_with_timestamps:!0});(!e.includes(u)||o[c+e.indexOf(u)]===u)&&(l.push(e),d.push(p),s.push(f),p=[],f=[],c+=e.length)}return[l,d,s]}splitTokensOnSpaces(n){let[o,u,l]=this.splitTokensOnUnicode(n),d=[],s=[],p=[];const f=new RegExp(`^[${PUNCTUATION_REGEX}]$`,"gu");for(let c=0;c<o.length;++c){const a=o[c],t=u[c],e=l[c],r=t[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),i=a.startsWith(" "),h=a.trim(),g=f.test(h);if(r||i||g||d.length===0)d.push(a),s.push(t),p.push(e);else{const m=d.length-1;d[m]+=a,s[m].push(...t),p[m].push(...e)}}return[d,s,p]}mergePunctuations(n,o,u,l,d){let s=structuredClone(n),p=structuredClone(o),f=structuredClone(u),c=s.length-2,a=s.length-1;for(;c>=0;)s[c].startsWith(" ")&&l.includes(s[c].trim())?(s[a]=s[c]+s[a],p[a]=mergeArrays(p[c],p[a]),f[a]=mergeArrays(f[c],f[a]),s[c]="",p[c]=[],f[c]=[]):a=c,--c;for(c=0,a=1;a<s.length;)!s[c].endsWith(" ")&&d.includes(s[a])?(s[c]+=s[a],p[c]=mergeArrays(p[c],p[a]),f[c]=mergeArrays(f[c],f[a]),s[a]="",p[a]=[],f[a]=[]):c=a,++a;return[s.filter(t=>t),p.filter(t=>t.length>0),f.filter(t=>t.length>0)]}get_decoder_prompt_ids({language:n=null,task:o=null,no_timestamps:u=!0}={}){let l=[];if(n){n=n.toLowerCase();let d=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(d===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))d=n;else{const f=n.length===2?WHISPER_LANGUAGE_MAPPING.keys():WHISPER_LANGUAGE_MAPPING.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(f)}`)}let s=this.model.tokens_to_ids.get(`<|${d}|>`);if(s===void 0)throw new Error(`Unable to find language "${d}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(s)}else l.push(null);if(o){if(o=o.toLowerCase(),o!=="transcribe"&&o!=="translate")throw new Error(`Task "${o}" is not supported. Must be one of: ["transcribe", "translate"]`);let d=this.model.tokens_to_ids.get(`<|${o}|>`);if(d===void 0)throw new Error(`Unable to find task "${o}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(d)}else l.push(null);if(u){let d=this.model.tokens_to_ids.get("<|notimestamps|>");if(d===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');l.push(d)}return l.map((d,s)=>[s+1,d]).filter(d=>d[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(u=>this.languageRegex.test(u)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(n){if(n===null)return null;let[o,...u]=n.trim().split(this.languageRegex);if(u.length===0)return super._encode_text(o);if(u.length===2){let[l,d]=u;return this.supported_language_codes.includes(l)||console.warn(`Unsupported language code "${l}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),mergeArrays([l],super._encode_text(d))}}}class Wav2Vec2CTCTokenizer extends PreTrainedTokenizer{}class BlenderbotTokenizer extends PreTrainedTokenizer{}class BlenderbotSmallTokenizer extends PreTrainedTokenizer{}class SpeechT5Tokenizer extends PreTrainedTokenizer{}class AutoTokenizer{static async from_pretrained(n,{quantized:o=!0,progress_callback:u=null,config:l=null,cache_dir:d=null,local_files_only:s=!1,revision:p="main"}={}){let[f,c]=await loadTokenizer(n,{quantized:o,progress_callback:u,config:l,cache_dir:d,local_files_only:s,revision:p}),a=c.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[a];return t||(console.warn(`Unknown tokenizer class "${a}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(f,c)}}le(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,CamembertTokenizer,DebertaTokenizer,DebertaV2Tokenizer,BertTokenizer,HerbertTokenizer,XLMTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,MBartTokenizer,MBart50Tokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,M2M100Tokenizer,LlamaTokenizer,CodeLlamaTokenizer,XLMRobertaTokenizer,MPNetTokenizer,FalconTokenizer,GPTNeoXTokenizer,Wav2Vec2CTCTokenizer,BlenderbotTokenizer,BlenderbotSmallTokenizer,SpeechT5Tokenizer,PreTrainedTokenizer});async function loadConfig(_,n){return await getModelJSON(_,"config.json",!0,n)}class PretrainedConfig{constructor(n){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,n)}static async from_pretrained(n,{progress_callback:o=null,config:u=null,cache_dir:l=null,local_files_only:d=!1,revision:s="main"}={}){let p=u??await loadConfig(n,{progress_callback:o,config:u,cache_dir:l,local_files_only:d,revision:s});return new this(p)}}class AutoConfig{static async from_pretrained(...n){return PretrainedConfig.from_pretrained(...n)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(n){this.processors.push(n)}extend(n){this.processors.push(...n)}_call(n,o){for(let u of o)this.processors.forEach(l=>l(n,u))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,o){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.force_token_map=Object.fromEntries(n??[])}_call(n,o){let u=this.force_token_map[n.length];return exists(u)&&(o.data.fill(-1/0),o.data[u]=0),o}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,o){return n.length===1&&(o.data.fill(-1/0),o.data[this.bos_token_id]=0),o}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.max_length=n,this.forced_eos_token_id=o}_call(n,o){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.begin_suppress_tokens=n,this.begin_index=o}_call(n,o){if(n.length===this.begin_index)for(let u of this.begin_suppress_tokens)o.data[u]=-1/0;return o}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.eos_token_id=n.eos_token_id,this.no_timestamps_token_id=n.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(n.forced_decoder_ids||[]).length+2,n.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=n.max_initial_timestamp_index}_call(n,o){if(o.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return o.data.fill(-1/0),o.data[this.timestamp_begin]=0,o;const u=n.slice(this.begin_index),l=u.length>=1&&u[u.length-1]>=this.timestamp_begin,d=u.length<2||u[u.length-2]>=this.timestamp_begin;if(l&&(d?o.data.subarray(this.timestamp_begin).fill(-1/0):o.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const c=this.timestamp_begin+this.max_initial_timestamp_index;o.data.subarray(c+1).fill(-1/0)}const s=log_softmax(o.data),p=Math.log(s.subarray(this.timestamp_begin).map(Math.exp).reduce((c,a)=>c+a)),f=max(s.subarray(0,this.timestamp_begin))[0];return p>f&&o.data.subarray(0,this.timestamp_begin).fill(-1/0),o}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const o=n.length,u=[];for(let d=0;d<o+1-this.no_repeat_ngram_size;++d){const s=[];for(let p=0;p<this.no_repeat_ngram_size;++p)s.push(n[d+p]);u.push(s)}const l=new Map;for(const d of u){const s=d.slice(0,d.length-1),p=JSON.stringify(s),f=l.get(p)??[];f.push(d[d.length-1]),l.set(p,f)}return l}getGeneratedNgrams(n,o){const u=o.slice(o.length+1-this.no_repeat_ngram_size,o.length);return n.get(JSON.stringify(u))??[]}calcBannedNgramTokens(n){const o=[];if(n.length+1<this.no_repeat_ngram_size)return o;{const u=this.getNgrams(n);return this.getGeneratedNgrams(u,n)}}_call(n,o){const u=this.calcBannedNgramTokens(n);for(const l of u)o.data[l]=-1/0;return o}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.penalty=n}_call(n,o){for(const u of n)o.data[u]<0?o.data[u]*=this.penalty:o.data[u]/=this.penalty;return o}}class MinLengthLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.min_length=n,this.eos_token_id=Array.isArray(o)?o:[o]}_call(n,o){if(n.length<this.min_length)for(const u of this.eos_token_id)o.data[u]=-1/0;return o}}class MinNewTokensLengthLogitsProcessor extends LogitsProcessor{constructor(n,o,u){super(),this.prompt_length_to_skip=n,this.min_new_tokens=o,this.eos_token_id=Array.isArray(u)?u:[u]}_call(n,o){if(n.length-this.prompt_length_to_skip<this.min_new_tokens)for(const l of this.eos_token_id)o.data[l]=-1/0;return o}}class GenerationConfig{constructor(n={}){this.max_length=n.max_length??20,this.max_new_tokens=n.max_new_tokens??null,this.min_length=n.min_length??0,this.min_new_tokens=n.min_new_tokens??null,this.early_stopping=n.early_stopping??!1,this.max_time=n.max_time??null,this.do_sample=n.do_sample??!1,this.num_beams=n.num_beams??1,this.num_beam_groups=n.num_beam_groups??1,this.penalty_alpha=n.penalty_alpha??null,this.use_cache=n.use_cache??!0,this.temperature=n.temperature??1,this.top_k=n.top_k??50,this.top_p=n.top_p??1,this.typical_p=n.typical_p??1,this.epsilon_cutoff=n.epsilon_cutoff??0,this.eta_cutoff=n.eta_cutoff??0,this.diversity_penalty=n.diversity_penalty??0,this.repetition_penalty=n.repetition_penalty??1,this.encoder_repetition_penalty=n.encoder_repetition_penalty??1,this.length_penalty=n.length_penalty??1,this.no_repeat_ngram_size=n.no_repeat_ngram_size??0,this.bad_words_ids=n.bad_words_ids??null,this.force_words_ids=n.force_words_ids??null,this.renormalize_logits=n.renormalize_logits??!1,this.constraints=n.constraints??null,this.forced_bos_token_id=n.forced_bos_token_id??null,this.forced_eos_token_id=n.forced_eos_token_id??null,this.remove_invalid_values=n.remove_invalid_values??!1,this.exponential_decay_length_penalty=n.exponential_decay_length_penalty??null,this.suppress_tokens=n.suppress_tokens??null,this.begin_suppress_tokens=n.begin_suppress_tokens??null,this.forced_decoder_ids=n.forced_decoder_ids??null,this.num_return_sequences=n.num_return_sequences??1,this.output_attentions=n.output_attentions??!1,this.output_hidden_states=n.output_hidden_states??!1,this.output_scores=n.output_scores??!1,this.return_dict_in_generate=n.return_dict_in_generate??!1,this.pad_token_id=n.pad_token_id??null,this.bos_token_id=n.bos_token_id??null,this.eos_token_id=n.eos_token_id??null,this.encoder_no_repeat_ngram_size=n.encoder_no_repeat_ngram_size??0,this.decoder_start_token_id=n.decoder_start_token_id??null,this.generation_kwargs=n.generation_kwargs??{}}}class Sampler extends Callable{constructor(n){super(),this.generation_config=n}_call(n,o=-1){return this.sample(n,o)}sample(n,o){throw Error("sample should be implemented in subclasses.")}getLogits(n,o){let u=n.dims.at(-1),l=n.data;if(o===-1)l=l.slice(-u);else{let d=o*u;l=l.slice(d,d+u)}return this.generation_config.temperature>0&&(l=l.map(d=>d/this.generation_config.temperature)),l}randomSelect(n){let o=n.reduce((l,d)=>l+d,0),u=Math.random()*o;for(let l=0;l<n.length;++l)if(u-=n[l],u<=0)return l;return 0}static getSampler(n){if(n.do_sample)return new MultinomialSampler(n);if(n.num_beams>1)return new BeamSearchSampler(n);if(n.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,o=-1){let u=this.getLogits(n,o);return[[max(u)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,o=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const l=this.getLogits(n,o),d=getTopItems(l,u),s=softmax(d.map(p=>p[1]));return Array.from({length:this.generation_config.num_beams},()=>{const p=this.randomSelect(s);return[d[p][0],Math.log(s[p])]})}}class BeamSearchSampler extends Sampler{sample(n,o=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const l=this.getLogits(n,o),d=getTopItems(l,u),s=softmax(d.map(p=>p[1]));return Array.from({length:this.generation_config.num_beams},(p,f)=>[d[f][0],Math.log(s[f])])}}const{InferenceSession,Tensor:ONNXTensor}=ONNX,MODEL_TYPES={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4},MODEL_TYPE_MAPPING=new Map,MODEL_NAME_TO_CLASS_MAPPING=new Map,MODEL_CLASS_TO_NAME_MAPPING=new Map;async function constructSession(_,n,o){let u=`onnx/${n}${o.quantized?"_quantized":""}.onnx`,l=await getModelFile(_,u,!0,o);try{return await InferenceSession.create(l,{executionProviders})}catch(d){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw d;return console.warn(d),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(l,{executionProviders:["wasm"]})}}async function validateInputs(_,n){const o={},u=[];for(let s of _.inputNames)n[s]===void 0?u.push(s):o[s]=n[s];if(u.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${u.join(", ")}.`);const l=Object.keys(n).length,d=_.inputNames.length;if(l>d){let s=Object.keys(n).filter(p=>!_.inputNames.includes(p));console.warn(`WARNING: Too many inputs were provided (${l} > ${d}). The following inputs will be ignored: "${s.join(", ")}".`)}return o}async function sessionRun(_,n){const o=await validateInputs(_,n);try{let u=await _.run(o);return u=replaceTensors(u),u}catch(u){throw console.error(`An error occurred during model execution: "${u}".`),console.error("Inputs given to model:",o),u}}function replaceTensors(_){for(let n in _)_[n]instanceof ONNXTensor?_[n]=new Tensor(_[n]):typeof _[n]=="object"&&replaceTensors(_[n]);return _}function toI64Tensor(_){if(_ instanceof Tensor)return _;if(_.length===0)throw Error("items must be non-empty");if(Array.isArray(_[0])){if(_.some(n=>n.length!==_[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Tensor("int64",BigInt64Array.from(_.flat().map(n=>BigInt(n))),[_.length,_[0].length])}else return new Tensor("int64",BigInt64Array.from(_.map(n=>BigInt(n))),[1,_.length])}function prepareAttentionMask(_,n){let o=_.config.pad_token_id??null,u=_.config.eos_token_id??null;isIntegralNumber(u)&&(u=[u]);let l=n.indexOf(o)!==-1,d=u===null||!u.includes(o);if(l&&d){let s=BigInt64Array.from(n.data.map(p=>p!=o));return new Tensor("int64",s,n.dims)}else return ones_like(n)}function boolTensor(_){return new Tensor("bool",[_],[1])}async function seq2seqForward(_,n){let{encoder_outputs:o,past_key_values:u}=n;o||(o=(await encoderForward(_,n)).last_hidden_state);let l={input_ids:n.decoder_input_ids,encoder_hidden_states:o,use_cache_branch:boolTensor(!!u)};_.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(l.encoder_attention_mask=n.attention_mask),_.addPastKeyValues(l,u);const d=await sessionRun(_.decoder_merged_session,l);let s=d.logits;u=_.getPastKeyValues(d,u);const p=_.getAttentions(d);return new Seq2SeqLMOutput({logits:s,past_key_values:u,encoder_outputs:o,...p})}function seq2seqStartBeams(_,n,o,u){let l=[],d=0;const s=_.requires_attention_mask??!0;let p=o.decoder_input_ids??o.decoder_start_token_id??o.bos_token_id??o.eos_token_id;p instanceof Tensor?p=p.tolist().flat():Array.isArray(p)||(p=[p]);for(let f of n){f.dims=[1,...f.dims];let c={inputs:f,encoder_outputs:null,prev_model_outputs:null,output_token_ids:p,done:!1,score:0,id:d++};s&&(c.attention_mask=prepareAttentionMask(_,f)),l.push(c)}return l}async function seq2seqRunBeam(_,n){var s;const o=_.main_input_name;let u=n.output_token_ids;n.prev_model_outputs&&(u=u.slice(-1));let l={[o]:n.inputs,decoder_input_ids:toI64Tensor(u),encoder_outputs:n.encoder_outputs,past_key_values:(s=n.prev_model_outputs)==null?void 0:s.past_key_values};n.attention_mask&&(l.attention_mask=n.attention_mask);let d=await _.forward(l);return n.prev_model_outputs=d,n.encoder_outputs=d.encoder_outputs,d}function seq2seqUpdatebeam(_,n){_.output_token_ids=[..._.output_token_ids,n]}async function encoderForward(_,n){let o={};for(let u of _.session.inputNames)o[u]=n[u];return await sessionRun(_.session,o)}async function decoderForward(_,n){let{input_ids:o,past_key_values:u,attention_mask:l}=n,d={input_ids:o,attention_mask:l??prepareAttentionMask(_,o),use_cache_branch:boolTensor(!!u)};_.addPastKeyValues(d,u);let s=await sessionRun(_.session,d),p=s.logits;return u=_.getPastKeyValues(s,u),{logits:p,past_key_values:u}}function decoderStartBeams(_,n,o,u,l){let d=[],s=0;for(let p of n){let f=p.tolist().map(Number);p.dims=[1,...p.dims];let c;l?(c=l[s],c.dims=[1,...c.dims]):c=prepareAttentionMask(_,p);let a={input:p,model_input_ids:p,attention_mask:c,prev_model_outputs:null,output_token_ids:f,num_output_tokens:u,done:!1,score:0,id:s++};d.push(a)}return d}async function decoderRunBeam(_,n){var d;let o=new BigInt64Array(n.output_token_ids.length).fill(1n),u={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",o,[1,o.length]),past_key_values:(d=n.prev_model_outputs)==null?void 0:d.past_key_values},l=await _.forward(u);return n.prev_model_outputs=l,l}function decoderUpdatebeam(_,n){_.output_token_ids=[..._.output_token_ids,n],_.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(o,u){super();le(this,"main_input_name","input_ids");this.config=o,this.session=u;const l=MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),d=MODEL_TYPE_MAPPING.get(l);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,d===MODEL_TYPES.DecoderOnly?(this.can_generate=!0,this._runBeam=decoderRunBeam,this._getStartBeams=decoderStartBeams,this._updateBeam=decoderUpdatebeam,this._forward=decoderForward):d===MODEL_TYPES.Seq2Seq||d===MODEL_TYPES.Vision2Seq?(this.can_generate=!0,this._runBeam=seq2seqRunBeam,this._getStartBeams=seq2seqStartBeams,this._updateBeam=seq2seqUpdatebeam,this._forward=seq2seqForward):d===MODEL_TYPES.EncoderDecoder?this._forward=encoderForward:this._forward=encoderForward}async dispose(){let o=[];for(let u of Object.keys(this)){let l=this[u];l instanceof InferenceSession&&o.push(l.handler.dispose())}return await Promise.all(o)}static async from_pretrained(o,{quantized:u=!0,progress_callback:l=null,config:d=null,cache_dir:s=null,local_files_only:p=!1,revision:f="main",model_file_name:c=null}={}){let a={quantized:u,progress_callback:l,config:d,cache_dir:s,local_files_only:p,revision:f,model_file_name:c};const t=MODEL_CLASS_TO_NAME_MAPPING.get(this),e=MODEL_TYPE_MAPPING.get(t);let r;return e===MODEL_TYPES.DecoderOnly?r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,a.model_file_name??"decoder_model_merged",a),getModelJSON(o,"generation_config.json",!1,a)]):e===MODEL_TYPES.Seq2Seq||e===MODEL_TYPES.Vision2Seq?r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,"encoder_model",a),constructSession(o,"decoder_model_merged",a),getModelJSON(o,"generation_config.json",!1,a)]):e===MODEL_TYPES.EncoderDecoder?r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,"encoder_model",a),constructSession(o,"decoder_model_merged",a)]):(e!==MODEL_TYPES.EncoderOnly&&console.warn(`Model type for '${t}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,a.model_file_name??"model",a)])),new this(...r)}async _call(o){return await this.forward(o)}async forward(o){return await this._forward(this,o)}_get_logits_processor(o,u,l=null){const d=new LogitsProcessorList;if(o.repetition_penalty!==null&&o.repetition_penalty!==1&&d.push(new RepetitionPenaltyLogitsProcessor(o.repetition_penalty)),o.no_repeat_ngram_size!==null&&o.no_repeat_ngram_size>0&&d.push(new NoRepeatNGramLogitsProcessor(o.no_repeat_ngram_size)),o.min_length!==null&&o.eos_token_id!==null&&o.min_length>0&&d.push(new MinLengthLogitsProcessor(o.min_length,o.eos_token_id)),o.min_new_tokens!==null&&o.eos_token_id!==null&&o.min_new_tokens>0&&d.push(new MinNewTokensLengthLogitsProcessor(u,o.min_new_tokens,o.eos_token_id)),o.forced_bos_token_id!==null&&d.push(new ForcedBOSTokenLogitsProcessor(o.forced_bos_token_id)),o.forced_eos_token_id!==null&&d.push(new ForcedEOSTokenLogitsProcessor(o.max_length,o.forced_eos_token_id)),o.begin_suppress_tokens!==null){let s=u>1||o.forced_bos_token_id===null?u:u+1;o.forced_decoder_ids!==null&&(s+=o.forced_decoder_ids[o.forced_decoder_ids.length-1][0]),d.push(new SuppressTokensAtBeginLogitsProcessor(o.begin_suppress_tokens,s))}return o.forced_decoder_ids!==null&&d.push(new ForceTokensLogitsProcessor(o.forced_decoder_ids)),l!==null&&d.extend(l),d}_get_generation_config(o){let u=new GenerationConfig(this.config);return"generation_config"in this&&Object.assign(u,this.generation_config),o!==null&&Object.assign(u,o),u}async generate(o,u=null,l=null,{inputs_attention_mask:d=null}={}){if(!this.can_generate){let m=`The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const b=this.config.model_type,y=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(b)??MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(b)??MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(b)??MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(b);throw y&&(m+=` Please use the following class instead: '${y[0]}'`),Error(m)}if(!(o instanceof Tensor)&&!isTypedArray(o)&&!Array.isArray(o))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${o.constructor.name}".`);let s;if(this.config.is_encoder_decoder)s=0;else if(s=o instanceof Tensor?o.dims.at(-1):o.length,s===0)throw Error("Must supply a non-empty array of input token ids.");u=this._get_generation_config(u),l=l??new LogitsProcessorList,l=this._get_logits_processor(u,s,l);let p=u.eos_token_id;p!==null&&!Array.isArray(p)&&(p=[p]);let f=1;const c=f+(u.max_new_tokens??1/0),a=Number.isInteger(u.max_length)&&(u.max_new_tokens??null)===null;let t=Sampler.getSampler(u),e=this.getStartBeams(o,u,f,d);for(;e.some(g=>!g.done)&&f<c;){let g=[];for(let m of e){if(m.done){g.push(m);continue}if(a&&m.output_token_ids.length>=u.max_length){m.done=!0,g.push(m);continue}let b=await this.runBeam(m);u.output_attentions&&this.addAttentionsToBeam(m,b),u.output_scores;let y=b.logits.slice(null,-1,null);l(m.output_token_ids,y);let w=t(y);for(let[T,S]of w){let A={...m};this.updateBeam(A,T),A.score+=S,p&&p.includes(T)&&(A.done=!0),g.push(A)}}++f,g=this.groupBeams(g).map(m=>m.sort((b,y)=>y.score-b.score).slice(0,u.num_beams)),e=g.flat(),u.callback_function&&u.callback_function(e)}const r=this.groupBeams(e),i=g=>r.map(m=>u.num_return_sequences>1?m.slice(0,u.num_return_sequences).map(b=>b[g]):[m[0][g]]).flat(),h=i("output_token_ids");if(u.return_dict_in_generate){const g=i("decoder_attentions"),m=i("cross_attentions");return{sequences:h,decoder_attentions:g,cross_attentions:m}}else return h}addAttentionsToBeam(o,u){if(this.config.is_encoder_decoder){if(!u.cross_attentions||u.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");o.cross_attentions||(o.cross_attentions=[]),o.cross_attentions.push(u.cross_attentions)}if(!u.decoder_attentions||u.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");o.decoder_attentions||(o.decoder_attentions=[]),o.decoder_attentions.push(u.decoder_attentions)}groupBeams(o){const u=Object.create(null);for(const l of o)u[l.id]===void 0?u[l.id]=[l]:u[l.id].push(l);return Object.values(u)}getPastKeyValues(o,u){const l=Object.create(null);for(const d in o)if(d.startsWith("present")){let s=d.replace("present","past_key_values");u&&d.includes("encoder")?l[s]=u[s]:l[s]=o[d]}return l}getAttentions(o){const u=Object.create(null);for(const l of["cross_attentions","decoder_attentions"]){const d=[];for(const s in o)if(s.startsWith(l)){const p=s.split(".").pop();d[p]=o[s]}u[l]=d}return u}addPastKeyValues(o,u){if(u)Object.assign(o,u);else if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let l=[1,this.num_encoder_heads,0,this.encoder_dim_kv],d=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let s=0;s<this.num_decoder_layers;++s)o[`past_key_values.${s}.encoder.key`]=new Tensor("float32",[],l),o[`past_key_values.${s}.encoder.value`]=new Tensor("float32",[],l),o[`past_key_values.${s}.decoder.key`]=new Tensor("float32",[],d),o[`past_key_values.${s}.decoder.value`]=new Tensor("float32",[],d)}else if(this.config.multi_query){let l=[1,0,2*this.dim_kv];for(let d=0;d<this.num_layers;++d)o[`past_key_values.${d}.key_value`]=new Tensor("float32",[],l)}else if(this.config.model_type==="bloom"){let l=[1*this.num_heads,this.dim_kv,0],d=[1*this.num_heads,0,this.dim_kv];for(let s=0;s<this.num_layers;++s)o[`past_key_values.${s}.key`]=new Tensor("float32",[],l),o[`past_key_values.${s}.value`]=new Tensor("float32",[],d)}else{let l=[1,this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)o[`past_key_values.${d}.key`]=new Tensor("float32",[],l),o[`past_key_values.${d}.value`]=new Tensor("float32",[],l)}}getStartBeams(o,u,l,d){return this._getStartBeams(this,o,u,l,d)}async runBeam(o){return await this._runBeam(this,o)}updateBeam(o,u){return this._updateBeam(o,u)}}class ModelOutput{}class BertPreTrainedModel extends PreTrainedModel{}class BertModel extends BertPreTrainedModel{}class BertForMaskedLM extends BertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class BertForSequenceClassification extends BertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class BertForTokenClassification extends BertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class BertForQuestionAnswering extends BertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class CamembertPreTrainedModel extends PreTrainedModel{}class CamembertModel extends CamembertPreTrainedModel{}class CamembertForMaskedLM extends CamembertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class CamembertForSequenceClassification extends CamembertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class CamembertForTokenClassification extends CamembertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class CamembertForQuestionAnswering extends CamembertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DebertaPreTrainedModel extends PreTrainedModel{}class DebertaModel extends DebertaPreTrainedModel{}class DebertaForMaskedLM extends DebertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class DebertaForSequenceClassification extends DebertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DebertaForTokenClassification extends DebertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class DebertaForQuestionAnswering extends DebertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DebertaV2PreTrainedModel extends PreTrainedModel{}class DebertaV2Model extends DebertaV2PreTrainedModel{}class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DistilBertPreTrainedModel extends PreTrainedModel{}class DistilBertModel extends DistilBertPreTrainedModel{}class DistilBertForSequenceClassification extends DistilBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DistilBertForTokenClassification extends DistilBertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DistilBertForMaskedLM extends DistilBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MobileBertPreTrainedModel extends PreTrainedModel{}class MobileBertModel extends MobileBertPreTrainedModel{}class MobileBertForMaskedLM extends MobileBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MobileBertForSequenceClassification extends MobileBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class MPNetPreTrainedModel extends PreTrainedModel{}class MPNetModel extends MPNetPreTrainedModel{}class MPNetForMaskedLM extends MPNetPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MPNetForSequenceClassification extends MPNetPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MPNetForTokenClassification extends MPNetPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class MPNetForQuestionAnswering extends MPNetPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class SqueezeBertPreTrainedModel extends PreTrainedModel{}class SqueezeBertModel extends SqueezeBertPreTrainedModel{}class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class AlbertPreTrainedModel extends PreTrainedModel{}class AlbertModel extends AlbertPreTrainedModel{}class AlbertForSequenceClassification extends AlbertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class AlbertForQuestionAnswering extends AlbertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class AlbertForMaskedLM extends AlbertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class T5PreTrainedModel extends PreTrainedModel{}class T5Model extends T5PreTrainedModel{}class T5ForConditionalGeneration extends T5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class LongT5PreTrainedModel extends PreTrainedModel{}class LongT5Model extends LongT5PreTrainedModel{}class LongT5ForConditionalGeneration extends LongT5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class MT5PreTrainedModel extends PreTrainedModel{}class MT5Model extends MT5PreTrainedModel{}class MT5ForConditionalGeneration extends MT5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class BartPretrainedModel extends PreTrainedModel{}class BartModel extends BartPretrainedModel{}class BartForConditionalGeneration extends BartPretrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BartForSequenceClassification extends BartPretrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MBartPreTrainedModel extends PreTrainedModel{}class MBartModel extends MBartPreTrainedModel{}class MBartForConditionalGeneration extends MBartPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class MBartForSequenceClassification extends MBartPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MBartForCausalLM extends MBartPreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BlenderbotPreTrainedModel extends PreTrainedModel{}class BlenderbotModel extends BlenderbotPreTrainedModel{}class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BlenderbotSmallPreTrainedModel extends PreTrainedModel{}class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel{}class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class RobertaPreTrainedModel extends PreTrainedModel{}class RobertaModel extends RobertaPreTrainedModel{}class RobertaForMaskedLM extends RobertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class RobertaForSequenceClassification extends RobertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class RobertaForTokenClassification extends RobertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class RobertaForQuestionAnswering extends RobertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class XLMPreTrainedModel extends PreTrainedModel{}class XLMModel extends XLMPreTrainedModel{}class XLMWithLMHeadModel extends XLMPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class XLMForSequenceClassification extends XLMPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class XLMForTokenClassification extends XLMPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class XLMForQuestionAnswering extends XLMPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class XLMRobertaPreTrainedModel extends PreTrainedModel{}class XLMRobertaModel extends XLMRobertaPreTrainedModel{}class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class WhisperPreTrainedModel extends PreTrainedModel{}class WhisperModel extends WhisperPreTrainedModel{}class WhisperForConditionalGeneration extends WhisperPreTrainedModel{constructor(o,u,l,d){super(o,u);le(this,"requires_attention_mask",!1);le(this,"main_input_name","input_features");this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}async generate(o,u=null,l=null){if(u=this._get_generation_config(u),u.return_timestamps??(u.return_timestamps=!1),u.return_timestamps&&(l=[new WhisperTimeStampLogitsProcessor(u)]),u.return_token_timestamps&&(u.output_attentions=!0,u.return_dict_in_generate=!0,u.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),!u.alignment_heads))throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");const d=await super.generate(o,u,l);return u.return_token_timestamps&&u.alignment_heads&&(d.token_timestamps=this._extract_token_timestamps(d,u.alignment_heads,u.num_frames)),d}_extract_token_timestamps(o,u,l=null,d=.02){if(!o.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let s=this.config.median_filter_width;s===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),s=7);const p=o.cross_attentions.map(a=>{let t=Array.from({length:this.config.decoder_layers},(m,b)=>cat(a.map(y=>y[b]),2)),e=stack(u.map(([m,b])=>l?t[m].slice(null,b,null,[0,l]):t[m].slice(null,b)));e=e.transpose(1,0,2,3);let[r,i]=std_mean(e,-2,0,!0),h=e.clone();for(let m=0;m<h.dims[0];++m){let b=h[m];for(let y=0;y<b.dims[0];++y){let w=b[y];const T=r[m][y][0],S=i[m][y][0];for(let A=0;A<w.dims[0];++A){let O=w[A];for(let v=0;v<O.data.length;++v)O.data[v]=(O.data[v]-S.data[v])/T.data[v];O.data.set(medianFilter(O.data,s))}}}return mean(h,1)}),f=[o.sequences.length,o.sequences[0].length],c=new Tensor("float32",new Float32Array(f[0]*f[1]),f);for(let a=0;a<f[0];++a){const t=p[a].neg().squeeze_(0);let[e,r]=dynamicTimeWarping(t),i=Array.from({length:e.length-1},(m,b)=>e[b+1]-e[b]),h=mergeArrays([1],i).map(m=>!!m),g=[];for(let m=0;m<h.length;++m)h[m]&&g.push(r[m]*d);c[a].data.set(g,1)}return c}}class VisionEncoderDecoderModel extends PreTrainedModel{constructor(o,u,l,d){super(o,u);le(this,"main_input_name","pixel_values");this.decoder_merged_session=l,this.generation_config=d;const s=this.config.encoder,p=this.config.decoder,f=s.model_type;(MODEL_MAPPING_NAMES_ENCODER_ONLY.get(f)??MODEL_MAPPING_NAMES_ENCODER_DECODER.get(f))||console.warn(`Model type for encoder '${f}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const a=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(p.model_type);if(!a)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const t=a[1],e=new t(p,l,d);this.add_encoder_pkv="num_decoder_layers"in e,this.add_encoder_pkv?(this.num_decoder_layers=e.num_decoder_layers,this.num_decoder_heads=e.num_decoder_heads,this.decoder_dim_kv=e.decoder_dim_kv,this.num_encoder_layers=e.num_encoder_layers,this.num_encoder_heads=e.num_encoder_heads,this.encoder_dim_kv=e.encoder_dim_kv):(this.num_layers=e.num_layers,this.num_heads=e.num_heads,this.dim_kv=e.dim_kv)}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class CLIPTextModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(n,o={}){return o.model_file_name??(o.model_file_name="text_model"),super.from_pretrained(n,o)}}class CLIPVisionModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(n,o={}){return o.model_file_name??(o.model_file_name="vision_model"),super.from_pretrained(n,o)}}class GPT2PreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPT2Model extends GPT2PreTrainedModel{}class GPT2LMHeadModel extends GPT2PreTrainedModel{}class GPTNeoPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_heads,this.num_layers=this.config.num_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class GPTNeoModel extends GPTNeoPreTrainedModel{}class GPTNeoForCausalLM extends GPTNeoPreTrainedModel{}class GPTNeoXPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class GPTNeoXModel extends GPTNeoXPreTrainedModel{}class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel{}class GPTJPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPTJModel extends GPTJPreTrainedModel{}class GPTJForCausalLM extends GPTJPreTrainedModel{}class GPTBigCodePreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPTBigCodeModel extends GPTBigCodePreTrainedModel{}class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel{}class CodeGenPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class CodeGenModel extends CodeGenPreTrainedModel{}class CodeGenForCausalLM extends CodeGenPreTrainedModel{}class LlamaPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class LlamaModel extends LlamaPreTrainedModel{}class LlamaForCausalLM extends LlamaPreTrainedModel{}class BloomPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.hidden_size/this.num_heads}}class BloomModel extends BloomPreTrainedModel{}class BloomForCausalLM extends BloomPreTrainedModel{}class MptPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_heads,this.num_layers=this.config.n_layers,this.dim_kv=this.config.d_model/this.num_heads}}class MptModel extends MptPreTrainedModel{}class MptForCausalLM extends MptPreTrainedModel{}class OPTPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class OPTModel extends OPTPreTrainedModel{}class OPTForCausalLM extends OPTPreTrainedModel{}class ViTPreTrainedModel extends PreTrainedModel{}class ViTModel extends ViTPreTrainedModel{}class ViTForImageClassification extends ViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MobileViTPreTrainedModel extends PreTrainedModel{}class MobileViTModel extends MobileViTPreTrainedModel{}class MobileViTForImageClassification extends MobileViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class BeitPreTrainedModel extends PreTrainedModel{}class BeitModel extends BeitPreTrainedModel{}class BeitForImageClassification extends BeitPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DetrPreTrainedModel extends PreTrainedModel{}class DetrModel extends DetrPreTrainedModel{}class DetrForObjectDetection extends DetrPreTrainedModel{async _call(n){return new DetrObjectDetectionOutput(await super._call(n))}}class DetrForSegmentation extends DetrPreTrainedModel{async _call(n){return new DetrSegmentationOutput(await super._call(n))}}class DetrObjectDetectionOutput extends ModelOutput{constructor({logits:n,pred_boxes:o}){super(),this.logits=n,this.pred_boxes=o}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:n,pred_boxes:o,pred_masks:u}){super(),this.logits=n,this.pred_boxes=o,this.pred_masks=u}}class DeiTPreTrainedModel extends PreTrainedModel{}class DeiTModel extends DeiTPreTrainedModel{}class DeiTForImageClassification extends DeiTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class ResNetPreTrainedModel extends PreTrainedModel{}class ResNetModel extends ResNetPreTrainedModel{}class ResNetForImageClassification extends ResNetPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class SwinPreTrainedModel extends PreTrainedModel{}class SwinModel extends SwinPreTrainedModel{}class SwinForImageClassification extends SwinPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DonutSwinPreTrainedModel extends PreTrainedModel{}class DonutSwinModel extends DonutSwinPreTrainedModel{}class YolosPreTrainedModel extends PreTrainedModel{}class YolosModel extends YolosPreTrainedModel{}class YolosForObjectDetection extends YolosPreTrainedModel{async _call(n){return new YolosObjectDetectionOutput(await super._call(n))}}class YolosObjectDetectionOutput extends ModelOutput{constructor({logits:n,pred_boxes:o}){super(),this.logits=n,this.pred_boxes=o}}class SamPreTrainedModel extends PreTrainedModel{}class SamModel extends SamPreTrainedModel{async _call(n){return new SamImageSegmentationOutput(await super._call(n))}}class SamImageSegmentationOutput extends ModelOutput{constructor({iou_scores:n,pred_masks:o}){super(),this.iou_scores=n,this.pred_masks=o}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{}class MarianMTModel extends MarianPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class M2M100PreTrainedModel extends PreTrainedModel{}class M2M100Model extends M2M100PreTrainedModel{}class M2M100ForConditionalGeneration extends M2M100PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Wav2Vec2PreTrainedModel extends PreTrainedModel{}class Wav2Vec2Model extends Wav2Vec2PreTrainedModel{}class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel{async _call(n){return new CausalLMOutput(await super._call(n))}}class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class WavLMPreTrainedModel extends PreTrainedModel{}class WavLMModel extends WavLMPreTrainedModel{}class WavLMForCTC extends WavLMPreTrainedModel{async _call(n){return new CausalLMOutput(await super._call(n))}}class WavLMForSequenceClassification extends WavLMPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class SpeechT5PreTrainedModel extends PreTrainedModel{}class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel{}class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.hidden_size/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.hidden_size/this.num_encoder_heads}async generate_speech(n,o,{threshold:u=.5,minlenratio:l=0,maxlenratio:d=20,vocoder:s=null}={}){const p={input_ids:n},{encoder_outputs:f,encoder_attention_mask:c}=await encoderForward(this,p),a=f.dims[1]/this.config.reduction_factor,t=Math.floor(a*d),e=Math.floor(a*l),r=this.config.num_mel_bins;let i=[],h=null,g=null,m=0;for(;;){++m;const w=boolTensor(!!g);let T;g?T=g.output_sequence_out:T=new Tensor("float32",new Float32Array(r),[1,1,r]);let S={use_cache_branch:w,output_sequence:T,encoder_attention_mask:c,speaker_embeddings:o,encoder_hidden_states:f};this.addPastKeyValues(S,h),g=await sessionRun(this.decoder_merged_session,S),h=this.getPastKeyValues(g,h);const{prob:A,spectrum:O}=g;if(i.push(O),m>=e&&(Array.from(A.data).filter(v=>v>=u).length>0||m>=t))break}const b=cat(i),{waveform:y}=await sessionRun(s.session,{spectrogram:b});return{spectrogram:b,waveform:y}}}class SpeechT5HifiGan extends PreTrainedModel{constructor(){super(...arguments);le(this,"main_input_name","spectrogram")}}class PretrainedMixin{static async from_pretrained(n,{quantized:o=!0,progress_callback:u=null,config:l=null,cache_dir:d=null,local_files_only:s=!1,revision:p="main",model_file_name:f=null}={}){let c={quantized:o,progress_callback:u,config:l,cache_dir:d,local_files_only:s,revision:p,model_file_name:f};if(l=await AutoConfig.from_pretrained(n,c),c.config||(c.config=l),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let a of this.MODEL_CLASS_MAPPINGS){const t=a.get(l.model_type);if(t)return await t[1].from_pretrained(n,c)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${l.model_type}", attempting to construct from base class.`),await PreTrainedModel.from_pretrained(n,c);throw Error(`Unsupported model type: ${l.model_type}`)}}le(PretrainedMixin,"MODEL_CLASS_MAPPINGS",null),le(PretrainedMixin,"BASE_IF_FAIL",!1);const MODEL_MAPPING_NAMES_ENCODER_ONLY=new Map([["bert",["BertModel",BertModel]],["camembert",["CamembertModel",CamembertModel]],["deberta",["DebertaModel",DebertaModel]],["deberta-v2",["DebertaV2Model",DebertaV2Model]],["mpnet",["MPNetModel",MPNetModel]],["albert",["AlbertModel",AlbertModel]],["distilbert",["DistilBertModel",DistilBertModel]],["roberta",["RobertaModel",RobertaModel]],["xlm",["XLMModel",XLMModel]],["xlm-roberta",["XLMRobertaModel",XLMRobertaModel]],["clip",["CLIPModel",CLIPModel]],["mobilebert",["MobileBertModel",MobileBertModel]],["squeezebert",["SqueezeBertModel",SqueezeBertModel]],["wav2vec2",["Wav2Vec2Model",Wav2Vec2Model]],["wavlm",["WavLMModel",WavLMModel]],["detr",["DetrModel",DetrModel]],["vit",["ViTModel",ViTModel]],["mobilevit",["MobileViTModel",MobileViTModel]],["beit",["BeitModel",BeitModel]],["deit",["DeiTModel",DeiTModel]],["resnet",["ResNetModel",ResNetModel]],["swin",["SwinModel",SwinModel]],["donut-swin",["DonutSwinModel",DonutSwinModel]],["yolos",["YolosModel",YolosModel]],["hifigan",["SpeechT5HifiGan",SpeechT5HifiGan]],["sam",["SamModel",SamModel]]]),MODEL_MAPPING_NAMES_ENCODER_DECODER=new Map([["t5",["T5Model",T5Model]],["longt5",["LongT5Model",LongT5Model]],["mt5",["MT5Model",MT5Model]],["bart",["BartModel",BartModel]],["mbart",["MBartModel",MBartModel]],["marian",["MarianModel",MarianModel]],["whisper",["WhisperModel",WhisperModel]],["m2m_100",["M2M100Model",M2M100Model]],["blenderbot",["BlenderbotModel",BlenderbotModel]],["blenderbot-small",["BlenderbotSmallModel",BlenderbotSmallModel]]]),MODEL_MAPPING_NAMES_DECODER_ONLY=new Map([["bloom",["BloomModel",BloomModel]],["gpt2",["GPT2Model",GPT2Model]],["gptj",["GPTJModel",GPTJModel]],["gpt_bigcode",["GPTBigCodeModel",GPTBigCodeModel]],["gpt_neo",["GPTNeoModel",GPTNeoModel]],["gpt_neox",["GPTNeoXModel",GPTNeoXModel]],["codegen",["CodeGenModel",CodeGenModel]],["llama",["LlamaModel",LlamaModel]],["mpt",["MptModel",MptModel]],["opt",["OPTModel",OPTModel]]]),MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES=new Map([["speecht5",["SpeechT5ForSpeechToText",SpeechT5ForSpeechToText]],["whisper",["WhisperForConditionalGeneration",WhisperForConditionalGeneration]]]),MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES=new Map([["speecht5",["SpeechT5ForTextToSpeech",SpeechT5ForTextToSpeech]]]),MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",["BertForSequenceClassification",BertForSequenceClassification]],["camembert",["CamembertForSequenceClassification",CamembertForSequenceClassification]],["deberta",["DebertaForSequenceClassification",DebertaForSequenceClassification]],["deberta-v2",["DebertaV2ForSequenceClassification",DebertaV2ForSequenceClassification]],["mpnet",["MPNetForSequenceClassification",MPNetForSequenceClassification]],["albert",["AlbertForSequenceClassification",AlbertForSequenceClassification]],["distilbert",["DistilBertForSequenceClassification",DistilBertForSequenceClassification]],["roberta",["RobertaForSequenceClassification",RobertaForSequenceClassification]],["xlm",["XLMForSequenceClassification",XLMForSequenceClassification]],["xlm-roberta",["XLMRobertaForSequenceClassification",XLMRobertaForSequenceClassification]],["bart",["BartForSequenceClassification",BartForSequenceClassification]],["mbart",["MBartForSequenceClassification",MBartForSequenceClassification]],["mobilebert",["MobileBertForSequenceClassification",MobileBertForSequenceClassification]],["squeezebert",["SqueezeBertForSequenceClassification",SqueezeBertForSequenceClassification]]]),MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",["BertForTokenClassification",BertForTokenClassification]],["camembert",["CamembertForTokenClassification",CamembertForTokenClassification]],["deberta",["DebertaForTokenClassification",DebertaForTokenClassification]],["deberta-v2",["DebertaV2ForTokenClassification",DebertaV2ForTokenClassification]],["mpnet",["MPNetForTokenClassification",MPNetForTokenClassification]],["distilbert",["DistilBertForTokenClassification",DistilBertForTokenClassification]],["roberta",["RobertaForTokenClassification",RobertaForTokenClassification]],["xlm",["XLMForTokenClassification",XLMForTokenClassification]],["xlm-roberta",["XLMRobertaForTokenClassification",XLMRobertaForTokenClassification]]]),MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES=new Map([["t5",["T5ForConditionalGeneration",T5ForConditionalGeneration]],["longt5",["LongT5ForConditionalGeneration",LongT5ForConditionalGeneration]],["mt5",["MT5ForConditionalGeneration",MT5ForConditionalGeneration]],["bart",["BartForConditionalGeneration",BartForConditionalGeneration]],["mbart",["MBartForConditionalGeneration",MBartForConditionalGeneration]],["marian",["MarianMTModel",MarianMTModel]],["m2m_100",["M2M100ForConditionalGeneration",M2M100ForConditionalGeneration]],["blenderbot",["BlenderbotForConditionalGeneration",BlenderbotForConditionalGeneration]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",BlenderbotSmallForConditionalGeneration]]]),MODEL_WITH_LM_HEAD_MAPPING_NAMES=new Map([["bloom",["BloomForCausalLM",BloomForCausalLM]],["gpt2",["GPT2LMHeadModel",GPT2LMHeadModel]],["gptj",["GPTJForCausalLM",GPTJForCausalLM]],["gpt_bigcode",["GPTBigCodeForCausalLM",GPTBigCodeForCausalLM]],["gpt_neo",["GPTNeoForCausalLM",GPTNeoForCausalLM]],["gpt_neox",["GPTNeoXForCausalLM",GPTNeoXForCausalLM]],["codegen",["CodeGenForCausalLM",CodeGenForCausalLM]],["llama",["LlamaForCausalLM",LlamaForCausalLM]],["mpt",["MptForCausalLM",MptForCausalLM]],["opt",["OPTForCausalLM",OPTForCausalLM]],["mbart",["MBartForCausalLM",MBartForCausalLM]]]),MODEL_FOR_MASKED_LM_MAPPING_NAMES=new Map([["bert",["BertForMaskedLM",BertForMaskedLM]],["camembert",["CamembertForMaskedLM",CamembertForMaskedLM]],["deberta",["DebertaForMaskedLM",DebertaForMaskedLM]],["deberta-v2",["DebertaV2ForMaskedLM",DebertaV2ForMaskedLM]],["mpnet",["MPNetForMaskedLM",MPNetForMaskedLM]],["albert",["AlbertForMaskedLM",AlbertForMaskedLM]],["distilbert",["DistilBertForMaskedLM",DistilBertForMaskedLM]],["roberta",["RobertaForMaskedLM",RobertaForMaskedLM]],["xlm",["XLMWithLMHeadModel",XLMWithLMHeadModel]],["xlm-roberta",["XLMRobertaForMaskedLM",XLMRobertaForMaskedLM]],["mobilebert",["MobileBertForMaskedLM",MobileBertForMaskedLM]],["squeezebert",["SqueezeBertForMaskedLM",SqueezeBertForMaskedLM]]]),MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES=new Map([["bert",["BertForQuestionAnswering",BertForQuestionAnswering]],["camembert",["CamembertForQuestionAnswering",CamembertForQuestionAnswering]],["deberta",["DebertaForQuestionAnswering",DebertaForQuestionAnswering]],["deberta-v2",["DebertaV2ForQuestionAnswering",DebertaV2ForQuestionAnswering]],["mpnet",["MPNetForQuestionAnswering",MPNetForQuestionAnswering]],["albert",["AlbertForQuestionAnswering",AlbertForQuestionAnswering]],["distilbert",["DistilBertForQuestionAnswering",DistilBertForQuestionAnswering]],["roberta",["RobertaForQuestionAnswering",RobertaForQuestionAnswering]],["xlm",["XLMForQuestionAnswering",XLMForQuestionAnswering]],["xlm-roberta",["XLMRobertaForQuestionAnswering",XLMRobertaForQuestionAnswering]],["mobilebert",["MobileBertForQuestionAnswering",MobileBertForQuestionAnswering]],["squeezebert",["SqueezeBertForQuestionAnswering",SqueezeBertForQuestionAnswering]]]),MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",VisionEncoderDecoderModel]]]),MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",VisionEncoderDecoderModel]]]),MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES=new Map([["vit",["ViTForImageClassification",ViTForImageClassification]],["mobilevit",["MobileViTForImageClassification",MobileViTForImageClassification]],["beit",["BeitForImageClassification",BeitForImageClassification]],["deit",["DeiTForImageClassification",DeiTForImageClassification]],["resnet",["ResNetForImageClassification",ResNetForImageClassification]],["swin",["SwinForImageClassification",SwinForImageClassification]]]),MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES=new Map([["detr",["DetrForObjectDetection",DetrForObjectDetection]],["yolos",["YolosForObjectDetection",YolosForObjectDetection]]]),MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES=new Map([["detr",["DetrForSegmentation",DetrForSegmentation]]]),MODEL_FOR_MASK_GENERATION_MAPPING_NAMES=new Map([["sam",["SamModel",SamModel]]]),MODEL_FOR_CTC_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForCTC",Wav2Vec2ForCTC]],["wavlm",["WavLMForCTC",WavLMForCTC]]]),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Wav2Vec2ForSequenceClassification]],["wavlm",["WavLMForSequenceClassification",WavLMForSequenceClassification]]]),MODEL_CLASS_TYPE_MAPPING=[[MODEL_MAPPING_NAMES_ENCODER_ONLY,MODEL_TYPES.EncoderOnly],[MODEL_MAPPING_NAMES_ENCODER_DECODER,MODEL_TYPES.EncoderDecoder],[MODEL_MAPPING_NAMES_DECODER_ONLY,MODEL_TYPES.DecoderOnly],[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,MODEL_TYPES.Seq2Seq],[MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,MODEL_TYPES.Seq2Seq],[MODEL_WITH_LM_HEAD_MAPPING_NAMES,MODEL_TYPES.DecoderOnly],[MODEL_FOR_MASKED_LM_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,MODEL_TYPES.Vision2Seq],[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_MASK_GENERATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_CTC_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES,MODEL_TYPES.Seq2Seq]];for(const[_,n]of MODEL_CLASS_TYPE_MAPPING)for(const[o,u]of _.values())MODEL_TYPE_MAPPING.set(o,n),MODEL_CLASS_TO_NAME_MAPPING.set(u,o),MODEL_NAME_TO_CLASS_MAPPING.set(o,u);const CUSTOM_MAPPING=[["CLIPTextModelWithProjection",CLIPTextModelWithProjection,MODEL_TYPES.EncoderOnly],["CLIPVisionModelWithProjection",CLIPVisionModelWithProjection,MODEL_TYPES.EncoderOnly]];for(const[_,n,o]of CUSTOM_MAPPING)MODEL_TYPE_MAPPING.set(_,o),MODEL_CLASS_TO_NAME_MAPPING.set(n,_),MODEL_NAME_TO_CLASS_MAPPING.set(_,n);class AutoModel extends PretrainedMixin{}le(AutoModel,"MODEL_CLASS_MAPPINGS",[MODEL_MAPPING_NAMES_ENCODER_ONLY,MODEL_MAPPING_NAMES_ENCODER_DECODER,MODEL_MAPPING_NAMES_DECODER_ONLY]),le(AutoModel,"BASE_IF_FAIL",!0);class AutoModelForSequenceClassification extends PretrainedMixin{}le(AutoModelForSequenceClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForTokenClassification extends PretrainedMixin{}le(AutoModelForTokenClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForSeq2SeqLM extends PretrainedMixin{}le(AutoModelForSeq2SeqLM,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]);class AutoModelForSpeechSeq2Seq extends PretrainedMixin{}le(AutoModelForSpeechSeq2Seq,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]);class AutoModelForTextToSpectrogram extends PretrainedMixin{}le(AutoModelForTextToSpectrogram,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]);class AutoModelForCausalLM extends PretrainedMixin{}le(AutoModelForCausalLM,"MODEL_CLASS_MAPPINGS",[MODEL_WITH_LM_HEAD_MAPPING_NAMES]);class AutoModelForMaskedLM extends PretrainedMixin{}le(AutoModelForMaskedLM,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_MASKED_LM_MAPPING_NAMES]);class AutoModelForQuestionAnswering extends PretrainedMixin{}le(AutoModelForQuestionAnswering,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]);class AutoModelForVision2Seq extends PretrainedMixin{}le(AutoModelForVision2Seq,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]);class AutoModelForImageClassification extends PretrainedMixin{}le(AutoModelForImageClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForImageSegmentation extends PretrainedMixin{}le(AutoModelForImageSegmentation,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]);class AutoModelForObjectDetection extends PretrainedMixin{}le(AutoModelForObjectDetection,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]);class AutoModelForCTC extends PretrainedMixin{}le(AutoModelForCTC,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_CTC_MAPPING_NAMES]);class AutoModelForAudioClassification extends PretrainedMixin{}le(AutoModelForAudioClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForDocumentQuestionAnswering extends PretrainedMixin{}le(AutoModelForDocumentQuestionAnswering,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]);class Seq2SeqLMOutput extends ModelOutput{constructor({logits:n,past_key_values:o,encoder_outputs:u,decoder_attentions:l=null,cross_attentions:d=null}){super(),this.logits=n,this.past_key_values=o,this.encoder_outputs=u,this.decoder_attentions=l,this.cross_attentions=d}}class SequenceClassifierOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class TokenClassifierOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class MaskedLMOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class QuestionAnsweringModelOutput extends ModelOutput{constructor({start_logits:n,end_logits:o}){super(),this.start_logits=n,this.end_logits=o}}class CausalLMOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}const BROWSER_ENV=typeof self<"u";let createCanvasFunction,ImageDataClass,loadImageFunction;if(BROWSER_ENV)createCanvasFunction=(_,n)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(_,n)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(sharp)loadImageFunction=async _=>{const o=(await _.metadata()).channels;let{data:u,info:l}=await _.raw().toBuffer({resolveWithObject:!0});const d=new RawImage(new Uint8ClampedArray(u),l.width,l.height,l.channels);return o!==void 0&&o!==l.channels&&d.convert(o),d};else throw new Error("Unable to load image processing library.");const RESAMPLING_MAPPING={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"};class RawImage{constructor(n,o,u,l){le(this,"_CONTENT_TYPE_MAP",{png:"image/png",jpg:"image/jpeg",jpeg:"image/jpeg",gif:"image/gif"});this.data=n,this.width=o,this.height=u,this.channels=l}static async read(n){if(n instanceof RawImage)return n;if(isString(n)||n instanceof URL)return await this.fromURL(n);throw new Error(`Unsupported input type: ${typeof n}`)}static async fromURL(n){let o=await getFile(n);if(o.status!==200)throw new Error(`Unable to read image from "${n}" (${o.status} ${o.statusText})`);let u=await o.blob();return this.fromBlob(u)}static async fromBlob(n){if(BROWSER_ENV){let o=await loadImageFunction(n);const u=createCanvasFunction(o.width,o.height).getContext("2d");return u.drawImage(o,0,0),new this(u.getImageData(0,0,o.width,o.height).data,o.width,o.height,4)}else{let o=sharp(await n.arrayBuffer());return await loadImageFunction(o)}}grayscale(){if(this.channels===1)return this;let n=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let o=0,u=0;o<this.data.length;o+=this.channels){const l=this.data[o],d=this.data[o+1],s=this.data[o+2];n[u++]=Math.round(.2989*l+.587*d+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,1)}rgb(){if(this.channels===3)return this;let n=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let o=0,u=0;o<this.data.length;++o)n[u++]=this.data[o],n[u++]=this.data[o],n[u++]=this.data[o];break;case 4:for(let o=0,u=0;o<this.data.length;o+=4)n[u++]=this.data[o],n[u++]=this.data[o+1],n[u++]=this.data[o+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,3)}rgba(){if(this.channels===4)return this;let n=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let o=0,u=0;o<this.data.length;++o)n[u++]=this.data[o],n[u++]=this.data[o],n[u++]=this.data[o],n[u++]=255;break;case 3:for(let o=0,u=0;o<this.data.length;o+=3)n[u++]=this.data[o],n[u++]=this.data[o+1],n[u++]=this.data[o+2],n[u++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,4)}async resize(n,o,{resample:u=2}={}){let l=RESAMPLING_MAPPING[u]??u;if(BROWSER_ENV){let d=this.channels,s=this.toCanvas();const p=createCanvasFunction(n,o).getContext("2d");return p.drawImage(s,0,0,n,o),new RawImage(p.getImageData(0,0,n,o).data,n,o,4).convert(d)}else{let d=this.toSharp();switch(l){case"box":case"hamming":(l==="box"||l==="hamming")&&(console.warn(`Resampling method ${l} is not yet supported. Using bilinear instead.`),l="bilinear");case"nearest":case"bilinear":case"bicubic":d=d.affine([n/this.width,0,0,o/this.height],{interpolator:l});break;case"lanczos":d=d.resize({width:n,height:o,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${l} is not supported.`)}return await loadImageFunction(d)}}async pad([n,o,u,l]){if(n=Math.max(n,0),o=Math.max(o,0),u=Math.max(u,0),l=Math.max(l,0),n===0&&o===0&&u===0&&l===0)return this;if(BROWSER_ENV){let d=this.channels,s=this.toCanvas(),p=this.width+n+o,f=this.height+u+l;const c=createCanvasFunction(p,f).getContext("2d");return c.drawImage(s,0,0,this.width,this.height,n,u,p,f),new RawImage(c.getImageData(0,0,p,f).data,p,f,4).convert(d)}else{let d=this.toSharp().extend({left:n,right:o,top:u,bottom:l});return await loadImageFunction(d)}}async center_crop(n,o){if(this.width===n&&this.height===o)return this;let u=(this.width-n)/2,l=(this.height-o)/2;if(BROWSER_ENV){let d=this.channels,s=this.toCanvas();const p=createCanvasFunction(n,o).getContext("2d");let f=0,c=0,a=0,t=0;return u>=0?f=u:a=-u,l>=0?c=l:t=-l,p.drawImage(s,f,c,n,o,a,t,n,o),new RawImage(p.getImageData(0,0,n,o).data,n,o,4).convert(d)}else{let d=this.toSharp();if(u>=0&&l>=0)d=d.extract({left:Math.floor(u),top:Math.floor(l),width:n,height:o});else if(u<=0&&l<=0){let s=Math.floor(-l),p=Math.floor(-u);d=d.extend({top:s,left:p,right:n-this.width-p,bottom:o-this.height-s})}else{let s=[0,0],p=0;l<0?(s[0]=Math.floor(-l),s[1]=o-this.height-s[0]):p=Math.floor(l);let f=[0,0],c=0;u<0?(f[0]=Math.floor(-u),f[1]=n-this.width-f[0]):c=Math.floor(u),d=d.extend({top:s[0],bottom:s[1],left:f[0],right:f[1]}).extract({left:c,top:p,width:n,height:o})}return await loadImageFunction(d)}}toCanvas(){if(!BROWSER_ENV)throw new Error("toCanvas() is only supported in browser environments.");let n=this.clone().rgba(),o=createCanvasFunction(n.width,n.height),u=new ImageDataClass(n.data,n.width,n.height);return o.getContext("2d").putImageData(u,0,0),o}_update(n,o,u,l=null){return this.data=n,this.width=o,this.height=u,l!==null&&(this.channels=l),this}clone(){return new RawImage(this.data.slice(),this.width,this.height,this.channels)}convert(n){if(this.channels===n)return this;switch(n){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}save(n){if(BROWSER_ENV){const o=n.split(".").pop().toLowerCase(),u=this._CONTENT_TYPE_MAP[o]??"image/png",d=this.toCanvas().toDataURL(u),s=document.createElement("a");s.href=d,s.download=n,s.click(),s.remove()}else if(env.useFS)this.toSharp().toFile(n);else throw new Error("Unable to save the image because filesystem is disabled in this environment.")}toSharp(){if(BROWSER_ENV)throw new Error("toSharp() is only supported in server-side environments.");return sharp(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function read_audio(_,n){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const o=await(await getFile(_)).arrayBuffer(),u=new AudioContext({sampleRate:n});typeof n>"u"&&console.warn(`No sampling rate provided, using default of ${u.sampleRate}Hz.`);const l=await u.decodeAudioData(o);let d;if(l.numberOfChannels===2){const s=Math.sqrt(2);let p=l.getChannelData(0),f=l.getChannelData(1);d=new Float32Array(p.length);for(let c=0;c<l.length;++c)d[c]=s*(p[c]+f[c])/2}else d=l.getChannelData(0);return d}function getMelFilters(_,n,o=128){o=Math.floor(o);const u=Math.floor(1+n/2),l=new Array(o),d=rfftfreq(n,1/_),s=0,c=(45.245640471924965-s)/(o+1),a=0,t=200/3,e=new Array(o+2),r=1e3,i=(r-a)/t,h=Math.log(6.4)/27,g=new Array(e.length);for(let b=0;b<e.length;++b){const y=b*c+s;y>=i?e[b]=r*Math.exp(h*(y-i)):e[b]=a+t*y,g[b]=d.map(w=>e[b]-w)}const m=e.slice(1).map((b,y)=>1/(b-e[y]));for(let b=0;b<l.length;++b){l[b]=new Array(u);const y=m[b],w=m[b+1],T=g[b],S=g[b+2],A=2/(e[b+2]-e[b]);for(let O=0;O<l[b].length;++O){const v=-T[O]*y,M=S[O]*w;l[b][O]=Math.max(0,Math.min(v,M))*A}}return l}function center_to_corners_format([_,n,o,u]){return[_-o/2,n-u/2,_+o/2,n+u/2]}function post_process_object_detection(_,n=.5,o=null){const u=_.logits,l=_.pred_boxes,[d,s,p]=u.dims;if(o!==null&&o.length!==d)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let f=[];for(let c=0;c<d;++c){let a=o!==null?o[c]:null,t={boxes:[],classes:[],scores:[]},e=u[c],r=l[c];for(let i=0;i<s;++i){let h=e[i],g=max(h.data)[1];if(g===p-1)continue;let b=softmax(h.data)[g];if(b>n){let y=r[i].data;y=center_to_corners_format(y),a!==null&&(y=y.map((w,T)=>w*a[(T+1)%2])),t.boxes.push(y),t.classes.push(g),t.scores.push(b)}}f.push(t)}return f}class FeatureExtractor extends Callable{constructor(n){super(),this.config=n}}class ImageFeatureExtractor extends FeatureExtractor{constructor(n){super(n),this.image_mean=this.config.image_mean,this.image_std=this.config.image_std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size)}async thumbnail(n,o,u=2){const l=n.height,d=n.width,s=o.height,p=o.width;let f=Math.min(l,s),c=Math.min(d,p);return f===l&&c===d?n:(l>d?c=Math.floor(d*f/l):d>l&&(f=Math.floor(l*c/d)),await n.resize(c,f,{resample:u}))}async preprocess(n){this.do_convert_rgb&&(n=n.rgb());const o=n.width,u=n.height;if(this.do_resize){let c,a;if(this.do_thumbnail){const{height:t,width:e}=this.size;c=Math.min(t,e)}else Number.isInteger(this.size)?(c=this.size,a=this.config.max_size??c):(c=this.size.shortest_edge,a=this.size.longest_edge);if(c!==void 0||a!==void 0){const t=c===void 0?1:Math.max(c/o,c/u),e=o*t,r=u*t,i=a===void 0?1:Math.min(a/e,a/r),h=Math.floor(Number((e*i).toFixed(2))),g=Math.floor(Number((r*i).toFixed(2)));n=await n.resize(h,g,{resample:this.resample})}else if(this.size.width!==void 0&&this.size.height!==void 0)n=await n.resize(this.size.width,this.size.height,{resample:this.resample});else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(this.size)}`)}if(this.do_thumbnail&&(n=await this.thumbnail(n,this.size,this.resample)),this.do_center_crop){let c,a;Number.isInteger(this.crop_size)?(c=this.crop_size,a=this.crop_size):(c=this.crop_size.width,a=this.crop_size.height),n=await n.center_crop(c,a)}let l=[n.height,n.width];if(this.do_pad&&this.pad_size){let c=0,a=this.pad_size.width-n.width,t=0,e=this.pad_size.height-n.height;n=await n.pad([c,a,t,e])}const d=Float32Array.from(n.data);if(this.do_rescale)for(let c=0;c<d.length;++c)d[c]=this.rescale_factor*d[c];if(this.do_normalize){let c=this.image_mean;Array.isArray(this.image_mean)||(c=new Array(n.channels).fill(c));let a=this.image_std;if(Array.isArray(this.image_std)||(a=new Array(n.channels).fill(c)),c.length!==n.channels||a.length!==n.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${c.length}) and \`image_std\` (${a.length}) must match the number of channels in the image (${n.channels}).`);for(let t=0;t<d.length;t+=n.channels)for(let e=0;e<n.channels;++e)d[t+e]=(d[t+e]-this.image_mean[e])/this.image_std[e]}let s=[n.height,n.width,n.channels],p=new Tensor("float32",d,s),f=transpose(p,[2,0,1]);return{original_size:[u,o],reshaped_input_size:l,pixel_values:f}}async _call(n,...o){Array.isArray(n)||(n=[n]);const u=await Promise.all(n.map(d=>this.preprocess(d)));return u.forEach(d=>d.pixel_values.dims=[1,...d.pixel_values.dims]),{pixel_values:cat(u.map(d=>d.pixel_values)),original_sizes:u.map(d=>d.original_size),reshaped_input_sizes:u.map(d=>d.reshaped_input_size)}}}class ConvNextFeatureExtractor extends ImageFeatureExtractor{}class ViTFeatureExtractor extends ImageFeatureExtractor{}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class DeiTFeatureExtractor extends ImageFeatureExtractor{}class BeitFeatureExtractor extends ImageFeatureExtractor{}class DonutFeatureExtractor extends ImageFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(n){const o=await super._call(n),u=[o.pixel_values.dims[0],64,64],l=new Tensor("int64",new BigInt64Array(u.reduce((d,s)=>d*s)).fill(1n),u);return{...o,pixel_mask:l}}post_process_object_detection(...n){return post_process_object_detection(...n)}remove_low_and_no_objects(n,o,u,l){let d=[],s=[],p=[];for(let f=0;f<n.dims[0];++f){let c=n[f],a=o[f],t=max(c.data)[1];if(t===l)continue;let r=softmax(c.data)[t];r>u&&(d.push(a),s.push(r),p.push(t))}return[d,s,p]}check_segment_validity(n,o,u,l=.5,d=.8){let s=[],p=0,f=0;for(let a=0;a<n.length;++a)n[a]===u&&(s.push(a),++p),o[u].data[a]>=l&&++f;let c=p>0&&f>0;return c&&(c=p/f>d),[c,s]}compute_segments(n,o,u,l,d,s=null,p=null){let[f,c]=p??n[0].dims,a=new Tensor("int32",new Int32Array(f*c),[f,c]),t=[];if(p!==null)for(let h=0;h<n.length;++h)n[h]=interpolate(n[h],p,"bilinear",!1);let e=new Int32Array(n[0].data.length),r=new Float32Array(n[0].data.length);for(let h=0;h<n.length;++h){let g=o[h];for(let m=0;m<n[h].data.length;++m)n[h].data[m]*=g,n[h].data[m]>r[m]&&(e[m]=h,r[m]=n[h].data[m])}let i=0;for(let h=0;h<u.length;++h){let g=u[h],[m,b]=this.check_segment_validity(e,n,h,l,d);if(m){++i;for(let y of b)a.data[y]=i;t.push({id:i,label_id:g,score:o[h]})}}return[a,t]}post_process_panoptic_segmentation(n,o=.5,u=.5,l=.8,d=null,s=null){d===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),d=new Set);const p=n.logits,c=n.pred_masks.sigmoid();let[a,t,e]=p.dims;if(e-=1,s!==null&&s.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let r=[];for(let i=0;i<a;++i){let h=s!==null?s[i]:null,g=p[i],m=c[i],[b,y,w]=this.remove_low_and_no_objects(g,m,o,e);if(w.length===0){let[A,O]=h??m.dims.slice(-2),v=new Tensor("int32",new Int32Array(A*O).fill(-1),[A,O]);r.push({segmentation:v,segments_info:[]});continue}let[T,S]=this.compute_segments(b,y,w,u,l,d,h);r.push({segmentation:T,segments_info:S})}return r}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class YolosFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...n){return post_process_object_detection(...n)}}class SamImageProcessor extends ImageFeatureExtractor{async _call(n,o){let{pixel_values:u,original_sizes:l,reshaped_input_sizes:d}=await super._call(n),s=calculateDimensions(o);if(s.length===3)s=[1,...s],o=[o];else if(s.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let f=0;f<o.length;++f){let c=l[f],a=d[f],t=[a[0]/c[0],a[1]/c[1]];for(let e=0;e<o[f].length;++e)for(let r=0;r<o[f][e].length;++r)for(let i=0;i<o[f][e][r].length;++i)o[f][e][r][i]*=t[i]}let p=new Tensor("int64",BigInt64Array.from(o.flat(1/0).map(f=>BigInt(Math.round(f)))),s);return{pixel_values:u,original_sizes:l,reshaped_input_sizes:d,input_points:p}}post_process_masks(n,o,u,{mask_threshold:l=0,binarize:d=!0,pad_size:s=null}={}){let p=[];s=s??this.pad_size;let f=[s.height,s.width];for(let c=0;c<o.length;++c){let a=o[c],t=u[c],e=n[c],r=[];for(let h=0;h<e.dims[0];++h){let g=e[h],m=interpolate(g,f,"bilinear",!1);m=m.slice(null,[0,t[0]],[0,t[1]]),m=interpolate(e,a,"bilinear",!1),d&&(m=new Tensor("bool",Array.from(m.data).map(b=>b>l),m.dims)),m.dims=[1,...m.dims],r.push(m)}let i=cat(r);p.push(i)}return p}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(n){var o;super(n),(o=this.config).mel_filters??(o.mel_filters=getMelFilters(this.config.sampling_rate,this.config.n_fft,this.config.feature_size))}calcOffset(n,o){return Math.abs((n+o)%(2*o)-o)}padReflect(n,o,u){const l=new Float32Array(n.length+o+u),d=n.length-1;for(let s=0;s<n.length;++s)l[o+s]=n[s];for(let s=1;s<=o;++s)l[o-s]=n[this.calcOffset(s,d)];for(let s=1;s<=u;++s)l[d+o+s]=n[this.calcOffset(d-s,d)];return l}stft(n,o){const u=this.config.n_fft,l=2*(u-1),d=2*(2*u-1),s=2**Math.ceil(Math.log2(d)),p=u+2,f=new Float32Array(p*n.length),c=new Float32Array(d),a=new Float32Array(s),t=new Float32Array(s),e=new Float32Array(s),r=new Float32Array(s),i=new Float32Array(s),h=new Float32Array(s),g=-2*Math.PI/u,m=Math.cos(g),b=Math.sin(g);for(let T=0;T<d>>1;++T){const S=(T+1-u)**2/2,A=Math.sqrt(m**2+b**2)**S,O=S*Math.atan2(b,m);let v=2*T;c[v]=A*Math.cos(O),c[v+1]=A*Math.sin(O),a[v]=c[v],a[v+1]=-c[v+1]}const y=c.subarray(l,d),w=new FFT(s>>1);w.transform(r,a);for(let T=0;T<n.length;++T){const S=n[T];for(let O=0;O<y.length;O+=2){const v=O+1,M=O>>1,F=S[M]*o[M];t[O]=F*y[O],t[v]=F*y[v]}w.transform(i,t);for(let O=0;O<r.length;O+=2){const v=O+1;e[O]=i[O]*r[O]-i[v]*r[v],e[v]=i[O]*r[v]+i[v]*r[O]}w.inverseTransform(h,e);const A=T*p;for(let O=0;O<p;O+=2){const v=h[O+l],M=h[O+l+1],F=y[O],R=y[O+1],$=A+O;f[$]=v*F-M*R,f[$+1]=v*R+M*F}}return{data:f,dims:[n.length,p]}}fram_wave(n,o=!0){const u=[],l=Math.floor((this.config.n_fft-1)/2)+1,d=n.length;for(let s=0;s<d+1;s+=this.config.hop_length){let p;if(o){let f=s>l?s-l:0,c=s<d-l?s+l:d;p=n.subarray(f,c),f===0?p=this.padReflect(p,-s+l,0):c===d&&(p=this.padReflect(p,0,s-d+l))}else{p=new Float32Array(this.config.n_fft);const f=n.subarray(s,s+this.config.n_fft);f.length<this.config.n_fft?(p.set(f),p.fill(0,f.length,this.config.n_fft)):p=f}u.push(p)}return u}hanning(n){if(n<1)return[];if(n===1)return[1];const o=n-1,u=new Float32Array(o);for(let l=0;l<o;++l){const d=2*l-n+1;u[l]=.5+.5*Math.cos(Math.PI*d/o)}return u}_extract_fbank_features(n){const o=new Float32Array(this.config.n_samples);o.set(n);const u=this.hanning(this.config.n_fft+1),l=this.fram_wave(o),d=this.stft(l,u),s=d.data,p=d.dims[0]-1,f=d.dims[1]>>1,c=new Float32Array(p*f);for(let m=0;m<p;++m)for(let b=0;b<f;++b){let y=m*f+b,w=y<<1,T=s[w]**2+s[w+1]**2;c[y]=T}const a=this.config.mel_filters,t=a.length,e=new Float32Array(t*p);let r=0;for(let m=0;m<t;++m){const b=a[m];for(let y=0;y<p;++y){let w=0;for(let T=0;T<f;++T)w+=b[T]*c[y*f+T];e[r++]=w}}const i=1e-10,h=new Float32Array(e.length);let g=0;for(let m=0;m<e.length;++m){const b=Math.max(i,e[m]),y=Math.log10(b);h[m]=y,g=Math.max(y,g)}for(let m=0;m<h.length;++m)h[m]=Math.max(h[m],g-8),h[m]=(h[m]+4)/4;return{data:h,dims:[t,p]}}async _call(n){var l;if(!(n instanceof Float32Array||n instanceof Float64Array))throw new Error(`WhisperFeatureExtractor expects input to be a Float32Array or a Float64Array, but got ${((l=n==null?void 0:n.constructor)==null?void 0:l.name)??typeof n} instead.If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`);n.length>this.config.n_samples&&console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`.");let o=n.slice(0,this.config.n_samples),u=this._extract_fbank_features(o);return{input_features:new Tensor("float32",u.data,[1,...u.dims])}}}class Wav2Vec2FeatureExtractor extends FeatureExtractor{_zero_mean_unit_var_norm(n){const u=n.reduce((d,s)=>d+s,0)/n.length,l=n.reduce((d,s)=>d+(s-u)**2,0)/n.length;return n.map(d=>(d-u)/Math.sqrt(l+1e-7))}async _call(n){var l;if(!(n instanceof Float32Array||n instanceof Float64Array))throw new Error(`Wav2Vec2FeatureExtractor expects input to be a Float32Array or a Float64Array, but got ${((l=n==null?void 0:n.constructor)==null?void 0:l.name)??typeof n} instead.If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`);n instanceof Float64Array&&(n=new Float32Array(n));let o=n;this.config.do_normalize&&(o=this._zero_mean_unit_var_norm(o));const u=[1,o.length];return{input_values:new Tensor("float32",o,u),attention_mask:new Tensor("int64",new BigInt64Array(o.length).fill(1n),u)}}}class SpeechT5FeatureExtractor extends FeatureExtractor{}class Processor extends Callable{constructor(n){super(),this.feature_extractor=n}async _call(n,...o){return await this.feature_extractor(n)}}class SamProcessor extends Processor{async _call(n,o){return await this.feature_extractor(n,o)}post_process_masks(...n){return this.feature_extractor.post_process_masks(...n)}}class WhisperProcessor extends Processor{async _call(n){return await this.feature_extractor(n)}}class Wav2Vec2ProcessorWithLM extends Processor{async _call(n){return await this.feature_extractor(n)}}class SpeechT5Processor extends Processor{async _call(n){return await this.feature_extractor(n)}}class AutoProcessor{static async from_pretrained(n,{progress_callback:o=null,config:u=null,cache_dir:l=null,local_files_only:d=!1,revision:s="main"}={}){let p=u??await getModelJSON(n,"preprocessor_config.json",!0,{progress_callback:o,config:u,cache_dir:l,local_files_only:d,revision:s}),f=p.feature_extractor_type??p.image_processor_type,c=this.FEATURE_EXTRACTOR_CLASS_MAPPING[f];if(!c)if(p.size!==void 0)console.warn(`Feature extractor type "${f}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),c=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${f}`);let a=this.PROCESSOR_CLASS_MAPPING[p.processor_class]??Processor,t=new c(p);return new a(t)}}le(AutoProcessor,"FEATURE_EXTRACTOR_CLASS_MAPPING",{WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,ConvNextFeatureExtractor,BeitFeatureExtractor,DeiTFeatureExtractor,DetrFeatureExtractor,YolosFeatureExtractor,DonutFeatureExtractor,SamImageProcessor,Wav2Vec2FeatureExtractor,SpeechT5FeatureExtractor}),le(AutoProcessor,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor,Wav2Vec2ProcessorWithLM,SamProcessor,SpeechT5Processor});async function prepareImages(_){return Array.isArray(_)||(_=[_]),_=await Promise.all(_.map(n=>RawImage.read(n))),_}class Pipeline extends Callable{constructor({task:n,model:o,tokenizer:u=null,processor:l=null}){super(),this.task=n,this.model=o,this.tokenizer=u,this.processor=l}async dispose(){await this.model.dispose()}async _call(n,...o){let u=this.tokenizer(n,{padding:!0,truncation:!0}),l=await this.model(u);return[u,l]}}class TextClassificationPipeline extends Pipeline{async _call(n,{topk:o=1}={}){let u=this.model.config.problem_type==="multi_label_classification"?f=>f.sigmoid().data:f=>softmax(f.data),[l,d]=await super._call(n),s=this.model.config.id2label,p=[];for(let f of d.logits){let c=u(f),t=getTopItems(c,o).map(function(e){return{label:s[e[0]],score:e[1]}});o===1?p.push(...t):p.push(t)}return Array.isArray(n)||o===1?p:p[0]}}class TokenClassificationPipeline extends Pipeline{async _call(n,{ignore_labels:o=["O"]}={}){let u=Array.isArray(n);u||(n=[n]);let l=this.tokenizer,[d,s]=await super._call(n),p=s.logits,f=this.model.config.id2label,c=[];for(let a=0;a<p.dims[0];++a){let t=d.input_ids[a],e=p[a],r=[];for(let i=0;i<e.dims[0];++i){let h=e[i],g=max(h.data)[1],m=f[g];if(o.includes(m))continue;let b=l.decode([t[i].item()],{skip_special_tokens:!0});if(b==="")continue;let y=softmax(h.data);r.push({entity:m,score:y[g],index:i,word:b,start:null,end:null})}c.push(r)}return u?c:c[0]}}class QuestionAnsweringPipeline extends Pipeline{async _call(n,o,{topk:u=1}={}){let l=this.tokenizer(n,{text_pair:o,padding:!0,truncation:!0}),d=await this.model(l),s=[];for(let p=0;p<d.start_logits.dims[0];++p){let f=l.input_ids[p],c=f.indexOf(this.tokenizer.sep_token_id),a=Array.from(softmax(d.start_logits[p].data)).map((r,i)=>[r,i]).filter(r=>r[1]>c),t=Array.from(softmax(d.end_logits[p].data)).map((r,i)=>[r,i]).filter(r=>r[1]>c),e=product(a,t).filter(r=>r[0][1]<=r[1][1]).map(r=>[r[0][1],r[1][1],r[0][0]*r[1][0]]).sort((r,i)=>i[2]-r[2]);for(let r=0;r<Math.min(e.length,u);++r){let[i,h,g]=e[r],m=[...f].slice(i,h+1),b=this.tokenizer.decode(m,{skip_special_tokens:!0});s.push({answer:b,score:g})}}return u===1?s[0]:s}}class FillMaskPipeline extends Pipeline{async _call(n,{topk:o=5}={}){let[u,l]=await super._call(n),d=this.tokenizer,s=[];for(let p=0;p<u.input_ids.dims[0];++p){let f=u.input_ids[p],c=f.indexOf(this.tokenizer.mask_token_id);if(c===-1)throw Error(`Mask token (${d.mask_token}) not found in text.`);let t=l.logits[p][c],e=getTopItems(softmax(t.data),o);s.push(e.map(r=>{let i=[...f];return i[c]=r[0],{score:r[1],token:r[0],token_str:d.model.vocab[r[0]],sequence:d.decode(i,{skip_special_tokens:!0})}}))}return Array.isArray(n)?s:s[0]}}class Text2TextGenerationPipeline extends Pipeline{constructor(){super(...arguments);le(this,"_key",null)}async _call(o,u={}){Array.isArray(o)||(o=[o]),this.model.config.prefix&&(o=o.map(c=>this.model.config.prefix+c));let l=this.model.config.task_specific_params;l&&l[this.task]&&l[this.task].prefix&&(o=o.map(c=>l[this.task].prefix+c));let d={padding:!0,truncation:!0},s;this instanceof TranslationPipeline&&"_build_translation_inputs"in this.tokenizer?s=this.tokenizer._build_translation_inputs(o,d,u).input_ids:s=this.tokenizer(o,d).input_ids;let p=await this.model.generate(s,u),f=this.tokenizer.batch_decode(p,{skip_special_tokens:!0});return this._key!==null&&(f=f.map(c=>this._key===null?c:{[this._key]:c})),f}}class SummarizationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);le(this,"_key","summary_text")}}class TranslationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);le(this,"_key","translation_text")}}class TextGenerationPipeline extends Pipeline{async _call(n,o={}){let u=typeof n=="string"||n instanceof String;u&&(n=[n]),this.tokenizer.padding_side="left";let l=this.tokenizer(n,{padding:!0,truncation:!0}),d=l.input_ids,s=l.attention_mask,p=await this.model.generate(d,o,null,{inputs_attention_mask:s});const f=this.tokenizer.batch_decode(p,{skip_special_tokens:!0}),c=Array.from({length:n.length},a=>[]);for(let a=0;a<f.length;++a){const t=Math.floor(a/p.length*n.length);c[t].push({generated_text:f[a]})}return u&&c.length===1?c[0]:c}}class ZeroShotClassificationPipeline extends Pipeline{constructor(n){super(n),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([o,u])=>[o.toLowerCase(),u])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(n,o,{hypothesis_template:u="This example is {}.",multi_label:l=!1}={}){let d=Array.isArray(n);d||(n=[n]),Array.isArray(o)||(o=[o]);let s=o.map(c=>u.replace("{}",c)),p=l||o.length===1,f=[];for(let c of n){let a=[];for(let r of s){let i=this.tokenizer(c,{text_pair:r,padding:!0,truncation:!0}),h=await this.model(i);p?a.push([h.logits.data[this.contradiction_id],h.logits.data[this.entailment_id]]):a.push(h.logits.data[this.entailment_id])}let t;p?t=a.map(r=>softmax(r)[1]):t=softmax(a);let e=t.map((r,i)=>[r,i]).sort((r,i)=>i[0]-r[0]);f.push({sequence:c,labels:e.map(r=>o[r[1]]),scores:e.map(r=>r[0])})}return d?f:f[0]}}class FeatureExtractionPipeline extends Pipeline{async _call(n,{pooling:o="none",normalize:u=!1}={}){let[l,d]=await super._call(n),s=d.last_hidden_state??d.logits;if(o!=="none")if(o==="mean")s=mean_pooling(s,l.attention_mask);else throw Error(`Pooling method '${o}' not supported.`);return u&&(s=s.normalize(2,-1)),s}}class AudioClassificationPipeline extends Pipeline{constructor(n){super(n)}async _preprocess(n,o){return isString(n)&&(n=await read_audio(n,o)),n}async _call(n,{topk:o=5}={}){let u=!Array.isArray(n);u&&(n=[n]);const l=this.model.config.id2label,d=this.processor.feature_extractor.config.sampling_rate;let s=[];for(let p of n){p=await this._preprocess(p,d);const f=await this.processor(p),a=(await this.model(f)).logits[0];let e=getTopItems(softmax(a.data),o).map(function(r){return{label:l[r[0]],score:r[1]}});o===1?s.push(...e):s.push(e)}return!u||o===1?s:s[0]}}class AutomaticSpeechRecognitionPipeline extends Pipeline{constructor(n){super(n)}async _preprocess(n,o){return isString(n)&&(n=await read_audio(n,o)),n}async _call(n,o={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(n,o);case"wav2vec2":return this._call_wav2vec2(n,o);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(n,o={}){o.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),o.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');let u=!Array.isArray(n);u&&(n=[n]);const l=this.processor.feature_extractor.config.sampling_rate;let d=[];for(let s of n){s=await this._preprocess(s,l);const p=await this.processor(s),c=(await this.model(p)).logits[0],a=[];for(let e of c)a.push(max(e.data)[1]);const t=this.tokenizer.decode(a);d.push({text:t})}return u?d[0]:d}async _call_whisper(n,o={}){let u=o.return_timestamps??!1,l=o.chunk_length_s??0,d=o.stride_length_s??null,s=o.chunk_callback??null,p=o.force_full_sequences??!1;u==="word"&&(o.return_token_timestamps=!0);let f=pop(o,"language",null),c=pop(o,"task",null);if(f||c||u){if(o.forced_decoder_ids)throw new Error("Cannot specify `language`/`task`/`return_timestamps` and `forced_decoder_ids` at the same time.");let h=this.tokenizer.get_decoder_prompt_ids({language:f,task:c,no_timestamps:!u});h.length>0&&(o.forced_decoder_ids=h)}let a=!Array.isArray(n);a&&(n=[n]);const t=this.processor.feature_extractor.config.sampling_rate,e=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,r=this.processor.feature_extractor.config.hop_length;let i=[];for(let h of n){h=await this._preprocess(h,t);let g=[];if(l>0){if(d===null)d=l/6;else if(l<=d)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const y=t*l,w=t*d,T=y-2*w;let S=0;for(;S<h.length;){let A=h.subarray(S,S+y),O=await this.processor(A),v=S===0,M=S+T>=h.length;g.push({stride:[A.length,v?0:w,M?0:w],input_features:O.input_features,is_last:M}),S+=T}}else g=[{stride:[h.length,0,0],input_features:(await this.processor(h)).input_features,is_last:!0}];for(let y of g){o.num_frames=Math.floor(y.stride[0]/r);let w=await this.model.generate(y.input_features,o);u==="word"?(y.tokens=w.sequences[0],y.token_timestamps=w.token_timestamps.tolist()[0].map(T=>round(T,2))):y.tokens=w[0],y.stride=y.stride.map(T=>T/t),s!==null&&s(y)}let[m,b]=this.tokenizer._decode_asr(g,{time_precision:e,return_timestamps:u,force_full_sequences:p});i.push({text:m,...b})}return a?i[0]:i}}class ImageToTextPipeline extends Pipeline{constructor(n){super(n)}async _call(n,o={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:l}=await this.processor(n),d=[];for(let s of l){s.dims=[1,...s.dims];let p=await this.model.generate(s,o),f=this.tokenizer.batch_decode(p,{skip_special_tokens:!0}).map(c=>({generated_text:c.trim()}));d.push(f)}return u?d:d[0]}}class ImageClassificationPipeline extends Pipeline{constructor(n){super(n)}async _call(n,{topk:o=1}={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:l}=await this.processor(n),d=await this.model({pixel_values:l}),s=this.model.config.id2label,p=[];for(let f of d.logits){let a=getTopItems(softmax(f.data),o).map(function(t){return{label:s[t[0]],score:t[1]}});o===1?p.push(...a):p.push(a)}return u||o===1?p:p[0]}}class ImageSegmentationPipeline extends Pipeline{constructor(n){super(n),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(n,{threshold:o=.5,mask_threshold:u=.5,overlap_mask_area_threshold:l=.8,label_ids_to_fuse:d=null,target_sizes:s=null,subtask:p=null}={}){if(Array.isArray(n)&&n.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let c=n.map(h=>[h.height,h.width]),{pixel_values:a,pixel_mask:t}=await this.processor(n),e=await this.model({pixel_values:a,pixel_mask:t}),r=null;if(p!==null)r=this.subtasks_mapping[p];else for(let[h,g]of Object.entries(this.subtasks_mapping))if(g in this.processor.feature_extractor){r=this.processor.feature_extractor[g].bind(this.processor.feature_extractor),p=h;break}let i=[];if(p==="panoptic"||p==="instance"){let h=r(e,o,u,l,d,s??c)[0],g=h.segmentation,m=this.model.config.id2label;for(let b of h.segments_info){let y=new Uint8ClampedArray(g.data.length);for(let T=0;T<g.data.length;++T)g.data[T]===b.id&&(y[T]=255);let w=new RawImage(y,g.dims[1],g.dims[0],1);i.push({score:b.score,label:m[b.label_id],mask:w})}}else throw Error(p==="semantic"?"semantic segmentation not yet supported.":`Subtask ${p} not supported.`);return i}}class ZeroShotImageClassificationPipeline extends Pipeline{constructor(n){super(n)}async _call(n,o,{hypothesis_template:u="This is a photo of {}"}={}){let l=Array.isArray(n);n=await prepareImages(n);let d=o.map(a=>u.replace("{}",a)),s=this.tokenizer(d,{padding:!0,truncation:!0}),{pixel_values:p}=await this.processor(n),f=await this.model({...s,pixel_values:p}),c=[];for(let a of f.logits_per_image){let t=softmax(a.data);c.push([...t].map((e,r)=>({score:e,label:o[r]})))}return l?c:c[0]}}class ObjectDetectionPipeline extends Pipeline{constructor(n){super(n)}async _call(n,{threshold:o=.9,percentage:u=!1}={}){let l=Array.isArray(n);if(l&&n.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let d=u?null:n.map(e=>[e.height,e.width]),{pixel_values:s,pixel_mask:p}=await this.processor(n),f=await this.model({pixel_values:s,pixel_mask:p}),c=this.processor.feature_extractor.post_process_object_detection(f,o,d),a=this.model.config.id2label;const t=c.map(e=>e.boxes.map((r,i)=>({score:e.scores[i],label:a[e.classes[i]],box:this._get_bounding_box(r,!u)})));return l?t:t[0]}_get_bounding_box(n,o){o&&(n=n.map(p=>p|0));const[u,l,d,s]=n;return{xmin:u,ymin:l,xmax:d,ymax:s}}}class DocumentQuestionAnsweringPipeline extends Pipeline{constructor(n){super(n)}async _call(n,o,u={}){n=(await prepareImages(n))[0];const{pixel_values:l}=await this.processor(n),d=`<s_docvqa><s_question>${o}</s_question><s_answer>`,s=this.tokenizer(d,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,p=await this.model.generate(l,{...u,decoder_input_ids:s,max_length:this.model.config.decoder.max_position_embeddings}),c=this.tokenizer.batch_decode(p)[0].match(/<s_answer>(.*?)<\/s_answer>/);let a=null;return c&&c.length>=2&&(a=c[1].trim()),[{answer:a}]}}class TextToAudioPipeline extends Pipeline{constructor(o){super(o);le(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=o.vocoder??null}async _call(o,{speaker_embeddings:u=null}={}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{quantized:!1})),(typeof u=="string"||u instanceof URL)&&(u=new Float32Array(await(await fetch(u)).arrayBuffer())),u instanceof Float32Array)u=new Tensor("float32",u,[1,u.length]);else if(!(u instanceof Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:l}=this.tokenizer(o,{padding:!0,truncation:!0}),{waveform:d}=await this.model.generate_speech(l,u,{vocoder:this.vocoder}),s=this.processor.feature_extractor.config.sampling_rate;return{audio:d.data,sampling_rate:s}}}const SUPPORTED_TASKS={"text-classification":{tokenizer:AutoTokenizer,pipeline:TextClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:AutoTokenizer,pipeline:TokenClassificationPipeline,model:AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:AutoTokenizer,pipeline:QuestionAnsweringPipeline,model:AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:AutoTokenizer,pipeline:FillMaskPipeline,model:AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:AutoTokenizer,pipeline:SummarizationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:AutoTokenizer,pipeline:TranslationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:AutoTokenizer,pipeline:Text2TextGenerationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:AutoTokenizer,pipeline:TextGenerationPipeline,model:AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:AudioClassificationPipeline,model:AutoModelForAudioClassification,processor:AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"automatic-speech-recognition":{tokenizer:AutoTokenizer,pipeline:AutomaticSpeechRecognitionPipeline,model:[AutoModelForSpeechSeq2Seq,AutoModelForCTC],processor:AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:AutoTokenizer,pipeline:TextToAudioPipeline,model:[AutoModelForTextToSpectrogram],processor:AutoProcessor,default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:AutoTokenizer,pipeline:ImageToTextPipeline,model:AutoModelForVision2Seq,processor:AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ImageClassificationPipeline,model:AutoModelForImageClassification,processor:AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ImageSegmentationPipeline,model:AutoModelForImageSegmentation,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotImageClassificationPipeline,model:AutoModel,processor:AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ObjectDetectionPipeline,model:AutoModelForObjectDetection,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"document-question-answering":{tokenizer:AutoTokenizer,pipeline:DocumentQuestionAnsweringPipeline,model:AutoModelForDocumentQuestionAnswering,processor:AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"feature-extraction":{tokenizer:AutoTokenizer,pipeline:FeatureExtractionPipeline,model:AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"}},TASK_ALIASES={"sentiment-analysis":"text-classification",ner:"token-classification",vqa:"visual-question-answering",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"};async function pipeline(_,n=null,{quantized:o=!0,progress_callback:u=null,config:l=null,cache_dir:d=null,local_files_only:s=!1,revision:p="main"}={}){_=TASK_ALIASES[_]??_;let f=SUPPORTED_TASKS[_.split("_",1)[0]];if(!f)throw Error(`Unsupported pipeline: ${_}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`);n||(n=f.default.model,console.log(`No model specified. 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diff --git a/spaces/Xenova/doodle-dash/assets/index-f42f7584.js b/spaces/Xenova/doodle-dash/assets/index-f42f7584.js
deleted file mode 100644
index e76dcc2bc3a446e822fb17ec31f612b476c127f1..0000000000000000000000000000000000000000
--- a/spaces/Xenova/doodle-dash/assets/index-f42f7584.js
+++ /dev/null
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t.flags&256?(t.flags&=-257,r=ms(Error(P(422))),Si(e,t,s,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(o=r.fallback,i=t.mode,r=_o({mode:"visible",children:r.children},i,0,null),o=fn(o,i,s,null),o.flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,t.mode&1&&Xn(t,e.child,null,s),t.child.memoizedState=wl(s),t.memoizedState=vl,o);if(!(t.mode&1))return Si(e,t,s,null);if(i.data==="$!"){if(r=i.nextSibling&&i.nextSibling.dataset,r)var l=r.dgst;return r=l,o=Error(P(419)),r=ms(o,r,void 0),Si(e,t,s,r)}if(l=(s&e.childLanes)!==0,De||l){if(r=ae,r!==null){switch(s&-s){case 4:i=2;break;case 16:i=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:i=32;break;case 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if(e.tag===19)oc(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(H(X,r),!(t.mode&1))t.memoizedState=null;else switch(i){case"forwards":for(n=t.child,i=null;n!==null;)e=n.alternate,e!==null&&io(e)===null&&(i=n),n=n.sibling;n=i,n===null?(i=t.child,t.child=null):(i=n.sibling,n.sibling=null),gs(t,!1,i,n,o);break;case"backwards":for(n=null,i=t.child,t.child=null;i!==null;){if(e=i.alternate,e!==null&&io(e)===null){t.child=i;break}e=i.sibling,i.sibling=n,n=i,i=e}gs(t,!0,n,null,o);break;case"together":gs(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function ji(e,t){!(t.mode&1)&&e!==null&&(e.alternate=null,t.alternate=null,t.flags|=2)}function St(e,t,n){if(e!==null&&(t.dependencies=e.dependencies),mn|=t.lanes,!(n&t.childLanes))return null;if(e!==null&&t.child!==e.child)throw Error(P(153));if(t.child!==null){for(e=t.child,n=Bt(e,e.pendingProps),t.child=n,n.return=t;e.sibling!==null;)e=e.sibling,n=n.sibling=Bt(e,e.pendingProps),n.return=t;n.sibling=null}return t.child}function dy(e,t,n){switch(t.tag){case 3:vp(t),Yn();break;case 5:Qd(t);break;case 1:Me(t.type)&&bi(t);break;case 4:wa(t,t.stateNode.containerInfo);break;case 10:var r=t.type._context,i=t.memoizedProps.value;H(to,r._currentValue),r._currentValue=i;break;case 13:if(r=t.memoizedState,r!==null)return r.dehydrated!==null?(H(X,X.current&1),t.flags|=128,null):n&t.child.childLanes?wp(e,t,n):(H(X,X.current&1),e=St(e,t,n),e!==null?e.sibling:null);H(X,X.current&1);break;case 19:if(r=(n&t.childLanes)!==0,e.flags&128){if(r)return xp(e,t,n);t.flags|=128}if(i=t.memoizedState,i!==null&&(i.rendering=null,i.tail=null,i.lastEffect=null),H(X,X.current),r)break;return null;case 22:case 23:return t.lanes=0,gp(e,t,n)}return St(e,t,n)}var Sp,xl,kp,Pp;Sp=function(e,t){for(var n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};xl=function(){};kp=function(e,t,n,r){var i=e.memoizedProps;if(i!==r){e=t.stateNode,an(at.current);var o=null;switch(n){case"input":i=$s(e,i),r=$s(e,r),o=[];break;case"select":i=b({},i,{value:void 0}),r=b({},r,{value:void 0}),o=[];break;case"textarea":i=Gs(e,i),r=Gs(e,r),o=[];break;default:typeof i.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=Zi)}Qs(n,r);var s;n=null;for(u in i)if(!r.hasOwnProperty(u)&&i.hasOwnProperty(u)&&i[u]!=null)if(u==="style"){var l=i[u];for(s in l)l.hasOwnProperty(s)&&(n||(n={}),n[s]="")}else u!=="dangerouslySetInnerHTML"&&u!=="children"&&u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&u!=="autoFocus"&&(Vr.hasOwnProperty(u)?o||(o=[]):(o=o||[]).push(u,null));for(u in r){var a=r[u];if(l=i!=null?i[u]:void 0,r.hasOwnProperty(u)&&a!==l&&(a!=null||l!=null))if(u==="style")if(l){for(s in l)!l.hasOwnProperty(s)||a&&a.hasOwnProperty(s)||(n||(n={}),n[s]="");for(s in a)a.hasOwnProperty(s)&&l[s]!==a[s]&&(n||(n={}),n[s]=a[s])}else n||(o||(o=[]),o.push(u,n)),n=a;else u==="dangerouslySetInnerHTML"?(a=a?a.__html:void 0,l=l?l.__html:void 0,a!=null&&l!==a&&(o=o||[]).push(u,a)):u==="children"?typeof a!="string"&&typeof a!="number"||(o=o||[]).push(u,""+a):u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&(Vr.hasOwnProperty(u)?(a!=null&&u==="onScroll"&&W("scroll",e),o||l===a||(o=[])):(o=o||[]).push(u,a))}n&&(o=o||[]).push("style",n);var u=o;(t.updateQueue=u)&&(t.flags|=4)}};Pp=function(e,t,n,r){n!==r&&(t.flags|=4)};function ur(e,t){if(!K)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function ye(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var i=e.child;i!==null;)n|=i.lanes|i.childLanes,r|=i.subtreeFlags&14680064,r|=i.flags&14680064,i.return=e,i=i.sibling;else for(i=e.child;i!==null;)n|=i.lanes|i.childLanes,r|=i.subtreeFlags,r|=i.flags,i.return=e,i=i.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function py(e,t,n){var r=t.pendingProps;switch(da(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return ye(t),null;case 1:return Me(t.type)&&qi(),ye(t),null;case 3:return r=t.stateNode,Zn(),G(Le),G(we),Sa(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),(e===null||e.child===null)&&(wi(t)?t.flags|=4:e===null||e.memoizedState.isDehydrated&&!(t.flags&256)||(t.flags|=1024,Je!==null&&(Ll(Je),Je=null))),xl(e,t),ye(t),null;case 5:xa(t);var i=an(Hr.current);if(n=t.type,e!==null&&t.stateNode!=null)kp(e,t,n,r,i),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(t.stateNode===null)throw Error(P(166));return ye(t),null}if(e=an(at.current),wi(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[st]=t,r[Ur]=o,e=(t.mode&1)!==0,n){case"dialog":W("cancel",r),W("close",r);break;case"iframe":case"object":case"embed":W("load",r);break;case"video":case"audio":for(i=0;i<gr.length;i++)W(gr[i],r);break;case"source":W("error",r);break;case"img":case"image":case"link":W("error",r),W("load",r);break;case"details":W("toggle",r);break;case"input":hu(r,o),W("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!o.multiple},W("invalid",r);break;case"textarea":gu(r,o),W("invalid",r)}Qs(n,o),i=null;for(var s in o)if(o.hasOwnProperty(s)){var l=o[s];s==="children"?typeof l=="string"?r.textContent!==l&&(o.suppressHydrationWarning!==!0&&vi(r.textContent,l,e),i=["children",l]):typeof l=="number"&&r.textContent!==""+l&&(o.suppressHydrationWarning!==!0&&vi(r.textContent,l,e),i=["children",""+l]):Vr.hasOwnProperty(s)&&l!=null&&s==="onScroll"&&W("scroll",r)}switch(n){case"input":ci(r),mu(r,o,!0);break;case"textarea":ci(r),yu(r);break;case"select":case"option":break;default:typeof o.onClick=="function"&&(r.onclick=Zi)}r=i,t.updateQueue=r,r!==null&&(t.flags|=4)}else{s=i.nodeType===9?i:i.ownerDocument,e==="http://www.w3.org/1999/xhtml"&&(e=Zf(n)),e==="http://www.w3.org/1999/xhtml"?n==="script"?(e=s.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=s.createElement(n,{is:r.is}):(e=s.createElement(n),n==="select"&&(s=e,r.multiple?s.multiple=!0:r.size&&(s.size=r.size))):e=s.createElementNS(e,n),e[st]=t,e[Ur]=r,Sp(e,t,!1,!1),t.stateNode=e;e:{switch(s=Ys(n,r),n){case"dialog":W("cancel",e),W("close",e),i=r;break;case"iframe":case"object":case"embed":W("load",e),i=r;break;case"video":case"audio":for(i=0;i<gr.length;i++)W(gr[i],e);i=r;break;case"source":W("error",e),i=r;break;case"img":case"image":case"link":W("error",e),W("load",e),i=r;break;case"details":W("toggle",e),i=r;break;case"input":hu(e,r),i=$s(e,r),W("invalid",e);break;case"option":i=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},i=b({},r,{value:void 0}),W("invalid",e);break;case"textarea":gu(e,r),i=Gs(e,r),W("invalid",e);break;default:i=r}Qs(n,i),l=i;for(o in l)if(l.hasOwnProperty(o)){var a=l[o];o==="style"?Jf(e,a):o==="dangerouslySetInnerHTML"?(a=a?a.__html:void 0,a!=null&&qf(e,a)):o==="children"?typeof a=="string"?(n!=="textarea"||a!=="")&&Ar(e,a):typeof a=="number"&&Ar(e,""+a):o!=="suppressContentEditableWarning"&&o!=="suppressHydrationWarning"&&o!=="autoFocus"&&(Vr.hasOwnProperty(o)?a!=null&&o==="onScroll"&&W("scroll",e):a!=null&&ql(e,o,a,s))}switch(n){case"input":ci(e),mu(e,r,!1);break;case"textarea":ci(e),yu(e);break;case"option":r.value!=null&&e.setAttribute("value",""+Ht(r.value));break;case"select":e.multiple=!!r.multiple,o=r.value,o!=null?zn(e,!!r.multiple,o,!1):r.defaultValue!=null&&zn(e,!!r.multiple,r.defaultValue,!0);break;default:typeof i.onClick=="function"&&(e.onclick=Zi)}switch(n){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}t.ref!==null&&(t.flags|=512,t.flags|=2097152)}return ye(t),null;case 6:if(e&&t.stateNode!=null)Pp(e,t,e.memoizedProps,r);else{if(typeof r!="string"&&t.stateNode===null)throw Error(P(166));if(n=an(Hr.current),an(at.current),wi(t)){if(r=t.stateNode,n=t.memoizedProps,r[st]=t,(o=r.nodeValue!==n)&&(e=Oe,e!==null))switch(e.tag){case 3:vi(r.nodeValue,n,(e.mode&1)!==0);break;case 5:e.memoizedProps.suppressHydrationWarning!==!0&&vi(r.nodeValue,n,(e.mode&1)!==0)}o&&(t.flags|=4)}else r=(n.nodeType===9?n:n.ownerDocument).createTextNode(r),r[st]=t,t.stateNode=r}return ye(t),null;case 13:if(G(X),r=t.memoizedState,e===null||e.memoizedState!==null&&e.memoizedState.dehydrated!==null){if(K&&Ne!==null&&t.mode&1&&!(t.flags&128))Bd(),Yn(),t.flags|=98560,o=!1;else if(o=wi(t),r!==null&&r.dehydrated!==null){if(e===null){if(!o)throw Error(P(318));if(o=t.memoizedState,o=o!==null?o.dehydrated:null,!o)throw Error(P(317));o[st]=t}else Yn(),!(t.flags&128)&&(t.memoizedState=null),t.flags|=4;ye(t),o=!1}else Je!==null&&(Ll(Je),Je=null),o=!0;if(!o)return t.flags&65536?t:null}return t.flags&128?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,t.mode&1&&(e===null||X.current&1?se===0&&(se=3):_a())),t.updateQueue!==null&&(t.flags|=4),ye(t),null);case 4:return Zn(),xl(e,t),e===null&&zr(t.stateNode.containerInfo),ye(t),null;case 10:return ga(t.type._context),ye(t),null;case 17:return Me(t.type)&&qi(),ye(t),null;case 19:if(G(X),o=t.memoizedState,o===null)return ye(t),null;if(r=(t.flags&128)!==0,s=o.rendering,s===null)if(r)ur(o,!1);else{if(se!==0||e!==null&&e.flags&128)for(e=t.child;e!==null;){if(s=io(e),s!==null){for(t.flags|=128,ur(o,!1),r=s.updateQueue,r!==null&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;n!==null;)o=n,e=r,o.flags&=14680066,s=o.alternate,s===null?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=s.childLanes,o.lanes=s.lanes,o.child=s.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=s.memoizedProps,o.memoizedState=s.memoizedState,o.updateQueue=s.updateQueue,o.type=s.type,e=s.dependencies,o.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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r=this.isDragging;if(this.cancel(),!r)return;const{velocity:i}=n;this.startAnimation(i);const{onDragEnd:o}=this.getProps();o&&Q.update(()=>o(t,n))}cancel(){this.isDragging=!1;const{projection:t,animationState:n}=this.visualElement;t&&(t.isAnimationBlocked=!1),this.panSession&&this.panSession.end(),this.panSession=void 0;const{dragPropagation:r}=this.getProps();!r&&this.openGlobalLock&&(this.openGlobalLock(),this.openGlobalLock=null),n&&n.setActive("whileDrag",!1)}updateAxis(t,n,r){const{drag:i}=this.getProps();if(!r||!Di(t,i,this.currentDirection))return;const o=this.getAxisMotionValue(t);let 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0)||0)}snapToCursor(t){ot(n=>{const{drag:r}=this.getProps();if(!Di(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,o=this.getAxisMotionValue(n);if(i&&i.layout){const{min:s,max:l}=i.layout.layoutBox[n];o.set(t[n]-Z(s,l,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!On(n)||!r||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};ot(s=>{const l=this.getAxisMotionValue(s);if(l){const a=l.get();i[s]=T1({min:a,max:a},this.constraints[s])}});const{transformTemplate:o}=this.visualElement.getProps();this.visualElement.current.style.transform=o?o({},""):"none",r.root&&r.root.updateScroll(),r.updateLayout(),this.resolveConstraints(),ot(s=>{if(!Di(s,t,null))return;const l=this.getAxisMotionValue(s),{min:a,max:u}=this.constraints[s];l.set(Z(a,u,i[s]))})}addListeners(){if(!this.visualElement.current)return;N1.set(this.visualElement,this);const 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Di(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function j1(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}class F1 extends Zt{constructor(t){super(t),this.removeGroupControls=oe,this.removeListeners=oe,this.controls=new O1(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||oe}unmount(){this.removeGroupControls(),this.removeListeners()}}const tf=e=>(t,n)=>{e&&Q.update(()=>e(t,n))};class I1 extends Zt{constructor(){super(...arguments),this.removePointerDownListener=oe}onPointerDown(t){this.session=new Gh(t,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint()})}createPanHandlers(){const{onPanSessionStart:t,onPanStart:n,onPan:r,onPanEnd:i}=this.node.getProps();return{onSessionStart:tf(t),onStart:tf(n),onMove:r,onEnd:(o,s)=>{delete 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Z1{constructor(){this.members=[]}add(t){nu(this.members,t),t.scheduleRender()}remove(t){if(ru(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){const r=this.lead;if(t!==r&&(this.prevLead=r,this.lead=t,t.show(),r)){r.instance&&r.scheduleRender(),t.scheduleRender(),t.resumeFrom=r,n&&(t.resumeFrom.preserveOpacity=!0),r.snapshot&&(t.snapshot=r.snapshot,t.snapshot.latestValues=r.animationValues||r.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:i}=t.options;i===!1&&r.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:n,resumingFrom:r}=t;n.onExitComplete&&n.onExitComplete(),r&&r.options.onExitComplete&&r.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function pf(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:a,rotateX:u,rotateY:c}=n;a&&(r+=`rotate(${a}deg) `),u&&(r+=`rotateX(${u}deg) `),c&&(r+=`rotateY(${c}deg) `)}const s=e.x.scale*t.x,l=e.y.scale*t.y;return(s!==1||l!==1)&&(r+=`scale(${s}, ${l})`),r||"none"}const q1=(e,t)=>e.depth-t.depth;class b1{constructor(){this.children=[],this.isDirty=!1}add(t){nu(this.children,t),this.isDirty=!0}remove(t){ru(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(q1),this.isDirty=!1,this.children.forEach(t)}}function J1(e,t){const n=performance.now(),r=({timestamp:i})=>{const o=i-n;o>=t&&(kt(r),e(o-t))};return Q.read(r,!0),()=>kt(r)}function ew(e){window.MotionDebug&&window.MotionDebug.record(e)}function tw(e){return e instanceof SVGElement&&e.tagName!=="svg"}function nw(e,t,n){const r=Ve(e)?e:Jn(e);return r.start(tu("",r,t,n)),r.animation}const hf=["","X","Y","Z"],mf=1e3;let rw=0;const on={type:"projectionFrame",totalNodes:0,resolvedTargetDeltas:0,recalculatedProjection:0};function tm({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(s={},l=t==null?void 0:t()){this.id=rw++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.updateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{on.totalNodes=on.resolvedTargetDeltas=on.recalculatedProjection=0,this.nodes.forEach(sw),this.nodes.forEach(fw),this.nodes.forEach(dw),this.nodes.forEach(lw),ew(on)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=s,this.root=l?l.root||l:this,this.path=l?[...l.path,l]:[],this.parent=l,this.depth=l?l.depth+1:0;for(let a=0;a<this.path.length;a++)this.path[a].shouldResetTransform=!0;this.root===this&&(this.nodes=new b1)}addEventListener(s,l){return this.eventHandlers.has(s)||this.eventHandlers.set(s,new iu),this.eventHandlers.get(s).add(l)}notifyListeners(s,...l){const a=this.eventHandlers.get(s);a&&a.notify(...l)}hasListeners(s){return this.eventHandlers.has(s)}mount(s,l=this.root.hasTreeAnimated){if(this.instance)return;this.isSVG=tw(s),this.instance=s;const{layoutId:a,layout:u,visualElement:c}=this.options;if(c&&!c.current&&c.mount(s),this.root.nodes.add(this),this.parent&&this.parent.children.add(this),l&&(u||a)&&(this.isLayoutDirty=!0),e){let f;const p=()=>this.root.updateBlockedByResize=!1;e(s,()=>{this.root.updateBlockedByResize=!0,f&&f(),f=J1(p,250),Ui.hasAnimatedSinceResize&&(Ui.hasAnimatedSinceResize=!1,this.nodes.forEach(yf))})}a&&this.root.registerSharedNode(a,this),this.options.animate!==!1&&c&&(a||u)&&this.addEventListener("didUpdate",({delta:f,hasLayoutChanged:p,hasRelativeTargetChanged:g,layout:y})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const v=this.options.transition||c.getDefaultTransition()||yw,{onLayoutAnimationStart:T,onLayoutAnimationComplete:m}=c.getProps(),d=!this.targetLayout||!Ul(this.targetLayout,y)||g,h=!p&&g;if(this.options.layoutRoot||this.resumeFrom&&this.resumeFrom.instance||h||p&&(d||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(f,h);const w={...Uh(v,"layout"),onPlay:T,onComplete:m};(c.shouldReduceMotion||this.options.layoutRoot)&&(w.delay=0,w.type=!1),this.startAnimation(w)}else p||yf(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=y})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const s=this.getStack();s&&s.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,kt(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(pw),this.animationId++)}getTransformTemplate(){const{visualElement:s}=this.options;return s&&s.getProps().transformTemplate}willUpdate(s=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let c=0;c<this.path.length;c++){const f=this.path[c];f.shouldResetTransform=!0,f.updateScroll("snapshot"),f.options.layoutRoot&&f.willUpdate(!1)}const{layoutId:l,layout:a}=this.options;if(l===void 0&&!a)return;const u=this.getTransformTemplate();this.prevTransformTemplateValue=u?u(this.latestValues,""):void 0,this.updateSnapshot(),s&&this.notifyListeners("willUpdate")}update(){if(this.updateScheduled=!1,this.isUpdateBlocked()){this.unblockUpdate(),this.clearAllSnapshots(),this.nodes.forEach(gf);return}this.isUpdating||this.nodes.forEach(uw),this.isUpdating=!1,this.nodes.forEach(cw),this.nodes.forEach(iw),this.nodes.forEach(ow),this.clearAllSnapshots();const l=performance.now();Y.delta=Gt(0,1e3/60,l-Y.timestamp),Y.timestamp=l,Y.isProcessing=!0,Gn.update.process(Y),Gn.preRender.process(Y),Gn.render.process(Y),Y.isProcessing=!1}didUpdate(){this.updateScheduled||(this.updateScheduled=!0,queueMicrotask(()=>this.update()))}clearAllSnapshots(){this.nodes.forEach(aw),this.sharedNodes.forEach(hw)}scheduleUpdateProjection(){Q.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){Q.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let a=0;a<this.path.length;a++)this.path[a].updateScroll();const s=this.layout;this.layout=this.measure(!1),this.layoutCorrected=ne(),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox);const{visualElement:l}=this.options;l&&l.notify("LayoutMeasure",this.layout.layoutBox,s?s.layoutBox:void 0)}updateScroll(s="measure"){let l=!!(this.options.layoutScroll&&this.instance);this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===s&&(l=!1),l&&(this.scroll={animationId:this.root.animationId,phase:s,isRoot:r(this.instance),offset:n(this.instance)})}resetTransform(){if(!i)return;const s=this.isLayoutDirty||this.shouldResetTransform,l=this.projectionDelta&&!em(this.projectionDelta),a=this.getTransformTemplate(),u=a?a(this.latestValues,""):void 0,c=u!==this.prevTransformTemplateValue;s&&(l||rn(this.latestValues)||c)&&(i(this.instance,u),this.shouldResetTransform=!1,this.scheduleRender())}measure(s=!0){const l=this.measurePageBox();let a=this.removeElementScroll(l);return s&&(a=this.removeTransform(a)),vw(a),{animationId:this.root.animationId,measuredBox:l,layoutBox:a,latestValues:{},source:this.id}}measurePageBox(){const{visualElement:s}=this.options;if(!s)return ne();const l=s.measureViewportBox(),{scroll:a}=this.root;return a&&(Lt(l.x,a.offset.x),Lt(l.y,a.offset.y)),l}removeElementScroll(s){const l=ne();$e(l,s);for(let a=0;a<this.path.length;a++){const u=this.path[a],{scroll:c,options:f}=u;if(u!==this.root&&c&&f.layoutScroll){if(c.isRoot){$e(l,s);const{scroll:p}=this.root;p&&(Lt(l.x,-p.offset.x),Lt(l.y,-p.offset.y))}Lt(l.x,c.offset.x),Lt(l.y,c.offset.y)}}return l}applyTransform(s,l=!1){const a=ne();$e(a,s);for(let u=0;u<this.path.length;u++){const c=this.path[u];!l&&c.options.layoutScroll&&c.scroll&&c!==c.root&&In(a,{x:-c.scroll.offset.x,y:-c.scroll.offset.y}),rn(c.latestValues)&&In(a,c.latestValues)}return rn(this.latestValues)&&In(a,this.latestValues),a}removeTransform(s){const l=ne();$e(l,s);for(let a=0;a<this.path.length;a++){const u=this.path[a];if(!u.instance||!rn(u.latestValues))continue;zl(u.latestValues)&&u.updateSnapshot();const c=ne(),f=u.measurePageBox();$e(c,f),cf(l,u.latestValues,u.snapshot?u.snapshot.layoutBox:void 0,c)}return rn(this.latestValues)&&cf(l,this.latestValues),l}setTargetDelta(s){this.targetDelta=s,this.root.scheduleUpdateProjection(),this.isProjectionDirty=!0}setOptions(s){this.options={...this.options,...s,crossfade:s.crossfade!==void 0?s.crossfade:!0}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}forceRelativeParentToResolveTarget(){this.relativeParent&&this.relativeParent.resolvedRelativeTargetAt!==Y.timestamp&&this.relativeParent.resolveTargetDelta(!0)}resolveTargetDelta(s=!1){var l;const a=this.getLead();this.isProjectionDirty||(this.isProjectionDirty=a.isProjectionDirty),this.isTransformDirty||(this.isTransformDirty=a.isTransformDirty),this.isSharedProjectionDirty||(this.isSharedProjectionDirty=a.isSharedProjectionDirty);const u=!!this.resumingFrom||this!==a;if(!(s||u&&this.isSharedProjectionDirty||this.isProjectionDirty||!((l=this.parent)===null||l===void 0)&&l.isProjectionDirty||this.attemptToResolveRelativeTarget))return;const{layout:f,layoutId:p}=this.options;if(!(!this.layout||!(f||p))){if(this.resolvedRelativeTargetAt=Y.timestamp,!this.targetDelta&&!this.relativeTarget){const g=this.getClosestProjectingParent();g&&g.layout&&this.animationProgress!==1?(this.relativeParent=g,this.forceRelativeParentToResolveTarget(),this.relativeTarget=ne(),this.relativeTargetOrigin=ne(),Rr(this.relativeTargetOrigin,this.layout.layoutBox,g.layout.layoutBox),$e(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}if(!(!this.relativeTarget&&!this.targetDelta)){if(this.target||(this.target=ne(),this.targetWithTransforms=ne()),this.relativeTarget&&this.relativeTargetOrigin&&this.relativeParent&&this.relativeParent.target?(this.forceRelativeParentToResolveTarget(),S1(this.target,this.relativeTarget,this.relativeParent.target)):this.targetDelta?(this.resumingFrom?this.target=this.applyTransform(this.layout.layoutBox):$e(this.target,this.layout.layoutBox),Xh(this.target,this.targetDelta)):$e(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget){this.attemptToResolveRelativeTarget=!1;const g=this.getClosestProjectingParent();g&&!!g.resumingFrom==!!this.resumingFrom&&!g.options.layoutScroll&&g.target&&this.animationProgress!==1?(this.relativeParent=g,this.forceRelativeParentToResolveTarget(),this.relativeTarget=ne(),this.relativeTargetOrigin=ne(),Rr(this.relativeTargetOrigin,this.target,g.target),$e(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}on.resolvedTargetDeltas++}}}getClosestProjectingParent(){if(!(!this.parent||zl(this.parent.latestValues)||Yh(this.parent.latestValues)))return this.parent.isProjecting()?this.parent:this.parent.getClosestProjectingParent()}isProjecting(){return!!((this.relativeTarget||this.targetDelta||this.options.layoutRoot)&&this.layout)}calcProjection(){var s;const l=this.getLead(),a=!!this.resumingFrom||this!==l;let u=!0;if((this.isProjectionDirty||!((s=this.parent)===null||s===void 0)&&s.isProjectionDirty)&&(u=!1),a&&(this.isSharedProjectionDirty||this.isTransformDirty)&&(u=!1),this.resolvedRelativeTargetAt===Y.timestamp&&(u=!1),u)return;const{layout:c,layoutId:f}=this.options;if(this.isTreeAnimating=!!(this.parent&&this.parent.isTreeAnimating||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!(c||f))return;$e(this.layoutCorrected,this.layout.layoutBox);const p=this.treeScale.x,g=this.treeScale.y;R1(this.layoutCorrected,this.treeScale,this.path,a),l.layout&&!l.target&&(this.treeScale.x!==1||this.treeScale.y!==1)&&(l.target=l.layout.layoutBox);const{target:y}=l;if(!y){this.projectionTransform&&(this.projectionDelta=Fn(),this.projectionTransform="none",this.scheduleRender());return}this.projectionDelta||(this.projectionDelta=Fn(),this.projectionDeltaWithTransform=Fn());const v=this.projectionTransform;Mr(this.projectionDelta,this.layoutCorrected,y,this.latestValues),this.projectionTransform=pf(this.projectionDelta,this.treeScale),(this.projectionTransform!==v||this.treeScale.x!==p||this.treeScale.y!==g)&&(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",y)),on.recalculatedProjection++}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(s=!0){if(this.options.scheduleRender&&this.options.scheduleRender(),s){const l=this.getStack();l&&l.scheduleRender()}this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(s,l=!1){const a=this.snapshot,u=a?a.latestValues:{},c={...this.latestValues},f=Fn();(!this.relativeParent||!this.relativeParent.options.layoutRoot)&&(this.relativeTarget=this.relativeTargetOrigin=void 0),this.attemptToResolveRelativeTarget=!l;const p=ne(),g=a?a.source:void 0,y=this.layout?this.layout.source:void 0,v=g!==y,T=this.getStack(),m=!T||T.members.length<=1,d=!!(v&&!m&&this.options.crossfade===!0&&!this.path.some(gw));this.animationProgress=0;let h;this.mixTargetDelta=w=>{const x=w/1e3;vf(f.x,s.x,x),vf(f.y,s.y,x),this.setTargetDelta(f),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(Rr(p,this.layout.layoutBox,this.relativeParent.layout.layoutBox),mw(this.relativeTarget,this.relativeTargetOrigin,p,x),h&&Ul(this.relativeTarget,h)&&(this.isProjectionDirty=!1),h||(h=ne()),$e(h,this.relativeTarget)),v&&(this.animationValues=c,W1(c,u,this.latestValues,x,d,m)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=x},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(s){this.notifyListeners("animationStart"),this.currentAnimation&&this.currentAnimation.stop(),this.resumingFrom&&this.resumingFrom.currentAnimation&&this.resumingFrom.currentAnimation.stop(),this.pendingAnimation&&(kt(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=Q.update(()=>{Ui.hasAnimatedSinceResize=!0,this.currentAnimation=nw(0,mf,{...s,onUpdate:l=>{this.mixTargetDelta(l),s.onUpdate&&s.onUpdate(l)},onComplete:()=>{s.onComplete&&s.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const s=this.getStack();s&&s.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(mf),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const s=this.getLead();let{targetWithTransforms:l,target:a,layout:u,latestValues:c}=s;if(!(!l||!a||!u)){if(this!==s&&this.layout&&u&&nm(this.options.animationType,this.layout.layoutBox,u.layoutBox)){a=this.target||ne();const f=Fe(this.layout.layoutBox.x);a.x.min=s.target.x.min,a.x.max=a.x.min+f;const p=Fe(this.layout.layoutBox.y);a.y.min=s.target.y.min,a.y.max=a.y.min+p}$e(l,a),In(l,c),Mr(this.projectionDeltaWithTransform,this.layoutCorrected,l,c)}}registerSharedNode(s,l){this.sharedNodes.has(s)||this.sharedNodes.set(s,new Z1),this.sharedNodes.get(s).add(l);const u=l.options.initialPromotionConfig;l.promote({transition:u?u.transition:void 0,preserveFollowOpacity:u&&u.shouldPreserveFollowOpacity?u.shouldPreserveFollowOpacity(l):void 0})}isLead(){const s=this.getStack();return s?s.lead===this:!0}getLead(){var s;const{layoutId:l}=this.options;return l?((s=this.getStack())===null||s===void 0?void 0:s.lead)||this:this}getPrevLead(){var s;const{layoutId:l}=this.options;return l?(s=this.getStack())===null||s===void 0?void 0:s.prevLead:void 0}getStack(){const{layoutId:s}=this.options;if(s)return this.root.sharedNodes.get(s)}promote({needsReset:s,transition:l,preserveFollowOpacity:a}={}){const u=this.getStack();u&&u.promote(this,a),s&&(this.projectionDelta=void 0,this.needsReset=!0),l&&this.setOptions({transition:l})}relegate(){const s=this.getStack();return s?s.relegate(this):!1}resetRotation(){const{visualElement:s}=this.options;if(!s)return;let l=!1;const{latestValues:a}=s;if((a.rotate||a.rotateX||a.rotateY||a.rotateZ)&&(l=!0),!l)return;const u={};for(let c=0;c<hf.length;c++){const f="rotate"+hf[c];a[f]&&(u[f]=a[f],s.setStaticValue(f,0))}s.render();for(const c in u)s.setStaticValue(c,u[c]);s.scheduleRender()}getProjectionStyles(s={}){var l,a;const u={};if(!this.instance||this.isSVG)return u;if(this.isVisible)u.visibility="";else return{visibility:"hidden"};const c=this.getTransformTemplate();if(this.needsReset)return this.needsReset=!1,u.opacity="",u.pointerEvents=Bi(s.pointerEvents)||"",u.transform=c?c(this.latestValues,""):"none",u;const f=this.getLead();if(!this.projectionDelta||!this.layout||!f.target){const v={};return this.options.layoutId&&(v.opacity=this.latestValues.opacity!==void 0?this.latestValues.opacity:1,v.pointerEvents=Bi(s.pointerEvents)||""),this.hasProjected&&!rn(this.latestValues)&&(v.transform=c?c({},""):"none",this.hasProjected=!1),v}const p=f.animationValues||f.latestValues;this.applyTransformsToTarget(),u.transform=pf(this.projectionDeltaWithTransform,this.treeScale,p),c&&(u.transform=c(p,u.transform));const{x:g,y}=this.projectionDelta;u.transformOrigin=`${g.origin*100}% ${y.origin*100}% 0`,f.animationValues?u.opacity=f===this?(a=(l=p.opacity)!==null&&l!==void 0?l:this.latestValues.opacity)!==null&&a!==void 0?a:1:this.preserveOpacity?this.latestValues.opacity:p.opacityExit:u.opacity=f===this?p.opacity!==void 0?p.opacity:"":p.opacityExit!==void 0?p.opacityExit:0;for(const v in ho){if(p[v]===void 0)continue;const{correct:T,applyTo:m}=ho[v],d=u.transform==="none"?p[v]:T(p[v],f);if(m){const h=m.length;for(let w=0;w<h;w++)u[m[w]]=d}else u[v]=d}return this.options.layoutId&&(u.pointerEvents=f===this?Bi(s.pointerEvents)||"":"none"),u}clearSnapshot(){this.resumeFrom=this.snapshot=void 0}resetTree(){this.root.nodes.forEach(s=>{var l;return(l=s.currentAnimation)===null||l===void 0?void 0:l.stop()}),this.root.nodes.forEach(gf),this.root.sharedNodes.clear()}}}function iw(e){e.updateLayout()}function ow(e){var t;const n=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&n&&e.hasListeners("didUpdate")){const{layoutBox:r,measuredBox:i}=e.layout,{animationType:o}=e.options,s=n.source!==e.layout.source;o==="size"?ot(f=>{const p=s?n.measuredBox[f]:n.layoutBox[f],g=Fe(p);p.min=r[f].min,p.max=p.min+g}):nm(o,n.layoutBox,r)&&ot(f=>{const p=s?n.measuredBox[f]:n.layoutBox[f],g=Fe(r[f]);p.max=p.min+g,e.relativeTarget&&!e.currentAnimation&&(e.isProjectionDirty=!0,e.relativeTarget[f].max=e.relativeTarget[f].min+g)});const l=Fn();Mr(l,r,n.layoutBox);const a=Fn();s?Mr(a,e.applyTransform(i,!0),n.measuredBox):Mr(a,r,n.layoutBox);const u=!em(l);let c=!1;if(!e.resumeFrom){const f=e.getClosestProjectingParent();if(f&&!f.resumeFrom){const{snapshot:p,layout:g}=f;if(p&&g){const y=ne();Rr(y,n.layoutBox,p.layoutBox);const v=ne();Rr(v,r,g.layoutBox),Ul(y,v)||(c=!0),f.options.layoutRoot&&(e.relativeTarget=v,e.relativeTargetOrigin=y,e.relativeParent=f)}}}e.notifyListeners("didUpdate",{layout:r,snapshot:n,delta:a,layoutDelta:l,hasLayoutChanged:u,hasRelativeTargetChanged:c})}else if(e.isLead()){const{onExitComplete:r}=e.options;r&&r()}e.options.transition=void 0}function 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diff --git a/spaces/XzJosh/Aatrox-Bert-VITS2/text/chinese_bert.py b/spaces/XzJosh/Aatrox-Bert-VITS2/text/chinese_bert.py
deleted file mode 100644
index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Aatrox-Bert-VITS2/text/chinese_bert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device)
-
-def get_bert_feature(text, word2ph):
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors='pt')
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)
-        res = model(**inputs, output_hidden_states=True)
-        res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
-
-    assert len(word2ph) == len(text)+2
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-
-    return phone_level_feature.T
-
-if __name__ == '__main__':
-    # feature = get_bert_feature('你好,我是说的道理。')
-    import torch
-
-    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
-    word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1]
-
-    # 计算总帧数
-    total_frames = sum(word2phone)
-    print(word_level_feature.shape)
-    print(word2phone)
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        print(word_level_feature[i].shape)
-
-        # 对每个词重复word2phone[i]次
-        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    print(phone_level_feature.shape)  # torch.Size([36, 1024])
-
diff --git a/spaces/YotamNitzan/domain-expansion/expansion_utils/io_utils.py b/spaces/YotamNitzan/domain-expansion/expansion_utils/io_utils.py
deleted file mode 100644
index 350f4eb671a03dada25d1e30a64154ff43074145..0000000000000000000000000000000000000000
--- a/spaces/YotamNitzan/domain-expansion/expansion_utils/io_utils.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# Copyright 2023 Adobe Research. All rights reserved.
-# To view a copy of the license, visit LICENSE.md.
-
-import json
-from pathlib import Path
-
-import torch
-from torch.utils.data import DataLoader
-import torchvision
-from PIL import Image, ImageDraw, ImageFont
-from expansion_utils import consts
-
-
-def max_num_not_in_list(max_num, lst):
-    for i in range(max_num, 0, -1):
-        if i not in lst:
-            return i
-
-
-def process_config(expansion_cfg_path):
-    with Path(expansion_cfg_path).open() as fp:
-        expansion_cfg = json.load(fp)
-    assert ("tasks" in expansion_cfg.keys()) and ("tasks_losses" in expansion_cfg.keys())
-
-    curr_max_dim = consts.LATENT_DIM - 1
-
-    used_dims = [x.get("dimension") for x in expansion_cfg["tasks"]]
-
-    for dim in used_dims:
-        if dim is not None and used_dims.count(dim) > 1:
-            raise ValueError(f"Config tries to repurpose the same dim {dim} more than once, unsupported...")
-
-    for task in expansion_cfg["tasks"]:
-        if task.get("dimension") is None:
-            curr_max_dim = max_num_not_in_list(curr_max_dim, used_dims)
-
-            if curr_max_dim is None:
-                raise ValueError("No available dimension was found")
-
-            task["dimension"] = curr_max_dim
-            used_dims.append(curr_max_dim)
-
-    print(f"Parsed config successfuly! Repurposing {len(used_dims)} dims, good luck!")
-
-    return expansion_cfg
-
-
-def label_image(img: torch.Tensor, label: str = None):
-    batch_size = img.shape[0]
-    img = torchvision.utils.make_grid(img, batch_size) # concat over W
-    if label is not None:
-        H, W = img.shape[-2:]
-        W = W // (4 * batch_size) 
-        H, W = W, H # will be rotated, so H is W
-        font = ImageFont.truetype("DejaVuSans.ttf", 60) # TODO: use different font sizes for different resolutions.
-        label_img = Image.new('RGB', (W ,H), color='white') 
-        draw = ImageDraw.Draw(label_img)
-        w, h = draw.textsize(label, font=font)
-        draw.text(((W - w) / 2, (H - h) / 2), label, font=font, fill=(0, 0, 0))
-        label_img = torchvision.transforms.functional.pil_to_tensor(label_img.rotate(90, expand=True))
-        label_img = label_img.to(torch.float32) / 127.5 - 1
-        img = torch.cat([label_img, img], dim=-1)
-    return img
-
-def save_batched_images(images: torch.Tensor, output_path: Path, labels: list = None, max_row_in_img=5):
-    num_rows = images.shape[0]
-    
-    if labels is not None:
-        if num_rows != len(labels):
-            raise ValueError('Number of labels should match number of batches')
-    else:
-        labels = [None] * num_rows
-
-    images = [label_image(image, label) for image, label in zip(images, labels)]
-    images = torch.stack(images)
-
-    batched_iter = DataLoader(images, batch_size=max_row_in_img)
-    for batch_idx, images_slice in enumerate(batched_iter):
-        save_images(
-            images_slice,
-            output_path.with_name(f"{output_path.stem}_batch_{batch_idx}"),
-            1,
-        )
-
-
-def save_images(frames: torch.Tensor, output_path: Path, nrow=None, size=None, separate=False):
-    parent_dir = output_path.parent
-    parent_dir.mkdir(exist_ok=True, parents=True)
-
-    if size:
-        frames = torch.nn.functional.interpolate(frames, size)
-
-    if separate:
-        base_name = output_path.stem
-        for i, frame in enumerate(frames):
-            torchvision.utils.save_image(
-                frame,
-                output_path.with_name(f"{i:05d}_{base_name}.jpg"),
-                nrow=len(frame),
-                normalize=True,
-                range=(-1, 1),
-            )
-    else:
-        torchvision.utils.save_image(
-            frames,
-            output_path.with_suffix(".jpg"),
-            nrow=nrow if nrow else len(frames),
-            normalize=True,
-            range=(-1, 1),
-        )
-
diff --git a/spaces/Yuliang/ECON/lib/common/seg3d_utils.py b/spaces/Yuliang/ECON/lib/common/seg3d_utils.py
deleted file mode 100644
index 8f2c547d2e02b40c9e149071818116160df0be3a..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/seg3d_utils.py
+++ /dev/null
@@ -1,341 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import matplotlib.pyplot as plt
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def plot_mask2D(mask, title="", point_coords=None, figsize=10, point_marker_size=5):
-    '''
-    Simple plotting tool to show intermediate mask predictions and points 
-    where PointRend is applied.
-
-    Args:
-    mask (Tensor): mask prediction of shape HxW
-    title (str): title for the plot
-    point_coords ((Tensor, Tensor)): x and y point coordinates
-    figsize (int): size of the figure to plot
-    point_marker_size (int): marker size for points
-    '''
-
-    H, W = mask.shape
-    plt.figure(figsize=(figsize, figsize))
-    if title:
-        title += ", "
-    plt.title("{}resolution {}x{}".format(title, H, W), fontsize=30)
-    plt.ylabel(H, fontsize=30)
-    plt.xlabel(W, fontsize=30)
-    plt.xticks([], [])
-    plt.yticks([], [])
-    plt.imshow(mask.detach(), interpolation="nearest", cmap=plt.get_cmap('gray'))
-    if point_coords is not None:
-        plt.scatter(
-            x=point_coords[0], y=point_coords[1], color="red", s=point_marker_size, clip_on=True
-        )
-    plt.xlim(-0.5, W - 0.5)
-    plt.ylim(H - 0.5, -0.5)
-    plt.show()
-
-
-def plot_mask3D(
-    mask=None, title="", point_coords=None, figsize=1500, point_marker_size=8, interactive=True
-):
-    '''
-    Simple plotting tool to show intermediate mask predictions and points 
-    where PointRend is applied.
-
-    Args:
-    mask (Tensor): mask prediction of shape DxHxW
-    title (str): title for the plot
-    point_coords ((Tensor, Tensor, Tensor)): x and y and z point coordinates
-    figsize (int): size of the figure to plot
-    point_marker_size (int): marker size for points
-    '''
-    import trimesh
-    import vtkplotter
-    from skimage import measure
-
-    vp = vtkplotter.Plotter(title=title, size=(figsize, figsize))
-    vis_list = []
-
-    if mask is not None:
-        mask = mask.detach().to("cpu").numpy()
-        mask = mask.transpose(2, 1, 0)
-
-        # marching cube to find surface
-        verts, faces, normals, values = measure.marching_cubes_lewiner(
-            mask, 0.5, gradient_direction='ascent'
-        )
-
-        # create a mesh
-        mesh = trimesh.Trimesh(verts, faces)
-        mesh.visual.face_colors = [200, 200, 250, 100]
-        vis_list.append(mesh)
-
-    if point_coords is not None:
-        point_coords = torch.stack(point_coords, 1).to("cpu").numpy()
-
-        # import numpy as np
-        # select_x = np.logical_and(point_coords[:, 0] >= 16, point_coords[:, 0] <= 112)
-        # select_y = np.logical_and(point_coords[:, 1] >= 48, point_coords[:, 1] <= 272)
-        # select_z = np.logical_and(point_coords[:, 2] >= 16, point_coords[:, 2] <= 112)
-        # select = np.logical_and(np.logical_and(select_x, select_y), select_z)
-        # point_coords = point_coords[select, :]
-
-        pc = vtkplotter.Points(point_coords, r=point_marker_size, c='red')
-        vis_list.append(pc)
-
-    vp.show(*vis_list, bg="white", axes=1, interactive=interactive, azimuth=30, elevation=30)
-
-
-def create_grid3D(min, max, steps):
-    if type(min) is int:
-        min = (min, min, min)    # (x, y, z)
-    if type(max) is int:
-        max = (max, max, max)    # (x, y)
-    if type(steps) is int:
-        steps = (steps, steps, steps)    # (x, y, z)
-    arrangeX = torch.linspace(min[0], max[0], steps[0]).long()
-    arrangeY = torch.linspace(min[1], max[1], steps[1]).long()
-    arrangeZ = torch.linspace(min[2], max[2], steps[2]).long()
-    gridD, girdH, gridW = torch.meshgrid([arrangeZ, arrangeY, arrangeX], indexing='ij')
-    coords = torch.stack([gridW, girdH, gridD])    # [2, steps[0], steps[1], steps[2]]
-    coords = coords.view(3, -1).t()    # [N, 3]
-    return coords
-
-
-def create_grid2D(min, max, steps):
-    if type(min) is int:
-        min = (min, min)    # (x, y)
-    if type(max) is int:
-        max = (max, max)    # (x, y)
-    if type(steps) is int:
-        steps = (steps, steps)    # (x, y)
-    arrangeX = torch.linspace(min[0], max[0], steps[0]).long()
-    arrangeY = torch.linspace(min[1], max[1], steps[1]).long()
-    girdH, gridW = torch.meshgrid([arrangeY, arrangeX], indexing='ij')
-    coords = torch.stack([gridW, girdH])    # [2, steps[0], steps[1]]
-    coords = coords.view(2, -1).t()    # [N, 2]
-    return coords
-
-
-class SmoothConv2D(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=3):
-        super().__init__()
-        assert kernel_size % 2 == 1, "kernel_size for smooth_conv must be odd: {3, 5, ...}"
-        self.padding = (kernel_size - 1) // 2
-
-        weight = torch.ones((in_channels, out_channels, kernel_size, kernel_size),
-                            dtype=torch.float32) / (kernel_size**2)
-        self.register_buffer('weight', weight)
-
-    def forward(self, input):
-        return F.conv2d(input, self.weight, padding=self.padding)
-
-
-class SmoothConv3D(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=3):
-        super().__init__()
-        assert kernel_size % 2 == 1, "kernel_size for smooth_conv must be odd: {3, 5, ...}"
-        self.padding = (kernel_size - 1) // 2
-
-        weight = torch.ones((in_channels, out_channels, kernel_size, kernel_size, kernel_size),
-                            dtype=torch.float32) / (kernel_size**3)
-        self.register_buffer('weight', weight)
-
-    def forward(self, input):
-        return F.conv3d(input, self.weight, padding=self.padding)
-
-
-def build_smooth_conv3D(in_channels=1, out_channels=1, kernel_size=3, padding=1):
-    smooth_conv = torch.nn.Conv3d(
-        in_channels=in_channels,
-        out_channels=out_channels,
-        kernel_size=kernel_size,
-        padding=padding
-    )
-    smooth_conv.weight.data = torch.ones(
-        (in_channels, out_channels, kernel_size, kernel_size, kernel_size), dtype=torch.float32
-    ) / (kernel_size**3)
-    smooth_conv.bias.data = torch.zeros(out_channels)
-    return smooth_conv
-
-
-def build_smooth_conv2D(in_channels=1, out_channels=1, kernel_size=3, padding=1):
-    smooth_conv = torch.nn.Conv2d(
-        in_channels=in_channels,
-        out_channels=out_channels,
-        kernel_size=kernel_size,
-        padding=padding
-    )
-    smooth_conv.weight.data = torch.ones((in_channels, out_channels, kernel_size, kernel_size),
-                                         dtype=torch.float32) / (kernel_size**2)
-    smooth_conv.bias.data = torch.zeros(out_channels)
-    return smooth_conv
-
-
-def get_uncertain_point_coords_on_grid3D(uncertainty_map, num_points, **kwargs):
-    """
-    Find `num_points` most uncertain points from `uncertainty_map` grid.
-    Args:
-        uncertainty_map (Tensor): A tensor of shape (N, 1, H, W, D) that contains uncertainty
-            values for a set of points on a regular H x W x D grid.
-        num_points (int): The number of points P to select.
-    Returns:
-        point_indices (Tensor): A tensor of shape (N, P) that contains indices from
-            [0, H x W x D) of the most uncertain points.
-        point_coords (Tensor): A tensor of shape (N, P, 3) that contains [0, 1] x [0, 1] normalized
-            coordinates of the most uncertain points from the H x W x D grid.
-    """
-    R, _, D, H, W = uncertainty_map.shape
-    # h_step = 1.0 / float(H)
-    # w_step = 1.0 / float(W)
-    # d_step = 1.0 / float(D)
-
-    num_points = min(D * H * W, num_points)
-    point_scores, point_indices = torch.topk(
-        uncertainty_map.view(R, D * H * W), k=num_points, dim=1
-    )
-    point_coords = torch.zeros(R, num_points, 3, dtype=torch.float, device=uncertainty_map.device)
-    # point_coords[:, :, 0] = h_step / 2.0 + (point_indices // (W * D)).to(torch.float) * h_step
-    # point_coords[:, :, 1] = w_step / 2.0 + (point_indices % (W * D) // D).to(torch.float) * w_step
-    # point_coords[:, :, 2] = d_step / 2.0 + (point_indices % D).to(torch.float) * d_step
-    point_coords[:, :, 0] = (point_indices % W).to(torch.float)    # x
-    point_coords[:, :, 1] = (point_indices % (H * W) // W).to(torch.float)    # y
-    point_coords[:, :, 2] = (point_indices // (H * W)).to(torch.float)    # z
-    print(f"resolution {D} x {H} x {W}", point_scores.min(), point_scores.max())
-    return point_indices, point_coords
-
-
-def get_uncertain_point_coords_on_grid3D_faster(uncertainty_map, num_points, clip_min):
-    """
-    Find `num_points` most uncertain points from `uncertainty_map` grid.
-    Args:
-        uncertainty_map (Tensor): A tensor of shape (N, 1, H, W, D) that contains uncertainty
-            values for a set of points on a regular H x W x D grid.
-        num_points (int): The number of points P to select.
-    Returns:
-        point_indices (Tensor): A tensor of shape (N, P) that contains indices from
-            [0, H x W x D) of the most uncertain points.
-        point_coords (Tensor): A tensor of shape (N, P, 3) that contains [0, 1] x [0, 1] normalized
-            coordinates of the most uncertain points from the H x W x D grid.
-    """
-    R, _, D, H, W = uncertainty_map.shape
-    # h_step = 1.0 / float(H)
-    # w_step = 1.0 / float(W)
-    # d_step = 1.0 / float(D)
-
-    assert R == 1, "batchsize > 1 is not implemented!"
-    uncertainty_map = uncertainty_map.view(D * H * W)
-    indices = (uncertainty_map >= clip_min).nonzero().squeeze(1)
-    num_points = min(num_points, indices.size(0))
-    point_scores, point_indices = torch.topk(uncertainty_map[indices], k=num_points, dim=0)
-    point_indices = indices[point_indices].unsqueeze(0)
-
-    point_coords = torch.zeros(R, num_points, 3, dtype=torch.float, device=uncertainty_map.device)
-    # point_coords[:, :, 0] = h_step / 2.0 + (point_indices // (W * D)).to(torch.float) * h_step
-    # point_coords[:, :, 1] = w_step / 2.0 + (point_indices % (W * D) // D).to(torch.float) * w_step
-    # point_coords[:, :, 2] = d_step / 2.0 + (point_indices % D).to(torch.float) * d_step
-    point_coords[:, :, 0] = (point_indices % W).to(torch.float)    # x
-    point_coords[:, :, 1] = (point_indices % (H * W) // W).to(torch.float)    # y
-    point_coords[:, :, 2] = (point_indices // (H * W)).to(torch.float)    # z
-    # print (f"resolution {D} x {H} x {W}", point_scores.min(), point_scores.max())
-    return point_indices, point_coords
-
-
-def get_uncertain_point_coords_on_grid2D(uncertainty_map, num_points, **kwargs):
-    """
-    Find `num_points` most uncertain points from `uncertainty_map` grid.
-    Args:
-        uncertainty_map (Tensor): A tensor of shape (N, 1, H, W) that contains uncertainty
-            values for a set of points on a regular H x W grid.
-        num_points (int): The number of points P to select.
-    Returns:
-        point_indices (Tensor): A tensor of shape (N, P) that contains indices from
-            [0, H x W) of the most uncertain points.
-        point_coords (Tensor): A tensor of shape (N, P, 2) that contains [0, 1] x [0, 1] normalized
-            coordinates of the most uncertain points from the H x W grid.
-    """
-    R, _, H, W = uncertainty_map.shape
-    # h_step = 1.0 / float(H)
-    # w_step = 1.0 / float(W)
-
-    num_points = min(H * W, num_points)
-    point_scores, point_indices = torch.topk(uncertainty_map.view(R, H * W), k=num_points, dim=1)
-    point_coords = torch.zeros(R, num_points, 2, dtype=torch.long, device=uncertainty_map.device)
-    # point_coords[:, :, 0] = w_step / 2.0 + (point_indices % W).to(torch.float) * w_step
-    # point_coords[:, :, 1] = h_step / 2.0 + (point_indices // W).to(torch.float) * h_step
-    point_coords[:, :, 0] = (point_indices % W).to(torch.long)
-    point_coords[:, :, 1] = (point_indices // W).to(torch.long)
-    # print (point_scores.min(), point_scores.max())
-    return point_indices, point_coords
-
-
-def get_uncertain_point_coords_on_grid2D_faster(uncertainty_map, num_points, clip_min):
-    """
-    Find `num_points` most uncertain points from `uncertainty_map` grid.
-    Args:
-        uncertainty_map (Tensor): A tensor of shape (N, 1, H, W) that contains uncertainty
-            values for a set of points on a regular H x W grid.
-        num_points (int): The number of points P to select.
-    Returns:
-        point_indices (Tensor): A tensor of shape (N, P) that contains indices from
-            [0, H x W) of the most uncertain points.
-        point_coords (Tensor): A tensor of shape (N, P, 2) that contains [0, 1] x [0, 1] normalized
-            coordinates of the most uncertain points from the H x W grid.
-    """
-    R, _, H, W = uncertainty_map.shape
-    # h_step = 1.0 / float(H)
-    # w_step = 1.0 / float(W)
-
-    assert R == 1, "batchsize > 1 is not implemented!"
-    uncertainty_map = uncertainty_map.view(H * W)
-    indices = (uncertainty_map >= clip_min).nonzero().squeeze(1)
-    num_points = min(num_points, indices.size(0))
-    point_scores, point_indices = torch.topk(uncertainty_map[indices], k=num_points, dim=0)
-    point_indices = indices[point_indices].unsqueeze(0)
-
-    point_coords = torch.zeros(R, num_points, 2, dtype=torch.long, device=uncertainty_map.device)
-    # point_coords[:, :, 0] = w_step / 2.0 + (point_indices % W).to(torch.float) * w_step
-    # point_coords[:, :, 1] = h_step / 2.0 + (point_indices // W).to(torch.float) * h_step
-    point_coords[:, :, 0] = (point_indices % W).to(torch.long)
-    point_coords[:, :, 1] = (point_indices // W).to(torch.long)
-    # print (point_scores.min(), point_scores.max())
-    return point_indices, point_coords
-
-
-def calculate_uncertainty(logits, classes=None, balance_value=0.5):
-    """
-    We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
-        foreground class in `classes`.
-    Args:
-        logits (Tensor): A tensor of shape (R, C, ...) or (R, 1, ...) for class-specific or
-            class-agnostic, where R is the total number of predicted masks in all images and C is
-            the number of foreground classes. The values are logits.
-        classes (list): A list of length R that contains either predicted of ground truth class
-            for eash predicted mask.
-    Returns:
-        scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
-            the most uncertain locations having the highest uncertainty score.
-    """
-    if logits.shape[1] == 1:
-        gt_class_logits = logits
-    else:
-        gt_class_logits = logits[torch.arange(logits.shape[0], device=logits.device),
-                                 classes].unsqueeze(1)
-    return -torch.abs(gt_class_logits - balance_value)
diff --git a/spaces/Yuliang/ECON/lib/net/BasePIFuNet.py b/spaces/Yuliang/ECON/lib/net/BasePIFuNet.py
deleted file mode 100644
index 3b5a77aec804f0fcbf72f6320b3ef30cdeb83ea1..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/net/BasePIFuNet.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import pytorch_lightning as pl
-import torch.nn as nn
-
-from .geometry import index, orthogonal, perspective
-
-
-class BasePIFuNet(pl.LightningModule):
-    def __init__(
-        self,
-        projection_mode="orthogonal",
-        error_term=nn.MSELoss(),
-    ):
-        """
-        :param projection_mode:
-        Either orthogonal or perspective.
-        It will call the corresponding function for projection.
-        :param error_term:
-        nn Loss between the predicted [B, Res, N] and the label [B, Res, N]
-        """
-        super(BasePIFuNet, self).__init__()
-        self.name = "base"
-
-        self.error_term = error_term
-
-        self.index = index
-        self.projection = orthogonal if projection_mode == "orthogonal" else perspective
-
-    def forward(self, points, images, calibs, transforms=None):
-        """
-        :param points: [B, 3, N] world space coordinates of points
-        :param images: [B, C, H, W] input images
-        :param calibs: [B, 3, 4] calibration matrices for each image
-        :param transforms: Optional [B, 2, 3] image space coordinate transforms
-        :return: [B, Res, N] predictions for each point
-        """
-        features = self.filter(images)
-        preds = self.query(features, points, calibs, transforms)
-        return preds
-
-    def filter(self, images):
-        """
-        Filter the input images
-        store all intermediate features.
-        :param images: [B, C, H, W] input images
-        """
-        return None
-
-    def query(self, features, points, calibs, transforms=None):
-        """
-        Given 3D points, query the network predictions for each point.
-        Image features should be pre-computed before this call.
-        store all intermediate features.
-        query() function may behave differently during training/testing.
-        :param points: [B, 3, N] world space coordinates of points
-        :param calibs: [B, 3, 4] calibration matrices for each image
-        :param transforms: Optional [B, 2, 3] image space coordinate transforms
-        :param labels: Optional [B, Res, N] gt labeling
-        :return: [B, Res, N] predictions for each point
-        """
-        return None
-
-    def get_error(self, preds, labels):
-        """
-        Get the network loss from the last query
-        :return: loss term
-        """
-        return self.error_term(preds, labels)
diff --git a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v2_2/util/fonts_opt.py b/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v2_2/util/fonts_opt.py
deleted file mode 100644
index c3590d012e5deb5cc2ed4851a4413682e3012bd3..0000000000000000000000000000000000000000
--- a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v2_2/util/fonts_opt.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# 字体管理
-# 创建人：曾逸夫
-# 创建时间：2022-05-01
-
-import os
-import sys
-from pathlib import Path
-
-import wget
-from rich.console import Console
-
-ROOT_PATH = sys.path[0]  # 项目根目录
-
-fonts_list = ["SimSun.ttf"]  # 字体列表
-fonts_suffix = ["ttc", "ttf", "otf"]  # 字体后缀
-
-data_url_dict = {
-    "SimSun.ttf": "https://gitee.com/CV_Lab/gradio_yolov5_det/attach_files/1053539/download/SimSun.ttf"
-}
-
-console = Console()
-
-
-# 创建字体库
-def add_fronts(font_diff):
-
-    global font_name
-
-    for k, v in data_url_dict.items():
-        if k in font_diff:
-            font_name = v.split("/")[-1]  # 字体名称
-            Path(f"{ROOT_PATH}/fonts").mkdir(parents=True, exist_ok=True)  # 创建目录
-
-            file_path = f"{ROOT_PATH}/fonts/{font_name}"  # 字体路径
-
-            try:
-                # 下载字体文件
-                wget.download(v, file_path)
-            except Exception as e:
-                print("路径错误！程序结束！")
-                print(e)
-                sys.exit()
-            else:
-                print()
-                console.print(
-                    f"{font_name} [bold green]字体文件下载完成！[/bold green] 已保存至：{file_path}"
-                )
-
-
-# 判断字体文件
-def is_fonts(fonts_dir):
-    if os.path.isdir(fonts_dir):
-        # 如果字体库存在
-        f_list = os.listdir(fonts_dir)  # 本地字体库
-
-        font_diff = list(set(fonts_list).difference(set(f_list)))
-
-        if font_diff != []:
-            # 字体不存在
-            console.print("[bold red]字体不存在，正在加载。。。[/bold red]")
-            add_fronts(font_diff)  # 创建字体库
-        else:
-            console.print(f"{fonts_list}[bold green]字体已存在！[/bold green]")
-    else:
-        # 字体库不存在，创建字体库
-        console.print("[bold red]字体库不存在，正在创建。。。[/bold red]")
-        add_fronts(fonts_list)  # 创建字体库
diff --git a/spaces/ZeroTwo3/WavJourney/scripts/download_models.py b/spaces/ZeroTwo3/WavJourney/scripts/download_models.py
deleted file mode 100644
index 51dda13bea02b8453cf7958e34cc9571c208c0df..0000000000000000000000000000000000000000
--- a/spaces/ZeroTwo3/WavJourney/scripts/download_models.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import yaml
-import os
-
-# Read the YAML file
-with open('config.yaml', 'r') as file:
-    config = yaml.safe_load(file)
-
-# Extract values for each application
-ttm_model_size = config['AudioCraft']['ttm_model_size']
-tta_model_size = config['AudioCraft']['tta_model_size']
-
-# Download nltk
-import nltk
-nltk.download('punkt')
-
-# Downloading the TTS models
-print('Step 1: Downloading TTS model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from transformers import BarkModel; BarkModel.from_pretrained("suno/bark")\'')
-
-print('Step 2: Downloading TTA model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from audiocraft.models import AudioGen; tta_model = AudioGen.get_pretrained("facebook/audiogen-{tta_model_size}")\'')
-
-print('Step 3: Downloading TTM model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from audiocraft.models import MusicGen; tta_model = MusicGen.get_pretrained("facebook/musicgen-{ttm_model_size}")\'')
-
-print('Step 4: Downloading SR model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from voicefixer import VoiceFixer; vf = VoiceFixer()\'')
-
-print('Step 5: Downloading VP model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from VoiceParser.model import VoiceParser; vp = VoiceParser(device="cpu")\'')
-
-print('All models successfully downloaded!')
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/pointrend_r50.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/pointrend_r50.py
deleted file mode 100644
index 9d323dbf9466d41e0800aa57ef84045f3d874bdf..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/pointrend_r50.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='CascadeEncoderDecoder',
-    num_stages=2,
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 1, 1),
-        strides=(1, 2, 2, 2),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=4),
-    decode_head=[
-        dict(
-            type='FPNHead',
-            in_channels=[256, 256, 256, 256],
-            in_index=[0, 1, 2, 3],
-            feature_strides=[4, 8, 16, 32],
-            channels=128,
-            dropout_ratio=-1,
-            num_classes=19,
-            norm_cfg=norm_cfg,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-        dict(
-            type='PointHead',
-            in_channels=[256],
-            in_index=[0],
-            channels=256,
-            num_fcs=3,
-            coarse_pred_each_layer=True,
-            dropout_ratio=-1,
-            num_classes=19,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
-    ],
-    # model training and testing settings
-    train_cfg=dict(
-        num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75),
-    test_cfg=dict(
-        mode='whole',
-        subdivision_steps=2,
-        subdivision_num_points=8196,
-        scale_factor=2))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/utils/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/utils/__init__.py
deleted file mode 100644
index a263e31c1e3977712827ca229bbc04910b4e928e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/utils/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .flops_counter import get_model_complexity_info
-from .fuse_conv_bn import fuse_conv_bn
-from .sync_bn import revert_sync_batchnorm
-from .weight_init import (INITIALIZERS, Caffe2XavierInit, ConstantInit,
-                          KaimingInit, NormalInit, PretrainedInit,
-                          TruncNormalInit, UniformInit, XavierInit,
-                          bias_init_with_prob, caffe2_xavier_init,
-                          constant_init, initialize, kaiming_init, normal_init,
-                          trunc_normal_init, uniform_init, xavier_init)
-
-__all__ = [
-    'get_model_complexity_info', 'bias_init_with_prob', 'caffe2_xavier_init',
-    'constant_init', 'kaiming_init', 'normal_init', 'trunc_normal_init',
-    'uniform_init', 'xavier_init', 'fuse_conv_bn', 'initialize',
-    'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit',
-    'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
-    'Caffe2XavierInit', 'revert_sync_batchnorm'
-]
diff --git a/spaces/abhishek/sketch-to-image/lib/ddpm_multi.py b/spaces/abhishek/sketch-to-image/lib/ddpm_multi.py
deleted file mode 100644
index 7dcd2b1544cb7cc57cc32d560c5bf57639cd61e0..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/lib/ddpm_multi.py
+++ /dev/null
@@ -1,1801 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
-'''
-
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager, nullcontext
-from functools import partial
-import itertools
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-from omegaconf import ListConfig
-
-from utils import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from lib.distributions import normal_kl, DiagonalGaussianDistribution
-from lib.autoencoder import IdentityFirstStage, AutoencoderKL
-from lib.util import make_beta_schedule, extract_into_tensor, noise_like
-from lib.ddim import DDIMSampler
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DDPM(pl.LightningModule):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(self,
-                 unet_config,
-                 timesteps=1000,
-                 beta_schedule="linear",
-                 loss_type="l2",
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 load_only_unet=False,
-                 monitor="val/loss",
-                 use_ema=True,
-                 first_stage_key="image",
-                 image_size=256,
-                 channels=3,
-                 log_every_t=100,
-                 clip_denoised=True,
-                 linear_start=1e-4,
-                 linear_end=2e-2,
-                 cosine_s=8e-3,
-                 given_betas=None,
-                 original_elbo_weight=0.,
-                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-                 l_simple_weight=1.,
-                 conditioning_key=None,
-                 parameterization="eps",  # all assuming fixed variance schedules
-                 scheduler_config=None,
-                 use_positional_encodings=False,
-                 learn_logvar=False,
-                 logvar_init=0.,
-                 make_it_fit=False,
-                 ucg_training=None,
-                 reset_ema=False,
-                 reset_num_ema_updates=False,
-                 ):
-        super().__init__()
-        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
-        self.parameterization = parameterization
-        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-        self.image_size = image_size  # try conv?
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapper(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-        self.make_it_fit = make_it_fit
-        if reset_ema: assert exists(ckpt_path)
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-            if reset_ema:
-                assert self.use_ema
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-        else:
-            self.register_buffer('logvar', logvar)
-
-        self.ucg_training = ucg_training or dict()
-        if self.ucg_training:
-            self.ucg_prng = np.random.RandomState()
-
-    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        elif self.parameterization == "v":
-            lvlb_weights = torch.ones_like(self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
-        else:
-            raise NotImplementedError("mu not supported")
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    @torch.no_grad()
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        if self.make_it_fit:
-            n_params = len([name for name, _ in
-                            itertools.chain(self.named_parameters(),
-                                            self.named_buffers())])
-            for name, param in tqdm(
-                    itertools.chain(self.named_parameters(),
-                                    self.named_buffers()),
-                    desc="Fitting old weights to new weights",
-                    total=n_params
-            ):
-                if not name in sd:
-                    continue
-                old_shape = sd[name].shape
-                new_shape = param.shape
-                assert len(old_shape) == len(new_shape)
-                if len(new_shape) > 2:
-                    # we only modify first two axes
-                    assert new_shape[2:] == old_shape[2:]
-                # assumes first axis corresponds to output dim
-                if not new_shape == old_shape:
-                    new_param = param.clone()
-                    old_param = sd[name]
-                    if len(new_shape) == 1:
-                        for i in range(new_param.shape[0]):
-                            new_param[i] = old_param[i % old_shape[0]]
-                    elif len(new_shape) >= 2:
-                        for i in range(new_param.shape[0]):
-                            for j in range(new_param.shape[1]):
-                                new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
-
-                        n_used_old = torch.ones(old_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_old[j % old_shape[1]] += 1
-                        n_used_new = torch.zeros(new_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_new[j] = n_used_old[j % old_shape[1]]
-
-                        n_used_new = n_used_new[None, :]
-                        while len(n_used_new.shape) < len(new_shape):
-                            n_used_new = n_used_new.unsqueeze(-1)
-                        new_param /= n_used_new
-
-                    sd[name] = new_param
-
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys:\n {missing}")
-        if len(unexpected) > 0:
-            print(f"\nUnexpected Keys:\n {unexpected}")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def predict_start_from_z_and_v(self, x_t, t, v):
-        # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
-        )
-
-    def predict_eps_from_z_and_v(self, x_t, t, v):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
-                                clip_denoised=self.clip_denoised)
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size),
-                                  return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_v(self, x, noise, t):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
-        )
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def p_losses(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_out = self.model(x_noisy, t)
-
-        loss_dict = {}
-        if self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
-
-        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
-        log_prefix = 'train' if self.training else 'val'
-
-        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
-        loss_simple = loss.mean() * self.l_simple_weight
-
-        loss_vlb = (self.lvlb_weights[t] * loss).mean()
-        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
-        loss = loss_simple + self.original_elbo_weight * loss_vlb
-
-        loss_dict.update({f'{log_prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def forward(self, x, *args, **kwargs):
-        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def shared_step(self, batch):
-        x = self.get_input(batch, self.first_stage_key)
-        loss, loss_dict = self(x)
-        return loss, loss_dict
-
-    def training_step(self, batch, batch_idx):
-        for k in self.ucg_training:
-            p = self.ucg_training[k]["p"]
-            val = self.ucg_training[k]["val"]
-            if val is None:
-                val = ""
-            for i in range(len(batch[k])):
-                if self.ucg_prng.choice(2, p=[1 - p, p]):
-                    batch[k][i] = val
-
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        _, loss_dict_no_ema = self.shared_step(batch)
-        with self.ema_scope():
-            _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
-        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def _get_rows_from_list(self, samples):
-        n_imgs_per_row = len(samples)
-        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.first_stage_key)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        x = x.to(self.device)[:N]
-        log["inputs"] = x
-
-        # get diffusion row
-        diffusion_row = list()
-        x_start = x[:n_row]
-
-        for t in range(self.num_timesteps):
-            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                t = t.to(self.device).long()
-                noise = torch.randn_like(x_start)
-                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-                diffusion_row.append(x_noisy)
-
-        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
-            log["samples"] = samples
-            log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.learn_logvar:
-            params = params + [self.logvar]
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-
-class LatentDiffusion(DDPM):
-    """main class"""
-
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 force_null_conditioning=False,
-                 *args, **kwargs):
-        self.force_null_conditioning = force_null_conditioning
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        reset_ema = kwargs.pop("reset_ema", False)
-        reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.concat_mode = concat_mode
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-            if reset_ema:
-                assert self.use_ema
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                       force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None, return_x=False):
-        # batch['jpg'] -> [4, 512, 512, 3]
-        # batch['txt'] -> list of 4
-        # batch['hint'] -> [4, 512, 512, 3]
-
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None and not self.force_null_conditioning:
-            if cond_key is None:
-                cond_key = self.cond_stage_key
-            if cond_key != self.first_stage_key:
-                if cond_key in ['caption', 'coordinates_bbox', "txt"]:
-                    xc = batch[cond_key]
-                elif cond_key in ['class_label', 'cls']:
-                    xc = batch
-                else:
-                    xc = super().get_input(batch, cond_key).to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    c = self.get_learned_conditioning(xc)
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_x:
-            out.extend([x])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-        return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)
-        loss = self(x, c)
-        return loss
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                c = self.get_learned_conditioning(c)
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-        if isinstance(cond, dict):
-            # hybrid case, cond is expected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None, **kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.image_size, self.image_size)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.image_size, self.image_size)
-            samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
-                                                         shape, cond, verbose=False, **kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True, **kwargs)
-
-        return samples, intermediates
-
-    @torch.no_grad()
-    def get_unconditional_conditioning(self, batch_size, null_label=None):
-        if null_label is not None:
-            xc = null_label
-            if isinstance(xc, ListConfig):
-                xc = list(xc)
-            if isinstance(xc, dict) or isinstance(xc, list):
-                c = self.get_learned_conditioning(xc)
-            else:
-                if hasattr(xc, "to"):
-                    xc = xc.to(self.device)
-                c = self.get_learned_conditioning(xc)
-        else:
-            if self.cond_stage_key in ["class_label", "cls"]:
-                xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
-                return self.get_learned_conditioning(xc)
-            else:
-                raise NotImplementedError("todo")
-        if isinstance(c, list):  # in case the encoder gives us a list
-            for i in range(len(c)):
-                c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
-        else:
-            c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
-        return c
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', "cls"]:
-                try:
-                    xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                    log['conditioning'] = xc
-                except KeyError:
-                    # probably no "human_label" in batch
-                    pass
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                             ddim_steps=ddim_steps, eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-        if unconditional_guidance_scale > 1.0:
-            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            if self.model.conditioning_key == "crossattn-adm":
-                uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        if inpaint:
-            # make a simple center square
-            b, h, w = z.shape[0], z.shape[2], z.shape[3]
-            mask = torch.ones(N, h, w).to(self.device)
-            # zeros will be filled in
-            mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-            mask = mask[:, None, ...]
-            with ema_scope("Plotting Inpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_inpainting"] = x_samples
-            log["mask"] = mask
-
-            # outpaint
-            mask = 1. - mask
-            with ema_scope("Plotting Outpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-
-
-class DiffusionWrapper(pl.LightningModule):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
-
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t)
-        elif self.conditioning_key == 'concat':
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t)
-        elif self.conditioning_key == 'crossattn':
-            if not self.sequential_cross_attn:
-                cc = torch.cat(c_crossattn, 1)
-            else:
-                cc = c_crossattn
-            out = self.diffusion_model(x, t, context=cc)
-        elif self.conditioning_key == 'hybrid':
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc)
-        elif self.conditioning_key == 'hybrid-adm':
-            assert c_adm is not None
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc, y=c_adm)
-        elif self.conditioning_key == 'crossattn-adm':
-            assert c_adm is not None
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc, y=c_adm)
-        elif self.conditioning_key == 'adm':
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc)
-        else:
-            raise NotImplementedError()
-
-        return out
-
-
-class LatentUpscaleDiffusion(LatentDiffusion):
-    def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
-        assert not self.cond_stage_trainable
-        self.instantiate_low_stage(low_scale_config)
-        self.low_scale_key = low_scale_key
-        self.noise_level_key = noise_level_key
-
-    def instantiate_low_stage(self, config):
-        model = instantiate_from_config(config)
-        self.low_scale_model = model.eval()
-        self.low_scale_model.train = disabled_train
-        for param in self.low_scale_model.parameters():
-            param.requires_grad = False
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
-        if not log_mode:
-            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
-        else:
-            z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                                  force_c_encode=True, return_original_cond=True, bs=bs)
-        x_low = batch[self.low_scale_key][:bs]
-        x_low = rearrange(x_low, 'b h w c -> b c h w')
-        x_low = x_low.to(memory_format=torch.contiguous_format).float()
-        zx, noise_level = self.low_scale_model(x_low)
-        if self.noise_level_key is not None:
-            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
-            raise NotImplementedError('TODO')
-
-        all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
-        if log_mode:
-            # TODO: maybe disable if too expensive
-            x_low_rec = self.low_scale_model.decode(zx)
-            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
-                   unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
-                                                                          log_mode=True)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        log["x_lr"] = x_low
-        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', 'cls']:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-        if unconditional_guidance_scale > 1.0:
-            uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            # TODO explore better "unconditional" choices for the other keys
-            # maybe guide away from empty text label and highest noise level and maximally degraded zx?
-            uc = dict()
-            for k in c:
-                if k == "c_crossattn":
-                    assert isinstance(c[k], list) and len(c[k]) == 1
-                    uc[k] = [uc_tmp]
-                elif k == "c_adm":  # todo: only run with text-based guidance?
-                    assert isinstance(c[k], torch.Tensor)
-                    #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
-                    uc[k] = c[k]
-                elif isinstance(c[k], list):
-                    uc[k] = [c[k][i] for i in range(len(c[k]))]
-                else:
-                    uc[k] = c[k]
-
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        if plot_progressive_rows:
-            with ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        return log
-
-
-class LatentFinetuneDiffusion(LatentDiffusion):
-    """
-         Basis for different finetunas, such as inpainting or depth2image
-         To disable finetuning mode, set finetune_keys to None
-    """
-
-    def __init__(self,
-                 concat_keys: tuple,
-                 finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
-                                "model_ema.diffusion_modelinput_blocks00weight"
-                                ),
-                 keep_finetune_dims=4,
-                 # if model was trained without concat mode before and we would like to keep these channels
-                 c_concat_log_start=None,  # to log reconstruction of c_concat codes
-                 c_concat_log_end=None,
-                 *args, **kwargs
-                 ):
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", list())
-        super().__init__(*args, **kwargs)
-        self.finetune_keys = finetune_keys
-        self.concat_keys = concat_keys
-        self.keep_dims = keep_finetune_dims
-        self.c_concat_log_start = c_concat_log_start
-        self.c_concat_log_end = c_concat_log_end
-        if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
-        if exists(ckpt_path):
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-
-            # make it explicit, finetune by including extra input channels
-            if exists(self.finetune_keys) and k in self.finetune_keys:
-                new_entry = None
-                for name, param in self.named_parameters():
-                    if name in self.finetune_keys:
-                        print(
-                            f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
-                        new_entry = torch.zeros_like(param)  # zero init
-                assert exists(new_entry), 'did not find matching parameter to modify'
-                new_entry[:, :self.keep_dims, ...] = sd[k]
-                sd[k] = new_entry
-
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
-        c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', 'cls']:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
-            log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                                         batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-        if unconditional_guidance_scale > 1.0:
-            uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            uc_cat = c_cat
-            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                                 batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc_full,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        return log
-
-
-class LatentInpaintDiffusion(LatentFinetuneDiffusion):
-    """
-    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
-    e.g. mask as concat and text via cross-attn.
-    To disable finetuning mode, set finetune_keys to None
-     """
-
-    def __init__(self,
-                 concat_keys=("mask", "masked_image"),
-                 masked_image_key="masked_image",
-                 *args, **kwargs
-                 ):
-        super().__init__(concat_keys, *args, **kwargs)
-        self.masked_image_key = masked_image_key
-        assert self.masked_image_key in concat_keys
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        c_cat = list()
-        for ck in self.concat_keys:
-            cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            bchw = z.shape
-            if ck != self.masked_image_key:
-                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
-            else:
-                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
-        log["masked_image"] = rearrange(args[0]["masked_image"],
-                                        'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
-        return log
-
-
-class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
-    """
-    condition on monocular depth estimation
-    """
-
-    def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
-        super().__init__(concat_keys=concat_keys, *args, **kwargs)
-        self.depth_model = instantiate_from_config(depth_stage_config)
-        self.depth_stage_key = concat_keys[0]
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        assert len(self.concat_keys) == 1
-        c_cat = list()
-        for ck in self.concat_keys:
-            cc = batch[ck]
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            cc = self.depth_model(cc)
-            cc = torch.nn.functional.interpolate(
-                cc,
-                size=z.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-
-            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
-                                                                                           keepdim=True)
-            cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super().log_images(*args, **kwargs)
-        depth = self.depth_model(args[0][self.depth_stage_key])
-        depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
-                               torch.amax(depth, dim=[1, 2, 3], keepdim=True)
-        log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
-        return log
-
-
-class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
-    """
-        condition on low-res image (and optionally on some spatial noise augmentation)
-    """
-    def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None,
-                 low_scale_config=None, low_scale_key=None, *args, **kwargs):
-        super().__init__(concat_keys=concat_keys, *args, **kwargs)
-        self.reshuffle_patch_size = reshuffle_patch_size
-        self.low_scale_model = None
-        if low_scale_config is not None:
-            print("Initializing a low-scale model")
-            assert exists(low_scale_key)
-            self.instantiate_low_stage(low_scale_config)
-            self.low_scale_key = low_scale_key
-
-    def instantiate_low_stage(self, config):
-        model = instantiate_from_config(config)
-        self.low_scale_model = model.eval()
-        self.low_scale_model.train = disabled_train
-        for param in self.low_scale_model.parameters():
-            param.requires_grad = False
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        assert len(self.concat_keys) == 1
-        # optionally make spatial noise_level here
-        c_cat = list()
-        noise_level = None
-        for ck in self.concat_keys:
-            cc = batch[ck]
-            cc = rearrange(cc, 'b h w c -> b c h w')
-            if exists(self.reshuffle_patch_size):
-                assert isinstance(self.reshuffle_patch_size, int)
-                cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
-                               p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size)
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            if exists(self.low_scale_model) and ck == self.low_scale_key:
-                cc, noise_level = self.low_scale_model(cc)
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        if exists(noise_level):
-            all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
-        else:
-            all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super().log_images(*args, **kwargs)
-        log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
-        return log
diff --git a/spaces/abidlabs/crowd-speech/README.md b/spaces/abidlabs/crowd-speech/README.md
deleted file mode 100644
index 209a621d9d8fc020b331b0a85826e0ef85945c53..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/crowd-speech/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Crowd Speech
-emoji: ⚡
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/akhaliq/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__init__.py b/spaces/akhaliq/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__init__.py
deleted file mode 100644
index 2b06b5ac538b63bdb9a6c82e4635b95bb5491d5b..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-from .ms_deform_attn_func import MSDeformAttnFunction
-
diff --git a/spaces/akhaliq/lama/saicinpainting/training/modules/base.py b/spaces/akhaliq/lama/saicinpainting/training/modules/base.py
deleted file mode 100644
index a50c3fc7753a0bba64a5ab8c1ed64ff97e62313f..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/saicinpainting/training/modules/base.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import abc
-from typing import Tuple, List
-
-import torch
-import torch.nn as nn
-
-from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
-from saicinpainting.training.modules.multidilated_conv import MultidilatedConv
-
-
-class BaseDiscriminator(nn.Module):
-    @abc.abstractmethod
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        """
-        Predict scores and get intermediate activations. Useful for feature matching loss
-        :return tuple (scores, list of intermediate activations)
-        """
-        raise NotImplemented()
-
-
-def get_conv_block_ctor(kind='default'):
-    if not isinstance(kind, str):
-        return kind
-    if kind == 'default':
-        return nn.Conv2d
-    if kind == 'depthwise':
-        return DepthWiseSeperableConv   
-    if kind == 'multidilated':
-        return MultidilatedConv
-    raise ValueError(f'Unknown convolutional block kind {kind}')
-
-
-def get_norm_layer(kind='bn'):
-    if not isinstance(kind, str):
-        return kind
-    if kind == 'bn':
-        return nn.BatchNorm2d
-    if kind == 'in':
-        return nn.InstanceNorm2d
-    raise ValueError(f'Unknown norm block kind {kind}')
-
-
-def get_activation(kind='tanh'):
-    if kind == 'tanh':
-        return nn.Tanh()
-    if kind == 'sigmoid':
-        return nn.Sigmoid()
-    if kind is False:
-        return nn.Identity()
-    raise ValueError(f'Unknown activation kind {kind}')
-
-
-class SimpleMultiStepGenerator(nn.Module):
-    def __init__(self, steps: List[nn.Module]):
-        super().__init__()
-        self.steps = nn.ModuleList(steps)
-
-    def forward(self, x):
-        cur_in = x
-        outs = []
-        for step in self.steps:
-            cur_out = step(cur_in)
-            outs.append(cur_out)
-            cur_in = torch.cat((cur_in, cur_out), dim=1)
-        return torch.cat(outs[::-1], dim=1)
-
-def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features):
-    if kind == 'convtranspose':
-        return [nn.ConvTranspose2d(min(max_features, ngf * mult), 
-                    min(max_features, int(ngf * mult / 2)), 
-                    kernel_size=3, stride=2, padding=1, output_padding=1),
-                    norm_layer(min(max_features, int(ngf * mult / 2))), activation]
-    elif kind == 'bilinear':
-        return [nn.Upsample(scale_factor=2, mode='bilinear'),
-                DepthWiseSeperableConv(min(max_features, ngf * mult), 
-                    min(max_features, int(ngf * mult / 2)), 
-                    kernel_size=3, stride=1, padding=1), 
-                norm_layer(min(max_features, int(ngf * mult / 2))), activation]
-    else:
-        raise Exception(f"Invalid deconv kind: {kind}")
\ No newline at end of file
diff --git a/spaces/alamin655/websurfx/src/cache/redis_cacher.rs b/spaces/alamin655/websurfx/src/cache/redis_cacher.rs
deleted file mode 100644
index e90344f3025035945fc84905f38833fb8ca2792f..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/src/cache/redis_cacher.rs
+++ /dev/null
@@ -1,167 +0,0 @@
-//! This module provides the functionality to cache the aggregated results fetched and aggregated
-//! from the upstream search engines in a json format.
-
-use error_stack::Report;
-use futures::future::try_join_all;
-use md5::compute;
-use redis::{aio::ConnectionManager, AsyncCommands, Client, RedisError};
-
-use super::error::CacheError;
-
-/// A named struct which stores the redis Connection url address to which the client will
-/// connect to.
-#[derive(Clone)]
-pub struct RedisCache {
-    /// It stores a pool of connections ready to be used.
-    connection_pool: Vec<ConnectionManager>,
-    /// It stores the size of the connection pool (in other words the number of
-    /// connections that should be stored in the pool).
-    pool_size: u8,
-    /// It stores the index of which connection is being used at the moment.
-    current_connection: u8,
-}
-
-impl RedisCache {
-    /// A function which fetches the cached json results as json string.
-    ///
-    /// # Arguments
-    ///
-    /// * `redis_connection_url` - It takes the redis Connection url address.
-    /// * `pool_size` - It takes the size of the connection pool (in other words the number of
-    /// connections that should be stored in the pool).
-    ///
-    /// # Error
-    ///
-    /// Returns a newly constructed `RedisCache` struct on success otherwise returns a standard
-    /// error type.
-    pub async fn new(
-        redis_connection_url: &str,
-        pool_size: u8,
-    ) -> Result<Self, Box<dyn std::error::Error>> {
-        let client = Client::open(redis_connection_url)?;
-        let mut tasks: Vec<_> = Vec::new();
-
-        for _ in 0..pool_size {
-            tasks.push(client.get_tokio_connection_manager());
-        }
-
-        let redis_cache = RedisCache {
-            connection_pool: try_join_all(tasks).await?,
-            pool_size,
-            current_connection: Default::default(),
-        };
-        Ok(redis_cache)
-    }
-
-    /// A helper function which computes the hash of the url and formats and returns it as string.
-    ///
-    /// # Arguments
-    ///
-    /// * `url` - It takes an url as string.
-    fn hash_url(&self, url: &str) -> String {
-        format!("{:?}", compute(url))
-    }
-
-    /// A function which fetches the cached json results as json string from the redis server.
-    ///
-    /// # Arguments
-    ///
-    /// * `url` - It takes an url as a string.
-    ///
-    /// # Error
-    ///
-    /// Returns the results as a String from the cache on success otherwise returns a `CacheError`
-    /// on a failure.
-    pub async fn cached_json(&mut self, url: &str) -> Result<String, Report<CacheError>> {
-        self.current_connection = Default::default();
-        let hashed_url_string: &str = &self.hash_url(url);
-
-        let mut result: Result<String, RedisError> = self.connection_pool
-            [self.current_connection as usize]
-            .get(hashed_url_string)
-            .await;
-
-        // Code to check whether the current connection being used is dropped with connection error
-        // or not. if it drops with the connection error then the current connection is replaced
-        // with a new connection from the pool which is then used to run the redis command then
-        // that connection is also checked whether it is dropped or not if it is not then the
-        // result is passed as a `Result` or else the same process repeats again and if all of the
-        // connections in the pool result in connection drop error then a custom pool error is
-        // returned.
-        loop {
-            match result {
-                Err(error) => match error.is_connection_dropped() {
-                    true => {
-                        self.current_connection += 1;
-                        if self.current_connection == self.pool_size {
-                            return Err(Report::new(
-                                CacheError::PoolExhaustionWithConnectionDropError,
-                            ));
-                        }
-                        result = self.connection_pool[self.current_connection as usize]
-                            .get(hashed_url_string)
-                            .await;
-                        continue;
-                    }
-                    false => return Err(Report::new(CacheError::RedisError(error))),
-                },
-                Ok(res) => return Ok(res),
-            }
-        }
-    }
-
-    /// A function which caches the results by using the hashed `url` as the key and
-    /// `json results` as the value and stores it in redis server with ttl(time to live)
-    /// set to 60 seconds.
-    ///
-    /// # Arguments
-    ///
-    /// * `json_results` - It takes the json results string as an argument.
-    /// * `url` - It takes the url as a String.
-    ///
-    /// # Error
-    ///
-    /// Returns an unit type if the results are cached succesfully otherwise returns a `CacheError`
-    /// on a failure.
-    pub async fn cache_results(
-        &mut self,
-        json_results: &str,
-        url: &str,
-    ) -> Result<(), Report<CacheError>> {
-        self.current_connection = Default::default();
-        let hashed_url_string: &str = &self.hash_url(url);
-
-        let mut result: Result<(), RedisError> = self.connection_pool
-            [self.current_connection as usize]
-            .set_ex(hashed_url_string, json_results, 60)
-            .await;
-
-        // Code to check whether the current connection being used is dropped with connection error
-        // or not. if it drops with the connection error then the current connection is replaced
-        // with a new connection from the pool which is then used to run the redis command then
-        // that connection is also checked whether it is dropped or not if it is not then the
-        // result is passed as a `Result` or else the same process repeats again and if all of the
-        // connections in the pool result in connection drop error then a custom pool error is
-        // returned.
-        loop {
-            match result {
-                Err(error) => match error.is_connection_dropped() {
-                    true => {
-                        self.current_connection += 1;
-                        if self.current_connection == self.pool_size {
-                            return Err(Report::new(
-                                CacheError::PoolExhaustionWithConnectionDropError,
-                            ));
-                        }
-                        result = self.connection_pool[self.current_connection as usize]
-                            .set_ex(hashed_url_string, json_results, 60)
-                            .await;
-                        continue;
-                    }
-                    false => return Err(Report::new(CacheError::RedisError(error))),
-                },
-                Ok(_) => return Ok(()),
-            }
-        }
-    }
-}
diff --git a/spaces/ali-ghamdan/realesrgan-models/app.py b/spaces/ali-ghamdan/realesrgan-models/app.py
deleted file mode 100644
index 47af3db1866a402ec9b228cf717dd22dbe316d79..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/realesrgan-models/app.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-
-# os.system("pip install gradio==2.9b23")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-from random import randint
-import sys
-from subprocess import call
-
-
-def run_cmd(command):
-    try:
-        print(command)
-        call(command, shell=True)
-    except KeyboardInterrupt:
-        print("Process interrupted")
-        sys.exit(1)
-
-
-run_cmd("pip install basicsr")
-run_cmd("pip freeze")
-
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P .")
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")
-
-
-def inference(img, model, ext="jpg", resizing="True"):
-    # if ext != "jpg" and ext != "png":
-    #     ext = "jpg"
-    if ext == 'png':
-        _id = randint(1, 10000)
-        INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
-        OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
-        run_cmd("rm -rf " + INPUT_DIR)
-        run_cmd("rm -rf " + OUTPUT_DIR)
-        run_cmd("mkdir " + INPUT_DIR)
-        run_cmd("mkdir " + OUTPUT_DIR)
-        if resizing == "True":
-            basewidth = 256
-            wpercent = (basewidth / float(img.size[0]))
-            hsize = int((float(img.size[1]) * float(wpercent)))
-            img = img.resize((basewidth, hsize))
-        img.save(INPUT_DIR + "1.png")
-        run_cmd("python inference_realesrgan.py --ext png -n " + model + " -i " + INPUT_DIR + " -o " + OUTPUT_DIR + "1_out.png")
-        return os.path.join(OUTPUT_DIR, "1_out.png")
-    else:
-        _id = randint(1, 10000)
-        INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
-        OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
-        run_cmd("rm -rf " + INPUT_DIR)
-        run_cmd("rm -rf " + OUTPUT_DIR)
-        run_cmd("mkdir " + INPUT_DIR)
-        run_cmd("mkdir " + OUTPUT_DIR)
-        if resizing == "True":
-            basewidth = 256
-            wpercent = (basewidth / float(img.size[0]))
-            hsize = int((float(img.size[1]) * float(wpercent)))
-        else:
-            basewidth = img.size[0]
-            hsize = img.size[1]
-        img = img.resize((basewidth,hsize), Image.ANTIALIAS)
-        img.save(INPUT_DIR + "1.jpg", "JPEG")
-        run_cmd("python inference_realesrgan.py  -n " + model + " -i " + INPUT_DIR + " -o " + OUTPUT_DIR + "1_out.jpg")
-        return os.path.join(OUTPUT_DIR, "1_out.jpg")
-
-
-title = "Real-ESRGAN"
-description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>"
-
-gr.Interface(
-    inference, [
-        gr.inputs.Image(type="pil", label="Input"),
-        gr.inputs.Radio(["RealESRGAN_x4plus.pth", "realesr-animevideov3.pth", "RealESRGAN_x4plus_anime_6B.pth"],
-                        type="value",
-                        default="realesr-animevideov3.pth",
-                        label="model type"),
-        gr.inputs.Radio(["jpg", "png"], type="value", default="png", label="image extension"),
-        gr.inputs.Radio(["True", "False"], type="value", default="True", label="resizing image")
-    ],
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article).launch()
diff --git a/spaces/aliabd/SummerTime/evaluation/summeval_metric.py b/spaces/aliabd/SummerTime/evaluation/summeval_metric.py
deleted file mode 100644
index 5b59ecbd5aa29bbf5a93ff0a95ab7bc31df8ae0c..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/evaluation/summeval_metric.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from .base_metric import SummMetric
-from summ_eval.metric import Metric as SEMetric
-from typing import List, Dict
-
-
-class SummEvalMetric(SummMetric):
-    """
-    Generic class for a summarization metric whose backend is SummEval.
-    """
-
-    def __init__(self, se_metric: SEMetric):
-        self.se_metric = se_metric
-
-    def evaluate(
-        self, inputs: List[str], targets: List[str], keys: List[str]
-    ) -> Dict[str, float]:
-        score_dict = self.se_metric.evaluate_batch(inputs, targets)
-        return {key: score_dict[key] if key in score_dict else None for key in keys}
diff --git a/spaces/all-diffusions/stable-diffusion-v1-5/README.md b/spaces/all-diffusions/stable-diffusion-v1-5/README.md
deleted file mode 100644
index 6fce347404bf17bd59f086b8cf99a867212f6e07..0000000000000000000000000000000000000000
--- a/spaces/all-diffusions/stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stable Diffusion 1.5
-emoji: 🛫
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/allandclive/Uganda_MMS/vits/models.py b/spaces/allandclive/Uganda_MMS/vits/models.py
deleted file mode 100644
index f5acdeb2bedd47897348407c0ae55c9a160da881..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/vits/models.py
+++ /dev/null
@@ -1,534 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/allknowingroger/Image-Models-Test60/README.md b/spaces/allknowingroger/Image-Models-Test60/README.md
deleted file mode 100644
index 8893a04c347ca7134ddd409c2459ca19fbe5cd7a..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test60/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image Models
-emoji: 👀
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test59
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/arch-123/bingo/Dockerfile b/spaces/arch-123/bingo/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/arch-123/bingo/src/components/theme-toggle.tsx b/spaces/arch-123/bingo/src/components/theme-toggle.tsx
deleted file mode 100644
index 67d3f1a2c163ccbeb52c40a7e42f107190237154..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/components/theme-toggle.tsx
+++ /dev/null
@@ -1,31 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useTheme } from 'next-themes'
-
-import { Button } from '@/components/ui/button'
-import { IconMoon, IconSun } from '@/components/ui/icons'
-
-export function ThemeToggle() {
-  const { setTheme, theme } = useTheme()
-  const [_, startTransition] = React.useTransition()
-
-  return (
-    <Button
-      variant="ghost"
-      size="icon"
-      onClick={() => {
-        startTransition(() => {
-          setTheme(theme === 'light' ? 'dark' : 'light')
-        })
-      }}
-    >
-      {!theme ? null : theme === 'dark' ? (
-        <IconMoon className="transition-all" />
-      ) : (
-        <IconSun className="transition-all" />
-      )}
-      <span className="sr-only">Toggle theme</span>
-    </Button>
-  )
-}
diff --git a/spaces/artificialguybr/video-dubbing/TTS/recipes/vctk/resnet_speaker_encoder/train_encoder.py b/spaces/artificialguybr/video-dubbing/TTS/recipes/vctk/resnet_speaker_encoder/train_encoder.py
deleted file mode 100644
index cd01754b72ef8e59a93412c5e31e2dfe5b58b90e..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/recipes/vctk/resnet_speaker_encoder/train_encoder.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import os
-
-from TTS.encoder.configs.speaker_encoder_config import SpeakerEncoderConfig
-
-# from TTS.encoder.configs.emotion_encoder_config import EmotionEncoderConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-
-CURRENT_PATH = os.getcwd()
-# change the root path to the TTS root path
-os.chdir("../../../")
-
-### Definitions ###
-# dataset
-VCTK_PATH = "/raid/datasets/VCTK_NEW_16khz_removed_silence_silero_vad/"  # download:  https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zipdddddddddd
-RIR_SIMULATED_PATH = "/raid/datasets/DA/RIRS_NOISES/simulated_rirs/"  # download: https://www.openslr.org/17/
-MUSAN_PATH = "/raid/datasets/DA/musan/"  # download: https://www.openslr.org/17/
-
-# training
-OUTPUT_PATH = os.path.join(
-    CURRENT_PATH, "resnet_speaker_encoder_training_output/"
-)  # path to save the train logs and checkpoint
-CONFIG_OUT_PATH = os.path.join(OUTPUT_PATH, "config_se.json")
-RESTORE_PATH = None  # Checkpoint to use for transfer learning if None ignore
-
-# instance the config
-# to speaker encoder
-config = SpeakerEncoderConfig()
-# to emotion encoder
-# config = EmotionEncoderConfig()
-
-
-#### DATASET CONFIG ####
-# The formatter need to return the key "speaker_name"  for the speaker encoder and the "emotion_name" for the emotion encoder
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", language="en-us", path=VCTK_PATH)
-
-# add the dataset to the config
-config.datasets = [dataset_config]
-
-
-#### TRAINING CONFIG ####
-# The encoder data loader balancer the dataset item equally to guarantee better training and to attend the losses requirements
-# It have two parameters to control the final batch size the number total of speaker used in each batch and the number of samples for each speaker
-
-# number total of speaker in batch in training
-config.num_classes_in_batch = 100
-# number of utterance per class/speaker in the batch in training
-config.num_utter_per_class = 4
-# final batch size = config.num_classes_in_batch * config.num_utter_per_class
-
-# number total of speaker in batch in evaluation
-config.eval_num_classes_in_batch = 100
-# number of utterance per class/speaker in the batch in evaluation
-config.eval_num_utter_per_class = 4
-
-# number of data loader workers
-config.num_loader_workers = 8
-config.num_val_loader_workers = 8
-
-# number of epochs
-config.epochs = 10000
-# loss to be used in training
-config.loss = "softmaxproto"
-
-# run eval
-config.run_eval = False
-
-# output path for the checkpoints
-config.output_path = OUTPUT_PATH
-
-# Save local checkpoint every save_step steps
-config.save_step = 2000
-
-### Model Config ###
-config.model_params = {
-    "model_name": "resnet",  # supported "lstm" and "resnet"
-    "input_dim": 64,
-    "use_torch_spec": True,
-    "log_input": True,
-    "proj_dim": 512,  # embedding dim
-}
-
-### Audio Config ###
-# To fast train the model divides the audio in small parts. it parameter defines the length in seconds of these "parts"
-config.voice_len = 2.0
-# all others configs
-config.audio = {
-    "fft_size": 512,
-    "win_length": 400,
-    "hop_length": 160,
-    "frame_shift_ms": None,
-    "frame_length_ms": None,
-    "stft_pad_mode": "reflect",
-    "sample_rate": 16000,
-    "resample": False,
-    "preemphasis": 0.97,
-    "ref_level_db": 20,
-    "do_sound_norm": False,
-    "do_trim_silence": False,
-    "trim_db": 60,
-    "power": 1.5,
-    "griffin_lim_iters": 60,
-    "num_mels": 64,
-    "mel_fmin": 0.0,
-    "mel_fmax": 8000.0,
-    "spec_gain": 20,
-    "signal_norm": False,
-    "min_level_db": -100,
-    "symmetric_norm": False,
-    "max_norm": 4.0,
-    "clip_norm": False,
-    "stats_path": None,
-    "do_rms_norm": True,
-    "db_level": -27.0,
-}
-
-
-### Augmentation Config ###
-config.audio_augmentation = {
-    # additive noise and room impulse response (RIR) simulation similar to: https://arxiv.org/pdf/2009.14153.pdf
-    "p": 0.5,  # probability to the use of one of the augmentation - 0 means disabled
-    "rir": {"rir_path": RIR_SIMULATED_PATH, "conv_mode": "full"},  # download: https://www.openslr.org/17/
-    "additive": {
-        "sounds_path": MUSAN_PATH,
-        "speech": {"min_snr_in_db": 13, "max_snr_in_db": 20, "min_num_noises": 1, "max_num_noises": 1},
-        "noise": {"min_snr_in_db": 0, "max_snr_in_db": 15, "min_num_noises": 1, "max_num_noises": 1},
-        "music": {"min_snr_in_db": 5, "max_snr_in_db": 15, "min_num_noises": 1, "max_num_noises": 1},
-    },
-    "gaussian": {"p": 0.7, "min_amplitude": 0.0, "max_amplitude": 1e-05},
-}
-
-config.save_json(CONFIG_OUT_PATH)
-
-print(CONFIG_OUT_PATH)
-if RESTORE_PATH is not None:
-    command = f"python TTS/bin/train_encoder.py --config_path {CONFIG_OUT_PATH} --restore_path {RESTORE_PATH}"
-else:
-    command = f"python TTS/bin/train_encoder.py --config_path {CONFIG_OUT_PATH}"
-
-os.system(command)
diff --git a/spaces/arunavsk1/Pubmed-Named-Entity-Recognition/app.py b/spaces/arunavsk1/Pubmed-Named-Entity-Recognition/app.py
deleted file mode 100644
index e092bebd82dfda91eeeafce50d66ec497f456987..0000000000000000000000000000000000000000
--- a/spaces/arunavsk1/Pubmed-Named-Entity-Recognition/app.py
+++ /dev/null
@@ -1,117 +0,0 @@
-
-from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer, DebertaV2Tokenizer, DebertaV2Model
-import sentencepiece
-import streamlit as st
-import pandas as pd
-import spacy
-
-example_list = [
-    "The primary outcome was overall survival (OS).", 
-    "Overall survival was not significantly different between the groups (hazard ratio [HR], 0.87; 95% CI, 0.66-1.16; P = .34).",
-     "Afatinib-an oral irreversible ErbB family blocker-improves progression-free survival compared with pemetrexed and cisplatin for first-line treatment of patients with EGFR mutation-positive advanced non-small-cell lung cancer (NSCLC)."
-]
-
-st.set_page_config(layout="wide")
-
-st.title("Demo for EIC NER")
-
-model_list = ['arunavsk1/my-awesome-pubmed-bert'
-             # 'akdeniz27/convbert-base-turkish-cased-ner',
-             # 'akdeniz27/xlm-roberta-base-turkish-ner',
-             # 'xlm-roberta-large-finetuned-conll03-english'
-             ]
-
-# st.sidebar.header("Select NER Model")
-model_checkpoint = st.sidebar.radio("", model_list)
-
-# st.sidebar.write("For details of models: 'https://huggingface.co/akdeniz27/")
-# st.sidebar.write("")
-
-# xlm_agg_strategy_info = "'aggregation_strategy' can be selected as 'simple' or 'none' for 'xlm-roberta' because of the RoBERTa model's tokenization approach."
-
-# st.sidebar.header("Select Aggregation Strategy Type")
-# if model_checkpoint == "akdeniz27/xlm-roberta-base-turkish-ner":
-#     aggregation = st.sidebar.radio("", ('simple', 'none'))
-#     st.sidebar.write(xlm_agg_strategy_info)
-# elif model_checkpoint == "xlm-roberta-large-finetuned-conll03-english":
-#     aggregation = st.sidebar.radio("", ('simple', 'none'))
-#     st.sidebar.write(xlm_agg_strategy_info)
-#     st.sidebar.write("")
-#     st.sidebar.write("This English NER model is included just to show the zero-shot transfer learning capability of XLM-Roberta.")
-# else:
-#     aggregation = st.sidebar.radio("", ('first', 'simple', 'average', 'max', 'none'))
-    
-# st.sidebar.write("Please refer 'https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html' for entity grouping with aggregation_strategy parameter.")
-aggregation = 'none'
-st.subheader("Select Text Input Method")
-input_method = st.radio("", ('Select from Examples', 'Write or Paste New Text'))
-if input_method == 'Select from Examples':
-    selected_text = st.selectbox('Select Text from List', example_list, index=0, key=1)
-    st.subheader("Text to Run")
-    input_text = st.text_area("Selected Text", selected_text, height=128, max_chars=None, key=2)
-elif input_method == "Write or Paste New Text":
-    st.subheader("Text to Run")
-    input_text = st.text_area('Write or Paste Text Below', value="", height=128, max_chars=None, key=2)
-
-@st.cache(allow_output_mutation=True)
-def setModel(model_checkpoint, aggregation):
-    model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)
-    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-    return pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy=aggregation)
-
-@st.cache(allow_output_mutation=True)
-def get_html(html: str):
-    WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
-    html = html.replace("\n", " ")
-    return WRAPPER.format(html)
-        
-Run_Button = st.button("Run", key=None)
-if Run_Button == True:
-    
-    ner_pipeline = setModel(model_checkpoint, aggregation)
-    output = ner_pipeline(input_text)
-
-    df = pd.DataFrame.from_dict(output)
-    if aggregation != "none":
-        cols_to_keep = ['word','entity_group','score','start','end']
-    else:
-        cols_to_keep = ['word','entity','score','start','end']
-    df_final = df[cols_to_keep]
-    
-    # st.subheader("Recognized Entities")
-    # st.dataframe(df_final)
-
-    st.subheader("Spacy Style Display")
-    spacy_display = {}
-    spacy_display["ents"] = []
-    spacy_display["text"] = input_text
-    spacy_display["title"] = None
-
-    entity_map = {'LABEL_0': 'O',
-                'LABEL_1': 'B-Intervention',
-                'LABEL_2': 'I-Intervention',
-                'LABEL_3': 'B-Outcome',
-                'LABEL_4': 'I-Outcome',
-                'LABEL_5': 'B-Value',
-                'LABEL_6': 'I-Value'}
-
-    for entity in output:
-        if aggregation != "none":
-            spacy_display["ents"].append({"start": entity["start"], "end": entity["end"], "label": entity_map[entity["entity_group"]]})
-        else:
-            spacy_display["ents"].append({"start": entity["start"], "end": entity["end"], "label": entity_map[entity["entity"]]})
-
-    entity_list = ['B-Intervention', 'I-Intervention', 'B-Outcome', 'I-Outcome', 'B-Value', 'I-Value']
-
-    colors = {'B-Intervention': '#85DCDF', 
-                'I-Intervention': '#85DCDF',
-                'B-Outcome': '#DF85DC', 
-                'I-Outcome': '#DF85DC',
-                'B-Value': '#DCDF85',
-                'I-Value': '#DCDF85'}
-    html = spacy.displacy.render(spacy_display, style="ent", minify=True, manual=True, options={"ents": entity_list, "colors": colors})
-    style = "<style>mark.entity { display: inline-block }</style>"
-    st.write(f"{style}{get_html(html)}", unsafe_allow_html=True)
-
-    st.subheader("Recognized Entities")
-    st.dataframe(df_final)
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/is64bit.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/is64bit.py
deleted file mode 100644
index bba12b435c118b58c601e424aa8166459370e471..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/is64bit.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""is64bit.Python() --> boolean value of detected Python word size. is64bit.os() --> os build version"""
-import sys
-
-
-def Python():
-    if sys.platform == "cli":  # IronPython
-        import System
-
-        return System.IntPtr.Size == 8
-    else:
-        try:
-            return sys.maxsize > 2147483647
-        except AttributeError:
-            return sys.maxint > 2147483647
-
-
-def os():
-    import platform
-
-    pm = platform.machine()
-    if pm != ".." and pm.endswith("64"):  # recent Python (not Iron)
-        return True
-    else:
-        import os
-
-        if "PROCESSOR_ARCHITEW6432" in os.environ:
-            return True  # 32 bit program running on 64 bit Windows
-        try:
-            return os.environ["PROCESSOR_ARCHITECTURE"].endswith(
-                "64"
-            )  # 64 bit Windows 64 bit program
-        except (IndexError, KeyError):
-            pass  # not Windows
-        try:
-            return "64" in platform.architecture()[0]  # this often works in Linux
-        except:
-            return False  # is an older version of Python, assume also an older os (best we can guess)
-
-
-if __name__ == "__main__":
-    print("is64bit.Python() =", Python(), "is64bit.os() =", os())
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_chart_with_cumsum.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_chart_with_cumsum.py
deleted file mode 100644
index 2b21b0845adc1bafb74de6786715686360bd5390..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_chart_with_cumsum.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""
-Line Chart with Cumulative Sum
-------------------------------
-This chart creates a simple line chart from the cumulative sum of a fields.
-"""
-# category: line charts
-import altair as alt
-from vega_datasets import data
-
-source = data.wheat()
-
-alt.Chart(source).mark_line().transform_window(
-    # Sort the data chronologically
-    sort=[{'field': 'year'}],
-    # Include all previous records before the current record and none after
-    # (This is the default value so you could skip it and it would still work.)
-    frame=[None, 0],
-    # What to add up as you go
-    cumulative_wheat='sum(wheat)'
-).encode(
-    x='year:O',
-    # Plot the calculated field created by the transformation
-    y='cumulative_wheat:Q'
-).properties(width=600)
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/_version.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/_version.py
deleted file mode 100644
index 382021f30ff6ce35872aa0de4ebe2a43f50c21d7..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/_version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "1.0.6"
diff --git a/spaces/ashkanforootan/af_chatGPT/app.py b/spaces/ashkanforootan/af_chatGPT/app.py
deleted file mode 100644
index 4cf6f8f62adf9f7b73a7974bf83a741f7bff8331..0000000000000000000000000000000000000000
--- a/spaces/ashkanforootan/af_chatGPT/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import streamlit as st
-from deep_translator import GoogleTranslator
-from xvfbwrapper import Xvfb
-from pyChatGPT import ChatGPT
-
-    
-    
-st.title("MY Name is AshkanForootan")
-
-token="eyJhbGciOiJkaXIiLCJlbmMiOiJBMjU2R0NNIn0..NHss60HSATOSRREE.Gg7JJlE8EEvOeMABf88qpovYaIGfl4s-FbU8G6Dv0jh6mna9ZEgw7Kj2wEbg2aOD0lAl5aqAf-o8OlEF2ZfcaAbpNQK6LdL1VstrnwfBCWWh36EYuCPn0cQ3NuOrplXQYSnfZfHmDb4QcoWO0a6NfLBWL7tbkVkZo8JoNKAo3DT3Mims5ikApF5-Ml8yhXfYoUAehmfqWAUiuEAYO5oW-_ZnvrMJqNfuGpbVEm4MxrN0Ah315o49_7Ym2B22w5zVayGqV7UDSOG9VOvUhT9KBSyjB5IbflU1MGR2pm8QsJaEqmmOTpHqz5_1p9KuiMUZ-0n0uhaMlTe3dFcQq1FkWEZBsU3I9kxHnC9qh4jxJJetjJzRE0xXT8Dtas-TpDOziKQ8ik1-u3KJMPnUqUCAduA6xR2ztwqMjPR5Qp41Y-uCNOC6tMqFXYkBsEMxFpE0TzzXAivd15VB6Pl7voaeDozG6gH5Fp7t82nMYK6OII06hYAQ0XQLo-Fa_tdlEk3mBUEvdhf_azPWqYoZpkNYpFrgqQ3g27B0nSMInwzfnNy8YgHnF0elo4IlwvaaDO5GyKTacw7jRvQMzHfTvkHMAIitu1nyQM23o6kXqbefNhTI2G3oLabvFjNpO4ao00PvEWYybNSo9pGN3xDa_8k3dVmRwxpQpH6gISMBChvpJDCVDzpTe4YeMVXf-FzWIFgPkOpDpcza0r4V73mst3kocQhmBjIgBrZBre7BJA-kcYpfFubwKWaabS8rM5iXhxoVAkHuAnluDnpTX4t63fyC75CjSQJ_roAKLvZ1SNbOhRgZm4MmSrdF4E-esK0zuJkVickc6l5-OWYssJhx4FaypVWhjexfGLyKypri8vlzpMTMWqii9MZ7iLj3tPwvZ-z1eCGlyDCOuQddMmd_WGT-4sWYf1Q_ZLjBtgB3sdRDxg03eFPmwW4eXs0jDufysQ31F6gHoQvTybqAS-l69uD8uoOdq-J4Aj6TCGoZsyVTwi1zUiCL0GTAzxfig-H2vyJcON8bz6t3OK_wU-_9bPow2DemHHT99lfhUn9nHN5ntGp1SwvD42W89WoTWZsEkBXyEQ3U-ErSRqrjStI4KRy34UaTt0QQp7xl4NQOSHm5rQCsG3VfQi-7muSg10zWBUcNjGGkvlpebyJlotQA7C7xQJ1bK3Y8X81_QdaSPdyPy_LpmDn9gs7SvIxwGclrNiXj2anNuTU_sqaQYtclRUh2HPU3oYpg8yAhHxlLA8oifwfoBFjsFBr2G_DwtvARJXqu1rAVxHbIZEOMjXwstQKaIKJa4n_neSK_GTfThCbkZGfaPMqlZ7I8Q1_rOWMyR61h01I9RUD2wLOMo8_NXUc14lTRlX9ueM1en082uPQSXLnnA7CbgAO2D2Wd9W9KPcSOGZF73tRILaSoiBUvrYa2nMi6k8R3CBRvgsHk1zKNYhbj0NzkTSEYLLAhftXgT6jsX9KB2Hpt8R_LhmcS-jtlS1BaDrjtNzewuzWvjzLWoTUVsc8rqrzNArTFMacqJGifZv2ihfwPcsfUbrfzK2_AeVtsNCXiOhPGk2yH9rMTaS0UMlio4rN2BIplGv69UcNcYFydxV7A_Up5aPrpMxn6Fr4809Q6dyQybqr9Xa8Pk5AU6iWtmxxyHtEDlpi0zVoZGksdrgXulYyZAyeSpPwLLH-isCGMWIpSa_GRmasmpnM15ZM3ZS0Fy42fHUK4F3GgNjXE5cby57bvWdqWTyn_atQvaqroTWip8mLwxXzAn9LdOlykfYE_Sc9pBFVE_sSZcA4rzOSFkvhzPPpO4Uiz25QQG58awCGZVsl3-UPC1gfTYy0FsQFsIcoW5uHNczZpKD34SMM5yilLMbOlTA4IXmn0m9nriyVopDUub3hkFd50UovP1vmMUKsHa5TY0Zt00tuQn0qg3I8CTvWGEuhcZ5DYPsMQ5UOjrlnnwPgv32ruatAAvmAEEgNyiErDzRjmKwLO76YZ1ny_npbvpVOzwMg2juWNyy1_AuSku0uJJZEpA56ptObaGo5jwkYY-P8cKM1YqW1sKQeQnsMKqWfRaJBL-2JbaOF6OAFTSP4m4obwJYpUsH0_qb6Jd9XaBun15KmAR8BC_K0lGpZfmbOxY_EZXk2LlWNdGwTjupG_fptPtUmU9msyu0d6fxLZlmTIeqIdI1sUdNucxaYZEHsrJc06nJOuLbm0dPJDVynSHUylWFEiAjrphrpJKQIrQxXZJ15zMiHHd19BV9PsV_V6o8uEv_0Pd-lXqq9flP3SBCoD_C1c02u83S4xMssEOSkq2oyTfiIMU7P8fLd8zyOu0_COGj7hGkL0EQwGRNt4zypeyKkZfh8aWgHTWRqjYsFDy2-XQOxUVxRyPRVFEOYp2lSDUVhvqNDahqDyG0uovV2d5VwEgOwAviwWEkvxcqpI7-N_KaLor-aJGfxMn-IyOoY0qcqMhE3qh2sUV5cXsZLh1Jot1UGPzDsA6NSeM4bmbDeK2gvsKT_p1p2kOA-CQQf-04IgJJHOXhyNATgwXBEMEaLEWnPaIl6GDAi0zm8P5bWz5L7eQxUCcPOjSpSdu9fBFjxVTA_Gyt9EzZJ-0HdZrzjg6-Q3Wfsj5tuOM7ROWVpeO2GU3wCYAtKdGW27eiQymONaEcsS9s_WL7DeUJVZHCYFFL-5YhAY1To.mK_BitHN5FWf_gs9g93PhQ"
-print("1")
-api = ChatGPT(token)
-
-
-with st.form('mm'):
-    inp = st.text_input('message')
-    translated = GoogleTranslator(source='fa', target='en').translate(inp)  # output -> Weiter so, du bist großartig
-    sub = st.form_submit_button('submit')
-    if sub :
-
-        x = api.send_message(translated)
-        target = GoogleTranslator(source='en', target='fa').translate(x['message'])
-        st.write(target)
-
-
diff --git a/spaces/awacke1/ChatGPTStreamlit7/README.md b/spaces/awacke1/ChatGPTStreamlit7/README.md
deleted file mode 100644
index 171d13797a6c388daa883c34807376ff4bf8bbad..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChatGPTStreamlit7/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGPTStreamlit7
-emoji: 🐢
-colorFrom: purple
-colorTo: red
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/awacke1/Streamlit-Clipboard-Monitor-Javascript/clipboard.html b/spaces/awacke1/Streamlit-Clipboard-Monitor-Javascript/clipboard.html
deleted file mode 100644
index bb2600526f46faee34ac62d75c525ebd38a958de..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Clipboard-Monitor-Javascript/clipboard.html
+++ /dev/null
@@ -1,23 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-    <style>
-        textarea {
-            width: 100%;
-            height: 200px;
-        }
-    </style>
-</head>
-<body>
-    <h1>Clipboard Monitor</h1>
-    <p>Paste your data in the textarea below:</p>
-    <textarea id="clipboard-text" oninput="updateClipboard()"></textarea>
-    <script>
-        function updateClipboard() {
-            let textArea = document.getElementById("clipboard-text");
-            let clipboardData = textArea.value;
-            window.parent.postMessage({type: "clipboard-update", data: clipboardData}, "*");
-        }
-    </script>
-</body>
-</html>
diff --git a/spaces/awacke1/StreamlitSharedChatToFiles/README.md b/spaces/awacke1/StreamlitSharedChatToFiles/README.md
deleted file mode 100644
index 928f8c1114dd7df90b7727120d69dfe8e091adf4..0000000000000000000000000000000000000000
--- a/spaces/awacke1/StreamlitSharedChatToFiles/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
----
-title: 🍪📚🤓StreamlitSharedChatToFiles
-emoji: 📢📚👩🏼‍🏫🤓
-colorFrom: green
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-📢📚👩🏼‍🏫🤓 Calling all bookworms, geeks, and knowledge seekers! 📢📚👩🏼‍🏫🤓
-
-👉🏼 Introducing Streamlit Chat, the funnest chat channel ever! 🎉🥳🤩
-
-If you're tired of boring chats and dull conversations, you're going to love Streamlit Chat. With this exciting new platform, you can connect with like-minded people from all over the world and chat about everything from your favorite books to the latest tech gadgets. 📱💻📚🌎
-
-And the best part? You can access a wealth of knowledge, all thanks to the incredible power of Streamlit Chat's Wikipedia integration. 🤯🌟👨🏻‍💻
-
-With Streamlit Chat, you don't need to be a librarian or a genius to access valuable information. You can chat with the encyclopedia itself, thanks to our innovative integration with Wikipedia. 🤓📚💻
-
-Whether you want to learn about the history of the Eiffel Tower 🗼 or discover the best way to bake a cake 🎂, Streamlit Chat has got you covered. And, with our fun and easy-to-use interface, you can connect with other users and share your knowledge with the world. 🌎👩‍👩‍👦‍👦💬
-
-So, if you're ready to take your chats to the next level, join Streamlit Chat today. It's the funnest way to connect with people and learn something new every day. 🤩🥳🎉
-
-Streamlit Chat - the cookiest librarian you'll ever meet! 🍪📚🤓
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/ContentVec768L12.py b/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/ContentVec768L12.py
deleted file mode 100644
index 0d1591c8843b920d5685e822354e8e6adc9a9e19..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/ContentVec768L12.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from vencoder.encoder import SpeechEncoder
-import torch
-from fairseq import checkpoint_utils
-
-class ContentVec768L12(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None):
-        print("load model(s) from {}".format(vec_path))
-        self.hidden_dim = 768
-        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-          [vec_path],
-          suffix="",
-        )
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.model = models[0].to(self.dev)
-        self.model.eval()
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        inputs = {
-          "source": feats.to(wav.device),
-          "padding_mask": padding_mask.to(wav.device),
-          "output_layer": 12,  # layer 12
-        }
-        with torch.no_grad():
-          logits = self.model.extract_features(**inputs)
-        return logits[0].transpose(1, 2)
\ No newline at end of file
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/hubert/__init__.py b/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/PRNG.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/PRNG.js
deleted file mode 100644
index ab811a820bc3e440b0fb6aecdeb806f4c02fbb77..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/PRNG.js
+++ /dev/null
@@ -1,23 +0,0 @@
-// Park-Miller-Carta Pseudo-Random Number Generator
-// https://github.com/pnitsch/BitmapData.js/blob/master/js/BitmapData.js
-
-var PRNG = function () {
-
-	this.seed = 1;
-	this.next = function() {
-
-		return ( this.gen() / 2147483647 );
-
-	};
-	this.nextRange = function( min, max )	{
-
-		return min + ( ( max - min ) * this.next() )
-
-	};
-	this.gen = function() {
-
-		return this.seed = ( this.seed * 16807 ) % 2147483647;
-
-	};
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/HalftonePass.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/HalftonePass.js
deleted file mode 100644
index 5fa841c1248c6232dd7a0373880b868f602e272f..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/HalftonePass.js
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @author meatbags / xavierburrow.com, github/meatbags
- *
- * RGB Halftone pass for three.js effects composer. Requires THREE.HalftoneShader.
- *
- */
-
-THREE.HalftonePass = function ( width, height, params ) {
-
-	THREE.Pass.call( this );
-
- 	if ( THREE.HalftoneShader === undefined ) {
-
- 		console.error( 'THREE.HalftonePass requires THREE.HalftoneShader' );
-
- 	}
-
- 	this.uniforms = THREE.UniformsUtils.clone( THREE.HalftoneShader.uniforms );
- 	this.material = new THREE.ShaderMaterial( {
- 		uniforms: this.uniforms,
- 		fragmentShader: THREE.HalftoneShader.fragmentShader,
- 		vertexShader: THREE.HalftoneShader.vertexShader
- 	} );
-
-	// set params
-	this.uniforms.width.value = width;
-	this.uniforms.height.value = height;
-
-	for ( var key in params ) {
-
-		if ( params.hasOwnProperty( key ) && this.uniforms.hasOwnProperty( key ) ) {
-
-			this.uniforms[ key ].value = params[ key ];
-
-		}
-
-	}
-
-	this.fsQuad = new THREE.Pass.FullScreenQuad( this.material );
-
-};
-
-THREE.HalftonePass.prototype = Object.assign( Object.create( THREE.Pass.prototype ), {
-
-	constructor: THREE.HalftonePass,
-
-	render: function ( renderer, writeBuffer, readBuffer, deltaTime, maskActive ) {
-
- 		this.material.uniforms[ "tDiffuse" ].value = readBuffer.texture;
-
- 		if ( this.renderToScreen ) {
-
- 			renderer.setRenderTarget( null );
- 			this.fsQuad.render( renderer );
-
-		} else {
-
- 			renderer.setRenderTarget( writeBuffer );
- 			if ( this.clear ) renderer.clear();
-			this.fsQuad.render( renderer );
-
-		}
-
- 	},
-
- 	setSize: function ( width, height ) {
-
- 		this.uniforms.width.value = width;
- 		this.uniforms.height.value = height;
-
- 	}
-} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshphong_vert.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshphong_vert.glsl.js
deleted file mode 100644
index 10588905821ea7bf1f775b26d5070b672b47a989..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshphong_vert.glsl.js
+++ /dev/null
@@ -1,59 +0,0 @@
-export default /* glsl */`
-#define PHONG
-
-varying vec3 vViewPosition;
-
-#ifndef FLAT_SHADED
-
-	varying vec3 vNormal;
-
-#endif
-
-#include <common>
-#include <uv_pars_vertex>
-#include <uv2_pars_vertex>
-#include <displacementmap_pars_vertex>
-#include <envmap_pars_vertex>
-#include <color_pars_vertex>
-#include <fog_pars_vertex>
-#include <morphtarget_pars_vertex>
-#include <skinning_pars_vertex>
-#include <shadowmap_pars_vertex>
-#include <logdepthbuf_pars_vertex>
-#include <clipping_planes_pars_vertex>
-
-void main() {
-
-	#include <uv_vertex>
-	#include <uv2_vertex>
-	#include <color_vertex>
-
-	#include <beginnormal_vertex>
-	#include <morphnormal_vertex>
-	#include <skinbase_vertex>
-	#include <skinnormal_vertex>
-	#include <defaultnormal_vertex>
-
-#ifndef FLAT_SHADED // Normal computed with derivatives when FLAT_SHADED
-
-	vNormal = normalize( transformedNormal );
-
-#endif
-
-	#include <begin_vertex>
-	#include <morphtarget_vertex>
-	#include <skinning_vertex>
-	#include <displacementmap_vertex>
-	#include <project_vertex>
-	#include <logdepthbuf_vertex>
-	#include <clipping_planes_vertex>
-
-	vViewPosition = - mvPosition.xyz;
-
-	#include <worldpos_vertex>
-	#include <envmap_vertex>
-	#include <shadowmap_vertex>
-	#include <fog_vertex>
-
-}
-`;
diff --git a/spaces/bigPear/digitalWDF/README.md b/spaces/bigPear/digitalWDF/README.md
deleted file mode 100644
index e032b1f34fb22e89ef8e717699f50ba1bfa2fe71..0000000000000000000000000000000000000000
--- a/spaces/bigPear/digitalWDF/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DigitalWDF
-emoji: 👁
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bioriAsaeru/text-to-voice/Crysis.3.Crackfix.2.INTERNAL-RELOADED mod The Best Way to Play Crysis 3 on PC.md b/spaces/bioriAsaeru/text-to-voice/Crysis.3.Crackfix.2.INTERNAL-RELOADED mod The Best Way to Play Crysis 3 on PC.md
deleted file mode 100644
index c8f7d6f37962b27b91cc924143bd0091848c7591..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Crysis.3.Crackfix.2.INTERNAL-RELOADED mod The Best Way to Play Crysis 3 on PC.md	
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index c44d89b6e25e2fecc8d15fa45c21c1f962d440f4..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Daniusoft Video Converter Ultimate V3.1.1 Crack [ChattChitto R 64 Bit VERIFIED.md	
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diff --git a/spaces/bioriAsaeru/text-to-voice/FSX - FS2004 CLS 747-200 300 V2 - Inkl. All LIVERIES CRACK (B- Download How to Install and Enjoy on Your PC.md b/spaces/bioriAsaeru/text-to-voice/FSX - FS2004 CLS 747-200 300 V2 - Inkl. All LIVERIES CRACK (B- Download How to Install and Enjoy on Your PC.md
deleted file mode 100644
index bb119982015b888743189434c7b1387de8337290..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/FSX - FS2004 CLS 747-200 300 V2 - Inkl. All LIVERIES CRACK (B- Download How to Install and Enjoy on Your PC.md	
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diff --git a/spaces/bla/tranny/stress_test.py b/spaces/bla/tranny/stress_test.py
deleted file mode 100644
index 7b3fad6b46dc4b7470197f975b67b7b33280f4da..0000000000000000000000000000000000000000
--- a/spaces/bla/tranny/stress_test.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import asyncio
-import aiohttp
-
-analysis = {"failed": 0, "passed": 0}
-
-
-async def send_post_request(session, number=0):
-    async with session.post(
-        url="https://app-mbonea-mjema.cloud.okteto.net/post/create",
-        json={"content": {}, "recommendations": {}},
-    ) as response:
-        response_text = await response.text()
-        if response.status == 200:
-            analysis["passed"] += 1
-            print(f"Response from {number}: {response_text}")
-        else:
-            analysis["failed"] += 1
-
-
-async def send_post_requests_async():
-    async with aiohttp.ClientSession() as session:
-        tasks = []
-        for number in range(1_000):
-            tasks.append(asyncio.ensure_future(send_post_request(session, number)))
-        await asyncio.gather(*tasks)
-
-
-if __name__ == "__main__":
-    asyncio.run(send_post_requests_async())
-    print(analysis)
diff --git a/spaces/blanchon/qrcode-diffusion/Dockerfile b/spaces/blanchon/qrcode-diffusion/Dockerfile
deleted file mode 100644
index e406c483e7d00a331d8d930768c450f1a02ae60c..0000000000000000000000000000000000000000
--- a/spaces/blanchon/qrcode-diffusion/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
-
-# Install dependencies
-COPY requirements.txt /tmp/requirements.txt
-RUN python -m pip install --upgrade pip && \
-    python -m pip install -r /tmp/requirements.txt
-
-# Copy source code
-WORKDIR /app
-RUN mkdir /app/cache
-COPY ./app.py /app/app.py
-
-# Run the application
-CMD ["python", "app.py"]
\ No newline at end of file
diff --git a/spaces/blogclif/CF25/app.py b/spaces/blogclif/CF25/app.py
deleted file mode 100644
index d6cc129dbf42082bcca87b1e93004ba5468dd368..0000000000000000000000000000000000000000
--- a/spaces/blogclif/CF25/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'gsdf/Counterfeit-V2.5'
-prefix = ''
-     
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
-  try:
-    if img is not None:
-      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>CF25</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/gsdf/Counterfeit-V2.5">Counterfeit V2.5</a> Stable Diffusion model.<br>
-               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
-              </p>
-              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/blogclif/CF25/settings'>Settings</a></b>"} after duplicating the space<br><br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/blogclif/CF25?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-              image_out = gr.Image(height=512)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
-    </div>
-    """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/COCO-Detection/fcos_R_50_FPN_1x.py b/spaces/brjathu/HMR2.0/vendor/detectron2/configs/COCO-Detection/fcos_R_50_FPN_1x.py
deleted file mode 100644
index 86f83c68786f5995c462ade5f3067072d69f047e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/COCO-Detection/fcos_R_50_FPN_1x.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.fcos import model
-from ..common.train import train
-
-dataloader.train.mapper.use_instance_mask = False
-optimizer.lr = 0.01
-
-model.backbone.bottom_up.freeze_at = 2
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/common/train.py b/spaces/brjathu/HMR2.0/vendor/detectron2/configs/common/train.py
deleted file mode 100644
index b6ed02bd59f540ca58df20bf72d462f195210a32..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/common/train.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Common training-related configs that are designed for "tools/lazyconfig_train_net.py"
-# You can use your own instead, together with your own train_net.py
-train = dict(
-    output_dir="./output",
-    init_checkpoint="",
-    max_iter=90000,
-    amp=dict(enabled=False),  # options for Automatic Mixed Precision
-    ddp=dict(  # options for DistributedDataParallel
-        broadcast_buffers=False,
-        find_unused_parameters=False,
-        fp16_compression=False,
-    ),
-    checkpointer=dict(period=5000, max_to_keep=100),  # options for PeriodicCheckpointer
-    eval_period=5000,
-    log_period=20,
-    device="cuda"
-    # ...
-)
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/transforms.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/wrappers.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/wrappers.py
deleted file mode 100644
index b59b4f3f6eeed008798e9b107dad59f18724e413..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/wrappers.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Wrappers around on some nn functions, mainly to support empty tensors.
-
-Ideally, add support directly in PyTorch to empty tensors in those functions.
-
-These can be removed once https://github.com/pytorch/pytorch/issues/12013
-is implemented
-"""
-
-import warnings
-from typing import List, Optional
-import torch
-from torch.nn import functional as F
-
-
-def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor:
-    """
-    Turn a list of integer scalars or integer Tensor scalars into a vector,
-    in a way that's both traceable and scriptable.
-
-    In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs.
-    In scripting or eager, `x` should be a list of int.
-    """
-    if torch.jit.is_scripting():
-        return torch.as_tensor(x, device=device)
-    if torch.jit.is_tracing():
-        assert all(
-            [isinstance(t, torch.Tensor) for t in x]
-        ), "Shape should be tensor during tracing!"
-        # as_tensor should not be used in tracing because it records a constant
-        ret = torch.stack(x)
-        if ret.device != device:  # avoid recording a hard-coded device if not necessary
-            ret = ret.to(device=device)
-        return ret
-    return torch.as_tensor(x, device=device)
-
-
-def cat(tensors: List[torch.Tensor], dim: int = 0):
-    """
-    Efficient version of torch.cat that avoids a copy if there is only a single element in a list
-    """
-    assert isinstance(tensors, (list, tuple))
-    if len(tensors) == 1:
-        return tensors[0]
-    return torch.cat(tensors, dim)
-
-
-def empty_input_loss_func_wrapper(loss_func):
-    def wrapped_loss_func(input, target, *, reduction="mean", **kwargs):
-        """
-        Same as `loss_func`, but returns 0 (instead of nan) for empty inputs.
-        """
-        if target.numel() == 0 and reduction == "mean":
-            return input.sum() * 0.0  # connect the gradient
-        return loss_func(input, target, reduction=reduction, **kwargs)
-
-    return wrapped_loss_func
-
-
-cross_entropy = empty_input_loss_func_wrapper(F.cross_entropy)
-
-
-class _NewEmptyTensorOp(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x, new_shape):
-        ctx.shape = x.shape
-        return x.new_empty(new_shape)
-
-    @staticmethod
-    def backward(ctx, grad):
-        shape = ctx.shape
-        return _NewEmptyTensorOp.apply(grad, shape), None
-
-
-class Conv2d(torch.nn.Conv2d):
-    """
-    A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
-    """
-
-    def __init__(self, *args, **kwargs):
-        """
-        Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:
-
-        Args:
-            norm (nn.Module, optional): a normalization layer
-            activation (callable(Tensor) -> Tensor): a callable activation function
-
-        It assumes that norm layer is used before activation.
-        """
-        norm = kwargs.pop("norm", None)
-        activation = kwargs.pop("activation", None)
-        super().__init__(*args, **kwargs)
-
-        self.norm = norm
-        self.activation = activation
-
-    def forward(self, x):
-        # torchscript does not support SyncBatchNorm yet
-        # https://github.com/pytorch/pytorch/issues/40507
-        # and we skip these codes in torchscript since:
-        # 1. currently we only support torchscript in evaluation mode
-        # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or
-        # later version, `Conv2d` in these PyTorch versions has already supported empty inputs.
-        if not torch.jit.is_scripting():
-            with warnings.catch_warnings(record=True):
-                if x.numel() == 0 and self.training:
-                    # https://github.com/pytorch/pytorch/issues/12013
-                    assert not isinstance(
-                        self.norm, torch.nn.SyncBatchNorm
-                    ), "SyncBatchNorm does not support empty inputs!"
-
-        x = F.conv2d(
-            x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
-        )
-        if self.norm is not None:
-            x = self.norm(x)
-        if self.activation is not None:
-            x = self.activation(x)
-        return x
-
-
-ConvTranspose2d = torch.nn.ConvTranspose2d
-BatchNorm2d = torch.nn.BatchNorm2d
-interpolate = F.interpolate
-Linear = torch.nn.Linear
-
-
-def nonzero_tuple(x):
-    """
-    A 'as_tuple=True' version of torch.nonzero to support torchscript.
-    because of https://github.com/pytorch/pytorch/issues/38718
-    """
-    if torch.jit.is_scripting():
-        if x.dim() == 0:
-            return x.unsqueeze(0).nonzero().unbind(1)
-        return x.nonzero().unbind(1)
-    else:
-        return x.nonzero(as_tuple=True)
-
-
-@torch.jit.script_if_tracing
-def move_device_like(src: torch.Tensor, dst: torch.Tensor) -> torch.Tensor:
-    """
-    Tracing friendly way to cast tensor to another tensor's device. Device will be treated
-    as constant during tracing, scripting the casting process as whole can workaround this issue.
-    """
-    return src.to(dst.device)
diff --git a/spaces/cccc-c/bingo/src/components/providers.tsx b/spaces/cccc-c/bingo/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/cccc-c/bingo/src/components/providers.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { ThemeProvider as NextThemesProvider } from 'next-themes'
-import { ThemeProviderProps } from 'next-themes/dist/types'
-
-import { TooltipProvider } from '@/components/ui/tooltip'
-
-export function Providers({ children, ...props }: ThemeProviderProps) {
-  return (
-    <NextThemesProvider {...props}>
-      <TooltipProvider>{children}</TooltipProvider>
-    </NextThemesProvider>
-  )
-}
diff --git a/spaces/chasemcdo/hf_localai/examples/k8sgpt/README.md b/spaces/chasemcdo/hf_localai/examples/k8sgpt/README.md
deleted file mode 100644
index 5676c9ae32c0945c18fb59d42f89775d56eb7b76..0000000000000000000000000000000000000000
--- a/spaces/chasemcdo/hf_localai/examples/k8sgpt/README.md
+++ /dev/null
@@ -1,70 +0,0 @@
-# k8sgpt example
-
-This example show how to use LocalAI with k8sgpt
-
-![Screenshot from 2023-06-19 23-58-47](https://github.com/go-skynet/go-ggml-transformers.cpp/assets/2420543/cab87409-ee68-44ae-8d53-41627fb49509)
-
-## Create the cluster locally with Kind (optional)
-
-If you want to test this locally without a remote Kubernetes cluster, you can use kind.
-
-Install [kind](https://kind.sigs.k8s.io/) and create a cluster:
-
-```
-kind create cluster
-```
-
-## Setup LocalAI
-
-We will use [helm](https://helm.sh/docs/intro/install/):
-
-```
-helm repo add go-skynet https://go-skynet.github.io/helm-charts/
-helm repo update
-
-# Clone LocalAI
-git clone https://github.com/go-skynet/LocalAI
-
-cd LocalAI/examples/k8sgpt
-
-# modify values.yaml preload_models with the models you want to install.
-# CHANGE the URL to a model in huggingface.
-helm install local-ai go-skynet/local-ai --create-namespace --namespace local-ai --values values.yaml
-```
-
-## Setup K8sGPT
-
-```
-# Install k8sgpt
-helm repo add k8sgpt https://charts.k8sgpt.ai/
-helm repo update
-helm install release k8sgpt/k8sgpt-operator -n k8sgpt-operator-system --create-namespace
-```
-
-Apply the k8sgpt-operator configuration:
-
-```
-kubectl apply -f - << EOF
-apiVersion: core.k8sgpt.ai/v1alpha1
-kind: K8sGPT
-metadata:
-  name: k8sgpt-local-ai
-  namespace: default
-spec:
-  backend: localai
-  baseUrl: http://local-ai.local-ai.svc.cluster.local:8080/v1
-  noCache: false
-  model: gpt-3.5-turbo
-  noCache: false
-  version: v0.3.0
-  enableAI: true
-EOF
-```
-
-## Test
-
-Apply a broken pod:
-
-```
-kubectl apply -f broken-pod.yaml
-```
\ No newline at end of file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/altair/vegalite/v5/api.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/altair/vegalite/v5/api.py
deleted file mode 100644
index ed449bcab3fe7b2679f1ffaadc97402f43381869..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/altair/vegalite/v5/api.py
+++ /dev/null
@@ -1,3434 +0,0 @@
-import warnings
-
-import hashlib
-import io
-import json
-import jsonschema
-import pandas as pd
-from toolz.curried import pipe as _pipe
-import itertools
-import sys
-from typing import cast
-
-# Have to rename it here as else it overlaps with schema.core.Type
-from typing import Type as TypingType
-
-from .schema import core, channels, mixins, Undefined, SCHEMA_URL
-
-from .data import data_transformers
-from ... import utils, expr
-from .display import renderers, VEGALITE_VERSION, VEGAEMBED_VERSION, VEGA_VERSION
-from .theme import themes
-
-if sys.version_info >= (3, 11):
-    from typing import Self
-else:
-    from typing_extensions import Self
-
-
-# ------------------------------------------------------------------------
-# Data Utilities
-def _dataset_name(values):
-    """Generate a unique hash of the data
-
-    Parameters
-    ----------
-    values : list or dict
-        A list/dict representation of data values.
-
-    Returns
-    -------
-    name : string
-        A unique name generated from the hash of the values.
-    """
-    if isinstance(values, core.InlineDataset):
-        values = values.to_dict()
-    if values == [{}]:
-        return "empty"
-    values_json = json.dumps(values, sort_keys=True)
-    hsh = hashlib.md5(values_json.encode()).hexdigest()
-    return "data-" + hsh
-
-
-def _consolidate_data(data, context):
-    """If data is specified inline, then move it to context['datasets']
-
-    This function will modify context in-place, and return a new version of data
-    """
-    values = Undefined
-    kwds = {}
-
-    if isinstance(data, core.InlineData):
-        if data.name is Undefined and data.values is not Undefined:
-            if isinstance(data.values, core.InlineDataset):
-                values = data.to_dict()["values"]
-            else:
-                values = data.values
-            kwds = {"format": data.format}
-
-    elif isinstance(data, dict):
-        if "name" not in data and "values" in data:
-            values = data["values"]
-            kwds = {k: v for k, v in data.items() if k != "values"}
-
-    if values is not Undefined:
-        name = _dataset_name(values)
-        data = core.NamedData(name=name, **kwds)
-        context.setdefault("datasets", {})[name] = values
-
-    return data
-
-
-def _prepare_data(data, context=None):
-    """Convert input data to data for use within schema
-
-    Parameters
-    ----------
-    data :
-        The input dataset in the form of a DataFrame, dictionary, altair data
-        object, or other type that is recognized by the data transformers.
-    context : dict (optional)
-        The to_dict context in which the data is being prepared. This is used
-        to keep track of information that needs to be passed up and down the
-        recursive serialization routine, such as global named datasets.
-    """
-    if data is Undefined:
-        return data
-
-    # convert dataframes  or objects with __geo_interface__ to dict
-    elif isinstance(data, pd.DataFrame) or hasattr(data, "__geo_interface__"):
-        data = _pipe(data, data_transformers.get())
-
-    # convert string input to a URLData
-    elif isinstance(data, str):
-        data = core.UrlData(data)
-
-    elif hasattr(data, "__dataframe__"):
-        data = _pipe(data, data_transformers.get())
-
-    # consolidate inline data to top-level datasets
-    if context is not None and data_transformers.consolidate_datasets:
-        data = _consolidate_data(data, context)
-
-    # if data is still not a recognized type, then return
-    if not isinstance(data, (dict, core.Data)):
-        warnings.warn("data of type {} not recognized".format(type(data)), stacklevel=1)
-
-    return data
-
-
-# ------------------------------------------------------------------------
-# Aliases & specializations
-Bin = core.BinParams
-Impute = core.ImputeParams
-Title = core.TitleParams
-
-
-class LookupData(core.LookupData):
-    @utils.use_signature(core.LookupData)
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def to_dict(self, *args, **kwargs):
-        """Convert the chart to a dictionary suitable for JSON export."""
-        copy = self.copy(deep=False)
-        copy.data = _prepare_data(copy.data, kwargs.get("context"))
-        return super(LookupData, copy).to_dict(*args, **kwargs)
-
-
-class FacetMapping(core.FacetMapping):
-    _class_is_valid_at_instantiation = False
-
-    @utils.use_signature(core.FacetMapping)
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def to_dict(self, *args, **kwargs):
-        copy = self.copy(deep=False)
-        context = kwargs.get("context", {})
-        data = context.get("data", None)
-        if isinstance(self.row, str):
-            copy.row = core.FacetFieldDef(**utils.parse_shorthand(self.row, data))
-        if isinstance(self.column, str):
-            copy.column = core.FacetFieldDef(**utils.parse_shorthand(self.column, data))
-        return super(FacetMapping, copy).to_dict(*args, **kwargs)
-
-
-# ------------------------------------------------------------------------
-# Encoding will contain channel objects that aren't valid at instantiation
-core.FacetedEncoding._class_is_valid_at_instantiation = False
-
-# ------------------------------------------------------------------------
-# These are parameters that are valid at the top level, but are not valid
-# for specs that are within a composite chart
-# (layer, hconcat, vconcat, facet, repeat)
-TOPLEVEL_ONLY_KEYS = {"background", "config", "autosize", "padding", "$schema"}
-
-
-def _get_channels_mapping():
-    mapping = {}
-    for attr in dir(channels):
-        cls = getattr(channels, attr)
-        if isinstance(cls, type) and issubclass(cls, core.SchemaBase):
-            mapping[cls] = attr.replace("Value", "").lower()
-    return mapping
-
-
-# -------------------------------------------------------------------------
-# Tools for working with parameters
-class Parameter(expr.core.OperatorMixin, object):
-    """A Parameter object"""
-
-    _counter = 0
-
-    @classmethod
-    def _get_name(cls):
-        cls._counter += 1
-        return f"param_{cls._counter}"
-
-    def __init__(self, name):
-        if name is None:
-            name = self._get_name()
-        self.name = name
-
-    @utils.deprecation.deprecated(
-        message="'ref' is deprecated. No need to call '.ref()' anymore."
-    )
-    def ref(self):
-        "'ref' is deprecated. No need to call '.ref()' anymore."
-        return self.to_dict()
-
-    def to_dict(self):
-        if self.param_type == "variable":
-            return {"expr": self.name}
-        elif self.param_type == "selection":
-            return {
-                "param": self.name.to_dict()
-                if hasattr(self.name, "to_dict")
-                else self.name
-            }
-
-    def __invert__(self):
-        if self.param_type == "selection":
-            return SelectionPredicateComposition({"not": {"param": self.name}})
-        else:
-            return expr.core.OperatorMixin.__invert__(self)
-
-    def __and__(self, other):
-        if self.param_type == "selection":
-            if isinstance(other, Parameter):
-                other = {"param": other.name}
-            return SelectionPredicateComposition({"and": [{"param": self.name}, other]})
-        else:
-            return expr.core.OperatorMixin.__and__(self, other)
-
-    def __or__(self, other):
-        if self.param_type == "selection":
-            if isinstance(other, Parameter):
-                other = {"param": other.name}
-            return SelectionPredicateComposition({"or": [{"param": self.name}, other]})
-        else:
-            return expr.core.OperatorMixin.__or__(self, other)
-
-    def __repr__(self):
-        return "Parameter({0!r}, {1})".format(self.name, self.param)
-
-    def _to_expr(self):
-        return self.name
-
-    def _from_expr(self, expr):
-        return ParameterExpression(expr=expr)
-
-    def __getattr__(self, field_name):
-        if field_name.startswith("__") and field_name.endswith("__"):
-            raise AttributeError(field_name)
-        _attrexpr = expr.core.GetAttrExpression(self.name, field_name)
-        # If self is a SelectionParameter and field_name is in its
-        # fields or encodings list, then we want to return an expression.
-        if check_fields_and_encodings(self, field_name):
-            return SelectionExpression(_attrexpr)
-        return expr.core.GetAttrExpression(self.name, field_name)
-
-    # TODO: Are there any special cases to consider for __getitem__?
-    # This was copied from v4.
-    def __getitem__(self, field_name):
-        return expr.core.GetItemExpression(self.name, field_name)
-
-
-# Enables use of ~, &, | with compositions of selection objects.
-class SelectionPredicateComposition(core.PredicateComposition):
-    def __invert__(self):
-        return SelectionPredicateComposition({"not": self.to_dict()})
-
-    def __and__(self, other):
-        return SelectionPredicateComposition({"and": [self.to_dict(), other.to_dict()]})
-
-    def __or__(self, other):
-        return SelectionPredicateComposition({"or": [self.to_dict(), other.to_dict()]})
-
-
-class ParameterExpression(expr.core.OperatorMixin, object):
-    def __init__(self, expr):
-        self.expr = expr
-
-    def to_dict(self):
-        return {"expr": repr(self.expr)}
-
-    def _to_expr(self):
-        return repr(self.expr)
-
-    def _from_expr(self, expr):
-        return ParameterExpression(expr=expr)
-
-
-class SelectionExpression(expr.core.OperatorMixin, object):
-    def __init__(self, expr):
-        self.expr = expr
-
-    def to_dict(self):
-        return {"expr": repr(self.expr)}
-
-    def _to_expr(self):
-        return repr(self.expr)
-
-    def _from_expr(self, expr):
-        return SelectionExpression(expr=expr)
-
-
-def check_fields_and_encodings(parameter, field_name):
-    for prop in ["fields", "encodings"]:
-        try:
-            if field_name in getattr(parameter.param.select, prop):
-                return True
-        except (AttributeError, TypeError):
-            pass
-
-    return False
-
-
-# ------------------------------------------------------------------------
-# Top-Level Functions
-
-
-def value(value, **kwargs):
-    """Specify a value for use in an encoding"""
-    return dict(value=value, **kwargs)
-
-
-def param(
-    name=None,
-    value=Undefined,
-    bind=Undefined,
-    empty=Undefined,
-    expr=Undefined,
-    **kwds,
-):
-    """Create a named parameter.  See https://altair-viz.github.io/user_guide/interactions.html for examples.  Although both variable parameters and selection parameters can be created using this 'param' function, to create a selection parameter, it is recommended to use either 'selection_point' or 'selection_interval' instead.
-
-    Parameters
-    ----------
-    name : string (optional)
-        The name of the parameter. If not specified, a unique name will be
-        created.
-    value : any (optional)
-        The default value of the parameter. If not specified, the parameter
-        will be created without a default value.
-    bind : :class:`Binding` (optional)
-        Binds the parameter to an external input element such as a slider,
-        selection list or radio button group.
-    empty : boolean (optional)
-        For selection parameters, the predicate of empty selections returns
-        True by default. Override this behavior, by setting this property
-        'empty=False'.
-    expr : :class:`Expr` (optional)
-        An expression for the value of the parameter. This expression may
-        include other parameters, in which case the parameter will
-        automatically update in response to upstream parameter changes.
-    **kwds :
-        additional keywords will be used to construct a parameter.  If 'select'
-        is among the keywords, then a selection parameter will be created.
-        Otherwise, a variable parameter will be created.
-
-    Returns
-    -------
-    parameter: Parameter
-        The parameter object that can be used in chart creation.
-    """
-    parameter = Parameter(name)
-
-    if empty is not Undefined:
-        parameter.empty = empty
-        if parameter.empty == "none":
-            warnings.warn(
-                """The value of 'empty' should be True or False.""",
-                utils.AltairDeprecationWarning,
-                stacklevel=1,
-            )
-            parameter.empty = False
-        elif parameter.empty == "all":
-            warnings.warn(
-                """The value of 'empty' should be True or False.""",
-                utils.AltairDeprecationWarning,
-                stacklevel=1,
-            )
-            parameter.empty = True
-        elif (parameter.empty is False) or (parameter.empty is True):
-            pass
-        else:
-            raise ValueError("The value of 'empty' should be True or False.")
-
-    if "init" in kwds:
-        warnings.warn(
-            """Use 'value' instead of 'init'.""",
-            utils.AltairDeprecationWarning,
-            stacklevel=1,
-        )
-        if value is Undefined:
-            kwds["value"] = kwds.pop("init")
-        else:
-            # If both 'value' and 'init' are set, we ignore 'init'.
-            kwds.pop("init")
-
-    if "select" not in kwds:
-        parameter.param = core.VariableParameter(
-            name=parameter.name, bind=bind, value=value, expr=expr, **kwds
-        )
-        parameter.param_type = "variable"
-    elif "views" in kwds:
-        parameter.param = core.TopLevelSelectionParameter(
-            name=parameter.name, bind=bind, value=value, expr=expr, **kwds
-        )
-        parameter.param_type = "selection"
-    else:
-        parameter.param = core.SelectionParameter(
-            name=parameter.name, bind=bind, value=value, expr=expr, **kwds
-        )
-        parameter.param_type = "selection"
-
-    return parameter
-
-
-def _selection(type=Undefined, **kwds):
-    # We separate out the parameter keywords from the selection keywords
-    param_kwds = {}
-
-    for kwd in {"name", "bind", "value", "empty", "init", "views"}:
-        if kwd in kwds:
-            param_kwds[kwd] = kwds.pop(kwd)
-
-    if type == "interval":
-        select = core.IntervalSelectionConfig(type=type, **kwds)
-    elif type == "point":
-        select = core.PointSelectionConfig(type=type, **kwds)
-    elif type in ["single", "multi"]:
-        select = core.PointSelectionConfig(type="point", **kwds)
-        warnings.warn(
-            """The types 'single' and 'multi' are now
-        combined and should be specified using "selection_point()".""",
-            utils.AltairDeprecationWarning,
-            stacklevel=1,
-        )
-    else:
-        raise ValueError("""'type' must be 'point' or 'interval'""")
-
-    return param(select=select, **param_kwds)
-
-
-@utils.deprecation.deprecated(
-    message="""'selection' is deprecated.
-   Use 'selection_point()' or 'selection_interval()' instead; these functions also include more helpful docstrings."""
-)
-def selection(type=Undefined, **kwds):
-    """
-    Users are recommended to use either 'selection_point' or 'selection_interval' instead, depending on the type of parameter they want to create.
-
-    Create a selection parameter.
-
-    Parameters
-    ----------
-    type : enum('point', 'interval') (required)
-        Determines the default event processing and data query for the
-        selection. Vega-Lite currently supports two selection types:
-        * "point" - to select multiple discrete data values; the first
-        value is selected on click and additional values toggled on
-        shift-click.
-        * "interval" - to select a continuous range of data values on
-        drag.
-    **kwds :
-        additional keywords to control the selection.
-    """
-
-    return _selection(type=type, **kwds)
-
-
-def selection_interval(
-    name=None,
-    value=Undefined,
-    bind=Undefined,
-    empty=Undefined,
-    expr=Undefined,
-    encodings=Undefined,
-    on=Undefined,
-    clear=Undefined,
-    resolve=Undefined,
-    mark=Undefined,
-    translate=Undefined,
-    zoom=Undefined,
-    **kwds,
-):
-    """Create an interval selection parameter. Selection parameters define data queries that are driven by direct manipulation from user input (e.g., mouse clicks or drags). Interval selection parameters are used to select a continuous range of data values on drag, whereas point selection parameters (`selection_point`) are used to select multiple discrete data values.)
-
-    Parameters
-    ----------
-    name : string (optional)
-        The name of the parameter. If not specified, a unique name will be
-        created.
-    value : any (optional)
-        The default value of the parameter. If not specified, the parameter
-        will be created without a default value.
-    bind : :class:`Binding` (optional)
-        Binds the parameter to an external input element such as a slider,
-        selection list or radio button group.
-    empty : boolean (optional)
-        For selection parameters, the predicate of empty selections returns
-        True by default. Override this behavior, by setting this property
-        'empty=False'.
-    expr : :class:`Expr` (optional)
-        An expression for the value of the parameter. This expression may
-        include other parameters, in which case the parameter will
-        automatically update in response to upstream parameter changes.
-    encodings : List[str] (optional)
-        A list of encoding channels. The corresponding data field values
-        must match for a data tuple to fall within the selection.
-    on : string (optional)
-        A Vega event stream (object or selector) that triggers the selection.
-        For interval selections, the event stream must specify a start and end.
-    clear : string or boolean (optional)
-        Clears the selection, emptying it of all values. This property can
-        be an Event Stream or False to disable clear.  Default is 'dblclick'.
-    resolve : enum('global', 'union', 'intersect') (optional)
-        With layered and multi-view displays, a strategy that determines
-        how selections' data queries are resolved when applied in a filter
-        transform, conditional encoding rule, or scale domain.
-        One of:
-
-        * 'global': only one brush exists for the entire SPLOM. When the
-          user begins to drag, any previous brushes are cleared, and a
-          new one is constructed.
-        * 'union': each cell contains its own brush, and points are
-          highlighted if they lie within any of these individual brushes.
-        * 'intersect': each cell contains its own brush, and points are
-          highlighted only if they fall within all of these individual
-          brushes.
-
-        The default is 'global'.
-    mark : :class:`Mark` (optional)
-        An interval selection also adds a rectangle mark to depict the
-        extents of the interval. The mark property can be used to
-        customize the appearance of the mark.
-    translate : string or boolean (optional)
-        When truthy, allows a user to interactively move an interval
-        selection back-and-forth. Can be True, False (to disable panning),
-        or a Vega event stream definition which must include a start and
-        end event to trigger continuous panning. Discrete panning (e.g.,
-        pressing the left/right arrow keys) will be supported in future
-        versions.
-        The default value is True, which corresponds to
-        [mousedown, window:mouseup] > window:mousemove!
-        This default allows users to click and drag within an interval
-        selection to reposition it.
-    zoom : string or boolean (optional)
-        When truthy, allows a user to interactively resize an interval
-        selection. Can be True, False (to disable zooming), or a Vega
-        event stream definition. Currently, only wheel events are supported,
-        but custom event streams can still be used to specify filters,
-        debouncing, and throttling. Future versions will expand the set of
-        events that can trigger this transformation.
-        The default value is True, which corresponds to wheel!. This
-        default allows users to use the mouse wheel to resize an interval
-        selection.
-    **kwds :
-        Additional keywords to control the selection.
-
-    Returns
-    -------
-    parameter: Parameter
-        The parameter object that can be used in chart creation.
-    """
-    return _selection(
-        type="interval",
-        name=name,
-        value=value,
-        bind=bind,
-        empty=empty,
-        expr=expr,
-        encodings=encodings,
-        on=on,
-        clear=clear,
-        resolve=resolve,
-        mark=mark,
-        translate=translate,
-        zoom=zoom,
-        **kwds,
-    )
-
-
-def selection_point(
-    name=None,
-    value=Undefined,
-    bind=Undefined,
-    empty=Undefined,
-    expr=Undefined,
-    encodings=Undefined,
-    fields=Undefined,
-    on=Undefined,
-    clear=Undefined,
-    resolve=Undefined,
-    toggle=Undefined,
-    nearest=Undefined,
-    **kwds,
-):
-    """Create a point selection parameter. Selection parameters define data queries that are driven by direct manipulation from user input (e.g., mouse clicks or drags). Point selection parameters are used to select multiple discrete data values; the first value is selected on click and additional values toggled on shift-click. To select a continuous range of data values on drag interval selection parameters (`selection_interval`) can be used instead.
-
-    Parameters
-    ----------
-    name : string (optional)
-        The name of the parameter. If not specified, a unique name will be
-        created.
-    value : any (optional)
-        The default value of the parameter. If not specified, the parameter
-        will be created without a default value.
-    bind : :class:`Binding` (optional)
-        Binds the parameter to an external input element such as a slider,
-        selection list or radio button group.
-    empty : boolean (optional)
-        For selection parameters, the predicate of empty selections returns
-        True by default. Override this behavior, by setting this property
-        'empty=False'.
-    expr : :class:`Expr` (optional)
-        An expression for the value of the parameter. This expression may
-        include other parameters, in which case the parameter will
-        automatically update in response to upstream parameter changes.
-    encodings : List[str] (optional)
-        A list of encoding channels. The corresponding data field values
-        must match for a data tuple to fall within the selection.
-    fields : List[str] (optional)
-        A list of field names whose values must match for a data tuple to
-        fall within the selection.
-    on : string (optional)
-        A Vega event stream (object or selector) that triggers the selection.
-        For interval selections, the event stream must specify a start and end.
-    clear : string or boolean (optional)
-        Clears the selection, emptying it of all values. This property can
-        be an Event Stream or False to disable clear.  Default is 'dblclick'.
-    resolve : enum('global', 'union', 'intersect') (optional)
-        With layered and multi-view displays, a strategy that determines
-        how selections' data queries are resolved when applied in a filter
-        transform, conditional encoding rule, or scale domain.
-        One of:
-
-        * 'global': only one brush exists for the entire SPLOM. When the
-          user begins to drag, any previous brushes are cleared, and a
-          new one is constructed.
-        * 'union': each cell contains its own brush, and points are
-          highlighted if they lie within any of these individual brushes.
-        * 'intersect': each cell contains its own brush, and points are
-          highlighted only if they fall within all of these individual
-          brushes.
-
-        The default is 'global'.
-    toggle : string or boolean (optional)
-        Controls whether data values should be toggled (inserted or
-        removed from a point selection) or only ever inserted into
-        point selections.
-        One of:
-
-        * True (default): the toggle behavior, which corresponds to
-          "event.shiftKey". As a result, data values are toggled
-          when the user interacts with the shift-key pressed.
-        * False: disables toggling behaviour; the selection will
-          only ever contain a single data value corresponding
-          to the most recent interaction.
-        * A Vega expression which is re-evaluated as the user interacts.
-          If the expression evaluates to True, the data value is
-          toggled into or out of the point selection. If the expression
-          evaluates to False, the point selection is first cleared, and
-          the data value is then inserted. For example, setting the
-          value to the Vega expression True will toggle data values
-          without the user pressing the shift-key.
-
-    nearest : boolean (optional)
-        When true, an invisible voronoi diagram is computed to accelerate
-        discrete selection. The data value nearest the mouse cursor is
-        added to the selection.  The default is False, which means that
-        data values must be interacted with directly (e.g., clicked on)
-        to be added to the selection.
-    **kwds :
-        Additional keywords to control the selection.
-
-    Returns
-    -------
-    parameter: Parameter
-        The parameter object that can be used in chart creation.
-    """
-    return _selection(
-        type="point",
-        name=name,
-        value=value,
-        bind=bind,
-        empty=empty,
-        expr=expr,
-        encodings=encodings,
-        fields=fields,
-        on=on,
-        clear=clear,
-        resolve=resolve,
-        toggle=toggle,
-        nearest=nearest,
-        **kwds,
-    )
-
-
-@utils.deprecation.deprecated(
-    message="'selection_multi' is deprecated.  Use 'selection_point'"
-)
-@utils.use_signature(core.PointSelectionConfig)
-def selection_multi(**kwargs):
-    """'selection_multi' is deprecated.  Use 'selection_point'"""
-    return _selection(type="point", **kwargs)
-
-
-@utils.deprecation.deprecated(
-    message="'selection_single' is deprecated.  Use 'selection_point'"
-)
-@utils.use_signature(core.PointSelectionConfig)
-def selection_single(**kwargs):
-    """'selection_single' is deprecated.  Use 'selection_point'"""
-    return _selection(type="point", **kwargs)
-
-
-@utils.use_signature(core.Binding)
-def binding(input, **kwargs):
-    """A generic binding"""
-    return core.Binding(input=input, **kwargs)
-
-
-@utils.use_signature(core.BindCheckbox)
-def binding_checkbox(**kwargs):
-    """A checkbox binding"""
-    return core.BindCheckbox(input="checkbox", **kwargs)
-
-
-@utils.use_signature(core.BindRadioSelect)
-def binding_radio(**kwargs):
-    """A radio button binding"""
-    return core.BindRadioSelect(input="radio", **kwargs)
-
-
-@utils.use_signature(core.BindRadioSelect)
-def binding_select(**kwargs):
-    """A select binding"""
-    return core.BindRadioSelect(input="select", **kwargs)
-
-
-@utils.use_signature(core.BindRange)
-def binding_range(**kwargs):
-    """A range binding"""
-    return core.BindRange(input="range", **kwargs)
-
-
-# TODO: update the docstring
-def condition(predicate, if_true, if_false, **kwargs):
-    """A conditional attribute or encoding
-
-    Parameters
-    ----------
-    predicate: Selection, PredicateComposition, expr.Expression, dict, or string
-        the selection predicate or test predicate for the condition.
-        if a string is passed, it will be treated as a test operand.
-    if_true:
-        the spec or object to use if the selection predicate is true
-    if_false:
-        the spec or object to use if the selection predicate is false
-    **kwargs:
-        additional keyword args are added to the resulting dict
-
-    Returns
-    -------
-    spec: dict or VegaLiteSchema
-        the spec that describes the condition
-    """
-    test_predicates = (str, expr.Expression, core.PredicateComposition)
-
-    if isinstance(predicate, Parameter):
-        if predicate.param_type == "selection" or predicate.param.expr is Undefined:
-            condition = {"param": predicate.name}
-            if "empty" in kwargs:
-                condition["empty"] = kwargs.pop("empty")
-            elif isinstance(predicate.empty, bool):
-                condition["empty"] = predicate.empty
-        else:
-            condition = {"test": predicate.param.expr}
-    elif isinstance(predicate, test_predicates):
-        condition = {"test": predicate}
-    elif isinstance(predicate, dict):
-        condition = predicate
-    else:
-        raise NotImplementedError(
-            "condition predicate of type {}" "".format(type(predicate))
-        )
-
-    if isinstance(if_true, core.SchemaBase):
-        # convert to dict for now; the from_dict call below will wrap this
-        # dict in the appropriate schema
-        if_true = if_true.to_dict()
-    elif isinstance(if_true, str):
-        if isinstance(if_false, str):
-            raise ValueError(
-                "A field cannot be used for both the `if_true` and `if_false` values of a condition. One of them has to specify a `value` or `datum` definition."
-            )
-        else:
-            if_true = utils.parse_shorthand(if_true)
-            if_true.update(kwargs)
-    condition.update(if_true)
-
-    if isinstance(if_false, core.SchemaBase):
-        # For the selection, the channel definitions all allow selections
-        # already. So use this SchemaBase wrapper if possible.
-        selection = if_false.copy()
-        selection.condition = condition
-    elif isinstance(if_false, str):
-        selection = {"condition": condition, "shorthand": if_false}
-        selection.update(kwargs)
-    else:
-        selection = dict(condition=condition, **if_false)
-
-    return selection
-
-
-# --------------------------------------------------------------------
-# Top-level objects
-
-
-class TopLevelMixin(mixins.ConfigMethodMixin):
-    """Mixin for top-level chart objects such as Chart, LayeredChart, etc."""
-
-    _class_is_valid_at_instantiation = False
-
-    def to_dict(self, *args, **kwargs) -> dict:
-        """Convert the chart to a dictionary suitable for JSON export"""
-        # We make use of three context markers:
-        # - 'data' points to the data that should be referenced for column type
-        #   inference.
-        # - 'top_level' is a boolean flag that is assumed to be true; if it's
-        #   true then a "$schema" arg is added to the dict.
-        # - 'datasets' is a dict of named datasets that should be inserted
-        #   in the top-level object
-
-        # note: not a deep copy because we want datasets and data arguments to
-        # be passed by reference
-        context = kwargs.get("context", {}).copy()
-        context.setdefault("datasets", {})
-        is_top_level = context.get("top_level", True)
-
-        # TopLevelMixin instance does not necessarily have copy defined but due to how
-        # Altair is set up this should hold. Too complex to type hint right now
-        copy = self.copy(deep=False)  # type: ignore[attr-defined]
-        original_data = getattr(copy, "data", Undefined)
-        copy.data = _prepare_data(original_data, context)
-
-        if original_data is not Undefined:
-            context["data"] = original_data
-
-        # remaining to_dict calls are not at top level
-        context["top_level"] = False
-        kwargs["context"] = context
-
-        # TopLevelMixin instance does not necessarily have to_dict defined
-        # but due to how Altair is set up this should hold.
-        # Too complex to type hint right now
-        dct = super(TopLevelMixin, copy).to_dict(*args, **kwargs)  # type: ignore[misc]
-
-        # TODO: following entries are added after validation. Should they be validated?
-        if is_top_level:
-            # since this is top-level we add $schema if it's missing
-            if "$schema" not in dct:
-                dct["$schema"] = SCHEMA_URL
-
-            # apply theme from theme registry
-            the_theme = themes.get()
-            # Use assert to tell type checkers that it is not None. Holds true
-            # as there is always a default theme set when importing Altair
-            assert the_theme is not None
-            dct = utils.update_nested(the_theme(), dct, copy=True)
-
-            # update datasets
-            if context["datasets"]:
-                dct.setdefault("datasets", {}).update(context["datasets"])
-
-        return dct
-
-    def to_html(
-        self,
-        base_url="https://cdn.jsdelivr.net/npm",
-        output_div="vis",
-        embed_options=None,
-        json_kwds=None,
-        fullhtml=True,
-        requirejs=False,
-    ) -> str:
-        return utils.spec_to_html(
-            self.to_dict(),
-            mode="vega-lite",
-            vegalite_version=VEGALITE_VERSION,
-            vegaembed_version=VEGAEMBED_VERSION,
-            vega_version=VEGA_VERSION,
-            base_url=base_url,
-            output_div=output_div,
-            embed_options=embed_options,
-            json_kwds=json_kwds,
-            fullhtml=fullhtml,
-            requirejs=requirejs,
-        )
-
-    def save(
-        self,
-        fp,
-        format=None,
-        override_data_transformer=True,
-        scale_factor=1.0,
-        vegalite_version=VEGALITE_VERSION,
-        vega_version=VEGA_VERSION,
-        vegaembed_version=VEGAEMBED_VERSION,
-        **kwargs,
-    ):
-        """Save a chart to file in a variety of formats
-
-        Supported formats are json, html, png, svg, pdf; the last three require
-        the altair_saver package to be installed.
-
-        Parameters
-        ----------
-        fp : string filename or file-like object
-            file in which to write the chart.
-        format : string (optional)
-            the format to write: one of ['json', 'html', 'png', 'svg', 'pdf'].
-            If not specified, the format will be determined from the filename.
-        override_data_transformer : `boolean` (optional)
-            If True (default), then the save action will be done with
-            the MaxRowsError disabled. If False, then do not change the data
-            transformer.
-        scale_factor : float
-            For svg or png formats, scale the image by this factor when saving.
-            This can be used to control the size or resolution of the output.
-            Default is 1.0
-        **kwargs :
-            Additional keyword arguments are passed to the output method
-            associated with the specified format.
-
-        """
-        from ...utils.save import save
-
-        kwds = dict(
-            chart=self,
-            fp=fp,
-            format=format,
-            scale_factor=scale_factor,
-            vegalite_version=vegalite_version,
-            vega_version=vega_version,
-            vegaembed_version=vegaembed_version,
-            **kwargs,
-        )
-
-        # By default we override the data transformer. This makes it so
-        # that save() will succeed even for large datasets that would
-        # normally trigger a MaxRowsError
-        if override_data_transformer:
-            with data_transformers.disable_max_rows():
-                result = save(**kwds)
-        else:
-            result = save(**kwds)
-        return result
-
-    # Fallback for when rendering fails; the full repr is too long to be
-    # useful in nearly all cases.
-    def __repr__(self):
-        return "alt.{}(...)".format(self.__class__.__name__)
-
-    # Layering and stacking
-    def __add__(self, other):
-        if not isinstance(other, TopLevelMixin):
-            raise ValueError("Only Chart objects can be layered.")
-        return layer(self, other)
-
-    def __and__(self, other):
-        if not isinstance(other, TopLevelMixin):
-            raise ValueError("Only Chart objects can be concatenated.")
-        return vconcat(self, other)
-
-    def __or__(self, other):
-        if not isinstance(other, TopLevelMixin):
-            raise ValueError("Only Chart objects can be concatenated.")
-        return hconcat(self, other)
-
-    def repeat(
-        self,
-        repeat=Undefined,
-        row=Undefined,
-        column=Undefined,
-        layer=Undefined,
-        columns=Undefined,
-        **kwargs,
-    ) -> "RepeatChart":
-        """Return a RepeatChart built from the chart
-
-        Fields within the chart can be set to correspond to the row or
-        column using `alt.repeat('row')` and `alt.repeat('column')`.
-
-        Parameters
-        ----------
-        repeat : list
-            a list of data column names to be repeated. This cannot be
-            used along with the ``row``, ``column`` or ``layer`` argument.
-        row : list
-            a list of data column names to be mapped to the row facet
-        column : list
-            a list of data column names to be mapped to the column facet
-        layer : list
-            a list of data column names to be layered. This cannot be
-            used along with the ``row``, ``column`` or ``repeat`` argument.
-        columns : int
-            the maximum number of columns before wrapping. Only referenced
-            if ``repeat`` is specified.
-        **kwargs :
-            additional keywords passed to RepeatChart.
-
-        Returns
-        -------
-        chart : RepeatChart
-            a repeated chart.
-        """
-        repeat_specified = repeat is not Undefined
-        rowcol_specified = row is not Undefined or column is not Undefined
-        layer_specified = layer is not Undefined
-
-        if repeat_specified and rowcol_specified:
-            raise ValueError(
-                "repeat argument cannot be combined with row/column argument."
-            )
-        elif repeat_specified and layer_specified:
-            raise ValueError("repeat argument cannot be combined with layer argument.")
-
-        if repeat_specified:
-            repeat = repeat
-        elif layer_specified:
-            repeat = core.LayerRepeatMapping(layer=layer, row=row, column=column)
-        else:
-            repeat = core.RepeatMapping(row=row, column=column)
-
-        return RepeatChart(spec=self, repeat=repeat, columns=columns, **kwargs)
-
-    def properties(self, **kwargs) -> Self:
-        """Set top-level properties of the Chart.
-
-        Argument names and types are the same as class initialization.
-        """
-        # ignore type as copy comes from another class for subclasses of TopLevelMixin
-        copy = self.copy(deep=False)  # type: ignore[attr-defined]
-        for key, val in kwargs.items():
-            if key == "selection" and isinstance(val, Parameter):
-                # TODO: Can this be removed
-                # For backward compatibility with old selection interface.
-                setattr(copy, key, {val.name: val.selection})
-            else:
-                # Don't validate data, because it hasn't been processed.
-                if key != "data":
-                    # ignore type as validate_property comes from SchemaBase,
-                    # not from TopLevelMixin
-                    self.validate_property(key, val)  # type: ignore[attr-defined]
-                setattr(copy, key, val)
-        return copy
-
-    def project(
-        self,
-        type=Undefined,
-        center=Undefined,
-        clipAngle=Undefined,
-        clipExtent=Undefined,
-        coefficient=Undefined,
-        distance=Undefined,
-        fraction=Undefined,
-        lobes=Undefined,
-        parallel=Undefined,
-        precision=Undefined,
-        radius=Undefined,
-        ratio=Undefined,
-        reflectX=Undefined,
-        reflectY=Undefined,
-        rotate=Undefined,
-        scale=Undefined,
-        spacing=Undefined,
-        tilt=Undefined,
-        translate=Undefined,
-        **kwds,
-    ) -> Self:
-        """Add a geographic projection to the chart.
-
-        This is generally used either with ``mark_geoshape`` or with the
-        ``latitude``/``longitude`` encodings.
-
-        Available projection types are
-        ['albers', 'albersUsa', 'azimuthalEqualArea', 'azimuthalEquidistant',
-        'conicConformal', 'conicEqualArea', 'conicEquidistant', 'equalEarth', 'equirectangular',
-        'gnomonic', 'identity', 'mercator', 'orthographic', 'stereographic', 'transverseMercator']
-
-        Parameters
-        ----------
-        type : ProjectionType
-            The cartographic projection to use. This value is case-insensitive, for example
-            `"albers"` and `"Albers"` indicate the same projection type. You can find all valid
-            projection types [in the
-            documentation](https://vega.github.io/vega-lite/docs/projection.html#projection-types).
-
-            **Default value:** `equalEarth`
-        center : List(float)
-            Sets the projection’s center to the specified center, a two-element array of
-            longitude and latitude in degrees.
-
-            **Default value:** `[0, 0]`
-        clipAngle : float
-            Sets the projection’s clipping circle radius to the specified angle in degrees. If
-            `null`, switches to [antimeridian](http://bl.ocks.org/mbostock/3788999) cutting
-            rather than small-circle clipping.
-        clipExtent : List(List(float))
-            Sets the projection’s viewport clip extent to the specified bounds in pixels. The
-            extent bounds are specified as an array `[[x0, y0], [x1, y1]]`, where `x0` is the
-            left-side of the viewport, `y0` is the top, `x1` is the right and `y1` is the
-            bottom. If `null`, no viewport clipping is performed.
-        coefficient : float
-
-        distance : float
-
-        fraction : float
-
-        lobes : float
-
-        parallel : float
-
-        precision : Mapping(required=[length])
-            Sets the threshold for the projection’s [adaptive
-            resampling](http://bl.ocks.org/mbostock/3795544) to the specified value in pixels.
-            This value corresponds to the [Douglas–Peucker
-            distance](http://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm).
-             If precision is not specified, returns the projection’s current resampling
-            precision which defaults to `√0.5 ≅ 0.70710…`.
-        radius : float
-
-        ratio : float
-
-        reflectX : boolean
-
-        reflectY : boolean
-
-        rotate : List(float)
-            Sets the projection’s three-axis rotation to the specified angles, which must be a
-            two- or three-element array of numbers [`lambda`, `phi`, `gamma`] specifying the
-            rotation angles in degrees about each spherical axis. (These correspond to yaw,
-            pitch and roll.)
-
-            **Default value:** `[0, 0, 0]`
-        scale : float
-            Sets the projection's scale (zoom) value, overriding automatic fitting.
-
-        spacing : float
-
-        tilt : float
-
-        translate : List(float)
-            Sets the projection's translation (pan) value, overriding automatic fitting.
-
-        """
-        projection = core.Projection(
-            center=center,
-            clipAngle=clipAngle,
-            clipExtent=clipExtent,
-            coefficient=coefficient,
-            distance=distance,
-            fraction=fraction,
-            lobes=lobes,
-            parallel=parallel,
-            precision=precision,
-            radius=radius,
-            ratio=ratio,
-            reflectX=reflectX,
-            reflectY=reflectY,
-            rotate=rotate,
-            scale=scale,
-            spacing=spacing,
-            tilt=tilt,
-            translate=translate,
-            type=type,
-            **kwds,
-        )
-        return self.properties(projection=projection)
-
-    def _add_transform(self, *transforms):
-        """Copy the chart and add specified transforms to chart.transform"""
-        copy = self.copy(deep=["transform"])
-        if copy.transform is Undefined:
-            copy.transform = []
-        copy.transform.extend(transforms)
-        return copy
-
-    def transform_aggregate(
-        self, aggregate=Undefined, groupby=Undefined, **kwds
-    ) -> Self:
-        """
-        Add an :class:`AggregateTransform` to the schema.
-
-        Parameters
-        ----------
-        aggregate : List(:class:`AggregatedFieldDef`)
-            Array of objects that define fields to aggregate.
-        groupby : List(string)
-            The data fields to group by. If not specified, a single group containing all data
-            objects will be used.
-        **kwds :
-            additional keywords are converted to aggregates using standard
-            shorthand parsing.
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        Examples
-        --------
-        The aggregate transform allows you to specify transforms directly using
-        the same shorthand syntax as used in encodings:
-
-        >>> import altair as alt
-        >>> chart1 = alt.Chart().transform_aggregate(
-        ...     mean_acc='mean(Acceleration)',
-        ...     groupby=['Origin']
-        ... )
-        >>> print(chart1.transform[0].to_json())  # doctest: +NORMALIZE_WHITESPACE
-        {
-          "aggregate": [
-            {
-              "as": "mean_acc",
-              "field": "Acceleration",
-              "op": "mean"
-            }
-          ],
-          "groupby": [
-            "Origin"
-          ]
-        }
-
-        It also supports including AggregatedFieldDef instances or dicts directly,
-        so you can create the above transform like this:
-
-        >>> chart2 = alt.Chart().transform_aggregate(
-        ...     [alt.AggregatedFieldDef(field='Acceleration', op='mean',
-        ...                             **{'as': 'mean_acc'})],
-        ...     groupby=['Origin']
-        ... )
-        >>> chart2.transform == chart1.transform
-        True
-
-        See Also
-        --------
-        alt.AggregateTransform : underlying transform object
-
-        """
-        if aggregate is Undefined:
-            aggregate = []
-        for key, val in kwds.items():
-            parsed = utils.parse_shorthand(val)
-            dct = {
-                "as": key,
-                "field": parsed.get("field", Undefined),
-                "op": parsed.get("aggregate", Undefined),
-            }
-            aggregate.append(core.AggregatedFieldDef(**dct))
-        return self._add_transform(
-            core.AggregateTransform(aggregate=aggregate, groupby=groupby)
-        )
-
-    def transform_bin(self, as_=Undefined, field=Undefined, bin=True, **kwargs) -> Self:
-        """
-        Add a :class:`BinTransform` to the schema.
-
-        Parameters
-        ----------
-        as_ : anyOf(string, List(string))
-            The output fields at which to write the start and end bin values.
-        bin : anyOf(boolean, :class:`BinParams`)
-            An object indicating bin properties, or simply ``true`` for using default bin
-            parameters.
-        field : string
-            The data field to bin.
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        Examples
-        --------
-        >>> import altair as alt
-        >>> chart = alt.Chart().transform_bin("x_binned", "x")
-        >>> chart.transform[0]
-        BinTransform({
-          as: 'x_binned',
-          bin: True,
-          field: 'x'
-        })
-
-        >>> chart = alt.Chart().transform_bin("x_binned", "x",
-        ...                                   bin=alt.Bin(maxbins=10))
-        >>> chart.transform[0]
-        BinTransform({
-          as: 'x_binned',
-          bin: BinParams({
-            maxbins: 10
-          }),
-          field: 'x'
-        })
-
-        See Also
-        --------
-        alt.BinTransform : underlying transform object
-
-        """
-        if as_ is not Undefined:
-            if "as" in kwargs:
-                raise ValueError(
-                    "transform_bin: both 'as_' and 'as' passed as arguments."
-                )
-            kwargs["as"] = as_
-        kwargs["bin"] = bin
-        kwargs["field"] = field
-        return self._add_transform(core.BinTransform(**kwargs))
-
-    def transform_calculate(self, as_=Undefined, calculate=Undefined, **kwargs) -> Self:
-        """
-        Add a :class:`CalculateTransform` to the schema.
-
-        Parameters
-        ----------
-        as_ : string
-            The field for storing the computed formula value.
-        calculate : string or alt.expr expression
-            A `expression <https://vega.github.io/vega-lite/docs/types.html#expression>`__
-            string. Use the variable ``datum`` to refer to the current data object.
-        **kwargs
-            transforms can also be passed by keyword argument; see Examples
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        Examples
-        --------
-        >>> import altair as alt
-        >>> from altair import datum, expr
-
-        >>> chart = alt.Chart().transform_calculate(y = 2 * expr.sin(datum.x))
-        >>> chart.transform[0]
-        CalculateTransform({
-          as: 'y',
-          calculate: (2 * sin(datum.x))
-        })
-
-        It's also possible to pass the ``CalculateTransform`` arguments directly:
-
-        >>> kwds = {'as': 'y', 'calculate': '2 * sin(datum.x)'}
-        >>> chart = alt.Chart().transform_calculate(**kwds)
-        >>> chart.transform[0]
-        CalculateTransform({
-          as: 'y',
-          calculate: '2 * sin(datum.x)'
-        })
-
-        As the first form is easier to write and understand, that is the
-        recommended method.
-
-        See Also
-        --------
-        alt.CalculateTransform : underlying transform object
-        """
-        if as_ is Undefined:
-            as_ = kwargs.pop("as", Undefined)
-        elif "as" in kwargs:
-            raise ValueError(
-                "transform_calculate: both 'as_' and 'as' passed as arguments."
-            )
-        if as_ is not Undefined or calculate is not Undefined:
-            dct = {"as": as_, "calculate": calculate}
-            self = self._add_transform(core.CalculateTransform(**dct))
-        for as_, calculate in kwargs.items():
-            dct = {"as": as_, "calculate": calculate}
-            self = self._add_transform(core.CalculateTransform(**dct))
-        return self
-
-    def transform_density(
-        self,
-        density,
-        as_=Undefined,
-        bandwidth=Undefined,
-        counts=Undefined,
-        cumulative=Undefined,
-        extent=Undefined,
-        groupby=Undefined,
-        maxsteps=Undefined,
-        minsteps=Undefined,
-        steps=Undefined,
-    ) -> Self:
-        """Add a :class:`DensityTransform` to the spec.
-
-        Parameters
-        ----------
-        density : str
-            The data field for which to perform density estimation.
-        as_ : [str, str]
-            The output fields for the sample value and corresponding density estimate.
-            **Default value:** ``["value", "density"]``
-        bandwidth : float
-            The bandwidth (standard deviation) of the Gaussian kernel. If unspecified or set to
-            zero, the bandwidth value is automatically estimated from the input data using
-            Scott’s rule.
-        counts : boolean
-            A boolean flag indicating if the output values should be probability estimates
-            (false) or smoothed counts (true).
-            **Default value:** ``false``
-        cumulative : boolean
-            A boolean flag indicating whether to produce density estimates (false) or cumulative
-            density estimates (true).
-            **Default value:** ``false``
-        extent : List([float, float])
-            A [min, max] domain from which to sample the distribution. If unspecified, the
-            extent will be determined by the observed minimum and maximum values of the density
-            value field.
-        groupby : List(str)
-            The data fields to group by. If not specified, a single group containing all data
-            objects will be used.
-        maxsteps : float
-            The maximum number of samples to take along the extent domain for plotting the
-            density. **Default value:** ``200``
-        minsteps : float
-            The minimum number of samples to take along the extent domain for plotting the
-            density. **Default value:** ``25``
-        steps : float
-            The exact number of samples to take along the extent domain for plotting the
-            density. If specified, overrides both minsteps and maxsteps to set an exact number
-            of uniform samples. Potentially useful in conjunction with a fixed extent to ensure
-            consistent sample points for stacked densities.
-        """
-        return self._add_transform(
-            core.DensityTransform(
-                density=density,
-                bandwidth=bandwidth,
-                counts=counts,
-                cumulative=cumulative,
-                extent=extent,
-                groupby=groupby,
-                maxsteps=maxsteps,
-                minsteps=minsteps,
-                steps=steps,
-                **{"as": as_},
-            )
-        )
-
-    def transform_impute(
-        self,
-        impute,
-        key,
-        frame=Undefined,
-        groupby=Undefined,
-        keyvals=Undefined,
-        method=Undefined,
-        value=Undefined,
-    ) -> Self:
-        """
-        Add an :class:`ImputeTransform` to the schema.
-
-        Parameters
-        ----------
-        impute : string
-            The data field for which the missing values should be imputed.
-        key : string
-            A key field that uniquely identifies data objects within a group.
-            Missing key values (those occurring in the data but not in the current group) will
-            be imputed.
-        frame : List(anyOf(None, float))
-            A frame specification as a two-element array used to control the window over which
-            the specified method is applied. The array entries should either be a number
-            indicating the offset from the current data object, or null to indicate unbounded
-            rows preceding or following the current data object.  For example, the value ``[-5,
-            5]`` indicates that the window should include five objects preceding and five
-            objects following the current object.
-            **Default value:** :  ``[null, null]`` indicating that the window includes all
-            objects.
-        groupby : List(string)
-            An optional array of fields by which to group the values.
-            Imputation will then be performed on a per-group basis.
-        keyvals : anyOf(List(Mapping(required=[])), :class:`ImputeSequence`)
-            Defines the key values that should be considered for imputation.
-            An array of key values or an object defining a `number sequence
-            <https://vega.github.io/vega-lite/docs/impute.html#sequence-def>`__.
-            If provided, this will be used in addition to the key values observed within the
-            input data.  If not provided, the values will be derived from all unique values of
-            the ``key`` field. For ``impute`` in ``encoding``, the key field is the x-field if
-            the y-field is imputed, or vice versa.
-            If there is no impute grouping, this property *must* be specified.
-        method : :class:`ImputeMethod`
-            The imputation method to use for the field value of imputed data objects.
-            One of ``value``, ``mean``, ``median``, ``max`` or ``min``.
-            **Default value:**  ``"value"``
-        value : Mapping(required=[])
-            The field value to use when the imputation ``method`` is ``"value"``.
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        alt.ImputeTransform : underlying transform object
-        """
-        return self._add_transform(
-            core.ImputeTransform(
-                impute=impute,
-                key=key,
-                frame=frame,
-                groupby=groupby,
-                keyvals=keyvals,
-                method=method,
-                value=value,
-            )
-        )
-
-    def transform_joinaggregate(
-        self, joinaggregate=Undefined, groupby=Undefined, **kwargs
-    ) -> Self:
-        """
-        Add a :class:`JoinAggregateTransform` to the schema.
-
-        Parameters
-        ----------
-        joinaggregate : List(:class:`JoinAggregateFieldDef`)
-            The definition of the fields in the join aggregate, and what calculations to use.
-        groupby : List(string)
-            The data fields for partitioning the data objects into separate groups. If
-            unspecified, all data points will be in a single group.
-        **kwargs
-            joinaggregates can also be passed by keyword argument; see Examples.
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        Examples
-        --------
-        >>> import altair as alt
-        >>> chart = alt.Chart().transform_joinaggregate(x='sum(y)')
-        >>> chart.transform[0]
-        JoinAggregateTransform({
-          joinaggregate: [JoinAggregateFieldDef({
-            as: 'x',
-            field: 'y',
-            op: 'sum'
-          })]
-        })
-
-        See Also
-        --------
-        alt.JoinAggregateTransform : underlying transform object
-        """
-        if joinaggregate is Undefined:
-            joinaggregate = []
-        for key, val in kwargs.items():
-            parsed = utils.parse_shorthand(val)
-            dct = {
-                "as": key,
-                "field": parsed.get("field", Undefined),
-                "op": parsed.get("aggregate", Undefined),
-            }
-            joinaggregate.append(core.JoinAggregateFieldDef(**dct))
-        return self._add_transform(
-            core.JoinAggregateTransform(joinaggregate=joinaggregate, groupby=groupby)
-        )
-
-    # TODO: Update docstring
-    def transform_filter(self, filter, **kwargs) -> Self:
-        """
-        Add a :class:`FilterTransform` to the schema.
-
-        Parameters
-        ----------
-        filter : a filter expression or :class:`PredicateComposition`
-            The `filter` property must be one of the predicate definitions:
-            (1) a string or alt.expr expression
-            (2) a range predicate
-            (3) a selection predicate
-            (4) a logical operand combining (1)-(3)
-            (5) a Selection object
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        alt.FilterTransform : underlying transform object
-
-        """
-        if isinstance(filter, Parameter):
-            new_filter = {"param": filter.name}
-            if "empty" in kwargs:
-                new_filter["empty"] = kwargs.pop("empty")
-            elif isinstance(filter.empty, bool):
-                new_filter["empty"] = filter.empty
-            filter = new_filter
-        return self._add_transform(core.FilterTransform(filter=filter, **kwargs))
-
-    def transform_flatten(self, flatten, as_=Undefined) -> Self:
-        """Add a :class:`FlattenTransform` to the schema.
-
-        Parameters
-        ----------
-        flatten : List(string)
-            An array of one or more data fields containing arrays to flatten.
-            If multiple fields are specified, their array values should have a parallel
-            structure, ideally with the same length.
-            If the lengths of parallel arrays do not match,
-            the longest array will be used with ``null`` values added for missing entries.
-        as : List(string)
-            The output field names for extracted array values.
-            **Default value:** The field name of the corresponding array field
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        alt.FlattenTransform : underlying transform object
-        """
-        return self._add_transform(
-            core.FlattenTransform(flatten=flatten, **{"as": as_})
-        )
-
-    def transform_fold(self, fold, as_=Undefined) -> Self:
-        """Add a :class:`FoldTransform` to the spec.
-
-        Parameters
-        ----------
-        fold : List(string)
-            An array of data fields indicating the properties to fold.
-        as : [string, string]
-            The output field names for the key and value properties produced by the fold
-            transform. Default: ``["key", "value"]``
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        Chart.transform_pivot : pivot transform - opposite of fold.
-        alt.FoldTransform : underlying transform object
-        """
-        return self._add_transform(core.FoldTransform(fold=fold, **{"as": as_}))
-
-    def transform_loess(
-        self,
-        on,
-        loess,
-        as_=Undefined,
-        bandwidth=Undefined,
-        groupby=Undefined,
-    ) -> Self:
-        """Add a :class:`LoessTransform` to the spec.
-
-        Parameters
-        ----------
-        on : str
-            The data field of the independent variable to use a predictor.
-        loess : str
-            The data field of the dependent variable to smooth.
-        as_ : [str, str]
-            The output field names for the smoothed points generated by the loess transform.
-            **Default value:** The field names of the input x and y values.
-        bandwidth : float
-            A bandwidth parameter in the range ``[0, 1]`` that determines the amount of
-            smoothing. **Default value:** ``0.3``
-        groupby : List(str)
-            The data fields to group by. If not specified, a single group containing all data
-            objects will be used.
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        Chart.transform_regression: regression transform
-        alt.LoessTransform : underlying transform object
-        """
-        return self._add_transform(
-            core.LoessTransform(
-                loess=loess, on=on, bandwidth=bandwidth, groupby=groupby, **{"as": as_}
-            )
-        )
-
-    def transform_lookup(
-        self,
-        lookup=Undefined,
-        from_=Undefined,
-        as_=Undefined,
-        default=Undefined,
-        **kwargs,
-    ) -> Self:
-        """Add a :class:`DataLookupTransform` or :class:`SelectionLookupTransform` to the chart
-
-        Parameters
-        ----------
-        lookup : string
-            Key in primary data source.
-        from_ : anyOf(:class:`LookupData`, :class:`LookupSelection`)
-            Secondary data reference.
-        as_ : anyOf(string, List(string))
-            The output fields on which to store the looked up data values.
-
-            For data lookups, this property may be left blank if ``from_.fields``
-            has been specified (those field names will be used); if ``from_.fields``
-            has not been specified, ``as_`` must be a string.
-
-            For selection lookups, this property is optional: if unspecified,
-            looked up values will be stored under a property named for the selection;
-            and if specified, it must correspond to ``from_.fields``.
-        default : string
-            The default value to use if lookup fails. **Default value:** ``null``
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        alt.DataLookupTransform : underlying transform object
-        alt.SelectionLookupTransform : underlying transform object
-        """
-        if as_ is not Undefined:
-            if "as" in kwargs:
-                raise ValueError(
-                    "transform_lookup: both 'as_' and 'as' passed as arguments."
-                )
-            kwargs["as"] = as_
-        if from_ is not Undefined:
-            if "from" in kwargs:
-                raise ValueError(
-                    "transform_lookup: both 'from_' and 'from' passed as arguments."
-                )
-            kwargs["from"] = from_
-        kwargs["lookup"] = lookup
-        kwargs["default"] = default
-        return self._add_transform(core.LookupTransform(**kwargs))
-
-    def transform_pivot(
-        self,
-        pivot,
-        value,
-        groupby=Undefined,
-        limit=Undefined,
-        op=Undefined,
-    ) -> Self:
-        """Add a :class:`PivotTransform` to the chart.
-
-        Parameters
-        ----------
-        pivot : str
-            The data field to pivot on. The unique values of this field become new field names
-            in the output stream.
-        value : str
-            The data field to populate pivoted fields. The aggregate values of this field become
-            the values of the new pivoted fields.
-        groupby : List(str)
-            The optional data fields to group by. If not specified, a single group containing
-            all data objects will be used.
-        limit : float
-            An optional parameter indicating the maximum number of pivoted fields to generate.
-            The default ( ``0`` ) applies no limit. The pivoted ``pivot`` names are sorted in
-            ascending order prior to enforcing the limit.
-            **Default value:** ``0``
-        op : string
-            The aggregation operation to apply to grouped ``value`` field values.
-            **Default value:** ``sum``
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        Chart.transform_fold : fold transform - opposite of pivot.
-        alt.PivotTransform : underlying transform object
-        """
-        return self._add_transform(
-            core.PivotTransform(
-                pivot=pivot, value=value, groupby=groupby, limit=limit, op=op
-            )
-        )
-
-    def transform_quantile(
-        self,
-        quantile,
-        as_=Undefined,
-        groupby=Undefined,
-        probs=Undefined,
-        step=Undefined,
-    ) -> Self:
-        """Add a :class:`QuantileTransform` to the chart
-
-        Parameters
-        ----------
-        quantile : str
-            The data field for which to perform quantile estimation.
-        as : [str, str]
-            The output field names for the probability and quantile values.
-        groupby : List(str)
-            The data fields to group by. If not specified, a single group containing all data
-            objects will be used.
-        probs : List(float)
-            An array of probabilities in the range (0, 1) for which to compute quantile values.
-            If not specified, the *step* parameter will be used.
-        step : float
-            A probability step size (default 0.01) for sampling quantile values. All values from
-            one-half the step size up to 1 (exclusive) will be sampled. This parameter is only
-            used if the *probs* parameter is not provided. **Default value:** ``["prob", "value"]``
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        alt.QuantileTransform : underlying transform object
-        """
-        return self._add_transform(
-            core.QuantileTransform(
-                quantile=quantile,
-                groupby=groupby,
-                probs=probs,
-                step=step,
-                **{"as": as_},
-            )
-        )
-
-    def transform_regression(
-        self,
-        on,
-        regression,
-        as_=Undefined,
-        extent=Undefined,
-        groupby=Undefined,
-        method=Undefined,
-        order=Undefined,
-        params=Undefined,
-    ) -> Self:
-        """Add a :class:`RegressionTransform` to the chart.
-
-        Parameters
-        ----------
-        on : str
-            The data field of the independent variable to use a predictor.
-        regression : str
-            The data field of the dependent variable to predict.
-        as_ : [str, str]
-            The output field names for the smoothed points generated by the regression
-            transform. **Default value:** The field names of the input x and y values.
-        extent : [float, float]
-            A [min, max] domain over the independent (x) field for the starting and ending
-            points of the generated trend line.
-        groupby : List(str)
-            The data fields to group by. If not specified, a single group containing all data
-            objects will be used.
-        method : enum('linear', 'log', 'exp', 'pow', 'quad', 'poly')
-            The functional form of the regression model. One of ``"linear"``, ``"log"``,
-            ``"exp"``, ``"pow"``, ``"quad"``, or ``"poly"``.  **Default value:** ``"linear"``
-        order : float
-            The polynomial order (number of coefficients) for the 'poly' method.
-            **Default value:** ``3``
-        params : boolean
-            A boolean flag indicating if the transform should return the regression model
-            parameters (one object per group), rather than trend line points.
-            The resulting objects include a ``coef`` array of fitted coefficient values
-            (starting with the intercept term and then including terms of increasing order)
-            and an ``rSquared`` value (indicating the total variance explained by the model).
-            **Default value:** ``false``
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        Chart.transform_loess : LOESS transform
-        alt.RegressionTransform : underlying transform object
-        """
-        return self._add_transform(
-            core.RegressionTransform(
-                regression=regression,
-                on=on,
-                extent=extent,
-                groupby=groupby,
-                method=method,
-                order=order,
-                params=params,
-                **{"as": as_},
-            )
-        )
-
-    def transform_sample(self, sample=1000) -> Self:
-        """
-        Add a :class:`SampleTransform` to the schema.
-
-        Parameters
-        ----------
-        sample : float
-            The maximum number of data objects to include in the sample. Default: 1000.
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        alt.SampleTransform : underlying transform object
-        """
-        return self._add_transform(core.SampleTransform(sample))
-
-    def transform_stack(
-        self, as_, stack, groupby, offset=Undefined, sort=Undefined
-    ) -> Self:
-        """
-        Add a :class:`StackTransform` to the schema.
-
-        Parameters
-        ----------
-        as_ : anyOf(string, List(string))
-            Output field names. This can be either a string or an array of strings with
-            two elements denoting the name for the fields for stack start and stack end
-            respectively.
-            If a single string(eg."val") is provided, the end field will be "val_end".
-        stack : string
-            The field which is stacked.
-        groupby : List(string)
-            The data fields to group by.
-        offset : enum('zero', 'center', 'normalize')
-            Mode for stacking marks. Default: 'zero'.
-        sort : List(:class:`SortField`)
-            Field that determines the order of leaves in the stacked charts.
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        See Also
-        --------
-        alt.StackTransform : underlying transform object
-        """
-        return self._add_transform(
-            core.StackTransform(
-                stack=stack, groupby=groupby, offset=offset, sort=sort, **{"as": as_}
-            )
-        )
-
-    def transform_timeunit(
-        self,
-        as_=Undefined,
-        field=Undefined,
-        timeUnit=Undefined,
-        **kwargs,
-    ) -> Self:
-        """
-        Add a :class:`TimeUnitTransform` to the schema.
-
-        Parameters
-        ----------
-        as_ : string
-            The output field to write the timeUnit value.
-        field : string
-            The data field to apply time unit.
-        timeUnit : :class:`TimeUnit`
-            The timeUnit.
-        **kwargs
-            transforms can also be passed by keyword argument; see Examples
-
-        Returns
-        -------
-        self : Chart object
-            returns chart to allow for chaining
-
-        Examples
-        --------
-        >>> import altair as alt
-        >>> from altair import datum, expr
-
-        >>> chart = alt.Chart().transform_timeunit(month='month(date)')
-        >>> chart.transform[0]
-        TimeUnitTransform({
-          as: 'month',
-          field: 'date',
-          timeUnit: 'month'
-        })
-
-        It's also possible to pass the ``TimeUnitTransform`` arguments directly;
-        this is most useful in cases where the desired field name is not a
-        valid python identifier:
-
-        >>> kwds = {'as': 'month', 'timeUnit': 'month', 'field': 'The Month'}
-        >>> chart = alt.Chart().transform_timeunit(**kwds)
-        >>> chart.transform[0]
-        TimeUnitTransform({
-          as: 'month',
-          field: 'The Month',
-          timeUnit: 'month'
-        })
-
-        As the first form is easier to write and understand, that is the
-        recommended method.
-
-        See Also
-        --------
-        alt.TimeUnitTransform : underlying transform object
-
-        """
-        if as_ is Undefined:
-            as_ = kwargs.pop("as", Undefined)
-        else:
-            if "as" in kwargs:
-                raise ValueError(
-                    "transform_timeunit: both 'as_' and 'as' passed as arguments."
-                )
-        if as_ is not Undefined:
-            dct = {"as": as_, "timeUnit": timeUnit, "field": field}
-            self = self._add_transform(core.TimeUnitTransform(**dct))
-        for as_, shorthand in kwargs.items():
-            dct = utils.parse_shorthand(
-                shorthand,
-                parse_timeunits=True,
-                parse_aggregates=False,
-                parse_types=False,
-            )
-            dct.pop("type", None)
-            dct["as"] = as_
-            if "timeUnit" not in dct:
-                raise ValueError("'{}' must include a valid timeUnit".format(shorthand))
-            self = self._add_transform(core.TimeUnitTransform(**dct))
-        return self
-
-    def transform_window(
-        self,
-        window=Undefined,
-        frame=Undefined,
-        groupby=Undefined,
-        ignorePeers=Undefined,
-        sort=Undefined,
-        **kwargs,
-    ) -> Self:
-        """Add a :class:`WindowTransform` to the schema
-
-        Parameters
-        ----------
-        window : List(:class:`WindowFieldDef`)
-            The definition of the fields in the window, and what calculations to use.
-        frame : List(anyOf(None, float))
-            A frame specification as a two-element array indicating how the sliding window
-            should proceed. The array entries should either be a number indicating the offset
-            from the current data object, or null to indicate unbounded rows preceding or
-            following the current data object. The default value is ``[null, 0]``, indicating
-            that the sliding window includes the current object and all preceding objects. The
-            value ``[-5, 5]`` indicates that the window should include five objects preceding
-            and five objects following the current object. Finally, ``[null, null]`` indicates
-            that the window frame should always include all data objects. The only operators
-            affected are the aggregation operations and the ``first_value``, ``last_value``, and
-            ``nth_value`` window operations. The other window operations are not affected by
-            this.
-
-            **Default value:** :  ``[null, 0]`` (includes the current object and all preceding
-            objects)
-        groupby : List(string)
-            The data fields for partitioning the data objects into separate windows. If
-            unspecified, all data points will be in a single group.
-        ignorePeers : boolean
-            Indicates if the sliding window frame should ignore peer values. (Peer values are
-            those considered identical by the sort criteria). The default is false, causing the
-            window frame to expand to include all peer values. If set to true, the window frame
-            will be defined by offset values only. This setting only affects those operations
-            that depend on the window frame, namely aggregation operations and the first_value,
-            last_value, and nth_value window operations.
-
-            **Default value:** ``false``
-        sort : List(:class:`SortField`)
-            A sort field definition for sorting data objects within a window. If two data
-            objects are considered equal by the comparator, they are considered “peer” values of
-            equal rank. If sort is not specified, the order is undefined: data objects are
-            processed in the order they are observed and none are considered peers (the
-            ignorePeers parameter is ignored and treated as if set to ``true`` ).
-        **kwargs
-            transforms can also be passed by keyword argument; see Examples
-
-        Examples
-        --------
-        A cumulative line chart
-
-        >>> import altair as alt
-        >>> import numpy as np
-        >>> import pandas as pd
-        >>> data = pd.DataFrame({'x': np.arange(100),
-        ...                      'y': np.random.randn(100)})
-        >>> chart = alt.Chart(data).mark_line().encode(
-        ...     x='x:Q',
-        ...     y='ycuml:Q'
-        ... ).transform_window(
-        ...     ycuml='sum(y)'
-        ... )
-        >>> chart.transform[0]
-        WindowTransform({
-          window: [WindowFieldDef({
-            as: 'ycuml',
-            field: 'y',
-            op: 'sum'
-          })]
-        })
-
-        """
-        if kwargs:
-            if window is Undefined:
-                window = []
-            for as_, shorthand in kwargs.items():
-                kwds = {"as": as_}
-                kwds.update(
-                    utils.parse_shorthand(
-                        shorthand,
-                        parse_aggregates=False,
-                        parse_window_ops=True,
-                        parse_timeunits=False,
-                        parse_types=False,
-                    )
-                )
-                window.append(core.WindowFieldDef(**kwds))
-
-        return self._add_transform(
-            core.WindowTransform(
-                window=window,
-                frame=frame,
-                groupby=groupby,
-                ignorePeers=ignorePeers,
-                sort=sort,
-            )
-        )
-
-    # Display-related methods
-
-    def _repr_mimebundle_(self, include=None, exclude=None):
-        """Return a MIME bundle for display in Jupyter frontends."""
-        # Catch errors explicitly to get around issues in Jupyter frontend
-        # see https://github.com/ipython/ipython/issues/11038
-        try:
-            dct = self.to_dict()
-        except Exception:
-            utils.display_traceback(in_ipython=True)
-            return {}
-        else:
-            return renderers.get()(dct)
-
-    def display(self, renderer=Undefined, theme=Undefined, actions=Undefined, **kwargs):
-        """Display chart in Jupyter notebook or JupyterLab
-
-        Parameters are passed as options to vega-embed within supported frontends.
-        See https://github.com/vega/vega-embed#options for details.
-
-        Parameters
-        ----------
-        renderer : string ('canvas' or 'svg')
-            The renderer to use
-        theme : string
-            The Vega theme name to use; see https://github.com/vega/vega-themes
-        actions : bool or dict
-            Specify whether action links ("Open In Vega Editor", etc.) are
-            included in the view.
-        **kwargs :
-            Additional parameters are also passed to vega-embed as options.
-
-        """
-        from IPython.display import display
-
-        if renderer is not Undefined:
-            kwargs["renderer"] = renderer
-        if theme is not Undefined:
-            kwargs["theme"] = theme
-        if actions is not Undefined:
-            kwargs["actions"] = actions
-
-        if kwargs:
-            options = renderers.options.copy()
-            options["embed_options"] = options.get("embed_options", {}).copy()
-            options["embed_options"].update(kwargs)
-            with renderers.enable(**options):
-                display(self)
-        else:
-            display(self)
-
-    @utils.deprecation.deprecated(message="'serve' is deprecated. Use 'show' instead.")
-    def serve(
-        self,
-        ip="127.0.0.1",
-        port=8888,
-        n_retries=50,
-        files=None,
-        jupyter_warning=True,
-        open_browser=True,
-        http_server=None,
-        **kwargs,
-    ):
-        """
-        'serve' is deprecated. Use 'show' instead.
-
-        Open a browser window and display a rendering of the chart
-
-        Parameters
-        ----------
-        html : string
-            HTML to serve
-        ip : string (default = '127.0.0.1')
-            ip address at which the HTML will be served.
-        port : int (default = 8888)
-            the port at which to serve the HTML
-        n_retries : int (default = 50)
-            the number of nearby ports to search if the specified port
-            is already in use.
-        files : dictionary (optional)
-            dictionary of extra content to serve
-        jupyter_warning : bool (optional)
-            if True (default), then print a warning if this is used
-            within the Jupyter notebook
-        open_browser : bool (optional)
-            if True (default), then open a web browser to the given HTML
-        http_server : class (optional)
-            optionally specify an HTTPServer class to use for showing the
-            figure. The default is Python's basic HTTPServer.
-        **kwargs :
-            additional keyword arguments passed to the save() method
-
-        """
-        from ...utils.server import serve
-
-        html = io.StringIO()
-        self.save(html, format="html", **kwargs)
-        html.seek(0)
-
-        serve(
-            html.read(),
-            ip=ip,
-            port=port,
-            n_retries=n_retries,
-            files=files,
-            jupyter_warning=jupyter_warning,
-            open_browser=open_browser,
-            http_server=http_server,
-        )
-
-    def show(self, embed_opt=None, open_browser=None):
-        """Show the chart in an external browser window.
-
-        This requires a recent version of the altair_viewer package.
-
-        Parameters
-        ----------
-        embed_opt : dict (optional)
-            The Vega embed options that control the dispay of the chart.
-        open_browser : bool (optional)
-            Specify whether a browser window should be opened. If not specified,
-            a browser window will be opened only if the server is not already
-            connected to a browser.
-        """
-        try:
-            import altair_viewer  # type: ignore
-        except ImportError as err:
-            raise ValueError(
-                "'show' method requires the altair_viewer package. "
-                "See http://github.com/altair-viz/altair_viewer"
-            ) from err
-        altair_viewer.show(self, embed_opt=embed_opt, open_browser=open_browser)
-
-    @utils.use_signature(core.Resolve)
-    def _set_resolve(self, **kwargs):
-        """Copy the chart and update the resolve property with kwargs"""
-        if not hasattr(self, "resolve"):
-            raise ValueError(
-                "{} object has no attribute " "'resolve'".format(self.__class__)
-            )
-        copy = self.copy(deep=["resolve"])
-        if copy.resolve is Undefined:
-            copy.resolve = core.Resolve()
-        for key, val in kwargs.items():
-            copy.resolve[key] = val
-        return copy
-
-    @utils.use_signature(core.AxisResolveMap)
-    def resolve_axis(self, *args, **kwargs) -> Self:
-        return self._set_resolve(axis=core.AxisResolveMap(*args, **kwargs))
-
-    @utils.use_signature(core.LegendResolveMap)
-    def resolve_legend(self, *args, **kwargs) -> Self:
-        return self._set_resolve(legend=core.LegendResolveMap(*args, **kwargs))
-
-    @utils.use_signature(core.ScaleResolveMap)
-    def resolve_scale(self, *args, **kwargs) -> Self:
-        return self._set_resolve(scale=core.ScaleResolveMap(*args, **kwargs))
-
-
-class _EncodingMixin:
-    @utils.use_signature(core.FacetedEncoding)
-    def encode(self, *args, **kwargs) -> Self:
-        # Convert args to kwargs based on their types.
-        kwargs = utils.infer_encoding_types(args, kwargs, channels)
-
-        # get a copy of the dict representation of the previous encoding
-        # ignore type as copy method comes from SchemaBase
-        copy = self.copy(deep=["encoding"])  # type: ignore[attr-defined]
-        encoding = copy._get("encoding", {})
-        if isinstance(encoding, core.VegaLiteSchema):
-            encoding = {k: v for k, v in encoding._kwds.items() if v is not Undefined}
-
-        # update with the new encodings, and apply them to the copy
-        encoding.update(kwargs)
-        copy.encoding = core.FacetedEncoding(**encoding)
-        return copy
-
-    def facet(
-        self,
-        facet=Undefined,
-        row=Undefined,
-        column=Undefined,
-        data=Undefined,
-        columns=Undefined,
-        **kwargs,
-    ) -> "FacetChart":
-        """Create a facet chart from the current chart.
-
-        Faceted charts require data to be specified at the top level; if data
-        is not specified, the data from the current chart will be used at the
-        top level.
-
-        Parameters
-        ----------
-        facet : string or alt.Facet (optional)
-            The data column to use as an encoding for a wrapped facet.
-            If specified, then neither row nor column may be specified.
-        column : string or alt.Column (optional)
-            The data column to use as an encoding for a column facet.
-            May be combined with row argument, but not with facet argument.
-        row : string or alt.Column (optional)
-            The data column to use as an encoding for a row facet.
-            May be combined with column argument, but not with facet argument.
-        data : string or dataframe (optional)
-            The dataset to use for faceting. If not supplied, then data must
-            be specified in the top-level chart that calls this method.
-        columns : integer
-            the maximum number of columns for a wrapped facet.
-
-        Returns
-        -------
-        self :
-            for chaining
-        """
-        facet_specified = facet is not Undefined
-        rowcol_specified = row is not Undefined or column is not Undefined
-
-        if facet_specified and rowcol_specified:
-            raise ValueError(
-                "facet argument cannot be combined with row/column argument."
-            )
-
-        # Remove "ignore" statement once Undefined is no longer typed as Any
-        if data is Undefined:  # type: ignore
-            # Remove "ignore" statement once Undefined is no longer typed as Any
-            if self.data is Undefined:  # type: ignore
-                raise ValueError(
-                    "Facet charts require data to be specified at the top level."
-                )
-            # ignore type as copy comes from another class
-            self = self.copy(deep=False)  # type: ignore[attr-defined]
-            # Remove "ignore" statement once Undefined is no longer typed as Any
-            data, self.data = self.data, Undefined  # type: ignore
-
-        if facet_specified:
-            if isinstance(facet, str):
-                facet = channels.Facet(facet)
-        else:
-            facet = FacetMapping(row=row, column=column)
-
-        return FacetChart(spec=self, facet=facet, data=data, columns=columns, **kwargs)
-
-
-class Chart(
-    TopLevelMixin, _EncodingMixin, mixins.MarkMethodMixin, core.TopLevelUnitSpec
-):
-    """Create a basic Altair/Vega-Lite chart.
-
-    Although it is possible to set all Chart properties as constructor attributes,
-    it is more idiomatic to use methods such as ``mark_point()``, ``encode()``,
-    ``transform_filter()``, ``properties()``, etc. See Altair's documentation
-    for details and examples: http://altair-viz.github.io/.
-
-    Parameters
-    ----------
-    data : Data
-        An object describing the data source
-    mark : AnyMark
-        A string describing the mark type (one of `"bar"`, `"circle"`, `"square"`, `"tick"`,
-         `"line"`, * `"area"`, `"point"`, `"rule"`, `"geoshape"`, and `"text"`) or a
-         MarkDef object.
-    encoding : FacetedEncoding
-        A key-value mapping between encoding channels and definition of fields.
-    autosize : anyOf(AutosizeType, AutoSizeParams)
-        Sets how the visualization size should be determined. If a string, should be one of
-        `"pad"`, `"fit"` or `"none"`. Object values can additionally specify parameters for
-        content sizing and automatic resizing. `"fit"` is only supported for single and
-        layered views that don't use `rangeStep`.  Default value: `pad`
-    background : string
-        CSS color property to use as the background of visualization.
-
-        **Default value:** none (transparent)
-    config : Config
-        Vega-Lite configuration object.  This property can only be defined at the top-level
-        of a specification.
-    description : string
-        Description of this mark for commenting purpose.
-    height : float
-        The height of a visualization.
-    name : string
-        Name of the visualization for later reference.
-    padding : Padding
-        The default visualization padding, in pixels, from the edge of the visualization
-        canvas to the data rectangle.  If a number, specifies padding for all sides. If an
-        object, the value should have the format `{"left": 5, "top": 5, "right": 5,
-        "bottom": 5}` to specify padding for each side of the visualization.  Default
-        value: `5`
-    projection : Projection
-        An object defining properties of geographic projection.  Works with `"geoshape"`
-        marks and `"point"` or `"line"` marks that have a channel (one or more of `"X"`,
-        `"X2"`, `"Y"`, `"Y2"`) with type `"latitude"`, or `"longitude"`.
-    selection : Mapping(required=[])
-        A key-value mapping between selection names and definitions.
-    title : anyOf(string, TitleParams)
-        Title for the plot.
-    transform : List(Transform)
-        An array of data transformations such as filter and new field calculation.
-    width : float
-        The width of a visualization.
-    """
-
-    def __init__(
-        self,
-        data=Undefined,
-        encoding=Undefined,
-        mark=Undefined,
-        width=Undefined,
-        height=Undefined,
-        **kwargs,
-    ):
-        super(Chart, self).__init__(
-            data=data,
-            encoding=encoding,
-            mark=mark,
-            width=width,
-            height=height,
-            **kwargs,
-        )
-
-    _counter = 0
-
-    @classmethod
-    def _get_name(cls):
-        cls._counter += 1
-        return f"view_{cls._counter}"
-
-    @classmethod
-    def from_dict(cls, dct, validate=True) -> "Chart":  # type: ignore[override]  # Not the same signature as SchemaBase.from_dict. Would ideally be aligned in the future
-        """Construct class from a dictionary representation
-
-        Parameters
-        ----------
-        dct : dictionary
-            The dict from which to construct the class
-        validate : boolean
-            If True (default), then validate the input against the schema.
-
-        Returns
-        -------
-        obj : Chart object
-            The wrapped schema
-
-        Raises
-        ------
-        jsonschema.ValidationError :
-            if validate=True and dct does not conform to the schema
-        """
-        for class_ in TopLevelMixin.__subclasses__():
-            if class_ is Chart:
-                class_ = cast(TypingType[TopLevelMixin], super(Chart, cls))
-            try:
-                # TopLevelMixin classes don't necessarily have from_dict defined
-                # but all classes which are used here have due to how Altair is
-                # designed. Too complex to type check right now.
-                return class_.from_dict(dct, validate=validate)  # type: ignore[attr-defined]
-            except jsonschema.ValidationError:
-                pass
-
-        # As a last resort, try using the Root vegalite object
-        return core.Root.from_dict(dct, validate)
-
-    def to_dict(self, *args, **kwargs) -> dict:
-        """Convert the chart to a dictionary suitable for JSON export."""
-        context = kwargs.get("context", {})
-        if self.data is Undefined and "data" not in context:
-            # No data specified here or in parent: inject empty data
-            # for easier specification of datum encodings.
-            copy = self.copy(deep=False)
-            copy.data = core.InlineData(values=[{}])
-            return super(Chart, copy).to_dict(*args, **kwargs)
-        return super().to_dict(*args, **kwargs)
-
-    def add_params(self, *params) -> Self:
-        """Add one or more parameters to the chart."""
-        if not params:
-            return self
-        copy = self.copy(deep=["params"])
-        if copy.params is Undefined:
-            copy.params = []
-
-        for s in params:
-            copy.params.append(s.param)
-        return copy
-
-    @utils.deprecation.deprecated(
-        message="'add_selection' is deprecated. Use 'add_params' instead."
-    )
-    def add_selection(self, *params) -> Self:
-        """'add_selection' is deprecated. Use 'add_params' instead."""
-        return self.add_params(*params)
-
-    def interactive(self, name=None, bind_x=True, bind_y=True) -> Self:
-        """Make chart axes scales interactive
-
-        Parameters
-        ----------
-        name : string
-            The parameter name to use for the axes scales. This name should be
-            unique among all parameters within the chart.
-        bind_x : boolean, default True
-            If true, then bind the interactive scales to the x-axis
-        bind_y : boolean, default True
-            If true, then bind the interactive scales to the y-axis
-
-        Returns
-        -------
-        chart :
-            copy of self, with interactive axes added
-
-        """
-        encodings = []
-        if bind_x:
-            encodings.append("x")
-        if bind_y:
-            encodings.append("y")
-        return self.add_params(selection_interval(bind="scales", encodings=encodings))
-
-
-def _check_if_valid_subspec(spec, classname):
-    """Check if the spec is a valid sub-spec.
-
-    If it is not, then raise a ValueError
-    """
-    err = (
-        'Objects with "{0}" attribute cannot be used within {1}. '
-        "Consider defining the {0} attribute in the {1} object instead."
-    )
-
-    if not isinstance(spec, (core.SchemaBase, dict)):
-        raise ValueError("Only chart objects can be used in {0}.".format(classname))
-    for attr in TOPLEVEL_ONLY_KEYS:
-        if isinstance(spec, core.SchemaBase):
-            val = getattr(spec, attr, Undefined)
-        else:
-            val = spec.get(attr, Undefined)
-        if val is not Undefined:
-            raise ValueError(err.format(attr, classname))
-
-
-def _check_if_can_be_layered(spec):
-    """Check if the spec can be layered."""
-
-    def _get(spec, attr):
-        if isinstance(spec, core.SchemaBase):
-            return spec._get(attr)
-        else:
-            return spec.get(attr, Undefined)
-
-    encoding = _get(spec, "encoding")
-    if encoding is not Undefined:
-        for channel in ["row", "column", "facet"]:
-            if _get(encoding, channel) is not Undefined:
-                raise ValueError(
-                    "Faceted charts cannot be layered. Instead, layer the charts before faceting."
-                )
-    if isinstance(spec, (Chart, LayerChart)):
-        return
-
-    if not isinstance(spec, (core.SchemaBase, dict)):
-        raise ValueError("Only chart objects can be layered.")
-    if _get(spec, "facet") is not Undefined:
-        raise ValueError(
-            "Faceted charts cannot be layered. Instead, layer the charts before faceting."
-        )
-    if isinstance(spec, FacetChart) or _get(spec, "facet") is not Undefined:
-        raise ValueError(
-            "Faceted charts cannot be layered. Instead, layer the charts before faceting."
-        )
-    if isinstance(spec, RepeatChart) or _get(spec, "repeat") is not Undefined:
-        raise ValueError(
-            "Repeat charts cannot be layered. Instead, layer the charts before repeating."
-        )
-    if isinstance(spec, ConcatChart) or _get(spec, "concat") is not Undefined:
-        raise ValueError(
-            "Concatenated charts cannot be layered. Instead, layer the charts before concatenating."
-        )
-    if isinstance(spec, HConcatChart) or _get(spec, "hconcat") is not Undefined:
-        raise ValueError(
-            "Concatenated charts cannot be layered. Instead, layer the charts before concatenating."
-        )
-    if isinstance(spec, VConcatChart) or _get(spec, "vconcat") is not Undefined:
-        raise ValueError(
-            "Concatenated charts cannot be layered. Instead, layer the charts before concatenating."
-        )
-
-
-class RepeatChart(TopLevelMixin, core.TopLevelRepeatSpec):
-    """A chart repeated across rows and columns with small changes"""
-
-    # Because TopLevelRepeatSpec is defined as a union as of Vega-Lite schema 4.9,
-    # we set the arguments explicitly here.
-    # TODO: Should we instead use tools/schemapi/codegen._get_args?
-    @utils.use_signature(core.TopLevelRepeatSpec)
-    def __init__(
-        self,
-        repeat=Undefined,
-        spec=Undefined,
-        align=Undefined,
-        autosize=Undefined,
-        background=Undefined,
-        bounds=Undefined,
-        center=Undefined,
-        columns=Undefined,
-        config=Undefined,
-        data=Undefined,
-        datasets=Undefined,
-        description=Undefined,
-        name=Undefined,
-        padding=Undefined,
-        params=Undefined,
-        resolve=Undefined,
-        spacing=Undefined,
-        title=Undefined,
-        transform=Undefined,
-        usermeta=Undefined,
-        **kwds,
-    ):
-        _check_if_valid_subspec(spec, "RepeatChart")
-        _spec_as_list = [spec]
-        params, _spec_as_list = _combine_subchart_params(params, _spec_as_list)
-        spec = _spec_as_list[0]
-        if isinstance(spec, (Chart, LayerChart)):
-            params = _repeat_names(params, repeat, spec)
-        super(RepeatChart, self).__init__(
-            repeat=repeat,
-            spec=spec,
-            align=align,
-            autosize=autosize,
-            background=background,
-            bounds=bounds,
-            center=center,
-            columns=columns,
-            config=config,
-            data=data,
-            datasets=datasets,
-            description=description,
-            name=name,
-            padding=padding,
-            params=params,
-            resolve=resolve,
-            spacing=spacing,
-            title=title,
-            transform=transform,
-            usermeta=usermeta,
-            **kwds,
-        )
-
-    def interactive(self, name=None, bind_x=True, bind_y=True) -> Self:
-        """Make chart axes scales interactive
-
-        Parameters
-        ----------
-        name : string
-            The parameter name to use for the axes scales. This name should be
-            unique among all parameters within the chart.
-        bind_x : boolean, default True
-            If true, then bind the interactive scales to the x-axis
-        bind_y : boolean, default True
-            If true, then bind the interactive scales to the y-axis
-
-        Returns
-        -------
-        chart :
-            copy of self, with interactive axes added
-
-        """
-        copy = self.copy(deep=False)
-        copy.spec = copy.spec.interactive(name=name, bind_x=bind_x, bind_y=bind_y)
-        return copy
-
-    def add_params(self, *params) -> Self:
-        """Add one or more parameters to the chart."""
-        if not params or self.spec is Undefined:
-            return self
-        copy = self.copy()
-        copy.spec = copy.spec.add_params(*params)
-        return copy.copy()
-
-    @utils.deprecation.deprecated(
-        message="'add_selection' is deprecated. Use 'add_params' instead."
-    )
-    def add_selection(self, *selections) -> Self:
-        """'add_selection' is deprecated. Use 'add_params' instead."""
-        return self.add_params(*selections)
-
-
-def repeat(repeater="repeat"):
-    """Tie a channel to the row or column within a repeated chart
-
-    The output of this should be passed to the ``field`` attribute of
-    a channel.
-
-    Parameters
-    ----------
-    repeater : {'row'|'column'|'repeat'|'layer'}
-        The repeater to tie the field to. Default is 'repeat'.
-
-    Returns
-    -------
-    repeat : RepeatRef object
-    """
-    if repeater not in ["row", "column", "repeat", "layer"]:
-        raise ValueError("repeater must be one of ['row', 'column', 'repeat', 'layer']")
-    return core.RepeatRef(repeat=repeater)
-
-
-class ConcatChart(TopLevelMixin, core.TopLevelConcatSpec):
-    """A chart with horizontally-concatenated facets"""
-
-    @utils.use_signature(core.TopLevelConcatSpec)
-    def __init__(self, data=Undefined, concat=(), columns=Undefined, **kwargs):
-        # TODO: move common data to top level?
-        for spec in concat:
-            _check_if_valid_subspec(spec, "ConcatChart")
-        super(ConcatChart, self).__init__(
-            data=data, concat=list(concat), columns=columns, **kwargs
-        )
-        self.data, self.concat = _combine_subchart_data(self.data, self.concat)
-        self.params, self.concat = _combine_subchart_params(self.params, self.concat)
-
-    def __ior__(self, other):
-        _check_if_valid_subspec(other, "ConcatChart")
-        self.concat.append(other)
-        self.data, self.concat = _combine_subchart_data(self.data, self.concat)
-        self.params, self.concat = _combine_subchart_params(self.params, self.concat)
-        return self
-
-    def __or__(self, other):
-        copy = self.copy(deep=["concat"])
-        copy |= other
-        return copy
-
-    def interactive(self, name=None, bind_x=True, bind_y=True) -> Self:
-        """Make chart axes scales interactive
-
-        Parameters
-        ----------
-        name : string
-            The parameter name to use for the axes scales. This name should be
-            unique among all parameters within the chart.
-        bind_x : boolean, default True
-            If true, then bind the interactive scales to the x-axis
-        bind_y : boolean, default True
-            If true, then bind the interactive scales to the y-axis
-
-        Returns
-        -------
-        chart :
-            copy of self, with interactive axes added
-
-        """
-        encodings = []
-        if bind_x:
-            encodings.append("x")
-        if bind_y:
-            encodings.append("y")
-        return self.add_params(selection_interval(bind="scales", encodings=encodings))
-
-    def add_params(self, *params) -> Self:
-        """Add one or more parameters to the chart."""
-        if not params or not self.concat:
-            return self
-        copy = self.copy()
-        copy.concat = [chart.add_params(*params) for chart in copy.concat]
-        return copy
-
-    @utils.deprecation.deprecated(
-        message="'add_selection' is deprecated. Use 'add_params' instead."
-    )
-    def add_selection(self, *selections) -> Self:
-        """'add_selection' is deprecated. Use 'add_params' instead."""
-        return self.add_params(*selections)
-
-
-def concat(*charts, **kwargs):
-    """Concatenate charts horizontally"""
-    return ConcatChart(concat=charts, **kwargs)
-
-
-class HConcatChart(TopLevelMixin, core.TopLevelHConcatSpec):
-    """A chart with horizontally-concatenated facets"""
-
-    @utils.use_signature(core.TopLevelHConcatSpec)
-    def __init__(self, data=Undefined, hconcat=(), **kwargs):
-        # TODO: move common data to top level?
-        for spec in hconcat:
-            _check_if_valid_subspec(spec, "HConcatChart")
-        super(HConcatChart, self).__init__(data=data, hconcat=list(hconcat), **kwargs)
-        self.data, self.hconcat = _combine_subchart_data(self.data, self.hconcat)
-        self.params, self.hconcat = _combine_subchart_params(self.params, self.hconcat)
-
-    def __ior__(self, other):
-        _check_if_valid_subspec(other, "HConcatChart")
-        self.hconcat.append(other)
-        self.data, self.hconcat = _combine_subchart_data(self.data, self.hconcat)
-        self.params, self.hconcat = _combine_subchart_params(self.params, self.hconcat)
-        return self
-
-    def __or__(self, other):
-        copy = self.copy(deep=["hconcat"])
-        copy |= other
-        return copy
-
-    def interactive(self, name=None, bind_x=True, bind_y=True) -> Self:
-        """Make chart axes scales interactive
-
-        Parameters
-        ----------
-        name : string
-            The parameter name to use for the axes scales. This name should be
-            unique among all parameters within the chart.
-        bind_x : boolean, default True
-            If true, then bind the interactive scales to the x-axis
-        bind_y : boolean, default True
-            If true, then bind the interactive scales to the y-axis
-
-        Returns
-        -------
-        chart :
-            copy of self, with interactive axes added
-
-        """
-        encodings = []
-        if bind_x:
-            encodings.append("x")
-        if bind_y:
-            encodings.append("y")
-        return self.add_params(selection_interval(bind="scales", encodings=encodings))
-
-    def add_params(self, *params) -> Self:
-        """Add one or more parameters to the chart."""
-        if not params or not self.hconcat:
-            return self
-        copy = self.copy()
-        copy.hconcat = [chart.add_params(*params) for chart in copy.hconcat]
-        return copy
-
-    @utils.deprecation.deprecated(
-        message="'add_selection' is deprecated. Use 'add_params' instead."
-    )
-    def add_selection(self, *selections) -> Self:
-        """'add_selection' is deprecated. Use 'add_params' instead."""
-        return self.add_params(*selections)
-
-
-def hconcat(*charts, **kwargs):
-    """Concatenate charts horizontally"""
-    return HConcatChart(hconcat=charts, **kwargs)
-
-
-class VConcatChart(TopLevelMixin, core.TopLevelVConcatSpec):
-    """A chart with vertically-concatenated facets"""
-
-    @utils.use_signature(core.TopLevelVConcatSpec)
-    def __init__(self, data=Undefined, vconcat=(), **kwargs):
-        # TODO: move common data to top level?
-        for spec in vconcat:
-            _check_if_valid_subspec(spec, "VConcatChart")
-        super(VConcatChart, self).__init__(data=data, vconcat=list(vconcat), **kwargs)
-        self.data, self.vconcat = _combine_subchart_data(self.data, self.vconcat)
-        self.params, self.vconcat = _combine_subchart_params(self.params, self.vconcat)
-
-    def __iand__(self, other):
-        _check_if_valid_subspec(other, "VConcatChart")
-        self.vconcat.append(other)
-        self.data, self.vconcat = _combine_subchart_data(self.data, self.vconcat)
-        self.params, self.vconcat = _combine_subchart_params(self.params, self.vconcat)
-        return self
-
-    def __and__(self, other):
-        copy = self.copy(deep=["vconcat"])
-        copy &= other
-        return copy
-
-    def interactive(self, name=None, bind_x=True, bind_y=True) -> Self:
-        """Make chart axes scales interactive
-
-        Parameters
-        ----------
-        name : string
-            The parameter name to use for the axes scales. This name should be
-            unique among all parameters within the chart.
-        bind_x : boolean, default True
-            If true, then bind the interactive scales to the x-axis
-        bind_y : boolean, default True
-            If true, then bind the interactive scales to the y-axis
-
-        Returns
-        -------
-        chart :
-            copy of self, with interactive axes added
-
-        """
-        encodings = []
-        if bind_x:
-            encodings.append("x")
-        if bind_y:
-            encodings.append("y")
-        return self.add_params(selection_interval(bind="scales", encodings=encodings))
-
-    def add_params(self, *params) -> Self:
-        """Add one or more parameters to the chart."""
-        if not params or not self.vconcat:
-            return self
-        copy = self.copy()
-        copy.vconcat = [chart.add_params(*params) for chart in copy.vconcat]
-        return copy
-
-    @utils.deprecation.deprecated(
-        message="'add_selection' is deprecated. Use 'add_params' instead."
-    )
-    def add_selection(self, *selections) -> Self:
-        """'add_selection' is deprecated. Use 'add_params' instead."""
-        return self.add_params(*selections)
-
-
-def vconcat(*charts, **kwargs):
-    """Concatenate charts vertically"""
-    return VConcatChart(vconcat=charts, **kwargs)
-
-
-class LayerChart(TopLevelMixin, _EncodingMixin, core.TopLevelLayerSpec):
-    """A Chart with layers within a single panel"""
-
-    @utils.use_signature(core.TopLevelLayerSpec)
-    def __init__(self, data=Undefined, layer=(), **kwargs):
-        # TODO: move common data to top level?
-        # TODO: check for conflicting interaction
-        for spec in layer:
-            _check_if_valid_subspec(spec, "LayerChart")
-            _check_if_can_be_layered(spec)
-        super(LayerChart, self).__init__(data=data, layer=list(layer), **kwargs)
-        self.data, self.layer = _combine_subchart_data(self.data, self.layer)
-        # Currently (Vega-Lite 5.5) the same param can't occur on two layers
-        self.layer = _remove_duplicate_params(self.layer)
-        self.params, self.layer = _combine_subchart_params(self.params, self.layer)
-
-        # Some properties are not allowed within layer; we'll move to parent.
-        layer_props = ("height", "width", "view")
-        combined_dict, self.layer = _remove_layer_props(self, self.layer, layer_props)
-
-        for prop in combined_dict:
-            self[prop] = combined_dict[prop]
-
-    def __iadd__(self, other):
-        _check_if_valid_subspec(other, "LayerChart")
-        _check_if_can_be_layered(other)
-        self.layer.append(other)
-        self.data, self.layer = _combine_subchart_data(self.data, self.layer)
-        self.params, self.layer = _combine_subchart_params(self.params, self.layer)
-        return self
-
-    def __add__(self, other):
-        copy = self.copy(deep=["layer"])
-        copy += other
-        return copy
-
-    def add_layers(self, *layers) -> Self:
-        copy = self.copy(deep=["layer"])
-        for layer in layers:
-            copy += layer
-        return copy
-
-    def interactive(self, name=None, bind_x=True, bind_y=True) -> Self:
-        """Make chart axes scales interactive
-
-        Parameters
-        ----------
-        name : string
-            The parameter name to use for the axes scales. This name should be
-            unique among all parameters within the chart.
-        bind_x : boolean, default True
-            If true, then bind the interactive scales to the x-axis
-        bind_y : boolean, default True
-            If true, then bind the interactive scales to the y-axis
-
-        Returns
-        -------
-        chart :
-            copy of self, with interactive axes added
-
-        """
-        if not self.layer:
-            raise ValueError(
-                "LayerChart: cannot call interactive() until a " "layer is defined"
-            )
-        copy = self.copy(deep=["layer"])
-        copy.layer[0] = copy.layer[0].interactive(
-            name=name, bind_x=bind_x, bind_y=bind_y
-        )
-        return copy
-
-    def add_params(self, *params) -> Self:
-        """Add one or more parameters to the chart."""
-        if not params or not self.layer:
-            return self
-        copy = self.copy()
-        copy.layer[0] = copy.layer[0].add_params(*params)
-        return copy.copy()
-
-    @utils.deprecation.deprecated(
-        message="'add_selection' is deprecated. Use 'add_params' instead."
-    )
-    def add_selection(self, *selections) -> Self:
-        """'add_selection' is deprecated. Use 'add_params' instead."""
-        return self.add_params(*selections)
-
-
-def layer(*charts, **kwargs):
-    """layer multiple charts"""
-    return LayerChart(layer=charts, **kwargs)
-
-
-class FacetChart(TopLevelMixin, core.TopLevelFacetSpec):
-    """A Chart with layers within a single panel"""
-
-    @utils.use_signature(core.TopLevelFacetSpec)
-    def __init__(
-        self,
-        data=Undefined,
-        spec=Undefined,
-        facet=Undefined,
-        params=Undefined,
-        **kwargs,
-    ):
-        _check_if_valid_subspec(spec, "FacetChart")
-        _spec_as_list = [spec]
-        params, _spec_as_list = _combine_subchart_params(params, _spec_as_list)
-        spec = _spec_as_list[0]
-        super(FacetChart, self).__init__(
-            data=data, spec=spec, facet=facet, params=params, **kwargs
-        )
-
-    def interactive(self, name=None, bind_x=True, bind_y=True) -> Self:
-        """Make chart axes scales interactive
-
-        Parameters
-        ----------
-        name : string
-            The parameter name to use for the axes scales. This name should be
-            unique among all parameters within the chart.
-        bind_x : boolean, default True
-            If true, then bind the interactive scales to the x-axis
-        bind_y : boolean, default True
-            If true, then bind the interactive scales to the y-axis
-
-        Returns
-        -------
-        chart :
-            copy of self, with interactive axes added
-
-        """
-        copy = self.copy(deep=False)
-        copy.spec = copy.spec.interactive(name=name, bind_x=bind_x, bind_y=bind_y)
-        return copy
-
-    def add_params(self, *params) -> Self:
-        """Add one or more parameters to the chart."""
-        if not params or self.spec is Undefined:
-            return self
-        copy = self.copy()
-        copy.spec = copy.spec.add_params(*params)
-        return copy.copy()
-
-    @utils.deprecation.deprecated(
-        message="'add_selection' is deprecated. Use 'add_params' instead."
-    )
-    def add_selection(self, *selections) -> Self:
-        """'add_selection' is deprecated. Use 'add_params' instead."""
-        return self.add_params(*selections)
-
-
-def topo_feature(url, feature, **kwargs):
-    """A convenience function for extracting features from a topojson url
-
-    Parameters
-    ----------
-    url : string
-        An URL from which to load the data set.
-
-    feature : string
-        The name of the TopoJSON object set to convert to a GeoJSON feature collection. For
-        example, in a map of the world, there may be an object set named `"countries"`.
-        Using the feature property, we can extract this set and generate a GeoJSON feature
-        object for each country.
-
-    **kwargs :
-        additional keywords passed to TopoDataFormat
-    """
-    return core.UrlData(
-        url=url, format=core.TopoDataFormat(type="topojson", feature=feature, **kwargs)
-    )
-
-
-def _combine_subchart_data(data, subcharts):
-    def remove_data(subchart):
-        if subchart.data is not Undefined:
-            subchart = subchart.copy()
-            subchart.data = Undefined
-        return subchart
-
-    if not subcharts:
-        # No subcharts = nothing to do.
-        pass
-    elif data is Undefined:
-        # Top level has no data; all subchart data must
-        # be identical to proceed.
-        subdata = subcharts[0].data
-        if subdata is not Undefined and all(c.data is subdata for c in subcharts):
-            data = subdata
-            subcharts = [remove_data(c) for c in subcharts]
-    else:
-        # Top level has data; subchart data must be either
-        # undefined or identical to proceed.
-        if all(c.data is Undefined or c.data is data for c in subcharts):
-            subcharts = [remove_data(c) for c in subcharts]
-
-    return data, subcharts
-
-
-def _viewless_dict(param):
-    d = param.to_dict()
-    d.pop("views", None)
-    return d
-
-
-def _needs_name(subchart):
-    # Only `Chart` objects need a name
-    if (subchart.name is not Undefined) or (not isinstance(subchart, Chart)):
-        return False
-
-    # Variable parameters won't receive a views property.
-    if all(isinstance(p, core.VariableParameter) for p in subchart.params):
-        return False
-
-    return True
-
-
-# Convert SelectionParameters to TopLevelSelectionParameters with a views property.
-def _prepare_to_lift(param):
-    param = param.copy()
-
-    if isinstance(param, core.VariableParameter):
-        return param
-
-    if isinstance(param, core.SelectionParameter):
-        return core.TopLevelSelectionParameter(**param.to_dict(), views=[])
-
-    if param.views is Undefined:
-        param.views = []
-
-    return param
-
-
-def _remove_duplicate_params(layer):
-    subcharts = [subchart.copy() for subchart in layer]
-    found_params = []
-
-    for subchart in subcharts:
-        if (not hasattr(subchart, "params")) or (subchart.params is Undefined):
-            continue
-
-        params = []
-
-        # Ensure the same selection parameter doesn't appear twice
-        for param in subchart.params:
-            if isinstance(param, core.VariableParameter):
-                params.append(param)
-                continue
-
-            p = param.copy()
-            pd = _viewless_dict(p)
-
-            if pd not in found_params:
-                params.append(p)
-                found_params.append(pd)
-
-        if len(params) == 0:
-            subchart.params = Undefined
-        else:
-            subchart.params = params
-
-    return subcharts
-
-
-def _combine_subchart_params(params, subcharts):
-    if params is Undefined:
-        params = []
-
-    # List of triples related to params, (param, dictionary minus views, views)
-    param_info = []
-
-    # Put parameters already found into `param_info` list.
-    for param in params:
-        p = _prepare_to_lift(param)
-        param_info.append(
-            (
-                p,
-                _viewless_dict(p),
-                [] if isinstance(p, core.VariableParameter) else p.views,
-            )
-        )
-
-    subcharts = [subchart.copy() for subchart in subcharts]
-
-    for subchart in subcharts:
-        if (not hasattr(subchart, "params")) or (subchart.params is Undefined):
-            continue
-
-        if _needs_name(subchart):
-            subchart.name = subchart._get_name()
-
-        for param in subchart.params:
-            p = _prepare_to_lift(param)
-            pd = _viewless_dict(p)
-
-            dlist = [d for _, d, _ in param_info]
-            found = pd in dlist
-
-            if isinstance(p, core.VariableParameter) and found:
-                continue
-
-            if isinstance(p, core.VariableParameter) and not found:
-                param_info.append((p, pd, []))
-                continue
-
-            # At this stage in the loop, p must be a TopLevelSelectionParameter.
-
-            if isinstance(subchart, Chart) and (subchart.name not in p.views):
-                p.views.append(subchart.name)
-
-            if found:
-                i = dlist.index(pd)
-                _, _, old_views = param_info[i]
-                new_views = [v for v in p.views if v not in old_views]
-                old_views += new_views
-            else:
-                param_info.append((p, pd, p.views))
-
-        subchart.params = Undefined
-
-    for p, _, v in param_info:
-        if len(v) > 0:
-            p.views = v
-
-    subparams = [p for p, _, _ in param_info]
-
-    if len(subparams) == 0:
-        subparams = Undefined
-
-    return subparams, subcharts
-
-
-def _get_repeat_strings(repeat):
-    if isinstance(repeat, list):
-        return repeat
-    elif isinstance(repeat, core.LayerRepeatMapping):
-        klist = ["row", "column", "layer"]
-    elif isinstance(repeat, core.RepeatMapping):
-        klist = ["row", "column"]
-    rclist = [k for k in klist if repeat[k] is not Undefined]
-    rcstrings = [[f"{k}_{v}" for v in repeat[k]] for k in rclist]
-    return ["".join(s) for s in itertools.product(*rcstrings)]
-
-
-def _extend_view_name(v, r, spec):
-    # prevent the same extension from happening more than once
-    if isinstance(spec, Chart):
-        if v.endswith("child__" + r):
-            return v
-        else:
-            return f"{v}_child__{r}"
-    elif isinstance(spec, LayerChart):
-        if v.startswith("child__" + r):
-            return v
-        else:
-            return f"child__{r}_{v}"
-
-
-def _repeat_names(params, repeat, spec):
-    if params is Undefined:
-        return params
-
-    repeat = _get_repeat_strings(repeat)
-    params_named = []
-
-    for param in params:
-        if not isinstance(param, core.TopLevelSelectionParameter):
-            params_named.append(param)
-            continue
-        p = param.copy()
-        views = []
-        repeat_strings = _get_repeat_strings(repeat)
-        for v in param.views:
-            if isinstance(spec, Chart):
-                if any(v.endswith(f"child__{r}") for r in repeat_strings):
-                    views.append(v)
-                else:
-                    views += [_extend_view_name(v, r, spec) for r in repeat_strings]
-            elif isinstance(spec, LayerChart):
-                if any(v.startswith(f"child__{r}") for r in repeat_strings):
-                    views.append(v)
-                else:
-                    views += [_extend_view_name(v, r, spec) for r in repeat_strings]
-
-        p.views = views
-        params_named.append(p)
-
-    return params_named
-
-
-def _remove_layer_props(chart, subcharts, layer_props):
-    def remove_prop(subchart, prop):
-        # If subchart is a UnitSpec, then subchart["height"] raises a KeyError
-        try:
-            if subchart[prop] is not Undefined:
-                subchart = subchart.copy()
-                subchart[prop] = Undefined
-        except KeyError:
-            pass
-        return subchart
-
-    output_dict = {}
-
-    if not subcharts:
-        # No subcharts = nothing to do.
-        return output_dict, subcharts
-
-    for prop in layer_props:
-        if chart[prop] is Undefined:
-            # Top level does not have this prop.
-            # Check for consistent props within the subcharts.
-            values = []
-            for c in subcharts:
-                # If c is a UnitSpec, then c["height"] raises a KeyError.
-                try:
-                    val = c[prop]
-                    if val is not Undefined:
-                        values.append(val)
-                except KeyError:
-                    pass
-            if len(values) == 0:
-                pass
-            elif all(v == values[0] for v in values[1:]):
-                output_dict[prop] = values[0]
-            else:
-                raise ValueError(f"There are inconsistent values {values} for {prop}")
-        else:
-            # Top level has this prop; subchart must either not have the prop
-            # or it must be Undefined or identical to proceed.
-            if all(
-                getattr(c, prop, Undefined) is Undefined or c[prop] == chart[prop]
-                for c in subcharts
-            ):
-                output_dict[prop] = chart[prop]
-            else:
-                raise ValueError(f"There are inconsistent values {values} for {prop}")
-        subcharts = [remove_prop(c, prop) for c in subcharts]
-
-    return output_dict, subcharts
-
-
-@utils.use_signature(core.SequenceParams)
-def sequence(start, stop=None, step=Undefined, as_=Undefined, **kwds):
-    """Sequence generator."""
-    if stop is None:
-        start, stop = 0, start
-    params = core.SequenceParams(start=start, stop=stop, step=step, **{"as": as_})
-    return core.SequenceGenerator(sequence=params, **kwds)
-
-
-@utils.use_signature(core.GraticuleParams)
-def graticule(**kwds):
-    """Graticule generator."""
-    if not kwds:
-        # graticule: True indicates default parameters
-        graticule = True
-    else:
-        graticule = core.GraticuleParams(**kwds)
-    return core.GraticuleGenerator(graticule=graticule)
-
-
-def sphere():
-    """Sphere generator."""
-    return core.SphereGenerator(sphere=True)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/blis/tests/test_gemm.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/blis/tests/test_gemm.py
deleted file mode 100644
index e74d89defb5736c4c6f0e312c12d9869e11be8c9..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/blis/tests/test_gemm.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# Copyright ExplosionAI GmbH, released under BSD.
-from __future__ import division
-
-from hypothesis import given, assume
-from math import sqrt, floor
-
-from blis.tests.common import *
-from blis.py import gemm
-
-
-def _stretch_matrix(data, m, n):
-    orig_len = len(data)
-    orig_m = m
-    orig_n = n
-    ratio = sqrt(len(data) / (m * n))
-    m = int(floor(m * ratio))
-    n = int(floor(n * ratio))
-    data = np.ascontiguousarray(data[: m * n], dtype=data.dtype)
-    return data.reshape((m, n)), m, n
-
-
-def _reshape_for_gemm(
-    A, B, a_rows, a_cols, out_cols, dtype, trans_a=False, trans_b=False
-):
-    A, a_rows, a_cols = _stretch_matrix(A, a_rows, a_cols)
-    if len(B) < a_cols or a_cols < 1:
-        return (None, None, None)
-    b_cols = int(floor(len(B) / a_cols))
-    B = np.ascontiguousarray(B.flatten()[: a_cols * b_cols], dtype=dtype)
-    B = B.reshape((a_cols, b_cols))
-    out_cols = B.shape[1]
-    C = np.zeros(shape=(A.shape[0], B.shape[1]), dtype=dtype)
-    if trans_a:
-        A = np.ascontiguousarray(A.T, dtype=dtype)
-    return A, B, C
-
-
-@given(
-    ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float64"),
-    ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float64"),
-    integers(min_value=2, max_value=1000),
-    integers(min_value=2, max_value=1000),
-    integers(min_value=2, max_value=1000),
-)
-def test_memoryview_double_notrans(A, B, a_rows, a_cols, out_cols):
-    A, B, C = _reshape_for_gemm(A, B, a_rows, a_cols, out_cols, "float64")
-    assume(A is not None)
-    assume(B is not None)
-    assume(C is not None)
-    assume(A.size >= 1)
-    assume(B.size >= 1)
-    assume(C.size >= 1)
-    gemm(A, B, out=C)
-    numpy_result = A.dot(B)
-    assert_allclose(numpy_result, C, atol=1e-4, rtol=1e-4)
-
-
-@given(
-    ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float32"),
-    ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float32"),
-    integers(min_value=2, max_value=1000),
-    integers(min_value=2, max_value=1000),
-    integers(min_value=2, max_value=1000),
-)
-def test_memoryview_float_notrans(A, B, a_rows, a_cols, out_cols):
-    A, B, C = _reshape_for_gemm(A, B, a_rows, a_cols, out_cols, dtype="float32")
-    assume(A is not None)
-    assume(B is not None)
-    assume(C is not None)
-    assume(A.size >= 1)
-    assume(B.size >= 1)
-    assume(C.size >= 1)
-    gemm(A, B, out=C)
-    numpy_result = A.dot(B)
-    assert_allclose(numpy_result, C, atol=1e-3, rtol=1e-3)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/zoneinfo/rebuild.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/zoneinfo/rebuild.py
deleted file mode 100644
index 684c6586f091350c347f2b6150935f5214ffec27..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/zoneinfo/rebuild.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import logging
-import os
-import tempfile
-import shutil
-import json
-from subprocess import check_call, check_output
-from tarfile import TarFile
-
-from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
-
-
-def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
-    """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
-
-    filename is the timezone tarball from ``ftp.iana.org/tz``.
-
-    """
-    tmpdir = tempfile.mkdtemp()
-    zonedir = os.path.join(tmpdir, "zoneinfo")
-    moduledir = os.path.dirname(__file__)
-    try:
-        with TarFile.open(filename) as tf:
-            for name in zonegroups:
-                tf.extract(name, tmpdir)
-            filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
-
-            _run_zic(zonedir, filepaths)
-
-        # write metadata file
-        with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
-            json.dump(metadata, f, indent=4, sort_keys=True)
-        target = os.path.join(moduledir, ZONEFILENAME)
-        with TarFile.open(target, "w:%s" % format) as tf:
-            for entry in os.listdir(zonedir):
-                entrypath = os.path.join(zonedir, entry)
-                tf.add(entrypath, entry)
-    finally:
-        shutil.rmtree(tmpdir)
-
-
-def _run_zic(zonedir, filepaths):
-    """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
-
-    Recent versions of ``zic`` default to ``-b slim``, while older versions
-    don't even have the ``-b`` option (but default to "fat" binaries). The
-    current version of dateutil does not support Version 2+ TZif files, which
-    causes problems when used in conjunction with "slim" binaries, so this
-    function is used to ensure that we always get a "fat" binary.
-    """
-
-    try:
-        help_text = check_output(["zic", "--help"])
-    except OSError as e:
-        _print_on_nosuchfile(e)
-        raise
-
-    if b"-b " in help_text:
-        bloat_args = ["-b", "fat"]
-    else:
-        bloat_args = []
-
-    check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
-
-
-def _print_on_nosuchfile(e):
-    """Print helpful troubleshooting message
-
-    e is an exception raised by subprocess.check_call()
-
-    """
-    if e.errno == 2:
-        logging.error(
-            "Could not find zic. Perhaps you need to install "
-            "libc-bin or some other package that provides it, "
-            "or it's not in your PATH?")
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/opc/package.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/opc/package.py
deleted file mode 100644
index 7ba87bab519dbe413f58b46dcde695613b5ca8b5..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/opc/package.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# encoding: utf-8
-
-"""Objects that implement reading and writing OPC packages."""
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-from docx.opc.constants import RELATIONSHIP_TYPE as RT
-from docx.opc.packuri import PACKAGE_URI, PackURI
-from docx.opc.part import PartFactory
-from docx.opc.parts.coreprops import CorePropertiesPart
-from docx.opc.pkgreader import PackageReader
-from docx.opc.pkgwriter import PackageWriter
-from docx.opc.rel import Relationships
-from docx.opc.shared import lazyproperty
-
-
-class OpcPackage(object):
-    """Main API class for |python-opc|.
-
-    A new instance is constructed by calling the :meth:`open` class method with a path
-    to a package file or file-like object containing one.
-    """
-
-    def __init__(self):
-        super(OpcPackage, self).__init__()
-
-    def after_unmarshal(self):
-        """
-        Entry point for any post-unmarshaling processing. May be overridden
-        by subclasses without forwarding call to super.
-        """
-        # don't place any code here, just catch call if not overridden by
-        # subclass
-        pass
-
-    @property
-    def core_properties(self):
-        """
-        |CoreProperties| object providing read/write access to the Dublin
-        Core properties for this document.
-        """
-        return self._core_properties_part.core_properties
-
-    def iter_rels(self):
-        """
-        Generate exactly one reference to each relationship in the package by
-        performing a depth-first traversal of the rels graph.
-        """
-        def walk_rels(source, visited=None):
-            visited = [] if visited is None else visited
-            for rel in source.rels.values():
-                yield rel
-                if rel.is_external:
-                    continue
-                part = rel.target_part
-                if part in visited:
-                    continue
-                visited.append(part)
-                new_source = part
-                for rel in walk_rels(new_source, visited):
-                    yield rel
-
-        for rel in walk_rels(self):
-            yield rel
-
-    def iter_parts(self):
-        """
-        Generate exactly one reference to each of the parts in the package by
-        performing a depth-first traversal of the rels graph.
-        """
-        def walk_parts(source, visited=list()):
-            for rel in source.rels.values():
-                if rel.is_external:
-                    continue
-                part = rel.target_part
-                if part in visited:
-                    continue
-                visited.append(part)
-                yield part
-                new_source = part
-                for part in walk_parts(new_source, visited):
-                    yield part
-
-        for part in walk_parts(self):
-            yield part
-
-    def load_rel(self, reltype, target, rId, is_external=False):
-        """
-        Return newly added |_Relationship| instance of *reltype* between this
-        part and *target* with key *rId*. Target mode is set to
-        ``RTM.EXTERNAL`` if *is_external* is |True|. Intended for use during
-        load from a serialized package, where the rId is well known. Other
-        methods exist for adding a new relationship to the package during
-        processing.
-        """
-        return self.rels.add_relationship(reltype, target, rId, is_external)
-
-    @property
-    def main_document_part(self):
-        """
-        Return a reference to the main document part for this package.
-        Examples include a document part for a WordprocessingML package, a
-        presentation part for a PresentationML package, or a workbook part
-        for a SpreadsheetML package.
-        """
-        return self.part_related_by(RT.OFFICE_DOCUMENT)
-
-    def next_partname(self, template):
-        """Return a |PackURI| instance representing partname matching *template*.
-
-        The returned part-name has the next available numeric suffix to distinguish it
-        from other parts of its type. *template* is a printf (%)-style template string
-        containing a single replacement item, a '%d' to be used to insert the integer
-        portion of the partname. Example: "/word/header%d.xml"
-        """
-        partnames = {part.partname for part in self.iter_parts()}
-        for n in range(1, len(partnames) + 2):
-            candidate_partname = template % n
-            if candidate_partname not in partnames:
-                return PackURI(candidate_partname)
-
-    @classmethod
-    def open(cls, pkg_file):
-        """
-        Return an |OpcPackage| instance loaded with the contents of
-        *pkg_file*.
-        """
-        pkg_reader = PackageReader.from_file(pkg_file)
-        package = cls()
-        Unmarshaller.unmarshal(pkg_reader, package, PartFactory)
-        return package
-
-    def part_related_by(self, reltype):
-        """
-        Return part to which this package has a relationship of *reltype*.
-        Raises |KeyError| if no such relationship is found and |ValueError|
-        if more than one such relationship is found.
-        """
-        return self.rels.part_with_reltype(reltype)
-
-    @property
-    def parts(self):
-        """
-        Return a list containing a reference to each of the parts in this
-        package.
-        """
-        return [part for part in self.iter_parts()]
-
-    def relate_to(self, part, reltype):
-        """
-        Return rId key of relationship to *part*, from the existing
-        relationship if there is one, otherwise a newly created one.
-        """
-        rel = self.rels.get_or_add(reltype, part)
-        return rel.rId
-
-    @lazyproperty
-    def rels(self):
-        """
-        Return a reference to the |Relationships| instance holding the
-        collection of relationships for this package.
-        """
-        return Relationships(PACKAGE_URI.baseURI)
-
-    def save(self, pkg_file):
-        """
-        Save this package to *pkg_file*, where *file* can be either a path to
-        a file (a string) or a file-like object.
-        """
-        for part in self.parts:
-            part.before_marshal()
-        PackageWriter.write(pkg_file, self.rels, self.parts)
-
-    @property
-    def _core_properties_part(self):
-        """
-        |CorePropertiesPart| object related to this package. Creates
-        a default core properties part if one is not present (not common).
-        """
-        try:
-            return self.part_related_by(RT.CORE_PROPERTIES)
-        except KeyError:
-            core_properties_part = CorePropertiesPart.default(self)
-            self.relate_to(core_properties_part, RT.CORE_PROPERTIES)
-            return core_properties_part
-
-
-class Unmarshaller(object):
-    """Hosts static methods for unmarshalling a package from a |PackageReader|."""
-
-    @staticmethod
-    def unmarshal(pkg_reader, package, part_factory):
-        """
-        Construct graph of parts and realized relationships based on the
-        contents of *pkg_reader*, delegating construction of each part to
-        *part_factory*. Package relationships are added to *pkg*.
-        """
-        parts = Unmarshaller._unmarshal_parts(
-            pkg_reader, package, part_factory
-        )
-        Unmarshaller._unmarshal_relationships(pkg_reader, package, parts)
-        for part in parts.values():
-            part.after_unmarshal()
-        package.after_unmarshal()
-
-    @staticmethod
-    def _unmarshal_parts(pkg_reader, package, part_factory):
-        """
-        Return a dictionary of |Part| instances unmarshalled from
-        *pkg_reader*, keyed by partname. Side-effect is that each part in
-        *pkg_reader* is constructed using *part_factory*.
-        """
-        parts = {}
-        for partname, content_type, reltype, blob in pkg_reader.iter_sparts():
-            parts[partname] = part_factory(
-                partname, content_type, reltype, blob, package
-            )
-        return parts
-
-    @staticmethod
-    def _unmarshal_relationships(pkg_reader, package, parts):
-        """
-        Add a relationship to the source object corresponding to each of the
-        relationships in *pkg_reader* with its target_part set to the actual
-        target part in *parts*.
-        """
-        for source_uri, srel in pkg_reader.iter_srels():
-            source = package if source_uri == '/' else parts[source_uri]
-            target = (srel.target_ref if srel.is_external
-                      else parts[srel.target_partname])
-            source.load_rel(srel.reltype, target, srel.rId, srel.is_external)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/dependencies/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/dependencies/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/cihyFjudo/fairness-paper-search/1dnest 1d cutting optimizer crack Download the best tool for linear material optimization.md b/spaces/cihyFjudo/fairness-paper-search/1dnest 1d cutting optimizer crack Download the best tool for linear material optimization.md
deleted file mode 100644
index 59110d911dca7369daa4b9aa5358d64a8b7b3d05..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/1dnest 1d cutting optimizer crack Download the best tool for linear material optimization.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<p><strong>1D</strong>-Nest linear <strong>cutting</strong> <strong>optimizer</strong> is the most efficient, friendly and powerful <strong>optimizer</strong> for <strong>cutting</strong> of materials provided in standard lengths, like bars, tubes, metallic profiles, extrusions, paper rolls, etc. Power: NO quantity limits for pieces to</p>
-<h2>1dnest 1d cutting optimizer crack</h2><br /><p><b><b>Download File</b> &#9881; <a href="https://tinurli.com/2uwiIJ">https://tinurli.com/2uwiIJ</a></b></p><br /><br />
-<p>The Glass <strong>Cutting</strong> <strong>optimizer</strong> based on the PLUS 2D <strong>cutting</strong> optimization technology consistently delivers high yield layouts and is ideal for <strong>cutting</strong> glass, whether manually or using a CNC <strong>cutting</strong> table.</p>
-<p><strong>1DNest</strong> is the most efficient, friendly and powerful <strong>optimizer</strong> for <strong>cutting</strong> of materials provided in standard lengths, like bars, tubes, metallic profiles, extrusions, paper rolls, etc. Power: NO quantity limits for pieces to cut or Bars to cut from.</p>
-<p><strong>1DNest</strong> is the most efficient, friendly and powerful <strong>optimizer</strong> for <strong>cutting</strong> of materials provided in standard lengths, like bars, tubes, metallic profiles, extrusions, paper rolls, etc. Some features: NO quantity limits for pieces to cut or Bars to</p>
-<p>1D cutting optimizer, 1DNest is the most efficient, friendly and productive one-dimensional cutting optimization software to make cutting plans with topl efficiency for manufacturers who cut in pieces of smaller size, materials provided in standard lengths (linear materials) like bars, tubes, structural profiles, beams, aluminium extrusions, paper rolls, etc.</p>
-<p>Using warez version, crack, warez passwords, patches, serial numbers, registration codes, key generator, pirate key, keymaker or keygen for<strong>1d cutting</strong> license key is illegal. Download links are directly from our mirrors or publisher's website,<strong>1d cutting</strong> torrent files or shared files from free file sharing and free upload services,including Rapidshare, MegaUpload, YouSendIt, Letitbit, DropSend, MediaMax, HellShare, HotFile, FileServe, LeapFile, MyOtherDrive or MediaFire,are not allowed!</p>
-<p>Your computer will be at risk getting infected with spyware, adware, viruses, worms, trojan horses, dialers, etcwhile you are searching and browsing these illegal sites which distribute a so called keygen, key generator, pirate key, serial number, warez full version or crack for<strong>1d cutting</strong>. These infections might corrupt your computer installation or breach your privacy.<strong>1d cutting</strong> keygen or key generator might contain a trojan horse opening a backdoor on your computer.</p>
-<p></p>
-<p>1D-Nest linear cutting optimizer is the most efficient, friendly and powerful optimization software to make efficient cutting plans for materials provided in standard lengths, like bars, tubes, metallic profiles, structural steel, extrusions, paper rolls, etc.Power:NO quantity limits for...</p>
-<p><strong>1D</strong>-Nest linear cutting optimizer is the most efficient, friendly and powerful optimizer for cutting of materials provided in standard lengths, like bars, tubes, metallic profiles, extrusions, paper rolls, etc. Power: NO quantity limits for pieces to</p>
-<p>1DNest is the most efficient, friendly and powerful optimizer for cutting of materials provided in standard lengths, like bars, tubes, metallic profiles, extrusions, paper rolls, etc. Some features: NO quantity limits for pieces to cut or Bars to</p>
-<p>This program is length-nesting optimizer and stock management software. It minimizes the wasted materials in the cutting process of any linear material like pipes, bars, tubes, profiles, paper rolls, extrusions,</p>
-<p>1D-Nest linear cutting optimizer is the most efficient, friendly and powerful optimizer for cutting of materials provided in standard lengths, like bars, tubes, metallic profiles, extrusions, paper rolls, etc. Power: NO quantity limits for pieces to</p>
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-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cjayic/sovits-overwatch2/monotonic_align/__init__.py b/spaces/cjayic/sovits-overwatch2/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/cjayic/sovits-overwatch2/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/security/base.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/security/base.py
deleted file mode 100644
index c43555deb8ea83b14241a5631c9ea451c96f6e7f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/security/base.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from fastapi.openapi.models import SecurityBase as SecurityBaseModel
-
-
-class SecurityBase:
-    model: SecurityBaseModel
-    scheme_name: str
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/__init__.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/__init__.py
deleted file mode 100644
index 10eff133fae5d025f940b962c232a39bd0c23a74..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/__init__.py
+++ /dev/null
@@ -1,211 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools import ttLib
-import fontTools.merge.base
-from fontTools.merge.cmap import (
-    computeMegaGlyphOrder,
-    computeMegaCmap,
-    renameCFFCharStrings,
-)
-from fontTools.merge.layout import layoutPreMerge, layoutPostMerge
-from fontTools.merge.options import Options
-import fontTools.merge.tables
-from fontTools.misc.loggingTools import Timer
-from functools import reduce
-import sys
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-timer = Timer(logger=logging.getLogger(__name__ + ".timer"), level=logging.INFO)
-
-
-class Merger(object):
-    """Font merger.
-
-    This class merges multiple files into a single OpenType font, taking into
-    account complexities such as OpenType layout (``GSUB``/``GPOS``) tables and
-    cross-font metrics (e.g. ``hhea.ascent`` is set to the maximum value across
-    all the fonts).
-
-    If multiple glyphs map to the same Unicode value, and the glyphs are considered
-    sufficiently different (that is, they differ in any of paths, widths, or
-    height), then subsequent glyphs are renamed and a lookup in the ``locl``
-    feature will be created to disambiguate them. For example, if the arguments
-    are an Arabic font and a Latin font and both contain a set of parentheses,
-    the Latin glyphs will be renamed to ``parenleft#1`` and ``parenright#1``,
-    and a lookup will be inserted into the to ``locl`` feature (creating it if
-    necessary) under the ``latn`` script to substitute ``parenleft`` with
-    ``parenleft#1`` etc.
-
-    Restrictions:
-
-    - All fonts must have the same units per em.
-    - If duplicate glyph disambiguation takes place as described above then the
-            fonts must have a ``GSUB`` table.
-
-    Attributes:
-            options: Currently unused.
-    """
-
-    def __init__(self, options=None):
-
-        if not options:
-            options = Options()
-
-        self.options = options
-
-    def _openFonts(self, fontfiles):
-        fonts = [ttLib.TTFont(fontfile) for fontfile in fontfiles]
-        for font, fontfile in zip(fonts, fontfiles):
-            font._merger__fontfile = fontfile
-            font._merger__name = font["name"].getDebugName(4)
-        return fonts
-
-    def merge(self, fontfiles):
-        """Merges fonts together.
-
-        Args:
-                fontfiles: A list of file names to be merged
-
-        Returns:
-                A :class:`fontTools.ttLib.TTFont` object. Call the ``save`` method on
-                this to write it out to an OTF file.
-        """
-        #
-        # Settle on a mega glyph order.
-        #
-        fonts = self._openFonts(fontfiles)
-        glyphOrders = [list(font.getGlyphOrder()) for font in fonts]
-        computeMegaGlyphOrder(self, glyphOrders)
-
-        # Take first input file sfntVersion
-        sfntVersion = fonts[0].sfntVersion
-
-        # Reload fonts and set new glyph names on them.
-        fonts = self._openFonts(fontfiles)
-        for font, glyphOrder in zip(fonts, glyphOrders):
-            font.setGlyphOrder(glyphOrder)
-            if "CFF " in font:
-                renameCFFCharStrings(self, glyphOrder, font["CFF "])
-
-        cmaps = [font["cmap"] for font in fonts]
-        self.duplicateGlyphsPerFont = [{} for _ in fonts]
-        computeMegaCmap(self, cmaps)
-
-        mega = ttLib.TTFont(sfntVersion=sfntVersion)
-        mega.setGlyphOrder(self.glyphOrder)
-
-        for font in fonts:
-            self._preMerge(font)
-
-        self.fonts = fonts
-
-        allTags = reduce(set.union, (list(font.keys()) for font in fonts), set())
-        allTags.remove("GlyphOrder")
-
-        for tag in sorted(allTags):
-            if tag in self.options.drop_tables:
-                continue
-
-            with timer("merge '%s'" % tag):
-                tables = [font.get(tag, NotImplemented) for font in fonts]
-
-                log.info("Merging '%s'.", tag)
-                clazz = ttLib.getTableClass(tag)
-                table = clazz(tag).merge(self, tables)
-                # XXX Clean this up and use:  table = mergeObjects(tables)
-
-                if table is not NotImplemented and table is not False:
-                    mega[tag] = table
-                    log.info("Merged '%s'.", tag)
-                else:
-                    log.info("Dropped '%s'.", tag)
-
-        del self.duplicateGlyphsPerFont
-        del self.fonts
-
-        self._postMerge(mega)
-
-        return mega
-
-    def mergeObjects(self, returnTable, logic, tables):
-        # Right now we don't use self at all.  Will use in the future
-        # for options and logging.
-
-        allKeys = set.union(
-            set(),
-            *(vars(table).keys() for table in tables if table is not NotImplemented),
-        )
-        for key in allKeys:
-            try:
-                mergeLogic = logic[key]
-            except KeyError:
-                try:
-                    mergeLogic = logic["*"]
-                except KeyError:
-                    raise Exception(
-                        "Don't know how to merge key %s of class %s"
-                        % (key, returnTable.__class__.__name__)
-                    )
-            if mergeLogic is NotImplemented:
-                continue
-            value = mergeLogic(getattr(table, key, NotImplemented) for table in tables)
-            if value is not NotImplemented:
-                setattr(returnTable, key, value)
-
-        return returnTable
-
-    def _preMerge(self, font):
-        layoutPreMerge(font)
-
-    def _postMerge(self, font):
-        layoutPostMerge(font)
-
-        if "OS/2" in font:
-            # https://github.com/fonttools/fonttools/issues/2538
-            # TODO: Add an option to disable this?
-            font["OS/2"].recalcAvgCharWidth(font)
-
-
-__all__ = ["Options", "Merger", "main"]
-
-
-@timer("make one with everything (TOTAL TIME)")
-def main(args=None):
-    """Merge multiple fonts into one"""
-    from fontTools import configLogger
-
-    if args is None:
-        args = sys.argv[1:]
-
-    options = Options()
-    args = options.parse_opts(args, ignore_unknown=["output-file"])
-    outfile = "merged.ttf"
-    fontfiles = []
-    for g in args:
-        if g.startswith("--output-file="):
-            outfile = g[14:]
-            continue
-        fontfiles.append(g)
-
-    if len(args) < 1:
-        print("usage: pyftmerge font...", file=sys.stderr)
-        return 1
-
-    configLogger(level=logging.INFO if options.verbose else logging.WARNING)
-    if options.timing:
-        timer.logger.setLevel(logging.DEBUG)
-    else:
-        timer.logger.disabled = True
-
-    merger = Merger(options=options)
-    font = merger.merge(fontfiles)
-    with timer("compile and save font"):
-        font.save(outfile)
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/pens/__init__.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/pens/__init__.py
deleted file mode 100644
index 156cb232a7aa80eee1526c7598f72043de10473f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/pens/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Empty __init__.py file to signal Python this directory is a package."""
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/qu2cu/qu2cu.c b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/qu2cu/qu2cu.c
deleted file mode 100644
index a03120c215700bbe0ef33f9a10a54e6f914fa978..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/qu2cu/qu2cu.c
+++ /dev/null
@@ -1,15471 +0,0 @@
-/* Generated by Cython 3.0.0 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "define_macros": [
-            [
-                "CYTHON_TRACE_NOGIL",
-                "1"
-            ]
-        ],
-        "name": "fontTools.qu2cu.qu2cu",
-        "sources": [
-            "Lib/fontTools/qu2cu/qu2cu.py"
-        ]
-    },
-    "module_name": "fontTools.qu2cu.qu2cu"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#if defined(CYTHON_LIMITED_API) && 0
-  #ifndef Py_LIMITED_API
-    #if CYTHON_LIMITED_API+0 > 0x03030000
-      #define Py_LIMITED_API CYTHON_LIMITED_API
-    #else
-      #define Py_LIMITED_API 0x03030000
-    #endif
-  #endif
-#endif
-
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
-    #error Cython requires Python 2.7+ or Python 3.3+.
-#else
-#define CYTHON_ABI "3_0_0"
-#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI
-#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "."
-#define CYTHON_HEX_VERSION 0x030000F0
-#define CYTHON_FUTURE_DIVISION 1
-#include <stddef.h>
-#ifndef offsetof
-  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
-  #define HAVE_LONG_LONG
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#if defined(GRAALVM_PYTHON)
-  /* For very preliminary testing purposes. Most variables are set the same as PyPy.
-     The existence of this section does not imply that anything works or is even tested */
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 1
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PYPY_VERSION)
-  #define CYTHON_COMPILING_IN_PYPY 1
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #if PY_VERSION_HEX < 0x03090000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00)
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(CYTHON_LIMITED_API)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 1
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_CLINE_IN_TRACEBACK
-  #define CYTHON_CLINE_IN_TRACEBACK 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 1
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #endif
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 1
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PY_NOGIL)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 1
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #ifndef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_TYPE_SPECS
-    #define CYTHON_USE_TYPE_SPECS 0
-  #endif
-  #ifndef CYTHON_USE_PYTYPE_LOOKUP
-    #define CYTHON_USE_PYTYPE_LOOKUP 1
-  #endif
-  #if PY_MAJOR_VERSION < 3
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_PYLONG_INTERNALS
-    #define CYTHON_USE_PYLONG_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
-    #undef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #elif !defined(CYTHON_USE_UNICODE_WRITER)
-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #ifndef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_GIL
-    #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6)
-  #endif
-  #ifndef CYTHON_METH_FASTCALL
-    #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1)
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL 1
-  #endif
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_MODULE_STATE
-    #define CYTHON_USE_MODULE_STATE 0
-  #endif
-  #if PY_VERSION_HEX < 0x030400a1
-    #undef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 0
-  #elif !defined(CYTHON_USE_TP_FINALIZE)
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #if PY_VERSION_HEX < 0x030600B1
-    #undef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS 0
-  #elif !defined(CYTHON_USE_DICT_VERSIONS)
-    #define CYTHON_USE_DICT_VERSIONS  (PY_VERSION_HEX < 0x030C00A5)
-  #endif
-  #if PY_VERSION_HEX < 0x030700A3
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK 1
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if !defined(CYTHON_VECTORCALL)
-#define CYTHON_VECTORCALL  (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1)
-#endif
-#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1)
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(maybe_unused)
-        #define CYTHON_UNUSED [[maybe_unused]]
-      #endif
-    #endif
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-  #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x)
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-            typedef unsigned char     uint8_t;
-            typedef unsigned short    uint16_t;
-            typedef unsigned int      uint32_t;
-        #else
-            typedef unsigned __int8   uint8_t;
-            typedef unsigned __int16  uint16_t;
-            typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-    #if _MSC_VER < 1300
-        #ifdef _WIN64
-            typedef unsigned long long  __pyx_uintptr_t;
-        #else
-            typedef unsigned int        __pyx_uintptr_t;
-        #endif
-    #else
-        #ifdef _WIN64
-            typedef unsigned __int64    __pyx_uintptr_t;
-        #else
-            typedef unsigned __int32    __pyx_uintptr_t;
-        #endif
-    #endif
-#else
-    #include <stdint.h>
-    typedef uintptr_t  __pyx_uintptr_t;
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(fallthrough) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(fallthrough)
-        #define CYTHON_FALLTHROUGH [[fallthrough]]
-      #endif
-    #endif
-    #ifndef CYTHON_FALLTHROUGH
-      #if __has_cpp_attribute(clang::fallthrough)
-        #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-      #elif __has_cpp_attribute(gnu::fallthrough)
-        #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-      #endif
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-#ifdef __cplusplus
-  template <typename T>
-  struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);};
-  #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL<type>::value)
-#else
-  #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0)
-#endif
-#if CYTHON_COMPILING_IN_PYPY == 1
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000)
-#else
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000)
-#endif
-#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer))
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_DefaultClassType PyClass_Type
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject *co=NULL, *result=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(p))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end;
-        if (!(empty = PyTuple_New(0))) goto end;
-        result = (PyCodeObject*) PyObject_Call(replace, empty, kwds);
-    end:
-        Py_XDECREF((PyObject*) co);
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return result;
-    }
-#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-#endif
-#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE)
-  #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type)
-#else
-  #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is)
-  #define __Pyx_Py_Is(x, y)  Py_Is(x, y)
-#else
-  #define __Pyx_Py_Is(x, y) ((x) == (y))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone)
-  #define __Pyx_Py_IsNone(ob) Py_IsNone(ob)
-#else
-  #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue)
-  #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob)
-#else
-  #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse)
-  #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob)
-#else
-  #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False)
-#endif
-#define __Pyx_NoneAsNull(obj)  (__Pyx_Py_IsNone(obj) ? NULL : (obj))
-#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o)
-#else
-  #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o)
-#endif
-#ifndef CO_COROUTINE
-  #define CO_COROUTINE 0x80
-#endif
-#ifndef CO_ASYNC_GENERATOR
-  #define CO_ASYNC_GENERATOR 0x200
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef Py_TPFLAGS_SEQUENCE
-  #define Py_TPFLAGS_SEQUENCE 0
-#endif
-#ifndef Py_TPFLAGS_MAPPING
-  #define Py_TPFLAGS_MAPPING 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_METH_FASTCALL
-  #define __Pyx_METH_FASTCALL METH_FASTCALL
-  #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast
-  #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords
-#else
-  #define __Pyx_METH_FASTCALL METH_VARARGS
-  #define __Pyx_PyCFunction_FastCall PyCFunction
-  #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords
-#endif
-#if CYTHON_VECTORCALL
-  #define __pyx_vectorcallfunc vectorcallfunc
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  PY_VECTORCALL_ARGUMENTS_OFFSET
-  #define __Pyx_PyVectorcall_NARGS(n)  PyVectorcall_NARGS((size_t)(n))
-#elif CYTHON_BACKPORT_VECTORCALL
-  typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args,
-                                            size_t nargsf, PyObject *kwnames);
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  ((size_t)1 << (8 * sizeof(size_t) - 1))
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET))
-#else
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  0
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(n))
-#endif
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  ((void)m, PyType_FromSpecWithBases(s, b))
-  typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *);
-#else
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  PyType_FromModuleAndSpec(m, s, b)
-  #define __Pyx_PyCMethod  PyCMethod
-#endif
-#ifndef METH_METHOD
-  #define METH_METHOD 0x200
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyThreadState_Current PyThreadState_Get()
-#elif !CYTHON_FAST_THREAD_STATE
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
-  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
-  #define __Pyx_PyThreadState_Current _PyThreadState_Current
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op)
-{
-    void *result;
-    result = PyModule_GetState(op);
-    if (!result)
-        Py_FatalError("Couldn't find the module state");
-    return result;
-}
-#endif
-#define __Pyx_PyObject_GetSlot(obj, name, func_ctype)  __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype)
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyType_GetSlot(type, name, func_ctype)  ((func_ctype) PyType_GetSlot((type), Py_##name))
-#else
-  #define __Pyx_PyType_GetSlot(type, name, func_ctype)  ((type)->name)
-#endif
-#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
-#include "pythread.h"
-#define Py_tss_NEEDS_INIT 0
-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
-  *key = PyThread_create_key();
-  return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
-  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
-  *key = Py_tss_NEEDS_INIT;
-  return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
-  PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
-  return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
-  PyThread_delete_key(*key);
-  *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
-  return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
-  return PyThread_get_key_value(*key);
-}
-#endif
-#if PY_MAJOR_VERSION < 3
-    #if CYTHON_COMPILING_IN_PYPY
-        #if PYPY_VERSION_NUM < 0x07030600
-            #if defined(__cplusplus) && __cplusplus >= 201402L
-                [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]]
-            #elif defined(__GNUC__) || defined(__clang__)
-                __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")))
-            #elif defined(_MSC_VER)
-                __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))
-            #endif
-            static CYTHON_INLINE int PyGILState_Check(void) {
-                return 0;
-            }
-        #else  // PYPY_VERSION_NUM < 0x07030600
-        #endif  // PYPY_VERSION_NUM < 0x07030600
-    #else
-        static CYTHON_INLINE int PyGILState_Check(void) {
-            PyThreadState * tstate = _PyThreadState_Current;
-            return tstate && (tstate == PyGILState_GetThisThreadState());
-        }
-    #endif
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStrWithError(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) {
-    PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name);
-    if (res == NULL) PyErr_Clear();
-    return res;
-}
-#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000)
-#define __Pyx_PyDict_GetItemStrWithError  PyDict_GetItemWithError
-#define __Pyx_PyDict_GetItemStr           PyDict_GetItem
-#else
-static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyDict_GetItem(dict, name);
-#else
-    PyDictEntry *ep;
-    PyDictObject *mp = (PyDictObject*) dict;
-    long hash = ((PyStringObject *) name)->ob_shash;
-    assert(hash != -1);
-    ep = (mp->ma_lookup)(mp, name, hash);
-    if (ep == NULL) {
-        return NULL;
-    }
-    return ep->me_value;
-#endif
-}
-#define __Pyx_PyDict_GetItemStr           PyDict_GetItem
-#endif
-#if CYTHON_USE_TYPE_SLOTS
-  #define __Pyx_PyType_GetFlags(tp)   (((PyTypeObject *)tp)->tp_flags)
-  #define __Pyx_PyType_HasFeature(type, feature)  ((__Pyx_PyType_GetFlags(type) & (feature)) != 0)
-  #define __Pyx_PyObject_GetIterNextFunc(obj)  (Py_TYPE(obj)->tp_iternext)
-#else
-  #define __Pyx_PyType_GetFlags(tp)   (PyType_GetFlags((PyTypeObject *)tp))
-  #define __Pyx_PyType_HasFeature(type, feature)  PyType_HasFeature(type, feature)
-  #define __Pyx_PyObject_GetIterNextFunc(obj)  PyIter_Next
-#endif
-#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000
-#define __Pyx_PyHeapTypeObject_GC_Del(obj)  {\
-    PyTypeObject *type = Py_TYPE(obj);\
-    assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\
-    PyObject_GC_Del(obj);\
-    Py_DECREF(type);\
-}
-#else
-#define __Pyx_PyHeapTypeObject_GC_Del(obj)  PyObject_GC_Del(obj)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define CYTHON_PEP393_ENABLED 1
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GetLength(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i)
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((void)u, 1114111U)
-  #define __Pyx_PyUnicode_KIND(u)         ((void)u, (0))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)k, PyUnicode_ReadChar((PyObject*)(d), i))
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GetLength(u))
-#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-  #define CYTHON_PEP393_ENABLED 1
-  #if PY_VERSION_HEX >= 0x030C0000
-    #define __Pyx_PyUnicode_READY(op)       (0)
-  #else
-    #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
-                                                0 : _PyUnicode_Ready((PyObject *)(op)))
-  #endif
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
-  #define __Pyx_PyUnicode_KIND(u)         ((int)PyUnicode_KIND(u))
-  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, (Py_UCS4) ch)
-  #if PY_VERSION_HEX >= 0x030C0000
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
-  #else
-    #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length))
-    #else
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
-    #endif
-  #endif
-#else
-  #define CYTHON_PEP393_ENABLED 0
-  #define PyUnicode_1BYTE_KIND  1
-  #define PyUnicode_2BYTE_KIND  2
-  #define PyUnicode_4BYTE_KIND  4
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535U : 1114111U)
-  #define __Pyx_PyUnicode_KIND(u)         ((int)sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = (Py_UNICODE) ch)
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
-#else
-  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
-      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #if !defined(PyUnicode_DecodeUnicodeEscape)
-    #define PyUnicode_DecodeUnicodeEscape(s, size, errors)  PyUnicode_Decode(s, size, "unicode_escape", errors)
-  #endif
-  #if !defined(PyUnicode_Contains) || (PY_MAJOR_VERSION == 2 && PYPY_VERSION_NUM < 0x07030500)
-    #undef PyUnicode_Contains
-    #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
-  #endif
-  #if !defined(PyByteArray_Check)
-    #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
-  #endif
-  #if !defined(PyObject_Format)
-    #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
-  #endif
-#endif
-#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
-#else
-  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
-  #define PyObject_ASCII(o)            PyObject_Repr(o)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBaseString_Type            PyUnicode_Type
-  #define PyStringObject               PyUnicodeObject
-  #define PyString_Type                PyUnicode_Type
-  #define PyString_Check               PyUnicode_Check
-  #define PyString_CheckExact          PyUnicode_CheckExact
-#ifndef PyObject_Unicode
-  #define PyObject_Unicode             PyObject_Str
-#endif
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
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-  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
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-  #define __Pyx_PySequence_ListKeepNew(obj)\
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-  #define PyInt_FromSize_t             PyLong_FromSize_t
-  #define PyInt_FromSsize_t            PyLong_FromSsize_t
-  #define PyInt_AsLong                 PyLong_AsLong
-  #define PyInt_AS_LONG                PyLong_AS_LONG
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-  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
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-#if PY_VERSION_HEX < 0x030200A4
-  typedef long Py_hash_t;
-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsHash_t
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-  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
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-  #if PY_VERSION_HEX >= 0x030500B1
-    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
-    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
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-    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
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-  #define __Pyx_PyType_AsAsync(obj) NULL
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-#ifndef __Pyx_PyAsyncMethodsStruct
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-#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS)
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-#define __Pyx_truncl trunc
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-    #define __PYX_EXTERN_C CYTHON_EXTERN_C
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-    #ifdef _MSC_VER
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-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
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-
-#define __PYX_HAVE__fontTools__qu2cu__qu2cu
-#define __PYX_HAVE_API__fontTools__qu2cu__qu2cu
-/* Early includes */
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-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
-    (sizeof(type) < sizeof(Py_ssize_t))  ||\
-    (sizeof(type) > sizeof(Py_ssize_t) &&\
-          likely(v < (type)PY_SSIZE_T_MAX ||\
-                 v == (type)PY_SSIZE_T_MAX)  &&\
-          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
-                                v == (type)PY_SSIZE_T_MIN)))  ||\
-    (sizeof(type) == sizeof(Py_ssize_t) &&\
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
-    return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
-    #include <cstdlib>
-    #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
-    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
-    #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
-    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u)
-{
-    const wchar_t *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#else
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
-{
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#endif
-#define __Pyx_PyUnicode_FromOrdinal(o)       PyUnicode_FromOrdinal((int)o)
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_VERSION_HEX >= 0x030C00A7
-  #ifndef _PyLong_SIGN_MASK
-    #define _PyLong_SIGN_MASK 3
-  #endif
-  #ifndef _PyLong_NON_SIZE_BITS
-    #define _PyLong_NON_SIZE_BITS 3
-  #endif
-  #define __Pyx_PyLong_Sign(x)  (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK)
-  #define __Pyx_PyLong_IsNeg(x)  ((__Pyx_PyLong_Sign(x) & 2) != 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (!__Pyx_PyLong_IsNeg(x))
-  #define __Pyx_PyLong_IsZero(x)  (__Pyx_PyLong_Sign(x) & 1)
-  #define __Pyx_PyLong_IsPos(x)  (__Pyx_PyLong_Sign(x) == 0)
-  #define __Pyx_PyLong_CompactValueUnsigned(x)  (__Pyx_PyLong_Digits(x)[0])
-  #define __Pyx_PyLong_DigitCount(x)  ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS))
-  #define __Pyx_PyLong_SignedDigitCount(x)\
-        ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x))
-  #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue)
-    #define __Pyx_PyLong_IsCompact(x)     PyUnstable_Long_IsCompact((PyLongObject*) x)
-    #define __Pyx_PyLong_CompactValue(x)  PyUnstable_Long_CompactValue((PyLongObject*) x)
-  #else
-    #define __Pyx_PyLong_IsCompact(x)     (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS))
-    #define __Pyx_PyLong_CompactValue(x)  ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0])
-  #endif
-  typedef Py_ssize_t  __Pyx_compact_pylong;
-  typedef size_t  __Pyx_compact_upylong;
-  #else  // Py < 3.12
-  #define __Pyx_PyLong_IsNeg(x)  (Py_SIZE(x) < 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (Py_SIZE(x) >= 0)
-  #define __Pyx_PyLong_IsZero(x)  (Py_SIZE(x) == 0)
-  #define __Pyx_PyLong_IsPos(x)  (Py_SIZE(x) > 0)
-  #define __Pyx_PyLong_CompactValueUnsigned(x)  ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0])
-  #define __Pyx_PyLong_DigitCount(x)  __Pyx_sst_abs(Py_SIZE(x))
-  #define __Pyx_PyLong_SignedDigitCount(x)  Py_SIZE(x)
-  #define __Pyx_PyLong_IsCompact(x)  (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1)
-  #define __Pyx_PyLong_CompactValue(x)\
-        ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0]))
-  typedef sdigit  __Pyx_compact_pylong;
-  typedef digit  __Pyx_compact_upylong;
-  #endif
-  #if PY_VERSION_HEX >= 0x030C00A5
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->long_value.ob_digit)
-  #else
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->ob_digit)
-  #endif
-#endif
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = (char) c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-#if !CYTHON_USE_MODULE_STATE
-static PyObject *__pyx_m = NULL;
-#endif
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm = __FILE__;
-static const char *__pyx_filename;
-
-/* Header.proto */
-#if !defined(CYTHON_CCOMPLEX)
-  #if defined(__cplusplus)
-    #define CYTHON_CCOMPLEX 1
-  #elif (defined(_Complex_I) && !defined(_MSC_VER)) || ((defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) && !defined(__STDC_NO_COMPLEX__))
-    #define CYTHON_CCOMPLEX 1
-  #else
-    #define CYTHON_CCOMPLEX 0
-  #endif
-#endif
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #include <complex>
-  #else
-    #include <complex.h>
-  #endif
-#endif
-#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
-  #undef _Complex_I
-  #define _Complex_I 1.0fj
-#endif
-
-/* #### Code section: filename_table ### */
-
-static const char *__pyx_f[] = {
-  "Lib/fontTools/qu2cu/qu2cu.py",
-};
-/* #### Code section: utility_code_proto_before_types ### */
-/* #### Code section: numeric_typedefs ### */
-/* #### Code section: complex_type_declarations ### */
-/* Declarations.proto */
-#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus)
-  #ifdef __cplusplus
-    typedef ::std::complex< double > __pyx_t_double_complex;
-  #else
-    typedef double _Complex __pyx_t_double_complex;
-  #endif
-#else
-    typedef struct { double real, imag; } __pyx_t_double_complex;
-#endif
-static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
-
-/* #### Code section: type_declarations ### */
-
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-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
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-#define __PYX_GET_DICT_VERSION(dict)  (0)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
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-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name)  do {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
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-#define __Pyx_GetModuleGlobalNameUncached(var, name)  do {\
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-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
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-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
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-/* PyFunctionFastCall.proto */
-#if CYTHON_FAST_PYCALL
-#if !CYTHON_VECTORCALL
-#define __Pyx_PyFunction_FastCall(func, args, nargs)\
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-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs);
-#endif
-#define __Pyx_BUILD_ASSERT_EXPR(cond)\
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-#ifndef Py_MEMBER_SIZE
-#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member)
-#endif
-#if !CYTHON_VECTORCALL
-#if PY_VERSION_HEX >= 0x03080000
-  #include "frameobject.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-  #define __Pxy_PyFrame_Initialize_Offsets()
-  #define __Pyx_PyFrame_GetLocalsplus(frame)  ((frame)->f_localsplus)
-#else
-  static size_t __pyx_pyframe_localsplus_offset = 0;
-  #include "frameobject.h"
-  #define __Pxy_PyFrame_Initialize_Offsets()\
-    ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\
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-  #define __Pyx_PyFrame_GetLocalsplus(frame)\
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-/* PyObjectCallMethO.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
-#endif
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-/* PyObjectFastCall.proto */
-#define __Pyx_PyObject_FastCall(func, args, nargs)  __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL)
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs);
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-/* SliceObject.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
-        PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
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-
-/* PyObjectCallNoArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-
-/* PyObjectCallOneArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
-
-/* PyObjectGetMethod.proto */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method);
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-/* PyObjectCallMethod0.proto */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name);
-
-/* pop.proto */
-static CYTHON_INLINE PyObject* __Pyx__PyObject_Pop(PyObject* L);
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE PyObject* __Pyx_PyList_Pop(PyObject* L);
-#define __Pyx_PyObject_Pop(L) (likely(PyList_CheckExact(L)) ?\
-    __Pyx_PyList_Pop(L) : __Pyx__PyObject_Pop(L))
-#else
-#define __Pyx_PyList_Pop(L)  __Pyx__PyObject_Pop(L)
-#define __Pyx_PyObject_Pop(L)  __Pyx__PyObject_Pop(L)
-#endif
-
-/* UnpackUnboundCMethod.proto */
-typedef struct {
-    PyObject *type;
-    PyObject **method_name;
-    PyCFunction func;
-    PyObject *method;
-    int flag;
-} __Pyx_CachedCFunction;
-
-/* CallUnboundCMethod0.proto */
-static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self);
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_CallUnboundCMethod0(cfunc, self)\
-    (likely((cfunc)->func) ?\
-        (likely((cfunc)->flag == METH_NOARGS) ?  (*((cfunc)->func))(self, NULL) :\
-         (PY_VERSION_HEX >= 0x030600B1 && likely((cfunc)->flag == METH_FASTCALL) ?\
-            (PY_VERSION_HEX >= 0x030700A0 ?\
-                (*(__Pyx_PyCFunctionFast)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0) :\
-                (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0, NULL)) :\
-          (PY_VERSION_HEX >= 0x030700A0 && (cfunc)->flag == (METH_FASTCALL | METH_KEYWORDS) ?\
-            (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0, NULL) :\
-            (likely((cfunc)->flag == (METH_VARARGS | METH_KEYWORDS)) ?  ((*(PyCFunctionWithKeywords)(void*)(PyCFunction)(cfunc)->func)(self, __pyx_empty_tuple, NULL)) :\
-               ((cfunc)->flag == METH_VARARGS ?  (*((cfunc)->func))(self, __pyx_empty_tuple) :\
-               __Pyx__CallUnboundCMethod0(cfunc, self)))))) :\
-        __Pyx__CallUnboundCMethod0(cfunc, self))
-#else
-#define __Pyx_CallUnboundCMethod0(cfunc, self)  __Pyx__CallUnboundCMethod0(cfunc, self)
-#endif
-
-/* ListExtend.proto */
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
-    if (unlikely(!none))
-        return -1;
-    Py_DECREF(none);
-    return 0;
-#else
-    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
-#endif
-}
-
-/* RaiseUnexpectedTypeError.proto */
-static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj);
-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
-    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
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-
-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
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-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* pyfrozenset_new.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it);
-
-/* PySetContains.proto */
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq);
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
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-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
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-/* IncludeStructmemberH.proto */
-#include <structmember.h>
-
-/* FixUpExtensionType.proto */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type);
-#endif
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-/* ValidateBasesTuple.proto */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases);
-#endif
-
-/* PyType_Ready.proto */
-CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t);
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2))
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyErr_ExceptionMatches2(err1, err2)  __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2)
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-/* ImportDottedModule.proto */
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple);
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple);
-#endif
-
-/* FetchSharedCythonModule.proto */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void);
-
-/* FetchCommonType.proto */
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-#else
-static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases);
-#endif
-
-/* PyMethodNew.proto */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
-    CYTHON_UNUSED_VAR(typ);
-    if (!self)
-        return __Pyx_NewRef(func);
-    return PyMethod_New(func, self);
-}
-#else
-    #define __Pyx_PyMethod_New PyMethod_New
-#endif
-
-/* PyVectorcallFastCallDict.proto */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw);
-#endif
-
-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CYFUNCTION_COROUTINE     0x08
-#define __Pyx_CyFunction_GetClosure(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_closure)
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#else
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      ((PyObject*) ((PyCMethodObject *) (f))->mm_class)
-#endif
-#define __Pyx_CyFunction_SetClassObj(f, classobj)\
-    __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj))
-#define __Pyx_CyFunction_Defaults(type, f)\
-    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
-    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
-typedef struct {
-#if PY_VERSION_HEX < 0x030900B1
-    PyCFunctionObject func;
-#else
-    PyCMethodObject func;
-#endif
-#if CYTHON_BACKPORT_VECTORCALL
-    __pyx_vectorcallfunc func_vectorcall;
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    PyObject *func_weakreflist;
-#endif
-    PyObject *func_dict;
-    PyObject *func_name;
-    PyObject *func_qualname;
-    PyObject *func_doc;
-    PyObject *func_globals;
-    PyObject *func_code;
-    PyObject *func_closure;
-#if PY_VERSION_HEX < 0x030900B1
-    PyObject *func_classobj;
-#endif
-    void *defaults;
-    int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
-    int flags;
-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-    PyObject *func_is_coroutine;
-} __pyx_CyFunctionObject;
-#define __Pyx_CyFunction_Check(obj)  __Pyx_TypeCheck(obj, __pyx_CyFunctionType)
-#define __Pyx_IsCyOrPyCFunction(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
-#define __Pyx_CyFunction_CheckExact(obj)  __Pyx_IS_TYPE(obj, __pyx_CyFunctionType)
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
-                                                            PyObject *tuple);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
-                                                             PyObject *dict);
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
-                                                              PyObject *dict);
-static int __pyx_CyFunction_init(PyObject *module);
-#if CYTHON_METH_FASTCALL
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-#if CYTHON_BACKPORT_VECTORCALL
-#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall)
-#else
-#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall)
-#endif
-#endif
-
-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-#endif
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-/* RealImag.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #define __Pyx_CREAL(z) ((z).real())
-    #define __Pyx_CIMAG(z) ((z).imag())
-  #else
-    #define __Pyx_CREAL(z) (__real__(z))
-    #define __Pyx_CIMAG(z) (__imag__(z))
-  #endif
-#else
-    #define __Pyx_CREAL(z) ((z).real)
-    #define __Pyx_CIMAG(z) ((z).imag)
-#endif
-#if defined(__cplusplus) && CYTHON_CCOMPLEX\
-        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
-    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
-    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
-#else
-    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
-    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus)
-    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_double(a, b) ((a)-(b))
-    #define __Pyx_c_prod_double(a, b) ((a)*(b))
-    #define __Pyx_c_quot_double(a, b) ((a)/(b))
-    #define __Pyx_c_neg_double(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
-    #define __Pyx_c_conj_double(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (::std::abs(z))
-        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_double(z) ((z)==0)
-    #define __Pyx_c_conj_double(z)    (conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (cabs(z))
-        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
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-/* FromPy.proto */
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-/* ToPy.proto */
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-/* CIntFromPy.proto */
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-/* CIntToPy.proto */
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-/* CIntToPy.proto */
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-/* FormatTypeName.proto */
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-typedef PyObject *__Pyx_TypeName;
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-static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp);
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-#define __Pyx_DECREF_TypeName(obj)
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-/* CIntFromPy.proto */
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-/* SwapException.proto */
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-/* PyObjectCall2Args.proto */
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-    __Pyx_ExcInfoStruct gi_exc_state;
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-    PyTypeObject *type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-    PyObject *name, PyObject *qualname, PyObject *module_name);
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-#define __Pyx_Coroutine_ResetAndClearException(self)  __Pyx_Coroutine_ExceptionClear(&(self)->gi_exc_state)
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-/* PatchModuleWithCoroutine.proto */
-static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code);
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-/* Generator.proto */
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-#define __Pyx_Generator_New(body, code, closure, name, qualname, module_name)\
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-/* CheckBinaryVersion.proto */
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-/* InitStrings.proto */
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-/* Module declarations from "cython" */
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-/* Module declarations from "fontTools.qu2cu.qu2cu" */
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-static PyObject *__pyx_f_9fontTools_5qu2cu_5qu2cu_merge_curves(PyObject *, int, int); /*proto*/
-/* #### Code section: typeinfo ### */
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-static PyObject *__pyx_builtin_AttributeError;
-static PyObject *__pyx_builtin_ImportError;
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_AssertionError;
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-static PyObject *__pyx_builtin_enumerate;
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-static PyObject *__pyx_builtin_zip;
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-static const char __pyx_k_off2[] = "off2";
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-static const char __pyx_k_curve[] = "curve";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_float[] = "float";
-static const char __pyx_k_i_sol[] = "i_sol";
-static const char __pyx_k_print[] = "print";
-static const char __pyx_k_quads[] = "quads";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_throw[] = "throw";
-static const char __pyx_k_curves[] = "curves";
-static const char __pyx_k_cython[] = "cython";
-static const char __pyx_k_enable[] = "enable";
-static const char __pyx_k_forced[] = "forced";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_main_2[] = "main";
-static const char __pyx_k_p1_2_3[] = "p1_2_3";
-static const char __pyx_k_return[] = "return";
-static const char __pyx_k_splits[] = "splits";
-static const char __pyx_k_typing[] = "typing";
-static const char __pyx_k_disable[] = "disable";
-static const char __pyx_k_genexpr[] = "genexpr";
-static const char __pyx_k_max_err[] = "max_err";
-static const char __pyx_k_reconst[] = "reconst";
-static const char __pyx_k_COMPILED[] = "COMPILED";
-static const char __pyx_k_Solution[] = "Solution";
-static const char __pyx_k_best_sol[] = "best_sol";
-static const char __pyx_k_is_cubic[] = "is_cubic";
-static const char __pyx_k_reversed[] = "reversed";
-static const char __pyx_k_all_cubic[] = "all_cubic";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_isenabled[] = "isenabled";
-static const char __pyx_k_tolerance[] = "tolerance";
-static const char __pyx_k_impossible[] = "impossible";
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-static const char __pyx_k_namedtuple[] = "namedtuple";
-static const char __pyx_k_num_points[] = "num_points";
-static const char __pyx_k_quadratics[] = "quadratics";
-static const char __pyx_k_this_count[] = "this_count";
-static const char __pyx_k_ImportError[] = "ImportError";
-static const char __pyx_k_collections[] = "collections";
-static const char __pyx_k_i_sol_count[] = "i_sol_count";
-static const char __pyx_k_i_sol_error[] = "i_sol_error";
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-static const char __pyx_k_j_sol_error[] = "j_sol_error";
-static const char __pyx_k_start_index[] = "start_index";
-static const char __pyx_k_initializing[] = "_initializing";
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-static const char __pyx_k_class_getitem[] = "__class_getitem__";
-static const char __pyx_k_num_offcurves[] = "num_offcurves";
-static const char __pyx_k_reconstructed[] = "reconstructed";
-static const char __pyx_k_AssertionError[] = "AssertionError";
-static const char __pyx_k_AttributeError[] = "AttributeError";
-static const char __pyx_k_Original_curve[] = "Original curve:";
-static const char __pyx_k_fontTools_misc[] = "fontTools.misc";
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-static const char __pyx_k_List_Tuple_Point[] = "List[Tuple[Point, ...]]";
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-static const char __pyx_k_ZeroDivisionError[] = "ZeroDivisionError";
-static const char __pyx_k_elevate_quadratic[] = "elevate_quadratic";
-static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_curve_to_quadratic[] = "curve_to_quadratic";
-static const char __pyx_k_reconstructed_iter[] = "reconstructed_iter";
-static const char __pyx_k_elevated_quadratics[] = "elevated_quadratics";
-static const char __pyx_k_quadratic_to_curves[] = "quadratic_to_curves";
-static const char __pyx_k_Reconstructed_curve_s[] = "Reconstructed curve(s):";
-static const char __pyx_k_fontTools_qu2cu_qu2cu[] = "fontTools.qu2cu.qu2cu";
-static const char __pyx_k_reconstruct_tolerance[] = "reconstruct_tolerance";
-static const char __pyx_k_add_implicit_on_curves[] = "add_implicit_on_curves";
-static const char __pyx_k_fontTools_cu2qu_benchmark[] = "fontTools.cu2qu.benchmark";
-static const char __pyx_k_fontTools_misc_bezierTools[] = "fontTools.misc.bezierTools";
-static const char __pyx_k_Lib_fontTools_qu2cu_qu2cu_py[] = "Lib/fontTools/qu2cu/qu2cu.py";
-static const char __pyx_k_spline_to_curves_locals_genexpr[] = "spline_to_curves.<locals>.genexpr";
-static const char __pyx_k_One_random_cubic_turned_into_d_q[] = "One random cubic turned into %d quadratics.";
-static const char __pyx_k_Those_quadratics_turned_back_int[] = "Those quadratics turned back into %d cubics. ";
-static const char __pyx_k_cu2qu_tolerance_g_qu2cu_toleranc[] = "cu2qu tolerance %g. qu2cu tolerance %g.";
-static const char __pyx_k_quadratic_spline_requires_at_lea[] = "quadratic spline requires at least 3 points";
-static const char __pyx_k_quadratic_to_curves_locals_genex[] = "quadratic_to_curves.<locals>.genexpr";
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-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_2add_implicit_on_curves(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_p); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_19quadratic_to_curves_8genexpr3_genexpr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_genexpr_arg_0); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_4quadratic_to_curves(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_quads, double __pyx_v_max_err, PyObject *__pyx_v_all_cubic); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_16spline_to_curves_genexpr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_genexpr_arg_0); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_6spline_to_curves(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_q, PyObject *__pyx_v_costs, double __pyx_v_tolerance, int __pyx_v_all_cubic); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_8main(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_tp_new_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__genexpr(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
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-/* #### Code section: module_state_defines ### */
-#define __pyx_d __pyx_mstate_global->__pyx_d
-#define __pyx_b __pyx_mstate_global->__pyx_b
-#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime
-#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple
-#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes
-#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode
-#ifdef __Pyx_CyFunction_USED
-#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType
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-#ifdef __Pyx_FusedFunction_USED
-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType
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-#ifdef __Pyx_Generator_USED
-#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType
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-#endif
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-#define __pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr __pyx_mstate_global->__pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr
-#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError
-#define __pyx_n_s_AttributeError __pyx_mstate_global->__pyx_n_s_AttributeError
-#define __pyx_n_s_COMPILED __pyx_mstate_global->__pyx_n_s_COMPILED
-#define __pyx_n_s_ImportError __pyx_mstate_global->__pyx_n_s_ImportError
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-#define __pyx_n_s_List __pyx_mstate_global->__pyx_n_s_List
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-#define __pyx_n_s_Point __pyx_mstate_global->__pyx_n_s_Point
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-#define __pyx_n_s_Solution __pyx_mstate_global->__pyx_n_s_Solution
-#define __pyx_n_u_Solution __pyx_mstate_global->__pyx_n_u_Solution
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-#define __pyx_n_s_Tuple __pyx_mstate_global->__pyx_n_s_Tuple
-#define __pyx_n_s_Union __pyx_mstate_global->__pyx_n_s_Union
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-#define __pyx_n_s__15 __pyx_mstate_global->__pyx_n_s__15
-#define __pyx_kp_u__3 __pyx_mstate_global->__pyx_kp_u__3
-#define __pyx_n_s__4 __pyx_mstate_global->__pyx_n_s__4
-#define __pyx_n_s_add_implicit_on_curves __pyx_mstate_global->__pyx_n_s_add_implicit_on_curves
-#define __pyx_n_s_all __pyx_mstate_global->__pyx_n_s_all
-#define __pyx_n_s_all_cubic __pyx_mstate_global->__pyx_n_s_all_cubic
-#define __pyx_n_s_args __pyx_mstate_global->__pyx_n_s_args
-#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines
-#define __pyx_n_s_best_sol __pyx_mstate_global->__pyx_n_s_best_sol
-#define __pyx_n_s_bool __pyx_mstate_global->__pyx_n_s_bool
-#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem
-#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback
-#define __pyx_n_s_close __pyx_mstate_global->__pyx_n_s_close
-#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections
-#define __pyx_n_s_cost __pyx_mstate_global->__pyx_n_s_cost
-#define __pyx_n_s_costs __pyx_mstate_global->__pyx_n_s_costs
-#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count
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-#define __pyx_n_s_cubic __pyx_mstate_global->__pyx_n_s_cubic
-#define __pyx_n_s_curve __pyx_mstate_global->__pyx_n_s_curve
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-#define __pyx_n_s_curves __pyx_mstate_global->__pyx_n_s_curves
-#define __pyx_n_s_cython __pyx_mstate_global->__pyx_n_s_cython
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-#define __pyx_n_s_elevate_quadratic __pyx_mstate_global->__pyx_n_s_elevate_quadratic
-#define __pyx_n_s_elevated_quadratics __pyx_mstate_global->__pyx_n_s_elevated_quadratics
-#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable
-#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate
-#define __pyx_n_s_err __pyx_mstate_global->__pyx_n_s_err
-#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error
-#define __pyx_n_u_error __pyx_mstate_global->__pyx_n_u_error
-#define __pyx_n_s_float __pyx_mstate_global->__pyx_n_s_float
-#define __pyx_n_s_fontTools_cu2qu __pyx_mstate_global->__pyx_n_s_fontTools_cu2qu
-#define __pyx_n_s_fontTools_cu2qu_benchmark __pyx_mstate_global->__pyx_n_s_fontTools_cu2qu_benchmark
-#define __pyx_n_s_fontTools_misc __pyx_mstate_global->__pyx_n_s_fontTools_misc
-#define __pyx_n_s_fontTools_misc_bezierTools __pyx_mstate_global->__pyx_n_s_fontTools_misc_bezierTools
-#define __pyx_n_s_fontTools_qu2cu_qu2cu __pyx_mstate_global->__pyx_n_s_fontTools_qu2cu_qu2cu
-#define __pyx_n_s_forced __pyx_mstate_global->__pyx_n_s_forced
-#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc
-#define __pyx_n_s_generate_curve __pyx_mstate_global->__pyx_n_s_generate_curve
-#define __pyx_n_s_genexpr __pyx_mstate_global->__pyx_n_s_genexpr
-#define __pyx_n_s_i __pyx_mstate_global->__pyx_n_s_i
-#define __pyx_n_s_i_sol __pyx_mstate_global->__pyx_n_s_i_sol
-#define __pyx_n_s_i_sol_count __pyx_mstate_global->__pyx_n_s_i_sol_count
-#define __pyx_n_s_i_sol_error __pyx_mstate_global->__pyx_n_s_i_sol_error
-#define __pyx_n_s_imag __pyx_mstate_global->__pyx_n_s_imag
-#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import
-#define __pyx_n_s_impossible __pyx_mstate_global->__pyx_n_s_impossible
-#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing
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-#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine
-#define __pyx_n_s_is_cubic __pyx_mstate_global->__pyx_n_s_is_cubic
-#define __pyx_n_u_is_cubic __pyx_mstate_global->__pyx_n_u_is_cubic
-#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled
-#define __pyx_n_s_j __pyx_mstate_global->__pyx_n_s_j
-#define __pyx_n_s_j_sol_count __pyx_mstate_global->__pyx_n_s_j_sol_count
-#define __pyx_n_s_j_sol_error __pyx_mstate_global->__pyx_n_s_j_sol_error
-#define __pyx_n_s_k __pyx_mstate_global->__pyx_n_s_k
-#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main
-#define __pyx_n_u_main __pyx_mstate_global->__pyx_n_u_main
-#define __pyx_n_s_main_2 __pyx_mstate_global->__pyx_n_s_main_2
-#define __pyx_n_s_math __pyx_mstate_global->__pyx_n_s_math
-#define __pyx_n_s_max_err __pyx_mstate_global->__pyx_n_s_max_err
-#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name
-#define __pyx_n_s_namedtuple __pyx_mstate_global->__pyx_n_s_namedtuple
-#define __pyx_n_s_num_offcurves __pyx_mstate_global->__pyx_n_s_num_offcurves
-#define __pyx_n_s_num_points __pyx_mstate_global->__pyx_n_s_num_points
-#define __pyx_n_u_num_points __pyx_mstate_global->__pyx_n_u_num_points
-#define __pyx_n_s_off1 __pyx_mstate_global->__pyx_n_s_off1
-#define __pyx_n_s_off2 __pyx_mstate_global->__pyx_n_s_off2
-#define __pyx_n_s_on __pyx_mstate_global->__pyx_n_s_on
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-#define __pyx_n_s_p __pyx_mstate_global->__pyx_n_s_p
-#define __pyx_n_s_p0 __pyx_mstate_global->__pyx_n_s_p0
-#define __pyx_n_s_p1 __pyx_mstate_global->__pyx_n_s_p1
-#define __pyx_n_s_p1_2_3 __pyx_mstate_global->__pyx_n_s_p1_2_3
-#define __pyx_n_s_p2 __pyx_mstate_global->__pyx_n_s_p2
-#define __pyx_n_s_p3 __pyx_mstate_global->__pyx_n_s_p3
-#define __pyx_n_s_pop __pyx_mstate_global->__pyx_n_s_pop
-#define __pyx_n_s_print __pyx_mstate_global->__pyx_n_s_print
-#define __pyx_n_s_q __pyx_mstate_global->__pyx_n_s_q
-#define __pyx_n_s_qq __pyx_mstate_global->__pyx_n_s_qq
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-#define __pyx_n_s_quadratic_to_curves __pyx_mstate_global->__pyx_n_s_quadratic_to_curves
-#define __pyx_n_u_quadratic_to_curves __pyx_mstate_global->__pyx_n_u_quadratic_to_curves
-#define __pyx_n_s_quadratic_to_curves_locals_genex __pyx_mstate_global->__pyx_n_s_quadratic_to_curves_locals_genex
-#define __pyx_n_s_quadratics __pyx_mstate_global->__pyx_n_s_quadratics
-#define __pyx_n_s_quads __pyx_mstate_global->__pyx_n_s_quads
-#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range
-#define __pyx_n_s_real __pyx_mstate_global->__pyx_n_s_real
-#define __pyx_n_s_reconst __pyx_mstate_global->__pyx_n_s_reconst
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-#define __pyx_n_s_reconstructed __pyx_mstate_global->__pyx_n_s_reconstructed
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-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds));
-    while (1) {
-        if (kwds_is_tuple) {
-            if (pos >= PyTuple_GET_SIZE(kwds)) break;
-            key = PyTuple_GET_ITEM(kwds, pos);
-            value = kwvalues[pos];
-            pos++;
-        }
-        else
-        {
-            if (!PyDict_Next(kwds, &pos, &key, &value)) break;
-        }
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key)
-                );
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (unlikely(!j)) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_subscript) {
-            PyObject *r, *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return NULL;
-            r = mm->mp_subscript(o, key);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    CYTHON_UNUSED_VAR(cause);
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-      #if PY_VERSION_HEX >= 0x030C00A6
-        PyException_SetTraceback(value, tb);
-      #elif CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* py_abs */
-#if CYTHON_USE_PYLONG_INTERNALS
-static PyObject *__Pyx_PyLong_AbsNeg(PyObject *n) {
-#if PY_VERSION_HEX >= 0x030C00A7
-    if (likely(__Pyx_PyLong_IsCompact(n))) {
-        return PyLong_FromSize_t(__Pyx_PyLong_CompactValueUnsigned(n));
-    }
-#else
-    if (likely(Py_SIZE(n) == -1)) {
-        return PyLong_FromUnsignedLong(__Pyx_PyLong_Digits(n)[0]);
-    }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-    {
-        PyObject *copy = _PyLong_Copy((PyLongObject*)n);
-        if (likely(copy)) {
-            #if PY_VERSION_HEX >= 0x030C00A7
-            ((PyLongObject*)copy)->long_value.lv_tag = ((PyLongObject*)copy)->long_value.lv_tag & ~_PyLong_SIGN_MASK;
-            #else
-            __Pyx_SET_SIZE(copy, -Py_SIZE(copy));
-            #endif
-        }
-        return copy;
-    }
-#else
-    return PyNumber_Negative(n);
-#endif
-}
-#endif
-
-/* SliceTupleAndList */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE void __Pyx_crop_slice(Py_ssize_t* _start, Py_ssize_t* _stop, Py_ssize_t* _length) {
-    Py_ssize_t start = *_start, stop = *_stop, length = *_length;
-    if (start < 0) {
-        start += length;
-        if (start < 0)
-            start = 0;
-    }
-    if (stop < 0)
-        stop += length;
-    else if (stop > length)
-        stop = length;
-    *_length = stop - start;
-    *_start = start;
-    *_stop = stop;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyList_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    Py_ssize_t length = PyList_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    if (length <= 0) {
-        return PyList_New(0);
-    }
-    return __Pyx_PyList_FromArray(((PyListObject*)src)->ob_item + start, length);
-}
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    Py_ssize_t length = PyTuple_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    return __Pyx_PyTuple_FromArray(((PyTupleObject*)src)->ob_item + start, length);
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_MAYBE_UNUSED_VAR(inplace);
-    CYTHON_UNUSED_VAR(zerodivision_check);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op2))) {
-        const long a = intval;
-        long x;
-        long b = PyInt_AS_LONG(op2);
-        
-            x = (long)((unsigned long)a - (unsigned long)b);
-            if (likely((x^a) >= 0 || (x^~b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op2))) {
-        const long a = intval;
-        long b, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG lla = intval;
-        PY_LONG_LONG llb, llx;
-#endif
-        if (unlikely(__Pyx_PyLong_IsZero(op2))) {
-            return __Pyx_NewRef(op1);
-        }
-        if (likely(__Pyx_PyLong_IsCompact(op2))) {
-            b = __Pyx_PyLong_CompactValue(op2);
-        } else {
-            const digit* digits = __Pyx_PyLong_Digits(op2);
-            const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op2);
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        b = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        b = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        b = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        b = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        b = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        b = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-            }
-        }
-                x = a - b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla - llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op2)) {
-        const long a = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double b = __pyx_PyFloat_AsDouble(op2);
-#else
-        double b = PyFloat_AS_DOUBLE(op2);
-#endif
-            double result;
-            
-            PyFPE_START_PROTECT("subtract", return NULL)
-            result = ((double)a) - (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
-}
-#endif
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    __Pyx_TypeName type_name;
-    __Pyx_TypeName obj_type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    type_name = __Pyx_PyType_GetName(type);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME
-        ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* RaiseUnboundLocalError */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type = NULL, *local_value, *local_tb = NULL;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if PY_VERSION_HEX >= 0x030C00A6
-    local_value = tstate->current_exception;
-    tstate->current_exception = 0;
-    if (likely(local_value)) {
-        local_type = (PyObject*) Py_TYPE(local_value);
-        Py_INCREF(local_type);
-        local_tb = PyException_GetTraceback(local_value);
-    }
-  #else
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-  #endif
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6
-    if (unlikely(tstate->current_exception))
-#elif CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-      #if PY_VERSION_HEX >= 0x030B00a4
-        tmp_value = exc_info->exc_value;
-        exc_info->exc_value = local_value;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-        Py_XDECREF(local_type);
-        Py_XDECREF(local_tb);
-      #else
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-      #endif
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* pep479 */
-static void __Pyx_Generator_Replace_StopIteration(int in_async_gen) {
-    PyObject *exc, *val, *tb, *cur_exc;
-    __Pyx_PyThreadState_declare
-    #ifdef __Pyx_StopAsyncIteration_USED
-    int is_async_stopiteration = 0;
-    #endif
-    CYTHON_MAYBE_UNUSED_VAR(in_async_gen);
-    cur_exc = PyErr_Occurred();
-    if (likely(!__Pyx_PyErr_GivenExceptionMatches(cur_exc, PyExc_StopIteration))) {
-        #ifdef __Pyx_StopAsyncIteration_USED
-        if (in_async_gen && unlikely(__Pyx_PyErr_GivenExceptionMatches(cur_exc, __Pyx_PyExc_StopAsyncIteration))) {
-            is_async_stopiteration = 1;
-        } else
-        #endif
-            return;
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_GetException(&exc, &val, &tb);
-    Py_XDECREF(exc);
-    Py_XDECREF(val);
-    Py_XDECREF(tb);
-    PyErr_SetString(PyExc_RuntimeError,
-        #ifdef __Pyx_StopAsyncIteration_USED
-        is_async_stopiteration ? "async generator raised StopAsyncIteration" :
-        in_async_gen ? "async generator raised StopIteration" :
-        #endif
-        "generator raised StopIteration");
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* IterFinish */
-static CYTHON_INLINE int __Pyx_IterFinish(void) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    PyObject* exc_type = __Pyx_PyErr_CurrentExceptionType();
-    if (unlikely(exc_type)) {
-        if (unlikely(!__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)))
-            return -1;
-        __Pyx_PyErr_Clear();
-        return 0;
-    }
-    return 0;
-}
-
-/* UnpackItemEndCheck */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
-    if (unlikely(retval)) {
-        Py_DECREF(retval);
-        __Pyx_RaiseTooManyValuesError(expected);
-        return -1;
-    }
-    return __Pyx_IterFinish();
-}
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#elif CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(!__pyx_m)) {
-        return NULL;
-    }
-    result = PyObject_GetAttr(__pyx_m, name);
-    if (likely(result)) {
-        return result;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectFastCall */
-static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) {
-    PyObject *argstuple;
-    PyObject *result;
-    size_t i;
-    argstuple = PyTuple_New((Py_ssize_t)nargs);
-    if (unlikely(!argstuple)) return NULL;
-    for (i = 0; i < nargs; i++) {
-        Py_INCREF(args[i]);
-        PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]);
-    }
-    result = __Pyx_PyObject_Call(func, argstuple, kwargs);
-    Py_DECREF(argstuple);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) {
-    Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (nargs == 0 && kwargs == NULL) {
-#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG)
-        if (__Pyx_IsCyOrPyCFunction(func))
-#else
-        if (PyCFunction_Check(func))
-#endif
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-                return __Pyx_PyObject_CallMethO(func, NULL);
-            }
-        }
-    }
-    else if (nargs == 1 && kwargs == NULL) {
-        if (PyCFunction_Check(func))
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-                return __Pyx_PyObject_CallMethO(func, args[0]);
-            }
-        }
-    }
-#endif
-    #if PY_VERSION_HEX < 0x030800B1
-    #if CYTHON_FAST_PYCCALL
-    if (PyCFunction_Check(func)) {
-        if (kwargs) {
-            return _PyCFunction_FastCallDict(func, args, nargs, kwargs);
-        } else {
-            return _PyCFunction_FastCallKeywords(func, args, nargs, NULL);
-        }
-    }
-    #if PY_VERSION_HEX >= 0x030700A1
-    if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) {
-        return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL);
-    }
-    #endif
-    #endif
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs);
-    }
-    #endif
-    #endif
-    #if CYTHON_VECTORCALL
-    vectorcallfunc f = _PyVectorcall_Function(func);
-    if (f) {
-        return f(func, args, (size_t)nargs, kwargs);
-    }
-    #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL
-    if (__Pyx_CyFunction_CheckExact(func)) {
-        __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func);
-        if (f) return f(func, args, (size_t)nargs, kwargs);
-    }
-    #endif
-    if (nargs == 0) {
-        return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs);
-    }
-    return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs);
-}
-
-/* SliceObject */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj,
-        Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
-        int has_cstart, int has_cstop, int wraparound) {
-    __Pyx_TypeName obj_type_name;
-#if CYTHON_USE_TYPE_SLOTS
-    PyMappingMethods* mp;
-#if PY_MAJOR_VERSION < 3
-    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
-    if (likely(ms && ms->sq_slice)) {
-        if (!has_cstart) {
-            if (_py_start && (*_py_start != Py_None)) {
-                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
-                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstart = 0;
-        }
-        if (!has_cstop) {
-            if (_py_stop && (*_py_stop != Py_None)) {
-                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
-                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstop = PY_SSIZE_T_MAX;
-        }
-        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
-            Py_ssize_t l = ms->sq_length(obj);
-            if (likely(l >= 0)) {
-                if (cstop < 0) {
-                    cstop += l;
-                    if (cstop < 0) cstop = 0;
-                }
-                if (cstart < 0) {
-                    cstart += l;
-                    if (cstart < 0) cstart = 0;
-                }
-            } else {
-                if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                    goto bad;
-                PyErr_Clear();
-            }
-        }
-        return ms->sq_slice(obj, cstart, cstop);
-    }
-#else
-    CYTHON_UNUSED_VAR(wraparound);
-#endif
-    mp = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(mp && mp->mp_subscript))
-#else
-    CYTHON_UNUSED_VAR(wraparound);
-#endif
-    {
-        PyObject* result;
-        PyObject *py_slice, *py_start, *py_stop;
-        if (_py_slice) {
-            py_slice = *_py_slice;
-        } else {
-            PyObject* owned_start = NULL;
-            PyObject* owned_stop = NULL;
-            if (_py_start) {
-                py_start = *_py_start;
-            } else {
-                if (has_cstart) {
-                    owned_start = py_start = PyInt_FromSsize_t(cstart);
-                    if (unlikely(!py_start)) goto bad;
-                } else
-                    py_start = Py_None;
-            }
-            if (_py_stop) {
-                py_stop = *_py_stop;
-            } else {
-                if (has_cstop) {
-                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
-                    if (unlikely(!py_stop)) {
-                        Py_XDECREF(owned_start);
-                        goto bad;
-                    }
-                } else
-                    py_stop = Py_None;
-            }
-            py_slice = PySlice_New(py_start, py_stop, Py_None);
-            Py_XDECREF(owned_start);
-            Py_XDECREF(owned_stop);
-            if (unlikely(!py_slice)) goto bad;
-        }
-#if CYTHON_USE_TYPE_SLOTS
-        result = mp->mp_subscript(obj, py_slice);
-#else
-        result = PyObject_GetItem(obj, py_slice);
-#endif
-        if (!_py_slice) {
-            Py_DECREF(py_slice);
-        }
-        return result;
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is unsliceable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-bad:
-    return NULL;
-}
-
-/* PyObjectCallNoArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-    PyObject *arg = NULL;
-    return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectCallOneArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *args[2] = {NULL, arg};
-    return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
-    PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
-    __Pyx_TypeName type_name;
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyObject *descr;
-    descrgetfunc f = NULL;
-    PyObject **dictptr, *dict;
-    int meth_found = 0;
-    assert (*method == NULL);
-    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
-        attr = __Pyx_PyObject_GetAttrStr(obj, name);
-        goto try_unpack;
-    }
-    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
-        return 0;
-    }
-    descr = _PyType_Lookup(tp, name);
-    if (likely(descr != NULL)) {
-        Py_INCREF(descr);
-#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR
-        if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR))
-#elif PY_MAJOR_VERSION >= 3
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type)))
-        #endif
-#else
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr)))
-        #endif
-#endif
-        {
-            meth_found = 1;
-        } else {
-            f = Py_TYPE(descr)->tp_descr_get;
-            if (f != NULL && PyDescr_IsData(descr)) {
-                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-                Py_DECREF(descr);
-                goto try_unpack;
-            }
-        }
-    }
-    dictptr = _PyObject_GetDictPtr(obj);
-    if (dictptr != NULL && (dict = *dictptr) != NULL) {
-        Py_INCREF(dict);
-        attr = __Pyx_PyDict_GetItemStr(dict, name);
-        if (attr != NULL) {
-            Py_INCREF(attr);
-            Py_DECREF(dict);
-            Py_XDECREF(descr);
-            goto try_unpack;
-        }
-        Py_DECREF(dict);
-    }
-    if (meth_found) {
-        *method = descr;
-        return 1;
-    }
-    if (f != NULL) {
-        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-        Py_DECREF(descr);
-        goto try_unpack;
-    }
-    if (likely(descr != NULL)) {
-        *method = descr;
-        return 0;
-    }
-    type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-#else
-    attr = __Pyx_PyObject_GetAttrStr(obj, name);
-    goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
-    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
-        PyObject *function = PyMethod_GET_FUNCTION(attr);
-        Py_INCREF(function);
-        Py_DECREF(attr);
-        *method = function;
-        return 1;
-    }
-#endif
-    *method = attr;
-    return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
-    PyObject *method = NULL, *result = NULL;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_CallOneArg(method, obj);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) goto bad;
-    result = __Pyx_PyObject_CallNoArg(method);
-    Py_DECREF(method);
-bad:
-    return result;
-}
-
-/* UnpackUnboundCMethod */
-static PyObject *__Pyx_SelflessCall(PyObject *method, PyObject *args, PyObject *kwargs) {
-    PyObject *selfless_args = PyTuple_GetSlice(args, 1, PyTuple_Size(args));
-    if (unlikely(!selfless_args)) return NULL;
-    PyObject *result = PyObject_Call(method, selfless_args, kwargs);
-    Py_DECREF(selfless_args);
-    return result;
-}
-static PyMethodDef __Pyx_UnboundCMethod_Def = {
-     "CythonUnboundCMethod",
-     __PYX_REINTERPRET_FUNCION(PyCFunction, __Pyx_SelflessCall),
-     METH_VARARGS | METH_KEYWORDS,
-     NULL
-};
-static int __Pyx_TryUnpackUnboundCMethod(__Pyx_CachedCFunction* target) {
-    PyObject *method;
-    method = __Pyx_PyObject_GetAttrStr(target->type, *target->method_name);
-    if (unlikely(!method))
-        return -1;
-    target->method = method;
-#if CYTHON_COMPILING_IN_CPYTHON
-    #if PY_MAJOR_VERSION >= 3
-    if (likely(__Pyx_TypeCheck(method, &PyMethodDescr_Type)))
-    #else
-    if (likely(!PyCFunction_Check(method)))
-    #endif
-    {
-        PyMethodDescrObject *descr = (PyMethodDescrObject*) method;
-        target->func = descr->d_method->ml_meth;
-        target->flag = descr->d_method->ml_flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_STACKLESS);
-    } else
-#endif
-#if defined(CYTHON_COMPILING_IN_PYPY)
-#elif PY_VERSION_HEX >= 0x03090000
-    if (PyCFunction_CheckExact(method))
-#else
-    if (PyCFunction_Check(method))
-#endif
-    {
-        PyObject *self;
-        int self_found;
-#if CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_PYPY
-        self = PyObject_GetAttrString(method, "__self__");
-        if (!self) {
-            PyErr_Clear();
-        }
-#else
-        self = PyCFunction_GET_SELF(method);
-#endif
-        self_found = (self && self != Py_None);
-#if CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_PYPY
-        Py_XDECREF(self);
-#endif
-        if (self_found) {
-            PyObject *unbound_method = PyCFunction_New(&__Pyx_UnboundCMethod_Def, method);
-            if (unlikely(!unbound_method)) return -1;
-            Py_DECREF(method);
-            target->method = unbound_method;
-        }
-    }
-    return 0;
-}
-
-/* CallUnboundCMethod0 */
-static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self) {
-    PyObject *args, *result = NULL;
-    if (unlikely(!cfunc->method) && unlikely(__Pyx_TryUnpackUnboundCMethod(cfunc) < 0)) return NULL;
-#if CYTHON_ASSUME_SAFE_MACROS
-    args = PyTuple_New(1);
-    if (unlikely(!args)) goto bad;
-    Py_INCREF(self);
-    PyTuple_SET_ITEM(args, 0, self);
-#else
-    args = PyTuple_Pack(1, self);
-    if (unlikely(!args)) goto bad;
-#endif
-    result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
-    Py_DECREF(args);
-bad:
-    return result;
-}
-
-/* pop */
-static CYTHON_INLINE PyObject* __Pyx__PyObject_Pop(PyObject* L) {
-    if (__Pyx_IS_TYPE(L, &PySet_Type)) {
-        return PySet_Pop(L);
-    }
-    return __Pyx_PyObject_CallMethod0(L, __pyx_n_s_pop);
-}
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE PyObject* __Pyx_PyList_Pop(PyObject* L) {
-    if (likely(PyList_GET_SIZE(L) > (((PyListObject*)L)->allocated >> 1))) {
-        __Pyx_SET_SIZE(L, Py_SIZE(L) - 1);
-        return PyList_GET_ITEM(L, PyList_GET_SIZE(L));
-    }
-    return __Pyx_CallUnboundCMethod0(&__pyx_umethod_PyList_Type_pop, L);
-}
-#endif
-
-/* RaiseUnexpectedTypeError */
-static int
-__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj)
-{
-    __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME,
-                 expected, obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_MAYBE_UNUSED_VAR(inplace);
-    CYTHON_UNUSED_VAR(zerodivision_check);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-        
-            x = (long)((unsigned long)a + (unsigned long)b);
-            if (likely((x^a) >= 0 || (x^b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_add(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        if (unlikely(__Pyx_PyLong_IsZero(op1))) {
-            return __Pyx_NewRef(op2);
-        }
-        if (likely(__Pyx_PyLong_IsCompact(op1))) {
-            a = __Pyx_PyLong_CompactValue(op1);
-        } else {
-            const digit* digits = __Pyx_PyLong_Digits(op1);
-            const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op1);
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
-            }
-        }
-                x = a + b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla + llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double a = __pyx_PyFloat_AsDouble(op1);
-#else
-        double a = PyFloat_AS_DOUBLE(op1);
-#endif
-            double result;
-            
-            PyFPE_START_PROTECT("add", return NULL)
-            result = ((double)a) + (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    PyObject *exc_value = exc_info->exc_value;
-    if (exc_value == NULL || exc_value == Py_None) {
-        *value = NULL;
-        *type = NULL;
-        *tb = NULL;
-    } else {
-        *value = exc_value;
-        Py_INCREF(*value);
-        *type = (PyObject*) Py_TYPE(exc_value);
-        Py_INCREF(*type);
-        *tb = PyException_GetTraceback(exc_value);
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #endif
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    PyObject *tmp_value = exc_info->exc_value;
-    exc_info->exc_value = value;
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-  #else
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-  #endif
-}
-#endif
-
-/* pyfrozenset_new */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it) {
-    if (it) {
-        PyObject* result;
-#if CYTHON_COMPILING_IN_PYPY
-        PyObject* args;
-        args = PyTuple_Pack(1, it);
-        if (unlikely(!args))
-            return NULL;
-        result = PyObject_Call((PyObject*)&PyFrozenSet_Type, args, NULL);
-        Py_DECREF(args);
-        return result;
-#else
-        if (PyFrozenSet_CheckExact(it)) {
-            Py_INCREF(it);
-            return it;
-        }
-        result = PyFrozenSet_New(it);
-        if (unlikely(!result))
-            return NULL;
-        if ((PY_VERSION_HEX >= 0x031000A1) || likely(PySet_GET_SIZE(result)))
-            return result;
-        Py_DECREF(result);
-#endif
-    }
-#if CYTHON_USE_TYPE_SLOTS
-    return PyFrozenSet_Type.tp_new(&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#else
-    return PyObject_Call((PyObject*)&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#endif
-}
-
-/* PySetContains */
-static int __Pyx_PySet_ContainsUnhashable(PyObject *set, PyObject *key) {
-    int result = -1;
-    if (PySet_Check(key) && PyErr_ExceptionMatches(PyExc_TypeError)) {
-        PyObject *tmpkey;
-        PyErr_Clear();
-        tmpkey = __Pyx_PyFrozenSet_New(key);
-        if (tmpkey != NULL) {
-            result = PySet_Contains(set, tmpkey);
-            Py_DECREF(tmpkey);
-        }
-    }
-    return result;
-}
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq) {
-    int result = PySet_Contains(set, key);
-    if (unlikely(result < 0)) {
-        result = __Pyx_PySet_ContainsUnhashable(set, key);
-    }
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *module = 0;
-    PyObject *empty_dict = 0;
-    PyObject *empty_list = 0;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (unlikely(!py_import))
-        goto bad;
-    if (!from_list) {
-        empty_list = PyList_New(0);
-        if (unlikely(!empty_list))
-            goto bad;
-        from_list = empty_list;
-    }
-    #endif
-    empty_dict = PyDict_New();
-    if (unlikely(!empty_dict))
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                #if CYTHON_COMPILING_IN_LIMITED_API
-                module = PyImport_ImportModuleLevelObject(
-                    name, empty_dict, empty_dict, from_list, 1);
-                #else
-                module = PyImport_ImportModuleLevelObject(
-                    name, __pyx_d, empty_dict, from_list, 1);
-                #endif
-                if (unlikely(!module)) {
-                    if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError)))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (unlikely(!py_level))
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            #if CYTHON_COMPILING_IN_LIMITED_API
-            module = PyImport_ImportModuleLevelObject(
-                name, empty_dict, empty_dict, from_list, level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, __pyx_d, empty_dict, from_list, level);
-            #endif
-            #endif
-        }
-    }
-bad:
-    Py_XDECREF(empty_dict);
-    Py_XDECREF(empty_list);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    return module;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        const char* module_name_str = 0;
-        PyObject* module_name = 0;
-        PyObject* module_dot = 0;
-        PyObject* full_name = 0;
-        PyErr_Clear();
-        module_name_str = PyModule_GetName(module);
-        if (unlikely(!module_name_str)) { goto modbad; }
-        module_name = PyUnicode_FromString(module_name_str);
-        if (unlikely(!module_name)) { goto modbad; }
-        module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__3);
-        if (unlikely(!module_dot)) { goto modbad; }
-        full_name = PyUnicode_Concat(module_dot, name);
-        if (unlikely(!full_name)) { goto modbad; }
-        #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-        {
-            PyObject *modules = PyImport_GetModuleDict();
-            if (unlikely(!modules))
-                goto modbad;
-            value = PyObject_GetItem(modules, full_name);
-        }
-        #else
-        value = PyImport_GetModule(full_name);
-        #endif
-      modbad:
-        Py_XDECREF(full_name);
-        Py_XDECREF(module_dot);
-        Py_XDECREF(module_name);
-    }
-    if (unlikely(!value)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* FixUpExtensionType */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) {
-#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    CYTHON_UNUSED_VAR(spec);
-    CYTHON_UNUSED_VAR(type);
-#else
-    const PyType_Slot *slot = spec->slots;
-    while (slot && slot->slot && slot->slot != Py_tp_members)
-        slot++;
-    if (slot && slot->slot == Py_tp_members) {
-        int changed = 0;
-#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON)
-        const
-#endif
-            PyMemberDef *memb = (PyMemberDef*) slot->pfunc;
-        while (memb && memb->name) {
-            if (memb->name[0] == '_' && memb->name[1] == '_') {
-#if PY_VERSION_HEX < 0x030900b1
-                if (strcmp(memb->name, "__weaklistoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_weaklistoffset = memb->offset;
-                    changed = 1;
-                }
-                else if (strcmp(memb->name, "__dictoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_dictoffset = memb->offset;
-                    changed = 1;
-                }
-#if CYTHON_METH_FASTCALL
-                else if (strcmp(memb->name, "__vectorcalloffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-#if PY_VERSION_HEX >= 0x030800b4
-                    type->tp_vectorcall_offset = memb->offset;
-#else
-                    type->tp_print = (printfunc) memb->offset;
-#endif
-                    changed = 1;
-                }
-#endif
-#else
-                if ((0));
-#endif
-#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON
-                else if (strcmp(memb->name, "__module__") == 0) {
-                    PyObject *descr;
-                    assert(memb->type == T_OBJECT);
-                    assert(memb->flags == 0 || memb->flags == READONLY);
-                    descr = PyDescr_NewMember(type, memb);
-                    if (unlikely(!descr))
-                        return -1;
-                    if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) {
-                        Py_DECREF(descr);
-                        return -1;
-                    }
-                    Py_DECREF(descr);
-                    changed = 1;
-                }
-#endif
-            }
-            memb++;
-        }
-        if (changed)
-            PyType_Modified(type);
-    }
-#endif
-    return 0;
-}
-#endif
-
-/* ValidateBasesTuple */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) {
-    Py_ssize_t i, n = PyTuple_GET_SIZE(bases);
-    for (i = 1; i < n; i++)
-    {
-        PyObject *b0 = PyTuple_GET_ITEM(bases, i);
-        PyTypeObject *b;
-#if PY_MAJOR_VERSION < 3
-        if (PyClass_Check(b0))
-        {
-            PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class",
-                         PyString_AS_STRING(((PyClassObject*)b0)->cl_name));
-            return -1;
-        }
-#endif
-        b = (PyTypeObject*) b0;
-        if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE))
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-        if (dictoffset == 0 && b->tp_dictoffset)
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "extension type '%.200s' has no __dict__ slot, "
-                "but base type '" __Pyx_FMT_TYPENAME "' has: "
-                "either add 'cdef dict __dict__' to the extension type "
-                "or add '__slots__ = [...]' to the base type",
-                type_name, b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-    }
-    return 0;
-}
-#endif
-
-/* PyType_Ready */
-static int __Pyx_PyType_Ready(PyTypeObject *t) {
-#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION)
-    (void)__Pyx_PyObject_CallMethod0;
-#if CYTHON_USE_TYPE_SPECS
-    (void)__Pyx_validate_bases_tuple;
-#endif
-    return PyType_Ready(t);
-#else
-    int r;
-    PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*);
-    if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1))
-        return -1;
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-    {
-        int gc_was_enabled;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        gc_was_enabled = PyGC_Disable();
-        (void)__Pyx_PyObject_CallMethod0;
-    #else
-        PyObject *ret, *py_status;
-        PyObject *gc = NULL;
-        #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400)
-        gc = PyImport_GetModule(__pyx_kp_u_gc);
-        #endif
-        if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc);
-        if (unlikely(!gc)) return -1;
-        py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled);
-        if (unlikely(!py_status)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-        gc_was_enabled = __Pyx_PyObject_IsTrue(py_status);
-        Py_DECREF(py_status);
-        if (gc_was_enabled > 0) {
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable);
-            if (unlikely(!ret)) {
-                Py_DECREF(gc);
-                return -1;
-            }
-            Py_DECREF(ret);
-        } else if (unlikely(gc_was_enabled == -1)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-    #endif
-        t->tp_flags |= Py_TPFLAGS_HEAPTYPE;
-#if PY_VERSION_HEX >= 0x030A0000
-        t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE;
-#endif
-#else
-        (void)__Pyx_PyObject_CallMethod0;
-#endif
-    r = PyType_Ready(t);
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-        t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        if (gc_was_enabled)
-            PyGC_Enable();
-    #else
-        if (gc_was_enabled) {
-            PyObject *tp, *v, *tb;
-            PyErr_Fetch(&tp, &v, &tb);
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable);
-            if (likely(ret || r == -1)) {
-                Py_XDECREF(ret);
-                PyErr_Restore(tp, v, tb);
-            } else {
-                Py_XDECREF(tp);
-                Py_XDECREF(v);
-                Py_XDECREF(tb);
-                r = -1;
-            }
-        }
-        Py_DECREF(gc);
-    #endif
-    }
-#endif
-    return r;
-#endif
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, attr_name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(attr_name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*);
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (cls == a || cls == b) return 1;
-    mro = cls->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            PyObject *base = PyTuple_GET_ITEM(mro, i);
-            if (base == (PyObject *)a || base == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    if (exc_type1) {
-        return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2);
-    } else {
-        return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* ImportDottedModule */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) {
-    PyObject *partial_name = NULL, *slice = NULL, *sep = NULL;
-    if (unlikely(PyErr_Occurred())) {
-        PyErr_Clear();
-    }
-    if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) {
-        partial_name = name;
-    } else {
-        slice = PySequence_GetSlice(parts_tuple, 0, count);
-        if (unlikely(!slice))
-            goto bad;
-        sep = PyUnicode_FromStringAndSize(".", 1);
-        if (unlikely(!sep))
-            goto bad;
-        partial_name = PyUnicode_Join(sep, slice);
-    }
-    PyErr_Format(
-#if PY_MAJOR_VERSION < 3
-        PyExc_ImportError,
-        "No module named '%s'", PyString_AS_STRING(partial_name));
-#else
-#if PY_VERSION_HEX >= 0x030600B1
-        PyExc_ModuleNotFoundError,
-#else
-        PyExc_ImportError,
-#endif
-        "No module named '%U'", partial_name);
-#endif
-bad:
-    Py_XDECREF(sep);
-    Py_XDECREF(slice);
-    Py_XDECREF(partial_name);
-    return NULL;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) {
-    PyObject *imported_module;
-#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-    PyObject *modules = PyImport_GetModuleDict();
-    if (unlikely(!modules))
-        return NULL;
-    imported_module = __Pyx_PyDict_GetItemStr(modules, name);
-    Py_XINCREF(imported_module);
-#else
-    imported_module = PyImport_GetModule(name);
-#endif
-    return imported_module;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) {
-    Py_ssize_t i, nparts;
-    nparts = PyTuple_GET_SIZE(parts_tuple);
-    for (i=1; i < nparts && module; i++) {
-        PyObject *part, *submodule;
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        part = PyTuple_GET_ITEM(parts_tuple, i);
-#else
-        part = PySequence_ITEM(parts_tuple, i);
-#endif
-        submodule = __Pyx_PyObject_GetAttrStrNoError(module, part);
-#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-        Py_DECREF(part);
-#endif
-        Py_DECREF(module);
-        module = submodule;
-    }
-    if (unlikely(!module)) {
-        return __Pyx__ImportDottedModule_Error(name, parts_tuple, i);
-    }
-    return module;
-}
-#endif
-static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if PY_MAJOR_VERSION < 3
-    PyObject *module, *from_list, *star = __pyx_n_s__4;
-    CYTHON_UNUSED_VAR(parts_tuple);
-    from_list = PyList_New(1);
-    if (unlikely(!from_list))
-        return NULL;
-    Py_INCREF(star);
-    PyList_SET_ITEM(from_list, 0, star);
-    module = __Pyx_Import(name, from_list, 0);
-    Py_DECREF(from_list);
-    return module;
-#else
-    PyObject *imported_module;
-    PyObject *module = __Pyx_Import(name, NULL, 0);
-    if (!parts_tuple || unlikely(!module))
-        return module;
-    imported_module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(imported_module)) {
-        Py_DECREF(module);
-        return imported_module;
-    }
-    PyErr_Clear();
-    return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple);
-#endif
-}
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1
-    PyObject *module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(module)) {
-        PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec);
-        if (likely(spec)) {
-            PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing);
-            if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) {
-                Py_DECREF(spec);
-                spec = NULL;
-            }
-            Py_XDECREF(unsafe);
-        }
-        if (likely(!spec)) {
-            PyErr_Clear();
-            return module;
-        }
-        Py_DECREF(spec);
-        Py_DECREF(module);
-    } else if (PyErr_Occurred()) {
-        PyErr_Clear();
-    }
-#endif
-    return __Pyx__ImportDottedModule(name, parts_tuple);
-}
-
-/* FetchSharedCythonModule */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
-    PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
-    if (unlikely(!abi_module)) return NULL;
-    Py_INCREF(abi_module);
-    return abi_module;
-}
-
-/* FetchCommonType */
-static int __Pyx_VerifyCachedType(PyObject *cached_type,
-                               const char *name,
-                               Py_ssize_t basicsize,
-                               Py_ssize_t expected_basicsize) {
-    if (!PyType_Check(cached_type)) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s is not a type object", name);
-        return -1;
-    }
-    if (basicsize != expected_basicsize) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s has the wrong size, try recompiling",
-            name);
-        return -1;
-    }
-    return 0;
-}
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* abi_module;
-    const char* object_name;
-    PyTypeObject *cached_type = NULL;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    object_name = strrchr(type->tp_name, '.');
-    object_name = object_name ? object_name+1 : type->tp_name;
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        if (__Pyx_VerifyCachedType(
-              (PyObject *)cached_type,
-              object_name,
-              cached_type->tp_basicsize,
-              type->tp_basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    if (PyType_Ready(type) < 0) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0)
-        goto bad;
-    Py_INCREF(type);
-    cached_type = type;
-done:
-    Py_DECREF(abi_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#else
-static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) {
-    PyObject *abi_module, *cached_type = NULL;
-    const char* object_name = strrchr(spec->name, '.');
-    object_name = object_name ? object_name+1 : spec->name;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    cached_type = PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        Py_ssize_t basicsize;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        PyObject *py_basicsize;
-        py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__");
-        if (unlikely(!py_basicsize)) goto bad;
-        basicsize = PyLong_AsSsize_t(py_basicsize);
-        Py_DECREF(py_basicsize);
-        py_basicsize = 0;
-        if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-#else
-        basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1;
-#endif
-        if (__Pyx_VerifyCachedType(
-              cached_type,
-              object_name,
-              basicsize,
-              spec->basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    CYTHON_UNUSED_VAR(module);
-    cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases);
-    if (unlikely(!cached_type)) goto bad;
-    if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad;
-done:
-    Py_DECREF(abi_module);
-    assert(cached_type == NULL || PyType_Check(cached_type));
-    return (PyTypeObject *) cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#endif
-
-/* PyVectorcallFastCallDict */
-#if CYTHON_METH_FASTCALL
-static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    PyObject *res = NULL;
-    PyObject *kwnames;
-    PyObject **newargs;
-    PyObject **kwvalues;
-    Py_ssize_t i, pos;
-    size_t j;
-    PyObject *key, *value;
-    unsigned long keys_are_strings;
-    Py_ssize_t nkw = PyDict_GET_SIZE(kw);
-    newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0]));
-    if (unlikely(newargs == NULL)) {
-        PyErr_NoMemory();
-        return NULL;
-    }
-    for (j = 0; j < nargs; j++) newargs[j] = args[j];
-    kwnames = PyTuple_New(nkw);
-    if (unlikely(kwnames == NULL)) {
-        PyMem_Free(newargs);
-        return NULL;
-    }
-    kwvalues = newargs + nargs;
-    pos = i = 0;
-    keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS;
-    while (PyDict_Next(kw, &pos, &key, &value)) {
-        keys_are_strings &= Py_TYPE(key)->tp_flags;
-        Py_INCREF(key);
-        Py_INCREF(value);
-        PyTuple_SET_ITEM(kwnames, i, key);
-        kwvalues[i] = value;
-        i++;
-    }
-    if (unlikely(!keys_are_strings)) {
-        PyErr_SetString(PyExc_TypeError, "keywords must be strings");
-        goto cleanup;
-    }
-    res = vc(func, newargs, nargs, kwnames);
-cleanup:
-    Py_DECREF(kwnames);
-    for (i = 0; i < nkw; i++)
-        Py_DECREF(kwvalues[i]);
-    PyMem_Free(newargs);
-    return res;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) {
-        return vc(func, args, nargs, NULL);
-    }
-    return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw);
-}
-#endif
-
-/* CythonFunctionShared */
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1
-    __Pyx_Py_XDECREF_SET(
-        __Pyx_CyFunction_GetClassObj(f),
-            ((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#else
-    __Pyx_Py_XDECREF_SET(
-        ((PyCMethodObject *) (f))->mm_class,
-        (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#endif
-}
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
-{
-    CYTHON_UNUSED_VAR(closure);
-    if (unlikely(op->func_doc == NULL)) {
-        if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_doc, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_dict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(op);
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyTuple_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_tuple, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_tuple;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_kwdict;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_annotations, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->func_annotations;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyObject *
-__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
-    int is_coroutine;
-    CYTHON_UNUSED_VAR(context);
-    if (op->func_is_coroutine) {
-        return __Pyx_NewRef(op->func_is_coroutine);
-    }
-    is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE;
-#if PY_VERSION_HEX >= 0x03050000
-    if (is_coroutine) {
-        PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine;
-        fromlist = PyList_New(1);
-        if (unlikely(!fromlist)) return NULL;
-        Py_INCREF(marker);
-        PyList_SET_ITEM(fromlist, 0, marker);
-        module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
-        Py_DECREF(fromlist);
-        if (unlikely(!module)) goto ignore;
-        op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker);
-        Py_DECREF(module);
-        if (likely(op->func_is_coroutine)) {
-            return __Pyx_NewRef(op->func_is_coroutine);
-        }
-ignore:
-        PyErr_Clear();
-    }
-#endif
-    op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
-    return __Pyx_NewRef(op->func_is_coroutine);
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
-#if CYTHON_METH_FASTCALL
-#if CYTHON_BACKPORT_VECTORCALL
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
-#else
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
-#endif
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
-#else
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
-#endif
-#endif
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args)
-{
-    CYTHON_UNUSED_VAR(args);
-#if PY_MAJOR_VERSION >= 3
-    Py_INCREF(m->func_qualname);
-    return m->func_qualname;
-#else
-    return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyCFunctionObject *cf = (PyCFunctionObject*) op;
-    if (unlikely(op == NULL))
-        return NULL;
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-    cf->m_ml = ml;
-    cf->m_self = (PyObject *) op;
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-    Py_XINCREF(module);
-    cf->m_module = module;
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1
-    op->func_classobj = NULL;
-#else
-    ((PyCMethodObject*)op)->mm_class = NULL;
-#endif
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    op->func_is_coroutine = NULL;
-#if CYTHON_METH_FASTCALL
-    switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) {
-    case METH_NOARGS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS;
-        break;
-    case METH_O:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O;
-        break;
-    case METH_METHOD | METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD;
-        break;
-    case METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS;
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = NULL;
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        Py_DECREF(op);
-        return NULL;
-    }
-#endif
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-    Py_CLEAR(((PyCFunctionObject*)m)->m_module);
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-#if PY_VERSION_HEX < 0x030900B1
-    Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
-#else
-    {
-        PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class;
-        ((PyCMethodObject *) (m))->mm_class = NULL;
-        Py_XDECREF(cls);
-    }
-#endif
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    Py_CLEAR(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    __Pyx_PyHeapTypeObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-    Py_VISIT(((PyCFunctionObject*)m)->m_module);
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-    Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    Py_VISIT(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        return NULL;
-    }
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-#if CYTHON_METH_FASTCALL
-     __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
-    if (vc) {
-#if CYTHON_ASSUME_SAFE_MACROS
-        return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
-#else
-        (void) &__Pyx_PyVectorcall_FastCallDict;
-        return PyVectorcall_Call(func, args, kw);
-#endif
-    }
-#endif
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-        argc = PyTuple_GET_SIZE(args);
-        new_args = PyTuple_GetSlice(args, 1, argc);
-        if (unlikely(!new_args))
-            return NULL;
-        self = PyTuple_GetItem(args, 0);
-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-#if PY_MAJOR_VERSION > 2
-            PyErr_Format(PyExc_TypeError,
-                         "unbound method %.200S() needs an argument",
-                         cyfunc->func_qualname);
-#else
-            PyErr_SetString(PyExc_TypeError,
-                            "unbound method needs an argument");
-#endif
-            return NULL;
-        }
-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
-}
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames)
-{
-    int ret = 0;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        if (unlikely(nargs < 1)) {
-            PyErr_Format(PyExc_TypeError, "%.200s() needs an argument",
-                         ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-            return -1;
-        }
-        ret = 1;
-    }
-    if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) {
-        PyErr_Format(PyExc_TypeError,
-                     "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-        return -1;
-    }
-    return ret;
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 0)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, NULL);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 1)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, args[0]);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-    PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc);
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames);
-}
-#endif
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_CyFunctionType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc},
-    {Py_tp_repr, (void *)__Pyx_CyFunction_repr},
-    {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod},
-    {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse},
-    {Py_tp_clear, (void *)__Pyx_CyFunction_clear},
-    {Py_tp_methods, (void *)__pyx_CyFunction_methods},
-    {Py_tp_members, (void *)__pyx_CyFunction_members},
-    {Py_tp_getset, (void *)__pyx_CyFunction_getsets},
-    {Py_tp_descr_get, (void *)__Pyx_PyMethod_New},
-    {0, 0},
-};
-static PyType_Spec __pyx_CyFunctionType_spec = {
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL)
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    __pyx_CyFunctionType_slots
-};
-#else
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-    (destructor) __Pyx_CyFunction_dealloc,
-#if !CYTHON_METH_FASTCALL
-    0,
-#elif CYTHON_BACKPORT_VECTORCALL
-    (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall),
-#else
-    offsetof(PyCFunctionObject, vectorcall),
-#endif
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#ifdef _Py_TPFLAGS_HAVE_VECTORCALL
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_PyMethod_New,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_CyFunction_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-#endif
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
-    memset(m->defaults, 0, size);
-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
-        ml, flags, qualname, closure, module, globals, code
-    );
-    if (likely(op)) {
-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index));
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError,
-            "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name);
-        __Pyx_DECREF_TypeName(index_type_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) {
-    __Pyx_TypeName obj_type_name;
-    if (likely(PyType_Check(obj))) {
-        PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem);
-        if (meth) {
-            PyObject *result = __Pyx_PyObject_CallOneArg(meth, key);
-            Py_DECREF(meth);
-            return result;
-        }
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) {
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyMappingMethods *mm = tp->tp_as_mapping;
-    PySequenceMethods *sm = tp->tp_as_sequence;
-    if (likely(mm && mm->mp_subscript)) {
-        return mm->mp_subscript(obj, key);
-    }
-    if (likely(sm && sm->sq_item)) {
-        return __Pyx_PyObject_GetIndex(obj, key);
-    }
-    return __Pyx_PyObject_GetItem_Slow(obj, key);
-}
-#endif
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    CYTHON_MAYBE_UNUSED_VAR(tstate);
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-#endif
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    if (c_line) {
-        (void) __pyx_cfilenm;
-        (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
-    }
-    _PyTraceback_Add(funcname, filename, py_line);
-}
-#else
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-#endif
-
-/* Declarations */
-#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus)
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return ::std::complex< double >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return x + y*(__pyx_t_double_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      __pyx_t_double_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus)
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabs(b.real) >= fabs(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                double r = b.imag / b.real;
-                double s = (double)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_double_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            double r = b.real / b.imag;
-            double s = (double)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_double_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            double denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_double_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrt(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypot(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-            __pyx_t_double_complex z;
-            double r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    double denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_double(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if ((b.imag == 0) && (a.real >= 0)) {
-                    z.real = pow(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_double(a);
-                theta = atan2(a.imag, a.real);
-            }
-            lnr = log(r);
-            z_r = exp(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cos(z_theta);
-            z.imag = z_r * sin(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* FromPy */
-static __pyx_t_double_complex __Pyx_PyComplex_As___pyx_t_double_complex(PyObject* o) {
-    Py_complex cval;
-#if !CYTHON_COMPILING_IN_PYPY
-    if (PyComplex_CheckExact(o))
-        cval = ((PyComplexObject *)o)->cval;
-    else
-#endif
-        cval = PyComplex_AsCComplex(o);
-    return __pyx_t_double_complex_from_parts(
-               (double)cval.real,
-               (double)cval.imag);
-}
-
-/* CIntFromPyVerify */
-#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* CIntFromPy */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(int) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) {
-                                return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) {
-                                return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) {
-                                return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(int) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(int) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (int) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (int) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (int) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (int) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (int) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((int) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((int) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* FormatTypeName */
-#if CYTHON_COMPILING_IN_LIMITED_API
-static __Pyx_TypeName
-__Pyx_PyType_GetName(PyTypeObject* tp)
-{
-    PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp,
-                                               __pyx_n_s_name);
-    if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
-        PyErr_Clear();
-        Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__15));
-    }
-    return name;
-}
-#endif
-
-/* CIntFromPy */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(long) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) {
-                                return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) {
-                                return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) {
-                                return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(long) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(long) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (long) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (long) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (long) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (long) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (long) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((long) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((long) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_value = exc_info->exc_value;
-    exc_info->exc_value = *value;
-    if (tmp_value == NULL || tmp_value == Py_None) {
-        Py_XDECREF(tmp_value);
-        tmp_value = NULL;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-    } else {
-        tmp_type = (PyObject*) Py_TYPE(tmp_value);
-        Py_INCREF(tmp_type);
-        #if CYTHON_COMPILING_IN_CPYTHON
-        tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback;
-        Py_XINCREF(tmp_tb);
-        #else
-        tmp_tb = PyException_GetTraceback(tmp_value);
-        #endif
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-  #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-  #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args[3] = {NULL, arg1, arg2};
-    return __Pyx_PyObject_FastCall(function, args+1, 2 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectCallMethod1 */
-static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) {
-    PyObject *result = __Pyx_PyObject_CallOneArg(method, arg);
-    Py_DECREF(method);
-    return result;
-}
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
-    PyObject *method = NULL, *result;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_Call2Args(method, obj, arg);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) return NULL;
-    return __Pyx__PyObject_CallMethod1(method, arg);
-}
-
-/* CoroutineBase */
-#include <frameobject.h>
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#define __Pyx_Coroutine_Undelegate(gen) Py_CLEAR((gen)->yieldfrom)
-static int __Pyx_PyGen__FetchStopIterationValue(PyThreadState *__pyx_tstate, PyObject **pvalue) {
-    PyObject *et, *ev, *tb;
-    PyObject *value = NULL;
-    CYTHON_UNUSED_VAR(__pyx_tstate);
-    __Pyx_ErrFetch(&et, &ev, &tb);
-    if (!et) {
-        Py_XDECREF(tb);
-        Py_XDECREF(ev);
-        Py_INCREF(Py_None);
-        *pvalue = Py_None;
-        return 0;
-    }
-    if (likely(et == PyExc_StopIteration)) {
-        if (!ev) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#if PY_VERSION_HEX >= 0x030300A0
-        else if (likely(__Pyx_IS_TYPE(ev, (PyTypeObject*)PyExc_StopIteration))) {
-            value = ((PyStopIterationObject *)ev)->value;
-            Py_INCREF(value);
-            Py_DECREF(ev);
-        }
-#endif
-        else if (unlikely(PyTuple_Check(ev))) {
-            if (PyTuple_GET_SIZE(ev) >= 1) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                value = PyTuple_GET_ITEM(ev, 0);
-                Py_INCREF(value);
-#else
-                value = PySequence_ITEM(ev, 0);
-#endif
-            } else {
-                Py_INCREF(Py_None);
-                value = Py_None;
-            }
-            Py_DECREF(ev);
-        }
-        else if (!__Pyx_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration)) {
-            value = ev;
-        }
-        if (likely(value)) {
-            Py_XDECREF(tb);
-            Py_DECREF(et);
-            *pvalue = value;
-            return 0;
-        }
-    } else if (!__Pyx_PyErr_GivenExceptionMatches(et, PyExc_StopIteration)) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    PyErr_NormalizeException(&et, &ev, &tb);
-    if (unlikely(!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration))) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    Py_XDECREF(tb);
-    Py_DECREF(et);
-#if PY_VERSION_HEX >= 0x030300A0
-    value = ((PyStopIterationObject *)ev)->value;
-    Py_INCREF(value);
-    Py_DECREF(ev);
-#else
-    {
-        PyObject* args = __Pyx_PyObject_GetAttrStr(ev, __pyx_n_s_args);
-        Py_DECREF(ev);
-        if (likely(args)) {
-            value = PySequence_GetItem(args, 0);
-            Py_DECREF(args);
-        }
-        if (unlikely(!value)) {
-            __Pyx_ErrRestore(NULL, NULL, NULL);
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-    }
-#endif
-    *pvalue = value;
-    return 0;
-}
-static CYTHON_INLINE
-void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *exc_state) {
-#if PY_VERSION_HEX >= 0x030B00a4
-    Py_CLEAR(exc_state->exc_value);
-#else
-    PyObject *t, *v, *tb;
-    t = exc_state->exc_type;
-    v = exc_state->exc_value;
-    tb = exc_state->exc_traceback;
-    exc_state->exc_type = NULL;
-    exc_state->exc_value = NULL;
-    exc_state->exc_traceback = NULL;
-    Py_XDECREF(t);
-    Py_XDECREF(v);
-    Py_XDECREF(tb);
-#endif
-}
-#define __Pyx_Coroutine_AlreadyRunningError(gen)  (__Pyx__Coroutine_AlreadyRunningError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyRunningError(__pyx_CoroutineObject *gen) {
-    const char *msg;
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check((PyObject*)gen)) {
-        msg = "coroutine already executing";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact((PyObject*)gen)) {
-        msg = "async generator already executing";
-    #endif
-    } else {
-        msg = "generator already executing";
-    }
-    PyErr_SetString(PyExc_ValueError, msg);
-}
-#define __Pyx_Coroutine_NotStartedError(gen)  (__Pyx__Coroutine_NotStartedError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_NotStartedError(PyObject *gen) {
-    const char *msg;
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check(gen)) {
-        msg = "can't send non-None value to a just-started coroutine";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact(gen)) {
-        msg = "can't send non-None value to a just-started async generator";
-    #endif
-    } else {
-        msg = "can't send non-None value to a just-started generator";
-    }
-    PyErr_SetString(PyExc_TypeError, msg);
-}
-#define __Pyx_Coroutine_AlreadyTerminatedError(gen, value, closing)  (__Pyx__Coroutine_AlreadyTerminatedError(gen, value, closing), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyTerminatedError(PyObject *gen, PyObject *value, int closing) {
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    CYTHON_MAYBE_UNUSED_VAR(closing);
-    #ifdef __Pyx_Coroutine_USED
-    if (!closing && __Pyx_Coroutine_Check(gen)) {
-        PyErr_SetString(PyExc_RuntimeError, "cannot reuse already awaited coroutine");
-    } else
-    #endif
-    if (value) {
-        #ifdef __Pyx_AsyncGen_USED
-        if (__Pyx_AsyncGen_CheckExact(gen))
-            PyErr_SetNone(__Pyx_PyExc_StopAsyncIteration);
-        else
-        #endif
-        PyErr_SetNone(PyExc_StopIteration);
-    }
-}
-static
-PyObject *__Pyx_Coroutine_SendEx(__pyx_CoroutineObject *self, PyObject *value, int closing) {
-    __Pyx_PyThreadState_declare
-    PyThreadState *tstate;
-    __Pyx_ExcInfoStruct *exc_state;
-    PyObject *retval;
-    assert(!self->is_running);
-    if (unlikely(self->resume_label == 0)) {
-        if (unlikely(value && value != Py_None)) {
-            return __Pyx_Coroutine_NotStartedError((PyObject*)self);
-        }
-    }
-    if (unlikely(self->resume_label == -1)) {
-        return __Pyx_Coroutine_AlreadyTerminatedError((PyObject*)self, value, closing);
-    }
-#if CYTHON_FAST_THREAD_STATE
-    __Pyx_PyThreadState_assign
-    tstate = __pyx_tstate;
-#else
-    tstate = __Pyx_PyThreadState_Current;
-#endif
-    exc_state = &self->gi_exc_state;
-    if (exc_state->exc_value) {
-        #if CYTHON_COMPILING_IN_PYPY
-        #else
-        PyObject *exc_tb;
-        #if PY_VERSION_HEX >= 0x030B00a4 && !CYTHON_COMPILING_IN_CPYTHON
-        exc_tb = PyException_GetTraceback(exc_state->exc_value);
-        #elif PY_VERSION_HEX >= 0x030B00a4
-        exc_tb = ((PyBaseExceptionObject*) exc_state->exc_value)->traceback;
-        #else
-        exc_tb = exc_state->exc_traceback;
-        #endif
-        if (exc_tb) {
-            PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
-            PyFrameObject *f = tb->tb_frame;
-            assert(f->f_back == NULL);
-            #if PY_VERSION_HEX >= 0x030B00A1
-            f->f_back = PyThreadState_GetFrame(tstate);
-            #else
-            Py_XINCREF(tstate->frame);
-            f->f_back = tstate->frame;
-            #endif
-            #if PY_VERSION_HEX >= 0x030B00a4 && !CYTHON_COMPILING_IN_CPYTHON
-            Py_DECREF(exc_tb);
-            #endif
-        }
-        #endif
-    }
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state->previous_item = tstate->exc_info;
-    tstate->exc_info = exc_state;
-#else
-    if (exc_state->exc_type) {
-        __Pyx_ExceptionSwap(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    } else {
-        __Pyx_Coroutine_ExceptionClear(exc_state);
-        __Pyx_ExceptionSave(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    }
-#endif
-    self->is_running = 1;
-    retval = self->body(self, tstate, value);
-    self->is_running = 0;
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state = &self->gi_exc_state;
-    tstate->exc_info = exc_state->previous_item;
-    exc_state->previous_item = NULL;
-    __Pyx_Coroutine_ResetFrameBackpointer(exc_state);
-#endif
-    return retval;
-}
-static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__Pyx_ExcInfoStruct *exc_state) {
-#if CYTHON_COMPILING_IN_PYPY
-    CYTHON_UNUSED_VAR(exc_state);
-#else
-    PyObject *exc_tb;
-    #if PY_VERSION_HEX >= 0x030B00a4
-    if (!exc_state->exc_value) return;
-    exc_tb = PyException_GetTraceback(exc_state->exc_value);
-    #else
-    exc_tb = exc_state->exc_traceback;
-    #endif
-    if (likely(exc_tb)) {
-        PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
-        PyFrameObject *f = tb->tb_frame;
-        Py_CLEAR(f->f_back);
-        #if PY_VERSION_HEX >= 0x030B00a4
-        Py_DECREF(exc_tb);
-        #endif
-    }
-#endif
-}
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_MethodReturn(PyObject* gen, PyObject *retval) {
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if (unlikely(!retval)) {
-        __Pyx_PyThreadState_declare
-        __Pyx_PyThreadState_assign
-        if (!__Pyx_PyErr_Occurred()) {
-            PyObject *exc = PyExc_StopIteration;
-            #ifdef __Pyx_AsyncGen_USED
-            if (__Pyx_AsyncGen_CheckExact(gen))
-                exc = __Pyx_PyExc_StopAsyncIteration;
-            #endif
-            __Pyx_PyErr_SetNone(exc);
-        }
-    }
-    return retval;
-}
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-static CYTHON_INLINE
-PyObject *__Pyx_PyGen_Send(PyGenObject *gen, PyObject *arg) {
-#if PY_VERSION_HEX <= 0x030A00A1
-    return _PyGen_Send(gen, arg);
-#else
-    PyObject *result;
-    if (PyIter_Send((PyObject*)gen, arg ? arg : Py_None, &result) == PYGEN_RETURN) {
-        if (PyAsyncGen_CheckExact(gen)) {
-            assert(result == Py_None);
-            PyErr_SetNone(PyExc_StopAsyncIteration);
-        }
-        else if (result == Py_None) {
-            PyErr_SetNone(PyExc_StopIteration);
-        }
-        else {
-            _PyGen_SetStopIterationValue(result);
-        }
-        Py_CLEAR(result);
-    }
-    return result;
-#endif
-}
-#endif
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_FinishDelegation(__pyx_CoroutineObject *gen) {
-    PyObject *ret;
-    PyObject *val = NULL;
-    __Pyx_Coroutine_Undelegate(gen);
-    __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, &val);
-    ret = __Pyx_Coroutine_SendEx(gen, val, 0);
-    Py_XDECREF(val);
-    return ret;
-}
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value) {
-    PyObject *retval;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-            ret = __Pyx_async_gen_asend_send(yf, value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03050000 && defined(PyCoro_CheckExact) && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyCoro_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        {
-            if (value == Py_None)
-                ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
-            else
-                ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value);
-        }
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        retval = __Pyx_Coroutine_FinishDelegation(gen);
-    } else {
-        retval = __Pyx_Coroutine_SendEx(gen, value, 0);
-    }
-    return __Pyx_Coroutine_MethodReturn(self, retval);
-}
-static int __Pyx_Coroutine_CloseIter(__pyx_CoroutineObject *gen, PyObject *yf) {
-    PyObject *retval = NULL;
-    int err = 0;
-    #ifdef __Pyx_Generator_USED
-    if (__Pyx_Generator_CheckExact(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_Coroutine_USED
-    if (__Pyx_Coroutine_Check(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-        retval = __Pyx_CoroutineAwait_Close((__pyx_CoroutineAwaitObject*)yf, NULL);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-        retval = __Pyx_async_gen_asend_close(yf, NULL);
-    } else
-    if (__pyx_PyAsyncGenAThrow_CheckExact(yf)) {
-        retval = __Pyx_async_gen_athrow_close(yf, NULL);
-    } else
-    #endif
-    {
-        PyObject *meth;
-        gen->is_running = 1;
-        meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_close);
-        if (unlikely(!meth)) {
-            if (unlikely(PyErr_Occurred())) {
-                PyErr_WriteUnraisable(yf);
-            }
-        } else {
-            retval = __Pyx_PyObject_CallNoArg(meth);
-            Py_DECREF(meth);
-            if (unlikely(!retval))
-                err = -1;
-        }
-        gen->is_running = 0;
-    }
-    Py_XDECREF(retval);
-    return err;
-}
-static PyObject *__Pyx_Generator_Next(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Generator_Next(yf);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, NULL);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, Py_None);
-        } else
-        #endif
-            ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        return __Pyx_Coroutine_FinishDelegation(gen);
-    }
-    return __Pyx_Coroutine_SendEx(gen, Py_None, 0);
-}
-static PyObject *__Pyx_Coroutine_Close_Method(PyObject *self, PyObject *arg) {
-    CYTHON_UNUSED_VAR(arg);
-    return __Pyx_Coroutine_Close(self);
-}
-static PyObject *__Pyx_Coroutine_Close(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *retval, *raised_exception;
-    PyObject *yf = gen->yieldfrom;
-    int err = 0;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        Py_INCREF(yf);
-        err = __Pyx_Coroutine_CloseIter(gen, yf);
-        __Pyx_Coroutine_Undelegate(gen);
-        Py_DECREF(yf);
-    }
-    if (err == 0)
-        PyErr_SetNone(PyExc_GeneratorExit);
-    retval = __Pyx_Coroutine_SendEx(gen, NULL, 1);
-    if (unlikely(retval)) {
-        const char *msg;
-        Py_DECREF(retval);
-        if ((0)) {
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_Coroutine_Check(self)) {
-            msg = "coroutine ignored GeneratorExit";
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        } else if (__Pyx_AsyncGen_CheckExact(self)) {
-#if PY_VERSION_HEX < 0x03060000
-            msg = "async generator ignored GeneratorExit - might require Python 3.6+ finalisation (PEP 525)";
-#else
-            msg = "async generator ignored GeneratorExit";
-#endif
-        #endif
-        } else {
-            msg = "generator ignored GeneratorExit";
-        }
-        PyErr_SetString(PyExc_RuntimeError, msg);
-        return NULL;
-    }
-    raised_exception = PyErr_Occurred();
-    if (likely(!raised_exception || __Pyx_PyErr_GivenExceptionMatches2(raised_exception, PyExc_GeneratorExit, PyExc_StopIteration))) {
-        if (raised_exception) PyErr_Clear();
-        Py_INCREF(Py_None);
-        return Py_None;
-    }
-    return NULL;
-}
-static PyObject *__Pyx__Coroutine_Throw(PyObject *self, PyObject *typ, PyObject *val, PyObject *tb,
-                                        PyObject *args, int close_on_genexit) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        Py_INCREF(yf);
-        if (__Pyx_PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit) && close_on_genexit) {
-            int err = __Pyx_Coroutine_CloseIter(gen, yf);
-            Py_DECREF(yf);
-            __Pyx_Coroutine_Undelegate(gen);
-            if (err < 0)
-                return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-            goto throw_here;
-        }
-        gen->is_running = 1;
-        if (0
-        #ifdef __Pyx_Generator_USED
-            || __Pyx_Generator_CheckExact(yf)
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-            || __Pyx_Coroutine_Check(yf)
-        #endif
-            ) {
-            ret = __Pyx__Coroutine_Throw(yf, typ, val, tb, args, close_on_genexit);
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-            ret = __Pyx__Coroutine_Throw(((__pyx_CoroutineAwaitObject*)yf)->coroutine, typ, val, tb, args, close_on_genexit);
-        #endif
-        } else {
-            PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_throw);
-            if (unlikely(!meth)) {
-                Py_DECREF(yf);
-                if (unlikely(PyErr_Occurred())) {
-                    gen->is_running = 0;
-                    return NULL;
-                }
-                __Pyx_Coroutine_Undelegate(gen);
-                gen->is_running = 0;
-                goto throw_here;
-            }
-            if (likely(args)) {
-                ret = __Pyx_PyObject_Call(meth, args, NULL);
-            } else {
-                PyObject *cargs[4] = {NULL, typ, val, tb};
-                ret = __Pyx_PyObject_FastCall(meth, cargs+1, 3 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-            }
-            Py_DECREF(meth);
-        }
-        gen->is_running = 0;
-        Py_DECREF(yf);
-        if (!ret) {
-            ret = __Pyx_Coroutine_FinishDelegation(gen);
-        }
-        return __Pyx_Coroutine_MethodReturn(self, ret);
-    }
-throw_here:
-    __Pyx_Raise(typ, val, tb, NULL);
-    return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-}
-static PyObject *__Pyx_Coroutine_Throw(PyObject *self, PyObject *args) {
-    PyObject *typ;
-    PyObject *val = NULL;
-    PyObject *tb = NULL;
-    if (unlikely(!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb)))
-        return NULL;
-    return __Pyx__Coroutine_Throw(self, typ, val, tb, args, 1);
-}
-static CYTHON_INLINE int __Pyx_Coroutine_traverse_excstate(__Pyx_ExcInfoStruct *exc_state, visitproc visit, void *arg) {
-#if PY_VERSION_HEX >= 0x030B00a4
-    Py_VISIT(exc_state->exc_value);
-#else
-    Py_VISIT(exc_state->exc_type);
-    Py_VISIT(exc_state->exc_value);
-    Py_VISIT(exc_state->exc_traceback);
-#endif
-    return 0;
-}
-static int __Pyx_Coroutine_traverse(__pyx_CoroutineObject *gen, visitproc visit, void *arg) {
-    Py_VISIT(gen->closure);
-    Py_VISIT(gen->classobj);
-    Py_VISIT(gen->yieldfrom);
-    return __Pyx_Coroutine_traverse_excstate(&gen->gi_exc_state, visit, arg);
-}
-static int __Pyx_Coroutine_clear(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    Py_CLEAR(gen->closure);
-    Py_CLEAR(gen->classobj);
-    Py_CLEAR(gen->yieldfrom);
-    __Pyx_Coroutine_ExceptionClear(&gen->gi_exc_state);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)gen)->ag_finalizer);
-    }
-#endif
-    Py_CLEAR(gen->gi_code);
-    Py_CLEAR(gen->gi_frame);
-    Py_CLEAR(gen->gi_name);
-    Py_CLEAR(gen->gi_qualname);
-    Py_CLEAR(gen->gi_modulename);
-    return 0;
-}
-static void __Pyx_Coroutine_dealloc(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject_GC_UnTrack(gen);
-    if (gen->gi_weakreflist != NULL)
-        PyObject_ClearWeakRefs(self);
-    if (gen->resume_label >= 0) {
-        PyObject_GC_Track(self);
-#if PY_VERSION_HEX >= 0x030400a1 && CYTHON_USE_TP_FINALIZE
-        if (unlikely(PyObject_CallFinalizerFromDealloc(self)))
-#else
-        Py_TYPE(gen)->tp_del(self);
-        if (unlikely(Py_REFCNT(self) > 0))
-#endif
-        {
-            return;
-        }
-        PyObject_GC_UnTrack(self);
-    }
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        /* We have to handle this case for asynchronous generators
-           right here, because this code has to be between UNTRACK
-           and GC_Del. */
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)self)->ag_finalizer);
-    }
-#endif
-    __Pyx_Coroutine_clear(self);
-    __Pyx_PyHeapTypeObject_GC_Del(gen);
-}
-static void __Pyx_Coroutine_del(PyObject *self) {
-    PyObject *error_type, *error_value, *error_traceback;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    __Pyx_PyThreadState_declare
-    if (gen->resume_label < 0) {
-        return;
-    }
-#if !CYTHON_USE_TP_FINALIZE
-    assert(self->ob_refcnt == 0);
-    __Pyx_SET_REFCNT(self, 1);
-#endif
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&error_type, &error_value, &error_traceback);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        __pyx_PyAsyncGenObject *agen = (__pyx_PyAsyncGenObject*)self;
-        PyObject *finalizer = agen->ag_finalizer;
-        if (finalizer && !agen->ag_closed) {
-            PyObject *res = __Pyx_PyObject_CallOneArg(finalizer, self);
-            if (unlikely(!res)) {
-                PyErr_WriteUnraisable(self);
-            } else {
-                Py_DECREF(res);
-            }
-            __Pyx_ErrRestore(error_type, error_value, error_traceback);
-            return;
-        }
-    }
-#endif
-    if (unlikely(gen->resume_label == 0 && !error_value)) {
-#ifdef __Pyx_Coroutine_USED
-#ifdef __Pyx_Generator_USED
-    if (!__Pyx_Generator_CheckExact(self))
-#endif
-        {
-        PyObject_GC_UnTrack(self);
-#if PY_MAJOR_VERSION >= 3  || defined(PyErr_WarnFormat)
-        if (unlikely(PyErr_WarnFormat(PyExc_RuntimeWarning, 1, "coroutine '%.50S' was never awaited", gen->gi_qualname) < 0))
-            PyErr_WriteUnraisable(self);
-#else
-        {PyObject *msg;
-        char *cmsg;
-        #if CYTHON_COMPILING_IN_PYPY
-        msg = NULL;
-        cmsg = (char*) "coroutine was never awaited";
-        #else
-        char *cname;
-        PyObject *qualname;
-        qualname = gen->gi_qualname;
-        cname = PyString_AS_STRING(qualname);
-        msg = PyString_FromFormat("coroutine '%.50s' was never awaited", cname);
-        if (unlikely(!msg)) {
-            PyErr_Clear();
-            cmsg = (char*) "coroutine was never awaited";
-        } else {
-            cmsg = PyString_AS_STRING(msg);
-        }
-        #endif
-        if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, cmsg, 1) < 0))
-            PyErr_WriteUnraisable(self);
-        Py_XDECREF(msg);}
-#endif
-        PyObject_GC_Track(self);
-        }
-#endif
-    } else {
-        PyObject *res = __Pyx_Coroutine_Close(self);
-        if (unlikely(!res)) {
-            if (PyErr_Occurred())
-                PyErr_WriteUnraisable(self);
-        } else {
-            Py_DECREF(res);
-        }
-    }
-    __Pyx_ErrRestore(error_type, error_value, error_traceback);
-#if !CYTHON_USE_TP_FINALIZE
-    assert(Py_REFCNT(self) > 0);
-    if (likely(--self->ob_refcnt == 0)) {
-        return;
-    }
-    {
-        Py_ssize_t refcnt = Py_REFCNT(self);
-        _Py_NewReference(self);
-        __Pyx_SET_REFCNT(self, refcnt);
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    assert(PyType_IS_GC(Py_TYPE(self)) &&
-           _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED);
-    _Py_DEC_REFTOTAL;
-#endif
-#ifdef COUNT_ALLOCS
-    --Py_TYPE(self)->tp_frees;
-    --Py_TYPE(self)->tp_allocs;
-#endif
-#endif
-}
-static PyObject *
-__Pyx_Coroutine_get_name(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *name = self->gi_name;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_name(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(self->gi_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_qualname(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *name = self->gi_qualname;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_qualname(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(self->gi_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_frame(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *frame = self->gi_frame;
-    CYTHON_UNUSED_VAR(context);
-    if (!frame) {
-        if (unlikely(!self->gi_code)) {
-            Py_RETURN_NONE;
-        }
-        frame = (PyObject *) PyFrame_New(
-            PyThreadState_Get(),            /*PyThreadState *tstate,*/
-            (PyCodeObject*) self->gi_code,  /*PyCodeObject *code,*/
-            __pyx_d,                 /*PyObject *globals,*/
-            0                               /*PyObject *locals*/
-        );
-        if (unlikely(!frame))
-            return NULL;
-        self->gi_frame = frame;
-    }
-    Py_INCREF(frame);
-    return frame;
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
-            PyTypeObject* type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    __pyx_CoroutineObject *gen = PyObject_GC_New(__pyx_CoroutineObject, type);
-    if (unlikely(!gen))
-        return NULL;
-    return __Pyx__Coroutine_NewInit(gen, body, code, closure, name, qualname, module_name);
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
-            __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    gen->body = body;
-    gen->closure = closure;
-    Py_XINCREF(closure);
-    gen->is_running = 0;
-    gen->resume_label = 0;
-    gen->classobj = NULL;
-    gen->yieldfrom = NULL;
-    #if PY_VERSION_HEX >= 0x030B00a4
-    gen->gi_exc_state.exc_value = NULL;
-    #else
-    gen->gi_exc_state.exc_type = NULL;
-    gen->gi_exc_state.exc_value = NULL;
-    gen->gi_exc_state.exc_traceback = NULL;
-    #endif
-#if CYTHON_USE_EXC_INFO_STACK
-    gen->gi_exc_state.previous_item = NULL;
-#endif
-    gen->gi_weakreflist = NULL;
-    Py_XINCREF(qualname);
-    gen->gi_qualname = qualname;
-    Py_XINCREF(name);
-    gen->gi_name = name;
-    Py_XINCREF(module_name);
-    gen->gi_modulename = module_name;
-    Py_XINCREF(code);
-    gen->gi_code = code;
-    gen->gi_frame = NULL;
-    PyObject_GC_Track(gen);
-    return gen;
-}
-
-/* PatchModuleWithCoroutine */
-static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    int result;
-    PyObject *globals, *result_obj;
-    globals = PyDict_New();  if (unlikely(!globals)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_coroutine_type",
-    #ifdef __Pyx_Coroutine_USED
-        (PyObject*)__pyx_CoroutineType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_generator_type",
-    #ifdef __Pyx_Generator_USED
-        (PyObject*)__pyx_GeneratorType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "_module", module) < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "__builtins__", __pyx_b) < 0)) goto ignore;
-    result_obj = PyRun_String(py_code, Py_file_input, globals, globals);
-    if (unlikely(!result_obj)) goto ignore;
-    Py_DECREF(result_obj);
-    Py_DECREF(globals);
-    return module;
-ignore:
-    Py_XDECREF(globals);
-    PyErr_WriteUnraisable(module);
-    if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, "Cython module failed to patch module with custom type", 1) < 0)) {
-        Py_DECREF(module);
-        module = NULL;
-    }
-#else
-    py_code++;
-#endif
-    return module;
-}
-
-/* PatchGeneratorABC */
-#ifndef CYTHON_REGISTER_ABCS
-#define CYTHON_REGISTER_ABCS 1
-#endif
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-static PyObject* __Pyx_patch_abc_module(PyObject *module);
-static PyObject* __Pyx_patch_abc_module(PyObject *module) {
-    module = __Pyx_Coroutine_patch_module(
-        module, ""
-"if _cython_generator_type is not None:\n"
-"    try: Generator = _module.Generator\n"
-"    except AttributeError: pass\n"
-"    else: Generator.register(_cython_generator_type)\n"
-"if _cython_coroutine_type is not None:\n"
-"    try: Coroutine = _module.Coroutine\n"
-"    except AttributeError: pass\n"
-"    else: Coroutine.register(_cython_coroutine_type)\n"
-    );
-    return module;
-}
-#endif
-static int __Pyx_patch_abc(void) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    static int abc_patched = 0;
-    if (CYTHON_REGISTER_ABCS && !abc_patched) {
-        PyObject *module;
-        module = PyImport_ImportModule((PY_MAJOR_VERSION >= 3) ? "collections.abc" : "collections");
-        if (unlikely(!module)) {
-            PyErr_WriteUnraisable(NULL);
-            if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning,
-                    ((PY_MAJOR_VERSION >= 3) ?
-                        "Cython module failed to register with collections.abc module" :
-                        "Cython module failed to register with collections module"), 1) < 0)) {
-                return -1;
-            }
-        } else {
-            module = __Pyx_patch_abc_module(module);
-            abc_patched = 1;
-            if (unlikely(!module))
-                return -1;
-            Py_DECREF(module);
-        }
-        module = PyImport_ImportModule("backports_abc");
-        if (module) {
-            module = __Pyx_patch_abc_module(module);
-            Py_XDECREF(module);
-        }
-        if (!module) {
-            PyErr_Clear();
-        }
-    }
-#else
-    if ((0)) __Pyx_Coroutine_patch_module(NULL, NULL);
-#endif
-    return 0;
-}
-
-/* Generator */
-static PyMethodDef __pyx_Generator_methods[] = {
-    {"send", (PyCFunction) __Pyx_Coroutine_Send, METH_O,
-     (char*) PyDoc_STR("send(arg) -> send 'arg' into generator,\nreturn next yielded value or raise StopIteration.")},
-    {"throw", (PyCFunction) __Pyx_Coroutine_Throw, METH_VARARGS,
-     (char*) PyDoc_STR("throw(typ[,val[,tb]]) -> raise exception in generator,\nreturn next yielded value or raise StopIteration.")},
-    {"close", (PyCFunction) __Pyx_Coroutine_Close_Method, METH_NOARGS,
-     (char*) PyDoc_STR("close() -> raise GeneratorExit inside generator.")},
-    {0, 0, 0, 0}
-};
-static PyMemberDef __pyx_Generator_memberlist[] = {
-    {(char *) "gi_running", T_BOOL, offsetof(__pyx_CoroutineObject, is_running), READONLY, NULL},
-    {(char*) "gi_yieldfrom", T_OBJECT, offsetof(__pyx_CoroutineObject, yieldfrom), READONLY,
-     (char*) PyDoc_STR("object being iterated by 'yield from', or None")},
-    {(char*) "gi_code", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_code), READONLY, NULL},
-    {(char *) "__module__", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_modulename), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CoroutineObject, gi_weakreflist), READONLY, 0},
-#endif
-    {0, 0, 0, 0, 0}
-};
-static PyGetSetDef __pyx_Generator_getsets[] = {
-    {(char *) "__name__", (getter)__Pyx_Coroutine_get_name, (setter)__Pyx_Coroutine_set_name,
-     (char*) PyDoc_STR("name of the generator"), 0},
-    {(char *) "__qualname__", (getter)__Pyx_Coroutine_get_qualname, (setter)__Pyx_Coroutine_set_qualname,
-     (char*) PyDoc_STR("qualified name of the generator"), 0},
-    {(char *) "gi_frame", (getter)__Pyx_Coroutine_get_frame, NULL,
-     (char*) PyDoc_STR("Frame of the generator"), 0},
-    {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_GeneratorType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_Coroutine_dealloc},
-    {Py_tp_traverse, (void *)__Pyx_Coroutine_traverse},
-    {Py_tp_iter, (void *)PyObject_SelfIter},
-    {Py_tp_iternext, (void *)__Pyx_Generator_Next},
-    {Py_tp_methods, (void *)__pyx_Generator_methods},
-    {Py_tp_members, (void *)__pyx_Generator_memberlist},
-    {Py_tp_getset, (void *)__pyx_Generator_getsets},
-    {Py_tp_getattro, (void *) __Pyx_PyObject_GenericGetAttrNoDict},
-#if CYTHON_USE_TP_FINALIZE
-    {Py_tp_finalize, (void *)__Pyx_Coroutine_del},
-#endif
-    {0, 0},
-};
-static PyType_Spec __pyx_GeneratorType_spec = {
-    __PYX_TYPE_MODULE_PREFIX "generator",
-    sizeof(__pyx_CoroutineObject),
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
-    __pyx_GeneratorType_slots
-};
-#else
-static PyTypeObject __pyx_GeneratorType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "generator",
-    sizeof(__pyx_CoroutineObject),
-    0,
-    (destructor) __Pyx_Coroutine_dealloc,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
-    0,
-    (traverseproc) __Pyx_Coroutine_traverse,
-    0,
-    0,
-    offsetof(__pyx_CoroutineObject, gi_weakreflist),
-    0,
-    (iternextfunc) __Pyx_Generator_Next,
-    __pyx_Generator_methods,
-    __pyx_Generator_memberlist,
-    __pyx_Generator_getsets,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if CYTHON_USE_TP_FINALIZE
-    0,
-#else
-    __Pyx_Coroutine_del,
-#endif
-    0,
-#if CYTHON_USE_TP_FINALIZE
-    __Pyx_Coroutine_del,
-#elif PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_Generator_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_GeneratorType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_GeneratorType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_GeneratorType_type.tp_getattro = __Pyx_PyObject_GenericGetAttrNoDict;
-    __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter;
-    __pyx_GeneratorType = __Pyx_FetchCommonType(&__pyx_GeneratorType_type);
-#endif
-    if (unlikely(!__pyx_GeneratorType)) {
-        return -1;
-    }
-    return 0;
-}
-
-/* CheckBinaryVersion */
-static int __Pyx_check_binary_version(void) {
-    char ctversion[5];
-    int same=1, i, found_dot;
-    const char* rt_from_call = Py_GetVersion();
-    PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    found_dot = 0;
-    for (i = 0; i < 4; i++) {
-        if (!ctversion[i]) {
-            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');
-            break;
-        }
-        if (rt_from_call[i] != ctversion[i]) {
-            same = 0;
-            break;
-        }
-    }
-    if (!same) {
-        char rtversion[5] = {'\0'};
-        char message[200];
-        for (i=0; i<4; ++i) {
-            if (rt_from_call[i] == '.') {
-                if (found_dot) break;
-                found_dot = 1;
-            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {
-                break;
-            }
-            rtversion[i] = rt_from_call[i];
-        }
-        PyOS_snprintf(message, sizeof(message),
-                      "compile time version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-#if PY_MAJOR_VERSION >= 3
-static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) {
-    if (t.is_unicode | t.is_str) {
-        if (t.intern) {
-            *str = PyUnicode_InternFromString(t.s);
-        } else if (t.encoding) {
-            *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL);
-        } else {
-            *str = PyUnicode_FromStringAndSize(t.s, t.n - 1);
-        }
-    } else {
-        *str = PyBytes_FromStringAndSize(t.s, t.n - 1);
-    }
-    if (!*str)
-        return -1;
-    if (PyObject_Hash(*str) == -1)
-        return -1;
-    return 0;
-}
-#endif
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION >= 3
-        __Pyx_InitString(*t, t->p);
-        #else
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        #endif
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-    __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result));
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type " __Pyx_FMT_TYPENAME ").  "
-                "The ability to return an instance of a strict subclass of int is deprecated, "
-                "and may be removed in a future version of Python.",
-                result_type_name)) {
-            __Pyx_DECREF_TypeName(result_type_name);
-            Py_DECREF(result);
-            return NULL;
-        }
-        __Pyx_DECREF_TypeName(result_type_name);
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")",
-                 type_name, type_name, result_type_name);
-    __Pyx_DECREF_TypeName(result_type_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(__Pyx_PyLong_IsCompact(b))) {
-        return __Pyx_PyLong_CompactValue(b);
-    } else {
-      const digit* digits = __Pyx_PyLong_Digits(b);
-      const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b);
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
-  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
-    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
-  } else if (likely(PyInt_CheckExact(o))) {
-    return PyInt_AS_LONG(o);
-#endif
-  } else {
-    Py_ssize_t ival;
-    PyObject *x;
-    x = PyNumber_Index(o);
-    if (!x) return -1;
-    ival = PyInt_AsLong(x);
-    Py_DECREF(x);
-    return ival;
-  }
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-/* #### Code section: utility_code_pragmas_end ### */
-#ifdef _MSC_VER
-#pragma warning( pop )
-#endif
-
-
-
-/* #### Code section: end ### */
-#endif /* Py_PYTHON_H */
diff --git a/spaces/cloudwp/DreamShaper-webui/app.py b/spaces/cloudwp/DreamShaper-webui/app.py
deleted file mode 100644
index ca155539f27483646bed7c4618c200fcffda3b37..0000000000000000000000000000000000000000
--- a/spaces/cloudwp/DreamShaper-webui/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-from subprocess import getoutput
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.17/xformers-0.0.17+b6be33a.d20230315-cp39-cp39-linux_x86_64.whl")
-elif("T4" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.17/xformers-0.0.17+b6be33a.d20230315-cp39-cp39-linux_x86_64.whl")
-
-os.system(f"git clone https://github.com/luongpham89/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/luongpham89/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/                show_progress=False,/                show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/               queue=False,  /                /g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/luongpham89/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-if "IS_SHARED_UI" in os.environ:
-    os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-    
-    os.system(f"wget -q https://github.com/luongpham89/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
-    os.system(f"wget -q https://github.com/luongpham89/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
-    os.system(f"python launch.py --precision full --no-half --use-cpu SD BSRGAN ESRGAN SCUNet CodeFormer --all --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
-    # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
-    os.system(f"wget -q https://gist.githubusercontent.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-    
-    # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
-    #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
-    os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
-    os.system(f"git clone https://github.com/camenduru/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-
-    # os.system(f"wget -q https://huggingface.co/luongphamit/DreamShaper/resolve/main/dreamshaper_4BakedVae.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/dreamshaper_4BakedVae.safetensors")
-    os.system(f"wget -q https://huggingface.co/Lykon/NeverEnding-Dream/resolve/main/NeverEndingDream_ft_mse.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/NeverEndingDream_baked_vae.safetensors")
-    os.system(f"wget -q https://huggingface.co/prompthero/openjourney/resolve/main/mdjrny-v4.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.safetensors")
-    os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.safetensors")
-    os.system(f"wget -q https://huggingface.co/andite/pastel-mix/blob/resolve/pastelmix.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/pastelmix.safetensors")
-    os.system(f"wget -q https://huggingface.co/AIARTCHAN/camelliamix_v2.0/blob/resolve/camelliamix_v20-no-ema.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/7camelliamix_v20-no-ema.safetensors")
-    
-
-    os.system(f"python launch.py --precision full --no-half --use-cpu SD BSRGAN ESRGAN SCUNet CodeFormer --all --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")
-    
\ No newline at end of file
diff --git a/spaces/cncn102/bingo1/src/components/ui/select.tsx b/spaces/cncn102/bingo1/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/cncn102/bingo1/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
-  IconArrowDown,
-  IconCheck,
-  IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Trigger>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Trigger>
->(({ className, children, ...props }, ref) => (
-  <SelectPrimitive.Trigger
-    ref={ref}
-    className={cn(
-      'flex h-9 w-full items-center justify-between rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow ring-offset-background placeholder:text-muted-foreground focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-      className
-    )}
-    {...props}
-  >
-    {children}
-    <SelectPrimitive.Icon asChild>
-      <IconChevronUpDown className="opacity-50" />
-    </SelectPrimitive.Icon>
-  </SelectPrimitive.Trigger>
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Content>
->(({ className, children, position = 'popper', ...props }, ref) => (
-  <SelectPrimitive.Portal>
-    <SelectPrimitive.Content
-      ref={ref}
-      className={cn(
-        'relative z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover text-popover-foreground shadow-md animate-in fade-in-80',
-        position === 'popper' && 'translate-y-1',
-        className
-      )}
-      position={position}
-      {...props}
-    >
-      <SelectPrimitive.Viewport
-        className={cn(
-          'p-1',
-          position === 'popper' &&
-            'h-[var(--radix-select-trigger-height)] w-full min-w-[var(--radix-select-trigger-width)]'
-        )}
-      >
-        {children}
-      </SelectPrimitive.Viewport>
-    </SelectPrimitive.Content>
-  </SelectPrimitive.Portal>
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Label>
->(({ className, ...props }, ref) => (
-  <SelectPrimitive.Label
-    ref={ref}
-    className={cn('py-1.5 pl-8 pr-2 text-sm font-semibold', className)}
-    {...props}
-  />
-))
-SelectLabel.displayName = SelectPrimitive.Label.displayName
-
-const SelectItem = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Item>
->(({ className, children, ...props }, ref) => (
-  <SelectPrimitive.Item
-    ref={ref}
-    className={cn(
-      'relative flex w-full cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50',
-      className
-    )}
-    {...props}
-  >
-    <span className="absolute left-2 flex h-3.5 w-3.5 items-center justify-center">
-      <SelectPrimitive.ItemIndicator>
-        <IconCheck className="h-4 w-4" />
-      </SelectPrimitive.ItemIndicator>
-    </span>
-    <SelectPrimitive.ItemText>{children}</SelectPrimitive.ItemText>
-  </SelectPrimitive.Item>
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <SelectPrimitive.Separator
-    ref={ref}
-    className={cn('-mx-1 my-1 h-px bg-muted', className)}
-    {...props}
-  />
-))
-SelectSeparator.displayName = SelectPrimitive.Separator.displayName
-
-export {
-  Select,
-  SelectGroup,
-  SelectValue,
-  SelectTrigger,
-  SelectContent,
-  SelectLabel,
-  SelectItem,
-  SelectSeparator
-}
diff --git a/spaces/conanwl/bingo/README.md b/spaces/conanwl/bingo/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/conanwl/bingo/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-</div>
-
-
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Candy Crush Saga Dnyann En Popler 3l Eletirme Oyununu ndir.md b/spaces/congsaPfin/Manga-OCR/logs/Candy Crush Saga Dnyann En Popler 3l Eletirme Oyununu ndir.md
deleted file mode 100644
index 5e1c51478131384daca12565f9d4205111883edb..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Candy Crush Saga Dnyann En Popler 3l Eletirme Oyununu ndir.md	
+++ /dev/null
@@ -1,83 +0,0 @@
-
-<h1>Candy Crush Indir: How to Download and Play the World's Sweetest Game</h1>
-<p>Do you love candy? Do you love puzzles? Do you love fun? If you answered yes to any of these questions, then you will love Candy Crush Saga, the legendary match 3 puzzle game that has captivated millions of players around the world. In this article, we will tell you everything you need to know about Candy Crush Saga, how to download it, how to play it, and why you should play it. Let's get started!</p>
- <h2>What is Candy Crush Saga?</h2>
-<p>Candy Crush Saga is a match 3 puzzle game developed by King, a leading mobile game developer. The game was released in 2012 and has since become one of the most popular and successful mobile games of all time. According to King, the game has been played by over a trillion times, and has over a billion downloads on Google Play Store alone. The game is also available on Microsoft Store and App Store for Windows, iOS, and Mac devices.</p>
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- <h3>The gameplay of Candy Crush Saga</h3>
-<p>The gameplay of Candy Crush Saga is simple but addictive. You have to match three or more candies of the same color to make them pop and clear them from the board. You have to complete different objectives in each level, such as reaching a target score, clearing all the jelly, collecting all the ingredients, or clearing all the orders. You have a limited number of moves or time to complete each level, so you have to plan your moves carefully and use smart strategies. If you run out of moves or time, you will lose a life. You have five lives at a time, and they regenerate over time or by asking your friends for help.</p>
- <h3>The features of Candy Crush Saga</h3>
-<p>Candy Crush Saga has many features that make it fun and exciting. Some of them are:</p>
-<ul>
-<li>Thousands of levels and puzzles to play, with new ones added every two weeks.</li>
-<li>Various game modes and challenges, such as Target Score, Clear the Jelly, Collect the Ingredients, Order Mode, Mixed Mode, Rainbow Rapids, and more.</li>
-<li>Different types of candies, such as striped candies, wrapped candies, color bombs, jelly fish, coconut wheels, lucky candies, and more.</li>
-<li>Different types of obstacles, such as chocolate, licorice, frosting, honey, waffles, popcorn, cake bombs, and more.</li>
-<li>Different types of boosters and power-ups, such as lollipop hammers, free switches, sweet teeth, striped brushes, color bombs, party poppers, and more.</li>
-<li>Daily rewards and bonuses, such as the Daily Booster Wheel, the Sugar Track, the Treat Calendar, the Daily Dash, and more.</li>
-<li>Events and competitions, such as the Episode Race, the Star Chaser, the Royal Championship, the Fantastic Five, and more.</li>
-<li>Social features, such as connecting with Facebook or King account, sending and receiving lives and messages from friends, comparing scores and progress with friends and other players on the leaderboard.</li>
-</ul>
- <h2>How to download Candy Crush Saga?</h2>
-<p>Candy Crush Saga is free to download and play on various platforms. However, some optional in-game items require payment. You can turn off the payment feature by disabling in-app purchases in your device’s settings. Here are the steps to download Candy Crush Saga on different platforms:</p>
- <h3>Download Candy Crush Saga from Google Play Store</h3 <p>To download Candy Crush Saga from Google Play Store, follow these steps:</p>
-<ol>
-<li>Open the Google Play Store app on your Android device.</li>
-<li>Search for "Candy Crush Saga" in the search bar.</li>
-<li>Tap on the game icon that appears in the search results.</li>
-<li>Tap on the "Install" button to start downloading the game.</li>
-<li>Wait for the download to finish and then tap on the "Open" button to launch the game.</li>
-</ol>
- <h3>Download Candy Crush Saga from Microsoft Store</h3>
-<p>To download Candy Crush Saga from Microsoft Store, follow these steps:</p>
-<ol>
-<li>Open the Microsoft Store app on your Windows or Mac device.</li>
-<li>Search for "Candy Crush Saga" in the search bar.</li>
-<li>Click on the game icon that appears in the search results.</li>
-<li>Click on the "Get" button to start downloading the game.</li>
-<li>Wait for the download to finish and then click on the "Play" button to launch the game.</li>
-</ol>
- <h3>Download Candy Crush Saga from App Store</h3>
-<p>To download Candy Crush Saga from App Store, follow these steps:</p>
-<p>[Candy Crush Saga - Apps on Google Play]<br />
-[Candy Crush Saga - Download]<br />
-[Candy Crush Saga Online - Play the game at King.com]<br />
-[Candy Crush Saga İndir - Ücretsiz ve Hızlı İndirme]<br />
-[Candy Crush Saga İndir - Android için Candy Crush Oyunu ...]</p>
-<ol>
-<li>Open the App Store app on your iOS or Mac device.</li>
-<li>Search for "Candy Crush Saga" in the search bar.</li>
-<li>Tap on the game icon that appears in the search results.</li>
-<li>Tap on the "Get" button to start downloading the game.</li>
-<li>Wait for the download to finish and then tap on the game icon to launch the game.</li>
-</ol>
- <h2>How to play Candy Crush Saga?</h2>
-<p>Candy Crush Saga is easy to play but hard to master. Here are some tips and tricks to help you play better and have more fun:</p>
- <h3>Match and pop candies to clear levels</h3 <p>The basic rule of Candy Crush Saga is to match three or more candies of the same color to make them pop and clear them from the board. You can also create special candies by matching four or more candies in different ways. For example, matching four candies in a row or column creates a striped candy, which can clear a whole row or column when matched. Matching five candies in a T or L shape creates a wrapped candy, which can explode twice when matched. Matching five candies in a row creates a color bomb, which can clear all the candies of one color when matched. You can also combine two special candies to create even more powerful effects. For example, combining a striped candy and a wrapped candy creates a giant candy that clears three rows and three columns. Combining a color bomb and a striped candy creates a rainbow blast that turns all the candies of one color into striped candies and activates them. Experiment with different combinations and see what happens!</p>
- <h3>Use boosters and power-ups to overcome challenges</h3 <p>Sometimes, you may encounter levels that are too hard or tricky to beat with normal moves. That's when you can use boosters and power-ups to help you out. Boosters are items that you can use before or during a level to give you an advantage. For example, you can use a lollipop hammer to smash any candy on the board, a free switch to swap any two adjacent candies, or a sweet tooth to eat any chocolate or licorice on the board. Power-ups are items that you can activate during a level to create special effects. For example, you can use a striped brush to turn any candy into a striped candy, a color bomb to create a color bomb on the board, or a party popper to clear all the candies of one color and get extra points. You can get boosters and power-ups by spinning the Daily Booster Wheel, completing quests, winning events, or buying them with gold bars.</p>
- <h3>Join events and competitions to win rewards</h3 <p>Candy Crush Saga is not only about clearing levels, but also about joining events and competitions to win rewards and have more fun. Events are special features that run for a limited time and offer different challenges and prizes. For example, you can join the Episode Race, where you compete with other players to finish an episode first and get extra boosters. You can also join the Star Chaser, where you collect stars by clearing levels and get rewards based on your star count. You can also join the Royal Championship, where you play against other players in real-time matches and climb up the ranks. Competitions are features that run permanently and offer different goals and rewards. For example, you can join the Fantastic Five, where you team up with four other players and work together to complete tasks and get team chests. You can also join the Sugar Track, where you collect sugar drops by clearing levels and get sweet rewards along the way.</ <h2>Why play Candy Crush Saga?</h2>
-<p>Candy Crush Saga is more than just a game, it's a lifestyle. There are many reasons why you should play Candy Crush Saga, but here are some of the most compelling ones:</p>
- <h3>Enjoy a sweet and addictive puzzle adventure</h3>
-<p>Candy Crush Saga is a game that will keep you entertained for hours, days, weeks, and even years. The game has a simple but addictive gameplay that will challenge your brain and your fingers. The game also has a colorful and charming graphics and sound that will make you feel like you are in a candy wonderland. The game also has a captivating story that will take you on a journey through the Candy Kingdom, where you will meet many lovable characters and help them with their quests. The game also has a humorous and witty dialogue that will make you laugh and smile.</p>
- <h3>Explore thousands of levels and modes</h3>
-<p>Candy Crush Saga is a game that will never run out of content. The game has thousands of levels and puzzles to play, with new ones added every two weeks. The game also has various game modes and challenges that will test your skills and creativity. The game also has different types of candies, obstacles, boosters, and power-ups that will spice up your gameplay. The game also has different types of events and competitions that will give you extra fun and rewards. The game also has different types of worlds and episodes that will take you to different places and themes.</p>
- <h3>Connect with friends and other players</h3>
-<p>Candy Crush Saga is a game that will make you feel connected with others. The game has social features that allow you to connect with your Facebook or King account, send and receive lives and messages from your friends, compare scores and progress with your friends and other players on the leaderboard, and join teams and clubs with other players. The game also has community features that allow you to interact with other players on the official website, Facebook page, Instagram account, Twitter account, YouTube channel, and blog. The game also has support features that allow you to contact the customer care team, report bugs and issues, give feedback and suggestions, and access the help center and FAQ.</p>
- <h2>Conclusion</h2>
-<p>Candy Crush Saga is a match 3 puzzle game that has become a global phenomenon. The game has a simple but addictive gameplay, a colorful and charming graphics and sound, a captivating story and characters, a humorous and witty dialogue, thousands of levels and puzzles, various game modes and challenges, different types of candies, obstacles, boosters, and power-ups, daily rewards and bonuses, events and competitions, social features, community features, and support features. The game is free to download and play on various platforms, such as Google Play Store, Microsoft Store, App Store, Facebook, or King.com. If you love candy, puzzles, fun, or all of the above, then you should download Candy Crush Saga today and join the sweetest adventure ever!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Candy Crush Saga:</p>
-<ul>
-<li><b>Q: How can I get more lives in Candy Crush Saga?</b></li>
-<li>A: You can get more lives in Candy Crush Saga by waiting for them to regenerate over time (one life every 30 minutes), by asking your friends for help (up to five lives per day), by spinning the Daily Booster Wheel (up to five lives per day), by completing quests (up to five lives per day), by winning events (varies depending on the event), or by buying them with gold bars (varies depending on the amount).</li>
-<li><b>Q: How can I get more gold bars in Candy Crush Saga?</b></li>
-<li>A: You can get more gold bars in Candy Crush Saga by completing achievements (varies depending on the achievement), by winning events (varies depending on the event), by joining teams or clubs (varies depending on the team or club), or by buying them with real money (varies depending on the amount).</li>
-<li><b>Q: How can I sync my progress across different devices?</b></li>
-<li>A: You can sync your progress across different devices by connecting your game to your Facebook or King account. This way, you can access your game data from any device that supports Candy Crush Saga.</li>
-<li><b>Q: How can I reset my progress in Candy Crush Saga?</b></li>
-<li>A: You can reset your progress in Candy Crush Saga by disconnecting your game from your Facebook or King account. This way, you can start from level 1 again. However, this will also erase all your achievements, scores, boosters, power-ups, gold bars, lives, friends, teams, and clubs.</li>
-<li><b>Q: How can I contact the customer care team of Candy Crush Saga?</b></li>
-<li>A: You can contact the customer care team of Candy Crush Saga by visiting the official website, Facebook page, Instagram account, Twitter account, YouTube channel, or blog of the game and clicking on the "Contact Us" or "Support" button. You can also access the help center and FAQ of the game by tapping on the settings icon in the game and selecting the "Help" or "FAQ" option.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Delphi DS15e 2015 R3 Software for Cars and Trucks Diagnosis (free download and activation).md b/spaces/congsaPfin/Manga-OCR/logs/Delphi DS15e 2015 R3 Software for Cars and Trucks Diagnosis (free download and activation).md
deleted file mode 100644
index a728d9da38ec69225abac5bd02e11a4d3142349c..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Delphi DS15e 2015 R3 Software for Cars and Trucks Diagnosis (free download and activation).md	
+++ /dev/null
@@ -1,147 +0,0 @@
-<br />
-<h1>Delphi Cars 2015 R3 Keygen Download: How to Install and Activate the Software</h1>
- <p>If you are looking for a reliable and professional diagnostic tool for cars and trucks, you might have heard of Delphi Cars 2015 R3. This software is one of the most popular products from Delphi Technologies, a global leader in automotive solutions. In this article, we will tell you what Delphi Cars 2015 R3 is, where to download its keygen and software, and how to install and activate it on your computer. Read on to find out more.</p>
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- <h2>What is Delphi Cars 2015 R3?</h2>
- <p>Delphi Cars 2015 R3 is a diagnostic software that works with various diagnostic tools, such as WOW, CDP, Auto-com, MVDiag tools TCS CDP, etc. It supports multi-brands cars and trucks till 2017, and it can perform various functions, such as reading and erasing fault codes, displaying live data, performing actuator tests, adjusting parameters, coding modules, and more. It also has a user-friendly interface that allows you to easily navigate through the menus and options.</p>
- <h3>Features and benefits of Delphi Cars 2015 R3</h3>
- <p>Some of the features and benefits of Delphi Cars 2015 R3 are:</p>
-<ul>
-<li>It covers over 54,000 vehicle systems on more than 4,000 models from over 48 vehicle manufacturers.</li>
-<li>It supports both OBD and OBDII protocols, as well as CAN-BUS, KWP2000, ISO9141-2, J1850 VPW, J1850 PWM, etc.</li>
-<li>It has a database of more than 26,000 fault codes with descriptions.</li>
-<li>It can display live data in graphs, gauges, or digital formats.</li>
-<li>It can record and playback live data for later analysis.</li>
-<li>It can print or save diagnostic reports in PDF format.</li>
-<li>It can update online via the Internet.</li>
-<li>It has a multilingual support for English, Deutsch, Francais, Hungarian, Espanol, Polish, Cesky, Dansk, Greek, Hollands, Italiano, Norsk, Romania, Russian, Srpski, Suomen Svenska, Turkish, etc.</li>
-</ul>
- <h3>Supported vehicles and protocols</h3>
- <p>Delphi Cars 2015 R3 supports a wide range of vehicles and protocols. Here are some examples of the supported vehicles:</p>
-<table>
-<tr><th>Vehicle Manufacturer</th><th>Vehicle Model</th></tr>
-<tr><td>Audi</td><td>A1,A2,A3,A4,A5,A6,A7,A8,Q2,Q3,Q5,Q7,R8,S1,S2,S3,S4,S5,S6,S7,S8,TTS</td></tr>
-<tr><td>Benz</td><td>A-Class,B-Class,C-Class,E-Class,G-Class,GL-Class,GLA-Class,GLC-Class,GLE-Class,GLK-Class,GLS-Class,M-Class,R-Class,S-Class,V-Class,Viano,Vito,X-Class</td></tr>
-<tr><td>Bmw</td><td>1 Series,2 Series,3 Series,4 Series,5 Series,6 Series,X1,X2,X3,X4,X5,X 6,X6,Z3,Z4,Z8,i3,i8</td></tr>
-<tr><td>Ford</td><td>C-Max,EcoSport,Edge,Escape,Expedition,Explorer,F-150,Fiesta,Focus,Fusion,Galaxy,Ka,Kuga,Mondeo,Mustang,Ranger,S-Max,Taurus,Transit</td></tr>
-<tr><td>Honda</td><td>Accord,Civic,CR-V,CR-Z,Fit,HR-V,Jazz,Odyssey,Pilot,Ridgeline</td></tr>
-<tr><td>Hyundai</td><td>Accent,Azera,Creta,Elantra,Genesis,I10,I20,I30,I40,Ioniq,Kona,Santa Fe,Sonata,Tucson,Veloster</td></tr>
-<tr><td>Kia</td><td>Cadenza,Carens,Carnival,Ceed,Cerato,Forte,K900,Niro,Optima,Picanto,Rio,Sedona,Sorento,Soul,Sportage,Stinger</td></tr>
-<tr><td>Mazda</td><td>2,3,5,6,CX-3,CX-5,CX-7,CX-9,MX-5,RX-8</td></tr>
-<tr><td>Nissan</td><td>Altima,Armada,Juke,Kicks,Leaf,March,Micra,Murano,Navara,Note,NV200,NV300,NV400,Pathfinder,Pulsar,Qashqai,Rogue,Sentra,Tiida,X-Trail</td></tr>
-<tr><td>Toyota</td><td>Auris,Avalon,Avensis,Aygo,Camry,Corolla,Fortuner,Hilux,Land Cruiser,Prius,Rav4,Verso,Yaris</td></tr>
-<tr><td>Volkswagen</td><td>Amarok,Arteon,Beetle,Bora,Caddy,Golf,Jetta,Lupo,Polo,P assat,Scirocco,Sharan,Tiguan,Touareg,Touran,Transporter,Up</td></tr>
-</table>
-<p>And here are some examples of the supported protocols:</p>
-<ul>
-<li>OBD: ISO 9141-2, ISO 14230-4 (KWP2000), ISO 15765-4 (CAN)</li>
-<li>OBDII: SAE J1850 VPW, SAE J1850 PWM, ISO 9141-2, ISO 14230-4 (KWP2000), ISO 15765-4 (CAN)</li>
-<li>CAN-BUS: ISO 11898-1, ISO 11898-2, ISO 11898-3, SAE J1939, SAE J1708</li>
-<li>KWP2000: ISO 14230-4, ISO 14230-3</li>
-<li>ISO9141-2: ISO 9141-2</li>
-<li>J1850 VPW: SAE J1850 VPW</li>
-<li>J1850 PWM: SAE J1850 PWM</li>
-</ul>
- <h2>Where to download Delphi Cars 2015 R3 keygen and software?</h2>
- <p>If you want to use Delphi Cars 2015 R3 software, you will need to download its keygen and software from a reliable source. There are two main options for this: the official website of Delphi Technologies or alternative sources of Delphi Cars 2015 R3 keygen and software.</p>
- <h3>Official website of Delphi Technologies</h3>
- <p>The official website of Delphi Technologies is <a href="">https://www.delphi.com/</a>. Here you can find information about the company, its products and services, its news and events, its support and training, and its contact details. You can also access the online shop of Delphi Technologies, where you can buy various diagnostic tools and software. However, to download Delphi Cars 2015 R3 keygen and software from the official website, you will need to register an account and pay a fee. The price of Delphi Cars 2015 R3 software is $149.00 USD. You will also need to provide your device serial number and activation code to get the keygen and software.</p>
- <h3>Alternative sources of Delphi Cars 2015 R3 keygen and software</h3>
- <p>If you don't want to pay for the official website of Delphi Technologies, you can try to find alternative sources of Delphi Cars 2015 R3 keygen and software on the Internet. However, you should be careful about the quality and safety of these sources, as some of them may contain viruses, malware, or fake files. You should also check the feedback and reviews of other users before downloading anything. Here are some examples of alternative sources of Delphi Cars 2015 R3 keygen and software:</p>
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- <p>VXDAS is a professional supplier of auto diagnostic tools and software. On its official blog <a href="">https://www.vxdas.com/blog/</a>, you can find various articles about auto diagnosis, repair, maintenance, programming, coding, etc. One of these articles is titled "How to Install & Activate Delphi Cars & Trucks Software". In this article, you can find a link to download Delphi Cars 2015 R3 keygen and software for free. You can also find a step-by-step guide for installation and activation.</p>
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- <p>MHH AUTO is a forum for automotive enthusiasts and professionals. On this forum <a href="">https://mhhauto.com/</a>, you can find various topics about auto diagnosis, repair, tuning, modification, etc. One of these topics is titled "Delphi Cars & Trucks DS150E [R3.2015] + KEYGEN". In this topic, you can find a link to download Delphi Cars 2015 R3 keygen and software for free. You can also find some instructions and tips for installation and activation.</p>
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- <p>MOTORCARSOFT.COM is another forum for automotive enthusiasts and professionals. On this forum <a href="">https://www.motorcarsoft.com/</a>, you can find various topics about auto diagnosis, repair, programming , coding, etc. One of these topics is titled "Delphi Cars 2015.R3 + Activation". In this topic, you can find a link to download Delphi Cars 2015 R3 keygen and software for free. You can also find some instructions and screenshots for installation and activation.</p>
- <h2>How to install and activate Delphi Cars 2015 R3 software?</h2>
- <p>After you download Delphi Cars 2015 R3 keygen and software from one of the sources mentioned above, you will need to install and activate it on your computer. The installation and activation process may vary depending on the source, but here is a general step-by-step guide for your reference:</p>
- <h3>Step-by-step guide for installation and activation</h3>
- <h4>Copy the software to your computer</h4>
- <p>The first step is to copy the software to your computer. You can use a USB flash drive, a CD, or a DVD to do this. You will need to have at least 4 GB of free space on your computer. You should also disable your antivirus software and firewall before copying the software, as they may interfere with the installation and activation.</p>
- <h4>Run the main.exe file to activate</h4>
- <p>The second step is to run the main.exe file to activate the software. You can find this file in the folder where you copied the software. You should run it as administrator by right-clicking on it and selecting "Run as administrator". You will see a window that asks you to select the language of the software. You can choose from English, Deutsch, Francais, Hungarian, Espanol, Polish, Cesky, Dansk, Greek, Hollands, Italiano, Norsk, Romania, Russian, Srpski, Suomen Svenska, Turkish, etc. After you select the language, you will see another window that asks you to select the product you want to activate. You can choose from Cars or Trucks. After you select the product, you will see another window that shows your device serial number and asks you to enter an activation code.</p>
- <h4>Save and send the FileActivation file to your supplier</h4>
- <p>The third step is to save and send the FileActivation file to your supplier. The FileActivation file is a file that contains information about your device serial number and activation code. You can find this file in the same folder where you copied the software. You should save this file on your computer or a USB flash drive. Then you should send this file to your supplier by email or other methods. Your supplier will use a keygen tool to generate an activated FileActivation file for you.</p>
- <h4>Import the activated FileActivation file to the software</h4>
- <p>The fourth and final step is to import the activated FileActivation file to the software. You should receive this file from your supplier within 24 hours after you send them the original FileActivation file. You should copy this file to the same folder where you copied the software. Then you should run the main.exe file again as administrator and select "Yes" when it asks you if you want to import an activated FileActivation file. You will see a message that says "File Activation Success". This means that your software is now installed and activated.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Delphi Cars 2015 R3 is a powerful and professional diagnostic software that works with various diagnostic tools and supports multi-brands cars and trucks till 2017. It has many features and benefits that can help you diagnose, repair, maintain, and optimize your vehicles. To use this software, you will need to download its keygen and software from a reliable source, such as the official website of Delphi Technologies or alternative sources of Delphi Cars 2015 R3 keygen and software. Then you will need to install and activate it on your computer by following a step-by-step guide.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Delphi Cars 2015 R3 keygen download:</p>
-<ul>
-<li><b>Q: Is Delphi Cars 2015 R3 compatible with Windows 10?</b></li>
-<li>A: Yes, Delphi Cars 2015 R3 is compatible with Windows 10, as well as Windows XP, Windows Vista, Windows 7, Windows 8, and Windows 8.1.</li>
-<li><b>Q: How can I update Delphi Cars 2015 R3 software?</b></li>
-<li>A: You can update Delphi Cars 2015 R3 software online via the Internet. You just need to connect your device to the Internet and run the main.exe file as administrator. Then you can select "Update" from the menu and follow the instructions.</li>
-<li><b>Q: How can I contact Delphi Technologies for support?</b <li>A: You can contact Delphi Technologies for support by visiting their website <a href="">https://www.delphi.com/</a> and clicking on "Contact Us" at the bottom of the page. You can also call them at +1 877 463 3574 or email them at <a href="mailto:customerservice@delphi.com">customerservice@delphi.com</a>.</li>
-<li><b>Q: What are the advantages of using Delphi Cars 2015 R3 over other diagnostic software?</b></li>
-<li>A: Some of the advantages of using Delphi Cars 2015 R3 over other diagnostic software are:</li>
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-<li>It has a wider coverage of vehicles and protocols than most other diagnostic software.</li>
-<li>It has a more user-friendly interface and a more intuitive operation than most other diagnostic software.</li>
-<li>It has a more comprehensive database of fault codes and live data than most other diagnostic software.</li>
-<li>It has a more advanced and accurate diagnostic function than most other diagnostic software.</li>
-<li>It has a more convenient and flexible update and activation method than most other diagnostic software.</li>
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-<li><b>Q: What are the disadvantages of using Delphi Cars 2015 R3 over other diagnostic software?</b></li>
-<li>A: Some of the disadvantages of using Delphi Cars 2015 R3 over other diagnostic software are:</li>
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-<li>It may not be compatible with some newer vehicles and protocols that are released after 2017.</li>
-<li>It may not be able to perform some specific functions that are available in some other diagnostic software.</li>
-<li>It may require a higher system requirement and a larger disk space than some other diagnostic software.</li>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download 8 Ball Pool 5.6.1 from Google Play - The Most Popular Pool Game in the World.md b/spaces/congsaPfin/Manga-OCR/logs/Download 8 Ball Pool 5.6.1 from Google Play - The Most Popular Pool Game in the World.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/Download 8 Ball Pool 5.6.1 from Google Play - The Most Popular Pool Game in the World.md	
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-<p>If you are a fan of pool games, you have probably heard of <strong>8 Ball Pool</strong>, the most popular and addictive pool game for mobile devices. 8 Ball Pool is a game that lets you play online against other players from around the world, or with your friends, in various modes and tournaments. You can also customize your cue, table, and avatar, and collect coins and cash to buy new items and upgrade your equipment.</p>
-<p>But did you know that there is a new version of 8 Ball Pool available for download? That's right, <strong>8 Ball Pool 5.6.1</strong> is the latest update of the game, and it comes with some exciting new features and improvements that will make your gaming experience even better. In this article, we will tell you everything you need to know about 8 Ball Pool 5.6.1, including what's new, how to download it, and how to play it like a pro.</p>
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-<h2>What is 8 Ball Pool and Why You Should Play It</h2>
-<h3>The basics of 8 Ball Pool</h3>
-<p>8 Ball Pool is a game that simulates the real-life pool game of the same name. The game follows the standard rules of 8-ball pool, where you have to pocket either the solid or striped balls, depending on your assigned group, and then pocket the black 8-ball before your opponent does.</p>
-<p>The game is played with a cue ball and 15 object balls, numbered from 1 to 15. One player must pocket the balls numbered from 1 to 7 (solid colors), while the other player must pocket the balls numbered from 9 to 15 (stripes). The player who pockets all their balls first can then try to pocket the 8-ball and win the game.</p>
-<h3>The benefits of playing 8 Ball Pool</h3>
-<p>Playing 8 Ball Pool is not only fun, but also beneficial for your brain and skills. Here are some of the benefits of playing this game:</p>
-<ul>
-<li>It improves your concentration and focus, as you have to aim carefully and plan your shots ahead.</li>
-<li>It enhances your hand-eye coordination and reflexes, as you have to control the cue ball and adjust to different angles and speeds.</li>
-<li>It develops your strategic thinking and problem-solving skills, as you have to choose the best shots and avoid fouls and scratches.</li>
-<li>It boosts your confidence and self-esteem, as you win matches and climb up the ranks.</li>
-<li>It relieves your stress and boredom, as you enjoy a relaxing and entertaining game.</li>
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-<h2>What's New in 8 Ball Pool 5.6.1</h2>
-<h3>The latest updates and improvements</h3>
-<p>8 Ball Pool is constantly updated by its developers, Miniclip.com, to provide the best gaming experience for its players. The latest version of the game, <strong>8 Ball Pool 5.6.1</strong>, was released on April 17, 2023, and it brings some bug fixes and performance improvements that will make the game run smoother and faster on your device.</p>
-<p>Some of the issues that were fixed in this update include:</p>
-<ul>
-<li>The problem with some - The problem with some cues not showing up in the cue collection. - The problem with some players not receiving their rewards after completing a club event. - The problem with some players not being able to access the game due to a network error. - The problem with some players not being able to see their friends online or chat with them. - The problem with some players not being able to play in certain modes or tournaments due to a loading error. </p>
-<p>If you encounter any other issues or bugs while playing 8 Ball Pool, you can contact the support team through the game settings or the official website. They will try to help you as soon as possible.</p>
-<h3>The new features and modes</h3>
-<p>Aside from the bug fixes and improvements, 8 Ball Pool 5.6.1 also introduces some new features and modes that will make the game more fun and exciting. Here are some of the new additions that you can enjoy in this version:</p>
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-<li>A new season pass that will give you access to exclusive rewards, such as cues, avatars, chat packs, and more. You can buy the season pass with real money or earn it by completing challenges and missions.</li>
-<li>A new game mode called <strong>Pool Pass</strong> that will let you compete with other players in a weekly leaderboard and win amazing prizes. You can earn points by playing matches and completing tasks, and you can unlock different tiers of rewards as you progress.</li>
-<li>A new feature called <strong>Clubs</strong> that will let you join or create a club with other players and participate in club events and tournaments. You can chat with your club members, share tips and strategies, and earn club points and coins by playing matches.</li>
-<li>A new feature called <strong>Legends</strong> that will let you play against the best players in the world and learn from their skills and techniques. You can watch their replays, challenge them to a match, or follow them to see their stats and achievements.</li>
-</ul>
-<h2>How to Download 8 Ball Pool 5.6.1 on Your Device</h2>
-<h3>The steps to download 8 Ball Pool 5.6.1 on Android</h3>
-<p>If you have an Android device, you can download 8 Ball Pool 5.6.1 from the Google Play Store. Here are the steps to do so:</p>
-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for 8 Ball Pool in the search bar.</li>
-<li>Select the game from the list of results and tap on <strong>Install</strong>.</li>
-<li>Wait for the download and installation to finish.</li>
-<li>Tap on <strong>Open</strong> to launch the game and enjoy.</li>
-</ol>
-<p>Note: You may need to update your device's software or free up some storage space before downloading the game. You may also need an internet connection to play the game online.</p>
-<h3>The steps to download 8 Ball Pool 5.6.1 on PC</h3>
-<p>If you have a PC, you can download 8 Ball Pool 5.6.1 from the official website. Here are the steps to do so:</p>
-<ol>
-<li>Open your web browser and go to <a href="">https://www.miniclip.com/games/8-ball-pool-multiplayer/en/</a>.</li>
-<li>Click on <strong>Play Now</strong> to start the game.</li>
-<li>You may need to create an account or log in with your Facebook or Google account to play the game online.</li>
-<li>You may also need to install Adobe Flash Player or enable it in your browser settings to play the game.</li>
-<li>Enjoy the game on your PC screen.</li>
-</ol>
-<p>Note: You may need a stable internet connection to play the game online. You may also need a mouse or a keyboard to control the game.</p>
- <h2>How to Play 8 Ball Pool 5.6.1 Like a Pro</h2>
- <h3>Some tips and tricks to improve your skills</h3>
- <p>If you want to play 8 Ball Pool 5.6.1 like a pro, you need to practice your skills and learn some tips and tricks that will help you win more matches and earn more coins. Here are some of them:</p>
- <ul>
- <li>Aim carefully and use the guideline to help you align your shots. You can also adjust the power of your shots by dragging the power bar up or down.</li>
- <li>Use spin to control the cue ball and make it use spin to control the cue ball and make it go where you want. You can apply spin by tapping on the cue ball icon and moving it around.</li>
-<li>Plan your shots ahead and think about the best order to pocket your balls. You can also use the free view mode to look around the table and see the possible shots.</li>
-<li>Practice different game modes and challenges to improve your skills and earn more coins. You can also watch videos or tutorials to learn from other players.</li>
-</ul>
-<h3>Some resources to learn more about the game</h3>
-<p>If you want to learn more about 8 Ball Pool 5.6.1 and its features, you can check out some of the following resources:</p>
-<ul>
-<li>The official website of 8 Ball Pool, where you can find the latest news, updates, and events about the game. You can also contact the support team or join the community forum there.</li>
-<li>The official Facebook page of 8 Ball Pool, where you can follow the game's posts, videos, and live streams. You can also interact with other fans and players there.</li>
-<li>The official YouTube channel of 8 Ball Pool, where you can watch the game's trailers, tips, tricks, and gameplay videos. You can also subscribe to the channel and turn on the notifications to get updated on new videos.</li>
-<li>The official blog of 8 Ball Pool, where you can read the game's articles, guides, and stories. You can also comment on the blog posts and share your feedback.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>8 Ball Pool 5.6.1 is the latest version of the most popular and addictive pool game for mobile devices. It comes with some bug fixes and improvements, as well as some new features and modes that will make your gaming experience even better. You can download 8 Ball Pool 5.6.1 from the Google Play Store or the official website, depending on your device. You can also play 8 Ball Pool 5.6.1 like a pro by following some tips and tricks and checking out some resources to learn more about the game.</p>
-<p>We hope you enjoyed this article and found it helpful. If you have any questions or comments, feel free to leave them below. Happy gaming!</p>
-<h2>FAQs</h2>
-<h4>Q: Is 8 Ball Pool 5.6.1 free to play?</h4>
-<p>A: Yes, 8 Ball Pool 5.6.1 is free to play, but it contains some in-app purchases that you can buy with real money if you want to get more coins, cash, or items.</p>
-<h4>Q: Can I play 8 Ball Pool 5.6.1 offline?</h4>
-<p>A: No, you need an internet connection to play 8 Ball Pool 5.6.1 online with other players or access some of its features.</p>
-<h4>Q: Can I play 8 Ball Pool 5.6.1 on other devices?</h4>
-<p>A: Yes, you can play 8 Ball Pool 5.6.1 on other devices, such as iOS devices or Windows phones, by downloading it from their respective app stores.</p>
-<h4>Q: How can I get more coins and cash in 8 Ball Pool 5.6.1?</h4>
-<p>A: You can get more coins and cash in 8 Ball Pool 5.6.1 by playing matches and winning them, completing challenges and missions, participating in tournaments and events, spinning the wheel of fortune, watching ads or videos, or buying them with real money.</p>
-<h4>Q: How can I contact the support team of 8 Ball Pool 5.6.1?</h4>
-<p>A: You can contact the support team of 8 Ball Pool 5.6.1 by going to the game settings and tapping on <strong>Contact Us</strong>. You can also email them at <a href="mailto:support@miniclip.com">support@miniclip.com</a>.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Knife Hit Hile APK and Challenge Yourself with the Ultimate Knife Game.md b/spaces/congsaPfin/Manga-OCR/logs/Download Knife Hit Hile APK and Challenge Yourself with the Ultimate Knife Game.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/Download Knife Hit Hile APK and Challenge Yourself with the Ultimate Knife Game.md	
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-<p>If you manage to beat the boss, you will get an exclusive knife that is related to the boss theme. For example, if you beat the pineapple boss, you will get a pineapple knife. These knives are very rare and cool to have, so don't miss the chance to get them.</p>
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-<p>Next, you have to download the APK file of Knife Hit Hile APK from a trusted source. You can use the link below to get the latest version of the game. Make sure you have enough storage space on your device before downloading the file.</p>
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-<h3>Step 3: Locate and install the APK file on your device</h3>
-<p>Once you have downloaded the APK file, you have to locate it on your device using a file manager app. You can usually find it in the Downloads folder. Tap on the file and follow the instructions to install it on your device. It may take a few seconds to complete the installation.</p>
-<h3>Step 4: Launch the game and enjoy</h3>
-<p>After installing the game, you can launch it from your app drawer or home screen. You will see the game icon with the word "Hile" on it. Tap on it and start playing Knife Hit Hile APK. You will see that you have unlimited coins, lives, and knives in the game. You can also access all the modes and levels in the game without any restrictions.</p>
-<h2>Tips and Tricks for Playing Knife Hit Hile APK</h2>
-<h3>Aim for the apples and avoid the spikes</h3>
-<p>One of the most important tips for playing Knife Hit Hile APK is to aim for the apples that appear on the logs. Apples are valuable because they give you extra knives and power-ups that can help you in the game. Power-ups include magnets, lasers, bombs, and more. They can make your knives stick better, destroy more parts of the log, or clear all the obstacles on the log.</p>
-<p>However, you also have to avoid hitting the spikes that are also on the logs. Spikes are dangerous because they can break your knives or make you lose a life. If you hit a spike, you will hear a loud sound and see a red flash on the screen. If you lose all your lives, you will have to start over from the beginning of the stage.</p>
-<h3>Use the power-ups wisely</h3>
-<p>As mentioned above, power-ups are very useful in Knife Hit Hile APK, but you have to use them wisely. Power-ups are activated automatically when you hit an apple that contains them, but they only last for a limited time. You have to make sure that you use them at the right moment and not waste them.</p>
-<p>For example, if you have a magnet power-up, you should use it when there are many knives or spikes on the log, so that your knives will stick better and avoid hitting them. If you have a laser power-up, you should use it when there are few knives or spikes on the log, so that you can cut through them easily. If you have a bomb power-up, you should use it when the log is almost broken, so that you can finish it off quickly.</p>
-<h3>Collect coins and unlock new knives</h3>
-<p>Another tip for playing Knife Hit Hile APK is to collect coins and unlock new knives in the game. Coins are the currency of the game, and you can use them to buy new knives from the shop. You can earn coins by hitting apples, breaking logs, defeating bosses, or watching ads. You can also get coins for free by spinning the wheel or opening the chest every day.</p>
-<p>There are more than 100 knives to unlock in the game, and each one costs a different amount of coins. Some knives are cheap and easy to get, while others are expensive and rare. You can also get some knives by completing certain achievements or challenges in the game. For example, you can get a pizza knife by hitting 1000 apples, or a unicorn knife by beating 10 bosses.</p>
-<p>New knives are not only cosmetic, but they also have different effects on the gameplay. Some knives may be faster, sharper, heavier, or lighter than others. Some knives may also have special abilities, such as bouncing, splitting, or exploding. You should try different knives and see which ones suit your style and preference.</p>
-<h3>Challenge yourself with different modes and levels</h3>
-<p>The last tip for playing Knife Hit Hile APK is to challenge yourself with different modes and levels in the game. The game has four modes to choose from: Classic, Challenge, Arcade, and Event. Each mode has its own rules and objectives, and you can switch between them anytime you want.</p>
-<p>Classic mode is the default mode of the game, where you have to break logs and beat bosses as you progress through the stages. Challenge mode is where you have to complete specific tasks or missions in each stage, such as hitting a certain number of apples or avoiding a certain number of spikes. Arcade mode is where you have to break as many logs as you can in a row without missing or hitting anything else. Event mode is where you can play special events that are available for a limited time, such as Halloween or Christmas.</p>
-<p>Each mode also has different levels of difficulty, ranging from easy to hard. The difficulty affects the speed, size, shape, and obstacles of the logs, as well as the number of knives and lives you have. The higher the difficulty, the more challenging and rewarding the game becomes. You can change the difficulty level anytime you want in the settings menu.</p>
-<h2>Conclusion</h2>
-<p>Knife Hit Hile APK is a fun and challenging game for Android users who love throwing knives and breaking things. It has simple and addictive gameplay, different types of knives and targets, boss battles and exclusive rewards, and no internet connection required. It also gives you unlimited coins, lives, and knives, as well as access to all the modes and levels in the game. You can download and install Knife Hit Hile APK easily by following the steps above. You can also improve your skills and enjoy the game more by following the tips and tricks above.</p>
-<p>If you are looking for a game that will test your reflexes and accuracy, while also giving you a lot of fun and excitement, then you should try Knife Hit Hile APK today. It is one of the best arcade games for Android devices that will keep you entertained for hours.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Knife Hit Hile APK:</p>
-<ul>
-<li><b>Is Knife Hit Hile APK safe to download and install?</b></li>
-<li>Yes, Knife Hit Hile APK is safe to download and install on your Android device. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a trusted source and enable unknown sources on your device before installing it.</li>
-<li><b>Is Knife Hit Hile APK compatible with my device?</b></li>
-<li>Knife Hit Hile APK is compatible with most Android devices that run on Android 4.1 or higher. However, some devices may experience performance issues or glitches due to hardware limitations or software compatibility. If you encounter any problems while playing the game, you can try lowering the graphics quality or closing other apps in the background.</li>
-<li><b>How can I update Knife Hit Hile APK?</b></li>
-<li>Knife Hit Hile APK is updated regularly by its developers to fix bugs and add new features. You can check for updates by visiting the source website or by opening the game and tapping on the update button on the main menu. You can also enable automatic updates on your device settings to get notified when a new version is available.</li>
-<li><b>How can I contact the developers of Knife Hit Hile APK?</b></li>
-<li>If you have any questions, feedback, or suggestions about Knife Hit Hile APK, you can contact the developers by sending them an email at ketchappgames@gmail.com. You can also follow them on their social media accounts, such as Facebook, Twitter, and Instagram, to get the latest news and updates about the game.</li>
-<li><b>Can I play Knife Hit Hile APK with my friends?</b></li>
-<li>Yes, you can play Knife Hit Hile APK with your friends by using the multiplayer mode in the game. You can challenge your friends to see who can break more logs and get more points in a limited time. You can also compare your scores and achievements with your friends on the leaderboard and see who is the best knife thrower among you.</li>
-</ul></p> 401be4b1e0<br />
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-<h1>FIFA Mobile Mod APK Unlimited Money Download</h1>
-<p>If you are a fan of soccer games, you must have heard of FIFA Mobile, the official mobile game of the FIFA World Cup 2022. This game lets you build your ultimate team of soccer stars, compete in various modes, and relive the world's greatest soccer tournament. But what if you want to enjoy the game without any limitations or restrictions? That's where FIFA Mobile Mod APK comes in. This is a modified version of the original game that gives you unlimited money, unlocked players and teams, menu mod with cheats and hacks, and more. In this article, we will tell you everything you need to know about FIFA Mobile Mod APK, including its features, how to download and install it, its pros and cons, and some FAQs. Read on to find out more.</p>
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-<h2>What is FIFA Mobile Mod APK?</h2>
-<p>FIFA Mobile Mod APK is a hacked version of FIFA Mobile, the official mobile game of the FIFA World Cup 2022. This mod apk gives you access to features that are not available in the original game, such as unlimited money and coins, unlocked players and teams, menu mod with cheats and hacks, and more. With these features, you can easily build your dream team of soccer legends, customize your tactics and strategies, dominate the pvp modes, and enjoy the realistic soccer simulation. You can also replay the official World Cup brackets with any of the 32 qualified nations, play in World Cup stadiums, and listen to localized World Cup commentary.</p>
-<h3>Features of FIFA Mobile Mod APK</h3>
-<p>FIFA Mobile Mod APK has many features that make it different from the original game. Here are some of the main features that you can enjoy with this mod apk:</p>
-<h4>Unlocked all players and teams</h4>
-<p>One of the best features of FIFA Mobile Mod APK is that it unlocks all the players and teams in the game. You can choose from over 15,000 authentic soccer stars from over 30 leagues, including world-class talent like Kylian Mbappé, Christian Pulisic, Vinicius Jr, Son Heung-min, and more. You can also play with over 600 teams from around the world, including Chelsea, Paris SG, Real Madrid, Liverpool, Juventus, and more. You can even play with soccer icons and heroes like Paolo Maldini, Ronaldinho, Zinedine Zidane, David Beckham, and more.</p>
-<h4>Unlimited money and coins</h4>
-<p>Another great feature of FIFA Mobile Mod APK is that it gives you unlimited money and coins in the game. You can use these resources to buy player items, upgrade your team, unlock new kits and badges, and more. You don't have to worry about running out of money or coins or spending real money to get them. You can enjoy the game without any financial constraints.</p>
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-<li>God mode: You can make your players invincible and immune to injuries.</li>
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-<li>No ads: You can remove all the annoying ads from the game.</li>
-<li>No root: You don't need to root your device to use this mod apk.</li>
-<li>And more...</li>
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-<h3>How to download and install FIFA Mobile Mod APK?</h3>
-<p>If you want to download and install FIFA Mobile Mod APK on your device , you need to follow these simple steps:</p>
-<h4>Step 1: Download the mod apk file from a trusted source</h4>
-<p>The first step is to download the mod apk file from a trusted source. You can use the link below to download the latest version of FIFA Mobile Mod APK for free. This link is safe and secure and will not harm your device. Make sure you have enough storage space on your device before downloading the file.</p>
-<p><a href="">Download FIFA Mobile Mod APK</a></p>
-<h4>Step 2: Enable unknown sources on your device settings</h4>
-<p>The next step is to enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on. You may see a warning message, but don't worry, it's safe to proceed.</p>
-<h4>Step 3: Install the mod apk file and launch the game</h4>
-<p>The final step is to install the mod apk file and launch the game. To do this, locate the downloaded file on your device, tap on it, and follow the instructions on the screen. Once the installation is complete, you can open the game and enjoy FIFA Mobile Mod APK with unlimited money, unlocked players and teams, menu mod with cheats and hacks, and more.</p>
-<h3>Pros and cons of FIFA Mobile Mod APK</h3>
-<p>FIFA Mobile Mod APK has many advantages, but it also has some disadvantages. Here are some of the pros and cons of using this mod apk:</p>
-<h4>Pros</h4>
-<ul>
-<li>You can enjoy FIFA Mobile without any limitations or restrictions.</li>
-<li>You can build your ultimate team of soccer stars with unlimited money and coins.</li>
-<li>You can unlock all the players and teams in the game, including soccer icons and heroes.</li>
-<li>You can use menu mod with cheats and hacks to enhance your gameplay experience and have more fun.</li>
-<li>You can replay the official World Cup brackets with any of the 32 qualified nations.</li>
-<li>You can play in World Cup stadiums and listen to localized World Cup commentary.</li>
-<li>You can remove all the annoying ads from the game.</li>
-<li>You don't need to root your device to use this mod apk.</li>
-</ul>
-<h4>Cons</h4>
-<ul>
-<li>You may face some compatibility issues with some devices or Android versions.</li>
-<li>You may encounter some bugs or glitches in the game.</li>
-<li>You may get banned from online modes or leaderboards if you use cheats and hacks.</li>
-<li>You may miss out on some updates or features from the original game.</li>
-</ul>
-<h3>FAQs about FIFA Mobile Mod APK</h3>
-<p>Here are some of the frequently asked questions about FIFA Mobile Mod APK:</p>
- <table border="1">
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>Is FIFA Mobile Mod APK safe to use?</td><td>Yes, FIFA Mobile Mod APK is safe to use as long as you download it from a trusted source. However, you should always be careful when installing apps from unknown sources and scan them for viruses or malware before installing them.</td></tr>
-<tr><td>Is FIFA Mobile Mod APK legal to use?</td><td>No, FIFA Mobile Mod APK is not legal to use as it violates the terms and conditions of the original game. Using this mod apk may result in legal action from EA Sports or FIFA. Therefore, we do not recommend using this mod apk for any illegal purposes.</td></tr>
-<tr><td>Can I play online modes with FIFA Mobile Mod APK?</td><td>Yes, you can play online modes with FIFA Mobile Mod APK, but you may face some issues or risks. For example, you may not be able to connect to other players or servers, you may experience lag or crashes, or you may get banned from online modes or leaderboards if you use cheats and hacks. Therefore, we advise you to play online modes at your own risk.</td></tr>
-<tr><td>Can I update FIFA Mobile Mod APK?</td><td>No, you cannot update FIFA Mobile Mod APK as it is a modified version of the original game. Updating this mod apk may cause it to stop working or lose its features. Therefore, we suggest you to stick with the current version of FIFA Mobile Mod APK or wait for a new version from the modder.</td></tr>
-<tr><td>Can I use FIFA Mobile Mod APK with my existing account?</td><td>No, you cannot use FIFA Mobile Mod APK with your existing account as it may cause your account to get banned or corrupted. Therefore, we recommend you to create a new account or use a guest account to play with FIFA Mobile Mod APK.</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>FIFA Mobile Mod APK is a modified version of FIFA Mobile, the official mobile game of the FIFA World Cup 2022. This mod apk gives you unlimited money, unlocked players and teams, menu mod with cheats and hacks, and more. You can enjoy the game without any limitations or restrictions, but you may also face some risks or drawbacks. Therefore, you should use this mod apk at your own discretion and responsibility.</p>
-<p>We hope this article has helped you learn more about FIFA Mobile Mod APK and how to download and install it on your device. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and have fun playing FIFA Mobile Mod APK.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Install FINAL FANTASY TACTICS  WotL 2.1 0 APK on Your Android Phone or Tablet.md b/spaces/congsaPfin/Manga-OCR/logs/How to Install FINAL FANTASY TACTICS  WotL 2.1 0 APK on Your Android Phone or Tablet.md
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-<h1>Final Fantasy Tactics 2.1 0 apk: A Classic Strategy RPG for Android</h1>
- <p>Final Fantasy Tactics is a strategy role-playing game that was released in 1997 for the PlayStation console. It is one of the most acclaimed games in the Final Fantasy series, with a complex and engaging story, a rich and diverse job system, and a challenging and rewarding tactical combat. The game has been re-released several times, with enhanced graphics, sound, and content. The latest version is Final Fantasy Tactics 2.1 0 apk, which is available for Android devices.</p>
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- <p>In this article, we will explore the features of Final Fantasy Tactics 2.1 0 apk and how to download it on your Android device. Whether you are a fan of the original game or a newcomer to the world of Ivalice, you will find something to enjoy in this classic strategy RPG.</p>
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- <p>Final Fantasy Tactics is set in the medieval fantasy world of Ivalice, where two rival factions are fighting for the throne of the kingdom. You play as Ramza Beoulve, a young noble who gets involved in the conflict and uncovers a sinister plot behind it. Along the way, you will meet various characters, some of whom will join your party, while others will oppose you.</p>
- <p>The story of Final Fantasy Tactics is rich and complex, with multiple twists and turns, political intrigue, moral dilemmas, and hidden secrets. The game also features multiple endings, depending on your choices and actions throughout the game.</p>
- <h2>The gameplay and job system of Final Fantasy Tactics</h2>
- <p>Final Fantasy Tactics is a tactical role-playing game, where you control a party of up to five characters in turn-based battles on grid-based maps. You can move your characters around the map, attack enemies, use items, cast spells, and perform various actions depending on their job class.</p>
- <p>The job system is one of the most distinctive features of Final Fantasy Tactics. There are over 20 types of jobs in the game, each with its own abilities, strengths, and weaknesses. You can change your characters' jobs at any time outside of battle, as long as they meet the requirements for that job. You can also learn new abilities by spending job points (JP) that you earn from battles.</p>
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- <p>The job system allows you to customize your characters according to your preferences and strategies. You can mix and match abilities from different jobs to create unique combinations and synergies. For example, you can have a Knight who can also cast White Magic, or a Ninja who can also use Geomancy.</p>
- <h2>The improvements and additions of Final Fantasy Tactics 2.1 0 apk</h2>
- <p>Final Fantasy Tactics 2.1 0 apk is based on the enhanced port of the game that was released for the PlayStation Portable in 2007, titled Final Fantasy Tactics: The War of the Lions. This version of the game features several improvements and additions over the original PlayStation version, such as:</p>
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-<li>New animated cutscenes with voice acting</li>
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-<li>New jobs, such as Onion Knight and Dark Knight</li>
-<li>New items, abilities, monsters, and locations</li>
-<li>New multiplayer modes via local wireless or online connection</li>
-<li>New touch screen controls for easier navigation</li>
-<li>New autosave function for convenience</li>
-</ul>
- <p>Final Fantasy Tactics 2.1 0 apk also fixes some bugs and glitches that were present in the original version of the game.</p>
- <h2>The steps to download and install Final Fantasy Tactics 2.1 0 apk</h2>
- <p>If you want to play Final Fantasy Tactics 2.1 0 apk on your Android device, you will need to follow these steps:</p>
- <ol>
-<li>Download the Final Fantasy Tactics 2.1 0 apk file from a reliable source, such as [this one].</li>
-<li>Download the OBB data file for the game from the same source, such as [this one].</li>
-<li>Install the Final Fantasy Tactics 2.1 0 apk file on your device by tapping on it and allowing the installation from unknown sources.</li>
-<li>Extract the OBB data file using a file manager app, such as [this one], and copy the folder com.square_enix.android_googleplay.FFT_en2 to the Android/OBB directory on your device.</li>
-<li>Launch the game and enjoy!</li>
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- <p>Final Fantasy Tactics 2.1 0 apk is a great way to experience one of the best strategy RPGs ever made on your Android device. The game offers a captivating story, a deep and flexible job system, and a challenging and satisfying tactical combat. The game also features improved graphics, sound, and content over the original version, as well as new touch screen controls and autosave function. If you are a fan of Final Fantasy or strategy games, you should definitely give this game a try.</p>
- <h2>FAQs</h2>
- <h3>What are the system requirements for Final Fantasy Tactics 2.1 0 apk?</h3>
- <p>The game requires Android 4.0.3 or higher, and at least 1 GB of RAM and 700 MB of free storage space.</p>
- <h3>How can I save my progress in Final Fantasy Tactics 2.1 0 apk?</h3>
- <p>The game has an autosave function that saves your progress every time you enter or exit a battle or a location. You can also manually save your progress by accessing the menu and selecting Save Game.</p>
- <h3>How can I change the language of Final Fantasy Tactics 2.1 0 apk?</h3>
- <p>The game supports English, French, German, Italian, Spanish, Japanese, Korean, Traditional Chinese, and Simplified Chinese languages. You can change the language by accessing the menu and selecting Options, then Language.</p>
- <h3>How can I play multiplayer mode in Final Fantasy Tactics 2.1 0 apk?</h3>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Play Parking Jam 3D with Everything Unlocked using this Mod APK.md b/spaces/congsaPfin/Manga-OCR/logs/Play Parking Jam 3D with Everything Unlocked using this Mod APK.md
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-<h1>Parking Jam 3D Mod APK Unlocked Everything: A Fun and Addictive Puzzle Game</h1>
- <p>If you are looking for a puzzle game that is fun, addictive, and challenging, then you should check out <strong>Parking Jam 3D</strong>. This is a game that will test your logic skills, critical thinking, and timing precision as you try to empty a parking lot full of cars. But there is more to this game than just parking. You will also experience jams, angry grannies, bonus levels, idle money, and much more. And if you want to enjoy all the features of this game without any limitations, then you should download <strong>Parking Jam 3D Mod APK Unlocked Everything</strong>. This is a modified version of the game that will give you access to all the cars, skins, scenes, buildings, and money that you want. In this article, we will tell you what Parking Jam 3D is, what Parking Jam 3D Mod APK Unlocked Everything is, how to download and install it, and what are its features and benefits.</p>
- <h2>What is Parking Jam 3D?</h2>
- <p>Parking Jam 3D is one of the most downloaded Puzzle Board games with more than 80 million installs. It is developed by Popcore Games, a studio that has created other popular games like Pull the Pin, Paint Puzzle, Sandwich!, Clash of Blocks, Paint The Cube and more. Here are some of the aspects of Parking Jam 3D that make it a unique and entertaining game.</p>
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- <h3>A game about emptying a parking lot</h3>
- <p>The main objective of Parking Jam 3D is to get all the cars out of the parking lot. Sounds simple enough, right? Well, not quite. You see, the parking lot is full of obstacles and other cars that are blocking your way. You need to move them in the right order and direction to clear a path for your car. But be careful not to hit anything or anyone, especially granny. She might look harmless, but she can flip your car over with her enormous strength. You need to use your logic skills, critical thinking, and timing precision to solve this tricky parking puzzle.</p>
- <h3>A game with more than just <h3>A game with more than just parking</h3>
- <p>Parking Jam 3D is not only a game about parking, but also a game about jams. Yes, you heard that right. Jams are bonus levels that you can unlock by completing certain levels. In these levels, you will have to deal with a traffic jam that is caused by various factors, such as accidents, roadworks, police cars, ambulances, fire trucks, and more. You will have to find a way to clear the jam and get all the cars moving again. But be careful not to cause more chaos or damage. Jams are fun and challenging levels that will test your patience and problem-solving skills.</p>
- <h3>A game with different levels, maps, cars, skins and scenes</h3>
- <p>Parking Jam 3D is a game that will never get boring or repetitive. That's because it has hundreds of levels that get harder and harder every time. You will have to face different parking scenarios and situations that will require different strategies and tactics. You will also have to deal with different maps that have different layouts and features. Some maps are small and simple, while others are large and complex. Some maps have ramps, bridges, tunnels, elevators, and other elements that will add more variety and fun to the game. You will also have access to different cars that have different shapes, sizes, colors, and speeds. You can choose from sedans, SUVs, trucks, buses, sports cars, and more. You can also unlock skins for your cars that will make them look more cool and stylish. And if that's not enough, you can also unlock scenes for your maps that will change the background and the atmosphere of the game. You can choose from city, beach, forest, desert, snow, and more. Parking Jam 3D is a game that has something for everyone.</p>
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- <h2>What is Parking Jam 3D Mod APK Unlocked Everything?</h2>
- <p>Parking Jam 3D is a free game that you can download from the Google Play Store or the App Store. However, the game also has some limitations and restrictions that might affect your gaming experience. For example, the game has ads that might interrupt your gameplay or annoy you. The game also has in-app purchases that might tempt you to spend real money to get more coins or unlock more features. The game also has some locked features that you can only access by completing certain levels or tasks. For example, some cars, skins, scenes, buildings, and money are locked until you reach a certain level or collect a certain amount of rent.</p>
- <p>If you want to get rid of these limitations and restrictions, then you should download <strong>Parking Jam 3D Mod APK Unlocked Everything</strong>. This is a modified version of the game that will give you access to all the features of the game without any limitations or restrictions. You will be able to enjoy the game without ads or in-app purchases. You will also be able to customize your cars and buildings with any skin or scene that you want. You will also have unlimited money that you can use to buy anything in the game.</p>
- <h2>How to download and install Parking Jam 3D Mod APK Unlocked Everything?</h2>
- <p>If you want to download and install Parking Jam 3D Mod APK Unlocked Everything on your Android device, then you need to follow these simple steps:</p>
- <h3>The steps to download the mod apk file from a reliable source</h3>
- <p>First of all, you need to find a reliable source that provides the mod apk file for Parking Jam 3D Mod APK Unlocked Everything. There are many websites that claim to offer this file, but not all of them are trustworthy or safe. Some of them might contain viruses or malware that could harm your device or steal your personal information. Therefore, you need to be careful and do some research before downloading anything from the internet.</p>
- <p>One of the websites that we recommend is [APKPure]. This is a popular website that provides mod apk files for various games and apps. It has a good reputation and a large user base. It also has a verification system that ensures the safety and quality of the files. You can download Parking Jam 3D Mod APK Unlocked Everything from this website by following these steps:</p>
- - Go to [APKPure] website on your browser. - Search for Parking Jam 3D Mod APK Unlocked Everything in the search bar. - Click on the result that matches your query. - Click on the download button and wait for the file to be downloaded on your device. <h3>The steps to enable unknown sources on your device</h3>
- <p>Before you can install Parking Jam 3D Mod APK Unlocked Everything on your device, you need to enable unknown sources on your device. <p>Unknown sources are sources that are not verified by Google or the device manufacturer. By default, your device does not allow you to install apps from unknown sources for security reasons. However, since Parking Jam 3D Mod APK Unlocked Everything is not available on the Google Play Store, you need to enable unknown sources to install it. You can do this by following these steps:</p>
- - Go to your device settings and look for security or privacy options. - Find the option that says unknown sources or install unknown apps and toggle it on. - Confirm your choice by tapping OK or Allow. <h3>The steps to install the mod apk file and launch the game</h3>
- <p>After you have downloaded the mod apk file and enabled unknown sources, you can install Parking Jam 3D Mod APK Unlocked Everything on your device. You can do this by following these steps:</p>
- - Locate the mod apk file on your device using a file manager app or your browser's downloads folder. - Tap on the file and select install. - Wait for the installation process to finish and tap open. - Enjoy the game with all the features unlocked. <h2>What are the features and benefits of Parking Jam 3D Mod APK Unlocked Everything?</h2>
- <p>Parking Jam 3D Mod APK Unlocked Everything is a great way to enjoy Parking Jam 3D without any limitations or restrictions. Here are some of the features and benefits that you will get from this mod apk:</p>
- <h3>The features that make the game more fun and challenging</h3>
- <p>Parking Jam 3D Mod APK Unlocked Everything has some features that will make the game more fun and challenging for you. These include:</p>
- <h4>Relieve your stress by hitting cars or avoiding granny</h4>
- <p>If you are feeling stressed or frustrated, you can vent your anger by hitting cars or avoiding granny in Parking Jam 3D Mod APK Unlocked Everything. You can hit cars with your car or with other cars to cause damage and chaos. You can also avoid granny by driving away from her or hiding behind other cars. However, be careful not to hit her or she will flip your car over. This feature will make the game more fun and relaxing for you.</p>
- <h4>Solve puzzles that get harder every time and require skills and critical thinking</h4>
- <p>If you are looking for a challenge, you will find it in Parking Jam 3D Mod APK Unlocked Everything. The game has hundreds of levels that get harder and harder every time. You will have to face different parking scenarios and situations that will require different strategies and tactics. You will also have to deal with different maps that have different layouts and features. You will need to use your skills and critical thinking to solve these puzzles and clear the parking lot.</p>
- <h4>Learn how to unpark smoothly without hitting anything or anyone</h4>
- <p>If you want to improve your driving skills, you can learn how to unpark smoothly without hitting anything or anyone in Parking Jam 3D Mod APK Unlocked Everything. The game will teach you how to move your car in the right order and direction to clear a path for your car. You will also learn how to avoid obstacles and other cars that are blocking your way. You will also learn how to park your car in tight spaces without scratching it. This feature will make the game more educational and useful for you.</p>
- <h3>The benefits that make the game more rewarding and satisfying</h3>
- <p>Parking Jam 3D Mod APK Unlocked Everything has some benefits that will make the game more rewarding and satisfying for you. These include:</p>
- <h4>Earn money by completing levels and collecting rent from buildings</h4>
- <p>If you want to earn money, you can do so by completing levels and collecting rent from buildings in Parking Jam 3D Mod APK Unlocked Everything. You will earn money by clearing the parking lot and getting all the cars out. You will also earn money by collecting rent from buildings that you own or manage. You can use this money to buy more cars, skins, scenes, buildings, and more.</p>
- <h4>Unlock skins for your cars and scenes for your maps</h4>
- <p>If you want to customize your cars and maps, you can do so by unlocking skins for your cars and scenes for your maps in Parking Jam 3D Mod APK Unlocked Everything. You will unlock skins for your cars by completing levels or buying them with money. You can choose from different colors, patterns, designs, logos, stickers, and more. You will unlock scenes for your maps by completing levels or buying them with money. You can choose from different backgrounds, atmospheres, themes, weather, and more.</p>
- <h4>Play offline and on the go without internet connection</h4>
- <p>If you want to play offline and on the go without internet connection, you can do so in Parking Jam 3D Mod APK Un Everything. The game does not require internet connection to play, so you can play it anytime and anywhere. You can play it on your phone, tablet, or laptop without worrying about data usage or wifi availability. This feature will make the game more convenient and accessible for you.</p>
- <h2>Conclusion</h2>
- <p>Parking Jam 3D is a fun and addictive puzzle game that will test your logic skills, critical thinking, and timing precision as you try to empty a parking lot full of cars. But there is more to this game than just parking. You will also experience jams, angry grannies, bonus levels, idle money, and much more. And if you want to enjoy all the features of this game without any limitations or restrictions, then you should download Parking Jam 3D Mod APK Unlocked Everything. This is a modified version of the game that will give you access to all the cars, skins, scenes, buildings, and money that you want. You will also be able to customize your cars and buildings with any skin or scene that you want. You will also have unlimited money that you can use to buy anything in the game.</p>
- <p>If you are looking for a puzzle game that is fun, addictive, and challenging, then you should try out Parking Jam 3D and Parking Jam 3D Mod APK Unlocked Everything. You will not regret it. You will have hours of entertainment and satisfaction as you solve puzzles and clear parking lots. You will also have fun and relax as you hit cars or avoid granny. You will also learn and improve as you unpark smoothly without hitting anything or anyone. You will also earn and reward as you complete levels and collect rent from buildings. You will also customize and enjoy as you unlock skins for your cars and scenes for your maps.</p>
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- <p>Here are some of the frequently asked questions about Parking Jam 3D and Parking Jam 3D Mod APK Unlocked Everything:</p>
- <h4>Q: Is Parking Jam 3D safe to play?</h4>
-<p>A: Yes, Parking Jam 3D is safe to play. It does not contain any viruses or malware that could harm your device or steal your personal information. It is also suitable for all ages and does not contain any inappropriate or offensive content.</p>
- <h4>Q: Is Parking Jam 3D Mod APK Unlocked Everything safe to download and install?</h4>
-<p>A: Yes, Parking Jam 3D Mod APK Unlocked Everything is safe to download and install. However, you need to make sure that you download it from a reliable source like [APKPure]. You also need to enable unknown sources on your device before installing it.</p>
- <h4>Q: How can I update Parking Jam 3D Mod APK Unlocked Everything?</h4>
-<p>A: If there is a new version of Parking Jam 3D Mod APK Unlocked Everything available, you can update it by following these steps:</p>
-- Go to [APKPure] website on your browser. - Search for Parking Jam 3D Mod APK Unlocked Everything in the search bar. - Click on the result that matches your query. - Click on the update button and wait for the file to be downloaded on your device. - Install the file over the existing one and launch the game. <h4>Q: How can I contact the developers of Parking Jam 3D?</h4>
-<p>A: If you have any questions, suggestions, feedback, or issues about Parking Jam 3D, you can contact the developers of the game by emailing them at support@popcore.com. You can also visit their website at https://popcore.com/ or follow them on Facebook at https://www.facebook.com/popcoregames/.</p>
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-<p>A: If you want to share your opinion about Parking Jam 3D or Parking Jam 3D Mod APK Unlocked Everything, you can do so by leaving a comment below this article. You can also rate and review the game on the Google Play Store or the App Store. We appreciate your feedback and we hope you enjoy the game.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Quick Live 1.15.1 APK Play PSP Games on Your Android Device.md b/spaces/congsaPfin/Manga-OCR/logs/Quick Live 1.15.1 APK Play PSP Games on Your Android Device.md
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+++ /dev/null
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-<h1>Quick Live 1.15.1 APK Download: A Guide for Android Users</h1>
- <p>If you are looking for a fun and easy way to stream live video and chat with friends, you might want to try Quick Live app. Quick Live is a social streaming platform that allows you to create your own live shows, join live battles, watch other streamers, and interact with other users. You can also enjoy party games, music, voice chat, and more on Quick Live.</p>
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- <p>An APK file is a package file format used by the Android operating system for the distribution and installation of mobile applications. It contains all the elements that an app needs to run properly on your device, such as code, resources, manifest, certificates, etc. You can think of it as a zip file that contains the app.</p>
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- <h2>What is Quick Live app and what are its features?</h2>
- <p>Quick Live is a live streaming app that lets you broadcast your own live shows or watch other streamers from around the world. You can also join live battles for huge rewards, invite friends to co-host with you, tag your location when streaming, and more.</p>
- <p>Some of the features of Quick Live app are:</p>
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- <p>Congratulations! You have successfully downloaded and installed Quick Live 1.15.1 APK file on your Android device. You can now launch the app and enjoy its features.</p>
- <h2>What are some alternatives to Quick Live app for live streaming?</h2>
- <p>If you are looking for some other apps that can help you stream live video and chat with friends, you may want to check out these alternatives:</p>
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- <h2>Conclusion: Summarize the main points and provide some tips for using Quick Live app.</h2>
- <p>In conclusion, Quick Live is a fun and easy way to stream live video and chat with friends on your Android device. You can create your own live shows, join live battles, watch other streamers, and interact with other users on Quick Live. However, you may not be able to find Quick Live app on the Google Play Store, so you will need to download and install the APK file of Quick Live 1.15.1 from a third-party source.</p>
- <p>To download and install Quick Live 1.15.1 APK file on your Android device, you will need to allow unknown apps, download the APK file from a reliable source, and install the APK file on your device. You can then launch the app and enjoy its features.</p>
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-<li><strong>Follow the rules:</strong> Make sure you follow the community guidelines and terms of service of Quick Live app when streaming or watching live videos. Do not engage in any illegal, harmful, or offensive activities or content.</li>
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- <h3>Q: Is Quick Live app safe to use?</h3>
- <p>A: Quick Live app is generally safe to use as long as you download it from a trusted source and scan it for any malware or viruses before installing it. However, you should also be careful about what information you share or what permissions you grant to the app.</p>
- <h3>Q: How do I update Quick Live app?</h3>
- <p>A: If you have downloaded Quick Live app from a third-party source, you will not receive automatic updates from the Google Play Store. You will need to check for updates manually from the source where you downloaded the APK file. Alternatively, you can uninstall the app and install the latest version of the APK file from the same or a different source.</p>
- <h3>Q: How do I uninstall Quick Live app?</h3>
- <p>A: If you want to uninstall Quick Live app from your device, you can do so by following these steps:</p>
- <ol>
-<li>Go to Settings > Apps > Quick Live (or Settings > Applications > Application manager > Quick Live).</li>
-<li>Tap on Uninstall and confirm your action.</li>
-<li>Wait for the app to be removed from your device.</li>
-</ol>
- <h3>Q: How do I contact Quick Live app support?</h3>
- <p>A: If you have any questions, feedback, or issues with Quick Live app, you can contact the app support team by using the following methods:</p>
- <ul>
-<li>Email: support@quicklive.com</li>
-<li>Facebook: https://www.facebook.com/quickliveapp</li>
-<li>Twitter: https://twitter.com/quickliveapp</li>
-<li>Instagram: https://www.instagram.com/quickliveapp</li>
-</ul>
- <h3>Q: What are the minimum requirements for Quick Live app?</h3>
- <p>A: To run Quick Live app on your device, you will need to have the following minimum requirements:</p>
- <ul>
-<li>Android version: 4.4 or higher</li>
-<li>RAM: 2 GB or higher</li>
-<li>Storage space: 100 MB or higher</li>
-<li>Internet connection: Wi-Fi or cellular data</li>
-</ul>
- <h3>Q: How do I stream to multiple platforms with Quick Live app?</h3>
- <p>A: To stream to multiple platforms with Quick Live app, you will need to follow these steps:</p>
- <ol>
-<li>Launch Quick Live app and tap on the camera icon at the bottom.</li>
-<li>Select the platforms that you want to stream to, such as YouTube Live or Facebook Live, and log in with your accounts.</li>
-<li>Enter a title and a description for your live stream and choose a category and a tag.</li>
-<li>Tap on Start Streaming and enjoy your live show.</li>
-</ol></p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Xtreme Motorbikes Raja APK The Best 3D Sport Motorbike Game for Android.md b/spaces/congsaPfin/Manga-OCR/logs/Xtreme Motorbikes Raja APK The Best 3D Sport Motorbike Game for Android.md
deleted file mode 100644
index 306432a6c56708ec07ff24ddbf3712da36fa64c3..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Xtreme Motorbikes Raja APK The Best 3D Sport Motorbike Game for Android.md	
+++ /dev/null
@@ -1,118 +0,0 @@
-
-<h1>Xtreme Motorbikes Raja APK: A Review</h1>
-<p>If you are a fan of motorbike racing games, you might want to check out Xtreme Motorbikes Raja APK. This is a free Android game that lets you experience the thrill of riding powerful and exciting sport motorbikes on realistic tracks. In this article, we will review the features, gameplay, pros and cons of Xtreme Motorbikes Raja APK, and also show you how to download and install it on your device.</p>
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- <h2>What is Xtreme Motorbikes Raja APK?</h2>
-<p>Xtreme Motorbikes Raja APK is an Android game developed by Mehdi Rabiee. It is a simulation game that lets you drive more than 20 different motorbikes with realistic physics, sounds, and graphics. You can customize your motorbikes with exclusive paint jobs and rims, and choose from various game modes and challenges to test your skills. You can also compete with other players online or offline, and share your achievements on social media.</p>
- <h3>Features of Xtreme Motorbikes Raja APK</h3>
-<h4>Realistic physics and graphics</h4>
-<p>One of the main attractions of Xtreme Motorbikes Raja APK is its realistic physics engine, which simulates every aspect of motorbike behavior. You can feel the acceleration, braking, steering, suspension, and traction of your motorbike as you race on different tracks. The game also has stunning 3D graphics that create a immersive environment for your racing experience. You can see the details of your motorbike, the track, the weather, and the surroundings as you zoom past them.</p>
- <h4>Variety of motorbikes and customization options</h4>
-<p>Xtreme Motorbikes Raja APK offers you a wide range of motorbikes to choose from, each with its own specifications and performance. You can drive more than 20 powerful and exciting sport motorbikes, such as Ducati, Yamaha, Kawasaki, Honda, Suzuki, BMW, and more. You can also customize your motorbikes with exclusive paint jobs and rims, to make them look more stylish and unique. You can change the color, design, pattern, and material of your motorbike's body and wheels.</p>
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- <h4>Different game modes and challenges</h4>
-<p>Xtreme Motorbikes Raja APK has various game modes and challenges to keep you entertained and challenged. You can choose from free ride mode, where you can explore the tracks at your own pace; career mode, where you can complete missions and earn money; time trial mode, where you can race against the clock; or multiplayer mode, where you can race against other players online or offline. You can also take part in different events and tournaments, where you can win prizes and trophies.</p>
- <h3>How to download and install Xtreme Motorbikes Raja APK?</h3>
-<h4>Requirements and compatibility</h4>
-<p>To download and install Xtreme Motorbikes Raja APK on your device, you need to have an Android device that runs on Android 5.0 or higher. You also need to have at least 100 MB of free storage space on your device. The game is compatible with most Android devices, but some features may vary depending on your device's specifications.</p>
- <h4>Steps to download and install</h4>
-<p>To download and install Xtreme Motorbikes Raja APK on your device, follow these steps:</p>
-<ol>
-<li>Go to [Xtreme Motorbikes](^1^) website and click on the download button.</li>
-<li>Wait for the download to finish and then open the downloaded file (xtreme-motorbikes-raja.apk) in your device's file manager.</li>
-<li>Tap on the file and allow the installation from unknown sources if prompted.</li>
-<li>Follow the on-screen instructions and wait for the installation to finish.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
- <h4>Tips and tricks to play the game</h4>
-<p>To play Xtreme Motorbikes Raja APK, you need to master the controls and the physics of your motorbike. Here are some tips and tricks to help you:</p>
-<ul>
-<li>Use the accelerator and brake buttons on the right side of the screen to control your speed.</li>
-<li>Use the tilt or touch buttons on the left side of the screen to steer your motorbike.</li>
-<li>Use the nitro button on the bottom right corner of the screen to boost your speed when needed.</li>
-<li>Use the camera button on the top right corner of the screen to change your view angle.</li>
-<li>Avoid crashing into obstacles or other vehicles, as this will damage your motorbike and reduce your performance.</li>
-<li>Collect coins and gems on the track to buy new motorbikes and upgrades.</li>
-<li>Complete missions and challenges to earn rewards and unlock new tracks.</li>
-<li>Compete with other players online or offline to improve your ranking and reputation.</li>
-</ul>
- <h3>Pros and cons of Xtreme Motorbikes Raja APK</h3>
-<h4>Pros</h4>
-<p>Xtreme Motorbikes Raja APK is a fun and addictive game that offers many benefits, such as:</p>
-<ul>
-<li>It is free to download and play, with no in-app purchases or ads.</li>
-<li>It has realistic physics and graphics that create a immersive racing experience.</li>
-<li>It has a variety of motorbikes and customization options that suit different preferences and styles.</li>
-<li>It has different game modes and challenges that keep you entertained and challenged.</li>
-<li>It has a multiplayer mode that lets you race with other players online or offline.</li>
-</ul>
- <h4>Cons</h4>
-<p>Xtreme Motorbikes Raja APK also has some drawbacks, such as:</p>
-<ul>
-<li>It requires a lot of storage space and a good internet connection to run smoothly.</li>
-<li>It may not be compatible with some older or low-end devices.</li>
-<li>It may have some bugs or glitches that affect the gameplay or performance.</li>
-<li>It may be too difficult or frustrating for some players who are not used to motorbike racing games.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Xtreme Motorbikes Raja APK is a great game for motorbike racing enthusiasts who want to enjoy a realistic and exciting racing experience on their Android devices. It has many features, game modes, challenges, and motorbikes that make it fun and addictive. It is also free to download and play, with no in-app purchases or ads. However, it also has some drawbacks, such as requiring a lot of storage space, a good internet connection, and a compatible device. It may also have some bugs or glitches, or be too difficult for some players. Overall, Xtreme Motorbikes Raja APK is a game worth trying if you love motorbike racing games.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Xtreme Motorbikes Raja APK:</p>
- <ol>
-<li><strong>What is the latest version of Xtreme Motorbikes Raja APK?</strong></li>
-<p>The latest version of Xtreme Motorbikes Raja APK is 1.0.1, which was released on June 15, 2023. It fixed some minor bugs and improved the performance of the game.</p>
- <li><strong>Is Xtreme Motorbikes Raja APK safe to download and install?</strong></li>
-<p>Xtreme Motorbikes Raja APK is safe to download and install, as long as you download it from a trusted source, such as [Xtreme Motorbikes] website. It does not contain any viruses, malware, or spyware that can harm your device or data. However, you should always be careful when downloading any file from unknown sources, as they may contain harmful content or software.</p>
- <li><strong>Can I play Xtreme Motorbikes Raja APK offline?</strong></li>
-<p>You can play Xtreme Motorbikes Raja APK offline, but you will not be able to access some features, such as multiplayer mode, online events, or social media sharing. You will also need an internet connection to download and install the game, and to update it when needed.</p>
- <li><strong>How can I contact the developer of Xtreme Motorbikes Raja APK?</strong></li>
-<p>If you have any questions, feedback, or suggestions about Xtreme Motorbikes Raja APK, you can contact the developer of the game by sending an email to mehdirabiee@gmail.com. You can also visit their [Facebook page] or their [YouTube channel] to get more information and updates about the game.</p>
- <li><strong>What are some alternatives to Xtreme Motorbikes Raja APK?</strong></li>
-<p>If you are looking for some other motorbike racing games for your Android device, you can try some of these alternatives:</p>
-<ul>
-<li><strong>MotoGP Racing '23</strong>: This is a official game of the MotoGP championship, where you can race with the best riders and teams in the world. You can customize your bike and rider, and compete in different modes and circuits. You can also join online tournaments and leagues, and challenge your friends.</li>
-<li><strong>Traffic Rider</strong>: This is a first-person motorbike racing game, where you have to drive your bike through endless highway traffic. You can choose from 29 different motorbikes, and upgrade them with various features. You can also play in different modes, such as career, free ride, time trial, or endless.</li>
-<li><strong>Real Bike Racing</strong>: This is a 3D motorbike racing game, where you can experience the adrenaline of high-speed racing. You can choose from 10 different motorbikes, and race on 9 realistic tracks. You can also compete with other players online, and show off your skills.</li>
-</ul>
-</ol></p> 197e85843d<br />
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\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/ChashmeBaddoor720phindimovietorrentdownloadkickass The remake of the classic film you dont want to miss.md b/spaces/contluForse/HuggingGPT/assets/ChashmeBaddoor720phindimovietorrentdownloadkickass The remake of the classic film you dont want to miss.md
deleted file mode 100644
index 171c7b0194e43471eb7b06770fec4f1c1c6dcc74..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/ChashmeBaddoor720phindimovietorrentdownloadkickass The remake of the classic film you dont want to miss.md	
+++ /dev/null
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diff --git a/spaces/contluForse/HuggingGPT/assets/Download Modern Economics By Robert 19.md b/spaces/contluForse/HuggingGPT/assets/Download Modern Economics By Robert 19.md
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index 0bea379ca27cca7e4933ecb9553953b5a11552de..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Download Modern Economics By Robert 19.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>my aim here is not to show that the modern theory is incorrect, but to point out that it has little to say about the population question. i will say more about population in my last chapter. to assess the broad outlines of the modern theory and to gain perspective on the questions asked by the classical theory, it helps to focus on the most recent version of the modern theory. it is best introduced as a complement to the first version, which is the classic growth model in the sense that the malthusian theory is complementary to ricardo’s law of rent. </p>
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diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/mlp_mixer.py b/spaces/cooelf/Multimodal-CoT/timm/models/mlp_mixer.py
deleted file mode 100644
index f128b9c916a68839a11fbd3ed6409743ac7a6a4c..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/mlp_mixer.py
+++ /dev/null
@@ -1,625 +0,0 @@
-""" MLP-Mixer, ResMLP, and gMLP in PyTorch
-
-This impl originally based on MLP-Mixer paper.
-
-Official JAX impl: https://github.com/google-research/vision_transformer/blob/linen/vit_jax/models_mixer.py
-
-Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-
-@article{tolstikhin2021,
-  title={MLP-Mixer: An all-MLP Architecture for Vision},
-  author={Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner,
-        Thomas and Yung, Jessica and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and Dosovitskiy, Alexey},
-  journal={arXiv preprint arXiv:2105.01601},
-  year={2021}
-}
-
-Also supporting ResMlp, and a preliminary (not verified) implementations of gMLP
-
-Code: https://github.com/facebookresearch/deit
-Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-@misc{touvron2021resmlp,
-      title={ResMLP: Feedforward networks for image classification with data-efficient training},
-      author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and Matthieu Cord and Alaaeldin El-Nouby and
-        Edouard Grave and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Hervé Jégou},
-      year={2021},
-      eprint={2105.03404},
-}
-
-Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
-@misc{liu2021pay,
-      title={Pay Attention to MLPs},
-      author={Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le},
-      year={2021},
-      eprint={2105.08050},
-}
-
-A thank you to paper authors for releasing code and weights.
-
-Hacked together by / Copyright 2021 Ross Wightman
-"""
-import math
-from copy import deepcopy
-from functools import partial
-
-import torch
-import torch.nn as nn
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg, overlay_external_default_cfg, named_apply
-from .layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple
-from .registry import register_model
-
-
-def _cfg(url='', **kwargs):
-    return {
-        'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
-        'crop_pct': 0.875, 'interpolation': 'bicubic', 'fixed_input_size': True,
-        'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
-        'first_conv': 'stem.proj', 'classifier': 'head',
-        **kwargs
-    }
-
-
-default_cfgs = dict(
-    mixer_s32_224=_cfg(),
-    mixer_s16_224=_cfg(),
-    mixer_b32_224=_cfg(),
-    mixer_b16_224=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224-76587d61.pth',
-    ),
-    mixer_b16_224_in21k=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224_in21k-617b3de2.pth',
-        num_classes=21843
-    ),
-    mixer_l32_224=_cfg(),
-    mixer_l16_224=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224-92f9adc4.pth',
-    ),
-    mixer_l16_224_in21k=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224_in21k-846aa33c.pth',
-        num_classes=21843
-    ),
-
-    # Mixer ImageNet-21K-P pretraining
-    mixer_b16_224_miil_in21k=_cfg(
-        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil_in21k.pth',
-        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
-    ),
-    mixer_b16_224_miil=_cfg(
-        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil.pth',
-        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
-    ),
-
-    gmixer_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-    gmixer_24_224=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmixer_24_224_raa-7daf7ae6.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-
-    resmlp_12_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlp_12_no_dist.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-    resmlp_24_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlp_24_no_dist.pth',
-        #url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resmlp_24_224_raa-a8256759.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-    resmlp_36_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlp_36_no_dist.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-    resmlp_big_24_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_no_dist.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-
-    resmlp_12_distilled_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dist.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-    resmlp_24_distilled_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dist.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-    resmlp_36_distilled_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlp_36_dist.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-    resmlp_big_24_distilled_224=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_dist.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-
-    resmlp_big_24_224_in22ft1k=_cfg(
-        url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth',
-        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-
-    gmlp_ti16_224=_cfg(),
-    gmlp_s16_224=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
-    ),
-    gmlp_b16_224=_cfg(),
-)
-
-
-class MixerBlock(nn.Module):
-    """ Residual Block w/ token mixing and channel MLPs
-    Based on: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    def __init__(
-            self, dim, seq_len, mlp_ratio=(0.5, 4.0), mlp_layer=Mlp,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.):
-        super().__init__()
-        tokens_dim, channels_dim = [int(x * dim) for x in to_2tuple(mlp_ratio)]
-        self.norm1 = norm_layer(dim)
-        self.mlp_tokens = mlp_layer(seq_len, tokens_dim, act_layer=act_layer, drop=drop)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        self.mlp_channels = mlp_layer(dim, channels_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.drop_path(self.mlp_tokens(self.norm1(x).transpose(1, 2)).transpose(1, 2))
-        x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
-        return x
-
-
-class Affine(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
-        self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
-
-    def forward(self, x):
-        return torch.addcmul(self.beta, self.alpha, x)
-
-
-class ResBlock(nn.Module):
-    """ Residual MLP block w/ LayerScale and Affine 'norm'
-
-    Based on: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    def __init__(
-            self, dim, seq_len, mlp_ratio=4, mlp_layer=Mlp, norm_layer=Affine,
-            act_layer=nn.GELU, init_values=1e-4, drop=0., drop_path=0.):
-        super().__init__()
-        channel_dim = int(dim * mlp_ratio)
-        self.norm1 = norm_layer(dim)
-        self.linear_tokens = nn.Linear(seq_len, seq_len)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, drop=drop)
-        self.ls1 = nn.Parameter(init_values * torch.ones(dim))
-        self.ls2 = nn.Parameter(init_values * torch.ones(dim))
-
-    def forward(self, x):
-        x = x + self.drop_path(self.ls1 * self.linear_tokens(self.norm1(x).transpose(1, 2)).transpose(1, 2))
-        x = x + self.drop_path(self.ls2 * self.mlp_channels(self.norm2(x)))
-        return x
-
-
-class SpatialGatingUnit(nn.Module):
-    """ Spatial Gating Unit
-
-    Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
-    """
-    def __init__(self, dim, seq_len, norm_layer=nn.LayerNorm):
-        super().__init__()
-        gate_dim = dim // 2
-        self.norm = norm_layer(gate_dim)
-        self.proj = nn.Linear(seq_len, seq_len)
-
-    def init_weights(self):
-        # special init for the projection gate, called as override by base model init
-        nn.init.normal_(self.proj.weight, std=1e-6)
-        nn.init.ones_(self.proj.bias)
-
-    def forward(self, x):
-        u, v = x.chunk(2, dim=-1)
-        v = self.norm(v)
-        v = self.proj(v.transpose(-1, -2))
-        return u * v.transpose(-1, -2)
-
-
-class SpatialGatingBlock(nn.Module):
-    """ Residual Block w/ Spatial Gating
-
-    Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
-    """
-    def __init__(
-            self, dim, seq_len, mlp_ratio=4, mlp_layer=GatedMlp,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.):
-        super().__init__()
-        channel_dim = int(dim * mlp_ratio)
-        self.norm = norm_layer(dim)
-        sgu = partial(SpatialGatingUnit, seq_len=seq_len)
-        self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, gate_layer=sgu, drop=drop)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-
-    def forward(self, x):
-        x = x + self.drop_path(self.mlp_channels(self.norm(x)))
-        return x
-
-
-class MlpMixer(nn.Module):
-
-    def __init__(
-            self,
-            num_classes=1000,
-            img_size=224,
-            in_chans=3,
-            patch_size=16,
-            num_blocks=8,
-            embed_dim=512,
-            mlp_ratio=(0.5, 4.0),
-            block_layer=MixerBlock,
-            mlp_layer=Mlp,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6),
-            act_layer=nn.GELU,
-            drop_rate=0.,
-            drop_path_rate=0.,
-            nlhb=False,
-            stem_norm=False,
-    ):
-        super().__init__()
-        self.num_classes = num_classes
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-
-        self.stem = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans,
-            embed_dim=embed_dim, norm_layer=norm_layer if stem_norm else None)
-        # FIXME drop_path (stochastic depth scaling rule or all the same?)
-        self.blocks = nn.Sequential(*[
-            block_layer(
-                embed_dim, self.stem.num_patches, mlp_ratio, mlp_layer=mlp_layer, norm_layer=norm_layer,
-                act_layer=act_layer, drop=drop_rate, drop_path=drop_path_rate)
-            for _ in range(num_blocks)])
-        self.norm = norm_layer(embed_dim)
-        self.head = nn.Linear(embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
-
-        self.init_weights(nlhb=nlhb)
-
-    def init_weights(self, nlhb=False):
-        head_bias = -math.log(self.num_classes) if nlhb else 0.
-        named_apply(partial(_init_weights, head_bias=head_bias), module=self)  # depth-first
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        x = self.stem(x)
-        x = self.blocks(x)
-        x = self.norm(x)
-        x = x.mean(dim=1)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-
-def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):
-    """ Mixer weight initialization (trying to match Flax defaults)
-    """
-    if isinstance(module, nn.Linear):
-        if name.startswith('head'):
-            nn.init.zeros_(module.weight)
-            nn.init.constant_(module.bias, head_bias)
-        else:
-            if flax:
-                # Flax defaults
-                lecun_normal_(module.weight)
-                if module.bias is not None:
-                    nn.init.zeros_(module.bias)
-            else:
-                # like MLP init in vit (my original init)
-                nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    if 'mlp' in name:
-                        nn.init.normal_(module.bias, std=1e-6)
-                    else:
-                        nn.init.zeros_(module.bias)
-    elif isinstance(module, nn.Conv2d):
-        lecun_normal_(module.weight)
-        if module.bias is not None:
-            nn.init.zeros_(module.bias)
-    elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
-        nn.init.ones_(module.weight)
-        nn.init.zeros_(module.bias)
-    elif hasattr(module, 'init_weights'):
-        # NOTE if a parent module contains init_weights method, it can override the init of the
-        # child modules as this will be called in depth-first order.
-        module.init_weights()
-
-
-def checkpoint_filter_fn(state_dict, model):
-    """ Remap checkpoints if needed """
-    if 'patch_embed.proj.weight' in state_dict:
-        # Remap FB ResMlp models -> timm
-        out_dict = {}
-        for k, v in state_dict.items():
-            k = k.replace('patch_embed.', 'stem.')
-            k = k.replace('attn.', 'linear_tokens.')
-            k = k.replace('mlp.', 'mlp_channels.')
-            k = k.replace('gamma_', 'ls')
-            if k.endswith('.alpha') or k.endswith('.beta'):
-                v = v.reshape(1, 1, -1)
-            out_dict[k] = v
-        return out_dict
-    return state_dict
-
-
-def _create_mixer(variant, pretrained=False, **kwargs):
-    if kwargs.get('features_only', None):
-        raise RuntimeError('features_only not implemented for MLP-Mixer models.')
-
-    model = build_model_with_cfg(
-        MlpMixer, variant, pretrained,
-        default_cfg=default_cfgs[variant],
-        pretrained_filter_fn=checkpoint_filter_fn,
-        **kwargs)
-    return model
-
-
-@register_model
-def mixer_s32_224(pretrained=False, **kwargs):
-    """ Mixer-S/32 224x224
-    Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=32, num_blocks=8, embed_dim=512, **kwargs)
-    model = _create_mixer('mixer_s32_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_s16_224(pretrained=False, **kwargs):
-    """ Mixer-S/16 224x224
-    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=16, num_blocks=8, embed_dim=512, **kwargs)
-    model = _create_mixer('mixer_s16_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_b32_224(pretrained=False, **kwargs):
-    """ Mixer-B/32 224x224
-    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=32, num_blocks=12, embed_dim=768, **kwargs)
-    model = _create_mixer('mixer_b32_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_b16_224(pretrained=False, **kwargs):
-    """ Mixer-B/16 224x224. ImageNet-1k pretrained weights.
-    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
-    model = _create_mixer('mixer_b16_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_b16_224_in21k(pretrained=False, **kwargs):
-    """ Mixer-B/16 224x224. ImageNet-21k pretrained weights.
-    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
-    model = _create_mixer('mixer_b16_224_in21k', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_l32_224(pretrained=False, **kwargs):
-    """ Mixer-L/32 224x224.
-    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=32, num_blocks=24, embed_dim=1024, **kwargs)
-    model = _create_mixer('mixer_l32_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_l16_224(pretrained=False, **kwargs):
-    """ Mixer-L/16 224x224. ImageNet-1k pretrained weights.
-    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=16, num_blocks=24, embed_dim=1024, **kwargs)
-    model = _create_mixer('mixer_l16_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_l16_224_in21k(pretrained=False, **kwargs):
-    """ Mixer-L/16 224x224. ImageNet-21k pretrained weights.
-    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
-    """
-    model_args = dict(patch_size=16, num_blocks=24, embed_dim=1024, **kwargs)
-    model = _create_mixer('mixer_l16_224_in21k', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_b16_224_miil(pretrained=False, **kwargs):
-    """ Mixer-B/16 224x224. ImageNet-21k pretrained weights.
-    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
-    """
-    model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
-    model = _create_mixer('mixer_b16_224_miil', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def mixer_b16_224_miil_in21k(pretrained=False, **kwargs):
-    """ Mixer-B/16 224x224. ImageNet-1k pretrained weights.
-    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
-    """
-    model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
-    model = _create_mixer('mixer_b16_224_miil_in21k', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def gmixer_12_224(pretrained=False, **kwargs):
-    """ Glu-Mixer-12 224x224
-    Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=(1.0, 4.0),
-        mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
-    model = _create_mixer('gmixer_12_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def gmixer_24_224(pretrained=False, **kwargs):
-    """ Glu-Mixer-24 224x224
-    Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=(1.0, 4.0),
-        mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
-    model = _create_mixer('gmixer_24_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_12_224(pretrained=False, **kwargs):
-    """ ResMLP-12
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_12_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_24_224(pretrained=False, **kwargs):
-    """ ResMLP-24
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
-        block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_24_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_36_224(pretrained=False, **kwargs):
-    """ ResMLP-36
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=36, embed_dim=384, mlp_ratio=4,
-        block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_36_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_big_24_224(pretrained=False, **kwargs):
-    """ ResMLP-B-24
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
-        block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_big_24_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_12_distilled_224(pretrained=False, **kwargs):
-    """ ResMLP-12
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_12_distilled_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_24_distilled_224(pretrained=False, **kwargs):
-    """ ResMLP-24
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
-        block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_24_distilled_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_36_distilled_224(pretrained=False, **kwargs):
-    """ ResMLP-36
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=36, embed_dim=384, mlp_ratio=4,
-        block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_36_distilled_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_big_24_distilled_224(pretrained=False, **kwargs):
-    """ ResMLP-B-24
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
-        block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_big_24_distilled_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def resmlp_big_24_224_in22ft1k(pretrained=False, **kwargs):
-    """ ResMLP-B-24
-    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
-    """
-    model_args = dict(
-        patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
-        block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
-    model = _create_mixer('resmlp_big_24_224_in22ft1k', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def gmlp_ti16_224(pretrained=False, **kwargs):
-    """ gMLP-Tiny
-    Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=30, embed_dim=128, mlp_ratio=6, block_layer=SpatialGatingBlock,
-        mlp_layer=GatedMlp, **kwargs)
-    model = _create_mixer('gmlp_ti16_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def gmlp_s16_224(pretrained=False, **kwargs):
-    """ gMLP-Small
-    Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=30, embed_dim=256, mlp_ratio=6, block_layer=SpatialGatingBlock,
-        mlp_layer=GatedMlp, **kwargs)
-    model = _create_mixer('gmlp_s16_224', pretrained=pretrained, **model_args)
-    return model
-
-
-@register_model
-def gmlp_b16_224(pretrained=False, **kwargs):
-    """ gMLP-Base
-    Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=30, embed_dim=512, mlp_ratio=6, block_layer=SpatialGatingBlock,
-        mlp_layer=GatedMlp, **kwargs)
-    model = _create_mixer('gmlp_b16_224', pretrained=pretrained, **model_args)
-    return model
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/midas/midas/transforms.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/cc_attention.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/cc_attention.py
deleted file mode 100644
index 8982f467185b5d839832baa2e51722613a8b87a2..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/cc_attention.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.mmpkg.mmcv.cnn import PLUGIN_LAYERS, Scale
-
-
-def NEG_INF_DIAG(n, device):
-    """Returns a diagonal matrix of size [n, n].
-
-    The diagonal are all "-inf". This is for avoiding calculating the
-    overlapped element in the Criss-Cross twice.
-    """
-    return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0)
-
-
-@PLUGIN_LAYERS.register_module()
-class CrissCrossAttention(nn.Module):
-    """Criss-Cross Attention Module.
-
-    .. note::
-        Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch
-        to a pure PyTorch and equivalent implementation. For more
-        details, please refer to https://github.com/open-mmlab/mmcv/pull/1201.
-
-        Speed comparison for one forward pass
-
-        - Input size: [2,512,97,97]
-        - Device: 1 NVIDIA GeForce RTX 2080 Ti
-
-        +-----------------------+---------------+------------+---------------+
-        |                       |PyTorch version|CUDA version|Relative speed |
-        +=======================+===============+============+===============+
-        |with torch.no_grad()   |0.00554402 s   |0.0299619 s |5.4x           |
-        +-----------------------+---------------+------------+---------------+
-        |no with torch.no_grad()|0.00562803 s   |0.0301349 s |5.4x           |
-        +-----------------------+---------------+------------+---------------+
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-    """
-
-    def __init__(self, in_channels):
-        super().__init__()
-        self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
-        self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
-        self.value_conv = nn.Conv2d(in_channels, in_channels, 1)
-        self.gamma = Scale(0.)
-        self.in_channels = in_channels
-
-    def forward(self, x):
-        """forward function of Criss-Cross Attention.
-
-        Args:
-            x (Tensor): Input feature. \
-                shape (batch_size, in_channels, height, width)
-        Returns:
-            Tensor: Output of the layer, with shape of \
-            (batch_size, in_channels, height, width)
-        """
-        B, C, H, W = x.size()
-        query = self.query_conv(x)
-        key = self.key_conv(x)
-        value = self.value_conv(x)
-        energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG(
-            H, query.device)
-        energy_H = energy_H.transpose(1, 2)
-        energy_W = torch.einsum('bchw,bchj->bhwj', query, key)
-        attn = F.softmax(
-            torch.cat([energy_H, energy_W], dim=-1), dim=-1)  # [B,H,W,(H+W)]
-        out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H])
-        out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:])
-
-        out = self.gamma(out) + x
-        out = out.contiguous()
-
-        return out
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(in_channels={self.in_channels})'
-        return s
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/se_layer.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/se_layer.py
deleted file mode 100644
index 42ab005e1fe2211e9ecb651d31de128cf95cfec7..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/se_layer.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import annotator.mmpkg.mmcv as mmcv
-import torch.nn as nn
-from annotator.mmpkg.mmcv.cnn import ConvModule
-
-from .make_divisible import make_divisible
-
-
-class SELayer(nn.Module):
-    """Squeeze-and-Excitation Module.
-
-    Args:
-        channels (int): The input (and output) channels of the SE layer.
-        ratio (int): Squeeze ratio in SELayer, the intermediate channel will be
-            ``int(channels/ratio)``. Default: 16.
-        conv_cfg (None or dict): Config dict for convolution layer.
-            Default: None, which means using conv2d.
-        act_cfg (dict or Sequence[dict]): Config dict for activation layer.
-            If act_cfg is a dict, two activation layers will be configured
-            by this dict. If act_cfg is a sequence of dicts, the first
-            activation layer will be configured by the first dict and the
-            second activation layer will be configured by the second dict.
-            Default: (dict(type='ReLU'), dict(type='HSigmoid', bias=3.0,
-            divisor=6.0)).
-    """
-
-    def __init__(self,
-                 channels,
-                 ratio=16,
-                 conv_cfg=None,
-                 act_cfg=(dict(type='ReLU'),
-                          dict(type='HSigmoid', bias=3.0, divisor=6.0))):
-        super(SELayer, self).__init__()
-        if isinstance(act_cfg, dict):
-            act_cfg = (act_cfg, act_cfg)
-        assert len(act_cfg) == 2
-        assert mmcv.is_tuple_of(act_cfg, dict)
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-        self.conv1 = ConvModule(
-            in_channels=channels,
-            out_channels=make_divisible(channels // ratio, 8),
-            kernel_size=1,
-            stride=1,
-            conv_cfg=conv_cfg,
-            act_cfg=act_cfg[0])
-        self.conv2 = ConvModule(
-            in_channels=make_divisible(channels // ratio, 8),
-            out_channels=channels,
-            kernel_size=1,
-            stride=1,
-            conv_cfg=conv_cfg,
-            act_cfg=act_cfg[1])
-
-    def forward(self, x):
-        out = self.global_avgpool(x)
-        out = self.conv1(out)
-        out = self.conv2(out)
-        return x * out
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/mobilenetv3.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/mobilenetv3.py
deleted file mode 100644
index b5966c28f7207e98ee50745b1bc8f3663c650f9d..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/mobilenetv3.py
+++ /dev/null
@@ -1,364 +0,0 @@
-""" MobileNet-V3
-
-A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
-
-Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .activations import get_act_fn, get_act_layer, HardSwish
-from .config import layer_config_kwargs
-from .conv2d_layers import select_conv2d
-from .helpers import load_pretrained
-from .efficientnet_builder import *
-
-__all__ = ['mobilenetv3_rw', 'mobilenetv3_large_075', 'mobilenetv3_large_100', 'mobilenetv3_large_minimal_100',
-           'mobilenetv3_small_075', 'mobilenetv3_small_100', 'mobilenetv3_small_minimal_100',
-           'tf_mobilenetv3_large_075', 'tf_mobilenetv3_large_100', 'tf_mobilenetv3_large_minimal_100',
-           'tf_mobilenetv3_small_075', 'tf_mobilenetv3_small_100', 'tf_mobilenetv3_small_minimal_100']
-
-model_urls = {
-    'mobilenetv3_rw':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
-    'mobilenetv3_large_075': None,
-    'mobilenetv3_large_100':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth',
-    'mobilenetv3_large_minimal_100': None,
-    'mobilenetv3_small_075': None,
-    'mobilenetv3_small_100': None,
-    'mobilenetv3_small_minimal_100': None,
-    'tf_mobilenetv3_large_075':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth',
-    'tf_mobilenetv3_large_100':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth',
-    'tf_mobilenetv3_large_minimal_100':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth',
-    'tf_mobilenetv3_small_075':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth',
-    'tf_mobilenetv3_small_100':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth',
-    'tf_mobilenetv3_small_minimal_100':
-        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth',
-}
-
-
-class MobileNetV3(nn.Module):
-    """ MobileNet-V3
-
-    A this model utilizes the MobileNet-v3 specific 'efficient head', where global pooling is done before the
-    head convolution without a final batch-norm layer before the classifier.
-
-    Paper: https://arxiv.org/abs/1905.02244
-    """
-
-    def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
-                 channel_multiplier=1.0, pad_type='', act_layer=HardSwish, drop_rate=0., drop_connect_rate=0.,
-                 se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, weight_init='goog'):
-        super(MobileNetV3, self).__init__()
-        self.drop_rate = drop_rate
-
-        stem_size = round_channels(stem_size, channel_multiplier)
-        self.conv_stem = select_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
-        self.bn1 = nn.BatchNorm2d(stem_size, **norm_kwargs)
-        self.act1 = act_layer(inplace=True)
-        in_chs = stem_size
-
-        builder = EfficientNetBuilder(
-            channel_multiplier, pad_type=pad_type, act_layer=act_layer, se_kwargs=se_kwargs,
-            norm_layer=norm_layer, norm_kwargs=norm_kwargs, drop_connect_rate=drop_connect_rate)
-        self.blocks = nn.Sequential(*builder(in_chs, block_args))
-        in_chs = builder.in_chs
-
-        self.global_pool = nn.AdaptiveAvgPool2d(1)
-        self.conv_head = select_conv2d(in_chs, num_features, 1, padding=pad_type, bias=head_bias)
-        self.act2 = act_layer(inplace=True)
-        self.classifier = nn.Linear(num_features, num_classes)
-
-        for m in self.modules():
-            if weight_init == 'goog':
-                initialize_weight_goog(m)
-            else:
-                initialize_weight_default(m)
-
-    def as_sequential(self):
-        layers = [self.conv_stem, self.bn1, self.act1]
-        layers.extend(self.blocks)
-        layers.extend([
-            self.global_pool, self.conv_head, self.act2,
-            nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier])
-        return nn.Sequential(*layers)
-
-    def features(self, x):
-        x = self.conv_stem(x)
-        x = self.bn1(x)
-        x = self.act1(x)
-        x = self.blocks(x)
-        x = self.global_pool(x)
-        x = self.conv_head(x)
-        x = self.act2(x)
-        return x
-
-    def forward(self, x):
-        x = self.features(x)
-        x = x.flatten(1)
-        if self.drop_rate > 0.:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
-        return self.classifier(x)
-
-
-def _create_model(model_kwargs, variant, pretrained=False):
-    as_sequential = model_kwargs.pop('as_sequential', False)
-    model = MobileNetV3(**model_kwargs)
-    if pretrained and model_urls[variant]:
-        load_pretrained(model, model_urls[variant])
-    if as_sequential:
-        model = model.as_sequential()
-    return model
-
-
-def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
-    """Creates a MobileNet-V3 model (RW variant).
-
-    Paper: https://arxiv.org/abs/1905.02244
-
-    This was my first attempt at reproducing the MobileNet-V3 from paper alone. It came close to the
-    eventual Tensorflow reference impl but has a few differences:
-    1. This model has no bias on the head convolution
-    2. This model forces no residual (noskip) on the first DWS block, this is different than MnasNet
-    3. This model always uses ReLU for the SE activation layer, other models in the family inherit their act layer
-       from their parent block
-    4. This model does not enforce divisible by 8 limitation on the SE reduction channel count
-
-    Overall the changes are fairly minor and result in a very small parameter count difference and no
-    top-1/5
-
-    Args:
-      channel_multiplier: multiplier to number of channels per layer.
-    """
-    arch_def = [
-        # stage 0, 112x112 in
-        ['ds_r1_k3_s1_e1_c16_nre_noskip'],  # relu
-        # stage 1, 112x112 in
-        ['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'],  # relu
-        # stage 2, 56x56 in
-        ['ir_r3_k5_s2_e3_c40_se0.25_nre'],  # relu
-        # stage 3, 28x28 in
-        ['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],  # hard-swish
-        # stage 4, 14x14in
-        ['ir_r2_k3_s1_e6_c112_se0.25'],  # hard-swish
-        # stage 5, 14x14in
-        ['ir_r3_k5_s2_e6_c160_se0.25'],  # hard-swish
-        # stage 6, 7x7 in
-        ['cn_r1_k1_s1_c960'],  # hard-swish
-    ]
-    with layer_config_kwargs(kwargs):
-        model_kwargs = dict(
-            block_args=decode_arch_def(arch_def),
-            head_bias=False,  # one of my mistakes
-            channel_multiplier=channel_multiplier,
-            act_layer=resolve_act_layer(kwargs, 'hard_swish'),
-            se_kwargs=dict(gate_fn=get_act_fn('hard_sigmoid'), reduce_mid=True),
-            norm_kwargs=resolve_bn_args(kwargs),
-            **kwargs,
-        )
-        model = _create_model(model_kwargs, variant, pretrained)
-    return model
-
-
-def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
-    """Creates a MobileNet-V3 large/small/minimal models.
-
-    Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v3.py
-    Paper: https://arxiv.org/abs/1905.02244
-
-    Args:
-      channel_multiplier: multiplier to number of channels per layer.
-    """
-    if 'small' in variant:
-        num_features = 1024
-        if 'minimal' in variant:
-            act_layer = 'relu'
-            arch_def = [
-                # stage 0, 112x112 in
-                ['ds_r1_k3_s2_e1_c16'],
-                # stage 1, 56x56 in
-                ['ir_r1_k3_s2_e4.5_c24', 'ir_r1_k3_s1_e3.67_c24'],
-                # stage 2, 28x28 in
-                ['ir_r1_k3_s2_e4_c40', 'ir_r2_k3_s1_e6_c40'],
-                # stage 3, 14x14 in
-                ['ir_r2_k3_s1_e3_c48'],
-                # stage 4, 14x14in
-                ['ir_r3_k3_s2_e6_c96'],
-                # stage 6, 7x7 in
-                ['cn_r1_k1_s1_c576'],
-            ]
-        else:
-            act_layer = 'hard_swish'
-            arch_def = [
-                # stage 0, 112x112 in
-                ['ds_r1_k3_s2_e1_c16_se0.25_nre'],  # relu
-                # stage 1, 56x56 in
-                ['ir_r1_k3_s2_e4.5_c24_nre', 'ir_r1_k3_s1_e3.67_c24_nre'],  # relu
-                # stage 2, 28x28 in
-                ['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r2_k5_s1_e6_c40_se0.25'],  # hard-swish
-                # stage 3, 14x14 in
-                ['ir_r2_k5_s1_e3_c48_se0.25'],  # hard-swish
-                # stage 4, 14x14in
-                ['ir_r3_k5_s2_e6_c96_se0.25'],  # hard-swish
-                # stage 6, 7x7 in
-                ['cn_r1_k1_s1_c576'],  # hard-swish
-            ]
-    else:
-        num_features = 1280
-        if 'minimal' in variant:
-            act_layer = 'relu'
-            arch_def = [
-                # stage 0, 112x112 in
-                ['ds_r1_k3_s1_e1_c16'],
-                # stage 1, 112x112 in
-                ['ir_r1_k3_s2_e4_c24', 'ir_r1_k3_s1_e3_c24'],
-                # stage 2, 56x56 in
-                ['ir_r3_k3_s2_e3_c40'],
-                # stage 3, 28x28 in
-                ['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],
-                # stage 4, 14x14in
-                ['ir_r2_k3_s1_e6_c112'],
-                # stage 5, 14x14in
-                ['ir_r3_k3_s2_e6_c160'],
-                # stage 6, 7x7 in
-                ['cn_r1_k1_s1_c960'],
-            ]
-        else:
-            act_layer = 'hard_swish'
-            arch_def = [
-                # stage 0, 112x112 in
-                ['ds_r1_k3_s1_e1_c16_nre'],  # relu
-                # stage 1, 112x112 in
-                ['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'],  # relu
-                # stage 2, 56x56 in
-                ['ir_r3_k5_s2_e3_c40_se0.25_nre'],  # relu
-                # stage 3, 28x28 in
-                ['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],  # hard-swish
-                # stage 4, 14x14in
-                ['ir_r2_k3_s1_e6_c112_se0.25'],  # hard-swish
-                # stage 5, 14x14in
-                ['ir_r3_k5_s2_e6_c160_se0.25'],  # hard-swish
-                # stage 6, 7x7 in
-                ['cn_r1_k1_s1_c960'],  # hard-swish
-            ]
-    with layer_config_kwargs(kwargs):
-        model_kwargs = dict(
-            block_args=decode_arch_def(arch_def),
-            num_features=num_features,
-            stem_size=16,
-            channel_multiplier=channel_multiplier,
-            act_layer=resolve_act_layer(kwargs, act_layer),
-            se_kwargs=dict(
-                act_layer=get_act_layer('relu'), gate_fn=get_act_fn('hard_sigmoid'), reduce_mid=True, divisor=8),
-            norm_kwargs=resolve_bn_args(kwargs),
-            **kwargs,
-        )
-        model = _create_model(model_kwargs, variant, pretrained)
-    return model
-
-
-def mobilenetv3_rw(pretrained=False, **kwargs):
-    """ MobileNet-V3 RW
-    Attn: See note in gen function for this variant.
-    """
-    # NOTE for train set drop_rate=0.2
-    if pretrained:
-        # pretrained model trained with non-default BN epsilon
-        kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
-    model = _gen_mobilenet_v3_rw('mobilenetv3_rw', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def mobilenetv3_large_075(pretrained=False, **kwargs):
-    """ MobileNet V3 Large 0.75"""
-    # NOTE for train set drop_rate=0.2
-    model = _gen_mobilenet_v3('mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
-    return model
-
-
-def mobilenetv3_large_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Large 1.0 """
-    # NOTE for train set drop_rate=0.2
-    model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Large (Minimalistic) 1.0 """
-    # NOTE for train set drop_rate=0.2
-    model = _gen_mobilenet_v3('mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def mobilenetv3_small_075(pretrained=False, **kwargs):
-    """ MobileNet V3 Small 0.75 """
-    model = _gen_mobilenet_v3('mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
-    return model
-
-
-def mobilenetv3_small_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Small 1.0 """
-    model = _gen_mobilenet_v3('mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Small (Minimalistic) 1.0 """
-    model = _gen_mobilenet_v3('mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def tf_mobilenetv3_large_075(pretrained=False, **kwargs):
-    """ MobileNet V3 Large 0.75. Tensorflow compat variant. """
-    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
-    kwargs['pad_type'] = 'same'
-    model = _gen_mobilenet_v3('tf_mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
-    return model
-
-
-def tf_mobilenetv3_large_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Large 1.0. Tensorflow compat variant. """
-    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
-    kwargs['pad_type'] = 'same'
-    model = _gen_mobilenet_v3('tf_mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def tf_mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Large Minimalistic 1.0. Tensorflow compat variant. """
-    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
-    kwargs['pad_type'] = 'same'
-    model = _gen_mobilenet_v3('tf_mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def tf_mobilenetv3_small_075(pretrained=False, **kwargs):
-    """ MobileNet V3 Small 0.75. Tensorflow compat variant. """
-    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
-    kwargs['pad_type'] = 'same'
-    model = _gen_mobilenet_v3('tf_mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
-    return model
-
-
-def tf_mobilenetv3_small_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Small 1.0. Tensorflow compat variant."""
-    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
-    kwargs['pad_type'] = 'same'
-    model = _gen_mobilenet_v3('tf_mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
-
-
-def tf_mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
-    """ MobileNet V3 Small Minimalistic 1.0. Tensorflow compat variant. """
-    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
-    kwargs['pad_type'] = 'same'
-    model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
-    return model
diff --git a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/apps/eval_spaces.py b/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/apps/eval_spaces.py
deleted file mode 100644
index b0cf689d24f70d95aa0d491fd04987296802e492..0000000000000000000000000000000000000000
--- a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/apps/eval_spaces.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import sys
-import os
-
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-import time
-import json
-import numpy as np
-import torch
-from torch.utils.data import DataLoader
-
-from lib.options import BaseOptions
-from lib.mesh_util import *
-from lib.sample_util import *
-from lib.train_util import *
-from lib.model import *
-
-from PIL import Image
-import torchvision.transforms as transforms
-
-import trimesh
-from datetime import datetime
-
-# get options
-opt = BaseOptions().parse()
-
-class Evaluator:
-    def __init__(self, opt, projection_mode='orthogonal'):
-        self.opt = opt
-        self.load_size = self.opt.loadSize
-        self.to_tensor = transforms.Compose([
-            transforms.Resize(self.load_size),
-            transforms.ToTensor(),
-            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-        ])
-        # set cuda
-        cuda = torch.device('cuda:%d' % opt.gpu_id) if torch.cuda.is_available() else torch.device('cpu')
-        print("CUDDAAAAA ???",  torch.cuda.get_device_name(0) if torch.cuda.is_available()  else "NO ONLY CPU")
-  
-        # create net
-        netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
-        print('Using Network: ', netG.name)
-
-        if opt.load_netG_checkpoint_path:
-            netG.load_state_dict(torch.load(opt.load_netG_checkpoint_path, map_location=cuda))
-
-        if opt.load_netC_checkpoint_path is not None:
-            print('loading for net C ...', opt.load_netC_checkpoint_path)
-            netC = ResBlkPIFuNet(opt).to(device=cuda)
-            netC.load_state_dict(torch.load(opt.load_netC_checkpoint_path, map_location=cuda))
-        else:
-            netC = None
-
-        os.makedirs(opt.results_path, exist_ok=True)
-        os.makedirs('%s/%s' % (opt.results_path, opt.name), exist_ok=True)
-
-        opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt')
-        with open(opt_log, 'w') as outfile:
-            outfile.write(json.dumps(vars(opt), indent=2))
-
-        self.cuda = cuda
-        self.netG = netG
-        self.netC = netC
-
-    def load_image(self, image_path, mask_path):
-        # Name
-        img_name = os.path.splitext(os.path.basename(image_path))[0]
-        # Calib
-        B_MIN = np.array([-1, -1, -1])
-        B_MAX = np.array([1, 1, 1])
-        projection_matrix = np.identity(4)
-        projection_matrix[1, 1] = -1
-        calib = torch.Tensor(projection_matrix).float()
-        # Mask
-        mask = Image.open(mask_path).convert('L')
-        mask = transforms.Resize(self.load_size)(mask)
-        mask = transforms.ToTensor()(mask).float()
-        # image
-        image = Image.open(image_path).convert('RGB')
-        image = self.to_tensor(image)
-        image = mask.expand_as(image) * image
-        return {
-            'name': img_name,
-            'img': image.unsqueeze(0),
-            'calib': calib.unsqueeze(0),
-            'mask': mask.unsqueeze(0),
-            'b_min': B_MIN,
-            'b_max': B_MAX,
-        }
-
-    def eval(self, data, use_octree=False):
-        '''
-        Evaluate a data point
-        :param data: a dict containing at least ['name'], ['image'], ['calib'], ['b_min'] and ['b_max'] tensors.
-        :return:
-        '''
-        opt = self.opt
-        with torch.no_grad():
-            self.netG.eval()
-            if self.netC:
-                self.netC.eval()
-            save_path = '%s/%s/result_%s.obj' % (opt.results_path, opt.name, data['name'])
-            if self.netC:
-                gen_mesh_color(opt, self.netG, self.netC, self.cuda, data, save_path, use_octree=use_octree)
-            else:
-                gen_mesh(opt, self.netG, self.cuda, data, save_path, use_octree=use_octree)
-
-
-if __name__ == '__main__':
-    evaluator = Evaluator(opt)
-
-    results_path = opt.results_path
-    name = opt.name
-    test_image_path = opt.img_path
-    test_mask_path = test_image_path[:-4] +'_mask.png'
-    test_img_name = os.path.splitext(os.path.basename(test_image_path))[0]
-    print("test_image: ", test_image_path)
-    print("test_mask: ", test_mask_path)
-
-    try:
-        time = datetime.now()
-        print("evaluating" , time)
-        data = evaluator.load_image(test_image_path, test_mask_path)
-        evaluator.eval(data, False)
-        print("done evaluating" , datetime.now() - time)
-    except Exception as e:
-        print("error:", e.args)
-
-    try:
-        mesh = trimesh.load(f'{results_path}/{name}/result_{test_img_name}.obj')
-        mesh.apply_transform([[1, 0, 0, 0],
-        [0, 1, 0, 0],
-        [0, 0, -1, 0],
-        [0, 0, 0, 1]])
-        mesh.export(file_obj=f'{results_path}/{name}/result_{test_img_name}.glb')
-    except Exception as e:
-        print("error generating MESH", e)
diff --git a/spaces/daddyjin/TalkingFaceGeneration/FONT/3DDFA_V2/utils/pose.py b/spaces/daddyjin/TalkingFaceGeneration/FONT/3DDFA_V2/utils/pose.py
deleted file mode 100644
index 55d1da6006e394578619697ca7217e9f86e4f975..0000000000000000000000000000000000000000
--- a/spaces/daddyjin/TalkingFaceGeneration/FONT/3DDFA_V2/utils/pose.py
+++ /dev/null
@@ -1,284 +0,0 @@
-# coding: utf-8
-
-"""
-Reference: https://github.com/YadiraF/PRNet/blob/master/utils/estimate_pose.py
-
-Calculating pose from the output 3DMM parameters, you can also try to use solvePnP to perform estimation
-"""
-
-__author__ = 'cleardusk'
-
-import cv2
-import numpy as np
-from math import cos, sin, atan2, asin, sqrt
-
-from .functions import calc_hypotenuse, plot_image
-
-
-def P2sRt(P):
-    """ decompositing camera matrix P.
-    Args:
-        P: (3, 4). Affine Camera Matrix.
-    Returns:
-        s: scale factor.
-        R: (3, 3). rotation matrix.
-        t2d: (2,). 2d translation.
-    """
-    t3d = P[:, 3]
-    R1 = P[0:1, :3]
-    R2 = P[1:2, :3]
-    s = (np.linalg.norm(R1) + np.linalg.norm(R2)) / 2.0
-    r1 = R1 / np.linalg.norm(R1)
-    r2 = R2 / np.linalg.norm(R2)
-    r3 = np.cross(r1, r2)
-
-    R = np.concatenate((r1, r2, r3), 0)
-    return s, R, t3d
-
-
-def matrix2angle(R):
-    """ compute three Euler angles from a Rotation Matrix. Ref: http://www.gregslabaugh.net/publications/euler.pdf
-    refined by: https://stackoverflow.com/questions/43364900/rotation-matrix-to-euler-angles-with-opencv
-    todo: check and debug
-     Args:
-         R: (3,3). rotation matrix
-     Returns:
-         x: yaw
-         y: pitch
-         z: roll
-     """
-    if R[2, 0] > 0.998:
-        z = 0
-        x = np.pi / 2
-        y = z + atan2(-R[0, 1], -R[0, 2])
-    elif R[2, 0] < -0.998:
-        z = 0
-        x = -np.pi / 2
-        y = -z + atan2(R[0, 1], R[0, 2])
-    else:
-        x = asin(R[2, 0])
-        y = atan2(R[2, 1] / cos(x), R[2, 2] / cos(x))
-        z = atan2(R[1, 0] / cos(x), R[0, 0] / cos(x))
-
-    return x, y, z
-
-def angle2matrix(theta):
-    """ compute three Euler angles from a Rotation Matrix. Ref: http://www.gregslabaugh.net/publications/euler.pdf
-    refined by: https://stackoverflow.com/questions/43364900/rotation-matrix-to-euler-angles-with-opencv
-    todo: check and debug
-     Args:
-         R: (3,3). rotation matrix
-     Returns:
-         x: yaw
-         y: pitch
-         z: roll
-     """
-    R_x = np.array([[1,         0,                  0         ],
-
-                    [0,         cos(theta[1]), -sin(theta[1]) ],
-
-                    [0,         sin(theta[1]), cos(theta[1])  ]
-
-                    ])
-
- 
-
-    R_y = np.array([[cos(theta[0]),    0,      sin(-theta[0])  ],
-
-                    [0,                     1,      0         ],
-
-                    [-sin(-theta[0]),   0,      cos(theta[0])  ]
-
-                    ])
-
- 
-
-    R_z = np.array([[cos(theta[2]),    -sin(theta[2]),    0],
-
-                    [sin(theta[2]),    cos(theta[2]),     0],
-
-                    [0,                     0,            1]
-
-                    ])
-
- 
-
-    R = np.dot(R_z, np.dot( R_y, R_x ))
-
- 
-
-    return R
-
-def angle2matrix_3ddfa(angles):
-    ''' get rotation matrix from three rotation angles(radian). The same as in 3DDFA.
-    Args:
-        angles: [3,]. x, y, z angles
-        x: pitch.
-        y: yaw. 
-        z: roll. 
-    Returns:
-        R: 3x3. rotation matrix.
-    '''
-    # x, y, z = np.deg2rad(angles[0]), np.deg2rad(angles[1]), np.deg2rad(angles[2])
-    x, y, z = angles[1], angles[0], angles[2]
-    
-    # x
-    Rx=np.array([[1,      0,       0],
-                 [0, cos(x),  sin(x)],
-                 [0, -sin(x),   cos(x)]])
-    # y
-    Ry=np.array([[ cos(y), 0, -sin(y)],
-                 [      0, 1,      0],
-                 [sin(y), 0, cos(y)]])
-    # z
-    Rz=np.array([[cos(z), sin(z), 0],
-                 [-sin(z),  cos(z), 0],
-                 [     0,       0, 1]])
-    R = Rx.dot(Ry).dot(Rz)
-    return R.astype(np.float32)
-
-def calc_pose(param):
-    P = param[:12].reshape(3, -1)  # camera matrix
-    s, R, t3d = P2sRt(P)
-    P = np.concatenate((R, t3d.reshape(3, -1)), axis=1)  # without scale
-    pose = matrix2angle(R)
-    pose = [p * 180 / np.pi for p in pose]
-
-    return P, pose
-
-
-def build_camera_box(rear_size=90):
-    point_3d = []
-    rear_depth = 0
-    point_3d.append((-rear_size, -rear_size, rear_depth))
-    point_3d.append((-rear_size, rear_size, rear_depth))
-    point_3d.append((rear_size, rear_size, rear_depth))
-    point_3d.append((rear_size, -rear_size, rear_depth))
-    point_3d.append((-rear_size, -rear_size, rear_depth))
-
-    front_size = int(4 / 3 * rear_size)
-    front_depth = int(4 / 3 * rear_size)
-    point_3d.append((-front_size, -front_size, front_depth))
-    point_3d.append((-front_size, front_size, front_depth))
-    point_3d.append((front_size, front_size, front_depth))
-    point_3d.append((front_size, -front_size, front_depth))
-    point_3d.append((-front_size, -front_size, front_depth))
-    point_3d = np.array(point_3d, dtype=np.float32).reshape(-1, 3)
-
-    return point_3d
-
-
-def plot_pose_box(img, P, ver, color=(40, 255, 0), line_width=2):
-    """ Draw a 3D box as annotation of pose.
-    Ref:https://github.com/yinguobing/head-pose-estimation/blob/master/pose_estimator.py
-    Args:
-        img: the input image
-        P: (3, 4). Affine Camera Matrix.
-        kpt: (2, 68) or (3, 68)
-    """
-    llength = calc_hypotenuse(ver)
-    point_3d = build_camera_box(llength)
-    # Map to 2d image points
-    point_3d_homo = np.hstack((point_3d, np.ones([point_3d.shape[0], 1])))  # n x 4
-    point_2d = point_3d_homo.dot(P.T)[:, :2]
-
-    point_2d[:, 1] = - point_2d[:, 1]
-    point_2d[:, :2] = point_2d[:, :2] - np.mean(point_2d[:4, :2], 0) + np.mean(ver[:2, :27], 1)
-    point_2d = np.int32(point_2d.reshape(-1, 2))
-
-    # Draw all the lines
-    cv2.polylines(img, [point_2d], True, color, line_width, cv2.LINE_AA)
-    cv2.line(img, tuple(point_2d[1]), tuple(
-        point_2d[6]), color, line_width, cv2.LINE_AA)
-    cv2.line(img, tuple(point_2d[2]), tuple(
-        point_2d[7]), color, line_width, cv2.LINE_AA)
-    cv2.line(img, tuple(point_2d[3]), tuple(
-        point_2d[8]), color, line_width, cv2.LINE_AA)
-
-    return img
-
-
-def viz_pose(img, param_lst, ver_lst, show_flag=False, wfp=None):
-    for param, ver in zip(param_lst, ver_lst):
-        P, pose = calc_pose(param)
-        img = plot_pose_box(img, P, ver)
-        # print(P[:, :3])
-        print(f'yaw: {pose[0]:.1f}, pitch: {pose[1]:.1f}, roll: {pose[2]:.1f}')
-
-    if wfp is not None:
-        cv2.imwrite(wfp, img)
-        print(f'Save visualization result to {wfp}')
-
-    if show_flag:
-        plot_image(img)
-
-    return img
-
-def pose_6(param):
-    P = param[:12].reshape(3, -1)  # camera matrix
-    s, R, t3d = P2sRt(P)
-    P = np.concatenate((R, t3d.reshape(3, -1)), axis=1)  # without scale
-    pose = matrix2angle(R)
-    print(t3d)
-    R1 = angle2matrix(pose)
-    print(R)
-    print(R1)
-    pose = [p * 180 / np.pi for p in pose]
-    
-    return s, pose, t3d, P
-
-
-def smooth_pose(img, param_lst, ver_lst, pose_new, show_flag=False, wfp=None, wnp = None):
-    for param, ver in zip(param_lst, ver_lst):
-        t3d = np.array([pose_new[4],pose_new[5],pose_new[6]])
-        
-        theta = np.array([pose_new[0],pose_new[1],pose_new[2]])
-        theta = [p * np.pi / 180 for p in theta]
-        R = angle2matrix(theta)
-        P = np.concatenate((R, t3d.reshape(3, -1)), axis=1) 
-        img = plot_pose_box(img, P, ver)
-    #    print(P,P.shape,t3d)
-        print(P,pose_new)
-        print(f'yaw: {theta[0]:.1f}, pitch: {theta[1]:.1f}, roll: {theta[2]:.1f}')
-        all_pose = [0]
-        all_pose = np.array(all_pose)
-
-    if wfp is not None:
-        cv2.imwrite(wfp, img)
-        print(f'Save visualization result to {wfp}')
-        
-    if wnp is not None:
-        np.save(wnp, all_pose)
-        print(f'Save visualization result to {wfp}')
-        
-    if show_flag:
-        plot_image(img)
-
-    return img
-
-    
-    
-    
-
-def get_pose(img, param_lst, ver_lst, show_flag=False, wfp=None, wnp = None):
-    for param, ver in zip(param_lst, ver_lst):
-        s, pose, t3d, P = pose_6(param)
-        img = plot_pose_box(img, P, ver)
-    #    print(P,P.shape,t3d)
-        print(f'yaw: {pose[0]:.1f}, pitch: {pose[1]:.1f}, roll: {pose[2]:.1f}')
-        all_pose = [pose[0],pose[1],pose[2],s,t3d[0],t3d[1],t3d[2]]
-        all_pose = np.array(all_pose)
-
-    if wfp is not None:
-        cv2.imwrite(wfp, img)
-        print(f'Save visualization result to {wfp}')
-        
-    if wnp is not None:
-        np.save(wnp, all_pose)
-        print(f'Save visualization result to {wfp}')
-        
-    if show_flag:
-        plot_image(img)
-
-    return all_pose
-
diff --git a/spaces/darveen/text_summarizer/model.py b/spaces/darveen/text_summarizer/model.py
deleted file mode 100644
index 8ddd11d84be38b908d0e8d9920aecedfda66ae23..0000000000000000000000000000000000000000
--- a/spaces/darveen/text_summarizer/model.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import nltk
-import ssl
-import re
-try:
-    _create_unverified_https_context = ssl._create_unverified_context
-except AttributeError:
-    pass
-else:
-    ssl._create_default_https_context = _create_unverified_https_context
-nltk.download('punkt')
-nltk.download('stopwords')
-from transformers import BartTokenizer, PegasusTokenizer
-from transformers import BartForConditionalGeneration, PegasusForConditionalGeneration
-from tqdm.notebook import tqdm
-
-
-
-class Abstractive_Summarization_Model:
-    def __init__(self):
-        self.text = None
-        self.IS_CNNDM = True # whether to use CNNDM dataset or XSum dataset
-        self.LOWER = False
-        self.max_length = 1024 #if self.IS_CNNDM else 512
-        self.model, self.tokenizer  = self.load_model()
-        
-    def load_model(self):
-        # Load our model checkpoints
-        print('[INFO]: Loading model ...')
-        if self.IS_CNNDM:
-            model = BartForConditionalGeneration.from_pretrained('Yale-LILY/brio-cnndm-uncased')
-            tokenizer = BartTokenizer.from_pretrained('Yale-LILY/brio-cnndm-uncased')
-        else:
-            model = PegasusForConditionalGeneration.from_pretrained('Yale-LILY/brio-xsum-cased')
-            tokenizer = PegasusTokenizer.from_pretrained('Yale-LILY/brio-xsum-cased')
-        print('[INFO]: Model Successfully Loaded :)')    
-        return model, tokenizer
-        
-        
-    def summarize(self, text):
-        # generation example
-        if self.LOWER:
-            article = text.lower()
-        else:
-            article = text
-        inputs = self.tokenizer([article], max_length=self.max_length, return_tensors="pt", truncation=True)
-        # Generate Summary
-        summary_ids = self.model.generate(inputs["input_ids"])
-        
-        return self.tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
\ No newline at end of file
diff --git a/spaces/dawdqd/ChuanhuChatGPT/assets/Kelpy-Codos.js b/spaces/dawdqd/ChuanhuChatGPT/assets/Kelpy-Codos.js
deleted file mode 100644
index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000
--- a/spaces/dawdqd/ChuanhuChatGPT/assets/Kelpy-Codos.js
+++ /dev/null
@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name         Kelpy Codos
-// @namespace    https://github.com/Keldos-Li/Kelpy-Codos
-// @version      1.0.5
-// @author       Keldos; https://keldos.me/
-// @description  Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. 
-//               Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license      GPL-3.0
-// @grant        none
-// ==/UserScript==
-
-(function () {
-    'use strict';
-
-    function addCopyButton(pre) {
-        var code = pre.querySelector('code');
-        if (!code) {
-            return; // 如果没有找到 <code> 元素，则不添加按钮
-        }
-        var firstChild = code.firstChild;
-        if (!firstChild) {
-            return; // 如果 <code> 元素没有子节点，则不添加按钮
-        }
-        var button = document.createElement('button');
-        button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
-        button.style.position = 'relative';
-        button.style.float = 'right';
-        button.style.fontSize = '1em'; // 可选：调整按钮大小
-        button.style.background = 'none'; // 可选：去掉背景颜色
-        button.style.border = 'none'; // 可选：去掉边框
-        button.style.cursor = 'pointer'; // 可选：显示指针样式
-        button.addEventListener('click', function () {
-            var range = document.createRange();
-            range.selectNodeContents(code);
-            range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
-            var selection = window.getSelection();
-            selection.removeAllRanges();
-            selection.addRange(range);
-
-            try {
-                var success = document.execCommand('copy');
-                if (success) {
-                    button.textContent = '\u2714';
-                    setTimeout(function () {
-                        button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
-                    }, 2000);
-                } else {
-                    button.textContent = '\u2716';
-                }
-            } catch (e) {
-                console.error(e);
-                button.textContent = '\u2716';
-            }
-
-            selection.removeAllRanges();
-        });
-        code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
-    }
-
-    function handleNewElements(mutationsList, observer) {
-        for (var mutation of mutationsList) {
-            if (mutation.type === 'childList') {
-                for (var node of mutation.addedNodes) {
-                    if (node.nodeName === 'PRE') {
-                        addCopyButton(node);
-                    }
-                }
-            }
-        }
-    }
-
-    var observer = new MutationObserver(handleNewElements);
-    observer.observe(document.documentElement, { childList: true, subtree: true });
-
-    document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/types.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/types.py
deleted file mode 100644
index 7adf565a7b6b7d4f1eed3adf6a96faab66fe517c..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/types.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import types
-from enum import Enum
-from typing import Any, Callable, Dict, Set, Type, TypeVar, Union
-
-from pydantic import BaseModel
-
-DecoratedCallable = TypeVar("DecoratedCallable", bound=Callable[..., Any])
-UnionType = getattr(types, "UnionType", Union)
-NoneType = getattr(types, "UnionType", None)
-ModelNameMap = Dict[Union[Type[BaseModel], Type[Enum]], str]
-IncEx = Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/iup.c b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/iup.c
deleted file mode 100644
index 64891ceac1811e09965675714ea67d1829d4ccb5..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/iup.c
+++ /dev/null
@@ -1,17999 +0,0 @@
-/* Generated by Cython 3.0.0 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "name": "fontTools.varLib.iup",
-        "sources": [
-            "Lib/fontTools/varLib/iup.py"
-        ]
-    },
-    "module_name": "fontTools.varLib.iup"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#if defined(CYTHON_LIMITED_API) && 0
-  #ifndef Py_LIMITED_API
-    #if CYTHON_LIMITED_API+0 > 0x03030000
-      #define Py_LIMITED_API CYTHON_LIMITED_API
-    #else
-      #define Py_LIMITED_API 0x03030000
-    #endif
-  #endif
-#endif
-
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
-    #error Cython requires Python 2.7+ or Python 3.3+.
-#else
-#define CYTHON_ABI "3_0_0"
-#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI
-#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "."
-#define CYTHON_HEX_VERSION 0x030000F0
-#define CYTHON_FUTURE_DIVISION 1
-#include <stddef.h>
-#ifndef offsetof
-  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
-  #define HAVE_LONG_LONG
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#if defined(GRAALVM_PYTHON)
-  /* For very preliminary testing purposes. Most variables are set the same as PyPy.
-     The existence of this section does not imply that anything works or is even tested */
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 1
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PYPY_VERSION)
-  #define CYTHON_COMPILING_IN_PYPY 1
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #if PY_VERSION_HEX < 0x03090000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00)
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(CYTHON_LIMITED_API)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 1
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_CLINE_IN_TRACEBACK
-  #define CYTHON_CLINE_IN_TRACEBACK 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 1
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #endif
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 1
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PY_NOGIL)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 1
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #ifndef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_TYPE_SPECS
-    #define CYTHON_USE_TYPE_SPECS 0
-  #endif
-  #ifndef CYTHON_USE_PYTYPE_LOOKUP
-    #define CYTHON_USE_PYTYPE_LOOKUP 1
-  #endif
-  #if PY_MAJOR_VERSION < 3
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_PYLONG_INTERNALS
-    #define CYTHON_USE_PYLONG_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
-    #undef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #elif !defined(CYTHON_USE_UNICODE_WRITER)
-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #ifndef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_GIL
-    #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6)
-  #endif
-  #ifndef CYTHON_METH_FASTCALL
-    #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1)
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL 1
-  #endif
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_MODULE_STATE
-    #define CYTHON_USE_MODULE_STATE 0
-  #endif
-  #if PY_VERSION_HEX < 0x030400a1
-    #undef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 0
-  #elif !defined(CYTHON_USE_TP_FINALIZE)
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #if PY_VERSION_HEX < 0x030600B1
-    #undef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS 0
-  #elif !defined(CYTHON_USE_DICT_VERSIONS)
-    #define CYTHON_USE_DICT_VERSIONS  (PY_VERSION_HEX < 0x030C00A5)
-  #endif
-  #if PY_VERSION_HEX < 0x030700A3
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK 1
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if !defined(CYTHON_VECTORCALL)
-#define CYTHON_VECTORCALL  (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1)
-#endif
-#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1)
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(maybe_unused)
-        #define CYTHON_UNUSED [[maybe_unused]]
-      #endif
-    #endif
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-  #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x)
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-            typedef unsigned char     uint8_t;
-            typedef unsigned short    uint16_t;
-            typedef unsigned int      uint32_t;
-        #else
-            typedef unsigned __int8   uint8_t;
-            typedef unsigned __int16  uint16_t;
-            typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-    #if _MSC_VER < 1300
-        #ifdef _WIN64
-            typedef unsigned long long  __pyx_uintptr_t;
-        #else
-            typedef unsigned int        __pyx_uintptr_t;
-        #endif
-    #else
-        #ifdef _WIN64
-            typedef unsigned __int64    __pyx_uintptr_t;
-        #else
-            typedef unsigned __int32    __pyx_uintptr_t;
-        #endif
-    #endif
-#else
-    #include <stdint.h>
-    typedef uintptr_t  __pyx_uintptr_t;
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(fallthrough) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(fallthrough)
-        #define CYTHON_FALLTHROUGH [[fallthrough]]
-      #endif
-    #endif
-    #ifndef CYTHON_FALLTHROUGH
-      #if __has_cpp_attribute(clang::fallthrough)
-        #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-      #elif __has_cpp_attribute(gnu::fallthrough)
-        #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-      #endif
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-#ifdef __cplusplus
-  template <typename T>
-  struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);};
-  #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL<type>::value)
-#else
-  #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0)
-#endif
-#if CYTHON_COMPILING_IN_PYPY == 1
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000)
-#else
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000)
-#endif
-#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer))
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_DefaultClassType PyClass_Type
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject *co=NULL, *result=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(p))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end;
-        if (!(empty = PyTuple_New(0))) goto end;
-        result = (PyCodeObject*) PyObject_Call(replace, empty, kwds);
-    end:
-        Py_XDECREF((PyObject*) co);
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return result;
-    }
-#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-#endif
-#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE)
-  #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type)
-#else
-  #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is)
-  #define __Pyx_Py_Is(x, y)  Py_Is(x, y)
-#else
-  #define __Pyx_Py_Is(x, y) ((x) == (y))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone)
-  #define __Pyx_Py_IsNone(ob) Py_IsNone(ob)
-#else
-  #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue)
-  #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob)
-#else
-  #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse)
-  #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob)
-#else
-  #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False)
-#endif
-#define __Pyx_NoneAsNull(obj)  (__Pyx_Py_IsNone(obj) ? NULL : (obj))
-#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o)
-#else
-  #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o)
-#endif
-#ifndef CO_COROUTINE
-  #define CO_COROUTINE 0x80
-#endif
-#ifndef CO_ASYNC_GENERATOR
-  #define CO_ASYNC_GENERATOR 0x200
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef Py_TPFLAGS_SEQUENCE
-  #define Py_TPFLAGS_SEQUENCE 0
-#endif
-#ifndef Py_TPFLAGS_MAPPING
-  #define Py_TPFLAGS_MAPPING 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_METH_FASTCALL
-  #define __Pyx_METH_FASTCALL METH_FASTCALL
-  #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast
-  #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords
-#else
-  #define __Pyx_METH_FASTCALL METH_VARARGS
-  #define __Pyx_PyCFunction_FastCall PyCFunction
-  #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords
-#endif
-#if CYTHON_VECTORCALL
-  #define __pyx_vectorcallfunc vectorcallfunc
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  PY_VECTORCALL_ARGUMENTS_OFFSET
-  #define __Pyx_PyVectorcall_NARGS(n)  PyVectorcall_NARGS((size_t)(n))
-#elif CYTHON_BACKPORT_VECTORCALL
-  typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args,
-                                            size_t nargsf, PyObject *kwnames);
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  ((size_t)1 << (8 * sizeof(size_t) - 1))
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET))
-#else
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  0
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(n))
-#endif
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  ((void)m, PyType_FromSpecWithBases(s, b))
-  typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *);
-#else
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  PyType_FromModuleAndSpec(m, s, b)
-  #define __Pyx_PyCMethod  PyCMethod
-#endif
-#ifndef METH_METHOD
-  #define METH_METHOD 0x200
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyThreadState_Current PyThreadState_Get()
-#elif !CYTHON_FAST_THREAD_STATE
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
-  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
-  #define __Pyx_PyThreadState_Current _PyThreadState_Current
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op)
-{
-    void *result;
-    result = PyModule_GetState(op);
-    if (!result)
-        Py_FatalError("Couldn't find the module state");
-    return result;
-}
-#endif
-#define __Pyx_PyObject_GetSlot(obj, name, func_ctype)  __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype)
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyType_GetSlot(type, name, func_ctype)  ((func_ctype) PyType_GetSlot((type), Py_##name))
-#else
-  #define __Pyx_PyType_GetSlot(type, name, func_ctype)  ((type)->name)
-#endif
-#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
-#include "pythread.h"
-#define Py_tss_NEEDS_INIT 0
-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
-  *key = PyThread_create_key();
-  return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
-  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
-  *key = Py_tss_NEEDS_INIT;
-  return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
-  PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
-  return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
-  PyThread_delete_key(*key);
-  *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
-  return PyThread_set_key_value(*key, value);
-}
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-{
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-{
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-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
-/* ListExtend.proto */
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
-    if (unlikely(!none))
-        return -1;
-    Py_DECREF(none);
-    return 0;
-#else
-    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
-#endif
-}
-
-/* ListAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PyObjectCall.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
-#else
-#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
-#endif
-
-/* TupleAndListFromArray.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n);
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n);
-#endif
-
-/* IncludeStringH.proto */
-#include <string.h>
-
-/* BytesEquals.proto */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* UnicodeEquals.proto */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* fastcall.proto */
-#define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i)
-#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds)
-#define __Pyx_KwValues_VARARGS(args, nargs) NULL
-#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s)
-#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw)
-#if CYTHON_METH_FASTCALL
-    #define __Pyx_Arg_FASTCALL(args, i) args[i]
-    #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds)
-    #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs))
-    static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s);
-    #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw)
-#else
-    #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS
-    #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS
-    #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS
-    #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS
-    #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start)
-#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start)
-#else
-#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop)
-#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop)
-#endif
-
-/* RaiseArgTupleInvalid.proto */
-static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
-    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
-
-/* RaiseDoubleKeywords.proto */
-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
-
-/* ParseKeywords.proto */
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,
-    const char* function_name);
-
-/* AssertionsEnabled.proto */
-#define __Pyx_init_assertions_enabled()
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
-  #define __pyx_assertions_enabled() (1)
-#elif PY_VERSION_HEX < 0x03080000  ||  CYTHON_COMPILING_IN_PYPY  ||  defined(Py_LIMITED_API)
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6
-  static int __pyx_assertions_enabled_flag;
-  #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag)
-  #undef __Pyx_init_assertions_enabled
-  static void __Pyx_init_assertions_enabled(void) {
-    __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level;
-  }
-#else
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#endif
-
-/* RaiseException.proto */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
-
-/* PySequenceContains.proto */
-static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
-    int result = PySequence_Contains(seq, item);
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
-    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
-#endif
-
-/* ListCompAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* IterNext.proto */
-#define __Pyx_PyIter_Next(obj) __Pyx_PyIter_Next2(obj, NULL)
-static CYTHON_INLINE PyObject *__Pyx_PyIter_Next2(PyObject *, PyObject *);
-
-/* PyIntCompare.proto */
-static CYTHON_INLINE int __Pyx_PyInt_BoolNeObjC(PyObject *op1, PyObject *op2, long intval, long inplace);
-
-/* SliceObject.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
-        PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
-        int has_cstart, int has_cstop, int wraparound);
-
-/* PyFunctionFastCall.proto */
-#if CYTHON_FAST_PYCALL
-#if !CYTHON_VECTORCALL
-#define __Pyx_PyFunction_FastCall(func, args, nargs)\
-    __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs);
-#endif
-#define __Pyx_BUILD_ASSERT_EXPR(cond)\
-    (sizeof(char [1 - 2*!(cond)]) - 1)
-#ifndef Py_MEMBER_SIZE
-#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member)
-#endif
-#if !CYTHON_VECTORCALL
-#if PY_VERSION_HEX >= 0x03080000
-  #include "frameobject.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-  #define __Pxy_PyFrame_Initialize_Offsets()
-  #define __Pyx_PyFrame_GetLocalsplus(frame)  ((frame)->f_localsplus)
-#else
-  static size_t __pyx_pyframe_localsplus_offset = 0;
-  #include "frameobject.h"
-  #define __Pxy_PyFrame_Initialize_Offsets()\
-    ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\
-     (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus)))
-  #define __Pyx_PyFrame_GetLocalsplus(frame)\
-    (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset))
-#endif
-#endif
-#endif
-
-/* PyObjectCallMethO.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
-#endif
-
-/* PyObjectFastCall.proto */
-#define __Pyx_PyObject_FastCall(func, args, nargs)  __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL)
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs);
-
-/* PyObjectCallOneArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_SubtractObjC(op1, op2, intval, inplace, zerodivision_check)\
-    (inplace ? PyNumber_InPlaceSubtract(op1, op2) : PyNumber_Subtract(op1, op2))
-#endif
-
-/* PyDictVersioning.proto */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-#define __PYX_DICT_VERSION_INIT  ((PY_UINT64_T) -1)
-#define __PYX_GET_DICT_VERSION(dict)  (((PyDictObject*)(dict))->ma_version_tag)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\
-    (version_var) = __PYX_GET_DICT_VERSION(dict);\
-    (cache_var) = (value);
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\
-        (VAR) = __pyx_dict_cached_value;\
-    } else {\
-        (VAR) = __pyx_dict_cached_value = (LOOKUP);\
-        __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
-    }\
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
-#else
-#define __PYX_GET_DICT_VERSION(dict)  (0)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
-#endif
-
-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name)  do {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
-        (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
-        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-} while(0)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  do {\
-    PY_UINT64_T __pyx_dict_version;\
-    PyObject *__pyx_dict_cached_value;\
-    (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-} while(0)
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
-#endif
-
-/* RaiseUnboundLocalError.proto */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* IterFinish.proto */
-static CYTHON_INLINE int __Pyx_IterFinish(void);
-
-/* UnpackItemEndCheck.proto */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
-
-/* py_abs.proto */
-#if CYTHON_USE_PYLONG_INTERNALS
-static PyObject *__Pyx_PyLong_AbsNeg(PyObject *num);
-#define __Pyx_PyNumber_Absolute(x)\
-    ((likely(PyLong_CheckExact(x))) ?\
-         (likely(__Pyx_PyLong_IsNonNeg(x)) ? (Py_INCREF(x), (x)) : __Pyx_PyLong_AbsNeg(x)) :\
-         PyNumber_Absolute(x))
-#else
-#define __Pyx_PyNumber_Absolute(x)  PyNumber_Absolute(x)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* pep479.proto */
-static void __Pyx_Generator_Replace_StopIteration(int in_async_gen);
-
-/* ArgTypeTest.proto */
-#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
-    ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\
-        __Pyx__ArgTypeTest(obj, type, name, exact))
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
-
-/* DictGetItem.proto */
-#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key);
-#define __Pyx_PyObject_Dict_GetItem(obj, name)\
-    (likely(PyDict_CheckExact(obj)) ?\
-     __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name))
-#else
-#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
-#define __Pyx_PyObject_Dict_GetItem(obj, name)  PyObject_GetItem(obj, name)
-#endif
-
-/* pyfrozenset_new.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it);
-
-/* PySetContains.proto */
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq);
-
-/* RaiseUnexpectedTypeError.proto */
-static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj);
-
-/* SliceTupleAndList.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyList_GetSlice(PyObject* src, Py_ssize_t start, Py_ssize_t stop);
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_GetSlice(PyObject* src, Py_ssize_t start, Py_ssize_t stop);
-#else
-#define __Pyx_PyList_GetSlice(seq, start, stop)   PySequence_GetSlice(seq, start, stop)
-#define __Pyx_PyTuple_GetSlice(seq, start, stop)  PySequence_GetSlice(seq, start, stop)
-#endif
-
-/* set_iter.proto */
-static CYTHON_INLINE PyObject* __Pyx_set_iterator(PyObject* iterable, int is_set,
-                                                  Py_ssize_t* p_orig_length, int* p_source_is_set);
-static CYTHON_INLINE int __Pyx_set_iter_next(
-        PyObject* iter_obj, Py_ssize_t orig_length,
-        Py_ssize_t* ppos, PyObject **value,
-        int source_is_set);
-
-/* RaiseClosureNameError.proto */
-static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname);
-
-/* PyIntCompare.proto */
-static CYTHON_INLINE int __Pyx_PyInt_BoolEqObjC(PyObject *op1, PyObject *op2, long intval, long inplace);
-
-/* py_set_remove.proto */
-static CYTHON_INLINE int __Pyx_PySet_Remove(PyObject *set, PyObject *key);
-
-/* IncludeStructmemberH.proto */
-#include <structmember.h>
-
-/* FixUpExtensionType.proto */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type);
-#endif
-
-/* PyObjectCallNoArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-
-/* PyObjectGetMethod.proto */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method);
-
-/* PyObjectCallMethod0.proto */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name);
-
-/* ValidateBasesTuple.proto */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases);
-#endif
-
-/* PyType_Ready.proto */
-CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t);
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
-
-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2))
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyErr_ExceptionMatches2(err1, err2)  __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2)
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-/* Import.proto */
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-/* ImportFrom.proto */
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-/* FetchSharedCythonModule.proto */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void);
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-static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases);
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-/* PyMethodNew.proto */
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-/* PyVectorcallFastCallDict.proto */
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-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw);
-#endif
-
-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CYFUNCTION_COROUTINE     0x08
-#define __Pyx_CyFunction_GetClosure(f)\
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-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#else
-  #define __Pyx_CyFunction_GetClassObj(f)\
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-#define __Pyx_CyFunction_SetClassObj(f, classobj)\
-    __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj))
-#define __Pyx_CyFunction_Defaults(type, f)\
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-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
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-    __pyx_vectorcallfunc func_vectorcall;
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-    PyObject *func_dict;
-    PyObject *func_name;
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-    PyObject *func_classobj;
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-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-    PyObject *func_is_coroutine;
-} __pyx_CyFunctionObject;
-#define __Pyx_CyFunction_Check(obj)  __Pyx_TypeCheck(obj, __pyx_CyFunctionType)
-#define __Pyx_IsCyOrPyCFunction(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
-#define __Pyx_CyFunction_CheckExact(obj)  __Pyx_IS_TYPE(obj, __pyx_CyFunctionType)
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
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-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
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-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
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-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
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-static int __pyx_CyFunction_init(PyObject *module);
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-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
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-#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall)
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-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
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-                                      PyObject *closure,
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-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
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-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
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-/* CodeObjectCache.proto */
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-struct __Pyx_CodeObjectCache {
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-    __Pyx_CodeObjectCacheEntry* entries;
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-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-#endif
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-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
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-/* GCCDiagnostics.proto */
-#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* FormatTypeName.proto */
-#if CYTHON_COMPILING_IN_LIMITED_API
-typedef PyObject *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%U"
-static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp);
-#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj)
-#else
-typedef const char *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%.200s"
-#define __Pyx_PyType_GetName(tp) ((tp)->tp_name)
-#define __Pyx_DECREF_TypeName(obj)
-#endif
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* PyObjectCall2Args.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2);
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-/* PyObjectCallMethod1.proto */
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg);
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-/* CoroutineBase.proto */
-struct __pyx_CoroutineObject;
-typedef PyObject *(*__pyx_coroutine_body_t)(struct __pyx_CoroutineObject *, PyThreadState *, PyObject *);
-#if CYTHON_USE_EXC_INFO_STACK
-#define __Pyx_ExcInfoStruct  _PyErr_StackItem
-#else
-typedef struct {
-    PyObject *exc_type;
-    PyObject *exc_value;
-    PyObject *exc_traceback;
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-typedef struct __pyx_CoroutineObject {
-    PyObject_HEAD
-    __pyx_coroutine_body_t body;
-    PyObject *closure;
-    __Pyx_ExcInfoStruct gi_exc_state;
-    PyObject *gi_weakreflist;
-    PyObject *classobj;
-    PyObject *yieldfrom;
-    PyObject *gi_name;
-    PyObject *gi_qualname;
-    PyObject *gi_modulename;
-    PyObject *gi_code;
-    PyObject *gi_frame;
-    int resume_label;
-    char is_running;
-} __pyx_CoroutineObject;
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
-    PyTypeObject *type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-    PyObject *name, PyObject *qualname, PyObject *module_name);
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
-            __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name);
-static CYTHON_INLINE void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *self);
-static int __Pyx_Coroutine_clear(PyObject *self);
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value);
-static PyObject *__Pyx_Coroutine_Close(PyObject *self);
-static PyObject *__Pyx_Coroutine_Throw(PyObject *gen, PyObject *args);
-#if CYTHON_USE_EXC_INFO_STACK
-#define __Pyx_Coroutine_SwapException(self)
-#define __Pyx_Coroutine_ResetAndClearException(self)  __Pyx_Coroutine_ExceptionClear(&(self)->gi_exc_state)
-#else
-#define __Pyx_Coroutine_SwapException(self) {\
-    __Pyx_ExceptionSwap(&(self)->gi_exc_state.exc_type, &(self)->gi_exc_state.exc_value, &(self)->gi_exc_state.exc_traceback);\
-    __Pyx_Coroutine_ResetFrameBackpointer(&(self)->gi_exc_state);\
-    }
-#define __Pyx_Coroutine_ResetAndClearException(self) {\
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-#endif
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyGen_FetchStopIterationValue(pvalue)\
-    __Pyx_PyGen__FetchStopIterationValue(__pyx_tstate, pvalue)
-#else
-#define __Pyx_PyGen_FetchStopIterationValue(pvalue)\
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-static int __Pyx_PyGen__FetchStopIterationValue(PyThreadState *tstate, PyObject **pvalue);
-static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__Pyx_ExcInfoStruct *exc_state);
-
-/* PatchModuleWithCoroutine.proto */
-static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code);
-
-/* PatchGeneratorABC.proto */
-static int __Pyx_patch_abc(void);
-
-/* Generator.proto */
-#define __Pyx_Generator_USED
-#define __Pyx_Generator_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_GeneratorType)
-#define __Pyx_Generator_New(body, code, closure, name, qualname, module_name)\
-    __Pyx__Coroutine_New(__pyx_GeneratorType, body, code, closure, name, qualname, module_name)
-static PyObject *__Pyx_Generator_Next(PyObject *self);
-static int __pyx_Generator_init(PyObject *module);
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-/* #### Code section: module_declarations ### */
-
-/* Module declarations from "cython" */
-
-/* Module declarations from "fontTools.varLib.iup" */
-static PyObject *__pyx_f_9fontTools_6varLib_3iup_iup_segment(PyObject *, PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/
-static CYTHON_INLINE int __pyx_f_9fontTools_6varLib_3iup_can_iup_in_between(PyObject *, PyObject *, int, int, double); /*proto*/
-/* #### Code section: typeinfo ### */
-/* #### Code section: before_global_var ### */
-#define __Pyx_MODULE_NAME "fontTools.varLib.iup"
-extern int __pyx_module_is_main_fontTools__varLib__iup;
-int __pyx_module_is_main_fontTools__varLib__iup = 0;
-
-/* Implementation of "fontTools.varLib.iup" */
-/* #### Code section: global_var ### */
-static PyObject *__pyx_builtin_AttributeError;
-static PyObject *__pyx_builtin_ImportError;
-static PyObject *__pyx_builtin_zip;
-static PyObject *__pyx_builtin_AssertionError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_max;
-/* #### Code section: string_decls ### */
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k_d[] = "d";
-static const char __pyx_k_i[] = "i";
-static const char __pyx_k_j[] = "j";
-static const char __pyx_k_k[] = "k";
-static const char __pyx_k_l[] = "l";
-static const char __pyx_k_n[] = "n";
-static const char __pyx_k_s[] = "s";
-static const char __pyx_k_v[] = "v";
-static const char __pyx_k__3[] = ".";
-static const char __pyx_k_c1[] = "c1";
-static const char __pyx_k_c2[] = "c2";
-static const char __pyx_k_cj[] = "cj";
-static const char __pyx_k_d0[] = "d0";
-static const char __pyx_k_d1[] = "d1";
-static const char __pyx_k_d2[] = "d2";
-static const char __pyx_k_dj[] = "dj";
-static const char __pyx_k_gc[] = "gc";
-static const char __pyx_k_i1[] = "i1";
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-static const char __pyx_k_lc[] = "lc";
-static const char __pyx_k_ld[] = "ld";
-static const char __pyx_k_nc[] = "nc";
-static const char __pyx_k_nd[] = "nd";
-static const char __pyx_k__23[] = "?";
-static const char __pyx_k_end[] = "end";
-static const char __pyx_k_int[] = "int";
-static const char __pyx_k_lcj[] = "lcj";
-static const char __pyx_k_ldj[] = "ldj";
-static const char __pyx_k_max[] = "max";
-static const char __pyx_k_ncj[] = "ncj";
-static const char __pyx_k_ndj[] = "ndj";
-static const char __pyx_k_out[] = "out";
-static const char __pyx_k_ri1[] = "ri1";
-static const char __pyx_k_ri2[] = "ri2";
-static const char __pyx_k_set[] = "set";
-static const char __pyx_k_zip[] = "zip";
-static const char __pyx_k_Real[] = "Real";
-static const char __pyx_k_args[] = "args";
-static const char __pyx_k_cost[] = "cost";
-static const char __pyx_k_ends[] = "ends";
-static const char __pyx_k_list[] = "list";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_name[] = "__name__";
-static const char __pyx_k_send[] = "send";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_Delta[] = "_Delta";
-static const char __pyx_k_Point[] = "_Point";
-static const char __pyx_k_Tuple[] = "Tuple";
-static const char __pyx_k_Union[] = "Union";
-static const char __pyx_k_chain[] = "chain";
-static const char __pyx_k_close[] = "close";
-static const char __pyx_k_costs[] = "costs";
-static const char __pyx_k_force[] = "force";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_throw[] = "throw";
-static const char __pyx_k_best_j[] = "best_j";
-static const char __pyx_k_coords[] = "coords";
-static const char __pyx_k_cython[] = "cython";
-static const char __pyx_k_deltas[] = "deltas";
-static const char __pyx_k_enable[] = "enable";
-static const char __pyx_k_forced[] = "forced";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_return[] = "return";
-static const char __pyx_k_typing[] = "typing";
-static const char __pyx_k_contour[] = "contour";
-static const char __pyx_k_disable[] = "disable";
-static const char __pyx_k_genexpr[] = "genexpr";
-static const char __pyx_k_indices[] = "indices";
-static const char __pyx_k_numbers[] = "numbers";
-static const char __pyx_k_rot_set[] = "_rot_set";
-static const char __pyx_k_COMPILED[] = "COMPILED";
-static const char __pyx_k_Integral[] = "Integral";
-static const char __pyx_k_Sequence[] = "Sequence";
-static const char __pyx_k_best_sol[] = "best_sol";
-static const char __pyx_k_lookback[] = "lookback";
-static const char __pyx_k_rot_list[] = "_rot_list";
-static const char __pyx_k_solution[] = "solution";
-static const char __pyx_k_Endpoints[] = "_Endpoints";
-static const char __pyx_k_best_cost[] = "best_cost";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_isenabled[] = "isenabled";
-static const char __pyx_k_iup_delta[] = "iup_delta";
-static const char __pyx_k_tolerance[] = "tolerance";
-static const char __pyx_k_DeltaOrNone[] = "_DeltaOrNone";
-static const char __pyx_k_ImportError[] = "ImportError";
-static const char __pyx_k_iup_contour[] = "iup_contour";
-static const char __pyx_k_DeltaSegment[] = "_DeltaSegment";
-static const char __pyx_k_MAX_LOOKBACK[] = "MAX_LOOKBACK";
-static const char __pyx_k_PointSegment[] = "_PointSegment";
-static const char __pyx_k_is_coroutine[] = "_is_coroutine";
-static const char __pyx_k_class_getitem[] = "__class_getitem__";
-static const char __pyx_k_AssertionError[] = "AssertionError";
-static const char __pyx_k_AttributeError[] = "AttributeError";
-static const char __pyx_k_fontTools_misc[] = "fontTools.misc";
-static const char __pyx_k_DeltaOrNoneSegment[] = "_DeltaOrNoneSegment";
-static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_iup_delta_optimize[] = "iup_delta_optimize";
-static const char __pyx_k_fontTools_varLib_iup[] = "fontTools.varLib.iup";
-static const char __pyx_k_iup_contour_optimize[] = "iup_contour_optimize";
-static const char __pyx_k_iup_contour_optimize_dp[] = "_iup_contour_optimize_dp";
-static const char __pyx_k_Lib_fontTools_varLib_iup_py[] = "Lib/fontTools/varLib/iup.py";
-static const char __pyx_k_iup_contour_bound_forced_set[] = "_iup_contour_bound_forced_set";
-static const char __pyx_k_can_iup_in_between_locals_genexp[] = "can_iup_in_between.<locals>.genexpr";
-static const char __pyx_k_iup_contour_optimize_locals_gene[] = "iup_contour_optimize.<locals>.genexpr";
-/* #### Code section: decls ### */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_iup_contour(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_deltas, PyObject *__pyx_v_coords); /* proto */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_2iup_delta(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_deltas, PyObject *__pyx_v_coords, PyObject *__pyx_v_ends); /* proto */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_18can_iup_in_between_genexpr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_genexpr_arg_0); /* proto */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_4_iup_contour_bound_forced_set(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_deltas, PyObject *__pyx_v_coords, PyObject *__pyx_v_tolerance); /* proto */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_16__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_6_iup_contour_optimize_dp(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_deltas, PyObject *__pyx_v_coords, PyObject *__pyx_v_forced, double __pyx_v_tolerance, PyObject *__pyx_v_lookback); /* proto */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_8_rot_list(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_l, PyObject *__pyx_v_k); /* proto */
-static PyObject *__pyx_pf_9fontTools_6varLib_3iup_10_rot_set(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_s, PyObject *__pyx_v_k, PyObject *__pyx_v_n); /* proto */
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-#if CYTHON_USE_MODULE_STATE
-static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) {
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-/* #### Code section: module_state_defines ### */
-#define __pyx_d __pyx_mstate_global->__pyx_d
-#define __pyx_b __pyx_mstate_global->__pyx_b
-#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime
-#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple
-#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes
-#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode
-#ifdef __Pyx_CyFunction_USED
-#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType
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-#ifdef __Pyx_FusedFunction_USED
-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType
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-#ifdef __Pyx_Generator_USED
-#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType
-#endif
-#ifdef __Pyx_IterableCoroutine_USED
-#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType
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-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
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-#define __pyx_type_9fontTools_6varLib_3iup___pyx_scope_struct_1_iup_contour_optimize __pyx_mstate_global->__pyx_type_9fontTools_6varLib_3iup___pyx_scope_struct_1_iup_contour_optimize
-#define __pyx_type_9fontTools_6varLib_3iup___pyx_scope_struct_2_genexpr __pyx_mstate_global->__pyx_type_9fontTools_6varLib_3iup___pyx_scope_struct_2_genexpr
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-#endif
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-#define __pyx_ptype_9fontTools_6varLib_3iup___pyx_scope_struct_1_iup_contour_optimize __pyx_mstate_global->__pyx_ptype_9fontTools_6varLib_3iup___pyx_scope_struct_1_iup_contour_optimize
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-#define __pyx_n_s_AttributeError __pyx_mstate_global->__pyx_n_s_AttributeError
-#define __pyx_n_s_COMPILED __pyx_mstate_global->__pyx_n_s_COMPILED
-#define __pyx_n_s_Delta __pyx_mstate_global->__pyx_n_s_Delta
-#define __pyx_n_s_DeltaOrNone __pyx_mstate_global->__pyx_n_s_DeltaOrNone
-#define __pyx_n_s_DeltaOrNoneSegment __pyx_mstate_global->__pyx_n_s_DeltaOrNoneSegment
-#define __pyx_n_s_DeltaSegment __pyx_mstate_global->__pyx_n_s_DeltaSegment
-#define __pyx_n_s_Endpoints __pyx_mstate_global->__pyx_n_s_Endpoints
-#define __pyx_n_s_ImportError __pyx_mstate_global->__pyx_n_s_ImportError
-#define __pyx_n_s_Integral __pyx_mstate_global->__pyx_n_s_Integral
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-#define __pyx_n_s_MAX_LOOKBACK __pyx_mstate_global->__pyx_n_s_MAX_LOOKBACK
-#define __pyx_n_s_Point __pyx_mstate_global->__pyx_n_s_Point
-#define __pyx_n_s_PointSegment __pyx_mstate_global->__pyx_n_s_PointSegment
-#define __pyx_n_s_Real __pyx_mstate_global->__pyx_n_s_Real
-#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence
-#define __pyx_n_s_Tuple __pyx_mstate_global->__pyx_n_s_Tuple
-#define __pyx_n_s_Union __pyx_mstate_global->__pyx_n_s_Union
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-#define __pyx_kp_u__3 __pyx_mstate_global->__pyx_kp_u__3
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-#define __pyx_n_s_best_j __pyx_mstate_global->__pyx_n_s_best_j
-#define __pyx_n_s_best_sol __pyx_mstate_global->__pyx_n_s_best_sol
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-#define __pyx_n_s_c1 __pyx_mstate_global->__pyx_n_s_c1
-#define __pyx_n_s_c2 __pyx_mstate_global->__pyx_n_s_c2
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-#define __pyx_n_s_chain __pyx_mstate_global->__pyx_n_s_chain
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-#define __pyx_n_s_close __pyx_mstate_global->__pyx_n_s_close
-#define __pyx_n_s_contour __pyx_mstate_global->__pyx_n_s_contour
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-#define __pyx_n_s_cost __pyx_mstate_global->__pyx_n_s_cost
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-#define __pyx_n_s_cython __pyx_mstate_global->__pyx_n_s_cython
-#define __pyx_n_s_d __pyx_mstate_global->__pyx_n_s_d
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-#define __pyx_n_s_d1 __pyx_mstate_global->__pyx_n_s_d1
-#define __pyx_n_s_d2 __pyx_mstate_global->__pyx_n_s_d2
-#define __pyx_n_s_deltas __pyx_mstate_global->__pyx_n_s_deltas
-#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable
-#define __pyx_n_s_dj __pyx_mstate_global->__pyx_n_s_dj
-#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable
-#define __pyx_n_s_end __pyx_mstate_global->__pyx_n_s_end
-#define __pyx_n_s_ends __pyx_mstate_global->__pyx_n_s_ends
-#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate
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-#define __pyx_n_s_fontTools_varLib_iup __pyx_mstate_global->__pyx_n_s_fontTools_varLib_iup
-#define __pyx_n_s_force __pyx_mstate_global->__pyx_n_s_force
-#define __pyx_n_s_forced __pyx_mstate_global->__pyx_n_s_forced
-#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc
-#define __pyx_n_s_genexpr __pyx_mstate_global->__pyx_n_s_genexpr
-#define __pyx_n_s_i __pyx_mstate_global->__pyx_n_s_i
-#define __pyx_n_s_i1 __pyx_mstate_global->__pyx_n_s_i1
-#define __pyx_n_s_i2 __pyx_mstate_global->__pyx_n_s_i2
-#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import
-#define __pyx_n_s_indices __pyx_mstate_global->__pyx_n_s_indices
-#define __pyx_n_s_int __pyx_mstate_global->__pyx_n_s_int
-#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine
-#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled
-#define __pyx_n_s_it __pyx_mstate_global->__pyx_n_s_it
-#define __pyx_n_s_iup_contour __pyx_mstate_global->__pyx_n_s_iup_contour
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-#define __pyx_n_s_iup_contour_optimize __pyx_mstate_global->__pyx_n_s_iup_contour_optimize
-#define __pyx_n_s_iup_contour_optimize_dp __pyx_mstate_global->__pyx_n_s_iup_contour_optimize_dp
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-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_subscript) {
-            PyObject *r, *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return NULL;
-            r = mm->mp_subscript(o, key);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* TupleAndListFromArray */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE void __Pyx_copy_object_array(PyObject *const *CYTHON_RESTRICT src, PyObject** CYTHON_RESTRICT dest, Py_ssize_t length) {
-    PyObject *v;
-    Py_ssize_t i;
-    for (i = 0; i < length; i++) {
-        v = dest[i] = src[i];
-        Py_INCREF(v);
-    }
-}
-static CYTHON_INLINE PyObject *
-__Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n)
-{
-    PyObject *res;
-    if (n <= 0) {
-        Py_INCREF(__pyx_empty_tuple);
-        return __pyx_empty_tuple;
-    }
-    res = PyTuple_New(n);
-    if (unlikely(res == NULL)) return NULL;
-    __Pyx_copy_object_array(src, ((PyTupleObject*)res)->ob_item, n);
-    return res;
-}
-static CYTHON_INLINE PyObject *
-__Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n)
-{
-    PyObject *res;
-    if (n <= 0) {
-        return PyList_New(0);
-    }
-    res = PyList_New(n);
-    if (unlikely(res == NULL)) return NULL;
-    __Pyx_copy_object_array(src, ((PyListObject*)res)->ob_item, n);
-    return res;
-}
-#endif
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000)
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* fastcall */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s)
-{
-    Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames);
-    for (i = 0; i < n; i++)
-    {
-        if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i];
-    }
-    for (i = 0; i < n; i++)
-    {
-        int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ);
-        if (unlikely(eq != 0)) {
-            if (unlikely(eq < 0)) return NULL;  // error
-            return kwvalues[i];
-        }
-    }
-    return NULL;  // not found (no exception set)
-}
-#endif
-
-/* RaiseArgTupleInvalid */
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
-}
-
-/* RaiseDoubleKeywords */
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-/* ParseKeywords */
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds));
-    while (1) {
-        if (kwds_is_tuple) {
-            if (pos >= PyTuple_GET_SIZE(kwds)) break;
-            key = PyTuple_GET_ITEM(kwds, pos);
-            value = kwvalues[pos];
-            pos++;
-        }
-        else
-        {
-            if (!PyDict_Next(kwds, &pos, &key, &value)) break;
-        }
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key)
-                );
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    CYTHON_UNUSED_VAR(cause);
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-      #if PY_VERSION_HEX >= 0x030C00A6
-        PyException_SetTraceback(value, tb);
-      #elif CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_MAYBE_UNUSED_VAR(inplace);
-    CYTHON_UNUSED_VAR(zerodivision_check);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-        
-            x = (long)((unsigned long)a + (unsigned long)b);
-            if (likely((x^a) >= 0 || (x^b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_add(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        if (unlikely(__Pyx_PyLong_IsZero(op1))) {
-            return __Pyx_NewRef(op2);
-        }
-        if (likely(__Pyx_PyLong_IsCompact(op1))) {
-            a = __Pyx_PyLong_CompactValue(op1);
-        } else {
-            const digit* digits = __Pyx_PyLong_Digits(op1);
-            const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op1);
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
-            }
-        }
-                x = a + b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla + llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double a = __pyx_PyFloat_AsDouble(op1);
-#else
-        double a = PyFloat_AS_DOUBLE(op1);
-#endif
-            double result;
-            
-            PyFPE_START_PROTECT("add", return NULL)
-            result = ((double)a) + (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* IterNext */
-static PyObject *__Pyx_PyIter_Next2Default(PyObject* defval) {
-    PyObject* exc_type;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    exc_type = __Pyx_PyErr_CurrentExceptionType();
-    if (unlikely(exc_type)) {
-        if (!defval || unlikely(!__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)))
-            return NULL;
-        __Pyx_PyErr_Clear();
-        Py_INCREF(defval);
-        return defval;
-    }
-    if (defval) {
-        Py_INCREF(defval);
-        return defval;
-    }
-    __Pyx_PyErr_SetNone(PyExc_StopIteration);
-    return NULL;
-}
-static void __Pyx_PyIter_Next_ErrorNoIterator(PyObject *iterator) {
-    __Pyx_TypeName iterator_type_name = __Pyx_PyType_GetName(Py_TYPE(iterator));
-    PyErr_Format(PyExc_TypeError,
-        __Pyx_FMT_TYPENAME " object is not an iterator", iterator_type_name);
-    __Pyx_DECREF_TypeName(iterator_type_name);
-}
-static CYTHON_INLINE PyObject *__Pyx_PyIter_Next2(PyObject* iterator, PyObject* defval) {
-    PyObject* next;
-    iternextfunc iternext = Py_TYPE(iterator)->tp_iternext;
-    if (likely(iternext)) {
-#if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY
-        next = iternext(iterator);
-        if (likely(next))
-            return next;
-#if CYTHON_COMPILING_IN_CPYTHON
-        if (unlikely(iternext == &_PyObject_NextNotImplemented))
-            return NULL;
-#endif
-#else
-        next = PyIter_Next(iterator);
-        if (likely(next))
-            return next;
-#endif
-    } else if (CYTHON_USE_TYPE_SLOTS || unlikely(!PyIter_Check(iterator))) {
-        __Pyx_PyIter_Next_ErrorNoIterator(iterator);
-        return NULL;
-    }
-#if !CYTHON_USE_TYPE_SLOTS
-    else {
-        next = PyIter_Next(iterator);
-        if (likely(next))
-            return next;
-    }
-#endif
-    return __Pyx_PyIter_Next2Default(defval);
-}
-
-/* PyIntCompare */
-static CYTHON_INLINE int __Pyx_PyInt_BoolNeObjC(PyObject *op1, PyObject *op2, long intval, long inplace) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_UNUSED_VAR(inplace);
-    if (op1 == op2) {
-        return 0;
-    }
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long a = PyInt_AS_LONG(op1);
-        return (a != b);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        int unequal;
-        unsigned long uintval;
-        Py_ssize_t size = __Pyx_PyLong_DigitCount(op1);
-        const digit* digits = __Pyx_PyLong_Digits(op1);
-        if (intval == 0) {
-            return (__Pyx_PyLong_IsZero(op1) != 1);
-        } else if (intval < 0) {
-            if (__Pyx_PyLong_IsNonNeg(op1))
-                return 1;
-            intval = -intval;
-        } else {
-            if (__Pyx_PyLong_IsNeg(op1))
-                return 1;
-        }
-        uintval = (unsigned long) intval;
-#if PyLong_SHIFT * 4 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 4)) {
-            unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 3 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 3)) {
-            unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 2 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 2)) {
-            unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 1 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 1)) {
-            unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-            unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
-        return (unequal != 0);
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double a = __pyx_PyFloat_AsDouble(op1);
-#else
-        double a = PyFloat_AS_DOUBLE(op1);
-#endif
-        return ((double)a != (double)b);
-    }
-    return __Pyx_PyObject_IsTrueAndDecref(
-        PyObject_RichCompare(op1, op2, Py_NE));
-}
-
-/* SliceObject */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj,
-        Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
-        int has_cstart, int has_cstop, int wraparound) {
-    __Pyx_TypeName obj_type_name;
-#if CYTHON_USE_TYPE_SLOTS
-    PyMappingMethods* mp;
-#if PY_MAJOR_VERSION < 3
-    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
-    if (likely(ms && ms->sq_slice)) {
-        if (!has_cstart) {
-            if (_py_start && (*_py_start != Py_None)) {
-                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
-                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstart = 0;
-        }
-        if (!has_cstop) {
-            if (_py_stop && (*_py_stop != Py_None)) {
-                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
-                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstop = PY_SSIZE_T_MAX;
-        }
-        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
-            Py_ssize_t l = ms->sq_length(obj);
-            if (likely(l >= 0)) {
-                if (cstop < 0) {
-                    cstop += l;
-                    if (cstop < 0) cstop = 0;
-                }
-                if (cstart < 0) {
-                    cstart += l;
-                    if (cstart < 0) cstart = 0;
-                }
-            } else {
-                if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                    goto bad;
-                PyErr_Clear();
-            }
-        }
-        return ms->sq_slice(obj, cstart, cstop);
-    }
-#else
-    CYTHON_UNUSED_VAR(wraparound);
-#endif
-    mp = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(mp && mp->mp_subscript))
-#else
-    CYTHON_UNUSED_VAR(wraparound);
-#endif
-    {
-        PyObject* result;
-        PyObject *py_slice, *py_start, *py_stop;
-        if (_py_slice) {
-            py_slice = *_py_slice;
-        } else {
-            PyObject* owned_start = NULL;
-            PyObject* owned_stop = NULL;
-            if (_py_start) {
-                py_start = *_py_start;
-            } else {
-                if (has_cstart) {
-                    owned_start = py_start = PyInt_FromSsize_t(cstart);
-                    if (unlikely(!py_start)) goto bad;
-                } else
-                    py_start = Py_None;
-            }
-            if (_py_stop) {
-                py_stop = *_py_stop;
-            } else {
-                if (has_cstop) {
-                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
-                    if (unlikely(!py_stop)) {
-                        Py_XDECREF(owned_start);
-                        goto bad;
-                    }
-                } else
-                    py_stop = Py_None;
-            }
-            py_slice = PySlice_New(py_start, py_stop, Py_None);
-            Py_XDECREF(owned_start);
-            Py_XDECREF(owned_stop);
-            if (unlikely(!py_slice)) goto bad;
-        }
-#if CYTHON_USE_TYPE_SLOTS
-        result = mp->mp_subscript(obj, py_slice);
-#else
-        result = PyObject_GetItem(obj, py_slice);
-#endif
-        if (!_py_slice) {
-            Py_DECREF(py_slice);
-        }
-        return result;
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is unsliceable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-bad:
-    return NULL;
-}
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectFastCall */
-static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) {
-    PyObject *argstuple;
-    PyObject *result;
-    size_t i;
-    argstuple = PyTuple_New((Py_ssize_t)nargs);
-    if (unlikely(!argstuple)) return NULL;
-    for (i = 0; i < nargs; i++) {
-        Py_INCREF(args[i]);
-        PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]);
-    }
-    result = __Pyx_PyObject_Call(func, argstuple, kwargs);
-    Py_DECREF(argstuple);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) {
-    Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (nargs == 0 && kwargs == NULL) {
-#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG)
-        if (__Pyx_IsCyOrPyCFunction(func))
-#else
-        if (PyCFunction_Check(func))
-#endif
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-                return __Pyx_PyObject_CallMethO(func, NULL);
-            }
-        }
-    }
-    else if (nargs == 1 && kwargs == NULL) {
-        if (PyCFunction_Check(func))
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-                return __Pyx_PyObject_CallMethO(func, args[0]);
-            }
-        }
-    }
-#endif
-    #if PY_VERSION_HEX < 0x030800B1
-    #if CYTHON_FAST_PYCCALL
-    if (PyCFunction_Check(func)) {
-        if (kwargs) {
-            return _PyCFunction_FastCallDict(func, args, nargs, kwargs);
-        } else {
-            return _PyCFunction_FastCallKeywords(func, args, nargs, NULL);
-        }
-    }
-    #if PY_VERSION_HEX >= 0x030700A1
-    if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) {
-        return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL);
-    }
-    #endif
-    #endif
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs);
-    }
-    #endif
-    #endif
-    #if CYTHON_VECTORCALL
-    vectorcallfunc f = _PyVectorcall_Function(func);
-    if (f) {
-        return f(func, args, (size_t)nargs, kwargs);
-    }
-    #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL
-    if (__Pyx_CyFunction_CheckExact(func)) {
-        __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func);
-        if (f) return f(func, args, (size_t)nargs, kwargs);
-    }
-    #endif
-    if (nargs == 0) {
-        return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs);
-    }
-    return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs);
-}
-
-/* PyObjectCallOneArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *args[2] = {NULL, arg};
-    return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index));
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError,
-            "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name);
-        __Pyx_DECREF_TypeName(index_type_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) {
-    __Pyx_TypeName obj_type_name;
-    if (likely(PyType_Check(obj))) {
-        PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem);
-        if (meth) {
-            PyObject *result = __Pyx_PyObject_CallOneArg(meth, key);
-            Py_DECREF(meth);
-            return result;
-        }
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) {
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyMappingMethods *mm = tp->tp_as_mapping;
-    PySequenceMethods *sm = tp->tp_as_sequence;
-    if (likely(mm && mm->mp_subscript)) {
-        return mm->mp_subscript(obj, key);
-    }
-    if (likely(sm && sm->sq_item)) {
-        return __Pyx_PyObject_GetIndex(obj, key);
-    }
-    return __Pyx_PyObject_GetItem_Slow(obj, key);
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_MAYBE_UNUSED_VAR(inplace);
-    CYTHON_UNUSED_VAR(zerodivision_check);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-        
-            x = (long)((unsigned long)a - (unsigned long)b);
-            if (likely((x^a) >= 0 || (x^~b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        if (unlikely(__Pyx_PyLong_IsZero(op1))) {
-            return PyLong_FromLong(-intval);
-        }
-        if (likely(__Pyx_PyLong_IsCompact(op1))) {
-            a = __Pyx_PyLong_CompactValue(op1);
-        } else {
-            const digit* digits = __Pyx_PyLong_Digits(op1);
-            const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op1);
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-            }
-        }
-                x = a - b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla - llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double a = __pyx_PyFloat_AsDouble(op1);
-#else
-        double a = PyFloat_AS_DOUBLE(op1);
-#endif
-            double result;
-            
-            PyFPE_START_PROTECT("subtract", return NULL)
-            result = ((double)a) - (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
-}
-#endif
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#elif CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(!__pyx_m)) {
-        return NULL;
-    }
-    result = PyObject_GetAttr(__pyx_m, name);
-    if (likely(result)) {
-        return result;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseUnboundLocalError */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* IterFinish */
-static CYTHON_INLINE int __Pyx_IterFinish(void) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    PyObject* exc_type = __Pyx_PyErr_CurrentExceptionType();
-    if (unlikely(exc_type)) {
-        if (unlikely(!__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)))
-            return -1;
-        __Pyx_PyErr_Clear();
-        return 0;
-    }
-    return 0;
-}
-
-/* UnpackItemEndCheck */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
-    if (unlikely(retval)) {
-        Py_DECREF(retval);
-        __Pyx_RaiseTooManyValuesError(expected);
-        return -1;
-    }
-    return __Pyx_IterFinish();
-}
-
-/* py_abs */
-#if CYTHON_USE_PYLONG_INTERNALS
-static PyObject *__Pyx_PyLong_AbsNeg(PyObject *n) {
-#if PY_VERSION_HEX >= 0x030C00A7
-    if (likely(__Pyx_PyLong_IsCompact(n))) {
-        return PyLong_FromSize_t(__Pyx_PyLong_CompactValueUnsigned(n));
-    }
-#else
-    if (likely(Py_SIZE(n) == -1)) {
-        return PyLong_FromUnsignedLong(__Pyx_PyLong_Digits(n)[0]);
-    }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-    {
-        PyObject *copy = _PyLong_Copy((PyLongObject*)n);
-        if (likely(copy)) {
-            #if PY_VERSION_HEX >= 0x030C00A7
-            ((PyLongObject*)copy)->long_value.lv_tag = ((PyLongObject*)copy)->long_value.lv_tag & ~_PyLong_SIGN_MASK;
-            #else
-            __Pyx_SET_SIZE(copy, -Py_SIZE(copy));
-            #endif
-        }
-        return copy;
-    }
-#else
-    return PyNumber_Negative(n);
-#endif
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type = NULL, *local_value, *local_tb = NULL;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if PY_VERSION_HEX >= 0x030C00A6
-    local_value = tstate->current_exception;
-    tstate->current_exception = 0;
-    if (likely(local_value)) {
-        local_type = (PyObject*) Py_TYPE(local_value);
-        Py_INCREF(local_type);
-        local_tb = PyException_GetTraceback(local_value);
-    }
-  #else
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-  #endif
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6
-    if (unlikely(tstate->current_exception))
-#elif CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-      #if PY_VERSION_HEX >= 0x030B00a4
-        tmp_value = exc_info->exc_value;
-        exc_info->exc_value = local_value;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-        Py_XDECREF(local_type);
-        Py_XDECREF(local_tb);
-      #else
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-      #endif
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* pep479 */
-static void __Pyx_Generator_Replace_StopIteration(int in_async_gen) {
-    PyObject *exc, *val, *tb, *cur_exc;
-    __Pyx_PyThreadState_declare
-    #ifdef __Pyx_StopAsyncIteration_USED
-    int is_async_stopiteration = 0;
-    #endif
-    CYTHON_MAYBE_UNUSED_VAR(in_async_gen);
-    cur_exc = PyErr_Occurred();
-    if (likely(!__Pyx_PyErr_GivenExceptionMatches(cur_exc, PyExc_StopIteration))) {
-        #ifdef __Pyx_StopAsyncIteration_USED
-        if (in_async_gen && unlikely(__Pyx_PyErr_GivenExceptionMatches(cur_exc, __Pyx_PyExc_StopAsyncIteration))) {
-            is_async_stopiteration = 1;
-        } else
-        #endif
-            return;
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_GetException(&exc, &val, &tb);
-    Py_XDECREF(exc);
-    Py_XDECREF(val);
-    Py_XDECREF(tb);
-    PyErr_SetString(PyExc_RuntimeError,
-        #ifdef __Pyx_StopAsyncIteration_USED
-        is_async_stopiteration ? "async generator raised StopAsyncIteration" :
-        in_async_gen ? "async generator raised StopIteration" :
-        #endif
-        "generator raised StopIteration");
-}
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    __Pyx_TypeName type_name;
-    __Pyx_TypeName obj_type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    type_name = __Pyx_PyType_GetName(type);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME
-        ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* DictGetItem */
-#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
-    PyObject *value;
-    value = PyDict_GetItemWithError(d, key);
-    if (unlikely(!value)) {
-        if (!PyErr_Occurred()) {
-            if (unlikely(PyTuple_Check(key))) {
-                PyObject* args = PyTuple_Pack(1, key);
-                if (likely(args)) {
-                    PyErr_SetObject(PyExc_KeyError, args);
-                    Py_DECREF(args);
-                }
-            } else {
-                PyErr_SetObject(PyExc_KeyError, key);
-            }
-        }
-        return NULL;
-    }
-    Py_INCREF(value);
-    return value;
-}
-#endif
-
-/* pyfrozenset_new */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it) {
-    if (it) {
-        PyObject* result;
-#if CYTHON_COMPILING_IN_PYPY
-        PyObject* args;
-        args = PyTuple_Pack(1, it);
-        if (unlikely(!args))
-            return NULL;
-        result = PyObject_Call((PyObject*)&PyFrozenSet_Type, args, NULL);
-        Py_DECREF(args);
-        return result;
-#else
-        if (PyFrozenSet_CheckExact(it)) {
-            Py_INCREF(it);
-            return it;
-        }
-        result = PyFrozenSet_New(it);
-        if (unlikely(!result))
-            return NULL;
-        if ((PY_VERSION_HEX >= 0x031000A1) || likely(PySet_GET_SIZE(result)))
-            return result;
-        Py_DECREF(result);
-#endif
-    }
-#if CYTHON_USE_TYPE_SLOTS
-    return PyFrozenSet_Type.tp_new(&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#else
-    return PyObject_Call((PyObject*)&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#endif
-}
-
-/* PySetContains */
-static int __Pyx_PySet_ContainsUnhashable(PyObject *set, PyObject *key) {
-    int result = -1;
-    if (PySet_Check(key) && PyErr_ExceptionMatches(PyExc_TypeError)) {
-        PyObject *tmpkey;
-        PyErr_Clear();
-        tmpkey = __Pyx_PyFrozenSet_New(key);
-        if (tmpkey != NULL) {
-            result = PySet_Contains(set, tmpkey);
-            Py_DECREF(tmpkey);
-        }
-    }
-    return result;
-}
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq) {
-    int result = PySet_Contains(set, key);
-    if (unlikely(result < 0)) {
-        result = __Pyx_PySet_ContainsUnhashable(set, key);
-    }
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* RaiseUnexpectedTypeError */
-static int
-__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj)
-{
-    __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME,
-                 expected, obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* SliceTupleAndList */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE void __Pyx_crop_slice(Py_ssize_t* _start, Py_ssize_t* _stop, Py_ssize_t* _length) {
-    Py_ssize_t start = *_start, stop = *_stop, length = *_length;
-    if (start < 0) {
-        start += length;
-        if (start < 0)
-            start = 0;
-    }
-    if (stop < 0)
-        stop += length;
-    else if (stop > length)
-        stop = length;
-    *_length = stop - start;
-    *_start = start;
-    *_stop = stop;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyList_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    Py_ssize_t length = PyList_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    if (length <= 0) {
-        return PyList_New(0);
-    }
-    return __Pyx_PyList_FromArray(((PyListObject*)src)->ob_item + start, length);
-}
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    Py_ssize_t length = PyTuple_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    return __Pyx_PyTuple_FromArray(((PyTupleObject*)src)->ob_item + start, length);
-}
-#endif
-
-/* set_iter */
-static CYTHON_INLINE PyObject* __Pyx_set_iterator(PyObject* iterable, int is_set,
-                                                  Py_ssize_t* p_orig_length, int* p_source_is_set) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    is_set = is_set || likely(PySet_CheckExact(iterable) || PyFrozenSet_CheckExact(iterable));
-    *p_source_is_set = is_set;
-    if (likely(is_set)) {
-        *p_orig_length = PySet_Size(iterable);
-        Py_INCREF(iterable);
-        return iterable;
-    }
-#else
-    CYTHON_UNUSED_VAR(is_set);
-    *p_source_is_set = 0;
-#endif
-    *p_orig_length = 0;
-    return PyObject_GetIter(iterable);
-}
-static CYTHON_INLINE int __Pyx_set_iter_next(
-        PyObject* iter_obj, Py_ssize_t orig_length,
-        Py_ssize_t* ppos, PyObject **value,
-        int source_is_set) {
-    if (!CYTHON_COMPILING_IN_CPYTHON || unlikely(!source_is_set)) {
-        *value = PyIter_Next(iter_obj);
-        if (unlikely(!*value)) {
-            return __Pyx_IterFinish();
-        }
-        CYTHON_UNUSED_VAR(orig_length);
-        CYTHON_UNUSED_VAR(ppos);
-        return 1;
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (unlikely(PySet_GET_SIZE(iter_obj) != orig_length)) {
-        PyErr_SetString(
-            PyExc_RuntimeError,
-            "set changed size during iteration");
-        return -1;
-    }
-    {
-        Py_hash_t hash;
-        int ret = _PySet_NextEntry(iter_obj, ppos, value, &hash);
-        assert (ret != -1);
-        if (likely(ret)) {
-            Py_INCREF(*value);
-            return 1;
-        }
-    }
-#endif
-    return 0;
-}
-
-/* RaiseClosureNameError */
-static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname) {
-    PyErr_Format(PyExc_NameError, "free variable '%s' referenced before assignment in enclosing scope", varname);
-}
-
-/* PyIntCompare */
-static CYTHON_INLINE int __Pyx_PyInt_BoolEqObjC(PyObject *op1, PyObject *op2, long intval, long inplace) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_UNUSED_VAR(inplace);
-    if (op1 == op2) {
-        return 1;
-    }
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long a = PyInt_AS_LONG(op1);
-        return (a == b);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        int unequal;
-        unsigned long uintval;
-        Py_ssize_t size = __Pyx_PyLong_DigitCount(op1);
-        const digit* digits = __Pyx_PyLong_Digits(op1);
-        if (intval == 0) {
-            return (__Pyx_PyLong_IsZero(op1) == 1);
-        } else if (intval < 0) {
-            if (__Pyx_PyLong_IsNonNeg(op1))
-                return 0;
-            intval = -intval;
-        } else {
-            if (__Pyx_PyLong_IsNeg(op1))
-                return 0;
-        }
-        uintval = (unsigned long) intval;
-#if PyLong_SHIFT * 4 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 4)) {
-            unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 3 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 3)) {
-            unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 2 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 2)) {
-            unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 1 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 1)) {
-            unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-            unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
-        return (unequal == 0);
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double a = __pyx_PyFloat_AsDouble(op1);
-#else
-        double a = PyFloat_AS_DOUBLE(op1);
-#endif
-        return ((double)a == (double)b);
-    }
-    return __Pyx_PyObject_IsTrueAndDecref(
-        PyObject_RichCompare(op1, op2, Py_EQ));
-}
-
-/* py_set_discard_unhashable */
-static int __Pyx_PySet_DiscardUnhashable(PyObject *set, PyObject *key) {
-    PyObject *tmpkey;
-    int rv;
-    if (likely(!PySet_Check(key) || !PyErr_ExceptionMatches(PyExc_TypeError)))
-        return -1;
-    PyErr_Clear();
-    tmpkey = __Pyx_PyFrozenSet_New(key);
-    if (tmpkey == NULL)
-        return -1;
-    rv = PySet_Discard(set, tmpkey);
-    Py_DECREF(tmpkey);
-    return rv;
-}
-
-/* py_set_remove */
-static int __Pyx_PySet_RemoveNotFound(PyObject *set, PyObject *key, int found) {
-    if (unlikely(found < 0)) {
-        found = __Pyx_PySet_DiscardUnhashable(set, key);
-    }
-    if (likely(found == 0)) {
-        PyObject *tup;
-        tup = PyTuple_Pack(1, key);
-        if (!tup)
-            return -1;
-        PyErr_SetObject(PyExc_KeyError, tup);
-        Py_DECREF(tup);
-        return -1;
-    }
-    return found;
-}
-static CYTHON_INLINE int __Pyx_PySet_Remove(PyObject *set, PyObject *key) {
-    int found = PySet_Discard(set, key);
-    if (unlikely(found != 1)) {
-        return __Pyx_PySet_RemoveNotFound(set, key, found);
-    }
-    return 0;
-}
-
-/* FixUpExtensionType */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) {
-#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    CYTHON_UNUSED_VAR(spec);
-    CYTHON_UNUSED_VAR(type);
-#else
-    const PyType_Slot *slot = spec->slots;
-    while (slot && slot->slot && slot->slot != Py_tp_members)
-        slot++;
-    if (slot && slot->slot == Py_tp_members) {
-        int changed = 0;
-#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON)
-        const
-#endif
-            PyMemberDef *memb = (PyMemberDef*) slot->pfunc;
-        while (memb && memb->name) {
-            if (memb->name[0] == '_' && memb->name[1] == '_') {
-#if PY_VERSION_HEX < 0x030900b1
-                if (strcmp(memb->name, "__weaklistoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_weaklistoffset = memb->offset;
-                    changed = 1;
-                }
-                else if (strcmp(memb->name, "__dictoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_dictoffset = memb->offset;
-                    changed = 1;
-                }
-#if CYTHON_METH_FASTCALL
-                else if (strcmp(memb->name, "__vectorcalloffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-#if PY_VERSION_HEX >= 0x030800b4
-                    type->tp_vectorcall_offset = memb->offset;
-#else
-                    type->tp_print = (printfunc) memb->offset;
-#endif
-                    changed = 1;
-                }
-#endif
-#else
-                if ((0));
-#endif
-#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON
-                else if (strcmp(memb->name, "__module__") == 0) {
-                    PyObject *descr;
-                    assert(memb->type == T_OBJECT);
-                    assert(memb->flags == 0 || memb->flags == READONLY);
-                    descr = PyDescr_NewMember(type, memb);
-                    if (unlikely(!descr))
-                        return -1;
-                    if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) {
-                        Py_DECREF(descr);
-                        return -1;
-                    }
-                    Py_DECREF(descr);
-                    changed = 1;
-                }
-#endif
-            }
-            memb++;
-        }
-        if (changed)
-            PyType_Modified(type);
-    }
-#endif
-    return 0;
-}
-#endif
-
-/* PyObjectCallNoArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-    PyObject *arg = NULL;
-    return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
-    PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
-    __Pyx_TypeName type_name;
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyObject *descr;
-    descrgetfunc f = NULL;
-    PyObject **dictptr, *dict;
-    int meth_found = 0;
-    assert (*method == NULL);
-    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
-        attr = __Pyx_PyObject_GetAttrStr(obj, name);
-        goto try_unpack;
-    }
-    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
-        return 0;
-    }
-    descr = _PyType_Lookup(tp, name);
-    if (likely(descr != NULL)) {
-        Py_INCREF(descr);
-#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR
-        if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR))
-#elif PY_MAJOR_VERSION >= 3
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type)))
-        #endif
-#else
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr)))
-        #endif
-#endif
-        {
-            meth_found = 1;
-        } else {
-            f = Py_TYPE(descr)->tp_descr_get;
-            if (f != NULL && PyDescr_IsData(descr)) {
-                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-                Py_DECREF(descr);
-                goto try_unpack;
-            }
-        }
-    }
-    dictptr = _PyObject_GetDictPtr(obj);
-    if (dictptr != NULL && (dict = *dictptr) != NULL) {
-        Py_INCREF(dict);
-        attr = __Pyx_PyDict_GetItemStr(dict, name);
-        if (attr != NULL) {
-            Py_INCREF(attr);
-            Py_DECREF(dict);
-            Py_XDECREF(descr);
-            goto try_unpack;
-        }
-        Py_DECREF(dict);
-    }
-    if (meth_found) {
-        *method = descr;
-        return 1;
-    }
-    if (f != NULL) {
-        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-        Py_DECREF(descr);
-        goto try_unpack;
-    }
-    if (likely(descr != NULL)) {
-        *method = descr;
-        return 0;
-    }
-    type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-#else
-    attr = __Pyx_PyObject_GetAttrStr(obj, name);
-    goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
-    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
-        PyObject *function = PyMethod_GET_FUNCTION(attr);
-        Py_INCREF(function);
-        Py_DECREF(attr);
-        *method = function;
-        return 1;
-    }
-#endif
-    *method = attr;
-    return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
-    PyObject *method = NULL, *result = NULL;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_CallOneArg(method, obj);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) goto bad;
-    result = __Pyx_PyObject_CallNoArg(method);
-    Py_DECREF(method);
-bad:
-    return result;
-}
-
-/* ValidateBasesTuple */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) {
-    Py_ssize_t i, n = PyTuple_GET_SIZE(bases);
-    for (i = 1; i < n; i++)
-    {
-        PyObject *b0 = PyTuple_GET_ITEM(bases, i);
-        PyTypeObject *b;
-#if PY_MAJOR_VERSION < 3
-        if (PyClass_Check(b0))
-        {
-            PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class",
-                         PyString_AS_STRING(((PyClassObject*)b0)->cl_name));
-            return -1;
-        }
-#endif
-        b = (PyTypeObject*) b0;
-        if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE))
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-        if (dictoffset == 0 && b->tp_dictoffset)
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "extension type '%.200s' has no __dict__ slot, "
-                "but base type '" __Pyx_FMT_TYPENAME "' has: "
-                "either add 'cdef dict __dict__' to the extension type "
-                "or add '__slots__ = [...]' to the base type",
-                type_name, b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-    }
-    return 0;
-}
-#endif
-
-/* PyType_Ready */
-static int __Pyx_PyType_Ready(PyTypeObject *t) {
-#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION)
-    (void)__Pyx_PyObject_CallMethod0;
-#if CYTHON_USE_TYPE_SPECS
-    (void)__Pyx_validate_bases_tuple;
-#endif
-    return PyType_Ready(t);
-#else
-    int r;
-    PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*);
-    if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1))
-        return -1;
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-    {
-        int gc_was_enabled;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        gc_was_enabled = PyGC_Disable();
-        (void)__Pyx_PyObject_CallMethod0;
-    #else
-        PyObject *ret, *py_status;
-        PyObject *gc = NULL;
-        #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400)
-        gc = PyImport_GetModule(__pyx_kp_u_gc);
-        #endif
-        if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc);
-        if (unlikely(!gc)) return -1;
-        py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled);
-        if (unlikely(!py_status)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-        gc_was_enabled = __Pyx_PyObject_IsTrue(py_status);
-        Py_DECREF(py_status);
-        if (gc_was_enabled > 0) {
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable);
-            if (unlikely(!ret)) {
-                Py_DECREF(gc);
-                return -1;
-            }
-            Py_DECREF(ret);
-        } else if (unlikely(gc_was_enabled == -1)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-    #endif
-        t->tp_flags |= Py_TPFLAGS_HEAPTYPE;
-#if PY_VERSION_HEX >= 0x030A0000
-        t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE;
-#endif
-#else
-        (void)__Pyx_PyObject_CallMethod0;
-#endif
-    r = PyType_Ready(t);
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-        t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        if (gc_was_enabled)
-            PyGC_Enable();
-    #else
-        if (gc_was_enabled) {
-            PyObject *tp, *v, *tb;
-            PyErr_Fetch(&tp, &v, &tb);
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable);
-            if (likely(ret || r == -1)) {
-                Py_XDECREF(ret);
-                PyErr_Restore(tp, v, tb);
-            } else {
-                Py_XDECREF(tp);
-                Py_XDECREF(v);
-                Py_XDECREF(tb);
-                r = -1;
-            }
-        }
-        Py_DECREF(gc);
-    #endif
-    }
-#endif
-    return r;
-#endif
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, attr_name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(attr_name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    PyObject *exc_value = exc_info->exc_value;
-    if (exc_value == NULL || exc_value == Py_None) {
-        *value = NULL;
-        *type = NULL;
-        *tb = NULL;
-    } else {
-        *value = exc_value;
-        Py_INCREF(*value);
-        *type = (PyObject*) Py_TYPE(exc_value);
-        Py_INCREF(*type);
-        *tb = PyException_GetTraceback(exc_value);
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #endif
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    PyObject *tmp_value = exc_info->exc_value;
-    exc_info->exc_value = value;
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-  #else
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-  #endif
-}
-#endif
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*);
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (cls == a || cls == b) return 1;
-    mro = cls->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            PyObject *base = PyTuple_GET_ITEM(mro, i);
-            if (base == (PyObject *)a || base == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    if (exc_type1) {
-        return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2);
-    } else {
-        return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *module = 0;
-    PyObject *empty_dict = 0;
-    PyObject *empty_list = 0;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (unlikely(!py_import))
-        goto bad;
-    if (!from_list) {
-        empty_list = PyList_New(0);
-        if (unlikely(!empty_list))
-            goto bad;
-        from_list = empty_list;
-    }
-    #endif
-    empty_dict = PyDict_New();
-    if (unlikely(!empty_dict))
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                #if CYTHON_COMPILING_IN_LIMITED_API
-                module = PyImport_ImportModuleLevelObject(
-                    name, empty_dict, empty_dict, from_list, 1);
-                #else
-                module = PyImport_ImportModuleLevelObject(
-                    name, __pyx_d, empty_dict, from_list, 1);
-                #endif
-                if (unlikely(!module)) {
-                    if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError)))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (unlikely(!py_level))
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            #if CYTHON_COMPILING_IN_LIMITED_API
-            module = PyImport_ImportModuleLevelObject(
-                name, empty_dict, empty_dict, from_list, level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, __pyx_d, empty_dict, from_list, level);
-            #endif
-            #endif
-        }
-    }
-bad:
-    Py_XDECREF(empty_dict);
-    Py_XDECREF(empty_list);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    return module;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        const char* module_name_str = 0;
-        PyObject* module_name = 0;
-        PyObject* module_dot = 0;
-        PyObject* full_name = 0;
-        PyErr_Clear();
-        module_name_str = PyModule_GetName(module);
-        if (unlikely(!module_name_str)) { goto modbad; }
-        module_name = PyUnicode_FromString(module_name_str);
-        if (unlikely(!module_name)) { goto modbad; }
-        module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__3);
-        if (unlikely(!module_dot)) { goto modbad; }
-        full_name = PyUnicode_Concat(module_dot, name);
-        if (unlikely(!full_name)) { goto modbad; }
-        #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-        {
-            PyObject *modules = PyImport_GetModuleDict();
-            if (unlikely(!modules))
-                goto modbad;
-            value = PyObject_GetItem(modules, full_name);
-        }
-        #else
-        value = PyImport_GetModule(full_name);
-        #endif
-      modbad:
-        Py_XDECREF(full_name);
-        Py_XDECREF(module_dot);
-        Py_XDECREF(module_name);
-    }
-    if (unlikely(!value)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* FetchSharedCythonModule */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
-    PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
-    if (unlikely(!abi_module)) return NULL;
-    Py_INCREF(abi_module);
-    return abi_module;
-}
-
-/* FetchCommonType */
-static int __Pyx_VerifyCachedType(PyObject *cached_type,
-                               const char *name,
-                               Py_ssize_t basicsize,
-                               Py_ssize_t expected_basicsize) {
-    if (!PyType_Check(cached_type)) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s is not a type object", name);
-        return -1;
-    }
-    if (basicsize != expected_basicsize) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s has the wrong size, try recompiling",
-            name);
-        return -1;
-    }
-    return 0;
-}
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* abi_module;
-    const char* object_name;
-    PyTypeObject *cached_type = NULL;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    object_name = strrchr(type->tp_name, '.');
-    object_name = object_name ? object_name+1 : type->tp_name;
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        if (__Pyx_VerifyCachedType(
-              (PyObject *)cached_type,
-              object_name,
-              cached_type->tp_basicsize,
-              type->tp_basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    if (PyType_Ready(type) < 0) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0)
-        goto bad;
-    Py_INCREF(type);
-    cached_type = type;
-done:
-    Py_DECREF(abi_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#else
-static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) {
-    PyObject *abi_module, *cached_type = NULL;
-    const char* object_name = strrchr(spec->name, '.');
-    object_name = object_name ? object_name+1 : spec->name;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    cached_type = PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        Py_ssize_t basicsize;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        PyObject *py_basicsize;
-        py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__");
-        if (unlikely(!py_basicsize)) goto bad;
-        basicsize = PyLong_AsSsize_t(py_basicsize);
-        Py_DECREF(py_basicsize);
-        py_basicsize = 0;
-        if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-#else
-        basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1;
-#endif
-        if (__Pyx_VerifyCachedType(
-              cached_type,
-              object_name,
-              basicsize,
-              spec->basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    CYTHON_UNUSED_VAR(module);
-    cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases);
-    if (unlikely(!cached_type)) goto bad;
-    if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad;
-done:
-    Py_DECREF(abi_module);
-    assert(cached_type == NULL || PyType_Check(cached_type));
-    return (PyTypeObject *) cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#endif
-
-/* PyVectorcallFastCallDict */
-#if CYTHON_METH_FASTCALL
-static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    PyObject *res = NULL;
-    PyObject *kwnames;
-    PyObject **newargs;
-    PyObject **kwvalues;
-    Py_ssize_t i, pos;
-    size_t j;
-    PyObject *key, *value;
-    unsigned long keys_are_strings;
-    Py_ssize_t nkw = PyDict_GET_SIZE(kw);
-    newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0]));
-    if (unlikely(newargs == NULL)) {
-        PyErr_NoMemory();
-        return NULL;
-    }
-    for (j = 0; j < nargs; j++) newargs[j] = args[j];
-    kwnames = PyTuple_New(nkw);
-    if (unlikely(kwnames == NULL)) {
-        PyMem_Free(newargs);
-        return NULL;
-    }
-    kwvalues = newargs + nargs;
-    pos = i = 0;
-    keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS;
-    while (PyDict_Next(kw, &pos, &key, &value)) {
-        keys_are_strings &= Py_TYPE(key)->tp_flags;
-        Py_INCREF(key);
-        Py_INCREF(value);
-        PyTuple_SET_ITEM(kwnames, i, key);
-        kwvalues[i] = value;
-        i++;
-    }
-    if (unlikely(!keys_are_strings)) {
-        PyErr_SetString(PyExc_TypeError, "keywords must be strings");
-        goto cleanup;
-    }
-    res = vc(func, newargs, nargs, kwnames);
-cleanup:
-    Py_DECREF(kwnames);
-    for (i = 0; i < nkw; i++)
-        Py_DECREF(kwvalues[i]);
-    PyMem_Free(newargs);
-    return res;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) {
-        return vc(func, args, nargs, NULL);
-    }
-    return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw);
-}
-#endif
-
-/* CythonFunctionShared */
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1
-    __Pyx_Py_XDECREF_SET(
-        __Pyx_CyFunction_GetClassObj(f),
-            ((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#else
-    __Pyx_Py_XDECREF_SET(
-        ((PyCMethodObject *) (f))->mm_class,
-        (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#endif
-}
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
-{
-    CYTHON_UNUSED_VAR(closure);
-    if (unlikely(op->func_doc == NULL)) {
-        if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_doc, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_dict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(op);
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyTuple_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_tuple, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_tuple;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_kwdict;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_annotations, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->func_annotations;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyObject *
-__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
-    int is_coroutine;
-    CYTHON_UNUSED_VAR(context);
-    if (op->func_is_coroutine) {
-        return __Pyx_NewRef(op->func_is_coroutine);
-    }
-    is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE;
-#if PY_VERSION_HEX >= 0x03050000
-    if (is_coroutine) {
-        PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine;
-        fromlist = PyList_New(1);
-        if (unlikely(!fromlist)) return NULL;
-        Py_INCREF(marker);
-        PyList_SET_ITEM(fromlist, 0, marker);
-        module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
-        Py_DECREF(fromlist);
-        if (unlikely(!module)) goto ignore;
-        op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker);
-        Py_DECREF(module);
-        if (likely(op->func_is_coroutine)) {
-            return __Pyx_NewRef(op->func_is_coroutine);
-        }
-ignore:
-        PyErr_Clear();
-    }
-#endif
-    op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
-    return __Pyx_NewRef(op->func_is_coroutine);
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
-#if CYTHON_METH_FASTCALL
-#if CYTHON_BACKPORT_VECTORCALL
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
-#else
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
-#endif
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
-#else
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
-#endif
-#endif
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args)
-{
-    CYTHON_UNUSED_VAR(args);
-#if PY_MAJOR_VERSION >= 3
-    Py_INCREF(m->func_qualname);
-    return m->func_qualname;
-#else
-    return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyCFunctionObject *cf = (PyCFunctionObject*) op;
-    if (unlikely(op == NULL))
-        return NULL;
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-    cf->m_ml = ml;
-    cf->m_self = (PyObject *) op;
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-    Py_XINCREF(module);
-    cf->m_module = module;
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1
-    op->func_classobj = NULL;
-#else
-    ((PyCMethodObject*)op)->mm_class = NULL;
-#endif
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    op->func_is_coroutine = NULL;
-#if CYTHON_METH_FASTCALL
-    switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) {
-    case METH_NOARGS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS;
-        break;
-    case METH_O:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O;
-        break;
-    case METH_METHOD | METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD;
-        break;
-    case METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS;
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = NULL;
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        Py_DECREF(op);
-        return NULL;
-    }
-#endif
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-    Py_CLEAR(((PyCFunctionObject*)m)->m_module);
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-#if PY_VERSION_HEX < 0x030900B1
-    Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
-#else
-    {
-        PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class;
-        ((PyCMethodObject *) (m))->mm_class = NULL;
-        Py_XDECREF(cls);
-    }
-#endif
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    Py_CLEAR(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    __Pyx_PyHeapTypeObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-    Py_VISIT(((PyCFunctionObject*)m)->m_module);
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-    Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    Py_VISIT(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        return NULL;
-    }
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-#if CYTHON_METH_FASTCALL
-     __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
-    if (vc) {
-#if CYTHON_ASSUME_SAFE_MACROS
-        return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
-#else
-        (void) &__Pyx_PyVectorcall_FastCallDict;
-        return PyVectorcall_Call(func, args, kw);
-#endif
-    }
-#endif
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-        argc = PyTuple_GET_SIZE(args);
-        new_args = PyTuple_GetSlice(args, 1, argc);
-        if (unlikely(!new_args))
-            return NULL;
-        self = PyTuple_GetItem(args, 0);
-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-#if PY_MAJOR_VERSION > 2
-            PyErr_Format(PyExc_TypeError,
-                         "unbound method %.200S() needs an argument",
-                         cyfunc->func_qualname);
-#else
-            PyErr_SetString(PyExc_TypeError,
-                            "unbound method needs an argument");
-#endif
-            return NULL;
-        }
-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
-}
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames)
-{
-    int ret = 0;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        if (unlikely(nargs < 1)) {
-            PyErr_Format(PyExc_TypeError, "%.200s() needs an argument",
-                         ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-            return -1;
-        }
-        ret = 1;
-    }
-    if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) {
-        PyErr_Format(PyExc_TypeError,
-                     "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-        return -1;
-    }
-    return ret;
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 0)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, NULL);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 1)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, args[0]);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-    PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc);
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames);
-}
-#endif
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_CyFunctionType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc},
-    {Py_tp_repr, (void *)__Pyx_CyFunction_repr},
-    {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod},
-    {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse},
-    {Py_tp_clear, (void *)__Pyx_CyFunction_clear},
-    {Py_tp_methods, (void *)__pyx_CyFunction_methods},
-    {Py_tp_members, (void *)__pyx_CyFunction_members},
-    {Py_tp_getset, (void *)__pyx_CyFunction_getsets},
-    {Py_tp_descr_get, (void *)__Pyx_PyMethod_New},
-    {0, 0},
-};
-static PyType_Spec __pyx_CyFunctionType_spec = {
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL)
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    __pyx_CyFunctionType_slots
-};
-#else
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-    (destructor) __Pyx_CyFunction_dealloc,
-#if !CYTHON_METH_FASTCALL
-    0,
-#elif CYTHON_BACKPORT_VECTORCALL
-    (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall),
-#else
-    offsetof(PyCFunctionObject, vectorcall),
-#endif
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#ifdef _Py_TPFLAGS_HAVE_VECTORCALL
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_PyMethod_New,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_CyFunction_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-#endif
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
-    memset(m->defaults, 0, size);
-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
-        ml, flags, qualname, closure, module, globals, code
-    );
-    if (likely(op)) {
-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    CYTHON_MAYBE_UNUSED_VAR(tstate);
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-#endif
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    if (c_line) {
-        (void) __pyx_cfilenm;
-        (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
-    }
-    _PyTraceback_Add(funcname, filename, py_line);
-}
-#else
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-#endif
-
-/* CIntFromPyVerify */
-#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* CIntFromPy */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(int) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) {
-                                return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) {
-                                return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) {
-                                return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(int) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(int) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (int) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (int) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (int) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (int) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (int) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((int) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((int) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(long) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) {
-                                return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) {
-                                return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) {
-                                return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(long) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(long) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (long) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (long) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (long) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (long) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (long) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((long) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((long) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* FormatTypeName */
-#if CYTHON_COMPILING_IN_LIMITED_API
-static __Pyx_TypeName
-__Pyx_PyType_GetName(PyTypeObject* tp)
-{
-    PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp,
-                                               __pyx_n_s_name);
-    if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
-        PyErr_Clear();
-        Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__23));
-    }
-    return name;
-}
-#endif
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_value = exc_info->exc_value;
-    exc_info->exc_value = *value;
-    if (tmp_value == NULL || tmp_value == Py_None) {
-        Py_XDECREF(tmp_value);
-        tmp_value = NULL;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-    } else {
-        tmp_type = (PyObject*) Py_TYPE(tmp_value);
-        Py_INCREF(tmp_type);
-        #if CYTHON_COMPILING_IN_CPYTHON
-        tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback;
-        Py_XINCREF(tmp_tb);
-        #else
-        tmp_tb = PyException_GetTraceback(tmp_value);
-        #endif
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-  #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-  #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args[3] = {NULL, arg1, arg2};
-    return __Pyx_PyObject_FastCall(function, args+1, 2 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectCallMethod1 */
-static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) {
-    PyObject *result = __Pyx_PyObject_CallOneArg(method, arg);
-    Py_DECREF(method);
-    return result;
-}
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
-    PyObject *method = NULL, *result;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_Call2Args(method, obj, arg);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) return NULL;
-    return __Pyx__PyObject_CallMethod1(method, arg);
-}
-
-/* CoroutineBase */
-#include <frameobject.h>
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#define __Pyx_Coroutine_Undelegate(gen) Py_CLEAR((gen)->yieldfrom)
-static int __Pyx_PyGen__FetchStopIterationValue(PyThreadState *__pyx_tstate, PyObject **pvalue) {
-    PyObject *et, *ev, *tb;
-    PyObject *value = NULL;
-    CYTHON_UNUSED_VAR(__pyx_tstate);
-    __Pyx_ErrFetch(&et, &ev, &tb);
-    if (!et) {
-        Py_XDECREF(tb);
-        Py_XDECREF(ev);
-        Py_INCREF(Py_None);
-        *pvalue = Py_None;
-        return 0;
-    }
-    if (likely(et == PyExc_StopIteration)) {
-        if (!ev) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#if PY_VERSION_HEX >= 0x030300A0
-        else if (likely(__Pyx_IS_TYPE(ev, (PyTypeObject*)PyExc_StopIteration))) {
-            value = ((PyStopIterationObject *)ev)->value;
-            Py_INCREF(value);
-            Py_DECREF(ev);
-        }
-#endif
-        else if (unlikely(PyTuple_Check(ev))) {
-            if (PyTuple_GET_SIZE(ev) >= 1) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                value = PyTuple_GET_ITEM(ev, 0);
-                Py_INCREF(value);
-#else
-                value = PySequence_ITEM(ev, 0);
-#endif
-            } else {
-                Py_INCREF(Py_None);
-                value = Py_None;
-            }
-            Py_DECREF(ev);
-        }
-        else if (!__Pyx_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration)) {
-            value = ev;
-        }
-        if (likely(value)) {
-            Py_XDECREF(tb);
-            Py_DECREF(et);
-            *pvalue = value;
-            return 0;
-        }
-    } else if (!__Pyx_PyErr_GivenExceptionMatches(et, PyExc_StopIteration)) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    PyErr_NormalizeException(&et, &ev, &tb);
-    if (unlikely(!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration))) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    Py_XDECREF(tb);
-    Py_DECREF(et);
-#if PY_VERSION_HEX >= 0x030300A0
-    value = ((PyStopIterationObject *)ev)->value;
-    Py_INCREF(value);
-    Py_DECREF(ev);
-#else
-    {
-        PyObject* args = __Pyx_PyObject_GetAttrStr(ev, __pyx_n_s_args);
-        Py_DECREF(ev);
-        if (likely(args)) {
-            value = PySequence_GetItem(args, 0);
-            Py_DECREF(args);
-        }
-        if (unlikely(!value)) {
-            __Pyx_ErrRestore(NULL, NULL, NULL);
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-    }
-#endif
-    *pvalue = value;
-    return 0;
-}
-static CYTHON_INLINE
-void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *exc_state) {
-#if PY_VERSION_HEX >= 0x030B00a4
-    Py_CLEAR(exc_state->exc_value);
-#else
-    PyObject *t, *v, *tb;
-    t = exc_state->exc_type;
-    v = exc_state->exc_value;
-    tb = exc_state->exc_traceback;
-    exc_state->exc_type = NULL;
-    exc_state->exc_value = NULL;
-    exc_state->exc_traceback = NULL;
-    Py_XDECREF(t);
-    Py_XDECREF(v);
-    Py_XDECREF(tb);
-#endif
-}
-#define __Pyx_Coroutine_AlreadyRunningError(gen)  (__Pyx__Coroutine_AlreadyRunningError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyRunningError(__pyx_CoroutineObject *gen) {
-    const char *msg;
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check((PyObject*)gen)) {
-        msg = "coroutine already executing";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact((PyObject*)gen)) {
-        msg = "async generator already executing";
-    #endif
-    } else {
-        msg = "generator already executing";
-    }
-    PyErr_SetString(PyExc_ValueError, msg);
-}
-#define __Pyx_Coroutine_NotStartedError(gen)  (__Pyx__Coroutine_NotStartedError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_NotStartedError(PyObject *gen) {
-    const char *msg;
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check(gen)) {
-        msg = "can't send non-None value to a just-started coroutine";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact(gen)) {
-        msg = "can't send non-None value to a just-started async generator";
-    #endif
-    } else {
-        msg = "can't send non-None value to a just-started generator";
-    }
-    PyErr_SetString(PyExc_TypeError, msg);
-}
-#define __Pyx_Coroutine_AlreadyTerminatedError(gen, value, closing)  (__Pyx__Coroutine_AlreadyTerminatedError(gen, value, closing), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyTerminatedError(PyObject *gen, PyObject *value, int closing) {
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    CYTHON_MAYBE_UNUSED_VAR(closing);
-    #ifdef __Pyx_Coroutine_USED
-    if (!closing && __Pyx_Coroutine_Check(gen)) {
-        PyErr_SetString(PyExc_RuntimeError, "cannot reuse already awaited coroutine");
-    } else
-    #endif
-    if (value) {
-        #ifdef __Pyx_AsyncGen_USED
-        if (__Pyx_AsyncGen_CheckExact(gen))
-            PyErr_SetNone(__Pyx_PyExc_StopAsyncIteration);
-        else
-        #endif
-        PyErr_SetNone(PyExc_StopIteration);
-    }
-}
-static
-PyObject *__Pyx_Coroutine_SendEx(__pyx_CoroutineObject *self, PyObject *value, int closing) {
-    __Pyx_PyThreadState_declare
-    PyThreadState *tstate;
-    __Pyx_ExcInfoStruct *exc_state;
-    PyObject *retval;
-    assert(!self->is_running);
-    if (unlikely(self->resume_label == 0)) {
-        if (unlikely(value && value != Py_None)) {
-            return __Pyx_Coroutine_NotStartedError((PyObject*)self);
-        }
-    }
-    if (unlikely(self->resume_label == -1)) {
-        return __Pyx_Coroutine_AlreadyTerminatedError((PyObject*)self, value, closing);
-    }
-#if CYTHON_FAST_THREAD_STATE
-    __Pyx_PyThreadState_assign
-    tstate = __pyx_tstate;
-#else
-    tstate = __Pyx_PyThreadState_Current;
-#endif
-    exc_state = &self->gi_exc_state;
-    if (exc_state->exc_value) {
-        #if CYTHON_COMPILING_IN_PYPY
-        #else
-        PyObject *exc_tb;
-        #if PY_VERSION_HEX >= 0x030B00a4 && !CYTHON_COMPILING_IN_CPYTHON
-        exc_tb = PyException_GetTraceback(exc_state->exc_value);
-        #elif PY_VERSION_HEX >= 0x030B00a4
-        exc_tb = ((PyBaseExceptionObject*) exc_state->exc_value)->traceback;
-        #else
-        exc_tb = exc_state->exc_traceback;
-        #endif
-        if (exc_tb) {
-            PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
-            PyFrameObject *f = tb->tb_frame;
-            assert(f->f_back == NULL);
-            #if PY_VERSION_HEX >= 0x030B00A1
-            f->f_back = PyThreadState_GetFrame(tstate);
-            #else
-            Py_XINCREF(tstate->frame);
-            f->f_back = tstate->frame;
-            #endif
-            #if PY_VERSION_HEX >= 0x030B00a4 && !CYTHON_COMPILING_IN_CPYTHON
-            Py_DECREF(exc_tb);
-            #endif
-        }
-        #endif
-    }
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state->previous_item = tstate->exc_info;
-    tstate->exc_info = exc_state;
-#else
-    if (exc_state->exc_type) {
-        __Pyx_ExceptionSwap(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    } else {
-        __Pyx_Coroutine_ExceptionClear(exc_state);
-        __Pyx_ExceptionSave(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    }
-#endif
-    self->is_running = 1;
-    retval = self->body(self, tstate, value);
-    self->is_running = 0;
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state = &self->gi_exc_state;
-    tstate->exc_info = exc_state->previous_item;
-    exc_state->previous_item = NULL;
-    __Pyx_Coroutine_ResetFrameBackpointer(exc_state);
-#endif
-    return retval;
-}
-static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__Pyx_ExcInfoStruct *exc_state) {
-#if CYTHON_COMPILING_IN_PYPY
-    CYTHON_UNUSED_VAR(exc_state);
-#else
-    PyObject *exc_tb;
-    #if PY_VERSION_HEX >= 0x030B00a4
-    if (!exc_state->exc_value) return;
-    exc_tb = PyException_GetTraceback(exc_state->exc_value);
-    #else
-    exc_tb = exc_state->exc_traceback;
-    #endif
-    if (likely(exc_tb)) {
-        PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
-        PyFrameObject *f = tb->tb_frame;
-        Py_CLEAR(f->f_back);
-        #if PY_VERSION_HEX >= 0x030B00a4
-        Py_DECREF(exc_tb);
-        #endif
-    }
-#endif
-}
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_MethodReturn(PyObject* gen, PyObject *retval) {
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if (unlikely(!retval)) {
-        __Pyx_PyThreadState_declare
-        __Pyx_PyThreadState_assign
-        if (!__Pyx_PyErr_Occurred()) {
-            PyObject *exc = PyExc_StopIteration;
-            #ifdef __Pyx_AsyncGen_USED
-            if (__Pyx_AsyncGen_CheckExact(gen))
-                exc = __Pyx_PyExc_StopAsyncIteration;
-            #endif
-            __Pyx_PyErr_SetNone(exc);
-        }
-    }
-    return retval;
-}
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-static CYTHON_INLINE
-PyObject *__Pyx_PyGen_Send(PyGenObject *gen, PyObject *arg) {
-#if PY_VERSION_HEX <= 0x030A00A1
-    return _PyGen_Send(gen, arg);
-#else
-    PyObject *result;
-    if (PyIter_Send((PyObject*)gen, arg ? arg : Py_None, &result) == PYGEN_RETURN) {
-        if (PyAsyncGen_CheckExact(gen)) {
-            assert(result == Py_None);
-            PyErr_SetNone(PyExc_StopAsyncIteration);
-        }
-        else if (result == Py_None) {
-            PyErr_SetNone(PyExc_StopIteration);
-        }
-        else {
-            _PyGen_SetStopIterationValue(result);
-        }
-        Py_CLEAR(result);
-    }
-    return result;
-#endif
-}
-#endif
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_FinishDelegation(__pyx_CoroutineObject *gen) {
-    PyObject *ret;
-    PyObject *val = NULL;
-    __Pyx_Coroutine_Undelegate(gen);
-    __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, &val);
-    ret = __Pyx_Coroutine_SendEx(gen, val, 0);
-    Py_XDECREF(val);
-    return ret;
-}
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value) {
-    PyObject *retval;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-            ret = __Pyx_async_gen_asend_send(yf, value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03050000 && defined(PyCoro_CheckExact) && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyCoro_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        {
-            if (value == Py_None)
-                ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
-            else
-                ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value);
-        }
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        retval = __Pyx_Coroutine_FinishDelegation(gen);
-    } else {
-        retval = __Pyx_Coroutine_SendEx(gen, value, 0);
-    }
-    return __Pyx_Coroutine_MethodReturn(self, retval);
-}
-static int __Pyx_Coroutine_CloseIter(__pyx_CoroutineObject *gen, PyObject *yf) {
-    PyObject *retval = NULL;
-    int err = 0;
-    #ifdef __Pyx_Generator_USED
-    if (__Pyx_Generator_CheckExact(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_Coroutine_USED
-    if (__Pyx_Coroutine_Check(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-        retval = __Pyx_CoroutineAwait_Close((__pyx_CoroutineAwaitObject*)yf, NULL);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-        retval = __Pyx_async_gen_asend_close(yf, NULL);
-    } else
-    if (__pyx_PyAsyncGenAThrow_CheckExact(yf)) {
-        retval = __Pyx_async_gen_athrow_close(yf, NULL);
-    } else
-    #endif
-    {
-        PyObject *meth;
-        gen->is_running = 1;
-        meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_close);
-        if (unlikely(!meth)) {
-            if (unlikely(PyErr_Occurred())) {
-                PyErr_WriteUnraisable(yf);
-            }
-        } else {
-            retval = __Pyx_PyObject_CallNoArg(meth);
-            Py_DECREF(meth);
-            if (unlikely(!retval))
-                err = -1;
-        }
-        gen->is_running = 0;
-    }
-    Py_XDECREF(retval);
-    return err;
-}
-static PyObject *__Pyx_Generator_Next(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Generator_Next(yf);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, NULL);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, Py_None);
-        } else
-        #endif
-            ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        return __Pyx_Coroutine_FinishDelegation(gen);
-    }
-    return __Pyx_Coroutine_SendEx(gen, Py_None, 0);
-}
-static PyObject *__Pyx_Coroutine_Close_Method(PyObject *self, PyObject *arg) {
-    CYTHON_UNUSED_VAR(arg);
-    return __Pyx_Coroutine_Close(self);
-}
-static PyObject *__Pyx_Coroutine_Close(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *retval, *raised_exception;
-    PyObject *yf = gen->yieldfrom;
-    int err = 0;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        Py_INCREF(yf);
-        err = __Pyx_Coroutine_CloseIter(gen, yf);
-        __Pyx_Coroutine_Undelegate(gen);
-        Py_DECREF(yf);
-    }
-    if (err == 0)
-        PyErr_SetNone(PyExc_GeneratorExit);
-    retval = __Pyx_Coroutine_SendEx(gen, NULL, 1);
-    if (unlikely(retval)) {
-        const char *msg;
-        Py_DECREF(retval);
-        if ((0)) {
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_Coroutine_Check(self)) {
-            msg = "coroutine ignored GeneratorExit";
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        } else if (__Pyx_AsyncGen_CheckExact(self)) {
-#if PY_VERSION_HEX < 0x03060000
-            msg = "async generator ignored GeneratorExit - might require Python 3.6+ finalisation (PEP 525)";
-#else
-            msg = "async generator ignored GeneratorExit";
-#endif
-        #endif
-        } else {
-            msg = "generator ignored GeneratorExit";
-        }
-        PyErr_SetString(PyExc_RuntimeError, msg);
-        return NULL;
-    }
-    raised_exception = PyErr_Occurred();
-    if (likely(!raised_exception || __Pyx_PyErr_GivenExceptionMatches2(raised_exception, PyExc_GeneratorExit, PyExc_StopIteration))) {
-        if (raised_exception) PyErr_Clear();
-        Py_INCREF(Py_None);
-        return Py_None;
-    }
-    return NULL;
-}
-static PyObject *__Pyx__Coroutine_Throw(PyObject *self, PyObject *typ, PyObject *val, PyObject *tb,
-                                        PyObject *args, int close_on_genexit) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        Py_INCREF(yf);
-        if (__Pyx_PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit) && close_on_genexit) {
-            int err = __Pyx_Coroutine_CloseIter(gen, yf);
-            Py_DECREF(yf);
-            __Pyx_Coroutine_Undelegate(gen);
-            if (err < 0)
-                return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-            goto throw_here;
-        }
-        gen->is_running = 1;
-        if (0
-        #ifdef __Pyx_Generator_USED
-            || __Pyx_Generator_CheckExact(yf)
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-            || __Pyx_Coroutine_Check(yf)
-        #endif
-            ) {
-            ret = __Pyx__Coroutine_Throw(yf, typ, val, tb, args, close_on_genexit);
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-            ret = __Pyx__Coroutine_Throw(((__pyx_CoroutineAwaitObject*)yf)->coroutine, typ, val, tb, args, close_on_genexit);
-        #endif
-        } else {
-            PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_throw);
-            if (unlikely(!meth)) {
-                Py_DECREF(yf);
-                if (unlikely(PyErr_Occurred())) {
-                    gen->is_running = 0;
-                    return NULL;
-                }
-                __Pyx_Coroutine_Undelegate(gen);
-                gen->is_running = 0;
-                goto throw_here;
-            }
-            if (likely(args)) {
-                ret = __Pyx_PyObject_Call(meth, args, NULL);
-            } else {
-                PyObject *cargs[4] = {NULL, typ, val, tb};
-                ret = __Pyx_PyObject_FastCall(meth, cargs+1, 3 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-            }
-            Py_DECREF(meth);
-        }
-        gen->is_running = 0;
-        Py_DECREF(yf);
-        if (!ret) {
-            ret = __Pyx_Coroutine_FinishDelegation(gen);
-        }
-        return __Pyx_Coroutine_MethodReturn(self, ret);
-    }
-throw_here:
-    __Pyx_Raise(typ, val, tb, NULL);
-    return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-}
-static PyObject *__Pyx_Coroutine_Throw(PyObject *self, PyObject *args) {
-    PyObject *typ;
-    PyObject *val = NULL;
-    PyObject *tb = NULL;
-    if (unlikely(!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb)))
-        return NULL;
-    return __Pyx__Coroutine_Throw(self, typ, val, tb, args, 1);
-}
-static CYTHON_INLINE int __Pyx_Coroutine_traverse_excstate(__Pyx_ExcInfoStruct *exc_state, visitproc visit, void *arg) {
-#if PY_VERSION_HEX >= 0x030B00a4
-    Py_VISIT(exc_state->exc_value);
-#else
-    Py_VISIT(exc_state->exc_type);
-    Py_VISIT(exc_state->exc_value);
-    Py_VISIT(exc_state->exc_traceback);
-#endif
-    return 0;
-}
-static int __Pyx_Coroutine_traverse(__pyx_CoroutineObject *gen, visitproc visit, void *arg) {
-    Py_VISIT(gen->closure);
-    Py_VISIT(gen->classobj);
-    Py_VISIT(gen->yieldfrom);
-    return __Pyx_Coroutine_traverse_excstate(&gen->gi_exc_state, visit, arg);
-}
-static int __Pyx_Coroutine_clear(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    Py_CLEAR(gen->closure);
-    Py_CLEAR(gen->classobj);
-    Py_CLEAR(gen->yieldfrom);
-    __Pyx_Coroutine_ExceptionClear(&gen->gi_exc_state);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)gen)->ag_finalizer);
-    }
-#endif
-    Py_CLEAR(gen->gi_code);
-    Py_CLEAR(gen->gi_frame);
-    Py_CLEAR(gen->gi_name);
-    Py_CLEAR(gen->gi_qualname);
-    Py_CLEAR(gen->gi_modulename);
-    return 0;
-}
-static void __Pyx_Coroutine_dealloc(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject_GC_UnTrack(gen);
-    if (gen->gi_weakreflist != NULL)
-        PyObject_ClearWeakRefs(self);
-    if (gen->resume_label >= 0) {
-        PyObject_GC_Track(self);
-#if PY_VERSION_HEX >= 0x030400a1 && CYTHON_USE_TP_FINALIZE
-        if (unlikely(PyObject_CallFinalizerFromDealloc(self)))
-#else
-        Py_TYPE(gen)->tp_del(self);
-        if (unlikely(Py_REFCNT(self) > 0))
-#endif
-        {
-            return;
-        }
-        PyObject_GC_UnTrack(self);
-    }
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        /* We have to handle this case for asynchronous generators
-           right here, because this code has to be between UNTRACK
-           and GC_Del. */
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)self)->ag_finalizer);
-    }
-#endif
-    __Pyx_Coroutine_clear(self);
-    __Pyx_PyHeapTypeObject_GC_Del(gen);
-}
-static void __Pyx_Coroutine_del(PyObject *self) {
-    PyObject *error_type, *error_value, *error_traceback;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    __Pyx_PyThreadState_declare
-    if (gen->resume_label < 0) {
-        return;
-    }
-#if !CYTHON_USE_TP_FINALIZE
-    assert(self->ob_refcnt == 0);
-    __Pyx_SET_REFCNT(self, 1);
-#endif
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&error_type, &error_value, &error_traceback);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        __pyx_PyAsyncGenObject *agen = (__pyx_PyAsyncGenObject*)self;
-        PyObject *finalizer = agen->ag_finalizer;
-        if (finalizer && !agen->ag_closed) {
-            PyObject *res = __Pyx_PyObject_CallOneArg(finalizer, self);
-            if (unlikely(!res)) {
-                PyErr_WriteUnraisable(self);
-            } else {
-                Py_DECREF(res);
-            }
-            __Pyx_ErrRestore(error_type, error_value, error_traceback);
-            return;
-        }
-    }
-#endif
-    if (unlikely(gen->resume_label == 0 && !error_value)) {
-#ifdef __Pyx_Coroutine_USED
-#ifdef __Pyx_Generator_USED
-    if (!__Pyx_Generator_CheckExact(self))
-#endif
-        {
-        PyObject_GC_UnTrack(self);
-#if PY_MAJOR_VERSION >= 3  || defined(PyErr_WarnFormat)
-        if (unlikely(PyErr_WarnFormat(PyExc_RuntimeWarning, 1, "coroutine '%.50S' was never awaited", gen->gi_qualname) < 0))
-            PyErr_WriteUnraisable(self);
-#else
-        {PyObject *msg;
-        char *cmsg;
-        #if CYTHON_COMPILING_IN_PYPY
-        msg = NULL;
-        cmsg = (char*) "coroutine was never awaited";
-        #else
-        char *cname;
-        PyObject *qualname;
-        qualname = gen->gi_qualname;
-        cname = PyString_AS_STRING(qualname);
-        msg = PyString_FromFormat("coroutine '%.50s' was never awaited", cname);
-        if (unlikely(!msg)) {
-            PyErr_Clear();
-            cmsg = (char*) "coroutine was never awaited";
-        } else {
-            cmsg = PyString_AS_STRING(msg);
-        }
-        #endif
-        if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, cmsg, 1) < 0))
-            PyErr_WriteUnraisable(self);
-        Py_XDECREF(msg);}
-#endif
-        PyObject_GC_Track(self);
-        }
-#endif
-    } else {
-        PyObject *res = __Pyx_Coroutine_Close(self);
-        if (unlikely(!res)) {
-            if (PyErr_Occurred())
-                PyErr_WriteUnraisable(self);
-        } else {
-            Py_DECREF(res);
-        }
-    }
-    __Pyx_ErrRestore(error_type, error_value, error_traceback);
-#if !CYTHON_USE_TP_FINALIZE
-    assert(Py_REFCNT(self) > 0);
-    if (likely(--self->ob_refcnt == 0)) {
-        return;
-    }
-    {
-        Py_ssize_t refcnt = Py_REFCNT(self);
-        _Py_NewReference(self);
-        __Pyx_SET_REFCNT(self, refcnt);
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    assert(PyType_IS_GC(Py_TYPE(self)) &&
-           _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED);
-    _Py_DEC_REFTOTAL;
-#endif
-#ifdef COUNT_ALLOCS
-    --Py_TYPE(self)->tp_frees;
-    --Py_TYPE(self)->tp_allocs;
-#endif
-#endif
-}
-static PyObject *
-__Pyx_Coroutine_get_name(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *name = self->gi_name;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_name(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(self->gi_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_qualname(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *name = self->gi_qualname;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_qualname(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(self->gi_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_frame(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *frame = self->gi_frame;
-    CYTHON_UNUSED_VAR(context);
-    if (!frame) {
-        if (unlikely(!self->gi_code)) {
-            Py_RETURN_NONE;
-        }
-        frame = (PyObject *) PyFrame_New(
-            PyThreadState_Get(),            /*PyThreadState *tstate,*/
-            (PyCodeObject*) self->gi_code,  /*PyCodeObject *code,*/
-            __pyx_d,                 /*PyObject *globals,*/
-            0                               /*PyObject *locals*/
-        );
-        if (unlikely(!frame))
-            return NULL;
-        self->gi_frame = frame;
-    }
-    Py_INCREF(frame);
-    return frame;
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
-            PyTypeObject* type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    __pyx_CoroutineObject *gen = PyObject_GC_New(__pyx_CoroutineObject, type);
-    if (unlikely(!gen))
-        return NULL;
-    return __Pyx__Coroutine_NewInit(gen, body, code, closure, name, qualname, module_name);
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
-            __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    gen->body = body;
-    gen->closure = closure;
-    Py_XINCREF(closure);
-    gen->is_running = 0;
-    gen->resume_label = 0;
-    gen->classobj = NULL;
-    gen->yieldfrom = NULL;
-    #if PY_VERSION_HEX >= 0x030B00a4
-    gen->gi_exc_state.exc_value = NULL;
-    #else
-    gen->gi_exc_state.exc_type = NULL;
-    gen->gi_exc_state.exc_value = NULL;
-    gen->gi_exc_state.exc_traceback = NULL;
-    #endif
-#if CYTHON_USE_EXC_INFO_STACK
-    gen->gi_exc_state.previous_item = NULL;
-#endif
-    gen->gi_weakreflist = NULL;
-    Py_XINCREF(qualname);
-    gen->gi_qualname = qualname;
-    Py_XINCREF(name);
-    gen->gi_name = name;
-    Py_XINCREF(module_name);
-    gen->gi_modulename = module_name;
-    Py_XINCREF(code);
-    gen->gi_code = code;
-    gen->gi_frame = NULL;
-    PyObject_GC_Track(gen);
-    return gen;
-}
-
-/* PatchModuleWithCoroutine */
-static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    int result;
-    PyObject *globals, *result_obj;
-    globals = PyDict_New();  if (unlikely(!globals)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_coroutine_type",
-    #ifdef __Pyx_Coroutine_USED
-        (PyObject*)__pyx_CoroutineType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_generator_type",
-    #ifdef __Pyx_Generator_USED
-        (PyObject*)__pyx_GeneratorType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "_module", module) < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "__builtins__", __pyx_b) < 0)) goto ignore;
-    result_obj = PyRun_String(py_code, Py_file_input, globals, globals);
-    if (unlikely(!result_obj)) goto ignore;
-    Py_DECREF(result_obj);
-    Py_DECREF(globals);
-    return module;
-ignore:
-    Py_XDECREF(globals);
-    PyErr_WriteUnraisable(module);
-    if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, "Cython module failed to patch module with custom type", 1) < 0)) {
-        Py_DECREF(module);
-        module = NULL;
-    }
-#else
-    py_code++;
-#endif
-    return module;
-}
-
-/* PatchGeneratorABC */
-#ifndef CYTHON_REGISTER_ABCS
-#define CYTHON_REGISTER_ABCS 1
-#endif
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-static PyObject* __Pyx_patch_abc_module(PyObject *module);
-static PyObject* __Pyx_patch_abc_module(PyObject *module) {
-    module = __Pyx_Coroutine_patch_module(
-        module, ""
-"if _cython_generator_type is not None:\n"
-"    try: Generator = _module.Generator\n"
-"    except AttributeError: pass\n"
-"    else: Generator.register(_cython_generator_type)\n"
-"if _cython_coroutine_type is not None:\n"
-"    try: Coroutine = _module.Coroutine\n"
-"    except AttributeError: pass\n"
-"    else: Coroutine.register(_cython_coroutine_type)\n"
-    );
-    return module;
-}
-#endif
-static int __Pyx_patch_abc(void) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    static int abc_patched = 0;
-    if (CYTHON_REGISTER_ABCS && !abc_patched) {
-        PyObject *module;
-        module = PyImport_ImportModule((PY_MAJOR_VERSION >= 3) ? "collections.abc" : "collections");
-        if (unlikely(!module)) {
-            PyErr_WriteUnraisable(NULL);
-            if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning,
-                    ((PY_MAJOR_VERSION >= 3) ?
-                        "Cython module failed to register with collections.abc module" :
-                        "Cython module failed to register with collections module"), 1) < 0)) {
-                return -1;
-            }
-        } else {
-            module = __Pyx_patch_abc_module(module);
-            abc_patched = 1;
-            if (unlikely(!module))
-                return -1;
-            Py_DECREF(module);
-        }
-        module = PyImport_ImportModule("backports_abc");
-        if (module) {
-            module = __Pyx_patch_abc_module(module);
-            Py_XDECREF(module);
-        }
-        if (!module) {
-            PyErr_Clear();
-        }
-    }
-#else
-    if ((0)) __Pyx_Coroutine_patch_module(NULL, NULL);
-#endif
-    return 0;
-}
-
-/* Generator */
-static PyMethodDef __pyx_Generator_methods[] = {
-    {"send", (PyCFunction) __Pyx_Coroutine_Send, METH_O,
-     (char*) PyDoc_STR("send(arg) -> send 'arg' into generator,\nreturn next yielded value or raise StopIteration.")},
-    {"throw", (PyCFunction) __Pyx_Coroutine_Throw, METH_VARARGS,
-     (char*) PyDoc_STR("throw(typ[,val[,tb]]) -> raise exception in generator,\nreturn next yielded value or raise StopIteration.")},
-    {"close", (PyCFunction) __Pyx_Coroutine_Close_Method, METH_NOARGS,
-     (char*) PyDoc_STR("close() -> raise GeneratorExit inside generator.")},
-    {0, 0, 0, 0}
-};
-static PyMemberDef __pyx_Generator_memberlist[] = {
-    {(char *) "gi_running", T_BOOL, offsetof(__pyx_CoroutineObject, is_running), READONLY, NULL},
-    {(char*) "gi_yieldfrom", T_OBJECT, offsetof(__pyx_CoroutineObject, yieldfrom), READONLY,
-     (char*) PyDoc_STR("object being iterated by 'yield from', or None")},
-    {(char*) "gi_code", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_code), READONLY, NULL},
-    {(char *) "__module__", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_modulename), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CoroutineObject, gi_weakreflist), READONLY, 0},
-#endif
-    {0, 0, 0, 0, 0}
-};
-static PyGetSetDef __pyx_Generator_getsets[] = {
-    {(char *) "__name__", (getter)__Pyx_Coroutine_get_name, (setter)__Pyx_Coroutine_set_name,
-     (char*) PyDoc_STR("name of the generator"), 0},
-    {(char *) "__qualname__", (getter)__Pyx_Coroutine_get_qualname, (setter)__Pyx_Coroutine_set_qualname,
-     (char*) PyDoc_STR("qualified name of the generator"), 0},
-    {(char *) "gi_frame", (getter)__Pyx_Coroutine_get_frame, NULL,
-     (char*) PyDoc_STR("Frame of the generator"), 0},
-    {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_GeneratorType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_Coroutine_dealloc},
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diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/line_plot.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/line_plot.py
deleted file mode 100644
index fcf6d8d49b7e8df39f2b571f7099de89b994fb57..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/line_plot.py
+++ /dev/null
@@ -1,430 +0,0 @@
-"""gr.LinePlot() component"""
-
-from __future__ import annotations
-
-from typing import Callable, Literal
-
-import altair as alt
-import pandas as pd
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.components.base import _Keywords
-from gradio.components.plot import AltairPlot, Plot
-
-set_documentation_group("component")
-
-
-@document()
-class LinePlot(Plot):
-    """
-    Create a line plot.
-
-    Preprocessing: this component does *not* accept input.
-    Postprocessing: expects a pandas dataframe with the data to plot.
-
-    Demos: line_plot, live_dashboard
-    """
-
-    def __init__(
-        self,
-        value: pd.DataFrame | Callable | None = None,
-        x: str | None = None,
-        y: str | None = None,
-        *,
-        color: str | None = None,
-        stroke_dash: str | None = None,
-        overlay_point: bool | None = None,
-        title: str | None = None,
-        tooltip: list[str] | str | None = None,
-        x_title: str | None = None,
-        y_title: str | None = None,
-        color_legend_title: str | None = None,
-        stroke_dash_legend_title: str | None = None,
-        color_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        stroke_dash_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        x_lim: list[int] | None = None,
-        y_lim: list[int] | None = None,
-        caption: str | None = None,
-        interactive: bool | None = True,
-        label: str | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        every: float | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-    ):
-        """
-        Parameters:
-            value: The pandas dataframe containing the data to display in a scatter plot.
-            x: Column corresponding to the x axis.
-            y: Column corresponding to the y axis.
-            color: The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.
-            stroke_dash: The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.
-            overlay_point: Whether to draw a point on the line for each (x, y) coordinate pair.
-            title: The title to display on top of the chart.
-            tooltip: The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
-            x_title: The title given to the x axis. By default, uses the value of the x parameter.
-            y_title: The title given to the y axis. By default, uses the value of the y parameter.
-            color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
-            stroke_dash_legend_title: The title given to the stroke_dash legend. By default, uses the value of the stroke_dash parameter.
-            color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
-            stroke_dash_legend_position: The position of the stoke_dash legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
-            height: The height of the plot in pixels.
-            width: The width of the plot in pixels.
-            x_lim: A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
-            y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
-            caption: The (optional) caption to display below the plot.
-            interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
-            label: The (optional) label to display on the top left corner of the plot.
-            show_label: Whether the label should be displayed.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            visible: Whether the plot should be visible.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        self.x = x
-        self.y = y
-        self.color = color
-        self.stroke_dash = stroke_dash
-        self.tooltip = tooltip
-        self.title = title
-        self.x_title = x_title
-        self.y_title = y_title
-        self.color_legend_title = color_legend_title
-        self.stroke_dash_legend_title = stroke_dash_legend_title
-        self.color_legend_position = color_legend_position
-        self.stroke_dash_legend_position = stroke_dash_legend_position
-        self.overlay_point = overlay_point
-        self.x_lim = x_lim
-        self.y_lim = y_lim
-        self.caption = caption
-        self.interactive_chart = interactive
-        self.width = width
-        self.height = height
-        super().__init__(
-            value=value,
-            label=label,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            every=every,
-        )
-
-    def get_config(self):
-        config = super().get_config()
-        config["caption"] = self.caption
-        return config
-
-    def get_block_name(self) -> str:
-        return "plot"
-
-    @staticmethod
-    def update(
-        value: pd.DataFrame | dict | Literal[_Keywords.NO_VALUE] = _Keywords.NO_VALUE,
-        x: str | None = None,
-        y: str | None = None,
-        color: str | None = None,
-        stroke_dash: str | None = None,
-        overlay_point: bool | None = None,
-        title: str | None = None,
-        tooltip: list[str] | str | None = None,
-        x_title: str | None = None,
-        y_title: str | None = None,
-        color_legend_title: str | None = None,
-        stroke_dash_legend_title: str | None = None,
-        color_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        stroke_dash_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        x_lim: list[int] | None = None,
-        y_lim: list[int] | None = None,
-        interactive: bool | None = None,
-        caption: str | None = None,
-        label: str | None = None,
-        show_label: bool | None = None,
-        container: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        visible: bool | None = None,
-    ):
-        """Update an existing plot component.
-
-        If updating any of the plot properties (color, size, etc) the value, x, and y parameters must be specified.
-
-        Parameters:
-            value: The pandas dataframe containing the data to display in a scatter plot.
-            x: Column corresponding to the x axis.
-            y: Column corresponding to the y axis.
-            color: The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.
-            stroke_dash: The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.
-            overlay_point: Whether to draw a point on the line for each (x, y) coordinate pair.
-            title: The title to display on top of the chart.
-            tooltip: The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
-            x_title: The title given to the x axis. By default, uses the value of the x parameter.
-            y_title: The title given to the y axis. By default, uses the value of the y parameter.
-            color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
-            stroke_dash_legend_title: The title given to the stroke legend. By default, uses the value of stroke parameter.
-            color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation
-            stroke_dash_legend_position: The position of the stoke_dash legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation
-            height: The height of the plot in pixels.
-            width: The width of the plot in pixels.
-            x_lim: A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
-            y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
-            caption: The (optional) caption to display below the plot.
-            interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
-            label: The (optional) label to display in the top left corner of the plot.
-            show_label: Whether the label should be displayed.
-            visible: Whether the plot should be visible.
-        """
-        properties = [
-            x,
-            y,
-            color,
-            stroke_dash,
-            overlay_point,
-            title,
-            tooltip,
-            x_title,
-            y_title,
-            color_legend_title,
-            stroke_dash_legend_title,
-            color_legend_position,
-            stroke_dash_legend_position,
-            height,
-            width,
-            x_lim,
-            y_lim,
-            interactive,
-        ]
-        if any(properties):
-            if not isinstance(value, pd.DataFrame):
-                raise ValueError(
-                    "In order to update plot properties the value parameter "
-                    "must be provided, and it must be a Dataframe. Please pass a value "
-                    "parameter to gr.LinePlot.update."
-                )
-            if x is None or y is None:
-                raise ValueError(
-                    "In order to update plot properties, the x and y axis data "
-                    "must be specified. Please pass valid values for x an y to "
-                    "gr.LinePlot.update."
-                )
-            chart = LinePlot.create_plot(value, *properties)
-            value = {"type": "altair", "plot": chart.to_json(), "chart": "line"}
-
-        updated_config = {
-            "label": label,
-            "show_label": show_label,
-            "container": container,
-            "scale": scale,
-            "min_width": min_width,
-            "visible": visible,
-            "value": value,
-            "caption": caption,
-            "__type__": "update",
-        }
-        return updated_config
-
-    @staticmethod
-    def create_plot(
-        value: pd.DataFrame,
-        x: str,
-        y: str,
-        color: str | None = None,
-        stroke_dash: str | None = None,
-        overlay_point: bool | None = None,
-        title: str | None = None,
-        tooltip: list[str] | str | None = None,
-        x_title: str | None = None,
-        y_title: str | None = None,
-        color_legend_title: str | None = None,
-        stroke_dash_legend_title: str | None = None,
-        color_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        stroke_dash_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        x_lim: list[int] | None = None,
-        y_lim: list[int] | None = None,
-        interactive: bool | None = None,
-    ):
-        """Helper for creating the scatter plot."""
-        interactive = True if interactive is None else interactive
-        encodings = {
-            "x": alt.X(
-                x,  # type: ignore
-                title=x_title or x,  # type: ignore
-                scale=AltairPlot.create_scale(x_lim),  # type: ignore
-            ),
-            "y": alt.Y(
-                y,  # type: ignore
-                title=y_title or y,  # type: ignore
-                scale=AltairPlot.create_scale(y_lim),  # type: ignore
-            ),
-        }
-        properties = {}
-        if title:
-            properties["title"] = title
-        if height:
-            properties["height"] = height
-        if width:
-            properties["width"] = width
-
-        if color:
-            domain = value[color].unique().tolist()
-            range_ = list(range(len(domain)))
-            encodings["color"] = {
-                "field": color,
-                "type": "nominal",
-                "scale": {"domain": domain, "range": range_},
-                "legend": AltairPlot.create_legend(
-                    position=color_legend_position, title=color_legend_title or color
-                ),
-            }
-
-        highlight = None
-        if interactive and any([color, stroke_dash]):
-            highlight = alt.selection(
-                type="single",  # type: ignore
-                on="mouseover",
-                fields=[c for c in [color, stroke_dash] if c],
-                nearest=True,
-            )
-
-        if stroke_dash:
-            stroke_dash = {
-                "field": stroke_dash,  # type: ignore
-                "legend": AltairPlot.create_legend(  # type: ignore
-                    position=stroke_dash_legend_position,  # type: ignore
-                    title=stroke_dash_legend_title or stroke_dash,  # type: ignore
-                ),  # type: ignore
-            }  # type: ignore
-        else:
-            stroke_dash = alt.value(alt.Undefined)  # type: ignore
-
-        if tooltip:
-            encodings["tooltip"] = tooltip
-
-        chart = alt.Chart(value).encode(**encodings)  # type: ignore
-
-        points = chart.mark_point(clip=True).encode(
-            opacity=alt.value(alt.Undefined) if overlay_point else alt.value(0),
-        )
-        lines = chart.mark_line(clip=True).encode(strokeDash=stroke_dash)
-
-        if highlight:
-            points = points.add_selection(highlight)
-
-            lines = lines.encode(
-                size=alt.condition(highlight, alt.value(4), alt.value(1)),
-            )
-
-        chart = (lines + points).properties(background="transparent", **properties)
-        if interactive:
-            chart = chart.interactive()
-
-        return chart
-
-    def postprocess(self, y: pd.DataFrame | dict | None) -> dict[str, str] | None:
-        # if None or update
-        if y is None or isinstance(y, dict):
-            return y
-        if self.x is None or self.y is None:
-            raise ValueError("No value provided for required parameters `x` and `y`.")
-        chart = self.create_plot(
-            value=y,
-            x=self.x,
-            y=self.y,
-            color=self.color,
-            overlay_point=self.overlay_point,
-            title=self.title,
-            tooltip=self.tooltip,
-            x_title=self.x_title,
-            y_title=self.y_title,
-            color_legend_title=self.color_legend_title,  # type: ignore
-            color_legend_position=self.color_legend_position,  # type: ignore
-            stroke_dash_legend_title=self.stroke_dash_legend_title,
-            stroke_dash_legend_position=self.stroke_dash_legend_position,  # type: ignore
-            x_lim=self.x_lim,
-            y_lim=self.y_lim,
-            stroke_dash=self.stroke_dash,
-            interactive=self.interactive_chart,
-            height=self.height,
-            width=self.width,
-        )
-
-        return {"type": "altair", "plot": chart.to_json(), "chart": "line"}
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-94005977.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-94005977.js
deleted file mode 100644
index af7acf3315af31245a6b74e3ece5e0c48f3d7ca2..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-94005977.js
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print Comment Script AssignStatement * BinaryExpression BitOp BitOp BitOp BitOp ArithOp ArithOp @ ArithOp ** UnaryExpression ArithOp BitOp AwaitExpression await ) ( ParenthesizedExpression BinaryExpression or and CompareOp in not is UnaryExpression ConditionalExpression if else LambdaExpression lambda ParamList VariableName AssignOp , : NamedExpression AssignOp YieldExpression yield from TupleExpression ComprehensionExpression async for LambdaExpression ] [ ArrayExpression ArrayComprehensionExpression } { DictionaryExpression DictionaryComprehensionExpression SetExpression SetComprehensionExpression CallExpression ArgList AssignOp MemberExpression . 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-    }
-
-    .lds-ellipsis div:nth-child(1) {
-      left: 8px;
-      animation: lds-ellipsis1 0.6s infinite;
-    }
-
-    .lds-ellipsis div:nth-child(2) {
-      left: 8px;
-      animation: lds-ellipsis2 0.6s infinite;
-    }
-
-    .lds-ellipsis div:nth-child(3) {
-      left: 32px;
-      animation: lds-ellipsis2 0.6s infinite;
-    }
-
-    .lds-ellipsis div:nth-child(4) {
-      left: 56px;
-      animation: lds-ellipsis3 0.6s infinite;
-    }
-
-    @keyframes lds-ellipsis1 {
-      0% {
-        transform: scale(0);
-      }
-
-      100% {
-        transform: scale(1);
-      }
-    }
-
-    @keyframes lds-ellipsis3 {
-      0% {
-        transform: scale(1);
-      }
-
-      100% {
-        transform: scale(0);
-      }
-    }
-
-    @keyframes lds-ellipsis2 {
-      0% {
-        transform: translate(0, 0);
-      }
-
-      100% {
-        transform: translate(24px, 0);
-      }
-    }
-  </style>
-</head>
-
-<body>
-  <div class="container">
-    <div class="controls">
-      <button id="draw" type="button" onclick="changeAction('draw')">Draw</button>
-      <button id="erase" type="button" onclick="changeAction('erase')">Erase</button>
-      <button id="submit" type="button" onclick="handleSubmit()">Submit</button>
-    </div>
-    <input type='text' class='prompt'></input>
-    <canvas id="canvas"></canvas>
-
-    <div id="overlay">
-      <!-- <div class="loader"></div> -->
-      <div class="lds-ellipsis">
-        <div></div>
-        <div></div>
-        <div></div>
-        <div></div>
-      </div>
-    </div>
-
-  </div>
-
-  <script>
-    function xorCipher(data, key) {
-      let result = '';
-      for (let i = 0; i < data.length; i++) {
-        result += String.fromCharCode(data.charCodeAt(i) ^ key.charCodeAt(i % key.length));
-      };
-      return result;
-    };
-    function decodeBase64(encodedText) {
-      const decodedText = atob(encodedText);
-      return decodedText;
-    };
-    const hardcodednum = 7;
-    const decr = async () => {
-      const _seeds = await fetch('/antiabuz');
-      const seeds_text = await _seeds.text();
-      const uncrypted_json_raw = JSON.parse(decodeBase64(seeds_text));
-      const { key, seedline, data: crypted_seeds_raw, cid } = uncrypted_json_raw;
-      const crypted3_seeds_raw = xorCipher(crypted_seeds_raw, key);
-      const crypted2_seeds_raw = decodeBase64(crypted3_seeds_raw);
-      const crypted_seeds_json = JSON.parse(crypted2_seeds_raw);
-      const seeds = [];
-      crypted_seeds_json.forEach(i => {
-        const x = decodeBase64(i);
-        const x_json = JSON.parse(x);
-        seeds.push(x_json);
-      });
-      return { num: seeds[seedline][hardcodednum], cid };
-    };
-    const tg = window.Telegram.WebApp; //получаем объект webapp телеграма 
-
-    const canvas = new fabric.Canvas('canvas');
-    canvas.selection = false;
-    let bg = null;
-
-    const url = new URLSearchParams(window.location.search);
-
-    fabric.Image.fromURL(`${url.get('code')}`,
-      function (img) {
-        bg = img;
-        img.erasable = false;
-        canvas.setBackgroundImage(img);
-        resizeCanvas();
-        canvas.renderAll();
-        tg.expand()
-        tg.ready()
-        console.log(tg.initDataUnsafe)
-      },
-      { crossOrigin: "anonymous" }
-    );
-
-
-    function resizeCanvas() {
-      const w = Math.min(512, window.innerWidth - 16, window.innerHeight - 16);
-
-      canvas.backgroundImage.scaleToWidth(w);
-      canvas.backgroundImage.scaleToHeight(w);
-      canvas.setDimensions({ width: w, height: w });
-
-      canvas.renderAll();
-    }
-
-    window.onresize = resizeCanvas;
-
-    function changeAction(action) {
-      ['erase', 'draw'].forEach((a) => {
-        const t = document.getElementById(a);
-        t.classList.remove('active');
-        if (a === action) {
-          t.classList.add('active');
-        }
-      });
-
-      switch (action) {
-        case "erase":
-          canvas.freeDrawingBrush = new fabric.EraserBrush(canvas);
-          canvas.freeDrawingBrush.width = 20;
-          canvas.isDrawingMode = true;
-          break;
-        case "draw":
-          canvas.freeDrawingBrush = new fabric.PencilBrush(canvas);
-          canvas.freeDrawingBrush.width = 35;
-          canvas.isDrawingMode = true;
-          break;
-        default:
-          break;
-      }
-    }
-
-    async function handleSubmit() {
-      // document.getElementById("overlay").style.display = "block";
-      document.getElementById("overlay").classList.add('open');
-      const hash = makeid(32);
-
-      canvas.backgroundImage.opacity = 0;
-      const mask = await saveCanvas(`${hash}-mask.png`);
-
-      canvas.clear();
-      canvas.setBackgroundImage(bg);
-      canvas.backgroundImage.opacity = 1;
-      const image = await saveCanvas(`${hash}-image.png`);
-
-      await Promise.all([
-        uploadFile(mask, `${hash}-mask.png`),
-        uploadFile(image, `${hash}-image.png`),
-      ]);
-
-      await fetch('/update', {
-        method: 'POST',
-        body: JSON.stringify({
-          ...(await decr()),
-          uid: tg.initDataUnsafe.user.id,
-          hash, prompt: document.querySelector('input.prompt').value
-        }),
-        headers: {
-          'Content-Type':'application/json'
-        }
-      })
-
-      // tg.sendData(`/sdxl_inpaint%20${hash}`)
-      tg.close()
-      // window.location.href = `tg://msg_url?url=/sdxl_inpaint%20${hash}`;
-    }
-
-    function saveCanvas(filename) {
-      return new Promise((resolve) => {
-        canvas.renderAll();
-        document.querySelector("#canvas").toBlob((blob) => {
-          const file = new File([blob], filename);
-
-          resolve(file);
-        });
-      });
-    }
-
-    async function uploadFile(file, filename) {
-      const data = new FormData()
-      data.append('file', file)
-      data.append('file.name', filename)
-      return fetch('/', { method: 'POST', body: data });
-    }
-
-    function makeid(length) {
-      let result = '';
-      const characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789';
-      const charactersLength = characters.length;
-      let counter = 0;
-      while (counter < length) {
-        result += characters.charAt(Math.floor(Math.random() * charactersLength));
-        counter += 1;
-      }
-      return result;
-    }
-
-    changeAction("draw");
-  </script>
-
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/deaf1296/finetuned_diffusion/README.md b/spaces/deaf1296/finetuned_diffusion/README.md
deleted file mode 100644
index c29108864c9d3c8ff637222d04f33fb0f576e74a..0000000000000000000000000000000000000000
--- a/spaces/deaf1296/finetuned_diffusion/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Finetuned Diffusion
-emoji: 🪄🖼️
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: true
-license: mit
-duplicated_from: anzorq/finetuned_diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dechantoine/PokeGAN/exploration.py b/spaces/dechantoine/PokeGAN/exploration.py
deleted file mode 100644
index fa119d89274f6a04d1337d478155fd79503d2241..0000000000000000000000000000000000000000
--- a/spaces/dechantoine/PokeGAN/exploration.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import numpy as np
-
-def convert_to_spherical(point):
-    sph = np.zeros(len(point))
-    sph[0] = np.sqrt(np.sum(np.square(point)))
-    for i in range(1, len(point)-1):
-        sph[i] = np.arccos(point[i-1]/np.sqrt(np.sum(np.square(point[i-1:]))))
-    if point[-1] >= 0:
-        sph[-1] = np.arccos(point[-2]/np.sqrt(np.sum(np.square(point[-2:]))))
-    else:
-        sph[-1] = np.pi*2 - np.arccos(point[-2]/np.sqrt(np.sum(np.square(point[-2:]))))
-    return sph
-
-def convert_to_cartesian(point):
-    crts = np.zeros(len(point))
-    crts[0] = point[0]*np.cos(point[1])
-    s = 1
-    for i in range(1, len(point)-1):
-        s = s*np.sin(point[i])
-        crts[i] = point[0]*s*np.cos(point[i+1])
-    s = s*np.sin(point[-1])
-    crts[-1] = point[0]*s
-    return crts
-
-def get_great_circle(point, axis_to_rotate=1, n_samples=100):
-    sph = convert_to_spherical(point)
-    circle = np.zeros(shape=(n_samples,len(point)))
-    for i in range(n_samples):
-        circle[i] = convert_to_cartesian(np.concatenate((sph[:axis_to_rotate],
-                                                         [2*np.pi/n_samples*i], sph[(axis_to_rotate+1):])))
-    return circle
-
-
-def get_arc_between_points(point1, point2, n_samples=100):
-    sph1 = convert_to_spherical(point1)
-    sph2 = convert_to_spherical(point2)
-
-    step = (sph2 - sph1) / n_samples
-    arc = np.zeros(shape=(n_samples, len(point1)))
-    for i in range(n_samples):
-        arc[i] = convert_to_cartesian(sph1 + step * i)
-    return arc
\ No newline at end of file
diff --git a/spaces/declare-lab/tango/diffusers/PHILOSOPHY.md b/spaces/declare-lab/tango/diffusers/PHILOSOPHY.md
deleted file mode 100644
index fbad5948e17e576d902176202060e8077b4198ec..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/PHILOSOPHY.md
+++ /dev/null
@@ -1,110 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Philosophy
-
-🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.
-Its purpose is to serve as a **modular toolbox** for both inference and training.
-
-We aim at building a library that stands the test of time and therefore take API design very seriously.
-
-In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:
-
-## Usability over Performance
-
-- While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library.
-- Diffusers aim at being a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.
-- Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired.
-
-## Simple over easy
-
-As PyTorch states, **explicit is better than implicit** and **simple is better than complex**. This design philosophy is reflected in multiple parts of the library: 
-- We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management.
-- Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible.
-- Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers.
-- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the unet, and the variational autoencoder, each have their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. Dreambooth or textual inversion training 
-is very simple thanks to diffusers' ability to separate single components of the diffusion pipeline.
-
-## Tweakable, contributor-friendly over abstraction
-
-For large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself). 
-In short, just like Transformers does for modeling files, diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.
-Functions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable. 
-**However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because:
-- Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions.
-- Machine Learning practitioners like to be able to quickly tweak existing code for ideation and research and therefore prefer self-contained code over one that contains many abstractions.
-- Open-source libraries rely on community contributions and therefore must build a library that is easy to contribute to. The more abstract the code, the more dependencies, the harder to read, and the harder to contribute to. Contributors simply stop contributing to very abstract libraries out of fear of breaking vital functionality. If contributing to a library cannot break other fundamental code, not only is it more inviting for potential new contributors, but it is also easier to review and contribute to multiple parts in parallel.
-
-At Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look
-at [this blog post](https://huggingface.co/blog/transformers-design-philosophy).
-
-In diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such 
-as [DDPM](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [UnCLIP (Dalle-2)](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/unclip#overview) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models#diffusers.UNet2DConditionModel).
-
-Great, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗. 
-We try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️  to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
-
-## Design Philosophy in Details
-
-Now, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consist of three major classes, [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
-Let's walk through more in-detail design decisions for each class.
-
-### Pipelines
-
-Pipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%)), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.
-
-The following design principles are followed:
-- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
-- Pipelines all inherit from [`DiffusionPipeline`]
-- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
-- Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.
-- Pipelines should be used **only** for inference.
-- Pipelines should be very readable, self-explanatory, and easy to tweak.
-- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.
-- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner)
-- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.
-- Pipelines should be named after the task they are intended to solve.
-- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.
-
-### Models
-
-Models are designed as configurable toolboxes that are natural extensions of [PyTorch's Module class](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). They only partly follow the **single-file policy**.
-
-The following design principles are followed:
-- Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
-- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
-- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
-- Models intend to expose complexity, just like PyTorch's module does, and give clear error messages.
-- Models all inherit from `ModelMixin` and `ConfigMixin`.
-- Models can be optimized for performance when it doesn’t demand major code changes, keeps backward compatibility, and gives significant memory or compute gain.
-- Models should by default have the highest precision and lowest performance setting.
-- To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.
-- Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.
-- The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and 
-readable longterm, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-### Schedulers
-
-Schedulers are responsible to guide the denoising process for inference as well as to define a noise schedule for training. They are designed as individual classes with loadable configuration files and strongly follow the **single-file policy**.
-
-The following design principles are followed:
-- All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers). 
-- Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained. 
-- One scheduler python file corresponds to one scheduler algorithm (as might be defined in a paper). 
-- If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.
-- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.mdx).
-- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
-- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon
-- The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).
-- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box".
-- In almost all cases, novel schedulers shall be implemented in a new scheduling file.
diff --git a/spaces/declare-lab/tango/diffusers/tests/repo_utils/test_check_dummies.py b/spaces/declare-lab/tango/diffusers/tests/repo_utils/test_check_dummies.py
deleted file mode 100644
index 52a75d7b02e85f70cb347afb1429ca8beb942d21..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/tests/repo_utils/test_check_dummies.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-import unittest
-
-
-git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
-sys.path.append(os.path.join(git_repo_path, "utils"))
-
-import check_dummies  # noqa: E402
-from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402
-
-
-# Align TRANSFORMERS_PATH in check_dummies with the current path
-check_dummies.PATH_TO_DIFFUSERS = os.path.join(git_repo_path, "src", "diffusers")
-
-
-class CheckDummiesTester(unittest.TestCase):
-    def test_find_backend(self):
-        simple_backend = find_backend("    if not is_torch_available():")
-        self.assertEqual(simple_backend, "torch")
-
-        # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
-        # self.assertEqual(backend_with_underscore, "tensorflow_text")
-
-        double_backend = find_backend("    if not (is_torch_available() and is_transformers_available()):")
-        self.assertEqual(double_backend, "torch_and_transformers")
-
-        # double_backend_with_underscore = find_backend(
-        #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
-        # )
-        # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
-
-        triple_backend = find_backend(
-            "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"
-        )
-        self.assertEqual(triple_backend, "torch_and_transformers_and_onnx")
-
-    def test_read_init(self):
-        objects = read_init()
-        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
-        self.assertIn("torch", objects)
-        self.assertIn("torch_and_transformers", objects)
-        self.assertIn("flax_and_transformers", objects)
-        self.assertIn("torch_and_transformers_and_onnx", objects)
-
-        # Likewise, we can't assert on the exact content of a key
-        self.assertIn("UNet2DModel", objects["torch"])
-        self.assertIn("FlaxUNet2DConditionModel", objects["flax"])
-        self.assertIn("StableDiffusionPipeline", objects["torch_and_transformers"])
-        self.assertIn("FlaxStableDiffusionPipeline", objects["flax_and_transformers"])
-        self.assertIn("LMSDiscreteScheduler", objects["torch_and_scipy"])
-        self.assertIn("OnnxStableDiffusionPipeline", objects["torch_and_transformers_and_onnx"])
-
-    def test_create_dummy_object(self):
-        dummy_constant = create_dummy_object("CONSTANT", "'torch'")
-        self.assertEqual(dummy_constant, "\nCONSTANT = None\n")
-
-        dummy_function = create_dummy_object("function", "'torch'")
-        self.assertEqual(
-            dummy_function, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"
-        )
-
-        expected_dummy_class = """
-class FakeClass(metaclass=DummyObject):
-    _backends = 'torch'
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, 'torch')
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, 'torch')
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, 'torch')
-"""
-        dummy_class = create_dummy_object("FakeClass", "'torch'")
-        self.assertEqual(dummy_class, expected_dummy_class)
-
-    def test_create_dummy_files(self):
-        expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-CONSTANT = None
-
-
-def function(*args, **kwargs):
-    requires_backends(function, ["torch"])
-
-
-class FakeClass(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-"""
-        dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
-        self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file)
diff --git a/spaces/deepusus/tts/README.md b/spaces/deepusus/tts/README.md
deleted file mode 100644
index b6b2f87908f373800949fe31221d396b714daf5b..0000000000000000000000000000000000000000
--- a/spaces/deepusus/tts/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: BanglaTTS
-emoji: 📈
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.49.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/diacanFperku/AutoGPT/BEST Free Sex Movie Long.md b/spaces/diacanFperku/AutoGPT/BEST Free Sex Movie Long.md
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index a016d5ab57e8e1447116bf7903af9c2e7bdb5645..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/BEST Free Sex Movie Long.md	
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index babc109b22598ec908fc70c8193c33ef6055e4c1..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Becker 7926 Dvd Europa Traffic Assist V3.0.torrent.md	
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diff --git a/spaces/diacanFperku/AutoGPT/HD Online Player (Korean Spellbound Full Movie Tagalog).md b/spaces/diacanFperku/AutoGPT/HD Online Player (Korean Spellbound Full Movie Tagalog).md
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diff --git a/spaces/dotmet/chatgpt_webui/style.css b/spaces/dotmet/chatgpt_webui/style.css
deleted file mode 100644
index 7f4f938bd243cf6708ee58b9dd6b935467bbaeeb..0000000000000000000000000000000000000000
--- a/spaces/dotmet/chatgpt_webui/style.css
+++ /dev/null
@@ -1,10 +0,0 @@
-.gradio-container {
-    max-width: 100%;
-    max-height: 100%;
-}
-
-[id$=chatbot] > div{
-    border: 0;
-    height: 70vh;
-    overflow-y: auto;
-}
diff --git a/spaces/duycse1603/math2tex/ScanSSD/test.py b/spaces/duycse1603/math2tex/ScanSSD/test.py
deleted file mode 100644
index 9c8feb4c542166e46f047c10d21affc9e08f7903..0000000000000000000000000000000000000000
--- a/spaces/duycse1603/math2tex/ScanSSD/test.py
+++ /dev/null
@@ -1,202 +0,0 @@
-'''
-This file contains functions to test and save the results
-'''
-from __future__ import print_function
-import os
-import argparse
-import torch.backends.cudnn as cudnn
-from ssd import build_ssd
-from utils import draw_boxes, helpers, save_boxes
-import logging
-import time
-import datetime
-from torch.autograd import Variable
-from torchvision import datasets, transforms
-from torch.utils.data import Dataset, DataLoader
-from data import *
-import shutil
-import torch.nn as nn
-
-def test_net_batch(args, net, gpu_id, dataset, transform, thresh):
-    '''
-    Batch testing
-    '''
-    num_images = len(dataset)
-
-    if args.limit != -1:
-        num_images = args.limit
-
-    data_loader = DataLoader(dataset, args.batch_size,
-                              num_workers=args.num_workers,
-                              shuffle=False, collate_fn=detection_collate,
-                              pin_memory=True)
-
-    total = len(dataset)
-
-    logging.debug('Test dataset size is {}'.format(total))
-
-    done = 0
-
-    for batch_idx, (images, targets, metadata) in enumerate(data_loader):
-
-        done = done + len(images)
-        logging.debug('processing {}/{}'.format(done, total))
-
-        if args.cuda:
-            images = images.cuda()
-            targets = [ann.cuda() for ann in targets]
-        else:
-            images = Variable(images)
-            targets = [Variable(ann, volatile=True) for ann in targets]
-            # targets = [ann for ann in targets]
-
-        y, debug_boxes, debug_scores = net(images)  # forward pass
-        detections = y.data
-
-        k = 0
-        for img, meta in zip(images, metadata):
-
-            img_id = meta[0]
-            x_l = meta[1]
-            y_l = meta[2]
-
-            img = img.permute(1,2,0)
-            # scale each detection back up to the image
-            scale = torch.Tensor([img.shape[1], img.shape[0],
-                                  img.shape[1], img.shape[0]])
-
-            recognized_boxes = []
-            recognized_scores = []
-
-            # [1,2,200,5]
-            # we only care about math class
-            # hence select detections[image_id, class, detection_id, detection_score]
-            # class=1 for math
-            i = 1
-            j = 0
-
-            while j < detections.size(2) and detections[k, i, j, 0] >= thresh:  # TODO it was 0.6
-
-                score = detections[k, i, j, 0]
-                pt = (detections[k, i, j, 1:] * args.window).cpu().numpy()
-                coords = (pt[0] + x_l, pt[1] + y_l, pt[2] + x_l, pt[3] + y_l)
-                #coords = (pt[0], pt[1], pt[2], pt[3])
-                recognized_boxes.append(coords)
-                recognized_scores.append(score.cpu().numpy())
-
-                j += 1
-
-            save_boxes(args, recognized_boxes, recognized_scores, img_id)
-            k = k + 1
-
-            if args.verbose:
-                draw_boxes(args, img.cpu().numpy(), recognized_boxes, recognized_scores,
-                           debug_boxes, debug_scores, scale, img_id)
-
-def test_gtdb(args):
-
-    gpu_id = 0
-    if args.cuda:
-        gpu_id = helpers.get_freer_gpu()
-        torch.cuda.set_device(gpu_id)
-
-    # load net
-    num_classes = 2 # +1 background
-
-    # initialize SSD
-    net = build_ssd(args, 'test', exp_cfg[args.cfg], gpu_id, args.model_type, num_classes)
-
-    logging.debug(net)
-    net.to(gpu_id)
-    net = nn.DataParallel(net)
-    net.load_state_dict(torch.load(args.trained_model, map_location={'cuda:1':'cuda:0'}))
-    net.eval()
-    logging.debug('Finished loading model!')
-
-    dataset = GTDBDetection(args, args.test_data, split='test',
-                            transform=BaseTransform(args.model_type, (246,246,246)),
-                            target_transform=GTDBAnnotationTransform())
-
-    if args.cuda:
-        net = net.to(gpu_id)
-        cudnn.benchmark = True
-
-    # evaluation
-    test_net_batch(args, net, gpu_id, dataset,
-                   BaseTransform(args.model_type, (246,246,246)),
-                   thresh=args.visual_threshold)
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Single Shot MultiBox Detection')
-    parser.add_argument('--trained_model', default='weights/ssd300_GTDB_990.pth',
-                        type=str, help='Trained state_dict file path to open')
-    parser.add_argument('--save_folder', default='eval/', type=str,
-                        help='Dir to save results')
-    parser.add_argument('--visual_threshold', default=0.6, type=float,
-                        help='Final confidence threshold')
-    parser.add_argument('--cuda', default=False, type=bool,
-                        help='Use cuda to train model')
-    parser.add_argument('--dataset_root', default=GTDB_ROOT, help='Location of VOC root directory')
-    parser.add_argument('--test_data', default="testing_data", help='testing data file')
-    parser.add_argument('--verbose', default=False, type=bool, help='plot output')
-    parser.add_argument('--suffix', default="_10", type=str, help='suffix of directory of images for testing')
-    parser.add_argument('--exp_name', default="SSD", help='Name of the experiment. Will be used to generate output')
-    parser.add_argument('--model_type', default=300, type=int,
-                        help='Type of ssd model, ssd300 or ssd512')
-    parser.add_argument('--use_char_info', default=False, type=bool, help='Whether or not to use char info')
-    parser.add_argument('--limit', default=-1, type=int, help='limit on number of test examples')
-    parser.add_argument('--cfg', default="gtdb", type=str,
-                        help='Type of network: either gtdb or math_gtdb_512')
-    parser.add_argument('--batch_size', default=16, type=int,
-                        help='Batch size for training')
-    parser.add_argument('--num_workers', default=4, type=int,
-                        help='Number of workers used in data loading')
-    parser.add_argument('--kernel', default="3 3", type=int, nargs='+',
-                        help='Kernel size for feature layers: 3 3 or 1 5')
-    parser.add_argument('--padding', default="1 1", type=int, nargs='+',
-                        help='Padding for feature layers: 1 1 or 0 2')
-    parser.add_argument('--neg_mining', default=True, type=bool,
-                        help='Whether or not to use hard negative mining with ratio 1:3')
-    parser.add_argument('--log_dir', default="logs", type=str,
-                        help='dir to save the logs')
-    parser.add_argument('--stride', default=0.1, type=float,
-                        help='Stride to use for sliding window')
-    parser.add_argument('--window', default=1200, type=int,
-                        help='Sliding window size')
-
-    parser.add_argument('-f', default=None, type=str, help="Dummy arg so we can load in Jupyter Notebooks")
-
-    args = parser.parse_args()
-
-    if args.cuda and torch.cuda.is_available():
-        torch.set_default_tensor_type('torch.cuda.FloatTensor')
-    else:
-        torch.set_default_tensor_type('torch.FloatTensor')
-
-    if not os.path.exists(args.save_folder):
-        os.mkdir(args.save_folder)
-
-    if os.path.exists(os.path.join(args.save_folder, args.exp_name)):
-        shutil.rmtree(os.path.join(args.save_folder, args.exp_name))
-
-    return args
-
-if __name__ == '__main__':
-
-    args = parse_args()
-    start = time.time()
-    try:
-        filepath=os.path.join(args.log_dir, args.exp_name + "_" + str(round(time.time())) + ".log")
-        print('Logging to ' + filepath)
-        logging.basicConfig(filename=filepath,
-                            filemode='w', format='%(process)d - %(asctime)s - %(message)s',
-                            datefmt='%d-%b-%y %H:%M:%S', level=logging.DEBUG)
-
-        test_gtdb(args)
-    except Exception as e:
-        logging.error("Exception occurred", exc_info=True)
-
-    end = time.time()
-    logging.debug('Toal time taken ' + str(datetime.timedelta(seconds=end-start)))
-    logging.debug("Testing done!")
-
diff --git a/spaces/enesbol/case_dif/modules/conv_modules.py b/spaces/enesbol/case_dif/modules/conv_modules.py
deleted file mode 100644
index 9b43a89622b3bbe60aa0ab41ae18ecb2fce013f5..0000000000000000000000000000000000000000
--- a/spaces/enesbol/case_dif/modules/conv_modules.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""
-author: Min Seok Lee and Wooseok Shin
-"""
-import torch.nn as nn
-
-
-class BasicConv2d(nn.Module):
-    def __init__(self, in_channel, out_channel, kernel_size, stride=(1, 1), padding=(0, 0), dilation=(1, 1)):
-        super(BasicConv2d, self).__init__()
-        self.conv = nn.Conv2d(in_channel, out_channel, kernel_size=kernel_size, stride=stride, padding=padding,
-                              dilation=dilation, bias=False)
-        self.bn = nn.BatchNorm2d(out_channel)
-        self.selu = nn.SELU()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.selu(x)
-
-        return x
-
-
-class DWConv(nn.Module):
-    def __init__(self, in_channel, out_channel, kernel, dilation, padding):
-        super(DWConv, self).__init__()
-        self.out_channel = out_channel
-        self.DWConv = nn.Conv2d(in_channel, out_channel, kernel_size=kernel, padding=padding, groups=in_channel,
-                                dilation=dilation, bias=False)
-        self.bn = nn.BatchNorm2d(out_channel)
-        self.selu = nn.SELU()
-
-    def forward(self, x):
-        x = self.DWConv(x)
-        out = self.selu(self.bn(x))
-
-        return out
-
-
-class DWSConv(nn.Module):
-    def __init__(self, in_channel, out_channel, kernel, padding, kernels_per_layer):
-        super(DWSConv, self).__init__()
-        self.out_channel = out_channel
-        self.DWConv = nn.Conv2d(in_channel, in_channel * kernels_per_layer, kernel_size=kernel, padding=padding,
-                                groups=in_channel, bias=False)
-        self.bn = nn.BatchNorm2d(in_channel * kernels_per_layer)
-        self.selu = nn.SELU()
-        self.PWConv = nn.Conv2d(in_channel * kernels_per_layer, out_channel, kernel_size=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(out_channel)
-
-    def forward(self, x):
-        x = self.DWConv(x)
-        x = self.selu(self.bn(x))
-        out = self.PWConv(x)
-        out = self.selu(self.bn2(out))
-
-        return out
\ No newline at end of file
diff --git a/spaces/enzostvs/stable-diffusion-tpu/components/modal/modal.tsx b/spaces/enzostvs/stable-diffusion-tpu/components/modal/modal.tsx
deleted file mode 100644
index 0cec31c31e7dada180e58032677efdcd88316bb0..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/stable-diffusion-tpu/components/modal/modal.tsx
+++ /dev/null
@@ -1,97 +0,0 @@
-import { useMemo } from "react";
-import { motion } from "framer-motion";
-import Image from "next/image";
-import { BsFillTrashFill } from "react-icons/bs";
-import { AiFillCheckCircle } from "react-icons/ai";
-
-import { useCollection } from "./useCollection";
-import { Button } from "../button";
-import { useUser } from "@/utils/useUser";
-
-interface Props {
-  id: string;
-  onClose: () => void;
-}
-
-const dropIn = {
-  hidden: {
-    opacity: 0,
-  },
-  visible: {
-    y: "0",
-    opacity: 1,
-    transition: {
-      duration: 0.1,
-      type: "spring",
-      damping: 25,
-      stiffness: 500,
-    },
-  },
-  exit: {
-    opacity: 0,
-  },
-};
-
-export const Modal: React.FC<Props> = ({ id, onClose }) => {
-  const { collection, updateVisibility, remove } = useCollection(id);
-  const { user } = useUser();
-
-  const formatDate = useMemo(() => {
-    if (!collection) return;
-    const date = new Date(collection?.createdAt);
-    return date.toLocaleDateString();
-  }, [collection?.createdAt]);
-
-  return (
-    <motion.div
-      onClick={onClose}
-      className="fixed top-0 w-screen h-screen left-0 bg-black/30 backdrop-blur-sm z-50 flex items-center justify-center p-6"
-      initial={{ opacity: 0 }}
-      animate={{ opacity: 1 }}
-      exit={{ opacity: 0 }}
-    >
-      <motion.div
-        onClick={(e) => e.stopPropagation()}
-        className="max-w-2xl h-auto w-full z-[1] rounded-3xl overflow-hidden relative flex items-center justify-center flex-col gap-4 bg-white/30 backdrop-blur-sm px-2 pb-2 pt-2"
-        variants={dropIn}
-        initial="hidden"
-        animate="visible"
-        exit="exit"
-      >
-        {user?.is_admin && (
-          <div className="absolute p-2 rounded-full top-4 left-4 flex items-center justify-start gap-2 bg-black/20 backdrop-blur">
-            {!collection?.is_visible && (
-              <Button theme="white" onClick={updateVisibility}>
-                <AiFillCheckCircle />
-                Validate
-              </Button>
-            )}
-            <Button theme="danger" onClick={remove}>
-              <BsFillTrashFill />
-              Delete
-            </Button>
-          </div>
-        )}
-        <Image
-          src={`https://huggingface.co/datasets/enzostvs/stable-diffusion-tpu-generations/resolve/main/images/${collection?.file_name}.png?raw=true`}
-          alt="Generated image"
-          className="object-center object-contain w-full h-full rounded-2xl"
-          width={1024}
-          height={1024}
-        />
-        <div
-          className="bg-cover bg-center w-full h-full rounded-2xl bg-no-repeat z-[-1] absolute top-0 left-0 opacity-90 blur-xl"
-          style={{
-            backgroundImage: `url(https://huggingface.co/datasets/enzostvs/stable-diffusion-tpu-generations/resolve/main/images/${collection?.file_name}.png?raw=true)`,
-          }}
-        />
-        <div className="text-left w-full px-4 pb-3 pt-2">
-          <p className="text-sm font-medium text-white/60 mb-1">{formatDate}</p>
-          <p className="text-xl font-semibold text-white lowercase leading-snug">
-            {collection?.prompt}
-          </p>
-        </div>
-      </motion.div>
-    </motion.div>
-  );
-};
diff --git a/spaces/everythingfades/Math-Stats-AP/app.py b/spaces/everythingfades/Math-Stats-AP/app.py
deleted file mode 100644
index c0adc9a08b3d3d8e61874f6e6f371e4f6c6a5499..0000000000000000000000000000000000000000
--- a/spaces/everythingfades/Math-Stats-AP/app.py
+++ /dev/null
@@ -1,426 +0,0 @@
-import gradio as gr
-import os
-os.system("pip install scipy")
-def birthdayCheck(people):
-    p = 1;
-    for i in range(people):
-        p = p*(365-i)/365
-    return 1-p
-import random
-import matplotlib.pyplot as plt
-def alter(x,y):
-    return x, y+3
-def getSame(birthdays):
-    cnt = 0
-    x = list()
-    y = list()
-    z = list()
-    for i in range(len(birthdays)):
-        for j in range(i+1,len(birthdays)):
-            if birthdays[i] == birthdays[j]:
-                cnt += 1
-                x.append(i)
-                y.append(j)
-                z.append(birthdays[i])
-    return cnt,x,y,z
-
-def simulation(people):
-    birthdays = list()
-    peoples = list()
-    for i in range(people):
-        birthdays.append(random.randint(1,365))
-        peoples.append(i)
-    return birthdays,peoples
-def tran(z):
-    a = [1,31,59,90,120,151,181,212,243,273,304,334]
-    str = ''
-    for j in z:
-        for i in range(len(a)):
-            if j < a[i]:
-                str += "{}/{}".format(i,j-a[i-1]+1)
-                str += "  |  "
-                break
-    return str if str != '' else "None"
-#birthdays,peoples = simulation(30)
-import matplotlib.pyplot as plt
-def alter(x,y):
-    return x, y+3
-def getSame(birthdays):
-    cnt = 0
-    x = list()
-    y = list()
-    z = list()
-    for i in range(len(birthdays)):
-        for j in range(i+1,len(birthdays)):
-            if birthdays[i] == birthdays[j]:
-                cnt += 1
-                x.append(i)
-                y.append(j)
-                z.append(birthdays[i])
-    return cnt,x,y,z
-import gradio as gr
-
-def getSame_gradio(people):
-    birthdays,peoples = simulation(people)
-    getSame(birthdays)
-    cnt = 0
-    x = list()
-    y = list()
-    z = list()
-    for i in range(len(birthdays)):
-        for j in range(i+1,len(birthdays)):
-            if birthdays[i] == birthdays[j]:
-                cnt += 1
-                x.append(i)
-                y.append(j)
-                z.append(birthdays[i])
-    temp = ""
-    for i in range(len(x)):
-        temp += ("No.{}and No.{} have same birthdays".format(x[i]+1,y[i]+1))
-    import matplotlib.pyplot as plt
-    import matplotlib as mpl
-    from matplotlib.patches import Ellipse
-    fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
-    ax.hlines(y=[31,59,90,120,151,181,212,243,273,304,334,365], xmin=0, xmax=len(peoples)+1, color='gray', alpha=0.7, linewidth=1, linestyles='dashdot')
-    ax.scatter(y=birthdays, x=peoples, s=75, color='black', alpha=0.7)
-
-    mpl.rcParams['font.sans-serif']=['SimHei']
-
-    # Title, Label, Ticks and Ylim
-    ax.set_title('birthdays:{}'.format(len(peoples)), fontdict={'size':22})
-    ax.set_xlabel('people No.')
-    ax.set_ylabel('date')
-    ax.set_yticklabels = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
-    ax.set_yticks([31,59,90,120,151,181,212,243,273,304,334,365])
-    for i in range(len(birthdays)):
-        textx,texty = alter(peoples[i],birthdays[i])
-        ax.text(textx,texty,birthdays[i])
-    if cnt != 0:
-        for i in range(len(x)):
-            plt.gcf().gca().add_artist(Ellipse(xy = (x[i],z[i]), width = 1, height = 17, fill = False, color = "red"))
-            plt.gcf().gca().add_artist(Ellipse(xy = (y[i],z[i]), width = 1, height = 17, fill = False, color = "red"))
-    return tran(birthdays), "we have {} pairs of same birthdays".format(cnt), "the repeated date is" + tran(z), temp, plt.gcf()
-def multi_simulation(times,people):
-    cnt_list = list()
-    cnt_check = list()
-    for i in range(times):
-        cnt = one_simulation(people)
-        cnt_list.append(cnt)
-        cnt_check.append(0 if cnt ==0 else 1)
-    return cnt_list, cnt_check
-#demo.launch()
-
-def one_simulation(people):
-    birthdays,peoples = simulation(people)
-    cnt,x,y,z = getSame(birthdays)
-    return cnt
-def draw_one_sim_one_zero_gradio(times, people):
-    fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
-    cnt_list, cnt_check = multi_simulation(times,people)
-    all_colors = list(plt.cm.colors.cnames.keys())
-    random.seed(100)
-    c = random.choices(all_colors, k=len(list(set(cnt_list))))
-    # Plot Bars
-    ax.bar(list(set(cnt_check)), find_class(cnt_check), color=c, width=.5)
-    #for i in range(len(list(set(cnt_check)))):
-    #    plt.annotate(float(find_class(cnt_check)[i]), 
-    #         xy=(list(set(cnt_check))[i],find_class(cnt_check)[i] + 4),#箭头末端位置
-    #         
-    #         xytext=(list(set(cnt_check))[i],find_class(cnt_check)[i] + 4),#文本起始位置
-    #         
-    #         #箭头属性设置
-    #        arrowprops=dict(facecolor='#74C476', 
-    #                        shrink=1,#箭头的收缩比
-    #                        alpha=0.6,
-    #                        width=7,#箭身宽
-    #                        headwidth=40,#箭头宽
-    #                        hatch='--',#填充形状
-    #                        frac=0.8,#身与头比
-    #                        #其它参考matplotlib.patches.Polygon中任何参数
-    #                       ),
-    #        )
-    return fig,float(find_class(cnt_check)[0]),float(find_class(cnt_check)[1]),float(find_class(cnt_check)[1]/times),birthdayCheck(people)
-import matplotlib
-import scipy.stats as stats
-import numpy as np
-def multi_multi_simulation_gradio(people, times_for_p, iter_times):
-    fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
-    results_30,a,b = multi_fixed_simulation(people, times_for_p, iter_times)
-    mu = np.mean(results_30)
-    std = np.std(results_30)
-    Bin = 80
-    n, bins, patches = ax.hist(results_30, bins = Bin)
-    y = stats.norm.pdf(bins,mu,std)
-    #y = matplotlib.mlab.normpdf(bins,mu,std)
-    print(mu,std)
-    plt.xlim((birthdayCheck(people)-0.08, birthdayCheck(people)+0.08)) 
-    ax.plot(bins,y,'r--')
-    return fig
-
-def find_class(list1):
-    classes = list()
-    for i in list(set(list1)):
-        temp_cnt= 0
-        for j in list1:
-            if i==j:
-                temp_cnt += 1
-        classes.append(temp_cnt)
-    if len(classes) == 1:
-        classes.append(classes[0])
-        classes[0] = 0
-    return classes
-def multi_fixed_simulation(people,times, iter_times):
-    results = list()
-    for i in range(iter_times):
-        results.append(get_ratio(people,times))
-        if i % 100 == 0:
-            print(i)
-    return results,people, times
-def get_ratio(people, times):
-    cnt_list1, cnt_check1 = multi_simulation(times,people)
-    return(find_class(cnt_check1)[1]/len(cnt_check1))
-import math
-import numpy as np
-import scipy
-def random_gen_data(population,p):
-    results = np.zeros(int(population))
-    for i in range(results.size):
-        if np.random.rand(1)[0] <= p:
-            results[i] = 1
-    return results
-def get_ratio1(results):
-    #print(results.size,results.sum(),"#########")
-    return results.sum()/results.size
-def simulation(p,SRS_size,population,times):
-    results = np.zeros(times)
-    for i in range(times):
-        sample1 = np.array(random.sample(list(population),int(SRS_size)))
-        results[i] = get_ratio1(sample1)
-    return results#,population
-def get_CI(p,alpha,sample_size):#size = (SRS_size)
-    sigma = math.sqrt(p*(1-p)/sample_size)
-    z = scipy.stats.norm.ppf(1-(1-alpha)/2)
-    return(sigma*z)
-def get_CI_new(alpha,sample):#size = (SRS_size)
-    p_hat = sample
-    sigma = np.sqrt(p_hat*(1-p_hat)/sample.shape[0])
-    z = scipy.stats.norm.ppf(1-(1-alpha)/2)
-    return(sigma*z)
-def draw(p,alpha,SRS_size,population_size,times):
-    population = random_gen_data(population_size,p)
-    #print(population)
-    sample = simulation(p,SRS_size,population,times)
-    CI = get_CI_new(alpha,sample)
-    #print(CI)
-    all_colors = list(plt.cm.colors.cnames.keys())
-    random.seed(42)
-    c = random.choices(all_colors, k=population_size)
-
-
-    fig,ax=plt.subplot_mosaic([['A','A','A'],
-                               ['C','C','C'],
-                                ['B','B','B'],
-                                ['B','B','B'],
-                                ['B','B','B']],figsize=(16, 10), constrained_layout=True)
-    ax['A'].set_xlim(0, 1)
-    #ax['B'].set_xlim(sample.min()-CI, sample.max()+CI)
-    ax['B'].set_xlim(0,1)
-    #ax['C'].set_xlim(sample.min()-CI, sample.max()+CI)
-    ax['C'].set_xlim(0,1)
-    mu = population.mean()
-    std = population.std()
-    n, bins, patches = ax['A'].hist(population,bins = 40)
-    y = normfun(bins,mu,std)
-    ax['A'].vlines(p,ax['A'].get_ylim()[0],ax['A'].get_ylim()[1],linestyles ="solid", colors ="k")
-    ax['C'].plot(bins,y,'r--',label = "population dis")
-    
-    mu = sample.mean()
-    std = sample.std()
-    n, bins, patches = ax['C'].hist(sample,bins = 40)
-    y = normfun(bins,mu,std)
-    ax['C'].plot(bins,y,':',label = "sample dis")
-    ax['C'].vlines(p,ax['C'].get_ylim()[0],ax['C'].get_ylim()[1],linestyles ="solid", colors ="k")
-    ax['C'].vlines(p-CI,ax['C'].get_ylim()[0],ax['C'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    ax['C'].vlines(p+CI,ax['C'].get_ylim()[0],ax['C'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    ax['C'].legend()
-
-    ax['B'].hlines(y = np.arange(0,times),xmin = sample-CI, xmax = sample+CI,color = "gray")
-    ax['C'].vlines(p,ax['C'].get_ylim()[0],ax['C'].get_ylim()[1],linestyles ="solid", colors ="k")
-    
-    ax['B'].scatter(sample,np.arange(0,times),c = "blue")
-    out = np.zeros(sample.shape[0])
-    ax['B'].vlines(p,ax['B'].get_ylim()[0],ax['B'].get_ylim()[1],linestyles ="solid", colors ="k")
-    ax['B'].vlines(p-CI,ax['B'].get_ylim()[0],ax['B'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    ax['B'].vlines(p+CI,ax['B'].get_ylim()[0],ax['B'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    check = (np.abs(sample - p)>CI)*sample
-    ax['B'].scatter(check,np.arange(0,times),c = "red")
-    ax['B'].scatter(np.zeros(times),np.arange(0,times),c = "white")
-    #print(check)
-    return fig
-
-import math
-import numpy as np
-import scipy
-def random_gen_data_mean(population,mu,sigma):
-    results = np.random.normal(mu, sigma,population)  # 均值,标准差,数量
-    return results
-def get_ratio1_mean(results):
-    #print(results.size,results.sum(),"#########")
-    return results.sum()/results.size
-def simulation_mean(SRS_size,population,times):
-    results = np.zeros(times)
-    for i in range(times):
-        sample1 = np.array(random.sample(population.tolist(),int(SRS_size)))
-        results[i] = get_ratio1_mean(sample1)
-    return results#,population
-def get_CI_mean(sample,alpha):#size = (SRS_size)
-    #print(sample)
-    sigma = sample.std()
-    sigma1 = sigma/math.sqrt(sample.shape[0])
-    z = scipy.stats.norm.ppf(1-(1-alpha)/2)
-    return(sigma*z)
-def get_CI_mean_new(sample,alpha):#size = (SRS_size)
-    #print(sample)
-    sigma = sample
-    sigma1 = sigma/math.sqrt(sample.shape[0])
-    z = scipy.stats.norm.ppf(1-(1-alpha)/2)
-    return(sigma*z)
-def normfun(x,mu,std):
-    pdf = np.exp(-((x - mu)**2)/(2*std**2)) / (std * np.sqrt(2*np.pi))
-    return pdf
-def normfun_d(x):
-    u = x-x.mean()
-    pdf = x * np.exp(-u*u/(2*x.std()*x.std()))/(x.std()*x.std()*x.std()*math.sqrt(math.pi))
-# 
-import matplotlib.pyplot as plt
-import random
-import math
-import numpy as np
-import scipy
-import gradio as gr
-from scipy.stats import norm
-def draw_mean(mu,sigma,alpha,SRS_size,population_size,times):
-    population = random_gen_data_mean(population_size,mu, sigma)
-    #print(population)
-    sample = simulation_mean(SRS_size,population,times)
-    CI = get_CI_mean(sample,alpha)
-    #print(CI)
-    #all_colors = list(plt.cm.colors.cnames.keys())
-    random.seed(42)
-    #c = random.choices(all_colors, k=population_size)
-
-
-    fig,ax=plt.subplot_mosaic([['A','A','A'],
-                               ['C','C','C'],
-                                ['B','B','B'],
-                                ['B','B','B'],
-                                ['B','B','B']],figsize=(16, 10), constrained_layout=True)
-    #ax['C'].set_ylim(0,0.5)
-    ax['A'].set_xlim(population.min()-CI, population.max()+CI)
-    ax['B'].set_xlim(population.min()-CI, population.max()+CI)
-    ax['C'].set_xlim(population.min()-CI, population.max()+CI)
-    mu = population.mean()
-    std = population.std()
-    n, bins, patches = ax['A'].hist(population,bins = 40)#,density = True)
-    y = normfun(bins,mu,std)
-    ax['A'].vlines(mu,ax['A'].get_ylim()[0],ax['A'].get_ylim()[1],linestyles ="solid", colors ="k")
-    #ax['A'].vlines(mu-CI,ax['A'].get_ylim()[0],ax['A'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    #ax['A'].vlines(mu+CI,ax['A'].get_ylim()[0],ax['A'].get_ylim()[1],linestyles ="dashed", colors ="k")
-
-    ax['C'].vlines(mu,ax['C'].get_ylim()[0],ax['C'].get_ylim()[1],linestyles ="solid", colors ="k")
-    ax['C'].vlines(mu-CI,ax['C'].get_ylim()[0],ax['C'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    ax['C'].vlines(mu+CI,ax['C'].get_ylim()[0],ax['C'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    
-    ax['C'].plot(bins,y,'r--',label = "population dis",color = "red")
-    
-    mu = sample.mean()
-    std = sample.std()
-    
-
-    n, bins, patches = ax['C'].hist(sample,bins = 40)#,density = True,weights = weights)
-    y = normfun(bins,mu,std)
-    ax['C'].plot(bins,y,'-',label = "sample dis",color = "red")
-    ax['C'].legend()
-
-    ax['B'].hlines(y = np.arange(0,times),xmin = sample-CI, xmax = sample+CI,color = "gray")
-    ax['B'].vlines(mu,ax['B'].get_ylim()[0],ax['B'].get_ylim()[1],linestyles ="solid", colors ="k")
-    ax['B'].vlines(mu-CI,ax['B'].get_ylim()[0],ax['B'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    ax['B'].vlines(mu+CI,ax['B'].get_ylim()[0],ax['B'].get_ylim()[1],linestyles ="dashed", colors ="k")
-    ax['B'].scatter(sample,np.arange(0,times))
-    
-    check = (np.abs(sample - mu)>CI)*sample
-    ax['B'].scatter(check,np.arange(0,times),c = "red")
-    ax['B'].scatter(np.zeros(times),np.arange(0,times),c = "white")
-    return fig
-
-
-with gr.Blocks() as demo:
-    with gr.Tab(label='Birthday paradox', open=False):
-        with gr.Accordion(label="intro", open = True):
-            gr.Markdown("The Birthday paradox illustrates a scnario to find whether or how many people are there with the same birthdays among the 365 days a year")
-            gr.Markdown("Basically, if we have n people, and each of the birthdays were randomly selected, the predicted probability for at least two people with the same birthdays is:")
-            gr.Markdown("1 - (365 * 364 * 363 * (3650n+1))/(365^n)")
-            gr.Markdown("since for the first person, his birthday could be any day. For the second, to avoid have same birthdays, there are 364 remaining, and so on")
-            gr.Markdown("we could use 1 minus the value to get the final probability")
-            birthday_intro_input1 = gr.Slider(2,1000,label = "number of people")
-            birthday_intro_output1 = gr.Textbox(label = "the probability to find same birthdays among the gien value of people is")
-            birthday_intro_button = gr.Button()
-        with gr.Accordion(label="single simulation", open = False):
-            gr.Markdown("this simulation is for one possible outcome given that the number of people is fixed(the slider)")
-            gr.Markdown("because I am to discuss the long term probability of multiple simulations combined, so the tag is single simulation")
-            birthday_single_simulation_input1 = gr.Slider(0,100,label = "number of people")
-            with gr.Row():
-                birthday_single_simulation_output1 = gr.Textbox("birthdays")
-                birthday_single_simulation_output2 = gr.Textbox()
-                birthday_single_simulation_output3 = gr.Textbox()
-                birthday_single_simulation_output4 = gr.Textbox()
-            birthday_single_simulation_output5 = gr.Plot()
-            birthday_single_simulation_button = gr.Button()
-        with gr.Accordion(label = "fix number of people, simulate n times to find approximate probability",open = False):
-            with gr.Row():
-                one_zero_input2 = gr.Slider(0,100,label = "number of people")
-                one_zero_input1 = gr.Slider(10,10000,10, label = "number of experiments")
-            one_zero_output = gr.Plot()
-            with gr.Row():
-                one_zero_output1 = gr.Textbox(label = "experiments with no same birthdays")
-                one_zero_output2 = gr.Textbox(label = "experiments with same birthdays")
-                one_zero_output3 = gr.Textbox(label = "probability of same birhtdays")
-                one_zero_output4 = gr.Textbox(label = "estimated probability")
-            one_zero_button = gr.Button()
-        with gr.Accordion("normal distribution", open = False):
-            gr.Markdown("it may took some time when the numbers are big")
-            multi_input1 = gr.Slider(1,100,label = "number of people")
-            multi_input2 = gr.Slider(10,1000,label = "number of experiments to get one probability")
-            multi_input3 = gr.Slider(1,1000,label = "number of probabilities")
-            multi_output = gr.Plot()
-            multi_button = gr.Button()
-    with gr.Tab(label="Confidence Interval"):
-        with gr.Accordion("CI for ratio"):
-            gr.Markdown("given that the probability to succeed in each trial is p")
-            gr.Markdown("find the Confidence Interval under certain significance level")
-            with gr.Row():
-                input1 = gr.Slider(0,1,0.001,label = "probability to succeed in each trial")
-                input2 = gr.Slider(0,1,0.001,label = "significane level")
-                input3 = gr.Slider(0,1000,1,label = "sample size")
-                input4 = gr.Slider(1,100000,1,label = "population")
-                input5 = gr.Slider(1,1000,1,label = "number of trials")
-            output = gr.Plot()
-            text_button = gr.Button()
-        with gr.Accordion("CI for mean"):
-            gr.Markdown("tip: the y axis has been normalized")
-            with gr.Row():
-                input11 = gr.Slider(0,100,1,label = "mean")
-                input21 = gr.Slider(0,100,1,label = "std")
-                input31 = gr.Slider(0,1,0.001,label = "signifigance level")
-                input41 = gr.Slider(0,1000,1,label = "sample size")
-                input51 = gr.Slider(1,100000,1,label = "population")
-                input61 = gr.Slider(1,1000,1,label = "sample times")
-            output1 = gr.Plot()
-            text_button1 = gr.Button()
-    text_button1.click(draw_mean, inputs=[input11,input21,input31,input41,input51,input61], outputs=[output1])
-    birthday_intro_button.click(birthdayCheck, inputs = [birthday_intro_input1], outputs = [birthday_intro_output1])
-    birthday_single_simulation_button.click(getSame_gradio, inputs = [birthday_single_simulation_input1], outputs = [birthday_single_simulation_output1,birthday_single_simulation_output2,birthday_single_simulation_output3,birthday_single_simulation_output4,birthday_single_simulation_output5])
-    one_zero_button.click(draw_one_sim_one_zero_gradio, inputs=[one_zero_input1,one_zero_input2], outputs=[one_zero_output,one_zero_output1,one_zero_output2,one_zero_output3,one_zero_output4])
-    multi_button.click(multi_multi_simulation_gradio,inputs = [multi_input1,multi_input2,multi_input3],outputs = multi_output)
-    text_button.click(draw, inputs=[input1,input2,input3,input4,input5], outputs=[output])
-demo.launch()
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/HD Online Player (Kabhi Alvida Naa Kehna Full !!EXCLUSIVE!! Hd Movie).md b/spaces/falterWliame/Face_Mask_Detection/HD Online Player (Kabhi Alvida Naa Kehna Full !!EXCLUSIVE!! Hd Movie).md
deleted file mode 100644
index 913abb2d7dae03fc543f09686a1946512573c31a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/HD Online Player (Kabhi Alvida Naa Kehna Full !!EXCLUSIVE!! Hd Movie).md	
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diff --git a/spaces/fatiXbelha/sd/Cch s dng phn mm VNPT-BHXH 2.0  k khai BHXH in t d dng.md b/spaces/fatiXbelha/sd/Cch s dng phn mm VNPT-BHXH 2.0  k khai BHXH in t d dng.md
deleted file mode 100644
index 08eedf8dcfe60996985d5fd4b9c690dc2d0691e2..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Cch s dng phn mm VNPT-BHXH 2.0  k khai BHXH in t d dng.md	
+++ /dev/null
@@ -1,157 +0,0 @@
-<br />
-<h1>Download BHXH VNPT 2.0 - A Convenient and Secure Software for Social Insurance Declaration</h1>
- <p>If you are looking for a software that can help you declare social insurance online, send and receive electronic documents, and check the status and feedback of your social insurance documents, then you should consider downloading BHXH VNPT 2.0.</p>
- <p>BHXH VNPT 2.0 is a software developed by Vietnam Posts and Telecommunications Group (VNPT) that allows users to perform various tasks related to social insurance declaration and management.</p>
-<h2>download bhxh vnpt 2.0</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt; <a href="https://urllie.com/2uNAxP">https://urllie.com/2uNAxP</a></b></p><br /><br />
- <p>In this article, we will introduce you to the features, benefits, reviews, alternatives, and instructions on how to download and use BHXH VNPT 2.0.</p>
- <h2>What is BHXH VNPT 2.0?</h2>
- <p>BHXH VNPT 2.0 is a software that enables users to declare social insurance online, send and receive electronic documents, check the status and feedback of social insurance documents, and update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0.</p>
- <p>BHXH VNPT 2.0 is compatible with Windows operating system and can be used with any type of digital signature.</p>
- <p>BHXH VNPT 2.0 is compliant with the regulations of Vietnam Social Security (VSS) and provides users with various forms of social insurance declaration.</p>
- <h3>Features of BHXH VNPT 2.0</h3>
- <p>Some of the main features of BHXH VNPT 2.0 are:</p>
- <ul>
-<li>Kê khai hồ sơ BHXH điện tử: Users can declare social insurance online with various forms that comply with the regulations of VSS.</li>
-<li>Gửi hồ sơ BHXH điện tử: Users can send electronic documents to VSS through a secure and fast connection.</li>
-<li>Tái sử dụng dữ liệu hồ sơ đã khai ở các kỳ trước: Users can reuse data from previous declarations to save time and avoid errors.</li>
-<li>Tra cứu tình trạng xử lý hồ sơ BHXH điện tử: Users can check the status and feedback of their social insurance documents online.</li>
-<li>Tra cứu thông báo phản hồi từ cơ quan BHXH: Users can check the notifications from VSS regarding their social insurance documents.</li>
-<li>Cập nhật và đồng bộ dữ liệu từ BHXH VNPT 2.0 sang BHXH VNPT 5.0: Users can update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0, which is the latest version of the software with more features.</li>
-</ul>
- <h3>Benefits of using BHXH VNPT 2 .0</h3>
- <p>Some of the benefits of using BHXH VNPT 2.0 are:</p>
- <ul>
-<li>It saves time and resources for users and VSS by reducing the need for paper documents and manual processing.</li>
-<li>It ensures the accuracy and security of the data by using digital signatures and encryption.</li>
-<li>It provides users with instant feedback and notifications from VSS regarding their social insurance documents.</li>
-<li>It allows users to update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0, which is the latest version of the software with more features.</li>
-</ul>
- <h3>Reviews of BHXH VNPT 2.0</h3>
- <p>BHXH VNPT 2.0 has received positive reviews from users who have used it for social insurance declaration and management. Here are some of the reviews from users:</p>
- <blockquote>
-<p>"I have been using BHXH VNPT 2.0 for a year and I am very satisfied with it. It is easy to use, fast, and secure. It helps me declare social insurance online without any hassle. I can also check the status and feedback of my documents anytime. I highly recommend it to anyone who needs to declare social insurance online."</p>
-<p>How to download bhxh vnpt 2.0 for Windows<br />
-Download bhxh vnpt 2.0 and sign offline<br />
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-<cite>- Nguyen Thi Lan, accountant at ABC Company</cite>
-</blockquote>
- <blockquote>
-<p>"BHXH VNPT 2.0 is a great software for social insurance declaration. It has many features that make it convenient and efficient. It allows me to reuse data from previous declarations, send and receive electronic documents, and update and synchronize data to BHXH VNPT 5.0. It also gives me instant feedback and notifications from VSS. It is a must-have software for social insurance declaration."</p>
-<cite>- Tran Van Minh, manager at XYZ Company</cite>
-</blockquote>
- <h3>Alternatives to BHXH VNPT 2.0</h3>
- <p>If you are looking for alternatives to BHXH VNPT 2.0, you may want to consider the following options:</p>
- <ul>
-<li>BHXH VNPT 5.0: This is the latest version of the software developed by VNPT that has more features and functions than BHXH VNPT 2.0. It allows users to declare social insurance online, send and receive electronic documents, check the status and feedback of social insurance documents, update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0, and perform other tasks related to social insurance declaration and management.</li>
-<li>BHXH Online: This is a web-based service provided by VSS that allows users to declare social insurance online, send and receive electronic documents, check the status and feedback of social insurance documents, and perform other tasks related to social insurance declaration and management.</li>
-<li>BHXH Mobile: This is a mobile application developed by VSS that allows users to declare social insurance online, send and receive electronic documents, check the status and feedback of social insurance documents, and perform other tasks related to social insurance declaration and management on their smartphones or tablets.</li>
-</ul>
- <h2>How to download and install BHXH VNPT 2.0?</h2>
- <p>If you want to download and install BHXH VNPT 2.0 on your computer, you can follow these steps:</p>
- <h3>Step 1: Choose the suitable version and download the software</h3>
- <p>You can choose between two versions of BHXH VNPT 2.0: one for Windows XP or Windows Server 2003 (32-bit), and one for Windows Vista or higher (64-bit). You can download the software from the official website of VNPT at <a href="">https://vnpt.com.vn/bhxh-vnpt-20</a> or from the official website of VSS at <a href="">https://bhxh.gov.vn/bhxh-vnpt-20</a>. You can also scan the QR code on these websites to download the software on your mobile device.</p>
- <h3>Step 2: Double-click on the downloaded file and follow the instructions</h3>
- <p>After downloading the software, you can double-click on the file named "BHXH_VNPT_20_Setup.exe" to start the installation process. You will see a welcome screen that asks you to choose the language for the installation. You can choose between Vietnamese or English. Then, you will see a license agreement screen that asks you to accept the terms and conditions of the software. You can click on "I Agree" to continue. Then, you will see a destination folder screen that asks you to choose where you want to install the software on your computer. You can click on " Browse" to select a different folder or click on "Next" to use the default folder. Then, you will see a ready to install screen that asks you to confirm the installation. You can click on "Install" to start the installation or click on "Back" to change any settings. The installation will take a few minutes and you will see a progress bar that shows the status of the installation. When the installation is complete, you will see a finish screen that asks you to launch the software. You can click on "Finish" to exit the installation or click on "Launch BHXH VNPT 2.0" to start using the software.</h3>
- <h3>Step 3: Complete the installation and start using the software</h3>
- <p>After launching the software, you will see a login screen that asks you to enter your username and password. You can use the username and password that you have registered with VSS or VNPT. If you have not registered yet, you can click on "Register" to create an account. You will need to provide some information such as your name, email, phone number, and digital signature. After registering, you can log in with your username and password. You will then see the main interface of the software that shows various options and functions that you can use for social insurance declaration and management.</p>
- <h2>How to use BHXH VNPT 2.0?</h2>
- <p>Once you have downloaded and installed BHXH VNPT 2.0, you can use it for various tasks related to social insurance declaration and management. Here are some of the main tasks that you can perform with BHXH VNPT 2.0:</p>
- <h3>How to declare social insurance online with BHXH VNPT 2.0?</h3>
- <p>To declare social insurance online with BHXH VNPT 2.0, you can follow these steps:</p>
- <ol>
-<li>On the main interface of the software, click on "Kê khai hồ sơ BHXH điện tử" (Declare social insurance online).</li>
-<li>Select the type of declaration that you want to make from the drop-down menu. You can choose between monthly declaration, quarterly declaration, annual declaration, or other types of declaration.</li>
-<li>Fill in the required information and data for your declaration. You can use the data from previous declarations or enter new data.</li>
-<li>Check and verify your declaration before submitting it. You can preview your declaration, print it, or save it as a file.</li>
-<li>Sign your declaration with your digital signature and click on "Gửi hồ sơ BHXH điện tử" (Send electronic documents) to submit your declaration to VSS.</li>
-</ol>
- <h3>How to send and receive electronic documents with BHXH VNPT 2.0?</h3>
- <p>To send and receive electronic documents with BHXH VNPT 2.0, you can follow these steps:</p>
- <ol>
-<li>On the main interface of the software, click on "Gửi hồ sơ BHXH điện tử" (Send electronic documents) or "Nhận hồ sơ BHXH điện tử" (Receive electronic documents) depending on what you want to do.</li>
-<li>Select the type of document that you want to send or receive from the list of available documents.</li>
-<li>Fill in the required information and data for your document. You can use the data from previous documents or enter new data.</li>
-<li>Check and verify your document before sending or receiving it. You can preview your document, print it, or save it as a file.</li>
-<li>Sign your document with your digital signature and click on "Gửi hồ sơ BHXH điện tử" (Send electronic documents) or "Nhận hồ sơ BHXH điện tử" (Receive electronic documents) to complete the process.</li>
-</ol>
- <h3>How to check the status and feedback of social insurance documents with BHXH VNPT 2.0?</h3>
- <p>To check the status and feedback of social insurance documents with BHXH VNPT 2.0, you can follow these steps:</p>
- <ol>
-<li>On the main interface of the software, click on "Tra cứu tình trạng xử lý hồ sơ BHXH điện tử" (Check the status and feedback of social insurance documents).</li>
-<li>Select the type of document that you want to check from the list of available documents.</li>
-<li>Enter the document number or other criteria to search for your document.</li>
-<li>View the status and feedback of your document on the screen. You can see if your document has been received, processed, approved, rejected, or returned by VSS. You can also see the reasons and suggestions for your document if it has been rejected or returned.</li>
-<li>Click on "Xem chi tiết" (View details) to see more information and feedback about your document. You can also download, print, or save your document as a file.</li>
-</ol>
- <h3>How to update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0?</h3>
- <p>To update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0, you can follow these steps:</p>
- <ol>
-<li>On the main interface of the software, click on "Cập nhật và đồng bộ dữ liệu từ BHXH VNPT 2.0 sang BHXH VNPT 5.0" (Update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0).</li>
-<li>Select the type of data that you want to update and synchronize from the list of available data.</li>
-<li>Enter the date range or other criteria to filter the data that you want to update and synchronize.</li>
-<li>Click on "Cập nhật và đồng bộ" (Update and synchronize) to start the process. You will see a progress bar that shows the status of the process.</li>
-<li>When the process is complete, you will see a confirmation message that shows the number of data records that have been updated and synchronized.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>BHXH VNPT 2.0 is a convenient and secure software for social insurance declaration and management. It allows users to declare social insurance online, send and receive electronic documents, check the status and feedback of social insurance documents, and update and synchronize data from BHXH VNPT 2.0 to BHXH VNPT 5.0.</p>
- <p>BHXH VNPT 2.0 is compatible with Windows operating system and can be used with any type of digital signature. It is compliant with the regulations of VSS and provides users with various forms of social insurance declaration.</p>
- <p>BHXH VNPT 2.0 has received positive reviews from users who have used it for social insurance declaration and management. It saves time and resources for users and VSS, ensures the accuracy and security of the data, provides instant feedback and notifications from VSS, and allows users to update and synchronize data to BHXH VNPT 5.0.</p>
- <p>If you want to download and use BHXH VNPT 2.0, you can follow the instructions in this article or visit the official websites of VNPT or VSS for more information.</p>
- <h3>FAQs</h3>
- <p>Here are some of the frequently asked questions about BHXH VNPT 2.0:</p>
- <ol>
-<li><strong>What is the difference between BHXH VNPT 2.0 and BHXH VNPT 5.0?</strong></li>
-<p>BHXH VNPT 2.0 is an older version of the software that has fewer features and functions than BHXH VNPT 5.0. BHXH VNPT 5.0 is the latest version of the software that has more features and functions than BHXH VNPT 2.0. For example, BHXH VNPT 5.0 allows users to declare health insurance online, manage payroll online, manage employee information online, and perform other tasks related to social insurance declaration and management.</p>
-<li><strong>How much does it cost to use BHXH VNPT 2.0?</strong></li>
-<p>BHXH VNPT 2.0 is free to use for users who have registered with VSS or VNPT. Users do not need to pay any fees or charges to use BHXH VNPT 2.0.</p>
-<li><strong>How secure is BHXH VNPT 2.0?</strong></li>
-<p>BHXH VNPT 2.0 is very secure as it uses digital signatures and encryption to protect the data and documents that are sent and received by users. Users can also choose their own passwords and change them regularly to prevent unauthorized access to their accounts.</p>
-<li><strong>How can I contact VSS or VNPT if I have any questions or problems with BHXH VNPT 2.0?</strong></li>
-<p>You can contact VSS or VNPT by phone, email, or online chat if you have any questions or problems with BHXH VNPT 2.0. You can find their contact information on their official websites or on the software interface.</p>
-<li><strong>Can I use BHXH VNPT 2 [user .0 on other devices besides computers?</strong></li>
-<p>Yes, you can use BHXH VNPT 2.0 on other devices besides computers, such as smartphones or tablets. You can download the software from the official websites of VNPT or VSS or scan the QR code on these websites to download the software on your mobile device. You can also use BHXH Mobile, which is a mobile application developed by VSS that has similar features and functions as BHXH VNPT 2.0.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Discover the Secrets of the Caribbean with Caribbean Treasures - A Free Game by Reflexive Entertainment.md b/spaces/fatiXbelha/sd/Discover the Secrets of the Caribbean with Caribbean Treasures - A Free Game by Reflexive Entertainment.md
deleted file mode 100644
index 4a75dc156fdb75adb372dd697f9b63f8ec2d8032..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Discover the Secrets of the Caribbean with Caribbean Treasures - A Free Game by Reflexive Entertainment.md	
+++ /dev/null
@@ -1,99 +0,0 @@
-<br />
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-<p>If you are looking for a fun and rewarding way to spend your time, you might want to check out Caribbean Treasures. Caribbean Treasures is an online gaming platform that offers exciting fish games and slot games that you can play anytime, anywhere. In this article, we will tell you everything you need to know about Caribbean Treasures, how to download and play it, and how to win big with it.</p>
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-<p>Caribbean Treasures is an online gaming platform that offers two types of games: fish games and slot games. Both types of games are designed to test your skills and luck, and reward you with big treasures. Here are some features of each type of game:</p>
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-<p>Downloading and playing Caribbean Treasures is very easy. Here are the steps you need to follow:</p>
- <h3>Download the game for Android or iOS</h3>
-<p>You can download the game for your mobile device from the official website. The game is compatible with both Android and iOS devices. The download is free and fast. You can also download the game for your desktop computer if you prefer.</p>
- <h3>Sign up for a free account</h3>
-<p>After downloading the game, you need to sign up for a free account. You don't need a credit card to sign up. You just need to provide some basic information, such as your name, email address, phone number, and username. The support team will activate your account and contact you with details. You can also contact them anytime if you have any questions or issues.</p>
- <h3>Choose your game and start playing</h3>
-<p>Once you have your account ready, you can choose your game and start playing. You can switch between the fish game and the slot game anytime you want. You can also choose from different rooms or tables depending on your preference. You can play with real money or with virtual coins. You can also play with other players or by yourself.</p>
- <h2>How to win big with Caribbean Treasures?</h2>
-<p>Playing Caribbean Treasures is not only fun but also rewarding. Here are some tips on how to win big with Caribbean Treasures:</p>
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- <h3>Use your skills and strategy</h3>
-<p>The fish game and the slot game both require some skills and strategy to win. For the fish game, you need to aim carefully, fire wisely, and avoid wasting bullets. You also need to know which fish are worth more coins, which fish have special effects, and when to use power-ups. For the slot game, you need to know how to adjust your bet size, how to choose the best paylines, and how to trigger the bonus features. You also need to manage your bankroll and set a limit for your losses and wins.</p>
- <h3>Take advantage of bonuses and promotions</h3>
-<p>Caribbean Treasures offers various bonuses and promotions to its players. You can get a welcome bonus when you sign up, a deposit bonus when you make your first deposit, a referral bonus when you invite your friends, and a loyalty bonus when you play regularly. You can also get free coins, free spins, or free tickets from time to time. You can use these bonuses and promotions to increase your chances of winning and have more fun.</p>
- <h3>Join the VIP club for more rewards</h3>
-<p>If you want to enjoy more benefits and rewards, you can join the VIP club of Caribbean Treasures. The VIP club is a membership program that gives you access to exclusive offers, discounts, gifts, and events. You can also get higher payouts, faster withdrawals, and better customer service. You can join the VIP club by earning points from playing the games or by paying a monthly fee.</p>
- <h2>Conclusion</h2>
-<p>Caribbean Treasures is an online gaming platform that offers exciting fish games and slot games that you can play anytime, anywhere. You can download the game for free for your mobile device or desktop computer. You can sign up for a free account and start playing with real money or virtual coins. You can use your skills and strategy, take advantage of bonuses and promotions, and join the VIP club to win big with Caribbean Treasures. So what are you waiting for? Download Caribbean Treasures today and discover the hidden treasures of the Caribbean!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Caribbean Treasures:</p>
-<ul>
-<li><b>Is Caribbean Treasures safe and secure?</b></li>
-<p>Yes, Caribbean Treasures is safe and secure. The game uses advanced encryption technology to protect your personal and financial information. The game also follows fair gaming practices and has a random number generator to ensure fair outcomes.</p>
-<li><b>How can I contact the support team of Caribbean Treasures?</b></li>
-<p>You can contact the support team of Caribbean Treasures by email, phone, or live chat. The support team is available 24/7 to assist you with any questions or issues you may have.</p>
-<li><b>Can I play Caribbean Treasures with my friends?</b></li>
-<p>Yes, you can play Caribbean Treasures with your friends. You can invite your friends to join the game and play together in the multiplayer mode of the fish game. You can also chat with your friends and other players in the game.</p>
-<li><b>What are the minimum requirements to play Caribbean Treasures?</b></li>
-<p>The minimum requirements to play Caribbean Treasures are as follows:</p>
-<table>
-<tr><td><b>Device</b></td><td><b>Operating System</b></td><td><b>Memory</b></td></tr>
-<tr><td>Android</td><td>4.4 or higher</td><td>1 GB or higher</td></tr>
-<tr><td>iOS</td><td>9.0 or higher</td><td>1 GB or higher</td></tr>
-<tr><td>Desktop</td><td>Windows 7 or higher / Mac OS X 10.10 or higher</td><td>2 GB or higher</td></tr>
-</table>
-<li><b>How can I withdraw my winnings from Caribbean Treasures?</b></li>
-<p>You can withdraw your winnings from Caribbean Treasures by using one of the following methods: PayPal, Skrill, Neteller, Bitcoin, or bank transfer. The minimum withdrawal amount is $20 and the maximum withdrawal amount is $10,000 per day. The withdrawal process may take up to 48 hours depending on the method you choose.</p>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Download Moneyland Mod Apk and Enjoy Unlimited Shopping and Cash.md b/spaces/fatiXbelha/sd/Download Moneyland Mod Apk and Enjoy Unlimited Shopping and Cash.md
deleted file mode 100644
index 7c2467a5cbfda96d9ed315e98d963c7be2263750..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Moneyland Mod Apk and Enjoy Unlimited Shopping and Cash.md	
+++ /dev/null
@@ -1,107 +0,0 @@
-<br />
-<h1>Moneyland Mod APK Free Shopping: How to Get Unlimited Money and Scooter in Moneyland Game</h1>
- <p>Do you love playing casual games that let you explore different places and earn money? If yes, then you might have heard of Moneyland, a popular game that lets you travel around the world and collect coins. But what if you want to enjoy the game without any limitations or restrictions? Well, that's where Moneyland Mod APK comes in handy. In this article, we will tell you everything you need to know about Moneyland Mod APK, including its features, benefits, and how to download and install it on your device. So, without further ado, let's get started!</p>
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- <h2>Introduction</h2>
- <h3>What is Moneyland?</h3>
- <p>Moneyland is a fun and addictive game that lets you explore different countries and cities and collect coins along the way. You can use the coins to buy various items and upgrades, such as houses, cars, boats, planes, and more. You can also unlock new locations and modes as you progress in the game. The game has simple controls and colorful graphics that make it suitable for all ages. You can also play with your friends and compete with them on the leaderboard.</p>
- <h3>What is Moneyland Mod APK?</h3>
- <p>Moneyland Mod APK is a modified version of the original Moneyland game that gives you access to all the premium features for free. By downloading the modded version of Moneyland, you will get unlimited money, free shopping, unlock scooter, no ads, and anti-ban option. This means that you can enjoy the game without any worries or hassles. You can buy anything you want, travel anywhere you want, and have fun without any interruptions.</p>
- <h3>Why do you need Moneyland Mod APK?</h3>
- <p>You might be wondering why you need Moneyland Mod APK when you can play the original game for free. Well, there are several reasons why you might want to try out the modded version of Moneyland. Here are some of them:</p>
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- <ul>
-<li>You want to get unlimited money and free shopping so that you can buy anything you want without spending real money.</li>
-<li>You want to unlock scooter so that you can travel faster and collect more coins.</li>
-<li>You want to remove ads so that you can play the game without any annoying pop-ups or banners.</li>
-<li>You want to use the anti-ban option so that you can play the game without any risk of getting banned by the developers.</li>
-</ul>
- <p>If any of these reasons apply to you, then you should definitely download Moneyland Mod APK and enjoy the game to the fullest.</p>
- <h2>Features of Moneyland Mod APK</h2>
- <h3>Unlimited Money</h3>
- <p>One of the best features of Moneyland Mod APK is that it gives you unlimited money in the game. This means that you can buy anything you want without worrying about running out of coins. You can buy houses, cars, boats, planes, and more with just a few taps. You can also upgrade your items and make them more luxurious and comfortable. With unlimited money, you can enjoy the game without any limitations or restrictions.</p>
- <h3>Free Shopping</h3>
- <p>Another great feature of Moneyland Mod APK is that it gives you free shopping in the game. This means that you don't have to spend any coins to buy anything in the game. You can simply select any item you want and get it for free. You don't have to wait for any timers or watch any ads to get your items. You can also get unlimited gems and keys for free, which are used to unlock new locations and modes in the game. With free shopping, you can get everything you want without spending a dime.</p>
- <h3>Unlock Scooter</h3>
- <p>Another cool feature of Moneyland Mod APK is that it lets you unlock scooter in the game. Scooter is a special vehicle that allows you to travel faster and collect more coins. You can also perform stunts and tricks with the scooter and impress your friends. Scooter is normally locked in the game and requires a lot of coins to unlock. But with Moneyland Mod APK, you can get it for free and enjoy the game more.</p>
- <h3>No Ads</h3>
- <p>Another awesome feature of Moneyland Mod APK is that it removes all the ads from the game. Ads are annoying and distracting and can ruin your gaming experience. They can also consume your data and battery and slow down your device. But with Moneyland Mod APK, you can play the game without any ads and have a smooth and uninterrupted gameplay. You can also save your data and battery and improve your device performance.</p>
- <h3>Anti-Ban Option</h3>
- <p>Another amazing feature of Moneyland Mod APK is that it gives you an anti-ban option in the game. This means that you can play the game without any risk of getting banned by the developers. The modded version of Moneyland has a built-in anti-ban system that protects your account from detection and suspension. You can use all the mod features without any worries or hassles. You can also update the game regularly and enjoy the latest features and bug fixes.</p>
- <h2>How to Download and Install Moneyland Mod APK</h2>
- <h3>Step 1: Enable Unknown Sources</h3>
- <p>The first step to download and install Moneyland Mod APK is to enable unknown sources on your device. This will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings and look for security or privacy options. Then, find the option that says unknown sources or allow installation from unknown sources and enable it. This will make your device ready for installing Moneyland Mod APK.</p>
- <h3>Step 2: Download the APK File</h3>
- <p>The next step is to download the APK file of Moneyland Mod APK from a reliable source. You can use the link below to download the latest version of Moneyland Mod APK for free. The file size is about 100 MB and it will take a few minutes to download depending on your internet speed. Make sure you have enough storage space on your device before downloading the file.</p>
- <p><a href="">Download Moneyland Mod APK Free Shopping</a></p>
- <h3>Step 3: Install the APK File</h3>
- <p>The final step is to install the APK file of Moneyland Mod APK on your device. To do this, locate the downloaded file in your file manager or downloads folder and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on install and wait for the process to complete. It will take a few seconds to install Moneyland Mod APK on your device.</p>
- <h3>Step 4: Enjoy the Game</h3>
- <p>Congratulations! You have successfully downloaded and installed Moneyland Mod APK on your device. Now, you can enjoy the game with all the mod features for free. You can open the game from your app drawer or home screen and start playing right away. You can explore different places, collect coins, buy items, unlock scooter, and have fun without any limitations or restrictions.</p>
- <h2>Conclusion</h2>
- <p>Moneyland Mod APK is a great way to enjoy Moneyland game without any limitations or restrictions. It gives you unlimited money, free shopping, unlock scooter, no ads, and anti-ban option for free. You can download and install it easily on your device and enjoy the game to the fullest. Moneyland Mod APK is safe, secure, and updated regularly. It is compatible with most Android devices and does not require root access or special permissions. If you love playing casual games that let you explore different places and earn money, then you should definitely try out Moneyland Mod APK and have fun.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Moneyland Mod APK:</p>
- <ul>
-<li><b>Q: Is Moneyland Mod APK safe to use?</b></li>
-<li>A: Yes, Moneyland Mod APK is safe to use as it does not contain any viruses or malware. It also has an anti-ban system that protects your account from getting banned by the developers.</li>
-<li><b>Q: Do I need to root my device to use Moneyland Mod APK?</b></li>
-<li>A: No, you do not need to root your device to use Moneyland Mod APK as it works fine on both rooted and non-rooted devices . You can download and install it without any problems.</li>
-<li><b>Q: How can I update Moneyland Mod APK?</b></li>
-<li>A: You can update Moneyland Mod APK by downloading the latest version from the same source you downloaded it from. You can also check this article regularly for any updates or news about Moneyland Mod APK.</li>
-<li><b>Q: Can I play Moneyland Mod APK with my friends?</b></li>
-<li>A: Yes, you can play Moneyland Mod APK with your friends and compete with them on the leaderboard. You can also share your progress and achievements with them on social media.</li>
-<li><b>Q: What are the minimum requirements to run Moneyland Mod APK?</b></li>
-<li>A: The minimum requirements to run Moneyland Mod APK are Android 4.4 or higher, 2 GB of RAM, and 200 MB of free storage space.</li>
-</ul>
- <p>I hope this article has helped you learn more about Moneyland Mod APK and how to download and install it on your device. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and happy gaming!</p> 401be4b1e0<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/fengmuxi/ChatGpt-Web/app/store/access.ts b/spaces/fengmuxi/ChatGpt-Web/app/store/access.ts
deleted file mode 100644
index faa53b80b8d6a3cfe13e46597f3c83f466e010eb..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/store/access.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-import { create } from "zustand";
-import { persist } from "zustand/middleware";
-import { StoreKey } from "../constant";
-import { BOT_HELLO } from "./chat";
-import { ALL_MODELS } from "./config";
-import { getHeaders } from "../requests";
-
-export interface AccessControlStore {
-  accessCode: string;
-  token: string;
-  auth: string;
-
-  needCode: number;
-  hideUserApiKey: boolean;
-  openaiUrl: string;
-
-  updateToken: (_: string) => void;
-  updateCode: (_: string) => void;
-  updateAuth: (_: string) => void;
-  enabledAccessControl: () => number;
-  isAuthorized: () => boolean;
-  fetch: () => void;
-}
-
-let fetchState = 0; // 0 not fetch, 1 fetching, 2 done
-
-export const useAccessStore = create<AccessControlStore>()(
-  persist(
-    (set, get) => ({
-      token: "",
-      auth: "",
-      accessCode: "",
-      needCode: 0,
-      hideUserApiKey: true,
-      openaiUrl: "/api/openai/",
-
-      enabledAccessControl() {
-        get().fetch();
-
-        return get().needCode;
-      },
-      updateCode(code: string) {
-        set(() => ({ accessCode: code }));
-      },
-      updateToken(token: string) {
-        set(() => ({ token }));
-      },
-      updateAuth(token: string) {
-        set(() => ({ auth:token }));
-      },
-      isAuthorized() {
-        get().fetch();
-        // has token or has code or disabled access control
-        return (
-          !!get().token || !!get().accessCode || !!get().enabledAccessControl() || !!get().auth
-        );
-      },
-      fetch() {
-        if (fetchState > 0) return;
-        fetchState = 1;
-        fetch("/api/config", {
-          method: "post",
-          headers: {
-            ...getHeaders(),
-          },
-          body: null,
-        })
-          .then((res) => res.json())
-          .then((res: DangerConfig) => {
-            console.log("[Config] got config from server", res);
-            set(() => ({ ...res }));
-
-            if (!res.enableGPT4) {
-              ALL_MODELS.forEach((model) => {
-                if (model.name.startsWith("gpt-4")) {
-                  (model as any).available = false;
-                }
-              });
-            }
-            
-            if ((res as any).botHello) {
-              BOT_HELLO.content = (res as any).botHello;
-            }
-          })
-          .catch(() => {
-            console.error("[Config] failed to fetch config");
-          })
-          .finally(() => {
-            fetchState = 2;
-          });
-      },
-    }),
-    {
-      name: StoreKey.Access,
-      version: 1,
-    },
-  ),
-);
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/transports/polling.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/transports/polling.js
deleted file mode 100644
index 4f855b387bdb5cd8e6d61c746f985c807358d6f2..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/transports/polling.js
+++ /dev/null
@@ -1,344 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.Polling = void 0;
-const transport_1 = require("../transport");
-const zlib_1 = require("zlib");
-const accepts = require("accepts");
-const debug_1 = require("debug");
-const debug = (0, debug_1.default)("engine:polling");
-const compressionMethods = {
-    gzip: zlib_1.createGzip,
-    deflate: zlib_1.createDeflate,
-};
-class Polling extends transport_1.Transport {
-    /**
-     * HTTP polling constructor.
-     *
-     * @api public.
-     */
-    constructor(req) {
-        super(req);
-        this.closeTimeout = 30 * 1000;
-    }
-    /**
-     * Transport name
-     *
-     * @api public
-     */
-    get name() {
-        return "polling";
-    }
-    get supportsFraming() {
-        return false;
-    }
-    /**
-     * Overrides onRequest.
-     *
-     * @param {http.IncomingMessage}
-     * @api private
-     */
-    onRequest(req) {
-        const res = req.res;
-        if ("GET" === req.method) {
-            this.onPollRequest(req, res);
-        }
-        else if ("POST" === req.method) {
-            this.onDataRequest(req, res);
-        }
-        else {
-            res.writeHead(500);
-            res.end();
-        }
-    }
-    /**
-     * The client sends a request awaiting for us to send data.
-     *
-     * @api private
-     */
-    onPollRequest(req, res) {
-        if (this.req) {
-            debug("request overlap");
-            // assert: this.res, '.req and .res should be (un)set together'
-            this.onError("overlap from client");
-            // TODO for the next major release: use an HTTP 400 status code (https://github.com/socketio/engine.io/issues/650)
-            res.writeHead(500);
-            res.end();
-            return;
-        }
-        debug("setting request");
-        this.req = req;
-        this.res = res;
-        const onClose = () => {
-            this.onError("poll connection closed prematurely");
-        };
-        const cleanup = () => {
-            req.removeListener("close", onClose);
-            this.req = this.res = null;
-        };
-        req.cleanup = cleanup;
-        req.on("close", onClose);
-        this.writable = true;
-        this.emit("drain");
-        // if we're still writable but had a pending close, trigger an empty send
-        if (this.writable && this.shouldClose) {
-            debug("triggering empty send to append close packet");
-            this.send([{ type: "noop" }]);
-        }
-    }
-    /**
-     * The client sends a request with data.
-     *
-     * @api private
-     */
-    onDataRequest(req, res) {
-        if (this.dataReq) {
-            // assert: this.dataRes, '.dataReq and .dataRes should be (un)set together'
-            this.onError("data request overlap from client");
-            // TODO for the next major release: use an HTTP 400 status code (https://github.com/socketio/engine.io/issues/650)
-            res.writeHead(500);
-            res.end();
-            return;
-        }
-        const isBinary = "application/octet-stream" === req.headers["content-type"];
-        if (isBinary && this.protocol === 4) {
-            return this.onError("invalid content");
-        }
-        this.dataReq = req;
-        this.dataRes = res;
-        let chunks = isBinary ? Buffer.concat([]) : "";
-        const cleanup = () => {
-            req.removeListener("data", onData);
-            req.removeListener("end", onEnd);
-            req.removeListener("close", onClose);
-            this.dataReq = this.dataRes = chunks = null;
-        };
-        const onClose = () => {
-            cleanup();
-            this.onError("data request connection closed prematurely");
-        };
-        const onData = (data) => {
-            let contentLength;
-            if (isBinary) {
-                chunks = Buffer.concat([chunks, data]);
-                contentLength = chunks.length;
-            }
-            else {
-                chunks += data;
-                contentLength = Buffer.byteLength(chunks);
-            }
-            if (contentLength > this.maxHttpBufferSize) {
-                res.writeHead(413).end();
-                cleanup();
-            }
-        };
-        const onEnd = () => {
-            this.onData(chunks);
-            const headers = {
-                // text/html is required instead of text/plain to avoid an
-                // unwanted download dialog on certain user-agents (GH-43)
-                "Content-Type": "text/html",
-                "Content-Length": 2,
-            };
-            res.writeHead(200, this.headers(req, headers));
-            res.end("ok");
-            cleanup();
-        };
-        req.on("close", onClose);
-        if (!isBinary)
-            req.setEncoding("utf8");
-        req.on("data", onData);
-        req.on("end", onEnd);
-    }
-    /**
-     * Processes the incoming data payload.
-     *
-     * @param {String} encoded payload
-     * @api private
-     */
-    onData(data) {
-        debug('received "%s"', data);
-        const callback = (packet) => {
-            if ("close" === packet.type) {
-                debug("got xhr close packet");
-                this.onClose();
-                return false;
-            }
-            this.onPacket(packet);
-        };
-        if (this.protocol === 3) {
-            this.parser.decodePayload(data, callback);
-        }
-        else {
-            this.parser.decodePayload(data).forEach(callback);
-        }
-    }
-    /**
-     * Overrides onClose.
-     *
-     * @api private
-     */
-    onClose() {
-        if (this.writable) {
-            // close pending poll request
-            this.send([{ type: "noop" }]);
-        }
-        super.onClose();
-    }
-    /**
-     * Writes a packet payload.
-     *
-     * @param {Object} packet
-     * @api private
-     */
-    send(packets) {
-        this.writable = false;
-        if (this.shouldClose) {
-            debug("appending close packet to payload");
-            packets.push({ type: "close" });
-            this.shouldClose();
-            this.shouldClose = null;
-        }
-        const doWrite = (data) => {
-            const compress = packets.some((packet) => {
-                return packet.options && packet.options.compress;
-            });
-            this.write(data, { compress });
-        };
-        if (this.protocol === 3) {
-            this.parser.encodePayload(packets, this.supportsBinary, doWrite);
-        }
-        else {
-            this.parser.encodePayload(packets, doWrite);
-        }
-    }
-    /**
-     * Writes data as response to poll request.
-     *
-     * @param {String} data
-     * @param {Object} options
-     * @api private
-     */
-    write(data, options) {
-        debug('writing "%s"', data);
-        this.doWrite(data, options, () => {
-            this.req.cleanup();
-        });
-    }
-    /**
-     * Performs the write.
-     *
-     * @api private
-     */
-    doWrite(data, options, callback) {
-        // explicit UTF-8 is required for pages not served under utf
-        const isString = typeof data === "string";
-        const contentType = isString
-            ? "text/plain; charset=UTF-8"
-            : "application/octet-stream";
-        const headers = {
-            "Content-Type": contentType,
-        };
-        const respond = (data) => {
-            headers["Content-Length"] =
-                "string" === typeof data ? Buffer.byteLength(data) : data.length;
-            this.res.writeHead(200, this.headers(this.req, headers));
-            this.res.end(data);
-            callback();
-        };
-        if (!this.httpCompression || !options.compress) {
-            respond(data);
-            return;
-        }
-        const len = isString ? Buffer.byteLength(data) : data.length;
-        if (len < this.httpCompression.threshold) {
-            respond(data);
-            return;
-        }
-        const encoding = accepts(this.req).encodings(["gzip", "deflate"]);
-        if (!encoding) {
-            respond(data);
-            return;
-        }
-        this.compress(data, encoding, (err, data) => {
-            if (err) {
-                this.res.writeHead(500);
-                this.res.end();
-                callback(err);
-                return;
-            }
-            headers["Content-Encoding"] = encoding;
-            respond(data);
-        });
-    }
-    /**
-     * Compresses data.
-     *
-     * @api private
-     */
-    compress(data, encoding, callback) {
-        debug("compressing");
-        const buffers = [];
-        let nread = 0;
-        compressionMethods[encoding](this.httpCompression)
-            .on("error", callback)
-            .on("data", function (chunk) {
-            buffers.push(chunk);
-            nread += chunk.length;
-        })
-            .on("end", function () {
-            callback(null, Buffer.concat(buffers, nread));
-        })
-            .end(data);
-    }
-    /**
-     * Closes the transport.
-     *
-     * @api private
-     */
-    doClose(fn) {
-        debug("closing");
-        let closeTimeoutTimer;
-        if (this.dataReq) {
-            debug("aborting ongoing data request");
-            this.dataReq.destroy();
-        }
-        const onClose = () => {
-            clearTimeout(closeTimeoutTimer);
-            fn();
-            this.onClose();
-        };
-        if (this.writable) {
-            debug("transport writable - closing right away");
-            this.send([{ type: "close" }]);
-            onClose();
-        }
-        else if (this.discarded) {
-            debug("transport discarded - closing right away");
-            onClose();
-        }
-        else {
-            debug("transport not writable - buffering orderly close");
-            this.shouldClose = onClose;
-            closeTimeoutTimer = setTimeout(onClose, this.closeTimeout);
-        }
-    }
-    /**
-     * Returns headers for a response.
-     *
-     * @param {http.IncomingMessage} request
-     * @param {Object} extra headers
-     * @api private
-     */
-    headers(req, headers) {
-        headers = headers || {};
-        // prevent XSS warnings on IE
-        // https://github.com/LearnBoost/socket.io/pull/1333
-        const ua = req.headers["user-agent"];
-        if (ua && (~ua.indexOf(";MSIE") || ~ua.indexOf("Trident/"))) {
-            headers["X-XSS-Protection"] = "0";
-        }
-        this.emit("headers", headers, req);
-        return headers;
-    }
-}
-exports.Polling = Polling;
diff --git a/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.h b/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.h
deleted file mode 100644
index a46c805ab80aab491f7f9508b3a008b149866bee..0000000000000000000000000000000000000000
--- a/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.h
+++ /dev/null
@@ -1,172 +0,0 @@
-
-// jpge.h - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// Alex Evans: Added RGBA support, linear memory allocator.
-#ifndef JPEG_ENCODER_H
-#define JPEG_ENCODER_H
-
-#include <stdint.h>
-
-namespace jpge
-{
-  typedef unsigned char  uint8;
-  typedef signed short   int16;
-  typedef signed int     int32;
-  typedef unsigned short uint16;
-  typedef unsigned int   uint32;
-  typedef unsigned int   uint;
-
-  // JPEG chroma subsampling factors. Y_ONLY (grayscale images) and H2V2 (color images) are the most common.
-  enum subsampling_t { Y_ONLY = 0, H1V1 = 1, H2V1 = 2, H2V2 = 3 };
-
-  // JPEG compression parameters structure.
-  struct params
-  {
-    inline params() : m_quality(85), m_subsampling(H2V2), m_no_chroma_discrim_flag(false), m_two_pass_flag(false) { }
-
-    inline bool check_valid() const
-    {
-      if ((m_quality < 1) || (m_quality > 100)) return false;
-      if ((uint)m_subsampling > (uint)H2V2) return false;
-      return true;
-    }
-
-    // Quality: 1-100, higher is better. Typical values are around 50-95.
-    int m_quality;
-
-    // m_subsampling:
-    // 0 = Y (grayscale) only
-    // 1 = YCbCr, no subsampling (H1V1, YCbCr 1x1x1, 3 blocks per MCU)
-    // 2 = YCbCr, H2V1 subsampling (YCbCr 2x1x1, 4 blocks per MCU)
-    // 3 = YCbCr, H2V2 subsampling (YCbCr 4x1x1, 6 blocks per MCU-- very common)
-    subsampling_t m_subsampling;
-
-    // Disables CbCr discrimination - only intended for testing.
-    // If true, the Y quantization table is also used for the CbCr channels.
-    bool m_no_chroma_discrim_flag;
-
-    bool m_two_pass_flag;
-  };
-  
-  // Writes JPEG image to a file. 
-  // num_channels must be 1 (Y) or 3 (RGB), image pitch must be width*num_channels.
-  bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
-
-  // Writes JPEG image to memory buffer. 
-  // On entry, buf_size is the size of the output buffer pointed at by pBuf, which should be at least ~1024 bytes. 
-  // If return value is true, buf_size will be set to the size of the compressed data.
-  bool compress_image_to_jpeg_file_in_memory(void *pBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
-    
-  // Output stream abstract class - used by the jpeg_encoder class to write to the output stream. 
-  // put_buf() is generally called with len==JPGE_OUT_BUF_SIZE bytes, but for headers it'll be called with smaller amounts.
-  class output_stream
-  {
-  public:
-    virtual ~output_stream() { };
-    virtual bool put_buf(const void* Pbuf, int64_t len) = 0;
-    template<class T> inline bool put_obj(const T& obj) { return put_buf(&obj, sizeof(T)); }
-  };
-    
-  // Lower level jpeg_encoder class - useful if more control is needed than the above helper functions.
-  class jpeg_encoder
-  {
-  public:
-    jpeg_encoder();
-    ~jpeg_encoder();
-
-    // Initializes the compressor.
-    // pStream: The stream object to use for writing compressed data.
-    // params - Compression parameters structure, defined above.
-    // width, height  - Image dimensions.
-    // channels - May be 1, or 3. 1 indicates grayscale, 3 indicates RGB source data.
-    // Returns false on out of memory or if a stream write fails.
-    bool init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params = params());
-    
-    const params &get_params() const { return m_params; }
-    
-    // Deinitializes the compressor, freeing any allocated memory. May be called at any time.
-    void deinit();
-
-    uint get_total_passes() const { return m_params.m_two_pass_flag ? 2 : 1; }
-    inline uint get_cur_pass() { return m_pass_num; }
-
-    // Call this method with each source scanline.
-    // width * src_channels bytes per scanline is expected (RGB or Y format).
-    // You must call with NULL after all scanlines are processed to finish compression.
-    // Returns false on out of memory or if a stream write fails.
-    bool process_scanline(const void* pScanline);
-        
-  private:
-    jpeg_encoder(const jpeg_encoder &);
-    jpeg_encoder &operator =(const jpeg_encoder &);
-
-    typedef int32 sample_array_t;
-        
-    output_stream *m_pStream;
-    params m_params;
-    uint8 m_num_components;
-    uint8 m_comp_h_samp[3], m_comp_v_samp[3];
-    int m_image_x, m_image_y, m_image_bpp, m_image_bpl;
-    int m_image_x_mcu, m_image_y_mcu;
-    int m_image_bpl_xlt, m_image_bpl_mcu;
-    int m_mcus_per_row;
-    int m_mcu_x, m_mcu_y;
-    uint8 *m_mcu_lines[16];
-    uint8 m_mcu_y_ofs;
-    sample_array_t m_sample_array[64];
-    int16 m_coefficient_array[64];
-    int32 m_quantization_tables[2][64];
-    uint m_huff_codes[4][256];
-    uint8 m_huff_code_sizes[4][256];
-    uint8 m_huff_bits[4][17];
-    uint8 m_huff_val[4][256];
-    uint32 m_huff_count[4][256];
-    int m_last_dc_val[3];
-    enum { JPGE_OUT_BUF_SIZE = 2048 };
-    uint8 m_out_buf[JPGE_OUT_BUF_SIZE];
-    uint8 *m_pOut_buf;
-    uint m_out_buf_left;
-    uint32 m_bit_buffer;
-    uint m_bits_in;
-    uint8 m_pass_num;
-    bool m_all_stream_writes_succeeded;
-        
-    void optimize_huffman_table(int table_num, int table_len);
-    void emit_byte(uint8 i);
-    void emit_word(uint i);
-    void emit_marker(int marker);
-    void emit_jfif_app0();
-    void emit_dqt();
-    void emit_sof();
-    void emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag);
-    void emit_dhts();
-    void emit_sos();
-    void emit_markers();
-    void compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val);
-    void compute_quant_table(int32 *dst, int16 *src);
-    void adjust_quant_table(int32 *dst, int32 *src);
-    void first_pass_init();
-    bool second_pass_init();
-    bool jpg_open(int p_x_res, int p_y_res, int src_channels);
-    void load_block_8_8_grey(int x);
-    void load_block_8_8(int x, int y, int c);
-    void load_block_16_8(int x, int c);
-    void load_block_16_8_8(int x, int c);
-    void load_quantized_coefficients(int component_num);
-    void flush_output_buffer();
-    void put_bits(uint bits, uint len);
-    void code_coefficients_pass_one(int component_num);
-    void code_coefficients_pass_two(int component_num);
-    void code_block(int component_num);
-    void process_mcu_row();
-    bool terminate_pass_one();
-    bool terminate_pass_two();
-    bool process_end_of_image();
-    void load_mcu(const void* src);
-    void clear();
-    void init();
-  };
-
-} // namespace jpge
-
-#endif // JPEG_ENCODER
\ No newline at end of file
diff --git a/spaces/florim/MedGPT/autogpt/json_utils/utilities.py b/spaces/florim/MedGPT/autogpt/json_utils/utilities.py
deleted file mode 100644
index eb9bb687750460fed2f4547b67e41f8e8c877a41..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/json_utils/utilities.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""Utilities for the json_fixes package."""
-import json
-import re
-
-from jsonschema import Draft7Validator
-
-from autogpt.config import Config
-from autogpt.logs import logger
-
-CFG = Config()
-
-
-def extract_char_position(error_message: str) -> int:
-    """Extract the character position from the JSONDecodeError message.
-
-    Args:
-        error_message (str): The error message from the JSONDecodeError
-          exception.
-
-    Returns:
-        int: The character position.
-    """
-
-    char_pattern = re.compile(r"\(char (\d+)\)")
-    if match := char_pattern.search(error_message):
-        return int(match[1])
-    else:
-        raise ValueError("Character position not found in the error message.")
-
-
-def validate_json(json_object: object, schema_name: object) -> object:
-    """
-    :type schema_name: object
-    :param schema_name:
-    :type json_object: object
-    """
-    with open(f"autogpt/json_utils/{schema_name}.json", "r") as f:
-        schema = json.load(f)
-    validator = Draft7Validator(schema)
-
-    if errors := sorted(validator.iter_errors(json_object), key=lambda e: e.path):
-        logger.error("The JSON object is invalid.")
-        if CFG.debug_mode:
-            logger.error(
-                json.dumps(json_object, indent=4)
-            )  # Replace 'json_object' with the variable containing the JSON data
-            logger.error("The following issues were found:")
-
-            for error in errors:
-                logger.error(f"Error: {error.message}")
-    elif CFG.debug_mode:
-        print("The JSON object is valid.")
-
-    return json_object
diff --git a/spaces/freddyaboulton/gradio_folium/src/backend/gradio_folium/templates/component/style.css b/spaces/freddyaboulton/gradio_folium/src/backend/gradio_folium/templates/component/style.css
deleted file mode 100644
index 53039f8f3554d2ebc72fc5bfc2733871aff9d81d..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/gradio_folium/src/backend/gradio_folium/templates/component/style.css
+++ /dev/null
@@ -1 +0,0 @@
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20px)}.unpadded_box.svelte-3w3rth{margin-top:0}.small_parent.svelte-3w3rth{min-height:100%!important}.dropdown-arrow.svelte-1in5nh4{fill:var(--body-text-color);margin-right:var(--size-2);width:var(--size-5)}.wrap.svelte-8ytugg{display:flex;flex-direction:column;justify-content:center;min-height:var(--size-60);color:var(--block-label-text-color);line-height:var(--line-md)}.or.svelte-8ytugg{color:var(--body-text-color-subdued)}@media (--screen-md){.wrap.svelte-8ytugg{font-size:var(--text-lg)}}svg.svelte-43sxxs.svelte-43sxxs{width:var(--size-20);height:var(--size-20)}svg.svelte-43sxxs path.svelte-43sxxs{fill:var(--loader-color)}div.svelte-43sxxs.svelte-43sxxs{z-index:var(--layer-2)}.margin.svelte-43sxxs.svelte-43sxxs{margin:var(--size-4)}.wrap.svelte-14miwb5.svelte-14miwb5{display:flex;flex-direction:column;justify-content:center;align-items:center;z-index:var(--layer-5);transition:opacity .1s ease-in-out;border-radius:var(--block-radius);background:var(--block-background-fill);padding:0 var(--size-6);max-height:var(--size-screen-h);overflow:hidden;pointer-events:none}.wrap.center.svelte-14miwb5.svelte-14miwb5{top:0;right:0;left:0}.wrap.default.svelte-14miwb5.svelte-14miwb5{top:0;right:0;bottom:0;left:0}.hide.svelte-14miwb5.svelte-14miwb5{opacity:0;pointer-events:none}.generating.svelte-14miwb5.svelte-14miwb5{animation:svelte-14miwb5-pulse 2s cubic-bezier(.4,0,.6,1) infinite;border:2px solid var(--color-accent);background:transparent}.translucent.svelte-14miwb5.svelte-14miwb5{background:none}@keyframes svelte-14miwb5-pulse{0%,to{opacity:1}50%{opacity:.5}}.loading.svelte-14miwb5.svelte-14miwb5{z-index:var(--layer-2);color:var(--body-text-color)}.eta-bar.svelte-14miwb5.svelte-14miwb5{position:absolute;top:0;right:0;bottom:0;left:0;transform-origin:left;opacity:.8;z-index:var(--layer-1);transition:10ms;background:var(--background-fill-secondary)}.progress-bar-wrap.svelte-14miwb5.svelte-14miwb5{border:1px solid 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.toast-text.warning.svelte-solcu7{color:var(--color-yellow-50)}.toast-text.info.svelte-solcu7{color:var(--color-grey-700)}.dark .toast-text.info.svelte-solcu7{color:var(--color-grey-50)}.toast-details.svelte-solcu7{margin:var(--size-3) var(--size-3) var(--size-3) 0;width:100%}.toast-icon.svelte-solcu7{display:flex;position:absolute;position:relative;flex-shrink:0;justify-content:center;align-items:center;margin:var(--size-2);border-radius:var(--radius-full);padding:var(--size-1);padding-left:calc(var(--size-1) - 1px);width:35px;height:35px}.toast-icon.error.svelte-solcu7{color:var(--color-red-700)}.dark .toast-icon.error.svelte-solcu7{color:var(--color-red-500)}.toast-icon.warning.svelte-solcu7{color:var(--color-yellow-700)}.dark .toast-icon.warning.svelte-solcu7{color:var(--color-yellow-500)}.toast-icon.info.svelte-solcu7{color:var(--color-grey-700)}.dark .toast-icon.info.svelte-solcu7{color:var(--color-grey-500)}@keyframes svelte-solcu7-countdown{0%{transform:scaleX(1)}to{transform:scaleX(0)}}.timer.svelte-solcu7{position:absolute;bottom:0;left:0;transform-origin:0 0;animation:svelte-solcu7-countdown 10s linear forwards;width:100%;height:var(--size-1)}.timer.error.svelte-solcu7{background:var(--color-red-700)}.dark .timer.error.svelte-solcu7{background:var(--color-red-500)}.timer.warning.svelte-solcu7{background:var(--color-yellow-700)}.dark .timer.warning.svelte-solcu7{background:var(--color-yellow-500)}.timer.info.svelte-solcu7{background:var(--color-grey-700)}.dark .timer.info.svelte-solcu7{background:var(--color-grey-500)}.toast-wrap.svelte-gatr8h{display:flex;position:fixed;top:var(--size-4);right:var(--size-4);flex-direction:column;align-items:end;gap:var(--size-2);z-index:var(--layer-top);width:calc(100% - var(--size-8))}@media (--screen-sm){.toast-wrap.svelte-gatr8h{width:calc(var(--size-96) + 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diff --git a/spaces/freshield/ChatGPT-gradio/MongdbClient.py b/spaces/freshield/ChatGPT-gradio/MongdbClient.py
deleted file mode 100644
index e01e08e339b5e202fd49193a8b82411c90a2e67a..0000000000000000000000000000000000000000
--- a/spaces/freshield/ChatGPT-gradio/MongdbClient.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# coding=utf-8
-"""
-@Author: Freshield
-@Contact: yangyufresh@163.com
-@File: MongdbClient.py
-@Time: 2023-03-03 20:25
-@Last_update: 2023-03-03 20:25
-@Desc: None
-@==============================================@
-@      _____             _   _     _   _       @
-@     |   __|___ ___ ___| |_|_|___| |_| |      @
-@     |   __|  _| -_|_ -|   | | -_| | . |      @
-@     |__|  |_| |___|___|_|_|_|___|_|___|      @
-@                                    Freshield @
-@==============================================@
-"""
-import pymongo
-from hashlib import sha256
-
-
-class MongodbClient(object):
-    """Mongodb客户端"""
-    def __init__(self):
-        self.myclient = pymongo.MongoClient("mongodb://localhost:27017/")
-        self.mydb = self.myclient["openai_bot"]
-        self.user_info = self.mydb['user_info']
-        self.user_history = self.mydb['user_history']
-
-    def insert_user(self, username, password):
-        """离线添加用户"""
-        username = sha256(username.encode('utf8')).hexdigest()
-        password = sha256(password.encode('utf8')).hexdigest()
-        mydict = {'username': username, 'password': password}
-        _ = self.user_info.insert_one(mydict)
-
-    def check_user_exist(self, username, password):
-        """检测用户是否存在"""
-        username = sha256(username.encode('utf8')).hexdigest()
-        password = sha256(password.encode('utf8')).hexdigest()
-        mydoc = self.user_info.find({'username': username, 'password': password}).limit(1)
-        res = [x for x in mydoc]
-
-        return len(res) >= 1
-
-    def update_user_access_token(self, username, access_token):
-        """更新数据库的access_token以便后续使用"""
-        username = sha256(username.encode('utf8')).hexdigest()
-        # 先看是否有这个用户
-        mydoc = self.user_history.find({'username': username}).limit(1)
-        res = [x for x in mydoc]
-        # 如果没有则直接创建
-        if len(res) < 1:
-            mydict = {
-                'username': username, 'access_token': access_token,
-                'role': '你是ChatGPT，OpenAI训练的大规模语言模型，简明的回答用户的问题。', 'history': []}
-            _ = self.user_history.insert_one(mydict)
-        # 如果有则更新
-        else:
-            self.user_history.update_one({'username': username}, {'$set': {'access_token': access_token}})
-
-    def get_user_chat_history(self, access_token):
-        """获取用户的聊天历史"""
-        mydoc = self.user_history.find({'access_token': access_token}).limit(1)
-        res = [x for x in mydoc]
-        history_str, history_list = '', []
-        role = '你是ChatGPT，OpenAI训练的大规模语言模型，简明的回答用户的问题。'
-        if len(res) >= 1:
-            # 遍历加到history中
-            history_list = res[0]['history']
-            role = res[0]['role']
-            for qus, ans in history_list[::-1]:
-                history_str += f'Q: {qus}\nA: {ans}\n'
-
-        return history_str, history_list, role
-
-    def update_user_chat_history(self, access_token, qus, ans):
-        """更新用户的聊天历史"""
-        mydoc = self.user_history.find({'access_token': access_token}).limit(1)
-        res = [x for x in mydoc]
-        if len(res) >= 1:
-            self.user_history.update_one({'access_token': access_token}, {'$push': {'history': (qus, ans)}})
-
-    def delete_user_chat_history(self, access_token):
-        """删除用户的聊天历史"""
-        mydoc = self.user_history.find({'access_token': access_token}).limit(1)
-        res = [x for x in mydoc]
-        if len(res) >= 1:
-            self.user_history.update_one({'access_token': access_token}, {'$set': {'history': []}})
-
-    def update_role(self, access_token, role):
-        """更新用户的聊天历史"""
-        mydoc = self.user_history.find({'access_token': access_token}).limit(1)
-        res = [x for x in mydoc]
-        if len(res) >= 1:
-            self.user_history.update_one({'access_token': access_token}, {'$set': {'role': role}})
diff --git a/spaces/ggffdd/White-box-Cartoonization/wbc/cartoonize.py b/spaces/ggffdd/White-box-Cartoonization/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/ggffdd/White-box-Cartoonization/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
-    h, w, c = np.shape(image)
-    if min(h, w) > 720:
-        if h > w:
-            h, w = int(720 * h / w), 720
-        else:
-            h, w = 720, int(720 * w / h)
-    image = cv2.resize(image, (w, h),
-                       interpolation=cv2.INTER_AREA)
-    h, w = (h // 8) * 8, (w // 8) * 8
-    image = image[:h, :w, :]
-    return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
-    print(model_path)
-    input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-    network_out = network.unet_generator(input_photo)
-    final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
-    all_vars = tf.trainable_variables()
-    gene_vars = [var for var in all_vars if 'generator' in var.name]
-    saver = tf.train.Saver(var_list=gene_vars)
-
-    config = tf.ConfigProto()
-    config.gpu_options.allow_growth = True
-    sess = tf.Session(config=config)
-
-    sess.run(tf.global_variables_initializer())
-    saver.restore(sess, tf.train.latest_checkpoint(model_path))
-    name_list = os.listdir(load_folder)
-    for name in tqdm(name_list):
-        try:
-            load_path = os.path.join(load_folder, name)
-            save_path = os.path.join(save_folder, name)
-            image = cv2.imread(load_path)
-            image = resize_crop(image)
-            batch_image = image.astype(np.float32) / 127.5 - 1
-            batch_image = np.expand_dims(batch_image, axis=0)
-            output = sess.run(final_out, feed_dict={input_photo: batch_image})
-            output = (np.squeeze(output) + 1) * 127.5
-            output = np.clip(output, 0, 255).astype(np.uint8)
-            cv2.imwrite(save_path, output)
-        except:
-            print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
-    def __init__(self, model_path):
-        print(model_path)
-        self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-        network_out = network.unet_generator(self.input_photo)
-        self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
-        all_vars = tf.trainable_variables()
-        gene_vars = [var for var in all_vars if 'generator' in var.name]
-        saver = tf.train.Saver(var_list=gene_vars)
-
-        config = tf.ConfigProto()
-        config.gpu_options.allow_growth = True
-        self.sess = tf.Session(config=config)
-
-        self.sess.run(tf.global_variables_initializer())
-        saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
-    def run(self, load_folder, save_folder):
-        name_list = os.listdir(load_folder)
-        for name in tqdm(name_list):
-            try:
-                load_path = os.path.join(load_folder, name)
-                save_path = os.path.join(save_folder, name)
-                image = cv2.imread(load_path)
-                image = resize_crop(image)
-                batch_image = image.astype(np.float32) / 127.5 - 1
-                batch_image = np.expand_dims(batch_image, axis=0)
-                output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
-                output = (np.squeeze(output) + 1) * 127.5
-                output = np.clip(output, 0, 255).astype(np.uint8)
-                cv2.imwrite(save_path, output)
-            except:
-                print('cartoonize {} failed'.format(load_path))
-
-    def run_sigle(self, load_path, save_path):
-        try:
-            image = cv2.imread(load_path)
-            image = resize_crop(image)
-            batch_image = image.astype(np.float32) / 127.5 - 1
-            batch_image = np.expand_dims(batch_image, axis=0)
-            output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
-            output = (np.squeeze(output) + 1) * 127.5
-            output = np.clip(output, 0, 255).astype(np.uint8)
-            cv2.imwrite(save_path, output)
-        except:
-            print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
-    model_path = 'saved_models'
-    load_folder = 'test_images'
-    save_folder = 'cartoonized_images'
-    if not os.path.exists(save_folder):
-        os.mkdir(save_folder)
-    cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Photoshop Cs7 Free Download Full Version With Crack _TOP_.md b/spaces/gotiQspiryo/whisper-ui/examples/Adobe Photoshop Cs7 Free Download Full Version With Crack _TOP_.md
deleted file mode 100644
index 8d3594f63fe45d82b55b2cea93fe59df4edf637a..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Photoshop Cs7 Free Download Full Version With Crack _TOP_.md	
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/CRACK Microsoft Office ProPlus 2013 SP1 VL X64 En-US Oct2014-murphy78-.md b/spaces/gotiQspiryo/whisper-ui/examples/CRACK Microsoft Office ProPlus 2013 SP1 VL X64 En-US Oct2014-murphy78-.md
deleted file mode 100644
index dd98ee204b2642e35db2bca9911901f0237ba298..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/CRACK Microsoft Office ProPlus 2013 SP1 VL X64 En-US Oct2014-murphy78-.md	
+++ /dev/null
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-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/DISCOGRAFIA ATAHUALPA YUPANQUI 9.md b/spaces/gotiQspiryo/whisper-ui/examples/DISCOGRAFIA ATAHUALPA YUPANQUI 9.md
deleted file mode 100644
index 3ad1397252c0360a6eb0a3709f3b9b411b439170..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/DISCOGRAFIA ATAHUALPA YUPANQUI 9.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<p>Son pseudonyme, choisi dès l'adolescence, est formé d'Atahualpa, et de Yupanqui. <i>Atahualpa yupanqui</i> signifierait « <i>celui qui vient de contrées lointaines pour dire quelque chose</i> », en langue quechua[3].</p>
-<h2>DISCOGRAFIA ATAHUALPA YUPANQUI 9</h2><br /><p><b><b>DOWNLOAD</b> &#10001; &#10001; &#10001; <a href="https://urlgoal.com/2uyLST">https://urlgoal.com/2uyLST</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/fairseq/modules/adaptive_softmax.py b/spaces/gradio/HuBERT/fairseq/modules/adaptive_softmax.py
deleted file mode 100644
index ae0c77ba0f6ee98501306d66cbc4a948b4ade0f7..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/modules/adaptive_softmax.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import functools
-import operator
-
-import torch
-import torch.nn.functional as F
-from fairseq.modules.fairseq_dropout import FairseqDropout
-from fairseq.modules.quant_noise import quant_noise
-from torch import nn
-
-
-class TiedLinear(nn.Module):
-    def __init__(self, weight, transpose):
-        super().__init__()
-        self.weight = weight
-        self.transpose = transpose
-
-    def forward(self, input):
-        return F.linear(input, self.weight.t() if self.transpose else self.weight)
-
-
-class TiedHeadModule(nn.Module):
-    def __init__(self, weights, input_dim, num_classes, q_noise, qn_block_size):
-        super().__init__()
-        tied_emb, _ = weights
-        self.num_words, emb_dim = tied_emb.size()
-
-        self.word_proj = quant_noise(
-            TiedLinear(tied_emb, transpose=False), q_noise, qn_block_size
-        )
-        if input_dim != emb_dim:
-            self.word_proj = nn.Sequential(
-                quant_noise(
-                    nn.Linear(input_dim, emb_dim, bias=False), q_noise, qn_block_size
-                ),
-                self.word_proj,
-            )
-
-        self.class_proj = quant_noise(
-            nn.Linear(input_dim, num_classes, bias=False), q_noise, qn_block_size
-        )
-        self.out_dim = self.num_words + num_classes
-
-        self.register_buffer("_float_tensor", torch.FloatTensor(1))
-
-    def forward(self, input):
-        inp_sz = functools.reduce(operator.mul, input.shape[:-1], 1)
-        out = self._float_tensor.new(inp_sz, self.out_dim)
-        out[:, : self.num_words] = self.word_proj(input.view(inp_sz, -1))
-        out[:, self.num_words :] = self.class_proj(input.view(inp_sz, -1))
-        return out
-
-
-class AdaptiveSoftmax(nn.Module):
-    """
-    This is an implementation of the efficient softmax approximation for
-    graphical processing units (GPU), described in the paper "Efficient softmax
-    approximation for GPUs" (http://arxiv.org/abs/1609.04309).
-    """
-
-    def __init__(
-        self,
-        vocab_size,
-        input_dim,
-        cutoff,
-        dropout,
-        factor=4.0,
-        adaptive_inputs=None,
-        tie_proj=False,
-        q_noise=0,
-        qn_block_size=8,
-    ):
-        super().__init__()
-
-        if vocab_size > cutoff[-1]:
-            cutoff = cutoff + [vocab_size]
-        else:
-            assert (
-                vocab_size == cutoff[-1]
-            ), "cannot specify cutoff larger than vocab size"
-
-        output_dim = cutoff[0] + len(cutoff) - 1
-
-        self.vocab_size = vocab_size
-        self.cutoff = cutoff
-        self.dropout_module = FairseqDropout(
-            dropout, module_name=self.__class__.__name__
-        )
-        self.input_dim = input_dim
-        self.factor = factor
-        self.q_noise = q_noise
-        self.qn_block_size = qn_block_size
-
-        self.lsm = nn.LogSoftmax(dim=1)
-
-        if adaptive_inputs is not None:
-            self.head = TiedHeadModule(
-                adaptive_inputs.weights_for_band(0),
-                input_dim,
-                len(cutoff) - 1,
-                self.q_noise,
-                self.qn_block_size,
-            )
-        else:
-            self.head = quant_noise(
-                nn.Linear(input_dim, output_dim, bias=False),
-                self.q_noise,
-                self.qn_block_size,
-            )
-
-        self._make_tail(adaptive_inputs, tie_proj)
-
-        def init_weights(m):
-            if (
-                hasattr(m, "weight")
-                and not isinstance(m, TiedLinear)
-                and not isinstance(m, TiedHeadModule)
-            ):
-                nn.init.xavier_uniform_(m.weight)
-
-        self.apply(init_weights)
-
-        self.register_buffer("version", torch.LongTensor([1]))
-
-    def _make_tail(self, adaptive_inputs=None, tie_proj=False):
-        self.tail = nn.ModuleList()
-        for i in range(len(self.cutoff) - 1):
-            dim = int(self.input_dim // self.factor ** (i + 1))
-
-            tied_emb, tied_proj = (
-                adaptive_inputs.weights_for_band(i + 1)
-                if adaptive_inputs is not None
-                else (None, None)
-            )
-
-            if tied_proj is not None:
-                if tie_proj:
-                    proj = quant_noise(
-                        TiedLinear(tied_proj, transpose=True),
-                        self.q_noise,
-                        self.qn_block_size,
-                    )
-                else:
-                    proj = quant_noise(
-                        nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False),
-                        self.q_noise,
-                        self.qn_block_size,
-                    )
-            else:
-                proj = quant_noise(
-                    nn.Linear(self.input_dim, dim, bias=False),
-                    self.q_noise,
-                    self.qn_block_size,
-                )
-
-            if tied_emb is None:
-                out_proj = nn.Linear(
-                    dim, self.cutoff[i + 1] - self.cutoff[i], bias=False
-                )
-            else:
-                out_proj = TiedLinear(tied_emb, transpose=False)
-
-            m = nn.Sequential(
-                proj,
-                nn.Dropout(self.dropout_module.p),
-                quant_noise(out_proj, self.q_noise, self.qn_block_size),
-            )
-
-            self.tail.append(m)
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        version_name = name + ".version"
-        if version_name not in state_dict:
-            raise Exception("This version of the model is no longer supported")
-
-    def adapt_target(self, target):
-        """
-        In order to be efficient, the AdaptiveSoftMax does not compute the
-        scores for all the word of the vocabulary for all the examples. It is
-        thus necessary to call the method adapt_target of the AdaptiveSoftMax
-        layer inside each forward pass.
-        """
-
-        target = target.view(-1)
-        new_target = [target.clone()]
-        target_idxs = []
-
-        for i in range(len(self.cutoff) - 1):
-            mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1]))
-            new_target[0][mask] = self.cutoff[0] + i
-
-            if mask.any():
-                target_idxs.append(mask.nonzero(as_tuple=False).squeeze(1))
-                new_target.append(target[mask].add(-self.cutoff[i]))
-            else:
-                target_idxs.append(None)
-                new_target.append(None)
-
-        return new_target, target_idxs
-
-    def forward(self, input, target):
-        """
-        Args:
-            input: (b x t x d)
-            target: (b x t)
-        Returns:
-            2 lists: output for each cutoff section and new targets by cut off
-        """
-
-        input = input.contiguous().view(-1, input.size(-1))
-        input = self.dropout_module(input)
-
-        new_target, target_idxs = self.adapt_target(target)
-        output = [self.head(input)]
-
-        for i in range(len(target_idxs)):
-            if target_idxs[i] is not None:
-                output.append(self.tail[i](input.index_select(0, target_idxs[i])))
-            else:
-                output.append(None)
-
-        return output, new_target
-
-    def get_log_prob(self, input, target):
-        """
-        Computes the log probabilities for all the words of the vocabulary,
-        given a 2D tensor of hidden vectors.
-        """
-
-        bsz, length, dim = input.size()
-        input = input.contiguous().view(-1, dim)
-
-        if target is not None:
-            _, target_idxs = self.adapt_target(target)
-        else:
-            target_idxs = None
-
-        head_y = self.head(input)
-        log_probs = head_y.new_zeros(input.size(0), self.vocab_size)
-
-        head_sz = self.cutoff[0] + len(self.tail)
-        log_probs[:, :head_sz] = self.lsm(head_y)
-        tail_priors = log_probs[:, self.cutoff[0] : head_sz].clone()
-
-        for i in range(len(self.tail)):
-            start = self.cutoff[i]
-            end = self.cutoff[i + 1]
-
-            if target_idxs is None:
-                tail_out = log_probs[:, start:end]
-                tail_out.copy_(self.tail[i](input))
-                log_probs[:, start:end] = self.lsm(tail_out).add_(
-                    tail_priors[:, i, None]
-                )
-            elif target_idxs[i] is not None:
-                idxs = target_idxs[i]
-                tail_out = log_probs[idxs, start:end]
-                tail_out.copy_(self.tail[i](input[idxs]))
-                log_probs[idxs, start:end] = self.lsm(tail_out).add_(
-                    tail_priors[idxs, i, None]
-                )
-
-        log_probs = log_probs.view(bsz, length, -1)
-        return log_probs
diff --git a/spaces/gradio/HuBERT/tests/test_backtranslation_dataset.py b/spaces/gradio/HuBERT/tests/test_backtranslation_dataset.py
deleted file mode 100644
index dffc3b49387dfdc046ea23d7db179377040b7cbc..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/tests/test_backtranslation_dataset.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-
-import tests.utils as test_utils
-import torch
-from fairseq.data import (
-    BacktranslationDataset,
-    LanguagePairDataset,
-    TransformEosDataset,
-)
-from fairseq.sequence_generator import SequenceGenerator
-
-
-class TestBacktranslationDataset(unittest.TestCase):
-    def setUp(self):
-        (
-            self.tgt_dict,
-            self.w1,
-            self.w2,
-            self.src_tokens,
-            self.src_lengths,
-            self.model,
-        ) = test_utils.sequence_generator_setup()
-
-        dummy_src_samples = self.src_tokens
-
-        self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
-        self.cuda = torch.cuda.is_available()
-
-    def _backtranslation_dataset_helper(
-        self,
-        remove_eos_from_input_src,
-        remove_eos_from_output_src,
-    ):
-        tgt_dataset = LanguagePairDataset(
-            src=self.tgt_dataset,
-            src_sizes=self.tgt_dataset.sizes,
-            src_dict=self.tgt_dict,
-            tgt=None,
-            tgt_sizes=None,
-            tgt_dict=None,
-        )
-
-        generator = SequenceGenerator(
-            [self.model],
-            tgt_dict=self.tgt_dict,
-            max_len_a=0,
-            max_len_b=200,
-            beam_size=2,
-            unk_penalty=0,
-        )
-
-        backtranslation_dataset = BacktranslationDataset(
-            tgt_dataset=TransformEosDataset(
-                dataset=tgt_dataset,
-                eos=self.tgt_dict.eos(),
-                # remove eos from the input src
-                remove_eos_from_src=remove_eos_from_input_src,
-            ),
-            src_dict=self.tgt_dict,
-            backtranslation_fn=(
-                lambda sample: generator.generate([self.model], sample)
-            ),
-            output_collater=TransformEosDataset(
-                dataset=tgt_dataset,
-                eos=self.tgt_dict.eos(),
-                # if we remove eos from the input src, then we need to add it
-                # back to the output tgt
-                append_eos_to_tgt=remove_eos_from_input_src,
-                remove_eos_from_src=remove_eos_from_output_src,
-            ).collater,
-            cuda=self.cuda,
-        )
-        dataloader = torch.utils.data.DataLoader(
-            backtranslation_dataset,
-            batch_size=2,
-            collate_fn=backtranslation_dataset.collater,
-        )
-        backtranslation_batch_result = next(iter(dataloader))
-
-        eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
-
-        # Note that we sort by src_lengths and add left padding, so actually
-        # ids will look like: [1, 0]
-        expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
-        if remove_eos_from_output_src:
-            expected_src = expected_src[:, :-1]
-        expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
-        generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
-        tgt_tokens = backtranslation_batch_result["target"]
-
-        self.assertTensorEqual(expected_src, generated_src)
-        self.assertTensorEqual(expected_tgt, tgt_tokens)
-
-    def test_backtranslation_dataset_no_eos_in_output_src(self):
-        self._backtranslation_dataset_helper(
-            remove_eos_from_input_src=False,
-            remove_eos_from_output_src=True,
-        )
-
-    def test_backtranslation_dataset_with_eos_in_output_src(self):
-        self._backtranslation_dataset_helper(
-            remove_eos_from_input_src=False,
-            remove_eos_from_output_src=False,
-        )
-
-    def test_backtranslation_dataset_no_eos_in_input_src(self):
-        self._backtranslation_dataset_helper(
-            remove_eos_from_input_src=True,
-            remove_eos_from_output_src=False,
-        )
-
-    def assertTensorEqual(self, t1, t2):
-        self.assertEqual(t1.size(), t2.size(), "size mismatch")
-        self.assertEqual(t1.ne(t2).long().sum(), 0)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/gstaff/MagicGen/colab-data-test/css/keyrune.css b/spaces/gstaff/MagicGen/colab-data-test/css/keyrune.css
deleted file mode 100644
index 1d8e6035e654f670f2e93e2eed831d33823b8b36..0000000000000000000000000000000000000000
--- a/spaces/gstaff/MagicGen/colab-data-test/css/keyrune.css
+++ /dev/null
@@ -1,697 +0,0 @@
-/**
- * Global */
-@font-face {
-  font-family: 'Keyrune';
-  src: url('../fonts/keyrune.eot?v=1.6.0');
-  src: url('../fonts/keyrune.eot?#iefix&v=1.6.0') format('embedded-opentype'), url('../fonts/keyrune.woff2?v=1.6.0') format('woff2'), url('../fonts/keyrune.woff?v=1.6.0') format('woff'), url('../fonts/keyrune.ttf?v=1.6.0') format('truetype'), url('../fonts/keyrune.svg?v=1.6.0#keyrune') format('svg');
-  font-weight: normal;
-  font-style: normal;
-}
-.ss {
-  display: inline-block;
-  font: normal normal normal 14px/1 Keyrune;
-  font-size: inherit;
-  line-height: 1em;
-  text-rendering: auto;
-  transform: translate(0, 0);
-  speak: none;
-  text-transform: none;
-  vertical-align: middle;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-.ss:before {
-  content: "\e684";
-}
-/**
- * Larger sizes */
-.ss-2x {
-  font-size: 2em;
-}
-.ss-3x {
-  font-size: 3em;
-}
-.ss-4x {
-  font-size: 4em;
-}
-.ss-5x {
-  font-size: 5em;
-}
-.ss-6x {
-  font-size: 6em;
-}
-/**
- * Rarity colors */
-.ss-common {
-  color: #1A1718;
-}
-.ss-common.ss-grad {
-  background: -webkit-gradient(linear, left top, right top, color-stop(1%, #302b2c), color-stop(50%, #474040), color-stop(100%, #302b2c));
-  /* Chrome,Safari4+ */
-  background: -webkit-linear-gradient(left, #302b2c 1%, #474040 50%, #302b2c 100%);
-  -webkit-text-stroke: 0.03em #000;
-  -webkit-background-clip: text;
-  -webkit-text-fill-color: transparent;
-}
-.ss-common.ss-grad.ss-no-border {
-  -webkit-text-stroke: 0;
-}
-.ss-uncommon {
-  color: #707883;
-}
-.ss-uncommon.ss-grad {
-  background: -webkit-gradient(linear, left top, right top, color-stop(0%, #5a6572), color-stop(50%, #9e9e9e), color-stop(100%, #5a6572));
-  /* Chrome,Safari4+ */
-  background: -webkit-linear-gradient(left, #5a6572 0%, #9e9e9e 50%, #5a6572 100%);
-  -webkit-text-stroke: 0.03em #111;
-  -webkit-background-clip: text;
-  -webkit-text-fill-color: transparent;
-}
-.ss-uncommon.ss-grad.ss-no-border {
-  -webkit-text-stroke: 0;
-}
-.ss-rare {
-  color: #A58E4A;
-}
-.ss-rare.ss-grad {
-  background: -webkit-gradient(linear, left top, right top, color-stop(0%, #876a3b), color-stop(50%, #dfbd6b), color-stop(100%, #876a3b));
-  /* Chrome,Safari4+ */
-  background: -webkit-linear-gradient(left, #876a3b 0%, #dfbd6b 50%, #876a3b 100%);
-  -webkit-text-stroke: 0.03em #333;
-  -webkit-background-clip: text;
-  -webkit-text-fill-color: transparent;
-}
-.ss-rare.ss-grad.ss-no-border {
-  -webkit-text-stroke: 0;
-}
-.ss-mythic {
-  color: #BF4427;
-}
-.ss-mythic.ss-grad {
-  background: -webkit-gradient(linear, left top, right top, color-stop(0%, #b21f0f), color-stop(50%, #f38300), color-stop(100%, #b21f0f));
-  /* Chrome,Safari4+ */
-  background: -webkit-linear-gradient(left, #b21f0f 0%, #f38300 50%, #b21f0f 100%);
-  -webkit-text-stroke: 0.03em #333;
-  -webkit-background-clip: text;
-  -webkit-text-fill-color: transparent;
-}
-.ss-mythic.ss-grad.ss-no-border {
-  -webkit-text-stroke: 0;
-}
-/**
- * Fixed width */
-.ss-fw {
-  width: 1.28571429em;
-  text-align: center;
-}
-/**
- * Core */
-.ss-lea:before {
-  content: "\e600";
-}
-.ss-leb:before {
-  content: "\e601";
-}
-.ss-2ed:before {
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-.ss-inv:before {
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-.ss-tor:before {
-  content: "\e62c";
-}
-.ss-jud:before {
-  content: "\e62d";
-}
-/* Onslaught Block */
-.ss-ons:before {
-  content: "\e62e";
-}
-.ss-lgn:before {
-  content: "\e62f";
-}
-.ss-scg:before {
-  content: "\e630";
-}
-/* Mirrodin Block */
-.ss-mrd:before {
-  content: "\e631";
-}
-.ss-dst:before {
-  content: "\e632";
-}
-.ss-5dn:before {
-  content: "\e633";
-}
-/* Kamigawa Block */
-.ss-chk:before {
-  content: "\e634";
-}
-.ss-bok:before {
-  content: "\e635";
-}
-.ss-sok:before {
-  content: "\e636";
-}
-/* Ravnica Block */
-.ss-rav:before {
-  content: "\e637";
-}
-.ss-gpt:before {
-  content: "\e638";
-}
-.ss-dis:before {
-  content: "\e639";
-}
-/* Time Spiral Block */
-.ss-tsp:before {
-  content: "\e63a";
-}
-.ss-plc:before {
-  content: "\e63b";
-}
-.ss-fut:before {
-  content: "\e63c";
-}
-/* Lorwyn Block */
-.ss-lrw:before {
-  content: "\e63d";
-}
-.ss-mor:before {
-  content: "\e63e";
-}
-/* Shadowmoor Block */
-.ss-shm:before {
-  content: "\e63f";
-}
-.ss-eve:before {
-  content: "\e640";
-}
-/* Alara Block */
-.ss-ala:before {
-  content: "\e641";
-}
-.ss-con:before {
-  content: "\e642";
-}
-.ss-arb:before {
-  content: "\e643";
-}
-/* Zendikar Block */
-.ss-zen:before {
-  content: "\e644";
-}
-.ss-wwk:before {
-  content: "\e645";
-}
-.ss-roe:before {
-  content: "\e646";
-}
-/* Scars Block */
-.ss-som:before {
-  content: "\e647";
-}
-.ss-mbs:before {
-  content: "\e648";
-}
-.ss-nph:before {
-  content: "\e649";
-}
-/* Innistrad Block */
-.ss-isd:before {
-  content: "\e64a";
-}
-.ss-dka:before {
-  content: "\e64b";
-}
-.ss-avr:before {
-  content: "\e64c";
-}
-/* RTR Block */
-.ss-rtr:before {
-  content: "\e64d";
-}
-.ss-gtc:before {
-  content: "\e64e";
-}
-.ss-dgm:before {
-  content: "\e64f";
-}
-/* Theros Block */
-.ss-ths:before {
-  content: "\e650";
-}
-.ss-bng:before {
-  content: "\e651";
-}
-.ss-jou:before {
-  content: "\e652";
-}
-/* Khans Block */
-.ss-ktk:before {
-  content: "\e653";
-}
-.ss-frf:before {
-  content: "\e654";
-}
-.ss-dtk:before {
-  content: "\e693";
-}
-/* Return to Zendikar Block */
-.ss-bfz:before {
-  content: "\e699";
-}
-.ss-ogw:before {
-  content: "\e901";
-}
-/* Return to Innistrad Block */
-.ss-soi:before {
-  content: "\e902";
-}
-.ss-emn:before {
-  content: "\e90b";
-}
-/**
- * Command Zone */
-.ss-van:before {
-  content: "\e655";
-}
-.ss-hop:before {
-  content: "\e656";
-}
-.ss-arc:before {
-  content: "\e657";
-}
-.ss-cmd:before {
-  content: "\e658";
-}
-.ss-pc2:before {
-  content: "\e659";
-}
-.ss-cm1:before {
-  content: "\e65a";
-}
-.ss-c13:before {
-  content: "\e65b";
-}
-.ss-cns:before {
-  content: "\e65c";
-}
-.ss-c14:before {
-  content: "\e65d";
-}
-.ss-c15:before {
-  content: "\e900";
-}
-.ss-cn2:before {
-  content: "\e904";
-}
-/**
- * Reprint */
-.ss-chr:before {
-  content: "\e65e";
-}
-.ss-ath:before {
-  content: "\e65f";
-}
-.ss-brb:before {
-  content: "\e660";
-}
-.ss-btd:before {
-  content: "\e661";
-}
-.ss-dkm:before {
-  content: "\e662";
-}
-.ss-mma:before {
-  content: "\e663";
-}
-.ss-mm2:before {
-  content: "\e695";
-}
-.ss-ema:before {
-  content: "\e903";
-}
-/**
- * Beginner */
-.ss-por:before {
-  content: "\e664";
-}
-.ss-po2:before {
-  content: "\e665";
-}
-.ss-ptk:before {
-  content: "\e666";
-}
-.ss-s99:before {
-  content: "\e667";
-}
-.ss-s00:before {
-  content: "\e668";
-}
-.ss-w16:before {
-  content: "\e907";
-}
-/**
- * Duel Decks */
-.ss-evg:before {
-  content: "\e669";
-}
-.ss-dd2:before {
-  content: "\e66a";
-}
-.ss-ddc:before {
-  content: "\e66b";
-}
-.ss-ddd:before {
-  content: "\e66c";
-}
-.ss-dde:before {
-  content: "\e66d";
-}
-.ss-ddf:before {
-  content: "\e66e";
-}
-.ss-ddg:before {
-  content: "\e66f";
-}
-.ss-ddh:before {
-  content: "\e670";
-}
-.ss-ddi:before {
-  content: "\e671";
-}
-.ss-ddj:before {
-  content: "\e672";
-}
-.ss-ddk:before {
-  content: "\e673";
-}
-.ss-ddl:before {
-  content: "\e674";
-}
-.ss-ddm:before {
-  content: "\e675";
-}
-.ss-ddn:before {
-  content: "\e676";
-}
-.ss-ddo:before {
-  content: "\e677";
-}
-.ss-ddp:before {
-  content: "\e698";
-}
-.ss-ddq:before {
-  content: "\e908";
-}
-/**
- * From the Vault */
-.ss-drb:before {
-  content: "\e678";
-}
-.ss-v09:before {
-  content: "\e679";
-}
-.ss-v10:before {
-  content: "\e67a";
-}
-.ss-v11:before {
-  content: "\e67b";
-}
-.ss-v12:before {
-  content: "\e67c";
-}
-.ss-v13:before {
-  content: "\e67d";
-}
-.ss-v14:before {
-  content: "\e67e";
-}
-.ss-v15:before {
-  content: "\e905";
-}
-.ss-v16:before {
-  content: "\e906";
-}
-/**
- * Premium Deck Series */
-.ss-h09:before {
-  content: "\e67f";
-}
-.ss-pd2:before {
-  content: "\e680";
-}
-.ss-pd3:before {
-  content: "\e681";
-}
-.ss-md1:before {
-  content: "\e682";
-}
-/**
- * Promotional */
-.ss-pgru:before {
-  content: "\e683";
-}
-.ss-pmtg1:before {
-  content: "\e684";
-}
-.ss-pmtg2:before {
-  content: "\e685";
-}
-.ss-pleaf:before {
-  content: "\e686";
-}
-.ss-pmei:before {
-  content: "\e687";
-}
-.ss-parl:before {
-  content: "\e688";
-}
-.ss-dpa:before {
-  content: "\e689";
-}
-.ss-pbook:before {
-  content: "\e68a";
-}
-.ss-past:before {
-  content: "\e68b";
-}
-.ss-parl2:before {
-  content: "\e68c";
-}
-.ss-exp:before {
-  content: "\e69a";
-}
-.ss-psalvat05:before {
-  content: "\e909";
-}
-.ss-psalvat11:before {
-  content: "\e90a";
-}
-/**
- * Online */
-.ss-med:before {
-  content: "\e68d";
-}
-.ss-me2:before {
-  content: "\e68e";
-}
-.ss-me3:before {
-  content: "\e68f";
-}
-.ss-me4:before {
-  content: "\e690";
-}
-.ss-tpr:before {
-  content: "\e694";
-}
-.ss-vma:before {
-  content: "\e696";
-}
-/**
- * Un-serious */
-.ss-ugl:before {
-  content: "\e691";
-}
-.ss-unh:before {
-  content: "\e692";
-}
-.ss-border:after {
-  content: "";
-  position: absolute;
-  left: -0.05em;
-  top: .0em;
-  color: #fff;
-  font-size: 1.15em;
-  z-index: -1;
-  background: #fff;
-  -webkit-text-stroke: 0.05em #fff;
-  -webkit-background-clip: text;
-  -webkit-text-fill-color: transparent;
-}
-.ss-border.ss-van:after {
-  content: "\e655";
-}
-.ss-border.ss-hop:after {
-  content: "\e656";
-}
-.ss-border.ss-arc:after {
-  content: "\e657";
-}
-.ss-border.ss-cmd:after {
-  content: "\e658";
-}
-.ss-border.ss-pc2:after {
-  content: "\e659";
-}
-.ss-border.ss-cm1:after {
-  content: "\e65a";
-}
-.ss-border.ss-c13:after {
-  content: "\e65b";
-}
-.ss-border.ss-cns:after {
-  content: "\e65c";
-}
-.ss-border.ss-c14:after {
-  content: "\e65d";
-}
-.ss-border.ss-c15:after {
-  content: "\e900";
-}
diff --git a/spaces/h2oai/wave-tour/examples/plot_step_after.py b/spaces/h2oai/wave-tour/examples/plot_step_after.py
deleted file mode 100644
index c5384061bd7bc96f092913ec93d03e3b9530cde8..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_step_after.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Plot / Line / Step / After
-# Make a line #plot with a step-after curve.
-# ---
-from h2o_wave import site, data, ui
-
-page = site['/demo']
-
-page.add('example', ui.plot_card(
-    box='1 1 4 5',
-    title='Line, step-after',
-    data=data('month price', 12, rows=[
-        ('Jan', 51),
-        ('Feb', 91),
-        ('Mar', 34),
-        ('Apr', 47),
-        ('May', 63),
-        ('June', 58),
-        ('July', 56),
-        ('Aug', 77),
-        ('Sep', 99),
-        ('Oct', 106),
-        ('Nov', 88),
-        ('Dec', 56),
-    ]),
-    plot=ui.plot([
-        ui.mark(type='line', x='=month', y='=price', curve='step-after', y_min=0)
-    ])
-))
-
-page.save()
diff --git a/spaces/h2oai/wave-tour/examples/table_pagination_download.py b/spaces/h2oai/wave-tour/examples/table_pagination_download.py
deleted file mode 100644
index 42e2f0d9b9d1b0aaab46b1cbfac9e036b8c78415..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/table_pagination_download.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Table / Pagination / Download
-# Use a #table with pagination to display large (100k+ rows) tabular data and provide data download option.
-# #form #table #pagination #download
-# ---
-
-from h2o_wave import main, app, Q, ui
-import csv
-
-
-rows = [str(i + 1) for i in range(100)]
-rows_per_page = 10
-
-
-@app('/demo')
-async def serve(q: Q):
-    if not q.app.initialized:
-        # Allow downloading all data since no filters/search/sort is allowed.
-        # Create and upload a CSV file for downloads.
-        # For multi-user apps, the tmp file name should be unique for each user, not hardcoded.
-        with open('data_download.csv', 'w') as csvfile:
-            csv_writer = csv.writer(csvfile, delimiter=',')
-            for r in rows:
-                csv_writer.writerow([r])
-        q.app.data_download, = await q.site.upload(['data_download.csv'])
-        q.app.initialized = True
-
-    if not q.client.initialized:
-        q.page['meta'] = ui.meta_card(box='')
-        q.page['form'] = ui.form_card(box='1 1 -1 -1', items=[
-            ui.table(
-                name='table',
-                columns=[ui.table_column(name='text', label='Text', link=False)],
-                rows=[ui.table_row(name=r, cells=[r]) for r in rows[0:rows_per_page]],
-                pagination=ui.table_pagination(total_rows=len(rows), rows_per_page=rows_per_page),
-                height='580px',
-                downloadable=True,
-                events=['page_change', 'download']
-            )
-        ])
-        q.client.initialized = True
-
-    if q.events.table:
-        if q.events.table.download:
-            q.page['meta'].script = ui.inline_script(f'window.open("{q.app.data_download}")')
-        if q.events.table.page_change:
-            offset = q.events.table.page_change.get('offset', 0)
-            new_rows = rows[offset:offset + rows_per_page]
-            q.page['form'].table.rows = [ui.table_row(name=r, cells=[r]) for r in new_rows]
-
-    await q.page.save()
diff --git a/spaces/haakohu/deep_privacy2/configs/anonymizers/deep_privacy1.py b/spaces/haakohu/deep_privacy2/configs/anonymizers/deep_privacy1.py
deleted file mode 100644
index 9bf116cefdbe716a1f9ba56b7f55d5949560cfbc..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/configs/anonymizers/deep_privacy1.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from .face_fdf128 import anonymizer, common, detector
-from dp2.detection.deep_privacy1_detector import DeepPrivacy1Detector
-from tops.config import LazyCall as L
-
-anonymizer.update(
-    face_G_cfg="configs/fdf/deep_privacy1.py",
-)
-
-anonymizer.detector = L(DeepPrivacy1Detector)(
-    face_detector_cfg=dict(name="DSFDDetector", clip_boxes=True),
-    face_post_process_cfg=dict(target_imsize=(128, 128), fdf128_expand=True),
-    score_threshold=0.3,
-    keypoint_threshold=0.3,
-    cache_directory=common.output_dir.joinpath("deep_privacy1_cache")
-)
diff --git a/spaces/hamzapehlivan/StyleRes/options/Settings.py b/spaces/hamzapehlivan/StyleRes/options/Settings.py
deleted file mode 100644
index 471af3abc5c715d6fad323d0054c25847c9a6570..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/options/Settings.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""
-Global settings file for all the classes. Only inference.py can should write to here.
-Other files can read from this file.
-"""
-
-device = 'cpu'
-interfacegan_directions = 'editings/interfacegan_directions'
-ganspace_directions = 'editings/ganspace_pca'
-styleclip_settings = 'editings/styleclip_directions'
-styleclip_mapper_directions = 'editings/styleclip_directions/styleclip_directions/mapper'
-styleclip_global_directions = 'editings/styleclip_directions/styleclip_directions/global_directions'
-
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/language_backbone/backbone.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/language_backbone/backbone.py
deleted file mode 100644
index 4699e97f8c15b3be92c4674bab4493d0c57e5260..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/language_backbone/backbone.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from collections import OrderedDict
-import torch
-from torch import nn
-
-from maskrcnn_benchmark.modeling import registry
-from . import bert_model
-from . import rnn_model
-from . import clip_model
-from . import word_utils
-
-
-@registry.LANGUAGE_BACKBONES.register("bert-base-uncased")
-def build_bert_backbone(cfg):
-    body = bert_model.BertEncoder(cfg)
-    model = nn.Sequential(OrderedDict([("body", body)]))
-    return model
-
-
-@registry.LANGUAGE_BACKBONES.register("roberta-base")
-def build_bert_backbone(cfg):
-    body = bert_model.BertEncoder(cfg)
-    model = nn.Sequential(OrderedDict([("body", body)]))
-    return model
-
-
-@registry.LANGUAGE_BACKBONES.register("rnn")
-def build_rnn_backbone(cfg):
-    body = rnn_model.RNNEnoder(cfg)
-    model = nn.Sequential(OrderedDict([("body", body)]))
-    return model
-
-
-@registry.LANGUAGE_BACKBONES.register("clip")
-def build_clip_backbone(cfg):
-    body = clip_model.CLIPTransformer(cfg)
-    model = nn.Sequential(OrderedDict([("body", body)]))
-    return model
-
-
-def build_backbone(cfg):
-    assert cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE in registry.LANGUAGE_BACKBONES, \
-        "cfg.MODEL.LANGUAGE_BACKBONE.TYPE: {} is not registered in registry".format(
-            cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE
-        )
-    return registry.LANGUAGE_BACKBONES[cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE](cfg)
diff --git a/spaces/hekbobo/bingo/src/components/toaster.tsx b/spaces/hekbobo/bingo/src/components/toaster.tsx
deleted file mode 100644
index 4d2693460b61307a1d4c127fd01df9bee16e59ff..0000000000000000000000000000000000000000
--- a/spaces/hekbobo/bingo/src/components/toaster.tsx
+++ /dev/null
@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'
diff --git a/spaces/hhhhardman/VITS/text/__init__.py b/spaces/hhhhardman/VITS/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/hhhhardman/VITS/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/hkunlp/Binder/utils/sql/process_sql.py b/spaces/hkunlp/Binder/utils/sql/process_sql.py
deleted file mode 100644
index 4df68314d6979f341d93d5b5e1c5e77bb371e745..0000000000000000000000000000000000000000
--- a/spaces/hkunlp/Binder/utils/sql/process_sql.py
+++ /dev/null
@@ -1,595 +0,0 @@
-################################
-# Assumptions:
-#   1. sql is correct
-#   2. only table name has alias
-#   3. only one intersect/union/except
-#
-# val: number(float)/string(str)/sql(dict)
-# col_unit: (agg_id, col_id, isDistinct(bool))
-# val_unit: (unit_op, col_unit1, col_unit2)
-# table_unit: (table_type, col_unit/sql)
-# cond_unit: (not_op, op_id, val_unit, val1, val2)
-# condition: [cond_unit1, 'and'/'or', cond_unit2, ...]
-# sql {
-#   'select': (isDistinct(bool), [(agg_id, val_unit), (agg_id, val_unit), ...])
-#   'from': {'table_units': [table_unit1, table_unit2, ...], 'conds': condition}
-#   'where': condition
-#   'groupBy': [col_unit1, col_unit2, ...]
-#   'orderBy': ('asc'/'desc', [val_unit1, val_unit2, ...])
-#   'having': condition
-#   'limit': None/limit value
-#   'intersect': None/sql
-#   'except': None/sql
-#   'union': None/sql
-# }
-################################
-
-import json
-import sqlite3
-from nltk import word_tokenize
-
-CLAUSE_KEYWORDS = ('select', 'from', 'where', 'group', 'order', 'limit', 'intersect', 'union', 'except')
-JOIN_KEYWORDS = ('join', 'on', 'as')
-
-WHERE_OPS = ('not', 'between', '=', '>', '<', '>=', '<=', '!=', 'in', 'like', 'is', 'exists')
-UNIT_OPS = ('none', '-', '+', "*", '/')
-AGG_OPS = ('none', 'max', 'min', 'count', 'sum', 'avg')
-TABLE_TYPE = {
-    'sql': "sql",
-    'table_unit': "table_unit",
-}
-
-COND_OPS = ('and', 'or')
-SQL_OPS = ('intersect', 'union', 'except')
-ORDER_OPS = ('desc', 'asc')
-
-
-
-class Schema:
-    """
-    Simple schema which maps table&column to a unique identifier
-    """
-    def __init__(self, schema):
-        self._schema = schema
-        self._idMap = self._map(self._schema)
-
-    @property
-    def schema(self):
-        return self._schema
-
-    @property
-    def idMap(self):
-        return self._idMap
-
-    def _map(self, schema):
-        idMap = {'*': "__all__"}
-        id = 1
-        for key, vals in schema.items():
-            for val in vals:
-                idMap[key.lower() + "." + val.lower()] = "__" + key.lower() + "." + val.lower() + "__"
-                id += 1
-
-        for key in schema:
-            idMap[key.lower()] = "__" + key.lower() + "__"
-            id += 1
-
-        return idMap
-
-
-def get_schema(db):
-    """
-    Get database's schema, which is a dict with table name as key
-    and list of column names as value
-    :param db: database path
-    :return: schema dict
-    """
-
-    schema = {}
-    conn = sqlite3.connect(db)
-    cursor = conn.cursor()
-
-    # fetch table names
-    cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
-    tables = [str(table[0].lower()) for table in cursor.fetchall()]
-
-    # fetch table info
-    for table in tables:
-        cursor.execute("PRAGMA table_info({})".format(table))
-        schema[table] = [str(col[1].lower()) for col in cursor.fetchall()]
-
-    return schema
-
-
-def get_schema_from_json(fpath):
-    with open(fpath) as f:
-        data = json.load(f)
-
-    schema = {}
-    for entry in data:
-        table = str(entry['table'].lower())
-        cols = [str(col['column_name'].lower()) for col in entry['col_data']]
-        schema[table] = cols
-
-    return schema
-
-
-def tokenize(string):
-    string = str(string)
-    string = string.replace("\'", "\"")  # ensures all string values wrapped by "" problem??
-    quote_idxs = [idx for idx, char in enumerate(string) if char == '"']
-    assert len(quote_idxs) % 2 == 0, "Unexpected quote"
-
-    # keep string value as token
-    vals = {}
-    for i in range(len(quote_idxs)-1, -1, -2):
-        qidx1 = quote_idxs[i-1]
-        qidx2 = quote_idxs[i]
-        val = string[qidx1: qidx2+1]
-        key = "__val_{}_{}__".format(qidx1, qidx2)
-        string = string[:qidx1] + key + string[qidx2+1:]
-        vals[key] = val
-
-    # tokenize sql
-    toks_tmp = [word.lower() for word in word_tokenize(string)]
-    toks = []
-    for tok in toks_tmp:
-        if tok.startswith('=__val_'):
-            tok = tok[1:]
-            toks.append('=')
-        toks.append(tok)
-
-    # replace with string value token
-    for i in range(len(toks)):
-        if toks[i] in vals:
-            toks[i] = vals[toks[i]]
-
-    # find if there exists !=, >=, <=
-    eq_idxs = [idx for idx, tok in enumerate(toks) if tok == "="]
-    eq_idxs.reverse()
-    prefix = ('!', '>', '<')
-    for eq_idx in eq_idxs:
-        pre_tok = toks[eq_idx-1]
-        if pre_tok in prefix:
-            toks = toks[:eq_idx-1] + [pre_tok + "="] + toks[eq_idx+1: ]
-
-    return toks
-
-
-def scan_alias(toks):
-    """Scan the index of 'as' and build the map for all alias"""
-    as_idxs = [idx for idx, tok in enumerate(toks) if tok == 'as']
-    alias = {}
-    for idx in as_idxs:
-        alias[toks[idx+1]] = toks[idx-1]
-    return alias
-
-
-def get_tables_with_alias(schema, toks):
-    tables = scan_alias(toks)
-    for key in schema:
-        assert key not in tables, "Alias {} has the same name in table".format(key)
-        tables[key] = key
-    return tables
-
-
-def parse_col(toks, start_idx, tables_with_alias, schema, default_tables=None):
-    """
-        :returns next idx, column id
-    """
-    tok = toks[start_idx]
-    if tok == "*":
-        return start_idx + 1, schema.idMap[tok]
-
-    if '.' in tok:  # if token is a composite
-        alias, col = tok.split('.')
-        key = tables_with_alias[alias] + "." + col
-        return start_idx+1, schema.idMap[key]
-
-    assert default_tables is not None and len(default_tables) > 0, "Default tables should not be None or empty"
-
-    for alias in default_tables:
-        table = tables_with_alias[alias]
-        if tok in schema.schema[table]:
-            key = table + "." + tok
-            return start_idx+1, schema.idMap[key]
-
-    assert False, "Error col: {}".format(tok)
-
-
-def parse_col_unit(toks, start_idx, tables_with_alias, schema, default_tables=None):
-    """
-        :returns next idx, (agg_op id, col_id)
-    """
-    idx = start_idx
-    len_ = len(toks)
-    isBlock = False
-    isDistinct = False
-    if toks[idx] == '(':
-        isBlock = True
-        idx += 1
-
-    if toks[idx] in AGG_OPS:
-        agg_id = AGG_OPS.index(toks[idx])
-        idx += 1
-        assert idx < len_ and toks[idx] == '('
-        idx += 1
-        if toks[idx] == "distinct":
-            idx += 1
-            isDistinct = True
-        idx, col_id = parse_col(toks, idx, tables_with_alias, schema, default_tables)
-        assert idx < len_ and toks[idx] == ')'
-        idx += 1
-        return idx, (agg_id, col_id, isDistinct)
-
-    if toks[idx] == "distinct":
-        idx += 1
-        isDistinct = True
-    agg_id = AGG_OPS.index("none")
-    idx, col_id = parse_col(toks, idx, tables_with_alias, schema, default_tables)
-
-    if isBlock:
-        assert toks[idx] == ')'
-        idx += 1  # skip ')'
-
-    return idx, (agg_id, col_id, isDistinct)
-
-
-def parse_val_unit(toks, start_idx, tables_with_alias, schema, default_tables=None):
-    idx = start_idx
-    len_ = len(toks)
-    isBlock = False
-    if toks[idx] == '(':
-        isBlock = True
-        idx += 1
-
-    col_unit1 = None
-    col_unit2 = None
-    unit_op = UNIT_OPS.index('none')
-
-    idx, col_unit1 = parse_col_unit(toks, idx, tables_with_alias, schema, default_tables)
-    if idx < len_ and toks[idx] in UNIT_OPS:
-        unit_op = UNIT_OPS.index(toks[idx])
-        idx += 1
-        idx, col_unit2 = parse_col_unit(toks, idx, tables_with_alias, schema, default_tables)
-
-    if isBlock:
-        assert toks[idx] == ')'
-        idx += 1  # skip ')'
-
-    return idx, (unit_op, col_unit1, col_unit2)
-
-
-def parse_table_unit(toks, start_idx, tables_with_alias, schema):
-    """
-        :returns next idx, table id, table name
-    """
-    idx = start_idx
-    len_ = len(toks)
-    key = tables_with_alias[toks[idx]]
-
-    if idx + 1 < len_ and toks[idx+1] == "as":
-        idx += 3
-    else:
-        idx += 1
-
-    return idx, schema.idMap[key], key
-
-
-def parse_value(toks, start_idx, tables_with_alias, schema, default_tables=None):
-    idx = start_idx
-    len_ = len(toks)
-
-    isBlock = False
-    if toks[idx] == '(':
-        isBlock = True
-        idx += 1
-
-    if toks[idx] == 'select':
-        idx, val = parse_sql(toks, idx, tables_with_alias, schema)
-    elif "\"" in toks[idx]:  # token is a string value
-        val = toks[idx]
-        idx += 1
-    else:
-        try:
-            val = float(toks[idx])
-            idx += 1
-        except:
-            end_idx = idx
-            while end_idx < len_ and toks[end_idx] != ',' and toks[end_idx] != ')'\
-                and toks[end_idx] != 'and' and toks[end_idx] not in CLAUSE_KEYWORDS and toks[end_idx] not in JOIN_KEYWORDS:
-                    end_idx += 1
-
-            idx, val = parse_col_unit(toks[start_idx: end_idx], 0, tables_with_alias, schema, default_tables)
-            idx = end_idx
-
-    if isBlock:
-        assert toks[idx] == ')'
-        idx += 1
-
-    return idx, val
-
-
-def parse_condition(toks, start_idx, tables_with_alias, schema, default_tables=None):
-    idx = start_idx
-    len_ = len(toks)
-    conds = []
-
-    while idx < len_:
-        idx, val_unit = parse_val_unit(toks, idx, tables_with_alias, schema, default_tables)
-        not_op = False
-        if toks[idx] == 'not':
-            not_op = True
-            idx += 1
-
-        assert idx < len_ and toks[idx] in WHERE_OPS, "Error condition: idx: {}, tok: {}".format(idx, toks[idx])
-        op_id = WHERE_OPS.index(toks[idx])
-        idx += 1
-        val1 = val2 = None
-        if op_id == WHERE_OPS.index('between'):  # between..and... special case: dual values
-            idx, val1 = parse_value(toks, idx, tables_with_alias, schema, default_tables)
-            assert toks[idx] == 'and'
-            idx += 1
-            idx, val2 = parse_value(toks, idx, tables_with_alias, schema, default_tables)
-        else:  # normal case: single value
-            idx, val1 = parse_value(toks, idx, tables_with_alias, schema, default_tables)
-            val2 = None
-
-        conds.append((not_op, op_id, val_unit, val1, val2))
-
-        if idx < len_ and (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";") or toks[idx] in JOIN_KEYWORDS):
-            break
-
-        if idx < len_ and toks[idx] in COND_OPS:
-            conds.append(toks[idx])
-            idx += 1  # skip and/or
-
-    return idx, conds
-
-
-def parse_select(toks, start_idx, tables_with_alias, schema, default_tables=None):
-    idx = start_idx
-    len_ = len(toks)
-
-    assert toks[idx] == 'select', "'select' not found"
-    idx += 1
-    isDistinct = False
-    if idx < len_ and toks[idx] == 'distinct':
-        idx += 1
-        isDistinct = True
-    val_units = []
-
-    while idx < len_ and toks[idx] not in CLAUSE_KEYWORDS:
-        agg_id = AGG_OPS.index("none")
-        if toks[idx] in AGG_OPS:
-            agg_id = AGG_OPS.index(toks[idx])
-            idx += 1
-        idx, val_unit = parse_val_unit(toks, idx, tables_with_alias, schema, default_tables)
-        val_units.append((agg_id, val_unit))
-        if idx < len_ and toks[idx] == ',':
-            idx += 1  # skip ','
-
-    return idx, (isDistinct, val_units)
-
-
-def parse_from(toks, start_idx, tables_with_alias, schema):
-    """
-    Assume in the from clause, all table units are combined with join
-    """
-    assert 'from' in toks[start_idx:], "'from' not found"
-
-    len_ = len(toks)
-    idx = toks.index('from', start_idx) + 1
-    default_tables = []
-    table_units = []
-    conds = []
-
-    while idx < len_:
-        isBlock = False
-        if toks[idx] == '(':
-            isBlock = True
-            idx += 1
-
-        if toks[idx] == 'select':
-            idx, sql = parse_sql(toks, idx, tables_with_alias, schema)
-            table_units.append((TABLE_TYPE['sql'], sql))
-        else:
-            if idx < len_ and toks[idx] == 'join':
-                idx += 1  # skip join
-            idx, table_unit, table_name = parse_table_unit(toks, idx, tables_with_alias, schema)
-            table_units.append((TABLE_TYPE['table_unit'],table_unit))
-            default_tables.append(table_name)
-        if idx < len_ and toks[idx] == "on":
-            idx += 1  # skip on
-            idx, this_conds = parse_condition(toks, idx, tables_with_alias, schema, default_tables)
-            if len(conds) > 0:
-                conds.append('and')
-            conds.extend(this_conds)
-
-        if isBlock:
-            assert toks[idx] == ')'
-            idx += 1
-        if idx < len_ and (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";")):
-            break
-
-    return idx, table_units, conds, default_tables
-
-
-def parse_where(toks, start_idx, tables_with_alias, schema, default_tables):
-    idx = start_idx
-    len_ = len(toks)
-
-    if idx >= len_ or toks[idx] != 'where':
-        return idx, []
-
-    idx += 1
-    idx, conds = parse_condition(toks, idx, tables_with_alias, schema, default_tables)
-    return idx, conds
-
-
-def parse_group_by(toks, start_idx, tables_with_alias, schema, default_tables):
-    idx = start_idx
-    len_ = len(toks)
-    col_units = []
-
-    if idx >= len_ or toks[idx] != 'group':
-        return idx, col_units
-
-    idx += 1
-    assert toks[idx] == 'by'
-    idx += 1
-
-    while idx < len_ and not (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";")):
-        idx, col_unit = parse_col_unit(toks, idx, tables_with_alias, schema, default_tables)
-        col_units.append(col_unit)
-        if idx < len_ and toks[idx] == ',':
-            idx += 1  # skip ','
-        else:
-            break
-
-    return idx, col_units
-
-
-def parse_order_by(toks, start_idx, tables_with_alias, schema, default_tables):
-    idx = start_idx
-    len_ = len(toks)
-    val_units = []
-    order_type = 'asc' # default type is 'asc'
-
-    if idx >= len_ or toks[idx] != 'order':
-        return idx, val_units
-
-    idx += 1
-    assert toks[idx] == 'by'
-    idx += 1
-
-    while idx < len_ and not (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";")):
-        idx, val_unit = parse_val_unit(toks, idx, tables_with_alias, schema, default_tables)
-        val_units.append(val_unit)
-        if idx < len_ and toks[idx] in ORDER_OPS:
-            order_type = toks[idx]
-            idx += 1
-        if idx < len_ and toks[idx] == ',':
-            idx += 1  # skip ','
-        else:
-            break
-
-    return idx, (order_type, val_units)
-
-
-def parse_having(toks, start_idx, tables_with_alias, schema, default_tables):
-    idx = start_idx
-    len_ = len(toks)
-
-    if idx >= len_ or toks[idx] != 'having':
-        return idx, []
-
-    idx += 1
-    idx, conds = parse_condition(toks, idx, tables_with_alias, schema, default_tables)
-    return idx, conds
-
-
-def parse_limit(toks, start_idx):
-    idx = start_idx
-    len_ = len(toks)
-
-    if idx < len_ and toks[idx] == 'limit':
-        idx += 2
-        # make limit value can work, cannot assume put 1 as a fake limit number
-        if type(toks[idx-1]) != int:
-            return idx, 1
-
-        return idx, int(toks[idx-1])
-
-    return idx, None
-
-
-def parse_sql(toks, start_idx, tables_with_alias, schema):
-    isBlock = False # indicate whether this is a block of sql/sub-sql
-    len_ = len(toks)
-    idx = start_idx
-
-    sql = {}
-    if toks[idx] == '(':
-        isBlock = True
-        idx += 1
-
-    # parse from clause in order to get default tables
-    from_end_idx, table_units, conds, default_tables = parse_from(toks, start_idx, tables_with_alias, schema)
-    sql['from'] = {'table_units': table_units, 'conds': conds}
-    # select clause
-    _, select_col_units = parse_select(toks, idx, tables_with_alias, schema, default_tables)
-    idx = from_end_idx
-    sql['select'] = select_col_units
-    # where clause
-    idx, where_conds = parse_where(toks, idx, tables_with_alias, schema, default_tables)
-    sql['where'] = where_conds
-    # group by clause
-    idx, group_col_units = parse_group_by(toks, idx, tables_with_alias, schema, default_tables)
-    sql['groupBy'] = group_col_units
-    # having clause
-    idx, having_conds = parse_having(toks, idx, tables_with_alias, schema, default_tables)
-    sql['having'] = having_conds
-    # order by clause
-    idx, order_col_units = parse_order_by(toks, idx, tables_with_alias, schema, default_tables)
-    sql['orderBy'] = order_col_units
-    # limit clause
-    idx, limit_val = parse_limit(toks, idx)
-    sql['limit'] = limit_val
-
-    idx = skip_semicolon(toks, idx)
-    if isBlock:
-        assert toks[idx] == ')'
-        idx += 1  # skip ')'
-    idx = skip_semicolon(toks, idx)
-
-    # intersect/union/except clause
-    for op in SQL_OPS:  # initialize IUE
-        sql[op] = None
-    if idx < len_ and toks[idx] in SQL_OPS:
-        sql_op = toks[idx]
-        idx += 1
-        idx, IUE_sql = parse_sql(toks, idx, tables_with_alias, schema)
-        sql[sql_op] = IUE_sql
-    return idx, sql
-
-
-def load_data(fpath):
-    with open(fpath) as f:
-        data = json.load(f)
-    return data
-
-
-def get_sql(schema, query):
-    toks = tokenize(query)
-    tables_with_alias = get_tables_with_alias(schema.schema, toks)
-    _, sql = parse_sql(toks, 0, tables_with_alias, schema)
-
-    return sql
-
-
-def skip_semicolon(toks, start_idx):
-    idx = start_idx
-    while idx < len(toks) and toks[idx] == ";":
-        idx += 1
-    return idx
-
-def get_schemas_from_json(fpath):
-    with open(fpath) as f:
-        data = json.load(f)
-    db_names = [db['db_id'] for db in data]
-
-    tables = {}
-    schemas = {}
-    for db in data:
-        db_id = db['db_id']
-        schema = {} #{'table': [col.lower, ..., ]} * -> __all__
-        column_names_original = db['column_names_original']
-        table_names_original = db['table_names_original']
-        tables[db_id] = {'column_names_original': column_names_original, 'table_names_original': table_names_original}
-        for i, tabn in enumerate(table_names_original):
-            table = str(tabn.lower())
-            cols = [str(col.lower()) for td, col in column_names_original if td == i]
-            schema[table] = cols
-        schemas[db_id] = schema
-
-    return schemas, db_names, tables
\ No newline at end of file
diff --git a/spaces/hkunlp/Binder/utils/wtq/utils.py b/spaces/hkunlp/Binder/utils/wtq/utils.py
deleted file mode 100644
index 539c24e0524c40345c30fbafe59beb57f59f8489..0000000000000000000000000000000000000000
--- a/spaces/hkunlp/Binder/utils/wtq/utils.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import re
-import json
-import records
-from typing import List, Dict
-from sqlalchemy.exc import SQLAlchemyError
-from utils.sql.all_keywords import ALL_KEY_WORDS
-
-
-class WTQDBEngine:
-    def __init__(self, fdb):
-        self.db = records.Database('sqlite:///{}'.format(fdb))
-        self.conn = self.db.get_connection()
-
-    def execute_wtq_query(self, sql_query: str):
-        out = self.conn.query(sql_query)
-        results = out.all()
-        merged_results = []
-        for i in range(len(results)):
-            merged_results.extend(results[i].values())
-        return merged_results
-
-    def delete_rows(self, row_indices: List[int]):
-        sql_queries = [
-            "delete from w where id == {}".format(row) for row in row_indices
-        ]
-        for query in sql_queries:
-            self.conn.query(query)
-
-
-def process_table_structure(_wtq_table_content: Dict, _add_all_column: bool = False):
-    # remove id and agg column
-    headers = [_.replace("\n", " ").lower() for _ in _wtq_table_content["headers"][2:]]
-    header_map = {}
-    for i in range(len(headers)):
-        header_map["c" + str(i + 1)] = headers[i]
-    header_types = _wtq_table_content["types"][2:]
-
-    all_headers = []
-    all_header_types = []
-    vertical_content = []
-    for column_content in _wtq_table_content["contents"][2:]:
-        # only take the first one
-        if _add_all_column:
-            for i in range(len(column_content)):
-                column_alias = column_content[i]["col"]
-                # do not add the numbered column
-                if "_number" in column_alias:
-                    continue
-                vertical_content.append([str(_).replace("\n", " ").lower() for _ in column_content[i]["data"]])
-                if "_" in column_alias:
-                    first_slash_pos = column_alias.find("_")
-                    column_name = header_map[column_alias[:first_slash_pos]] + " " + \
-                                  column_alias[first_slash_pos + 1:].replace("_", " ")
-                else:
-                    column_name = header_map[column_alias]
-                all_headers.append(column_name)
-                if column_content[i]["type"] == "TEXT":
-                    all_header_types.append("text")
-                else:
-                    all_header_types.append("number")
-        else:
-            vertical_content.append([str(_).replace("\n", " ").lower() for _ in column_content[0]["data"]])
-    row_content = list(map(list, zip(*vertical_content)))
-
-    if _add_all_column:
-        ret_header = all_headers
-        ret_types = all_header_types
-    else:
-        ret_header = headers
-        ret_types = header_types
-    return {
-        "header": ret_header,
-        "rows": row_content,
-        "types": ret_types,
-        "alias": list(_wtq_table_content["is_list"].keys())
-    }
-
-
-def retrieve_wtq_query_answer(_engine, _table_content, _sql_struct: List):
-    # do not append id / agg
-    headers = _table_content["header"]
-
-    def flatten_sql(_ex_sql_struct: List):
-        # [ "Keyword", "select", [] ], [ "Column", "c4", [] ]
-        _encode_sql = []
-        _execute_sql = []
-        for _ex_tuple in _ex_sql_struct:
-            keyword = str(_ex_tuple[1])
-            # upper the keywords.
-            if keyword in ALL_KEY_WORDS:
-                keyword = str(keyword).upper()
-
-            # extra column, which we do not need in result
-            if keyword == "w" or keyword == "from":
-                # add 'FROM w' make it executable
-                _encode_sql.append(keyword)
-            elif re.fullmatch(r"c\d+(_.+)?", keyword):
-                # only take the first part
-                index_key = int(keyword.split("_")[0][1:]) - 1
-                # wrap it with `` to make it executable
-                _encode_sql.append("`{}`".format(headers[index_key]))
-            else:
-                _encode_sql.append(keyword)
-            # c4_list, replace it with the original one
-            if "_address" in keyword or "_list" in keyword:
-                keyword = re.findall(r"c\d+", keyword)[0]
-
-            _execute_sql.append(keyword)
-
-        return " ".join(_execute_sql), " ".join(_encode_sql)
-
-    _exec_sql_str, _encode_sql_str = flatten_sql(_sql_struct)
-    try:
-        _sql_answers = _engine.execute_wtq_query(_exec_sql_str)
-    except SQLAlchemyError as e:
-        _sql_answers = []
-    _norm_sql_answers = [str(_).replace("\n", " ") for _ in _sql_answers if _ is not None]
-    if "none" in _norm_sql_answers:
-        _norm_sql_answers = []
-    return _encode_sql_str, _norm_sql_answers, _exec_sql_str
-
-
-def _load_table_w_page(table_path, page_title_path=None) -> dict:
-    """
-    attention: the table_path must be the .tsv path.
-    Load the WikiTableQuestion from csv file. Result in a dict format like:
-    {"header": [header1, header2,...], "rows": [[row11, row12, ...], [row21,...]... [...rownm]]}
-    """
-
-    from utils.utils import _load_table
-
-    table_item = _load_table(table_path)
-
-    # Load page title
-    if not page_title_path:
-        page_title_path = table_path.replace("csv", "page").replace(".tsv", ".json")
-    with open(page_title_path, "r") as f:
-        page_title = json.load(f)['title']
-    table_item['page_title'] = page_title
-
-    return table_item
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_GeLU.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_GeLU.py
deleted file mode 100644
index 16fb7f972d338a6fc2cf75c4930530f65908a03b..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_GeLU.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-import torch
-from nnunet.network_architecture.generic_UNet import Generic_UNet
-from nnunet.network_architecture.initialization import InitWeights_He
-
-from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2
-from nnunet.utilities.nd_softmax import softmax_helper
-from torch import nn
-
-try:
-    from torch.nn.functional import gelu
-except ImportError:
-    gelu = None
-
-
-class GeLU(nn.Module):
-    def __init__(self):
-        super().__init__()
-        if gelu is None:
-            raise ImportError('You need to have at least torch==1.7.0 to use GeLUs')
-
-    def forward(self, x):
-        return gelu(x)
-
-
-class nnUNetTrainerV2_GeLU(nnUNetTrainerV2):
-    def initialize_network(self):
-        """
-        - momentum 0.99
-        - SGD instead of Adam
-        - self.lr_scheduler = None because we do poly_lr
-        - deep supervision = True
-        - ReLU
-        - i am sure I forgot something here
-
-        Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though
-        :return:
-        """
-        if self.threeD:
-            conv_op = nn.Conv3d
-            dropout_op = nn.Dropout3d
-            norm_op = nn.InstanceNorm3d
-
-        else:
-            conv_op = nn.Conv2d
-            dropout_op = nn.Dropout2d
-            norm_op = nn.InstanceNorm2d
-
-        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
-        dropout_op_kwargs = {'p': 0, 'inplace': True}
-        net_nonlin = GeLU
-        net_nonlin_kwargs = {}
-        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
-                                    len(self.net_num_pool_op_kernel_sizes),
-                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
-                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(),
-                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
-        if torch.cuda.is_available():
-            self.network.cuda()
-        self.network.inference_apply_nonlin = softmax_helper
diff --git a/spaces/hshr/DeepFilterNet/README.md b/spaces/hshr/DeepFilterNet/README.md
deleted file mode 100644
index 78a40334d057612a2a047827095193821211db38..0000000000000000000000000000000000000000
--- a/spaces/hshr/DeepFilterNet/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: DeepFilterNet
-emoji: 💩
-colorFrom: gray
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/huggan/pix2pix-uavid/app.py b/spaces/huggan/pix2pix-uavid/app.py
deleted file mode 100644
index 0ebf45da4ef1ee1457b0891964441d760854775b..0000000000000000000000000000000000000000
--- a/spaces/huggan/pix2pix-uavid/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import gradio as gr
-from torchvision.transforms import Compose, Resize, ToTensor, Normalize
-from PIL import Image
-from torchvision.utils import save_image
-from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet
-
-def predict_fn(img):
-    inp = transform(img).unsqueeze(0)
-    out = model(inp)
-    save_image(out, 'out.png', normalize=True)
-    return 'out.png'
-
-
-transform = Compose(
-    [
-        Resize((1024, 1024), Image.BICUBIC),
-        ToTensor(),
-        Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-    ]
-)
-
-model = GeneratorUNet.from_pretrained('huggan/pix2pix-uavid-15')
-
-gr.Interface(predict_fn, inputs=gr.inputs.Image(type='pil'), outputs='image', examples=[['image1.jpg'], ['image2.jpg'], ['sample.jpg'], ['sample2.jpg'], ['sample3.jpg']]).launch()
\ No newline at end of file
diff --git a/spaces/huggan/sefa/SessionState.py b/spaces/huggan/sefa/SessionState.py
deleted file mode 100644
index 8686605b81753976c6f1f00bd774cc0e4ca20058..0000000000000000000000000000000000000000
--- a/spaces/huggan/sefa/SessionState.py
+++ /dev/null
@@ -1,129 +0,0 @@
-"""Adds pre-session state to StreamLit.
-
-This file is borrowed from
-https://gist.github.com/tvst/036da038ab3e999a64497f42de966a92
-"""
-
-# pylint: disable=protected-access
-
-try:
-    import streamlit.ReportThread as ReportThread
-    from streamlit.server.Server import Server
-except ModuleNotFoundError:
-    # Streamlit >= 0.65.0
-    import streamlit.report_thread as ReportThread
-    from streamlit.server.server import Server
-
-
-class SessionState(object):
-    """Hack to add per-session state to Streamlit.
-
-    Usage
-    -----
-
-    >>> import SessionState
-    >>>
-    >>> session_state = SessionState.get(user_name='', favorite_color='black')
-    >>> session_state.user_name
-    ''
-    >>> session_state.user_name = 'Mary'
-    >>> session_state.favorite_color
-    'black'
-
-    Since you set user_name above, next time your script runs this will be the
-    result:
-    >>> session_state = get(user_name='', favorite_color='black')
-    >>> session_state.user_name
-    'Mary'
-
-    """
-
-    def __init__(self, **kwargs):
-        """A new SessionState object.
-
-        Parameters
-        ----------
-        **kwargs : any
-            Default values for the session state.
-
-        Example
-        -------
-        >>> session_state = SessionState(user_name='', favorite_color='black')
-        >>> session_state.user_name = 'Mary'
-        ''
-        >>> session_state.favorite_color
-        'black'
-
-        """
-        for key, val in kwargs.items():
-            setattr(self, key, val)
-
-
-def get(**kwargs):
-    """Gets a SessionState object for the current session.
-
-    Creates a new object if necessary.
-
-    Parameters
-    ----------
-    **kwargs : any
-        Default values you want to add to the session state, if we're creating a
-        new one.
-
-    Example
-    -------
-    >>> session_state = get(user_name='', favorite_color='black')
-    >>> session_state.user_name
-    ''
-    >>> session_state.user_name = 'Mary'
-    >>> session_state.favorite_color
-    'black'
-
-    Since you set user_name above, next time your script runs this will be the
-    result:
-    >>> session_state = get(user_name='', favorite_color='black')
-    >>> session_state.user_name
-    'Mary'
-
-    """
-    # Hack to get the session object from Streamlit.
-
-    ctx = ReportThread.get_report_ctx()
-
-    this_session = None
-
-    current_server = Server.get_current()
-    if hasattr(current_server, '_session_infos'):
-        # Streamlit < 0.56
-        session_infos = Server.get_current()._session_infos.values()
-    else:
-        session_infos = Server.get_current()._session_info_by_id.values()
-
-    for session_info in session_infos:
-        s = session_info.session
-        if (
-            # Streamlit < 0.54.0
-            (hasattr(s, '_main_dg') and s._main_dg == ctx.main_dg)
-            or
-            # Streamlit >= 0.54.0
-            (not hasattr(s, '_main_dg') and s.enqueue == ctx.enqueue)
-            or
-            # Streamlit >= 0.65.2
-            (not hasattr(s, '_main_dg') and
-             s._uploaded_file_mgr == ctx.uploaded_file_mgr)
-        ):
-            this_session = s
-
-    if this_session is None:
-        raise RuntimeError(
-            "Oh noes. Couldn't get your Streamlit Session object. "
-            'Are you doing something fancy with threads?')
-
-    # Got the session object! Now let's attach some state into it.
-
-    if not hasattr(this_session, '_custom_session_state'):
-        this_session._custom_session_state = SessionState(**kwargs)
-
-    return this_session._custom_session_state
-
-# pylint: enable=protected-access
diff --git a/spaces/huggingchat/chat-ui/src/styles/highlight-js.css b/spaces/huggingchat/chat-ui/src/styles/highlight-js.css
deleted file mode 100644
index b262688368e9a946d72b21ae70fba7d711072fbb..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/src/styles/highlight-js.css
+++ /dev/null
@@ -1 +0,0 @@
-@import "highlight.js/styles/atom-one-dark";
diff --git a/spaces/hush1/anime-remove-background/app.py b/spaces/hush1/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/hush1/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/icon-it-tdtu/mt-vi-en-optimum/app.py b/spaces/icon-it-tdtu/mt-vi-en-optimum/app.py
deleted file mode 100644
index 65e556f39dcbf5c0e60e585fa528e0f6864cd676..0000000000000000000000000000000000000000
--- a/spaces/icon-it-tdtu/mt-vi-en-optimum/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import gradio as gr
-
-from optimum.onnxruntime import ORTModelForSeq2SeqLM
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("icon-it-tdtu/mt-vi-en-optimum")
-model = ORTModelForSeq2SeqLM.from_pretrained("icon-it-tdtu/mt-vi-en-optimum")
-
-def translate(text):
-    inputs = tokenizer(text, return_tensors='pt')
-    outputs = model.generate(**inputs)
-    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return result
-
-iface = gr.Interface(
-    fn=translate,
-    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter Vietnamese text to translate..."),
-    outputs="text",
-    theme="huggingface"
-)
-iface.launch()
\ No newline at end of file
diff --git "a/spaces/inamXcontru/PoeticTTS/Certainly No Alcoholic (Step Seven\302\240continued).md" "b/spaces/inamXcontru/PoeticTTS/Certainly No Alcoholic (Step Seven\302\240continued).md"
deleted file mode 100644
index acb56cc6c2adb0fb8bf6285502ccfc3a6f8083e3..0000000000000000000000000000000000000000
--- "a/spaces/inamXcontru/PoeticTTS/Certainly No Alcoholic (Step Seven\302\240continued).md"	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>People with alcoholic cirrhosis will almost certainly be dependent on alcohol and require medical treatment and a great deal of support. This scar tissue makes it difficult for the liver to perform its functions properly.</p>
-<h2>Certainly No Alcoholic (Step Seven continued)</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://gohhs.com/2uz3PV">https://gohhs.com/2uz3PV</a></b></p><br /><br /> aaccfb2cb3<br />
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diff --git a/spaces/inamXcontru/PoeticTTS/Descargar Programa Presto 10.22 ((NEW)) Crack.epub.md b/spaces/inamXcontru/PoeticTTS/Descargar Programa Presto 10.22 ((NEW)) Crack.epub.md
deleted file mode 100644
index f833898fa31b3dc922dece10329e4e1068a97d6f..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Descargar Programa Presto 10.22 ((NEW)) Crack.epub.md	
+++ /dev/null
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-<br />
-<p></p>
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/18 Wheels Of Steel Across America Patch Crack.md b/spaces/inplisQlawa/anything-midjourney-v4-1/18 Wheels Of Steel Across America Patch Crack.md
deleted file mode 100644
index da66522495d516b77998d0b137f787730778fb5e..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/18 Wheels Of Steel Across America Patch Crack.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<br />
-<p>18 wheels of steel across america patch v1.10. data, information, content and material published on this website may not be copied, duplicated or re-disseminated without the written permission of the owner. </p>
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Material Science And Metallurgy By Phaneesh Pdf Download [EXCLUSIVE].md b/spaces/inplisQlawa/anything-midjourney-v4-1/Material Science And Metallurgy By Phaneesh Pdf Download [EXCLUSIVE].md
deleted file mode 100644
index f562e4cc49ac68e17755e3257c44acc8be30692b..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Material Science And Metallurgy By Phaneesh Pdf Download [EXCLUSIVE].md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Mount And Blade Warband Custom Battle Mods.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Mount And Blade Warband Custom Battle Mods.md
deleted file mode 100644
index e60d1e4a2dec53e8525a48edec1a837f9451d7e8..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Mount And Blade Warband Custom Battle Mods.md	
+++ /dev/null
@@ -1,64 +0,0 @@
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-<h1>Mount and Blade Warband Custom Battle Mods: A Guide for Beginners</h1>
-<p>Mount and Blade Warband is a popular medieval action role-playing game that offers a lot of freedom and variety to the players. You can create your own character, join a faction, fight in battles, siege castles, start your own kingdom, and more. But what if you want to spice up your gameplay with some custom battle mods?</p>
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-<h2>How to Install Custom Battle Mods for Mount and Blade Warband</h2>
-<p>Installing custom battle mods for Mount and Blade Warband is not very difficult, but you need to follow some steps carefully. Here is a general guide on how to do it:</p>
-<ol>
-<li>Download the custom battle mod you want from a reliable source, such as Mod DB or Nexus Mods. Make sure the mod is compatible with your version of Mount and Blade Warband and Warband Script Enhancer (WSE), which is a tool that allows some mods to run properly.</li>
-<li>Extract the mod files to a folder of your choice. You can use a program like WinRAR or 7-Zip to do this.</li>
-<li>Copy the mod folder to the Modules folder of your Mount and Blade Warband installation directory. For example, if you installed the game in C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband, then you should copy the mod folder to C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband\Modules.</li>
-<li>If the mod requires WSE, then you need to copy the WSE files to the main folder of your Mount and Blade Warband installation directory. For example, if you installed the game in C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband, then you should copy the WSE files to C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband.</li>
-<li>Launch the game using WSE if the mod requires it, or using the normal launcher if it doesn't. You can find WSE in the main folder of your Mount and Blade Warband installation directory. It should be named WSELoader.exe.</li>
-<li>Select the mod from the drop-down menu in the launcher and click Play.</li>
-<li>Enjoy your custom battle mod!</li>
-</ol>
-<h2>Some Examples of Custom Battle Mods for Mount and Blade Warband</h2>
-<p>There are many custom battle mods for Mount and Blade Warband that you can try out. Here are some examples of popular and interesting ones:</p>
-<ul>
-<li><strong>BattlesMod for Warband</strong>: This mod adds several features and options to the battle mode, such as adaptive battles, random time of day and weather, drowning, elite units spawn, morale system, formation commands, siege AI pathing, helms deep map, arena maps, concept arena map, and more.</li>
-<li><strong>Deeds of Arms & Chivalry</strong>: This mod focuses on the Hundred Years War between France and England, specifically during the reigns of Charles V and Edward III. It adds new factions, units, weapons, armors, banners, scenes, sounds, music, quests, tournaments, historical characters, and more.</li>
-<li><strong>Sands of Faith</strong>: This mod is based on the Crusades in the Middle East during the 12th and 13th centuries. It adds new factions, units, weapons, armors, banners, scenes, sounds, music, quests, historical characters, diplomacy system</p>
-<h2>How to Play Custom Battle Mods for Mount and Blade Warband</h2>
-<p>Once you have installed your custom battle mod of choice, you can start playing it by launching the game and selecting the mod from the drop-down menu in the launcher. Then, you can choose the multiplayer option and either join an existing server that is running the mod, or host your own local session and invite your friends or play with bots.</p>
-<p>Depending on the mod, you may have different options and settings to customize your battle mode experience. For example, you can choose your faction, your troop type, your equipment, your map, your weather, your team size, your round time, and more. You can also enable or disable certain features, such as adaptive battles, morale system, formation commands, siege AI pathing, etc.</p>
-<p>Once you are ready, you can start the battle and enjoy the new challenges and possibilities that the custom battle mod offers. You can fight in historical or fictional scenarios, use new weapons and tactics, command your troops or follow orders, siege castles or defend them, and more. The custom battle mods for Mount and Blade Warband can provide endless hours of fun and replayability for fans of the game.</p>
-<h2>Why You Should Try Custom Battle Mods for Mount and Blade Warband</h2>
-<p>Custom battle mods for Mount and Blade Warband are a great way to enhance your gameplay and enjoy the game in new ways. Here are some reasons why you should try them:</p>
-<p></p>
-<ul>
-<li><strong>They add variety and diversity</strong>: Custom battle mods can introduce new factions, units, weapons, maps, weather effects, AI behaviors, and more to the game. This can make the battles more interesting and unpredictable, as you will face different enemies and allies, use different strategies and tactics, and fight in different environments and conditions.</li>
-<li><strong>They add challenge and difficulty</strong>: Custom battle mods can also make the battles more challenging and difficult, as you will face stronger opponents, larger armies, smarter AI, tougher sieges, and more. This can test your skills and abilities as a warrior and a leader, and make you feel more satisfied when you win.</li>
-<li><strong>They add immersion and realism</strong>: Custom battle mods can also make the battles more immersive and realistic, as they can be based on historical or fictional settings that are faithful to their sources. You can fight in the Hundred Years War, the Crusades, the Lord of the Rings, or other scenarios that are inspired by real or fictional events. You can also use realistic weapons and armors that match the period and culture of your faction.</li>
-<li><strong>They add fun and entertainment</strong>: Custom battle mods can also make the battles more fun and entertaining, as they can add creative and experimental features that are not possible in the vanilla game. You can play basketball, parkour, air combat, or other modes that are completely different from the usual medieval warfare. You can also use funny weapons and armors that are not meant to be taken seriously.</li>
-</ul>
-<p>Custom battle mods for Mount and Blade Warband are a great way to enjoy the game in new ways. They can add variety, challenge, immersion, realism
-<h2>How to Find Custom Battle Mods for Mount and Blade Warband</h2>
-<p>If you are looking for custom battle mods for Mount and Blade Warband, there are many sources where you can find them. Here are some of the most popular and reliable ones:</p>
-<ul>
-<li><strong>Mod DB</strong>: Mod DB is one of the largest and oldest websites for modding games. It has a huge collection of mods for Mount and Blade Warband, including custom battle mods. You can browse by category, popularity, rating, date, or name. You can also read articles, reviews, and comments about the mods, as well as download files, videos, and images. You can also join the modding community and interact with other modders and players.</li>
-<li><strong>Nexus Mods</strong>: Nexus Mods is another well-known website for modding games. It also has a large selection of mods for Mount and Blade Warband, including custom battle mods. You can browse by category, popularity, rating, date, or name. You can also read articles, reviews, and comments about the mods, as well as download files, videos, and images. You can also join the modding community and interact with other modders and players.</li>
-<li><strong>Steam Workshop</strong>: Steam Workshop is a feature of Steam that allows users to create and share content for games. It has some mods for Mount and Blade Warband, including custom battle mods. You can browse by category, popularity, rating, date, or name. You can also read articles, reviews, and comments about the mods, as well as download files, videos, and images. You can also join the modding community and interact with other modders and players.</li>
-<li><strong>Other Websites</strong>: There are also other websites that host mods for Mount and Blade Warband, including custom battle mods. Some examples are TaleWorlds Forum, ModdingWay Forum, ModDB Forum, etc. You can search for them on Google or other search engines. However, you should be careful when downloading mods from unknown sources, as they may contain viruses or malware that can harm your computer or game.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Custom battle mods for Mount and Blade Warband are a great way to enhance your gameplay and enjoy the game in new ways. They can add variety, challenge, immersion
-<h2>How to Uninstall Custom Battle Mods for Mount and Blade Warband</h2>
-<p>If you want to uninstall a custom battle mod for Mount and Blade Warband, you need to follow some steps carefully. Here is a general guide on how to do it:</p>
-<ol>
-<li>Launch the game and select the mod from the drop-down menu in the launcher. Then, exit the game.</li>
-<li>Go to the Modules folder of your Mount and Blade Warband installation directory. For example, if you installed the game in C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband, then you should go to C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband\Modules.</li>
-<li>Delete the mod folder that you want to uninstall. For example, if you want to uninstall Deeds of Arms & Chivalry mod, then you should delete the Deeds of Arms & Chivalry folder.</li>
-<li>If the mod required WSE, then you need to go to the main folder of your Mount and Blade Warband installation directory. For example, if you installed the game in C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband, then you should go to C:\Program Files (x86)\Steam\steamapps\common\MountBlade Warband.</li>
-<li>Delete the WSE files that came with the mod. For example, if you want to uninstall Deeds of Arms & Chivalry mod, then you should delete WSE_DeedsOfArmsAndChivalry.bat and WSE_DeedsOfArmsAndChivalry.ini files.</li>
-<li>Launch the game using the normal launcher or WSE if you still have other mods that require it. You can select the Native module or another mod from the drop-down menu in the launcher.</li>
-<li>Enjoy your unmodded or differently modded game!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Custom battle mods for Mount and Blade Warband are a great way to enhance your gameplay and enjoy the game in new ways. They can add variety, challenge, immersion, realism, fun, and entertainment to your battle mode experience. You can find many custom battle mods on various websites, such as Mod DB, Nexus Mods, Steam Workshop, and others. You can also install and uninstall them easily by following some simple steps. If you are a fan of Mount and Blade Warband, you should definitely try some custom battle mods and see what they can offer.</p>
-<h2>Conclusion</h2>
-<p>Custom battle mods for Mount and Blade Warband are a great way to enhance your gameplay and enjoy the game in new ways. They can add variety, challenge, immersion, realism, fun, and entertainment to your battle mode experience. You can find many custom battle mods on various websites, such as Mod DB, Nexus Mods, Steam Workshop, and others. You can also install and uninstall them easily by following some simple steps. If you are a fan of Mount and Blade Warband, you should definitely try some custom battle mods and see what they can offer.</p> 3cee63e6c2<br />
-<br />
-<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/Adjustment Program Epson Tx710w.md b/spaces/inreVtussa/clothingai/Examples/Adjustment Program Epson Tx710w.md
deleted file mode 100644
index 28f8c0dc69ee53b31be21975c2e52a0a1c4b3248..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Adjustment Program Epson Tx710w.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>YOUR USE OF THE SOFTWARE, PRODUCT AND RELATED PROGRAMS AND DOCUMENTATION IS AT YOUR OWN RISK AND DISCRETION. YOU ARE SOLELY RESPONSIBLE FOR (AND BELKIN DISCLAIMS) ANY AND ALL LOSS, LIABILITY, OR DAMAGES, INCLUDING TO YOUR HOME, HVAC SYSTEM, ELECTRICAL SYSTEM, PLUMBING, PRODUCT, OTHER PERIPHERALS CONNECTED TO THE PRODUCT, COMPUTER, MOBILE DEVICE, AND ALL OTHER ITEMS AND PETS IN YOUR HOME, RESULTING FROM YOUR MISUSE OF THE SOFTWARE, PRODUCT AND RELATED PROGRAMS AND DOCUMENTATION. YOU ARE RESPONSIBLE FOR COMPLYING WITH ANY SAFETY WARNINGS AND PRECAUTIONS THAT ACCOMPANY THE PRODUCT. IF YOU ARE NOT COMFORTABLE WITH USING THE PRODUCT AFTER READING THE SAFETY WARNINGS, YOU MUST RETURN THE PRODUCT TO YOUR PLACE OF PURCHASE AND STOP USING THE SOFTWARE. BELKIN IS NOT RESPONSIBLE FOR (I) YOUR FAILURE TO FOLLOW SAFETY WARNINGS, PRECAUTIONS OR ANY OTHER INSTRUCTIONS PROVIDED WITH THE PRODUCT AND/OR SOFTWARE, (II) YOUR NEGLIGENCE IN USE OF THE PRODUCT AND/OR SOFTWARE, OR (III) YOUR INTENTIONAL MISUSE OF THE PRODUCT OR SOFTWARE.</p>
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diff --git a/spaces/inreVtussa/clothingai/Examples/Adjustment Program For Epson Stylus Sx235.md b/spaces/inreVtussa/clothingai/Examples/Adjustment Program For Epson Stylus Sx235.md
deleted file mode 100644
index 77fb1ef2e63e8404c2506a9748ee3147940ee2a9..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Adjustment Program For Epson Stylus Sx235.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-
-<h1>How to Reset Your Epson Stylus SX235 Printer with Adjustment Program</h1>
-<p>If you own an Epson Stylus SX235 printer, you may encounter a problem where the printer stops working and displays an error message like "A printer's ink pad is at the end of its service life. Please contact Epson Support." This means that the printer's waste ink pad counter has reached its limit and needs to be reset.</p>
-<p>Fortunately, there is a solution to this problem. You can use a software tool called Adjustment Program to reset the waste ink pad counter and restore your printer's functionality. In this article, we will show you how to download and use Adjustment Program for Epson Stylus SX235 printer.</p>
-<h2>Adjustment Program For Epson Stylus Sx235</h2><br /><p><b><b>Download File</b> &#8230;&#8230;&#8230; <a href="https://tiurll.com/2uCiPL">https://tiurll.com/2uCiPL</a></b></p><br /><br />
-<h2>What is Adjustment Program?</h2>
-<p>Adjustment Program is a utility program that allows you to perform various maintenance tasks on your Epson printer, such as resetting the waste ink pad counter, cleaning the print head, checking the printer information, and more. It is an original program that works only with specific printer models and requires a license key to activate.</p>
-<h2>Where to Download Adjustment Program for Epson Stylus SX235?</h2>
-<p>You can download Adjustment Program for Epson Stylus SX235 from ORPYS, a website that provides various service programs for Epson printers. The program costs $9 and comes with free updates and unlimited use for one PC. You can pay with PayPal or credit card and receive the download link and license key by email.</p>
-<p>To download Adjustment Program for Epson Stylus SX235 from ORPYS, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://orpys.com/en/epson/103-epson-sx230-sx235w-adjustment-program.html">this page</a> and click on "Add to cart".</li>
-<li>Click on "Proceed to checkout" and enter your billing details.</li>
-<li>Choose your payment method and complete the payment.</li>
-<li>Check your email for the download link and license key.</li>
-<li>Download the program and extract it to a folder on your PC.</li>
-</ol>
-<h2>How to Use Adjustment Program for Epson Stylus SX235?</h2>
-<p>To use Adjustment Program for Epson Stylus SX235, follow these steps:</p>
-<p></p>
-<ol>
-<li>Connect your printer to your PC with a USB cable and turn it on.</li>
-<li>Run the program as administrator and enter the license key when prompted.</li>
-<li>Select your printer model and port from the drop-down menus.</li>
-<li>Click on "Particular adjustment mode".</li>
-<li>Select "Waste ink pad counter" from the list of options and click on "OK".</li>
-<li>Check the boxes next to "Main pad counter" and "Platen pad counter" and click on "Check".</li>
-<li>Note down the current values of the counters.</li>
-<li>Click on "Initialization" and confirm the message.</li>
-<li>Wait for the program to reset the counters and display a message like "Please turn off your printer".</li>
-<li>Turn off your printer and wait for a few seconds.</li>
-<li>Turn on your printer again and check if the error message is gone.</li>
-</ol>
-<p>Congratulations! You have successfully reset your Epson Stylus SX235 printer with Adjustment Program. You can now use your printer normally again. However, keep in mind that resetting the waste ink pad counter does not solve the problem of ink overflow. You may need to replace or clean the waste ink pad periodically to prevent ink leakage or damage to your printer.</p> d5da3c52bf<br />
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\ No newline at end of file
diff --git a/spaces/inreVtussa/clothingai/Examples/Dcpandeyopticsandmodernphysicspdfdownload !!INSTALL!!.md b/spaces/inreVtussa/clothingai/Examples/Dcpandeyopticsandmodernphysicspdfdownload !!INSTALL!!.md
deleted file mode 100644
index e1e19e7c804309d080d4e73e57e145cf5585828a..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Dcpandeyopticsandmodernphysicspdfdownload !!INSTALL!!.md	
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diff --git a/spaces/intelliarts/Car_parts_damage_detection/app.py b/spaces/intelliarts/Car_parts_damage_detection/app.py
deleted file mode 100644
index 46f133233d99e83dfe4a74faa15c7d1933cea309..0000000000000000000000000000000000000000
--- a/spaces/intelliarts/Car_parts_damage_detection/app.py
+++ /dev/null
@@ -1,212 +0,0 @@
-try:
-    import detectron2
-except:
-    import os 
-    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
-
-from matplotlib.pyplot import axis
-import gradio as gr
-import requests
-import numpy as np
-from torch import nn
-import requests
-
-import torch
-import detectron2
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog
-from detectron2.utils.visualizer import ColorMode
-
-damage_model_path = 'damage/model_final.pth'
-scratch_model_path = 'scratch/model_final.pth'
-parts_model_path = 'parts/model_final.pth'
-
-if torch.cuda.is_available():
-    device = 'cuda'
-else:
-    device = 'cpu'
-
-cfg_scratches = get_cfg()
-cfg_scratches.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-cfg_scratches.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8
-cfg_scratches.MODEL.ROI_HEADS.NUM_CLASSES = 1
-cfg_scratches.MODEL.WEIGHTS = scratch_model_path
-cfg_scratches.MODEL.DEVICE = device
-
-predictor_scratches = DefaultPredictor(cfg_scratches)
-
-metadata_scratch = MetadataCatalog.get("car_dataset_val")
-metadata_scratch.thing_classes = ["scratch"]
-
-cfg_damage = get_cfg()
-cfg_damage.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-cfg_damage.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7
-cfg_damage.MODEL.ROI_HEADS.NUM_CLASSES = 1
-cfg_damage.MODEL.WEIGHTS = damage_model_path
-cfg_damage.MODEL.DEVICE = device
-
-predictor_damage = DefaultPredictor(cfg_damage)
-
-metadata_damage = MetadataCatalog.get("car_damage_dataset_val")
-metadata_damage.thing_classes = ["damage"]
-
-cfg_parts = get_cfg()
-cfg_parts.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-cfg_parts.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.75
-cfg_parts.MODEL.ROI_HEADS.NUM_CLASSES = 19
-cfg_parts.MODEL.WEIGHTS = parts_model_path
-cfg_parts.MODEL.DEVICE = device
-
-predictor_parts = DefaultPredictor(cfg_parts)
-
-metadata_parts = MetadataCatalog.get("car_parts_dataset_val")
-metadata_parts.thing_classes = ['_background_',
- 'back_bumper',
- 'back_glass',
- 'back_left_door',
- 'back_left_light',
- 'back_right_door',
- 'back_right_light',
- 'front_bumper',
- 'front_glass',
- 'front_left_door',
- 'front_left_light',
- 'front_right_door',
- 'front_right_light',
- 'hood',
- 'left_mirror',
- 'right_mirror',
- 'tailgate',
- 'trunk',
- 'wheel']
-
-def merge_segment(pred_segm):
-    merge_dict = {}
-    for i in range(len(pred_segm)):
-        merge_dict[i] = []
-        for j in range(i+1,len(pred_segm)):
-            if torch.sum(pred_segm[i]*pred_segm[j])>0:
-                merge_dict[i].append(j)
-    
-    to_delete = []
-    for key in merge_dict:
-        for element in merge_dict[key]:
-            to_delete.append(element)
-    
-    for element in to_delete:
-        merge_dict.pop(element,None)
-        
-    empty_delete = []
-    for key in merge_dict:
-        if merge_dict[key] == []:
-            empty_delete.append(key)
-    
-    for element in empty_delete:
-        merge_dict.pop(element,None)
-        
-    for key in merge_dict:
-        for element in merge_dict[key]:
-            pred_segm[key]+=pred_segm[element]
-            
-    except_elem = list(set(to_delete))
-    
-    new_indexes = list(range(len(pred_segm)))
-    for elem in except_elem:
-        new_indexes.remove(elem)
-        
-    return pred_segm[new_indexes]
-
-    
-def inference(image):
-    img = np.array(image)
-    outputs_damage = predictor_damage(img)
-    outputs_parts = predictor_parts(img)
-    outputs_scratch = predictor_scratches(img)
-    out_dict = outputs_damage["instances"].to("cpu").get_fields()
-    merged_damage_masks = merge_segment(out_dict['pred_masks'])
-    scratch_data = outputs_scratch["instances"].get_fields()
-    scratch_masks = scratch_data['pred_masks']
-    damage_data = outputs_damage["instances"].get_fields()
-    damage_masks = damage_data['pred_masks']
-    parts_data = outputs_parts["instances"].get_fields()
-    parts_masks = parts_data['pred_masks']
-    parts_classes = parts_data['pred_classes']
-    new_inst = detectron2.structures.Instances((1024,1024))
-    new_inst.set('pred_masks',merge_segment(out_dict['pred_masks']))
-    
-    parts_damage_dict = {}
-    parts_list_damages = []
-    for part in parts_classes:
-        parts_damage_dict[metadata_parts.thing_classes[part]] = []
-    for mask in scratch_masks:
-        for i in range(len(parts_masks)):
-            if torch.sum(parts_masks[i]*mask)>0:
-                parts_damage_dict[metadata_parts.thing_classes[parts_classes[i]]].append('scratch')
-                parts_list_damages.append(f'{metadata_parts.thing_classes[parts_classes[i]]} has scratch')              
-                print(f'{metadata_parts.thing_classes[parts_classes[i]]} has scratch')
-    for mask in merged_damage_masks:
-        for i in range(len(parts_masks)):
-            if torch.sum(parts_masks[i]*mask)>0:
-                parts_damage_dict[metadata_parts.thing_classes[parts_classes[i]]].append('damage')
-                parts_list_damages.append(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
-                print(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
-
-    v_d = Visualizer(img[:, :, ::-1],
-                   metadata=metadata_damage, 
-                   scale=0.5, 
-                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
-    )
-    #v_d = Visualizer(img,scale=1.2)
-    #print(outputs["instances"].to('cpu'))
-    out_d = v_d.draw_instance_predictions(new_inst)
-    img1 = out_d.get_image()[:, :, ::-1]
-
-    v_s = Visualizer(img[:, :, ::-1],
-                   metadata=metadata_scratch, 
-                   scale=0.5, 
-                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
-    )
-    #v_s = Visualizer(img,scale=1.2)
-    out_s = v_s.draw_instance_predictions(outputs_scratch["instances"])
-    img2 = out_s.get_image()[:, :, ::-1]
-
-    v_p = Visualizer(img[:, :, ::-1],
-                   metadata=metadata_parts, 
-                   scale=0.5, 
-                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
-    )
-    #v_p = Visualizer(img,scale=1.2)
-    out_p = v_p.draw_instance_predictions(outputs_parts["instances"])
-    img3 = out_p.get_image()[:, :, ::-1]
-    
-    return img1, img2, img3, parts_list_damages
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("## Inputs")
-            image = gr.Image(type="pil",label="Input")
-            submit_button = gr.Button(value="Submit", label="Submit")
-        with gr.Column():
-            gr.Markdown("## Outputs")
-            with gr.Tab('Image of damages'):
-                im1 = gr.Image(type='numpy',label='Image of damages')
-            with gr.Tab('Image of scratches'):
-                im2 = gr.Image(type='numpy',label='Image of scratches')
-            with gr.Tab('Image of parts'):
-                im3 = gr.Image(type='numpy',label='Image of car parts')
-            with gr.Tab('Information about damaged parts'):
-                intersections = gr.Textbox(label='Information about type of damages on each part')
-    
-    #actions
-    submit_button.click(
-        fn=inference,
-        inputs = [image],
-        outputs = [im1,im2,im3,intersections]
-    )
-        
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/ismot/1702t1/dataset/build.py b/spaces/ismot/1702t1/dataset/build.py
deleted file mode 100644
index 6460ad7debbc459b72815b1199d8381c281daf52..0000000000000000000000000000000000000000
--- a/spaces/ismot/1702t1/dataset/build.py
+++ /dev/null
@@ -1,115 +0,0 @@
-"""
-@Date: 2021/07/18
-@description:
-"""
-import numpy as np
-import torch.utils.data
-from dataset.mp3d_dataset import MP3DDataset
-from dataset.pano_s2d3d_dataset import PanoS2D3DDataset
-from dataset.pano_s2d3d_mix_dataset import PanoS2D3DMixDataset
-from dataset.zind_dataset import ZindDataset
-
-
-def build_loader(config, logger):
-    name = config.DATA.DATASET
-    ddp = config.WORLD_SIZE > 1
-    train_dataset = None
-    train_data_loader = None
-    if config.MODE == 'train':
-        train_dataset = build_dataset(mode='train', config=config, logger=logger)
-
-    val_dataset = build_dataset(mode=config.VAL_NAME if config.MODE != 'test' else 'test', config=config, logger=logger)
-
-    train_sampler = None
-    val_sampler = None
-    if ddp:
-        if train_dataset:
-            train_sampler = torch.utils.data.DistributedSampler(train_dataset, shuffle=True)
-        val_sampler = torch.utils.data.DistributedSampler(val_dataset, shuffle=False)
-
-    batch_size = config.DATA.BATCH_SIZE
-    num_workers = 0 if config.DEBUG else config.DATA.NUM_WORKERS
-    pin_memory = config.DATA.PIN_MEMORY
-    if train_dataset:
-        logger.info(f'Train data loader batch size: {batch_size}')
-        train_data_loader = torch.utils.data.DataLoader(
-            train_dataset, sampler=train_sampler,
-            batch_size=batch_size,
-            shuffle=True,
-            num_workers=num_workers,
-            pin_memory=pin_memory,
-            drop_last=True,
-        )
-    batch_size = batch_size - (len(val_dataset) % np.arange(batch_size, 0, -1)).tolist().index(0)
-    logger.info(f'Val data loader batch size: {batch_size}')
-    val_data_loader = torch.utils.data.DataLoader(
-        val_dataset, sampler=val_sampler,
-        batch_size=batch_size,
-        shuffle=False,
-        num_workers=num_workers,
-        pin_memory=pin_memory,
-        drop_last=False
-    )
-    logger.info(f'Build data loader: num_workers:{num_workers} pin_memory:{pin_memory}')
-    return train_data_loader, val_data_loader
-
-
-def build_dataset(mode, config, logger):
-    name = config.DATA.DATASET
-    if name == 'mp3d':
-        dataset = MP3DDataset(
-            root_dir=config.DATA.DIR,
-            mode=mode,
-            shape=config.DATA.SHAPE,
-            max_wall_num=config.DATA.WALL_NUM,
-            aug=config.DATA.AUG if mode == 'train' else None,
-            camera_height=config.DATA.CAMERA_HEIGHT,
-            logger=logger,
-            for_test_index=config.DATA.FOR_TEST_INDEX,
-            keys=config.DATA.KEYS
-        )
-    elif name == 'pano_s2d3d':
-        dataset = PanoS2D3DDataset(
-            root_dir=config.DATA.DIR,
-            mode=mode,
-            shape=config.DATA.SHAPE,
-            max_wall_num=config.DATA.WALL_NUM,
-            aug=config.DATA.AUG if mode == 'train' else None,
-            camera_height=config.DATA.CAMERA_HEIGHT,
-            logger=logger,
-            for_test_index=config.DATA.FOR_TEST_INDEX,
-            subset=config.DATA.SUBSET,
-            keys=config.DATA.KEYS
-        )
-    elif name == 'pano_s2d3d_mix':
-        dataset = PanoS2D3DMixDataset(
-            root_dir=config.DATA.DIR,
-            mode=mode,
-            shape=config.DATA.SHAPE,
-            max_wall_num=config.DATA.WALL_NUM,
-            aug=config.DATA.AUG if mode == 'train' else None,
-            camera_height=config.DATA.CAMERA_HEIGHT,
-            logger=logger,
-            for_test_index=config.DATA.FOR_TEST_INDEX,
-            subset=config.DATA.SUBSET,
-            keys=config.DATA.KEYS
-        )
-    elif name == 'zind':
-        dataset = ZindDataset(
-            root_dir=config.DATA.DIR,
-            mode=mode,
-            shape=config.DATA.SHAPE,
-            max_wall_num=config.DATA.WALL_NUM,
-            aug=config.DATA.AUG if mode == 'train' else None,
-            camera_height=config.DATA.CAMERA_HEIGHT,
-            logger=logger,
-            for_test_index=config.DATA.FOR_TEST_INDEX,
-            is_simple=True,
-            is_ceiling_flat=False,
-            keys=config.DATA.KEYS,
-            vp_align=config.EVAL.POST_PROCESSING is not None and 'manhattan' in config.EVAL.POST_PROCESSING
-        )
-    else:
-        raise NotImplementedError(f"Unknown dataset: {name}")
-
-    return dataset
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/script_loading.py b/spaces/jackli888/stable-diffusion-webui/modules/script_loading.py
deleted file mode 100644
index b7611ea5f4489edc95f61040e4324124a2e6fefd..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/script_loading.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-import sys
-import traceback
-import importlib.util
-from types import ModuleType
-
-
-def load_module(path):
-    module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
-    module = importlib.util.module_from_spec(module_spec)
-    module_spec.loader.exec_module(module)
-
-    return module
-
-
-def preload_extensions(extensions_dir, parser):
-    if not os.path.isdir(extensions_dir):
-        return
-
-    for dirname in sorted(os.listdir(extensions_dir)):
-        preload_script = os.path.join(extensions_dir, dirname, "preload.py")
-        if not os.path.isfile(preload_script):
-            continue
-
-        try:
-            module = load_module(preload_script)
-            if hasattr(module, 'preload'):
-                module.preload(parser)
-
-        except Exception:
-            print(f"Error running preload() for {preload_script}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
diff --git a/spaces/jackli888/stable-diffusion-webui/webui.sh b/spaces/jackli888/stable-diffusion-webui/webui.sh
deleted file mode 100644
index 8cdad22d310fed20f229b09d7a3160aeb1731a85..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/webui.sh
+++ /dev/null
@@ -1,186 +0,0 @@
-#!/usr/bin/env bash
-#################################################
-# Please do not make any changes to this file,  #
-# change the variables in webui-user.sh instead #
-#################################################
-
-# If run from macOS, load defaults from webui-macos-env.sh
-if [[ "$OSTYPE" == "darwin"* ]]; then
-    if [[ -f webui-macos-env.sh ]]
-        then
-        source ./webui-macos-env.sh
-    fi
-fi
-
-# Read variables from webui-user.sh
-# shellcheck source=/dev/null
-if [[ -f webui-user.sh ]]
-then
-    source ./webui-user.sh
-fi
-
-# Set defaults
-# Install directory without trailing slash
-if [[ -z "${install_dir}" ]]
-then
-    install_dir="/home/$(whoami)"
-fi
-
-# Name of the subdirectory (defaults to stable-diffusion-webui)
-if [[ -z "${clone_dir}" ]]
-then
-    clone_dir="stable-diffusion-webui"
-fi
-
-# python3 executable
-if [[ -z "${python_cmd}" ]]
-then
-    python_cmd="python3"
-fi
-
-# git executable
-if [[ -z "${GIT}" ]]
-then
-    export GIT="git"
-fi
-
-# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
-if [[ -z "${venv_dir}" ]]
-then
-    venv_dir="venv"
-fi
-
-if [[ -z "${LAUNCH_SCRIPT}" ]]
-then
-    LAUNCH_SCRIPT="launch.py"
-fi
-
-# this script cannot be run as root by default
-can_run_as_root=0
-
-# read any command line flags to the webui.sh script
-while getopts "f" flag > /dev/null 2>&1
-do
-    case ${flag} in
-        f) can_run_as_root=1;;
-        *) break;;
-    esac
-done
-
-# Disable sentry logging
-export ERROR_REPORTING=FALSE
-
-# Do not reinstall existing pip packages on Debian/Ubuntu
-export PIP_IGNORE_INSTALLED=0
-
-# Pretty print
-delimiter="################################################################"
-
-printf "\n%s\n" "${delimiter}"
-printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n"
-printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
-printf "\n%s\n" "${delimiter}"
-
-# Do not run as root
-if [[ $(id -u) -eq 0 && can_run_as_root -eq 0 ]]
-then
-    printf "\n%s\n" "${delimiter}"
-    printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m"
-    printf "\n%s\n" "${delimiter}"
-    exit 1
-else
-    printf "\n%s\n" "${delimiter}"
-    printf "Running on \e[1m\e[32m%s\e[0m user" "$(whoami)"
-    printf "\n%s\n" "${delimiter}"
-fi
-
-if [[ -d .git ]]
-then
-    printf "\n%s\n" "${delimiter}"
-    printf "Repo already cloned, using it as install directory"
-    printf "\n%s\n" "${delimiter}"
-    install_dir="${PWD}/../"
-    clone_dir="${PWD##*/}"
-fi
-
-# Check prerequisites
-gpu_info=$(lspci 2>/dev/null | grep VGA)
-case "$gpu_info" in
-    *"Navi 1"*|*"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
-    ;;
-    *"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
-        printf "\n%s\n" "${delimiter}"
-        printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
-        printf "\n%s\n" "${delimiter}"
-    ;;
-    *) 
-    ;;
-esac
-if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
-then
-    export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2"
-fi  
-
-for preq in "${GIT}" "${python_cmd}"
-do
-    if ! hash "${preq}" &>/dev/null
-    then
-        printf "\n%s\n" "${delimiter}"
-        printf "\e[1m\e[31mERROR: %s is not installed, aborting...\e[0m" "${preq}"
-        printf "\n%s\n" "${delimiter}"
-        exit 1
-    fi
-done
-
-if ! "${python_cmd}" -c "import venv" &>/dev/null
-then
-    printf "\n%s\n" "${delimiter}"
-    printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m"
-    printf "\n%s\n" "${delimiter}"
-    exit 1
-fi
-
-cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; }
-if [[ -d "${clone_dir}" ]]
-then
-    cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-else
-    printf "\n%s\n" "${delimiter}"
-    printf "Clone stable-diffusion-webui"
-    printf "\n%s\n" "${delimiter}"
-    "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
-    cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-fi
-
-printf "\n%s\n" "${delimiter}"
-printf "Create and activate python venv"
-printf "\n%s\n" "${delimiter}"
-cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-if [[ ! -d "${venv_dir}" ]]
-then
-    "${python_cmd}" -m venv "${venv_dir}"
-    first_launch=1
-fi
-# shellcheck source=/dev/null
-if [[ -f "${venv_dir}"/bin/activate ]]
-then
-    source "${venv_dir}"/bin/activate
-else
-    printf "\n%s\n" "${delimiter}"
-    printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
-    printf "\n%s\n" "${delimiter}"
-    exit 1
-fi
-
-if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
-then
-    printf "\n%s\n" "${delimiter}"
-    printf "Accelerating launch.py..."
-    printf "\n%s\n" "${delimiter}"
-    exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
-else
-    printf "\n%s\n" "${delimiter}"
-    printf "Launching launch.py..."
-    printf "\n%s\n" "${delimiter}"      
-    exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
-fi
diff --git a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/bertwarper.py b/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/bertwarper.py
deleted file mode 100644
index f0cf9779b270e1aead32845006f8b881fcba37ad..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/bertwarper.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-from torchvision.ops.boxes import nms
-from transformers import BertConfig, BertModel, BertPreTrainedModel
-from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
-
-
-class BertModelWarper(nn.Module):
-    def __init__(self, bert_model):
-        super().__init__()
-        # self.bert = bert_modelc
-
-        self.config = bert_model.config
-        self.embeddings = bert_model.embeddings
-        self.encoder = bert_model.encoder
-        self.pooler = bert_model.pooler
-
-        self.get_extended_attention_mask = bert_model.get_extended_attention_mask
-        self.invert_attention_mask = bert_model.invert_attention_mask
-        self.get_head_mask = bert_model.get_head_mask
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = (
-            output_attentions if output_attentions is not None else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if self.config.is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        # past_key_values_length
-        past_key_values_length = (
-            past_key_values[0][0].shape[2] if past_key_values is not None else 0
-        )
-
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                ((batch_size, seq_length + past_key_values_length)), device=device
-            )
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
-            attention_mask, input_shape, device
-        )
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-        #     import ipdb; ipdb.set_trace()
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-class TextEncoderShell(nn.Module):
-    def __init__(self, text_encoder):
-        super().__init__()
-        self.text_encoder = text_encoder
-        self.config = self.text_encoder.config
-
-    def forward(self, **kw):
-        # feed into text encoder
-        return self.text_encoder(**kw)
-
-
-def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
-    """Generate attention mask between each pair of special tokens
-    Args:
-        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
-        special_tokens_mask (list): special tokens mask.
-    Returns:
-        torch.Tensor: attention mask between each special tokens.
-    """
-    input_ids = tokenized["input_ids"]
-    bs, num_token = input_ids.shape
-    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
-    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
-    for special_token in special_tokens_list:
-        special_tokens_mask |= input_ids == special_token
-
-    # idxs: each row is a list of indices of special tokens
-    idxs = torch.nonzero(special_tokens_mask)
-
-    # generate attention mask and positional ids
-    attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
-    )
-    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
-    previous_col = 0
-    for i in range(idxs.shape[0]):
-        row, col = idxs[i]
-        if (col == 0) or (col == num_token - 1):
-            attention_mask[row, col, col] = True
-            position_ids[row, col] = 0
-        else:
-            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
-            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
-                0, col - previous_col, device=input_ids.device
-            )
-
-        previous_col = col
-
-    # # padding mask
-    # padding_mask = tokenized['attention_mask']
-    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
-
-    return attention_mask, position_ids.to(torch.long)
-
-
-def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
-    """Generate attention mask between each pair of special tokens
-    Args:
-        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
-        special_tokens_mask (list): special tokens mask.
-    Returns:
-        torch.Tensor: attention mask between each special tokens.
-    """
-    input_ids = tokenized["input_ids"]
-    bs, num_token = input_ids.shape
-    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
-    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
-    for special_token in special_tokens_list:
-        special_tokens_mask |= input_ids == special_token
-
-    # idxs: each row is a list of indices of special tokens
-    idxs = torch.nonzero(special_tokens_mask)
-
-    # generate attention mask and positional ids
-    attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
-    )
-    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
-    cate_to_token_mask_list = [[] for _ in range(bs)]
-    previous_col = 0
-    for i in range(idxs.shape[0]):
-        row, col = idxs[i]
-        if (col == 0) or (col == num_token - 1):
-            attention_mask[row, col, col] = True
-            position_ids[row, col] = 0
-        else:
-            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
-            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
-                0, col - previous_col, device=input_ids.device
-            )
-            c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
-            c2t_maski[previous_col + 1 : col] = True
-            cate_to_token_mask_list[row].append(c2t_maski)
-        previous_col = col
-
-    cate_to_token_mask_list = [
-        torch.stack(cate_to_token_mask_listi, dim=0)
-        for cate_to_token_mask_listi in cate_to_token_mask_list
-    ]
-
-    # # padding mask
-    # padding_mask = tokenized['attention_mask']
-    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
-
-    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list
diff --git a/spaces/james-oldfield/PandA/networks/biggan/__init__.py b/spaces/james-oldfield/PandA/networks/biggan/__init__.py
deleted file mode 100644
index b570848421afd921fae635569c97d0f8f5b33c80..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/biggan/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .config import BigGANConfig
-from .model import BigGAN
-from .file_utils import PYTORCH_PRETRAINED_BIGGAN_CACHE, cached_path
-from .utils import (truncated_noise_sample, save_as_images,
-                    convert_to_images, display_in_terminal,
-                    one_hot_from_int, one_hot_from_names)
diff --git a/spaces/jamesjohnson763/ASRLiveSpeechRecognition-GR/README.md b/spaces/jamesjohnson763/ASRLiveSpeechRecognition-GR/README.md
deleted file mode 100644
index a549a4f50eec89dba4e78283712dc39c4fcaa81a..0000000000000000000000000000000000000000
--- a/spaces/jamesjohnson763/ASRLiveSpeechRecognition-GR/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ASRLiveSpeechRecognition GR
-emoji: 🌍
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jaskugler/timdettmers-guanaco-65b-merged/README.md b/spaces/jaskugler/timdettmers-guanaco-65b-merged/README.md
deleted file mode 100644
index ae09e02c1f491bdde0c23d67999af9ca47f97887..0000000000000000000000000000000000000000
--- a/spaces/jaskugler/timdettmers-guanaco-65b-merged/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Timdettmers Guanaco 65b Merged
-emoji: 🐠
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jbetker/tortoise/tortoise/models/classifier.py b/spaces/jbetker/tortoise/tortoise/models/classifier.py
deleted file mode 100644
index ce574eabb38b36b832ed27e61c836caf4d626185..0000000000000000000000000000000000000000
--- a/spaces/jbetker/tortoise/tortoise/models/classifier.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.utils.checkpoint import checkpoint
-
-from tortoise.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
-
-
-class ResBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        up=False,
-        down=False,
-        kernel_size=3,
-        do_checkpoint=True,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_scale_shift_norm = use_scale_shift_norm
-        self.do_checkpoint = do_checkpoint
-        padding = 1 if kernel_size == 3 else 2
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = nn.Conv1d(
-                dims, channels, self.out_channels, kernel_size, padding=padding
-            )
-        else:
-            self.skip_connection = nn.Conv1d(dims, channels, self.out_channels, 1)
-
-    def forward(self, x):
-        if self.do_checkpoint:
-            return checkpoint(
-                self._forward, x
-            )
-        else:
-            return self._forward(x)
-
-    def _forward(self, x):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AudioMiniEncoder(nn.Module):
-    def __init__(self,
-                 spec_dim,
-                 embedding_dim,
-                 base_channels=128,
-                 depth=2,
-                 resnet_blocks=2,
-                 attn_blocks=4,
-                 num_attn_heads=4,
-                 dropout=0,
-                 downsample_factor=2,
-                 kernel_size=3):
-        super().__init__()
-        self.init = nn.Sequential(
-            nn.Conv1d(spec_dim, base_channels, 3, padding=1)
-        )
-        ch = base_channels
-        res = []
-        self.layers = depth
-        for l in range(depth):
-            for r in range(resnet_blocks):
-                res.append(ResBlock(ch, dropout, do_checkpoint=False, kernel_size=kernel_size))
-            res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
-            ch *= 2
-        self.res = nn.Sequential(*res)
-        self.final = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            nn.Conv1d(ch, embedding_dim, 1)
-        )
-        attn = []
-        for a in range(attn_blocks):
-            attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=False))
-        self.attn = nn.Sequential(*attn)
-        self.dim = embedding_dim
-
-    def forward(self, x):
-        h = self.init(x)
-        h = self.res(h)
-        h = self.final(h)
-        for blk in self.attn:
-            h = checkpoint(blk, h)
-        return h[:, :, 0]
-
-
-class AudioMiniEncoderWithClassifierHead(nn.Module):
-    def __init__(self, classes, distribute_zero_label=True, **kwargs):
-        super().__init__()
-        self.enc = AudioMiniEncoder(**kwargs)
-        self.head = nn.Linear(self.enc.dim, classes)
-        self.num_classes = classes
-        self.distribute_zero_label = distribute_zero_label
-
-    def forward(self, x, labels=None):
-        h = self.enc(x)
-        logits = self.head(h)
-        if labels is None:
-            return logits
-        else:
-            if self.distribute_zero_label:
-                oh_labels = nn.functional.one_hot(labels, num_classes=self.num_classes)
-                zeros_indices = (labels == 0).unsqueeze(-1)
-                # Distribute 20% of the probability mass on all classes when zero is specified, to compensate for dataset noise.
-                zero_extra_mass = torch.full_like(oh_labels, dtype=torch.float, fill_value=.2/(self.num_classes-1))
-                zero_extra_mass[:, 0] = -.2
-                zero_extra_mass = zero_extra_mass * zeros_indices
-                oh_labels = oh_labels + zero_extra_mass
-            else:
-                oh_labels = labels
-            loss = nn.functional.cross_entropy(logits, oh_labels)
-            return loss
diff --git a/spaces/jbilcke-hf/Panoremix/src/lib/computeSecretFingerprint.ts b/spaces/jbilcke-hf/Panoremix/src/lib/computeSecretFingerprint.ts
deleted file mode 100644
index 19ba225b1fddb8e77b4fc143a8e380df8fb819ed..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/lib/computeSecretFingerprint.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { computeSha256 } from "./computeSha256"
-
-const secretFingerprint = `${process.env.FINGERPRINT_KEY || ""}`
-
-export function computeSecretFingerprint(input: string) {
-  return computeSha256(`${secretFingerprint}_${input}`)
-}
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/lib/dirtyLLMJsonParser.ts b/spaces/jbilcke-hf/ai-comic-factory/src/lib/dirtyLLMJsonParser.ts
deleted file mode 100644
index 869cb9cc5cabca4b45b954e9c6d3fb837d2597d2..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/src/lib/dirtyLLMJsonParser.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-import { LLMResponse } from "@/types"
-import { cleanJson } from "./cleanJson"
-import { parseBadJSON } from "./parseBadJSON"
-
-export function dirtyLLMJsonParser(input: string): LLMResponse {
-
-  if (input.includes("```")) {
-    input = input.split("```")[0]
-  }
-  // we only keep what's after the first [
-  let jsonOrNot = cleanJson(input)
-
-  const jsonData = parseBadJSON(jsonOrNot) as LLMResponse
-
-  const results = jsonData.map((item, i) => {
-    let panel = i
-    let caption = item.caption ? item.caption.trim() : ''
-    let instructions = item.instructions ? item.instructions.trim() : ''
-    if (!instructions && caption) {
-      instructions = caption
-    } 
-    if (!caption && instructions) {
-      caption = instructions
-    }
-    return { panel, caption, instructions }
-  })
-
-  return results
-}
\ No newline at end of file
diff --git a/spaces/jhonparra18/ocr-LLM-image-summarizer/image_processor.py b/spaces/jhonparra18/ocr-LLM-image-summarizer/image_processor.py
deleted file mode 100644
index 9df98fe09ecd603cff60e41e9a7e246248d79b34..0000000000000000000000000000000000000000
--- a/spaces/jhonparra18/ocr-LLM-image-summarizer/image_processor.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import cv2
-import pytesseract
-from config import PYTESSERACT_DEFAULT_CONFIG
-from pathlib import Path
-from tqdm import tqdm
-import numpy as np
-from langchain.tools import BaseTool
-from typing import Optional, Type
-from langchain.callbacks.manager import AsyncCallbackManagerForToolRun
-from PIL import Image
-
-class ImageProcessor(BaseTool):
-
-    name = "ImageProcessor"
-    description = "useful when you need to extract info from an image in an img_path corresponding to a receipt or invoice and tries to preprocess it returning all the text in the image using an OCR system."
-
-    def binarize(self,img_path):
-        """
-        This function is to binarize an input image
-        :param img: image in format of (h, w, channel)
-        :return: am image in format of (h, w)
-        """
-        img=cv2.imread(img_path)
-        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        #gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] #threshold may affect performance for invoices|receipts as seen in our test dataset
-        return gray
-    
-    def remove_watermark(self,img,alpha = 1.8,beta = -180):
-        """remove watermark from image 
-        img: cv2 image| np.array"""
-        new = alpha * img + beta
-        new = np.clip(new, 0, 255).astype(np.uint8)
-        return new
-        
-    def deskew(self,img):
-        coords = np.column_stack(np.where(img > 0))
-        angle = cv2.minAreaRect(coords)[-1]
-        if angle < -45:
-            angle = -(90 + angle)
-        else:
-            angle = -angle
-        (h, w) = img.shape[:2]
-        center = (w // 2, h // 2)
-        M = cv2.getRotationMatrix2D(center, angle, 1.0)
-        rotated = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
-        return rotated
-    
-    def dilate_erode(self,img):
-        """
-        apply an erosion and dilation kernel
-        img: cv2 image| np.array
-        """
-        kernel = np.ones((2, 1), np.uint8)
-        kernel2 = np.ones((1, 1), np.uint8)
-        img = cv2.blur(img,(6,5))
-        img=cv2.dilate(img, kernel, iterations=3)
-        img = cv2.erode(img, (2,1), iterations=1)
-        img = cv2.blur(img,(1,1))
-        img = cv2.bilateralFilter(img,10,35,30)
-        img= cv2.dilate(img, kernel2, iterations=1)
-        return img
-
-
-    def detect_angle(self,img_path):
-        """detects angle of rotation in the image using the text lines found"""
-        ##taken from https://stackoverflow.com/questions/13872331/rotating-an-image-with-orientation-specified-in-exif-using-python-without-pil-in
-        pil_img=Image.open(img_path)
-        img_exif = pil_img.getexif()
-        if len(img_exif):
-            if img_exif[274] == 3:
-                pil_img = pil_img.transpose(Image.ROTATE_180)
-            elif img_exif[274] == 6:
-                pil_img = pil_img.transpose(Image.ROTATE_270)
-            elif img_exif[274] == 8:
-                pil_img = pil_img.transpose(Image.ROTATE_90)
-
-        return np.array(pil_img)[:, :, ::-1] #convert to BGR
-    
-    def opening(self,image):
-        kernel = np.ones((5,5),np.uint8)
-        return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
-
-    def process_image(self,img_path):
-        img=self.binarize(img_path)
-        img=self.remove_watermark(img)
-        return img
-
-    def img_to_text(self,img,lang="spa"):
-        text=pytesseract.image_to_string(img,lang=lang,config=PYTESSERACT_DEFAULT_CONFIG)   
-        return text
-    
-    def _run(self,img_path,save_to_disk=False):
-        img=self.process_image(img_path)
-        text=self.img_to_text(img)
-        if save_to_disk:
-            with open(f"/tmp/{str(img_path).split('/')[-1].replace('.jpg','.txt')}",'w') as f:
-                f.write(text)
-            cv2.imwrite(f"images/rotated-{img_pth.name}",img)
-        return text
-
-    # as used in langchain documentation https://python.langchain.com/docs/modules/agents/tools/custom_tools
-    async def _arun(self, img_path: str,save_to_disk=False, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
-    ) -> str:
-        """Use the tool asynchronously."""
-        raise NotImplementedError("does not support async")
-
-
diff --git a/spaces/jie1/succ1/file/Preinput_Merge.py b/spaces/jie1/succ1/file/Preinput_Merge.py
deleted file mode 100644
index d0bc72e6398c6bc153e5d0a6a02b3df718496741..0000000000000000000000000000000000000000
--- a/spaces/jie1/succ1/file/Preinput_Merge.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import re
-
-from tname import *
-from Rfile import *
-
-
-def Strip(seq_file):
-    contents = j_reads(seq_file.name)
-    ina = Name()
-    ina = ina + r"input.tsv"  # 结果文件名称
-
-    # 去除序列文件中的换行,并写入新的文件中
-    for i in range(0, len(contents) - 1):
-        if contents[i][0] != '>' and contents[i + 1][0] != '>':
-            content = contents[i].split()
-            content = content[0]
-        else:
-            content = contents[i]
-        with open(ina, "a") as f:
-            f.write(content)
-    # 最后一行特殊，单独写入
-    with open(ina, "a") as f:
-        f.write(contents[len(contents) - 1])
-    return ina
-
-
-def Merge(smi_file, seq_file):
-    smile = j_read(smi_file.name)
-    smile = smile.strip("\n")
-
-    # 读取去掉换行后的文件
-    contents = j_reads(seq_file.name)
-
-    name = Name()
-    name = name + r"kcat_input.tsv"  # 结果文件名称
-
-    with open(name, "a") as f3:
-        f3.write("Substrate Name	Substrate SMILES	Protein Sequence")
-        f3.write("\n")
-
-    for i in range(0, len(contents)):
-        if i % 2 == 1:
-            with open(name, "a") as f3:
-                # 写入索引
-                f3.write(">seq" + str(int((i - 1) / 2)))
-                f3.write("\t")
-                # 写入smile名称
-                f3.write(smile)
-                f3.write("\t")
-                # 写入序列
-                f3.write(contents[i])
-    return name
-
-
-def Merge_All(smi_file, seq_file):
-    smile = j_read(smi_file.name)
-    smile = smile.strip("\n")
-
-    # 读取去掉换行后的文件
-    contents = j_reads(seq_file.name)
-
-    name = Name()
-    name = name + r"kcat_input.tsv"  # 结果文件名称
-
-    with open(name, "a") as f3:
-        f3.write("Substrate Name	Substrate SMILES	Protein Sequence")
-        f3.write("\n")
-
-    for i in range(0, len(contents)):
-        if i % 2 == 1:
-            with open(name, "a") as f3:
-                # 写入索引
-                # f3.write(">seq" + str(int((i - 1) / 2)))
-                info = re.sub(' ', '_', contents[i - 1])
-                info = re.sub('\n', '', info)
-                f3.write(info)
-                f3.write("\t")
-                # 写入smile名称
-                f3.write(smile)
-                f3.write("\t")
-                # 写入序列
-                f3.write(contents[i])
-    return name
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Hash/test_keccak.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Hash/test_keccak.py
deleted file mode 100644
index 54cdf27d8be70a1a7295db0d2abe40c0cc39d381..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Hash/test_keccak.py
+++ /dev/null
@@ -1,250 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2015, Legrandin <helderijs@gmail.com>
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-"""Self-test suite for Crypto.Hash.keccak"""
-
-import unittest
-from binascii import hexlify, unhexlify
-
-from Crypto.SelfTest.loader import load_test_vectors
-from Crypto.SelfTest.st_common import list_test_cases
-
-from Crypto.Hash import keccak
-from Crypto.Util.py3compat import b, tobytes, bchr
-
-class KeccakTest(unittest.TestCase):
-
-    def test_new_positive(self):
-
-        for digest_bits in (224, 256, 384, 512):
-            hobj = keccak.new(digest_bits=digest_bits)
-            self.assertEqual(hobj.digest_size, digest_bits // 8)
-
-            hobj2 = hobj.new()
-            self.assertEqual(hobj2.digest_size, digest_bits // 8)
-
-        for digest_bytes in (28, 32, 48, 64):
-            hobj = keccak.new(digest_bytes=digest_bytes)
-            self.assertEqual(hobj.digest_size, digest_bytes)
-
-            hobj2 = hobj.new()
-            self.assertEqual(hobj2.digest_size, digest_bytes)
-
-    def test_new_positive2(self):
-
-        digest1 = keccak.new(data=b("\x90"), digest_bytes=64).digest()
-        digest2 = keccak.new(digest_bytes=64).update(b("\x90")).digest()
-        self.assertEqual(digest1, digest2)
-
-    def test_new_negative(self):
-
-        # keccak.new needs digest size
-        self.assertRaises(TypeError, keccak.new)
-
-        h = keccak.new(digest_bits=512)
-
-        # Either bits or bytes can be specified
-        self.assertRaises(TypeError, keccak.new,
-                              digest_bytes=64,
-                              digest_bits=512)
-
-        # Range
-        self.assertRaises(ValueError, keccak.new, digest_bytes=0)
-        self.assertRaises(ValueError, keccak.new, digest_bytes=1)
-        self.assertRaises(ValueError, keccak.new, digest_bytes=65)
-        self.assertRaises(ValueError, keccak.new, digest_bits=0)
-        self.assertRaises(ValueError, keccak.new, digest_bits=1)
-        self.assertRaises(ValueError, keccak.new, digest_bits=513)
-
-    def test_update(self):
-        pieces = [bchr(10) * 200, bchr(20) * 300]
-        h = keccak.new(digest_bytes=64)
-        h.update(pieces[0]).update(pieces[1])
-        digest = h.digest()
-        h = keccak.new(digest_bytes=64)
-        h.update(pieces[0] + pieces[1])
-        self.assertEqual(h.digest(), digest)
-
-    def test_update_negative(self):
-        h = keccak.new(digest_bytes=64)
-        self.assertRaises(TypeError, h.update, u"string")
-
-    def test_digest(self):
-        h = keccak.new(digest_bytes=64)
-        digest = h.digest()
-
-        # hexdigest does not change the state
-        self.assertEqual(h.digest(), digest)
-        # digest returns a byte string
-        self.assertTrue(isinstance(digest, type(b("digest"))))
-
-    def test_hex_digest(self):
-        mac = keccak.new(digest_bits=512)
-        digest = mac.digest()
-        hexdigest = mac.hexdigest()
-
-        # hexdigest is equivalent to digest
-        self.assertEqual(hexlify(digest), tobytes(hexdigest))
-        # hexdigest does not change the state
-        self.assertEqual(mac.hexdigest(), hexdigest)
-        # hexdigest returns a string
-        self.assertTrue(isinstance(hexdigest, type("digest")))
-
-    def test_update_after_digest(self):
-        msg=b("rrrrttt")
-
-        # Normally, update() cannot be done after digest()
-        h = keccak.new(digest_bits=512, data=msg[:4])
-        dig1 = h.digest()
-        self.assertRaises(TypeError, h.update, msg[4:])
-        dig2 = keccak.new(digest_bits=512, data=msg).digest()
-
-        # With the proper flag, it is allowed
-        h = keccak.new(digest_bits=512, data=msg[:4], update_after_digest=True)
-        self.assertEqual(h.digest(), dig1)
-        # ... and the subsequent digest applies to the entire message
-        # up to that point
-        h.update(msg[4:])
-        self.assertEqual(h.digest(), dig2)
-
-
-class KeccakVectors(unittest.TestCase):
-    pass
-
-    # TODO: add ExtremelyLong tests
-
-
-test_vectors_224 =  load_test_vectors(("Hash", "keccak"),
-                                "ShortMsgKAT_224.txt",
-                                "Short Messages KAT 224",
-                                {"len": lambda x: int(x)}) or []
-
-test_vectors_224 += load_test_vectors(("Hash", "keccak"),
-                                "LongMsgKAT_224.txt",
-                                "Long Messages KAT 224",
-                                {"len": lambda x: int(x)}) or []
-
-for idx, tv in enumerate(test_vectors_224):
-    if tv.len == 0:
-        data = b("")
-    else:
-        data = tobytes(tv.msg)
-
-    def new_test(self, data=data, result=tv.md):
-        hobj = keccak.new(digest_bits=224, data=data)
-        self.assertEqual(hobj.digest(), result)
-
-    setattr(KeccakVectors, "test_224_%d" % idx, new_test)
-
-# ---
-
-test_vectors_256 =  load_test_vectors(("Hash", "keccak"),
-                                "ShortMsgKAT_256.txt",
-                                "Short Messages KAT 256",
-                                { "len" : lambda x: int(x) } ) or []
-
-test_vectors_256 += load_test_vectors(("Hash", "keccak"),
-                                "LongMsgKAT_256.txt",
-                                "Long Messages KAT 256",
-                                { "len" : lambda x: int(x) } ) or []
-
-for idx, tv in enumerate(test_vectors_256):
-    if tv.len == 0:
-        data = b("")
-    else:
-        data = tobytes(tv.msg)
-
-    def new_test(self, data=data, result=tv.md):
-        hobj = keccak.new(digest_bits=256, data=data)
-        self.assertEqual(hobj.digest(), result)
-
-    setattr(KeccakVectors, "test_256_%d" % idx, new_test)
-
-
-# ---
-
-test_vectors_384 =  load_test_vectors(("Hash", "keccak"),
-                                "ShortMsgKAT_384.txt",
-                                "Short Messages KAT 384",
-                                {"len": lambda x: int(x)}) or []
-
-test_vectors_384 += load_test_vectors(("Hash", "keccak"),
-                                "LongMsgKAT_384.txt",
-                                "Long Messages KAT 384",
-                                {"len": lambda x: int(x)}) or []
-
-for idx, tv in enumerate(test_vectors_384):
-    if tv.len == 0:
-        data = b("")
-    else:
-        data = tobytes(tv.msg)
-
-    def new_test(self, data=data, result=tv.md):
-        hobj = keccak.new(digest_bits=384, data=data)
-        self.assertEqual(hobj.digest(), result)
-
-    setattr(KeccakVectors, "test_384_%d" % idx, new_test)
-
-# ---
-
-test_vectors_512 =  load_test_vectors(("Hash", "keccak"),
-                                "ShortMsgKAT_512.txt",
-                                "Short Messages KAT 512",
-                                {"len": lambda x: int(x)}) or []
-
-test_vectors_512 += load_test_vectors(("Hash", "keccak"),
-                                "LongMsgKAT_512.txt",
-                                "Long Messages KAT 512",
-                                {"len": lambda x: int(x)}) or []
-
-for idx, tv in enumerate(test_vectors_512):
-    if tv.len == 0:
-        data = b("")
-    else:
-        data = tobytes(tv.msg)
-
-    def new_test(self, data=data, result=tv.md):
-        hobj = keccak.new(digest_bits=512, data=data)
-        self.assertEqual(hobj.digest(), result)
-
-    setattr(KeccakVectors, "test_512_%d" % idx, new_test)
-
-
-def get_tests(config={}):
-    tests = []
-    tests += list_test_cases(KeccakTest)
-    tests += list_test_cases(KeccakVectors)
-    return tests
-
-
-if __name__ == '__main__':
-    import unittest
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
diff --git a/spaces/johnberg/CLIPInverter/models/encoders/model_irse.py b/spaces/johnberg/CLIPInverter/models/encoders/model_irse.py
deleted file mode 100644
index ea6c6091c1e71279ff0bc7e013b0cea287cb01b3..0000000000000000000000000000000000000000
--- a/spaces/johnberg/CLIPInverter/models/encoders/model_irse.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
-from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
-
-"""
-Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Backbone(Module):
-    def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
-        super(Backbone, self).__init__()
-        assert input_size in [112, 224], "input_size should be 112 or 224"
-        assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
-        assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        if input_size == 112:
-            self.output_layer = Sequential(BatchNorm2d(512),
-                                           Dropout(drop_ratio),
-                                           Flatten(),
-                                           Linear(512 * 7 * 7, 512),
-                                           BatchNorm1d(512, affine=affine))
-        else:
-            self.output_layer = Sequential(BatchNorm2d(512),
-                                           Dropout(drop_ratio),
-                                           Flatten(),
-                                           Linear(512 * 14 * 14, 512),
-                                           BatchNorm1d(512, affine=affine))
-
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_layer(x)
-        return l2_norm(x)
-
-
-def IR_50(input_size):
-    """Constructs a ir-50 model."""
-    model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_101(input_size):
-    """Constructs a ir-101 model."""
-    model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_152(input_size):
-    """Constructs a ir-152 model."""
-    model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_50(input_size):
-    """Constructs a ir_se-50 model."""
-    model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_101(input_size):
-    """Constructs a ir_se-101 model."""
-    model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_152(input_size):
-    """Constructs a ir_se-152 model."""
-    model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
diff --git a/spaces/jonigata/PoseTweak/pose.py b/spaces/jonigata/PoseTweak/pose.py
deleted file mode 100644
index cd2d1d0e45d0519a68d9bc7ca052cc8d423abb07..0000000000000000000000000000000000000000
--- a/spaces/jonigata/PoseTweak/pose.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
-                         process_mmdet_results, vis_pose_result)
-from mmpose.datasets import DatasetInfo
-from mmdet.apis import inference_detector, init_detector
-
-det_model = init_detector(
-    "./external/faster_rcnn_r50_fpn_coco.py", 
-    "./faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth",
-    device="cpu")
-pose_model = init_pose_model(
-    "./external/hrnet_w48_coco_256x192.py",
-    "./hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth",
-    device="cpu")
-
-dataset = pose_model.cfg.data['test']['type']
-dataset_info = pose_model.cfg.data['test'].get('dataset_info', None)
-
-dataset_info = DatasetInfo(dataset_info)
-
-def infer(image):
-    mmdet_results = inference_detector(det_model, image)
-    person_results = process_mmdet_results(mmdet_results, 1)
-
-    pose_results, returned_outputs = inference_top_down_pose_model(
-        pose_model,
-        image,
-        person_results,
-        bbox_thr=0.3,
-        format='xyxy',
-        dataset=dataset,
-        dataset_info=dataset_info,
-        return_heatmap=False,
-        outputs=None)
-    # print(pose_results)
-    # print(returned_outputs)
-
-    return pose_results, returned_outputs
-
-def draw(image, results):
-    return vis_pose_result(
-        pose_model,
-        image,
-        results,
-        dataset=dataset,
-        dataset_info=dataset_info,
-        kpt_score_thr=0.3,
-        radius=4,
-        thickness=3,
-        show=False,
-        out_file=None)
diff --git a/spaces/josegabmuz/gradio-test/app.py b/spaces/josegabmuz/gradio-test/app.py
deleted file mode 100644
index 34a37eff273335a001dfdff51c225239e0841687..0000000000000000000000000000000000000000
--- a/spaces/josegabmuz/gradio-test/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import gradio as gr
-def bmi(name, height, weight,feeling):
-  bmi_val=weight/((height/100) ** 2)
-  result_emoticon="( ͡❛ ‿‿ ͡❛)" if bmi_val < 30 else "( ͡❛ 👅 ͡❛)"
-
-  txt=" i am happy" if feeling else " i feel like fuck this shit yo"
-  return("hello " +name+" your bmi is "+str(bmi_val)+result_emoticon+txt)
-
-
-interface=gr.Interface(
-    fn=bmi,
-    inputs=["text",gr.Slider(1,200,label="altura"),gr.Slider(1,100,label="weight"),gr.Checkbox("your feeling today ")],
-    outputs=["text"],
-    examples=[["jose",100,50,True],
-              ["pedro",50,72,False]],
-    
-    description="usa flag si encuentras algun problema",
-    allow_flagging="manual",
-    css="""
-    body {background-color:green}
-    """,
-    flagging_options=["wrong sign", "off by one", "other"]
-)
-
-interface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/kTonpa/Text2Cryptopunks/text2punks/text2punk.py b/spaces/kTonpa/Text2Cryptopunks/text2punks/text2punk.py
deleted file mode 100644
index 0238c7560267b2b20b49621d8c355f9a6da64279..0000000000000000000000000000000000000000
--- a/spaces/kTonpa/Text2Cryptopunks/text2punks/text2punk.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import math
-
-from einops import rearrange, repeat
-
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-
-from axial_positional_embedding import AxialPositionalEmbedding
-from text2punks.transformer import Transformer
-
-
-# helpers fns
-
-def exists(val):
-    return val is not None
-
-def default(val, d):
-    return val if exists(val) else d
-
-def set_requires_grad(model, value):
-    for param in model.parameters():
-        param.requires_grad = value
-
-def eval_decorator(fn):
-    def inner(model, *args, **kwargs):
-        was_training = model.training
-        model.eval()
-        out = fn(model, *args, **kwargs)
-        model.train(was_training)
-        return out
-    return inner
-
-# sampling helpers fn
-
-def top_k(logits, thres = 0.5):
-    num_logits = logits.shape[-1]
-    k = max(int((1 - thres) * num_logits), 1)
-    val, ind = torch.topk(logits, k)
-    probs = torch.full_like(logits, float('-inf'))
-    probs.scatter_(1, ind, val)
-    return probs
-
-# main CLIP class
-
-class CLIP(nn.Module):
-    def __init__(
-        self,
-        *,
-        dim_text = 512,
-        dim_image = 512,
-        dim_latent = 512,
-        num_text_tokens = 10000,
-        text_enc_depth = 6,
-        text_seq_len = 256,
-        text_heads = 8,
-        num_visual_tokens = 256,
-        visual_enc_depth = 6,
-        visual_image_seq_len = 256,
-        visual_image_size = 24,
-        visual_heads = 8,
-        attn_pdrop = 0.1,
-        resid_pdrop = 0.1,
-        embd_pdrop = 0.1,
-        ff_dropout = 0.1,
-        attn_types = None
-    ):
-        super().__init__()
-
-        # Texts
-
-        self.text_emb = nn.Embedding(num_text_tokens, dim_text)
-        self.text_pos_emb = nn.Embedding(text_seq_len, dim_text)
-
-        self.text_transformer = Transformer(
-            dim = dim_text,
-            causal = False,
-            seq_len = text_seq_len,
-            depth = text_enc_depth,
-            heads = text_heads,
-            dim_head = dim_text // text_heads,
-            attn_dropout = attn_pdrop,
-            resid_dropout = resid_pdrop,
-            embd_dropout = embd_pdrop,
-            ff_dropout = ff_dropout,
-            attn_types = attn_types
-        )
-
-        self.text_ln = nn.LayerNorm(dim_text)
-        self.to_text_latent = nn.Linear(dim_text, dim_latent, bias = False)
-
-        # Images
-
-        self.image_emb = nn.Embedding(num_visual_tokens, dim_image)
-        self.image_pos_emb = nn.Embedding(visual_image_seq_len, dim_image)
-
-        self.visual_transformer = Transformer(
-            dim = dim_image,
-            causal = False,
-            seq_len = visual_image_seq_len,
-            depth = visual_enc_depth,
-            heads = visual_heads,
-            dim_head = dim_image // visual_heads,
-            attn_dropout = attn_pdrop,
-            resid_dropout = resid_pdrop,
-            embd_dropout = embd_pdrop,
-            ff_dropout = ff_dropout,
-            attn_types = attn_types,
-            image_size = visual_image_size,
-        )
-
-        self.image_ln = nn.LayerNorm(dim_image)
-        self.to_visual_latent = nn.Linear(dim_image, dim_latent, bias = False)
-
-        self.temperature = nn.Parameter(torch.ones([]) * math.log(1 / 0.07))
-        
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(
-        self,
-        text,
-        image,
-        return_loss = False
-    ):
-        b, device= text.shape[0], text.device
-
-        text_emb = self.text_emb(text)
-        text_emb += self.text_pos_emb(torch.arange(text.shape[1], device = device))
-
-        image_emb = self.image_emb(image)
-        image_emb += self.image_pos_emb(torch.arange(image.shape[1], device = device))
-
-        enc_text = self.text_transformer(text_emb)
-        enc_image = self.visual_transformer(image_emb)
-
-        text_latents = enc_text.mean(dim = 1)
-        image_latents = enc_image.mean(dim = 1)
-
-        text_latents = self.text_ln(text_latents)
-        image_latents = self.image_ln(image_latents)
-
-        text_latents = self.to_text_latent(text_latents)
-        image_latents = self.to_visual_latent(image_latents)
-
-        text_latents, image_latents = map(lambda t: F.normalize(t, p = 2, dim = -1), (text_latents, image_latents))
-
-        temp = self.temperature.exp()
-
-        if not return_loss:
-            sim = einsum('n d, n d -> n', text_latents, image_latents) * temp
-            return sim
-
-        sim = einsum('i d, j d -> i j', text_latents, image_latents) * temp
-        labels = torch.arange(b, device = device)
-        loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
-        return loss
-
-# main Text2Punks class
-
-class Text2Punks(nn.Module):
-    def __init__(
-        self,
-        *,
-        n_embd,
-        n_layer = 12,
-        n_head = 12,
-        d_head = 64,
-        num_text_tokens = 10000,
-        text_seq_len = 256,
-        num_image_tokens = 222,
-        image_seq_len = 576,
-        image_size = 24,
-        attn_pdrop = 0.1,
-        resid_pdrop = 0.1,
-        embd_pdrop = 0.1,
-        ff_dropout = 0.1,
-        attn_types = None,
-        loss_img_weight = 7,
-        loss_txt_weight = 7,
-    ):
-        super().__init__()
-
-        num_text_tokens = num_text_tokens + text_seq_len  # reserve unique padding tokens for each position (text seq len)
-
-        self.text_emb = nn.Embedding(num_text_tokens, n_embd)
-        self.image_emb = nn.Embedding(num_image_tokens, n_embd)
-
-        self.text_pos_emb = nn.Embedding(text_seq_len + 1, n_embd) # +1 for <bos> a.k.a <sos>
-        # self.image_pos_emb = nn.Embedding(image_seq_len, n_embd)
-        self.image_pos_emb = nn.Parameter(torch.zeros(1, image_seq_len, n_embd))
-        # self.image_pos_emb = AxialPositionalEmbedding(n_embd, axial_shape=(image_size, image_size))
-
-        self.num_text_tokens = num_text_tokens # for offsetting logits index and calculating cross entropy loss
-        self.num_image_tokens = num_image_tokens
-        self.text_seq_len = text_seq_len
-        self.image_seq_len = image_seq_len
-
-        seq_len = text_seq_len + image_seq_len
-        total_tokens = num_text_tokens + num_image_tokens
-        self.total_seq_len = seq_len
-        self.total_tokens = total_tokens
-
-        self.transformer = Transformer(
-            dim = n_embd,
-            causal = True,
-            seq_len = seq_len,
-            depth = n_layer,
-            heads = n_head,
-            dim_head = d_head,
-            attn_dropout = attn_pdrop,
-            resid_dropout = resid_pdrop,
-            embd_dropout = embd_pdrop,
-            ff_dropout = ff_dropout,
-            attn_types = attn_types,
-            image_size = image_size,
-        )
-
-        self.to_logits = nn.Sequential(
-            nn.LayerNorm(n_embd),
-            nn.Linear(n_embd, self.total_tokens),
-        )
-
-        seq_range = torch.arange(seq_len)
-        logits_range = torch.arange(total_tokens)
-
-        seq_range = rearrange(seq_range, 'n -> () n ()')
-        logits_range = rearrange(logits_range, 'd -> () () d')
-
-        logits_mask = (
-            ((seq_range >= text_seq_len) & (logits_range < num_text_tokens)) |
-            ((seq_range < text_seq_len) & (logits_range >= num_text_tokens))
-        )
-
-        self.register_buffer('logits_mask', logits_mask, persistent=False)
-        self.loss_img_weight = loss_img_weight
-        self.loss_txt_weight = loss_txt_weight
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    @torch.no_grad()
-    @eval_decorator
-    def generate_images(
-        self,
-        text,
-        decoder,
-        *,
-        clip = None,
-        filter_thres = 0.5,
-        temperature = 1.,
-        img = None,
-        num_init_img_tokens = None
-    ):
-        text_seq_len, image_seq_len, num_text_tokens = self.text_seq_len, self.image_seq_len, self.num_text_tokens
-        total_len = text_seq_len + image_seq_len
-
-        batch = text.shape[0]
-        text = text[:, :text_seq_len] # make sure text is within bounds
-        out = text
-
-        if exists(img):
-            assert img.shape[1] == image_seq_len, f'input image must have the correct image size {image_seq_len}'
-
-            num_img_tokens = default(num_init_img_tokens, int(0.4375 * image_seq_len))  # OpenAI used 14 * 32 initial tokens to prime
-            assert num_img_tokens < image_seq_len, 'number of initial image tokens for priming must be less than the total image token sequence length'
-
-            trunc_img = img[:, :num_img_tokens]
-            out = torch.cat((out, trunc_img), dim = -1)
-
-        for cur_len in range(out.shape[1], total_len):
-            is_image = cur_len >= text_seq_len
-
-            text, image = out[:, :text_seq_len], out[:, text_seq_len:]
-
-            logits = self(text, image)[:, -1, :]
-
-            filtered_logits = top_k(logits, thres = filter_thres)
-            probs = F.softmax(filtered_logits / temperature, dim = -1)
-            sample = torch.multinomial(probs, 1)
-
-            sample -= (num_text_tokens if is_image else 0) # offset sampled token if it is an image token, since logit space is composed of text and then image tokens
-            out = torch.cat((out, sample), dim=-1)
-
-        text_seq = out[:, :text_seq_len]
-        img_seq = out[:, -image_seq_len:]
-
-        scores = None
-        if exists(clip):
-            scores = clip(text_seq, img_seq, return_loss = False)
-
-        img_seq = repeat(img_seq, 'b p -> b p c', c=3)
-        decoder = repeat(decoder, 'p c -> b p c', b=batch)
-        images = torch.gather(decoder, 1, img_seq)
-        images = rearrange(images, 'b (h w) c-> b c h w', h=24, w =24)
-        images = images.float()
-
-        return images, scores
-
-    def forward(
-        self,
-        text,
-        image = None,
-        return_loss = False
-    ):
-        assert text.shape[-1] == self.text_seq_len, f'the length {text.shape[-1]} of the text tokens you passed in does not have the correct length ({self.text_seq_len})'
-        device, total_seq_len = text.device, self.total_seq_len
-
-        text_range = torch.arange(self.text_seq_len, device = device) + (self.num_text_tokens - self.text_seq_len)
-        text = torch.where(text == 0, text_range, text)
-
-        text = F.pad(text, (1, 0), value = 0) # add <bos>
-
-        tokens = self.text_emb(text)
-        tokens += self.text_pos_emb(torch.arange(text.shape[1], device = device))
-
-        seq_len = tokens.shape[1]
-        
-        image_len = image.shape[1]
-        image_emb = self.image_emb(image)
-        # image_emb += self.image_pos_emb(torch.arange(image_len, device = device))
-        image_emb += self.image_pos_emb[:, :image_len, :]
-
-        # image_emb += self.image_pos_emb(image_emb)
-
-        tokens = torch.cat((tokens, image_emb), dim = 1)
-
-        seq_len += image_len
-
-        # when training, if the length exceeds the total text + image length
-        # remove the last token, since it needs not to be trained
-
-        if tokens.shape[1] > total_seq_len:
-            seq_len -= 1
-            tokens = tokens[:, :-1]
-
-        out = self.transformer(tokens)
-        logits = self.to_logits(out)
-
-        # mask logits to make sure text predicts text (except last token), and image predicts image
-
-        logits_mask = self.logits_mask[:, :seq_len]
-        max_neg_value = -torch.finfo(logits.dtype).max
-        logits.masked_fill_(logits_mask, max_neg_value)
-
-        if not return_loss:
-            return logits
-
-        assert exists(image), 'when training, image must be supplied'
-
-        offsetted_image = image + self.num_text_tokens
-        labels = torch.cat((text[:, 1:], offsetted_image), dim = 1)
-
-        logits = rearrange(logits, 'b n c -> b c n')
-
-        loss_text = F.cross_entropy(logits[:, :, :self.text_seq_len], labels[:, :self.text_seq_len])
-        loss_img = F.cross_entropy(logits[:, :, self.text_seq_len:], labels[:, self.text_seq_len:])
-
-        loss = (self.loss_txt_weight * loss_text + self.loss_img_weight * loss_img) / (self.loss_img_weight + self.loss_txt_weight)
-        return loss, loss_text, loss_img
diff --git a/spaces/kdrkdrkdr/YuukaTTS/monotonic_align/core.py b/spaces/kdrkdrkdr/YuukaTTS/monotonic_align/core.py
deleted file mode 100644
index 1f940605fe4fd0738fa0006149fcba14ef88223a..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/YuukaTTS/monotonic_align/core.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]),
-           nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-    b = paths.shape[0]
-    max_neg_val = -1e9
-    for i in range(int(b)):
-        path = paths[i]
-        value = values[i]
-        t_y = t_ys[i]
-        t_x = t_xs[i]
-
-        v_prev = v_cur = 0.0
-        index = t_x - 1
-
-        for y in range(t_y):
-            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-                if x == y:
-                    v_cur = max_neg_val
-                else:
-                    v_cur = value[y - 1, x]
-                if x == 0:
-                    if y == 0:
-                        v_prev = 0.
-                    else:
-                        v_prev = max_neg_val
-                else:
-                    v_prev = value[y - 1, x - 1]
-                value[y, x] += max(v_prev, v_cur)
-
-        for y in range(t_y - 1, -1, -1):
-            path[y, index] = 1
-            if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]):
-                index = index - 1
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/vocoder/vocoder_dataset.py b/spaces/keithhon/Real-Time-Voice-Cloning/vocoder/vocoder_dataset.py
deleted file mode 100644
index 9eae1b5f20117feef0a06e264a99b3c0c6143bac..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/vocoder/vocoder_dataset.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from torch.utils.data import Dataset
-from pathlib import Path
-from vocoder import audio
-import vocoder.hparams as hp
-import numpy as np
-import torch
-
-
-class VocoderDataset(Dataset):
-    def __init__(self, metadata_fpath: Path, mel_dir: Path, wav_dir: Path):
-        print("Using inputs from:\n\t%s\n\t%s\n\t%s" % (metadata_fpath, mel_dir, wav_dir))
-        
-        with metadata_fpath.open("r") as metadata_file:
-            metadata = [line.split("|") for line in metadata_file]
-        
-        gta_fnames = [x[1] for x in metadata if int(x[4])]
-        gta_fpaths = [mel_dir.joinpath(fname) for fname in gta_fnames]
-        wav_fnames = [x[0] for x in metadata if int(x[4])]
-        wav_fpaths = [wav_dir.joinpath(fname) for fname in wav_fnames]
-        self.samples_fpaths = list(zip(gta_fpaths, wav_fpaths))
-        
-        print("Found %d samples" % len(self.samples_fpaths))
-    
-    def __getitem__(self, index):  
-        mel_path, wav_path = self.samples_fpaths[index]
-        
-        # Load the mel spectrogram and adjust its range to [-1, 1]
-        mel = np.load(mel_path).T.astype(np.float32) / hp.mel_max_abs_value
-        
-        # Load the wav
-        wav = np.load(wav_path)
-        if hp.apply_preemphasis:
-            wav = audio.pre_emphasis(wav)
-        wav = np.clip(wav, -1, 1)
-        
-        # Fix for missing padding   # TODO: settle on whether this is any useful
-        r_pad =  (len(wav) // hp.hop_length + 1) * hp.hop_length - len(wav)
-        wav = np.pad(wav, (0, r_pad), mode='constant')
-        assert len(wav) >= mel.shape[1] * hp.hop_length
-        wav = wav[:mel.shape[1] * hp.hop_length]
-        assert len(wav) % hp.hop_length == 0
-        
-        # Quantize the wav
-        if hp.voc_mode == 'RAW':
-            if hp.mu_law:
-                quant = audio.encode_mu_law(wav, mu=2 ** hp.bits)
-            else:
-                quant = audio.float_2_label(wav, bits=hp.bits)
-        elif hp.voc_mode == 'MOL':
-            quant = audio.float_2_label(wav, bits=16)
-            
-        return mel.astype(np.float32), quant.astype(np.int64)
-
-    def __len__(self):
-        return len(self.samples_fpaths)
-        
-        
-def collate_vocoder(batch):
-    mel_win = hp.voc_seq_len // hp.hop_length + 2 * hp.voc_pad
-    max_offsets = [x[0].shape[-1] -2 - (mel_win + 2 * hp.voc_pad) for x in batch]
-    mel_offsets = [np.random.randint(0, offset) for offset in max_offsets]
-    sig_offsets = [(offset + hp.voc_pad) * hp.hop_length for offset in mel_offsets]
-
-    mels = [x[0][:, mel_offsets[i]:mel_offsets[i] + mel_win] for i, x in enumerate(batch)]
-
-    labels = [x[1][sig_offsets[i]:sig_offsets[i] + hp.voc_seq_len + 1] for i, x in enumerate(batch)]
-
-    mels = np.stack(mels).astype(np.float32)
-    labels = np.stack(labels).astype(np.int64)
-
-    mels = torch.tensor(mels)
-    labels = torch.tensor(labels).long()
-
-    x = labels[:, :hp.voc_seq_len]
-    y = labels[:, 1:]
-
-    bits = 16 if hp.voc_mode == 'MOL' else hp.bits
-
-    x = audio.label_2_float(x.float(), bits)
-
-    if hp.voc_mode == 'MOL' :
-        y = audio.label_2_float(y.float(), bits)
-
-    return x, y, mels
\ No newline at end of file
diff --git a/spaces/kevinwang676/Bert-VITS2/README_zh.md b/spaces/kevinwang676/Bert-VITS2/README_zh.md
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bert-VITS2/README_zh.md
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/utils/videoio.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/utils/videoio.py
deleted file mode 100644
index d16ee667713a16e3f9644fcc3cb3e023bc2c9102..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/utils/videoio.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import shutil
-import uuid
-
-import os
-
-import cv2
-
-def load_video_to_cv2(input_path):
-    video_stream = cv2.VideoCapture(input_path)
-    fps = video_stream.get(cv2.CAP_PROP_FPS)
-    full_frames = [] 
-    while 1:
-        still_reading, frame = video_stream.read()
-        if not still_reading:
-            video_stream.release()
-            break 
-        full_frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-    return full_frames
-
-def save_video_with_watermark(video, audio, save_path, watermark=False):
-    temp_file = str(uuid.uuid4())+'.mp4'
-    cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -vcodec mpeg4 "%s"' % (video, audio, temp_file)
-    os.system(cmd)
-
-    if watermark is False:
-        shutil.move(temp_file, save_path)
-    else:
-        # watermark
-        try:
-            ##### check if stable-diffusion-webui
-            import webui
-            from modules import paths
-            watarmark_path = paths.script_path+"/extensions/SadTalker/docs/sadtalker_logo.png"
-        except:
-            # get the root path of sadtalker.
-            dir_path = os.path.dirname(os.path.realpath(__file__))
-            watarmark_path = dir_path+"/../../docs/sadtalker_logo.png"
-
-        cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -filter_complex "[1]scale=100:-1[wm];[0][wm]overlay=(main_w-overlay_w)-10:10" "%s"' % (temp_file, watarmark_path, save_path)
-        os.system(cmd)
-        os.remove(temp_file)
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/configs/base.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/configs/base.py
deleted file mode 100644
index 78e4b36a9142b649ec39a8c59331bb2557f2ad57..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/configs/base.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = "ms1mv3_arcface_r50"
-
-config.dataset = "ms1m-retinaface-t1"
-config.embedding_size = 512
-config.sample_rate = 1
-config.fp16 = False
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-if config.dataset == "emore":
-    config.rec = "/train_tmp/faces_emore"
-    config.num_classes = 85742
-    config.num_image = 5822653
-    config.num_epoch = 16
-    config.warmup_epoch = -1
-    config.decay_epoch = [8, 14, ]
-    config.val_targets = ["lfw", ]
-
-elif config.dataset == "ms1m-retinaface-t1":
-    config.rec = "/train_tmp/ms1m-retinaface-t1"
-    config.num_classes = 93431
-    config.num_image = 5179510
-    config.num_epoch = 25
-    config.warmup_epoch = -1
-    config.decay_epoch = [11, 17, 22]
-    config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
-
-elif config.dataset == "glint360k":
-    config.rec = "/train_tmp/glint360k"
-    config.num_classes = 360232
-    config.num_image = 17091657
-    config.num_epoch = 20
-    config.warmup_epoch = -1
-    config.decay_epoch = [8, 12, 15, 18]
-    config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
-
-elif config.dataset == "webface":
-    config.rec = "/train_tmp/faces_webface_112x112"
-    config.num_classes = 10572
-    config.num_image = "forget"
-    config.num_epoch = 34
-    config.warmup_epoch = -1
-    config.decay_epoch = [20, 28, 32]
-    config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/eval/verification.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/eval/verification.py
deleted file mode 100644
index 253343b83dbf9d1bd154d14ec068e098bf0968db..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/eval/verification.py
+++ /dev/null
@@ -1,407 +0,0 @@
-"""Helper for evaluation on the Labeled Faces in the Wild dataset 
-"""
-
-# MIT License
-#
-# Copyright (c) 2016 David Sandberg
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-
-import datetime
-import os
-import pickle
-
-import mxnet as mx
-import numpy as np
-import sklearn
-import torch
-from mxnet import ndarray as nd
-from scipy import interpolate
-from sklearn.decomposition import PCA
-from sklearn.model_selection import KFold
-
-
-class LFold:
-    def __init__(self, n_splits=2, shuffle=False):
-        self.n_splits = n_splits
-        if self.n_splits > 1:
-            self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle)
-
-    def split(self, indices):
-        if self.n_splits > 1:
-            return self.k_fold.split(indices)
-        else:
-            return [(indices, indices)]
-
-
-def calculate_roc(thresholds,
-                  embeddings1,
-                  embeddings2,
-                  actual_issame,
-                  nrof_folds=10,
-                  pca=0):
-    assert (embeddings1.shape[0] == embeddings2.shape[0])
-    assert (embeddings1.shape[1] == embeddings2.shape[1])
-    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
-    nrof_thresholds = len(thresholds)
-    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
-    tprs = np.zeros((nrof_folds, nrof_thresholds))
-    fprs = np.zeros((nrof_folds, nrof_thresholds))
-    accuracy = np.zeros((nrof_folds))
-    indices = np.arange(nrof_pairs)
-
-    if pca == 0:
-        diff = np.subtract(embeddings1, embeddings2)
-        dist = np.sum(np.square(diff), 1)
-
-    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
-        if pca > 0:
-            print('doing pca on', fold_idx)
-            embed1_train = embeddings1[train_set]
-            embed2_train = embeddings2[train_set]
-            _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
-            pca_model = PCA(n_components=pca)
-            pca_model.fit(_embed_train)
-            embed1 = pca_model.transform(embeddings1)
-            embed2 = pca_model.transform(embeddings2)
-            embed1 = sklearn.preprocessing.normalize(embed1)
-            embed2 = sklearn.preprocessing.normalize(embed2)
-            diff = np.subtract(embed1, embed2)
-            dist = np.sum(np.square(diff), 1)
-
-        # Find the best threshold for the fold
-        acc_train = np.zeros((nrof_thresholds))
-        for threshold_idx, threshold in enumerate(thresholds):
-            _, _, acc_train[threshold_idx] = calculate_accuracy(
-                threshold, dist[train_set], actual_issame[train_set])
-        best_threshold_index = np.argmax(acc_train)
-        for threshold_idx, threshold in enumerate(thresholds):
-            tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(
-                threshold, dist[test_set],
-                actual_issame[test_set])
-        _, _, accuracy[fold_idx] = calculate_accuracy(
-            thresholds[best_threshold_index], dist[test_set],
-            actual_issame[test_set])
-
-    tpr = np.mean(tprs, 0)
-    fpr = np.mean(fprs, 0)
-    return tpr, fpr, accuracy
-
-
-def calculate_accuracy(threshold, dist, actual_issame):
-    predict_issame = np.less(dist, threshold)
-    tp = np.sum(np.logical_and(predict_issame, actual_issame))
-    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
-    tn = np.sum(
-        np.logical_and(np.logical_not(predict_issame),
-                       np.logical_not(actual_issame)))
-    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
-
-    tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
-    fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
-    acc = float(tp + tn) / dist.size
-    return tpr, fpr, acc
-
-
-def calculate_val(thresholds,
-                  embeddings1,
-                  embeddings2,
-                  actual_issame,
-                  far_target,
-                  nrof_folds=10):
-    assert (embeddings1.shape[0] == embeddings2.shape[0])
-    assert (embeddings1.shape[1] == embeddings2.shape[1])
-    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
-    nrof_thresholds = len(thresholds)
-    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
-    val = np.zeros(nrof_folds)
-    far = np.zeros(nrof_folds)
-
-    diff = np.subtract(embeddings1, embeddings2)
-    dist = np.sum(np.square(diff), 1)
-    indices = np.arange(nrof_pairs)
-
-    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
-
-        # Find the threshold that gives FAR = far_target
-        far_train = np.zeros(nrof_thresholds)
-        for threshold_idx, threshold in enumerate(thresholds):
-            _, far_train[threshold_idx] = calculate_val_far(
-                threshold, dist[train_set], actual_issame[train_set])
-        if np.max(far_train) >= far_target:
-            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
-            threshold = f(far_target)
-        else:
-            threshold = 0.0
-
-        val[fold_idx], far[fold_idx] = calculate_val_far(
-            threshold, dist[test_set], actual_issame[test_set])
-
-    val_mean = np.mean(val)
-    far_mean = np.mean(far)
-    val_std = np.std(val)
-    return val_mean, val_std, far_mean
-
-
-def calculate_val_far(threshold, dist, actual_issame):
-    predict_issame = np.less(dist, threshold)
-    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
-    false_accept = np.sum(
-        np.logical_and(predict_issame, np.logical_not(actual_issame)))
-    n_same = np.sum(actual_issame)
-    n_diff = np.sum(np.logical_not(actual_issame))
-    # print(true_accept, false_accept)
-    # print(n_same, n_diff)
-    val = float(true_accept) / float(n_same)
-    far = float(false_accept) / float(n_diff)
-    return val, far
-
-
-def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
-    # Calculate evaluation metrics
-    thresholds = np.arange(0, 4, 0.01)
-    embeddings1 = embeddings[0::2]
-    embeddings2 = embeddings[1::2]
-    tpr, fpr, accuracy = calculate_roc(thresholds,
-                                       embeddings1,
-                                       embeddings2,
-                                       np.asarray(actual_issame),
-                                       nrof_folds=nrof_folds,
-                                       pca=pca)
-    thresholds = np.arange(0, 4, 0.001)
-    val, val_std, far = calculate_val(thresholds,
-                                      embeddings1,
-                                      embeddings2,
-                                      np.asarray(actual_issame),
-                                      1e-3,
-                                      nrof_folds=nrof_folds)
-    return tpr, fpr, accuracy, val, val_std, far
-
-@torch.no_grad()
-def load_bin(path, image_size):
-    try:
-        with open(path, 'rb') as f:
-            bins, issame_list = pickle.load(f)  # py2
-    except UnicodeDecodeError as e:
-        with open(path, 'rb') as f:
-            bins, issame_list = pickle.load(f, encoding='bytes')  # py3
-    data_list = []
-    for flip in [0, 1]:
-        data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1]))
-        data_list.append(data)
-    for idx in range(len(issame_list) * 2):
-        _bin = bins[idx]
-        img = mx.image.imdecode(_bin)
-        if img.shape[1] != image_size[0]:
-            img = mx.image.resize_short(img, image_size[0])
-        img = nd.transpose(img, axes=(2, 0, 1))
-        for flip in [0, 1]:
-            if flip == 1:
-                img = mx.ndarray.flip(data=img, axis=2)
-            data_list[flip][idx][:] = torch.from_numpy(img.asnumpy())
-        if idx % 1000 == 0:
-            print('loading bin', idx)
-    print(data_list[0].shape)
-    return data_list, issame_list
-
-@torch.no_grad()
-def test(data_set, backbone, batch_size, nfolds=10):
-    print('testing verification..')
-    data_list = data_set[0]
-    issame_list = data_set[1]
-    embeddings_list = []
-    time_consumed = 0.0
-    for i in range(len(data_list)):
-        data = data_list[i]
-        embeddings = None
-        ba = 0
-        while ba < data.shape[0]:
-            bb = min(ba + batch_size, data.shape[0])
-            count = bb - ba
-            _data = data[bb - batch_size: bb]
-            time0 = datetime.datetime.now()
-            img = ((_data / 255) - 0.5) / 0.5
-            net_out: torch.Tensor = backbone(img)
-            _embeddings = net_out.detach().cpu().numpy()
-            time_now = datetime.datetime.now()
-            diff = time_now - time0
-            time_consumed += diff.total_seconds()
-            if embeddings is None:
-                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
-            embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
-            ba = bb
-        embeddings_list.append(embeddings)
-
-    _xnorm = 0.0
-    _xnorm_cnt = 0
-    for embed in embeddings_list:
-        for i in range(embed.shape[0]):
-            _em = embed[i]
-            _norm = np.linalg.norm(_em)
-            _xnorm += _norm
-            _xnorm_cnt += 1
-    _xnorm /= _xnorm_cnt
-
-    acc1 = 0.0
-    std1 = 0.0
-    embeddings = embeddings_list[0] + embeddings_list[1]
-    embeddings = sklearn.preprocessing.normalize(embeddings)
-    print(embeddings.shape)
-    print('infer time', time_consumed)
-    _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds)
-    acc2, std2 = np.mean(accuracy), np.std(accuracy)
-    return acc1, std1, acc2, std2, _xnorm, embeddings_list
-
-
-def dumpR(data_set,
-          backbone,
-          batch_size,
-          name='',
-          data_extra=None,
-          label_shape=None):
-    print('dump verification embedding..')
-    data_list = data_set[0]
-    issame_list = data_set[1]
-    embeddings_list = []
-    time_consumed = 0.0
-    for i in range(len(data_list)):
-        data = data_list[i]
-        embeddings = None
-        ba = 0
-        while ba < data.shape[0]:
-            bb = min(ba + batch_size, data.shape[0])
-            count = bb - ba
-
-            _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb)
-            time0 = datetime.datetime.now()
-            if data_extra is None:
-                db = mx.io.DataBatch(data=(_data,), label=(_label,))
-            else:
-                db = mx.io.DataBatch(data=(_data, _data_extra),
-                                     label=(_label,))
-            model.forward(db, is_train=False)
-            net_out = model.get_outputs()
-            _embeddings = net_out[0].asnumpy()
-            time_now = datetime.datetime.now()
-            diff = time_now - time0
-            time_consumed += diff.total_seconds()
-            if embeddings is None:
-                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
-            embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
-            ba = bb
-        embeddings_list.append(embeddings)
-    embeddings = embeddings_list[0] + embeddings_list[1]
-    embeddings = sklearn.preprocessing.normalize(embeddings)
-    actual_issame = np.asarray(issame_list)
-    outname = os.path.join('temp.bin')
-    with open(outname, 'wb') as f:
-        pickle.dump((embeddings, issame_list),
-                    f,
-                    protocol=pickle.HIGHEST_PROTOCOL)
-
-
-# if __name__ == '__main__':
-#
-#     parser = argparse.ArgumentParser(description='do verification')
-#     # general
-#     parser.add_argument('--data-dir', default='', help='')
-#     parser.add_argument('--model',
-#                         default='../model/softmax,50',
-#                         help='path to load model.')
-#     parser.add_argument('--target',
-#                         default='lfw,cfp_ff,cfp_fp,agedb_30',
-#                         help='test targets.')
-#     parser.add_argument('--gpu', default=0, type=int, help='gpu id')
-#     parser.add_argument('--batch-size', default=32, type=int, help='')
-#     parser.add_argument('--max', default='', type=str, help='')
-#     parser.add_argument('--mode', default=0, type=int, help='')
-#     parser.add_argument('--nfolds', default=10, type=int, help='')
-#     args = parser.parse_args()
-#     image_size = [112, 112]
-#     print('image_size', image_size)
-#     ctx = mx.gpu(args.gpu)
-#     nets = []
-#     vec = args.model.split(',')
-#     prefix = args.model.split(',')[0]
-#     epochs = []
-#     if len(vec) == 1:
-#         pdir = os.path.dirname(prefix)
-#         for fname in os.listdir(pdir):
-#             if not fname.endswith('.params'):
-#                 continue
-#             _file = os.path.join(pdir, fname)
-#             if _file.startswith(prefix):
-#                 epoch = int(fname.split('.')[0].split('-')[1])
-#                 epochs.append(epoch)
-#         epochs = sorted(epochs, reverse=True)
-#         if len(args.max) > 0:
-#             _max = [int(x) for x in args.max.split(',')]
-#             assert len(_max) == 2
-#             if len(epochs) > _max[1]:
-#                 epochs = epochs[_max[0]:_max[1]]
-#
-#     else:
-#         epochs = [int(x) for x in vec[1].split('|')]
-#     print('model number', len(epochs))
-#     time0 = datetime.datetime.now()
-#     for epoch in epochs:
-#         print('loading', prefix, epoch)
-#         sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
-#         # arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
-#         all_layers = sym.get_internals()
-#         sym = all_layers['fc1_output']
-#         model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
-#         # model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
-#         model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0],
-#                                           image_size[1]))])
-#         model.set_params(arg_params, aux_params)
-#         nets.append(model)
-#     time_now = datetime.datetime.now()
-#     diff = time_now - time0
-#     print('model loading time', diff.total_seconds())
-#
-#     ver_list = []
-#     ver_name_list = []
-#     for name in args.target.split(','):
-#         path = os.path.join(args.data_dir, name + ".bin")
-#         if os.path.exists(path):
-#             print('loading.. ', name)
-#             data_set = load_bin(path, image_size)
-#             ver_list.append(data_set)
-#             ver_name_list.append(name)
-#
-#     if args.mode == 0:
-#         for i in range(len(ver_list)):
-#             results = []
-#             for model in nets:
-#                 acc1, std1, acc2, std2, xnorm, embeddings_list = test(
-#                     ver_list[i], model, args.batch_size, args.nfolds)
-#                 print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
-#                 print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
-#                 print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
-#                 results.append(acc2)
-#             print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))
-#     elif args.mode == 1:
-#         raise ValueError
-#     else:
-#         model = nets[0]
-#         dumpR(ver_list[0], model, args.batch_size, args.target)
diff --git a/spaces/kevinwang676/FreeVC/speaker_encoder/params_data.py b/spaces/kevinwang676/FreeVC/speaker_encoder/params_data.py
deleted file mode 100644
index bdb1716ed45617f2b127a7fb8885afe6cc74fb71..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/FreeVC/speaker_encoder/params_data.py
+++ /dev/null
@@ -1,29 +0,0 @@
-
-## Mel-filterbank
-mel_window_length = 25  # In milliseconds
-mel_window_step = 10    # In milliseconds
-mel_n_channels = 40
-
-
-## Audio
-sampling_rate = 16000
-# Number of spectrogram frames in a partial utterance
-partials_n_frames = 160     # 1600 ms
-# Number of spectrogram frames at inference
-inference_n_frames = 80     #  800 ms
-
-
-## Voice Activation Detection
-# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
-# This sets the granularity of the VAD. Should not need to be changed.
-vad_window_length = 30  # In milliseconds
-# Number of frames to average together when performing the moving average smoothing.
-# The larger this value, the larger the VAD variations must be to not get smoothed out. 
-vad_moving_average_width = 8
-# Maximum number of consecutive silent frames a segment can have.
-vad_max_silence_length = 6
-
-
-## Audio volume normalization
-audio_norm_target_dBFS = -30
-
diff --git a/spaces/kevinwang676/VITS2-Mandarin/commons.py b/spaces/kevinwang676/VITS2-Mandarin/commons.py
deleted file mode 100644
index 9ad0444b61cbadaa388619986c2889c707d873ce..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VITS2-Mandarin/commons.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/kevinwang676/VoiceChangers/src/utils/init_path.py b/spaces/kevinwang676/VoiceChangers/src/utils/init_path.py
deleted file mode 100644
index 5f38d11907bd0dc789992062ce7f02d8876c638f..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/src/utils/init_path.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os
-import glob
-
-def init_path(checkpoint_dir, config_dir, size=512, old_version=False, preprocess='crop'):
-
-    if old_version:
-        #### load all the checkpoint of `pth`
-        sadtalker_paths = {
-                'wav2lip_checkpoint' : os.path.join(checkpoint_dir, 'wav2lip.pth'),
-                'audio2pose_checkpoint' : os.path.join(checkpoint_dir, 'auido2pose_00140-model.pth'),
-                'audio2exp_checkpoint' : os.path.join(checkpoint_dir, 'auido2exp_00300-model.pth'),
-                'free_view_checkpoint' : os.path.join(checkpoint_dir, 'facevid2vid_00189-model.pth.tar'),
-                'path_of_net_recon_model' : os.path.join(checkpoint_dir, 'epoch_20.pth')
-        }
-
-        use_safetensor = False
-    elif len(glob.glob(os.path.join(checkpoint_dir, '*.safetensors'))):
-        print('using safetensor as default')
-        sadtalker_paths = {
-            "checkpoint":os.path.join(checkpoint_dir, 'SadTalker_V0.0.2_'+str(size)+'.safetensors'),
-            }
-        use_safetensor = True
-    else:
-        print("WARNING: The new version of the model will be updated by safetensor, you may need to download it mannully. We run the old version of the checkpoint this time!")
-        use_safetensor = False
-        
-        sadtalker_paths = {
-                'wav2lip_checkpoint' : os.path.join(checkpoint_dir, 'wav2lip.pth'),
-                'audio2pose_checkpoint' : os.path.join(checkpoint_dir, 'auido2pose_00140-model.pth'),
-                'audio2exp_checkpoint' : os.path.join(checkpoint_dir, 'auido2exp_00300-model.pth'),
-                'free_view_checkpoint' : os.path.join(checkpoint_dir, 'facevid2vid_00189-model.pth.tar'),
-                'path_of_net_recon_model' : os.path.join(checkpoint_dir, 'epoch_20.pth')
-        }
-
-    sadtalker_paths['dir_of_BFM_fitting'] = os.path.join(config_dir) # , 'BFM_Fitting'
-    sadtalker_paths['audio2pose_yaml_path'] = os.path.join(config_dir, 'auido2pose.yaml')
-    sadtalker_paths['audio2exp_yaml_path'] = os.path.join(config_dir, 'auido2exp.yaml')
-    sadtalker_paths['use_safetensor'] =  use_safetensor # os.path.join(config_dir, 'auido2exp.yaml')
-
-    if 'full' in preprocess:
-        sadtalker_paths['mappingnet_checkpoint'] = os.path.join(checkpoint_dir, 'mapping_00109-model.pth.tar')
-        sadtalker_paths['facerender_yaml'] = os.path.join(config_dir, 'facerender_still.yaml')
-    else:
-        sadtalker_paths['mappingnet_checkpoint'] = os.path.join(checkpoint_dir, 'mapping_00229-model.pth.tar')
-        sadtalker_paths['facerender_yaml'] = os.path.join(config_dir, 'facerender.yaml')
-
-    return sadtalker_paths
\ No newline at end of file
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/feaLib/ast.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/feaLib/ast.py
deleted file mode 100644
index bbe6e6e740c421acfe158a9784c8f47c5d56ded6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/feaLib/ast.py
+++ /dev/null
@@ -1,2131 +0,0 @@
-from fontTools.feaLib.error import FeatureLibError
-from fontTools.feaLib.location import FeatureLibLocation
-from fontTools.misc.encodingTools import getEncoding
-from fontTools.misc.textTools import byteord, tobytes
-from collections import OrderedDict
-import itertools
-
-SHIFT = " " * 4
-
-__all__ = [
-    "Element",
-    "FeatureFile",
-    "Comment",
-    "GlyphName",
-    "GlyphClass",
-    "GlyphClassName",
-    "MarkClassName",
-    "AnonymousBlock",
-    "Block",
-    "FeatureBlock",
-    "NestedBlock",
-    "LookupBlock",
-    "GlyphClassDefinition",
-    "GlyphClassDefStatement",
-    "MarkClass",
-    "MarkClassDefinition",
-    "AlternateSubstStatement",
-    "Anchor",
-    "AnchorDefinition",
-    "AttachStatement",
-    "AxisValueLocationStatement",
-    "BaseAxis",
-    "CVParametersNameStatement",
-    "ChainContextPosStatement",
-    "ChainContextSubstStatement",
-    "CharacterStatement",
-    "ConditionsetStatement",
-    "CursivePosStatement",
-    "ElidedFallbackName",
-    "ElidedFallbackNameID",
-    "Expression",
-    "FeatureNameStatement",
-    "FeatureReferenceStatement",
-    "FontRevisionStatement",
-    "HheaField",
-    "IgnorePosStatement",
-    "IgnoreSubstStatement",
-    "IncludeStatement",
-    "LanguageStatement",
-    "LanguageSystemStatement",
-    "LigatureCaretByIndexStatement",
-    "LigatureCaretByPosStatement",
-    "LigatureSubstStatement",
-    "LookupFlagStatement",
-    "LookupReferenceStatement",
-    "MarkBasePosStatement",
-    "MarkLigPosStatement",
-    "MarkMarkPosStatement",
-    "MultipleSubstStatement",
-    "NameRecord",
-    "OS2Field",
-    "PairPosStatement",
-    "ReverseChainSingleSubstStatement",
-    "ScriptStatement",
-    "SinglePosStatement",
-    "SingleSubstStatement",
-    "SizeParameters",
-    "Statement",
-    "STATAxisValueStatement",
-    "STATDesignAxisStatement",
-    "STATNameStatement",
-    "SubtableStatement",
-    "TableBlock",
-    "ValueRecord",
-    "ValueRecordDefinition",
-    "VheaField",
-]
-
-
-def deviceToString(device):
-    if device is None:
-        return "<device NULL>"
-    else:
-        return "<device %s>" % ", ".join("%d %d" % t for t in device)
-
-
-fea_keywords = set(
-    [
-        "anchor",
-        "anchordef",
-        "anon",
-        "anonymous",
-        "by",
-        "contour",
-        "cursive",
-        "device",
-        "enum",
-        "enumerate",
-        "excludedflt",
-        "exclude_dflt",
-        "feature",
-        "from",
-        "ignore",
-        "ignorebaseglyphs",
-        "ignoreligatures",
-        "ignoremarks",
-        "include",
-        "includedflt",
-        "include_dflt",
-        "language",
-        "languagesystem",
-        "lookup",
-        "lookupflag",
-        "mark",
-        "markattachmenttype",
-        "markclass",
-        "nameid",
-        "null",
-        "parameters",
-        "pos",
-        "position",
-        "required",
-        "righttoleft",
-        "reversesub",
-        "rsub",
-        "script",
-        "sub",
-        "substitute",
-        "subtable",
-        "table",
-        "usemarkfilteringset",
-        "useextension",
-        "valuerecorddef",
-        "base",
-        "gdef",
-        "head",
-        "hhea",
-        "name",
-        "vhea",
-        "vmtx",
-    ]
-)
-
-
-def asFea(g):
-    if hasattr(g, "asFea"):
-        return g.asFea()
-    elif isinstance(g, tuple) and len(g) == 2:
-        return asFea(g[0]) + " - " + asFea(g[1])  # a range
-    elif g.lower() in fea_keywords:
-        return "\\" + g
-    else:
-        return g
-
-
-class Element(object):
-    """A base class representing "something" in a feature file."""
-
-    def __init__(self, location=None):
-        #: location of this element as a `FeatureLibLocation` object.
-        if location and not isinstance(location, FeatureLibLocation):
-            location = FeatureLibLocation(*location)
-        self.location = location
-
-    def build(self, builder):
-        pass
-
-    def asFea(self, indent=""):
-        """Returns this element as a string of feature code. For block-type
-        elements (such as :class:`FeatureBlock`), the `indent` string is
-        added to the start of each line in the output."""
-        raise NotImplementedError
-
-    def __str__(self):
-        return self.asFea()
-
-
-class Statement(Element):
-    pass
-
-
-class Expression(Element):
-    pass
-
-
-class Comment(Element):
-    """A comment in a feature file."""
-
-    def __init__(self, text, location=None):
-        super(Comment, self).__init__(location)
-        #: Text of the comment
-        self.text = text
-
-    def asFea(self, indent=""):
-        return self.text
-
-
-class NullGlyph(Expression):
-    """The NULL glyph, used in glyph deletion substitutions."""
-
-    def __init__(self, location=None):
-        Expression.__init__(self, location)
-        #: The name itself as a string
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return ()
-
-    def asFea(self, indent=""):
-        return "NULL"
-
-
-class GlyphName(Expression):
-    """A single glyph name, such as ``cedilla``."""
-
-    def __init__(self, glyph, location=None):
-        Expression.__init__(self, location)
-        #: The name itself as a string
-        self.glyph = glyph
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return (self.glyph,)
-
-    def asFea(self, indent=""):
-        return asFea(self.glyph)
-
-
-class GlyphClass(Expression):
-    """A glyph class, such as ``[acute cedilla grave]``."""
-
-    def __init__(self, glyphs=None, location=None):
-        Expression.__init__(self, location)
-        #: The list of glyphs in this class, as :class:`GlyphName` objects.
-        self.glyphs = glyphs if glyphs is not None else []
-        self.original = []
-        self.curr = 0
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphs)
-
-    def asFea(self, indent=""):
-        if len(self.original):
-            if self.curr < len(self.glyphs):
-                self.original.extend(self.glyphs[self.curr :])
-                self.curr = len(self.glyphs)
-            return "[" + " ".join(map(asFea, self.original)) + "]"
-        else:
-            return "[" + " ".join(map(asFea, self.glyphs)) + "]"
-
-    def extend(self, glyphs):
-        """Add a list of :class:`GlyphName` objects to the class."""
-        self.glyphs.extend(glyphs)
-
-    def append(self, glyph):
-        """Add a single :class:`GlyphName` object to the class."""
-        self.glyphs.append(glyph)
-
-    def add_range(self, start, end, glyphs):
-        """Add a range (e.g. ``A-Z``) to the class. ``start`` and ``end``
-        are either :class:`GlyphName` objects or strings representing the
-        start and end glyphs in the class, and ``glyphs`` is the full list of
-        :class:`GlyphName` objects in the range."""
-        if self.curr < len(self.glyphs):
-            self.original.extend(self.glyphs[self.curr :])
-        self.original.append((start, end))
-        self.glyphs.extend(glyphs)
-        self.curr = len(self.glyphs)
-
-    def add_cid_range(self, start, end, glyphs):
-        """Add a range to the class by glyph ID. ``start`` and ``end`` are the
-        initial and final IDs, and ``glyphs`` is the full list of
-        :class:`GlyphName` objects in the range."""
-        if self.curr < len(self.glyphs):
-            self.original.extend(self.glyphs[self.curr :])
-        self.original.append(("\\{}".format(start), "\\{}".format(end)))
-        self.glyphs.extend(glyphs)
-        self.curr = len(self.glyphs)
-
-    def add_class(self, gc):
-        """Add glyphs from the given :class:`GlyphClassName` object to the
-        class."""
-        if self.curr < len(self.glyphs):
-            self.original.extend(self.glyphs[self.curr :])
-        self.original.append(gc)
-        self.glyphs.extend(gc.glyphSet())
-        self.curr = len(self.glyphs)
-
-
-class GlyphClassName(Expression):
-    """A glyph class name, such as ``@FRENCH_MARKS``. This must be instantiated
-    with a :class:`GlyphClassDefinition` object."""
-
-    def __init__(self, glyphclass, location=None):
-        Expression.__init__(self, location)
-        assert isinstance(glyphclass, GlyphClassDefinition)
-        self.glyphclass = glyphclass
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphclass.glyphSet())
-
-    def asFea(self, indent=""):
-        return "@" + self.glyphclass.name
-
-
-class MarkClassName(Expression):
-    """A mark class name, such as ``@FRENCH_MARKS`` defined with ``markClass``.
-    This must be instantiated with a :class:`MarkClass` object."""
-
-    def __init__(self, markClass, location=None):
-        Expression.__init__(self, location)
-        assert isinstance(markClass, MarkClass)
-        self.markClass = markClass
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return self.markClass.glyphSet()
-
-    def asFea(self, indent=""):
-        return "@" + self.markClass.name
-
-
-class AnonymousBlock(Statement):
-    """An anonymous data block."""
-
-    def __init__(self, tag, content, location=None):
-        Statement.__init__(self, location)
-        self.tag = tag  #: string containing the block's "tag"
-        self.content = content  #: block data as string
-
-    def asFea(self, indent=""):
-        res = "anon {} {{\n".format(self.tag)
-        res += self.content
-        res += "}} {};\n\n".format(self.tag)
-        return res
-
-
-class Block(Statement):
-    """A block of statements: feature, lookup, etc."""
-
-    def __init__(self, location=None):
-        Statement.__init__(self, location)
-        self.statements = []  #: Statements contained in the block
-
-    def build(self, builder):
-        """When handed a 'builder' object of comparable interface to
-        :class:`fontTools.feaLib.builder`, walks the statements in this
-        block, calling the builder callbacks."""
-        for s in self.statements:
-            s.build(builder)
-
-    def asFea(self, indent=""):
-        indent += SHIFT
-        return (
-            indent
-            + ("\n" + indent).join([s.asFea(indent=indent) for s in self.statements])
-            + "\n"
-        )
-
-
-class FeatureFile(Block):
-    """The top-level element of the syntax tree, containing the whole feature
-    file in its ``statements`` attribute."""
-
-    def __init__(self):
-        Block.__init__(self, location=None)
-        self.markClasses = {}  # name --> ast.MarkClass
-
-    def asFea(self, indent=""):
-        return "\n".join(s.asFea(indent=indent) for s in self.statements)
-
-
-class FeatureBlock(Block):
-    """A named feature block."""
-
-    def __init__(self, name, use_extension=False, location=None):
-        Block.__init__(self, location)
-        self.name, self.use_extension = name, use_extension
-
-    def build(self, builder):
-        """Call the ``start_feature`` callback on the builder object, visit
-        all the statements in this feature, and then call ``end_feature``."""
-        # TODO(sascha): Handle use_extension.
-        builder.start_feature(self.location, self.name)
-        # language exclude_dflt statements modify builder.features_
-        # limit them to this block with temporary builder.features_
-        features = builder.features_
-        builder.features_ = {}
-        Block.build(self, builder)
-        for key, value in builder.features_.items():
-            features.setdefault(key, []).extend(value)
-        builder.features_ = features
-        builder.end_feature()
-
-    def asFea(self, indent=""):
-        res = indent + "feature %s " % self.name.strip()
-        if self.use_extension:
-            res += "useExtension "
-        res += "{\n"
-        res += Block.asFea(self, indent=indent)
-        res += indent + "} %s;\n" % self.name.strip()
-        return res
-
-
-class NestedBlock(Block):
-    """A block inside another block, for example when found inside a
-    ``cvParameters`` block."""
-
-    def __init__(self, tag, block_name, location=None):
-        Block.__init__(self, location)
-        self.tag = tag
-        self.block_name = block_name
-
-    def build(self, builder):
-        Block.build(self, builder)
-        if self.block_name == "ParamUILabelNameID":
-            builder.add_to_cv_num_named_params(self.tag)
-
-    def asFea(self, indent=""):
-        res = "{}{} {{\n".format(indent, self.block_name)
-        res += Block.asFea(self, indent=indent)
-        res += "{}}};\n".format(indent)
-        return res
-
-
-class LookupBlock(Block):
-    """A named lookup, containing ``statements``."""
-
-    def __init__(self, name, use_extension=False, location=None):
-        Block.__init__(self, location)
-        self.name, self.use_extension = name, use_extension
-
-    def build(self, builder):
-        # TODO(sascha): Handle use_extension.
-        builder.start_lookup_block(self.location, self.name)
-        Block.build(self, builder)
-        builder.end_lookup_block()
-
-    def asFea(self, indent=""):
-        res = "lookup {} ".format(self.name)
-        if self.use_extension:
-            res += "useExtension "
-        res += "{\n"
-        res += Block.asFea(self, indent=indent)
-        res += "{}}} {};\n".format(indent, self.name)
-        return res
-
-
-class TableBlock(Block):
-    """A ``table ... { }`` block."""
-
-    def __init__(self, name, location=None):
-        Block.__init__(self, location)
-        self.name = name
-
-    def asFea(self, indent=""):
-        res = "table {} {{\n".format(self.name.strip())
-        res += super(TableBlock, self).asFea(indent=indent)
-        res += "}} {};\n".format(self.name.strip())
-        return res
-
-
-class GlyphClassDefinition(Statement):
-    """Example: ``@UPPERCASE = [A-Z];``."""
-
-    def __init__(self, name, glyphs, location=None):
-        Statement.__init__(self, location)
-        self.name = name  #: class name as a string, without initial ``@``
-        self.glyphs = glyphs  #: a :class:`GlyphClass` object
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphs.glyphSet())
-
-    def asFea(self, indent=""):
-        return "@" + self.name + " = " + self.glyphs.asFea() + ";"
-
-
-class GlyphClassDefStatement(Statement):
-    """Example: ``GlyphClassDef @UPPERCASE, [B], [C], [D];``. The parameters
-    must be either :class:`GlyphClass` or :class:`GlyphClassName` objects, or
-    ``None``."""
-
-    def __init__(
-        self, baseGlyphs, markGlyphs, ligatureGlyphs, componentGlyphs, location=None
-    ):
-        Statement.__init__(self, location)
-        self.baseGlyphs, self.markGlyphs = (baseGlyphs, markGlyphs)
-        self.ligatureGlyphs = ligatureGlyphs
-        self.componentGlyphs = componentGlyphs
-
-    def build(self, builder):
-        """Calls the builder's ``add_glyphClassDef`` callback."""
-        base = self.baseGlyphs.glyphSet() if self.baseGlyphs else tuple()
-        liga = self.ligatureGlyphs.glyphSet() if self.ligatureGlyphs else tuple()
-        mark = self.markGlyphs.glyphSet() if self.markGlyphs else tuple()
-        comp = self.componentGlyphs.glyphSet() if self.componentGlyphs else tuple()
-        builder.add_glyphClassDef(self.location, base, liga, mark, comp)
-
-    def asFea(self, indent=""):
-        return "GlyphClassDef {}, {}, {}, {};".format(
-            self.baseGlyphs.asFea() if self.baseGlyphs else "",
-            self.ligatureGlyphs.asFea() if self.ligatureGlyphs else "",
-            self.markGlyphs.asFea() if self.markGlyphs else "",
-            self.componentGlyphs.asFea() if self.componentGlyphs else "",
-        )
-
-
-class MarkClass(object):
-    """One `or more` ``markClass`` statements for the same mark class.
-
-    While glyph classes can be defined only once, the feature file format
-    allows expanding mark classes with multiple definitions, each using
-    different glyphs and anchors. The following are two ``MarkClassDefinitions``
-    for the same ``MarkClass``::
-
-        markClass [acute grave] <anchor 350 800> @FRENCH_ACCENTS;
-        markClass [cedilla] <anchor 350 -200> @FRENCH_ACCENTS;
-
-    The ``MarkClass`` object is therefore just a container for a list of
-    :class:`MarkClassDefinition` statements.
-    """
-
-    def __init__(self, name):
-        self.name = name
-        self.definitions = []
-        self.glyphs = OrderedDict()  # glyph --> ast.MarkClassDefinitions
-
-    def addDefinition(self, definition):
-        """Add a :class:`MarkClassDefinition` statement to this mark class."""
-        assert isinstance(definition, MarkClassDefinition)
-        self.definitions.append(definition)
-        for glyph in definition.glyphSet():
-            if glyph in self.glyphs:
-                otherLoc = self.glyphs[glyph].location
-                if otherLoc is None:
-                    end = ""
-                else:
-                    end = f" at {otherLoc}"
-                raise FeatureLibError(
-                    "Glyph %s already defined%s" % (glyph, end), definition.location
-                )
-            self.glyphs[glyph] = definition
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphs.keys())
-
-    def asFea(self, indent=""):
-        res = "\n".join(d.asFea() for d in self.definitions)
-        return res
-
-
-class MarkClassDefinition(Statement):
-    """A single ``markClass`` statement. The ``markClass`` should be a
-    :class:`MarkClass` object, the ``anchor`` an :class:`Anchor` object,
-    and the ``glyphs`` parameter should be a `glyph-containing object`_ .
-
-    Example:
-
-        .. code:: python
-
-            mc = MarkClass("FRENCH_ACCENTS")
-            mc.addDefinition( MarkClassDefinition(mc, Anchor(350, 800),
-                GlyphClass([ GlyphName("acute"), GlyphName("grave") ])
-            ) )
-            mc.addDefinition( MarkClassDefinition(mc, Anchor(350, -200),
-                GlyphClass([ GlyphName("cedilla") ])
-            ) )
-
-            mc.asFea()
-            # markClass [acute grave] <anchor 350 800> @FRENCH_ACCENTS;
-            # markClass [cedilla] <anchor 350 -200> @FRENCH_ACCENTS;
-
-    """
-
-    def __init__(self, markClass, anchor, glyphs, location=None):
-        Statement.__init__(self, location)
-        assert isinstance(markClass, MarkClass)
-        assert isinstance(anchor, Anchor) and isinstance(glyphs, Expression)
-        self.markClass, self.anchor, self.glyphs = markClass, anchor, glyphs
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return self.glyphs.glyphSet()
-
-    def asFea(self, indent=""):
-        return "markClass {} {} @{};".format(
-            self.glyphs.asFea(), self.anchor.asFea(), self.markClass.name
-        )
-
-
-class AlternateSubstStatement(Statement):
-    """A ``sub ... from ...`` statement.
-
-    ``prefix``, ``glyph``, ``suffix`` and ``replacement`` should be lists of
-    `glyph-containing objects`_. ``glyph`` should be a `one element list`."""
-
-    def __init__(self, prefix, glyph, suffix, replacement, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyph, self.suffix = (prefix, glyph, suffix)
-        self.replacement = replacement
-
-    def build(self, builder):
-        """Calls the builder's ``add_alternate_subst`` callback."""
-        glyph = self.glyph.glyphSet()
-        assert len(glyph) == 1, glyph
-        glyph = list(glyph)[0]
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        replacement = self.replacement.glyphSet()
-        builder.add_alternate_subst(self.location, prefix, glyph, suffix, replacement)
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix):
-            if len(self.prefix):
-                res += " ".join(map(asFea, self.prefix)) + " "
-            res += asFea(self.glyph) + "'"  # even though we really only use 1
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += asFea(self.glyph)
-        res += " from "
-        res += asFea(self.replacement)
-        res += ";"
-        return res
-
-
-class Anchor(Expression):
-    """An ``Anchor`` element, used inside a ``pos`` rule.
-
-    If a ``name`` is given, this will be used in preference to the coordinates.
-    Other values should be integer.
-    """
-
-    def __init__(
-        self,
-        x,
-        y,
-        name=None,
-        contourpoint=None,
-        xDeviceTable=None,
-        yDeviceTable=None,
-        location=None,
-    ):
-        Expression.__init__(self, location)
-        self.name = name
-        self.x, self.y, self.contourpoint = x, y, contourpoint
-        self.xDeviceTable, self.yDeviceTable = xDeviceTable, yDeviceTable
-
-    def asFea(self, indent=""):
-        if self.name is not None:
-            return "<anchor {}>".format(self.name)
-        res = "<anchor {} {}".format(self.x, self.y)
-        if self.contourpoint:
-            res += " contourpoint {}".format(self.contourpoint)
-        if self.xDeviceTable or self.yDeviceTable:
-            res += " "
-            res += deviceToString(self.xDeviceTable)
-            res += " "
-            res += deviceToString(self.yDeviceTable)
-        res += ">"
-        return res
-
-
-class AnchorDefinition(Statement):
-    """A named anchor definition. (2.e.viii). ``name`` should be a string."""
-
-    def __init__(self, name, x, y, contourpoint=None, location=None):
-        Statement.__init__(self, location)
-        self.name, self.x, self.y, self.contourpoint = name, x, y, contourpoint
-
-    def asFea(self, indent=""):
-        res = "anchorDef {} {}".format(self.x, self.y)
-        if self.contourpoint:
-            res += " contourpoint {}".format(self.contourpoint)
-        res += " {};".format(self.name)
-        return res
-
-
-class AttachStatement(Statement):
-    """A ``GDEF`` table ``Attach`` statement."""
-
-    def __init__(self, glyphs, contourPoints, location=None):
-        Statement.__init__(self, location)
-        self.glyphs = glyphs  #: A `glyph-containing object`_
-        self.contourPoints = contourPoints  #: A list of integer contour points
-
-    def build(self, builder):
-        """Calls the builder's ``add_attach_points`` callback."""
-        glyphs = self.glyphs.glyphSet()
-        builder.add_attach_points(self.location, glyphs, self.contourPoints)
-
-    def asFea(self, indent=""):
-        return "Attach {} {};".format(
-            self.glyphs.asFea(), " ".join(str(c) for c in self.contourPoints)
-        )
-
-
-class ChainContextPosStatement(Statement):
-    r"""A chained contextual positioning statement.
-
-    ``prefix``, ``glyphs``, and ``suffix`` should be lists of
-    `glyph-containing objects`_ .
-
-    ``lookups`` should be a list of elements representing what lookups
-    to apply at each glyph position. Each element should be a
-    :class:`LookupBlock` to apply a single chaining lookup at the given
-    position, a list of :class:`LookupBlock`\ s to apply multiple
-    lookups, or ``None`` to apply no lookup. The length of the outer
-    list should equal the length of ``glyphs``; the inner lists can be
-    of variable length."""
-
-    def __init__(self, prefix, glyphs, suffix, lookups, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyphs, self.suffix = prefix, glyphs, suffix
-        self.lookups = list(lookups)
-        for i, lookup in enumerate(lookups):
-            if lookup:
-                try:
-                    (_ for _ in lookup)
-                except TypeError:
-                    self.lookups[i] = [lookup]
-
-    def build(self, builder):
-        """Calls the builder's ``add_chain_context_pos`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        glyphs = [g.glyphSet() for g in self.glyphs]
-        suffix = [s.glyphSet() for s in self.suffix]
-        builder.add_chain_context_pos(
-            self.location, prefix, glyphs, suffix, self.lookups
-        )
-
-    def asFea(self, indent=""):
-        res = "pos "
-        if (
-            len(self.prefix)
-            or len(self.suffix)
-            or any([x is not None for x in self.lookups])
-        ):
-            if len(self.prefix):
-                res += " ".join(g.asFea() for g in self.prefix) + " "
-            for i, g in enumerate(self.glyphs):
-                res += g.asFea() + "'"
-                if self.lookups[i]:
-                    for lu in self.lookups[i]:
-                        res += " lookup " + lu.name
-                if i < len(self.glyphs) - 1:
-                    res += " "
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += " ".join(map(asFea, self.glyph))
-        res += ";"
-        return res
-
-
-class ChainContextSubstStatement(Statement):
-    r"""A chained contextual substitution statement.
-
-    ``prefix``, ``glyphs``, and ``suffix`` should be lists of
-    `glyph-containing objects`_ .
-
-    ``lookups`` should be a list of elements representing what lookups
-    to apply at each glyph position. Each element should be a
-    :class:`LookupBlock` to apply a single chaining lookup at the given
-    position, a list of :class:`LookupBlock`\ s to apply multiple
-    lookups, or ``None`` to apply no lookup. The length of the outer
-    list should equal the length of ``glyphs``; the inner lists can be
-    of variable length."""
-
-    def __init__(self, prefix, glyphs, suffix, lookups, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyphs, self.suffix = prefix, glyphs, suffix
-        self.lookups = list(lookups)
-        for i, lookup in enumerate(lookups):
-            if lookup:
-                try:
-                    (_ for _ in lookup)
-                except TypeError:
-                    self.lookups[i] = [lookup]
-
-    def build(self, builder):
-        """Calls the builder's ``add_chain_context_subst`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        glyphs = [g.glyphSet() for g in self.glyphs]
-        suffix = [s.glyphSet() for s in self.suffix]
-        builder.add_chain_context_subst(
-            self.location, prefix, glyphs, suffix, self.lookups
-        )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if (
-            len(self.prefix)
-            or len(self.suffix)
-            or any([x is not None for x in self.lookups])
-        ):
-            if len(self.prefix):
-                res += " ".join(g.asFea() for g in self.prefix) + " "
-            for i, g in enumerate(self.glyphs):
-                res += g.asFea() + "'"
-                if self.lookups[i]:
-                    for lu in self.lookups[i]:
-                        res += " lookup " + lu.name
-                if i < len(self.glyphs) - 1:
-                    res += " "
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += " ".join(map(asFea, self.glyph))
-        res += ";"
-        return res
-
-
-class CursivePosStatement(Statement):
-    """A cursive positioning statement. Entry and exit anchors can either
-    be :class:`Anchor` objects or ``None``."""
-
-    def __init__(self, glyphclass, entryAnchor, exitAnchor, location=None):
-        Statement.__init__(self, location)
-        self.glyphclass = glyphclass
-        self.entryAnchor, self.exitAnchor = entryAnchor, exitAnchor
-
-    def build(self, builder):
-        """Calls the builder object's ``add_cursive_pos`` callback."""
-        builder.add_cursive_pos(
-            self.location, self.glyphclass.glyphSet(), self.entryAnchor, self.exitAnchor
-        )
-
-    def asFea(self, indent=""):
-        entry = self.entryAnchor.asFea() if self.entryAnchor else "<anchor NULL>"
-        exit = self.exitAnchor.asFea() if self.exitAnchor else "<anchor NULL>"
-        return "pos cursive {} {} {};".format(self.glyphclass.asFea(), entry, exit)
-
-
-class FeatureReferenceStatement(Statement):
-    """Example: ``feature salt;``"""
-
-    def __init__(self, featureName, location=None):
-        Statement.__init__(self, location)
-        self.location, self.featureName = (location, featureName)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_feature_reference`` callback."""
-        builder.add_feature_reference(self.location, self.featureName)
-
-    def asFea(self, indent=""):
-        return "feature {};".format(self.featureName)
-
-
-class IgnorePosStatement(Statement):
-    """An ``ignore pos`` statement, containing `one or more` contexts to ignore.
-
-    ``chainContexts`` should be a list of ``(prefix, glyphs, suffix)`` tuples,
-    with each of ``prefix``, ``glyphs`` and ``suffix`` being
-    `glyph-containing objects`_ ."""
-
-    def __init__(self, chainContexts, location=None):
-        Statement.__init__(self, location)
-        self.chainContexts = chainContexts
-
-    def build(self, builder):
-        """Calls the builder object's ``add_chain_context_pos`` callback on each
-        rule context."""
-        for prefix, glyphs, suffix in self.chainContexts:
-            prefix = [p.glyphSet() for p in prefix]
-            glyphs = [g.glyphSet() for g in glyphs]
-            suffix = [s.glyphSet() for s in suffix]
-            builder.add_chain_context_pos(self.location, prefix, glyphs, suffix, [])
-
-    def asFea(self, indent=""):
-        contexts = []
-        for prefix, glyphs, suffix in self.chainContexts:
-            res = ""
-            if len(prefix) or len(suffix):
-                if len(prefix):
-                    res += " ".join(map(asFea, prefix)) + " "
-                res += " ".join(g.asFea() + "'" for g in glyphs)
-                if len(suffix):
-                    res += " " + " ".join(map(asFea, suffix))
-            else:
-                res += " ".join(map(asFea, glyphs))
-            contexts.append(res)
-        return "ignore pos " + ", ".join(contexts) + ";"
-
-
-class IgnoreSubstStatement(Statement):
-    """An ``ignore sub`` statement, containing `one or more` contexts to ignore.
-
-    ``chainContexts`` should be a list of ``(prefix, glyphs, suffix)`` tuples,
-    with each of ``prefix``, ``glyphs`` and ``suffix`` being
-    `glyph-containing objects`_ ."""
-
-    def __init__(self, chainContexts, location=None):
-        Statement.__init__(self, location)
-        self.chainContexts = chainContexts
-
-    def build(self, builder):
-        """Calls the builder object's ``add_chain_context_subst`` callback on
-        each rule context."""
-        for prefix, glyphs, suffix in self.chainContexts:
-            prefix = [p.glyphSet() for p in prefix]
-            glyphs = [g.glyphSet() for g in glyphs]
-            suffix = [s.glyphSet() for s in suffix]
-            builder.add_chain_context_subst(self.location, prefix, glyphs, suffix, [])
-
-    def asFea(self, indent=""):
-        contexts = []
-        for prefix, glyphs, suffix in self.chainContexts:
-            res = ""
-            if len(prefix):
-                res += " ".join(map(asFea, prefix)) + " "
-            res += " ".join(g.asFea() + "'" for g in glyphs)
-            if len(suffix):
-                res += " " + " ".join(map(asFea, suffix))
-            contexts.append(res)
-        return "ignore sub " + ", ".join(contexts) + ";"
-
-
-class IncludeStatement(Statement):
-    """An ``include()`` statement."""
-
-    def __init__(self, filename, location=None):
-        super(IncludeStatement, self).__init__(location)
-        self.filename = filename  #: String containing name of file to include
-
-    def build(self):
-        # TODO: consider lazy-loading the including parser/lexer?
-        raise FeatureLibError(
-            "Building an include statement is not implemented yet. "
-            "Instead, use Parser(..., followIncludes=True) for building.",
-            self.location,
-        )
-
-    def asFea(self, indent=""):
-        return indent + "include(%s);" % self.filename
-
-
-class LanguageStatement(Statement):
-    """A ``language`` statement within a feature."""
-
-    def __init__(self, language, include_default=True, required=False, location=None):
-        Statement.__init__(self, location)
-        assert len(language) == 4
-        self.language = language  #: A four-character language tag
-        self.include_default = include_default  #: If false, "exclude_dflt"
-        self.required = required
-
-    def build(self, builder):
-        """Call the builder object's ``set_language`` callback."""
-        builder.set_language(
-            location=self.location,
-            language=self.language,
-            include_default=self.include_default,
-            required=self.required,
-        )
-
-    def asFea(self, indent=""):
-        res = "language {}".format(self.language.strip())
-        if not self.include_default:
-            res += " exclude_dflt"
-        if self.required:
-            res += " required"
-        res += ";"
-        return res
-
-
-class LanguageSystemStatement(Statement):
-    """A top-level ``languagesystem`` statement."""
-
-    def __init__(self, script, language, location=None):
-        Statement.__init__(self, location)
-        self.script, self.language = (script, language)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_language_system`` callback."""
-        builder.add_language_system(self.location, self.script, self.language)
-
-    def asFea(self, indent=""):
-        return "languagesystem {} {};".format(self.script, self.language.strip())
-
-
-class FontRevisionStatement(Statement):
-    """A ``head`` table ``FontRevision`` statement. ``revision`` should be a
-    number, and will be formatted to three significant decimal places."""
-
-    def __init__(self, revision, location=None):
-        Statement.__init__(self, location)
-        self.revision = revision
-
-    def build(self, builder):
-        builder.set_font_revision(self.location, self.revision)
-
-    def asFea(self, indent=""):
-        return "FontRevision {:.3f};".format(self.revision)
-
-
-class LigatureCaretByIndexStatement(Statement):
-    """A ``GDEF`` table ``LigatureCaretByIndex`` statement. ``glyphs`` should be
-    a `glyph-containing object`_, and ``carets`` should be a list of integers."""
-
-    def __init__(self, glyphs, carets, location=None):
-        Statement.__init__(self, location)
-        self.glyphs, self.carets = (glyphs, carets)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_ligatureCaretByIndex_`` callback."""
-        glyphs = self.glyphs.glyphSet()
-        builder.add_ligatureCaretByIndex_(self.location, glyphs, set(self.carets))
-
-    def asFea(self, indent=""):
-        return "LigatureCaretByIndex {} {};".format(
-            self.glyphs.asFea(), " ".join(str(x) for x in self.carets)
-        )
-
-
-class LigatureCaretByPosStatement(Statement):
-    """A ``GDEF`` table ``LigatureCaretByPos`` statement. ``glyphs`` should be
-    a `glyph-containing object`_, and ``carets`` should be a list of integers."""
-
-    def __init__(self, glyphs, carets, location=None):
-        Statement.__init__(self, location)
-        self.glyphs, self.carets = (glyphs, carets)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_ligatureCaretByPos_`` callback."""
-        glyphs = self.glyphs.glyphSet()
-        builder.add_ligatureCaretByPos_(self.location, glyphs, set(self.carets))
-
-    def asFea(self, indent=""):
-        return "LigatureCaretByPos {} {};".format(
-            self.glyphs.asFea(), " ".join(str(x) for x in self.carets)
-        )
-
-
-class LigatureSubstStatement(Statement):
-    """A chained contextual substitution statement.
-
-    ``prefix``, ``glyphs``, and ``suffix`` should be lists of
-    `glyph-containing objects`_; ``replacement`` should be a single
-    `glyph-containing object`_.
-
-    If ``forceChain`` is True, this is expressed as a chaining rule
-    (e.g. ``sub f' i' by f_i``) even when no context is given."""
-
-    def __init__(self, prefix, glyphs, suffix, replacement, forceChain, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyphs, self.suffix = (prefix, glyphs, suffix)
-        self.replacement, self.forceChain = replacement, forceChain
-
-    def build(self, builder):
-        prefix = [p.glyphSet() for p in self.prefix]
-        glyphs = [g.glyphSet() for g in self.glyphs]
-        suffix = [s.glyphSet() for s in self.suffix]
-        builder.add_ligature_subst(
-            self.location, prefix, glyphs, suffix, self.replacement, self.forceChain
-        )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(g.asFea() for g in self.prefix) + " "
-            res += " ".join(g.asFea() + "'" for g in self.glyphs)
-            if len(self.suffix):
-                res += " " + " ".join(g.asFea() for g in self.suffix)
-        else:
-            res += " ".join(g.asFea() for g in self.glyphs)
-        res += " by "
-        res += asFea(self.replacement)
-        res += ";"
-        return res
-
-
-class LookupFlagStatement(Statement):
-    """A ``lookupflag`` statement. The ``value`` should be an integer value
-    representing the flags in use, but not including the ``markAttachment``
-    class and ``markFilteringSet`` values, which must be specified as
-    glyph-containing objects."""
-
-    def __init__(
-        self, value=0, markAttachment=None, markFilteringSet=None, location=None
-    ):
-        Statement.__init__(self, location)
-        self.value = value
-        self.markAttachment = markAttachment
-        self.markFilteringSet = markFilteringSet
-
-    def build(self, builder):
-        """Calls the builder object's ``set_lookup_flag`` callback."""
-        markAttach = None
-        if self.markAttachment is not None:
-            markAttach = self.markAttachment.glyphSet()
-        markFilter = None
-        if self.markFilteringSet is not None:
-            markFilter = self.markFilteringSet.glyphSet()
-        builder.set_lookup_flag(self.location, self.value, markAttach, markFilter)
-
-    def asFea(self, indent=""):
-        res = []
-        flags = ["RightToLeft", "IgnoreBaseGlyphs", "IgnoreLigatures", "IgnoreMarks"]
-        curr = 1
-        for i in range(len(flags)):
-            if self.value & curr != 0:
-                res.append(flags[i])
-            curr = curr << 1
-        if self.markAttachment is not None:
-            res.append("MarkAttachmentType {}".format(self.markAttachment.asFea()))
-        if self.markFilteringSet is not None:
-            res.append("UseMarkFilteringSet {}".format(self.markFilteringSet.asFea()))
-        if not res:
-            res = ["0"]
-        return "lookupflag {};".format(" ".join(res))
-
-
-class LookupReferenceStatement(Statement):
-    """Represents a ``lookup ...;`` statement to include a lookup in a feature.
-
-    The ``lookup`` should be a :class:`LookupBlock` object."""
-
-    def __init__(self, lookup, location=None):
-        Statement.__init__(self, location)
-        self.location, self.lookup = (location, lookup)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_lookup_call`` callback."""
-        builder.add_lookup_call(self.lookup.name)
-
-    def asFea(self, indent=""):
-        return "lookup {};".format(self.lookup.name)
-
-
-class MarkBasePosStatement(Statement):
-    """A mark-to-base positioning rule. The ``base`` should be a
-    `glyph-containing object`_. The ``marks`` should be a list of
-    (:class:`Anchor`, :class:`MarkClass`) tuples."""
-
-    def __init__(self, base, marks, location=None):
-        Statement.__init__(self, location)
-        self.base, self.marks = base, marks
-
-    def build(self, builder):
-        """Calls the builder object's ``add_mark_base_pos`` callback."""
-        builder.add_mark_base_pos(self.location, self.base.glyphSet(), self.marks)
-
-    def asFea(self, indent=""):
-        res = "pos base {}".format(self.base.asFea())
-        for a, m in self.marks:
-            res += "\n" + indent + SHIFT + "{} mark @{}".format(a.asFea(), m.name)
-        res += ";"
-        return res
-
-
-class MarkLigPosStatement(Statement):
-    """A mark-to-ligature positioning rule. The ``ligatures`` must be a
-    `glyph-containing object`_. The ``marks`` should be a list of lists: each
-    element in the top-level list represents a component glyph, and is made
-    up of a list of (:class:`Anchor`, :class:`MarkClass`) tuples representing
-    mark attachment points for that position.
-
-    Example::
-
-        m1 = MarkClass("TOP_MARKS")
-        m2 = MarkClass("BOTTOM_MARKS")
-        # ... add definitions to mark classes...
-
-        glyph = GlyphName("lam_meem_jeem")
-        marks = [
-            [ (Anchor(625,1800), m1) ], # Attachments on 1st component (lam)
-            [ (Anchor(376,-378), m2) ], # Attachments on 2nd component (meem)
-            [ ]                         # No attachments on the jeem
-        ]
-        mlp = MarkLigPosStatement(glyph, marks)
-
-        mlp.asFea()
-        # pos ligature lam_meem_jeem <anchor 625 1800> mark @TOP_MARKS
-        # ligComponent <anchor 376 -378> mark @BOTTOM_MARKS;
-
-    """
-
-    def __init__(self, ligatures, marks, location=None):
-        Statement.__init__(self, location)
-        self.ligatures, self.marks = ligatures, marks
-
-    def build(self, builder):
-        """Calls the builder object's ``add_mark_lig_pos`` callback."""
-        builder.add_mark_lig_pos(self.location, self.ligatures.glyphSet(), self.marks)
-
-    def asFea(self, indent=""):
-        res = "pos ligature {}".format(self.ligatures.asFea())
-        ligs = []
-        for l in self.marks:
-            temp = ""
-            if l is None or not len(l):
-                temp = "\n" + indent + SHIFT * 2 + "<anchor NULL>"
-            else:
-                for a, m in l:
-                    temp += (
-                        "\n"
-                        + indent
-                        + SHIFT * 2
-                        + "{} mark @{}".format(a.asFea(), m.name)
-                    )
-            ligs.append(temp)
-        res += ("\n" + indent + SHIFT + "ligComponent").join(ligs)
-        res += ";"
-        return res
-
-
-class MarkMarkPosStatement(Statement):
-    """A mark-to-mark positioning rule. The ``baseMarks`` must be a
-    `glyph-containing object`_. The ``marks`` should be a list of
-    (:class:`Anchor`, :class:`MarkClass`) tuples."""
-
-    def __init__(self, baseMarks, marks, location=None):
-        Statement.__init__(self, location)
-        self.baseMarks, self.marks = baseMarks, marks
-
-    def build(self, builder):
-        """Calls the builder object's ``add_mark_mark_pos`` callback."""
-        builder.add_mark_mark_pos(self.location, self.baseMarks.glyphSet(), self.marks)
-
-    def asFea(self, indent=""):
-        res = "pos mark {}".format(self.baseMarks.asFea())
-        for a, m in self.marks:
-            res += "\n" + indent + SHIFT + "{} mark @{}".format(a.asFea(), m.name)
-        res += ";"
-        return res
-
-
-class MultipleSubstStatement(Statement):
-    """A multiple substitution statement.
-
-    Args:
-        prefix: a list of `glyph-containing objects`_.
-        glyph: a single glyph-containing object.
-        suffix: a list of glyph-containing objects.
-        replacement: a list of glyph-containing objects.
-        forceChain: If true, the statement is expressed as a chaining rule
-            (e.g. ``sub f' i' by f_i``) even when no context is given.
-    """
-
-    def __init__(
-        self, prefix, glyph, suffix, replacement, forceChain=False, location=None
-    ):
-        Statement.__init__(self, location)
-        self.prefix, self.glyph, self.suffix = prefix, glyph, suffix
-        self.replacement = replacement
-        self.forceChain = forceChain
-
-    def build(self, builder):
-        """Calls the builder object's ``add_multiple_subst`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        if hasattr(self.glyph, "glyphSet"):
-            originals = self.glyph.glyphSet()
-        else:
-            originals = [self.glyph]
-        count = len(originals)
-        replaces = []
-        for r in self.replacement:
-            if hasattr(r, "glyphSet"):
-                replace = r.glyphSet()
-            else:
-                replace = [r]
-            if len(replace) == 1 and len(replace) != count:
-                replace = replace * count
-            replaces.append(replace)
-        replaces = list(zip(*replaces))
-
-        for i, original in enumerate(originals):
-            builder.add_multiple_subst(
-                self.location,
-                prefix,
-                original,
-                suffix,
-                replaces and replaces[i] or (),
-                self.forceChain,
-            )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(map(asFea, self.prefix)) + " "
-            res += asFea(self.glyph) + "'"
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += asFea(self.glyph)
-        replacement = self.replacement or [NullGlyph()]
-        res += " by "
-        res += " ".join(map(asFea, replacement))
-        res += ";"
-        return res
-
-
-class PairPosStatement(Statement):
-    """A pair positioning statement.
-
-    ``glyphs1`` and ``glyphs2`` should be `glyph-containing objects`_.
-    ``valuerecord1`` should be a :class:`ValueRecord` object;
-    ``valuerecord2`` should be either a :class:`ValueRecord` object or ``None``.
-    If ``enumerated`` is true, then this is expressed as an
-    `enumerated pair <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#6.b.ii>`_.
-    """
-
-    def __init__(
-        self,
-        glyphs1,
-        valuerecord1,
-        glyphs2,
-        valuerecord2,
-        enumerated=False,
-        location=None,
-    ):
-        Statement.__init__(self, location)
-        self.enumerated = enumerated
-        self.glyphs1, self.valuerecord1 = glyphs1, valuerecord1
-        self.glyphs2, self.valuerecord2 = glyphs2, valuerecord2
-
-    def build(self, builder):
-        """Calls a callback on the builder object:
-
-        * If the rule is enumerated, calls ``add_specific_pair_pos`` on each
-          combination of first and second glyphs.
-        * If the glyphs are both single :class:`GlyphName` objects, calls
-          ``add_specific_pair_pos``.
-        * Else, calls ``add_class_pair_pos``.
-        """
-        if self.enumerated:
-            g = [self.glyphs1.glyphSet(), self.glyphs2.glyphSet()]
-            seen_pair = False
-            for glyph1, glyph2 in itertools.product(*g):
-                seen_pair = True
-                builder.add_specific_pair_pos(
-                    self.location, glyph1, self.valuerecord1, glyph2, self.valuerecord2
-                )
-            if not seen_pair:
-                raise FeatureLibError(
-                    "Empty glyph class in positioning rule", self.location
-                )
-            return
-
-        is_specific = isinstance(self.glyphs1, GlyphName) and isinstance(
-            self.glyphs2, GlyphName
-        )
-        if is_specific:
-            builder.add_specific_pair_pos(
-                self.location,
-                self.glyphs1.glyph,
-                self.valuerecord1,
-                self.glyphs2.glyph,
-                self.valuerecord2,
-            )
-        else:
-            builder.add_class_pair_pos(
-                self.location,
-                self.glyphs1.glyphSet(),
-                self.valuerecord1,
-                self.glyphs2.glyphSet(),
-                self.valuerecord2,
-            )
-
-    def asFea(self, indent=""):
-        res = "enum " if self.enumerated else ""
-        if self.valuerecord2:
-            res += "pos {} {} {} {};".format(
-                self.glyphs1.asFea(),
-                self.valuerecord1.asFea(),
-                self.glyphs2.asFea(),
-                self.valuerecord2.asFea(),
-            )
-        else:
-            res += "pos {} {} {};".format(
-                self.glyphs1.asFea(), self.glyphs2.asFea(), self.valuerecord1.asFea()
-            )
-        return res
-
-
-class ReverseChainSingleSubstStatement(Statement):
-    """A reverse chaining substitution statement. You don't see those every day.
-
-    Note the unusual argument order: ``suffix`` comes `before` ``glyphs``.
-    ``old_prefix``, ``old_suffix``, ``glyphs`` and ``replacements`` should be
-    lists of `glyph-containing objects`_. ``glyphs`` and ``replacements`` should
-    be one-item lists.
-    """
-
-    def __init__(self, old_prefix, old_suffix, glyphs, replacements, location=None):
-        Statement.__init__(self, location)
-        self.old_prefix, self.old_suffix = old_prefix, old_suffix
-        self.glyphs = glyphs
-        self.replacements = replacements
-
-    def build(self, builder):
-        prefix = [p.glyphSet() for p in self.old_prefix]
-        suffix = [s.glyphSet() for s in self.old_suffix]
-        originals = self.glyphs[0].glyphSet()
-        replaces = self.replacements[0].glyphSet()
-        if len(replaces) == 1:
-            replaces = replaces * len(originals)
-        builder.add_reverse_chain_single_subst(
-            self.location, prefix, suffix, dict(zip(originals, replaces))
-        )
-
-    def asFea(self, indent=""):
-        res = "rsub "
-        if len(self.old_prefix) or len(self.old_suffix):
-            if len(self.old_prefix):
-                res += " ".join(asFea(g) for g in self.old_prefix) + " "
-            res += " ".join(asFea(g) + "'" for g in self.glyphs)
-            if len(self.old_suffix):
-                res += " " + " ".join(asFea(g) for g in self.old_suffix)
-        else:
-            res += " ".join(map(asFea, self.glyphs))
-        res += " by {};".format(" ".join(asFea(g) for g in self.replacements))
-        return res
-
-
-class SingleSubstStatement(Statement):
-    """A single substitution statement.
-
-    Note the unusual argument order: ``prefix`` and suffix come `after`
-    the replacement ``glyphs``. ``prefix``, ``suffix``, ``glyphs`` and
-    ``replace`` should be lists of `glyph-containing objects`_. ``glyphs`` and
-    ``replace`` should be one-item lists.
-    """
-
-    def __init__(self, glyphs, replace, prefix, suffix, forceChain, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.suffix = prefix, suffix
-        self.forceChain = forceChain
-        self.glyphs = glyphs
-        self.replacements = replace
-
-    def build(self, builder):
-        """Calls the builder object's ``add_single_subst`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        originals = self.glyphs[0].glyphSet()
-        replaces = self.replacements[0].glyphSet()
-        if len(replaces) == 1:
-            replaces = replaces * len(originals)
-        builder.add_single_subst(
-            self.location,
-            prefix,
-            suffix,
-            OrderedDict(zip(originals, replaces)),
-            self.forceChain,
-        )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(asFea(g) for g in self.prefix) + " "
-            res += " ".join(asFea(g) + "'" for g in self.glyphs)
-            if len(self.suffix):
-                res += " " + " ".join(asFea(g) for g in self.suffix)
-        else:
-            res += " ".join(asFea(g) for g in self.glyphs)
-        res += " by {};".format(" ".join(asFea(g) for g in self.replacements))
-        return res
-
-
-class ScriptStatement(Statement):
-    """A ``script`` statement."""
-
-    def __init__(self, script, location=None):
-        Statement.__init__(self, location)
-        self.script = script  #: the script code
-
-    def build(self, builder):
-        """Calls the builder's ``set_script`` callback."""
-        builder.set_script(self.location, self.script)
-
-    def asFea(self, indent=""):
-        return "script {};".format(self.script.strip())
-
-
-class SinglePosStatement(Statement):
-    """A single position statement. ``prefix`` and ``suffix`` should be
-    lists of `glyph-containing objects`_.
-
-    ``pos`` should be a one-element list containing a (`glyph-containing object`_,
-    :class:`ValueRecord`) tuple."""
-
-    def __init__(self, pos, prefix, suffix, forceChain, location=None):
-        Statement.__init__(self, location)
-        self.pos, self.prefix, self.suffix = pos, prefix, suffix
-        self.forceChain = forceChain
-
-    def build(self, builder):
-        """Calls the builder object's ``add_single_pos`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        pos = [(g.glyphSet(), value) for g, value in self.pos]
-        builder.add_single_pos(self.location, prefix, suffix, pos, self.forceChain)
-
-    def asFea(self, indent=""):
-        res = "pos "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(map(asFea, self.prefix)) + " "
-            res += " ".join(
-                [
-                    asFea(x[0]) + "'" + ((" " + x[1].asFea()) if x[1] else "")
-                    for x in self.pos
-                ]
-            )
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += " ".join(
-                [asFea(x[0]) + " " + (x[1].asFea() if x[1] else "") for x in self.pos]
-            )
-        res += ";"
-        return res
-
-
-class SubtableStatement(Statement):
-    """Represents a subtable break."""
-
-    def __init__(self, location=None):
-        Statement.__init__(self, location)
-
-    def build(self, builder):
-        """Calls the builder objects's ``add_subtable_break`` callback."""
-        builder.add_subtable_break(self.location)
-
-    def asFea(self, indent=""):
-        return "subtable;"
-
-
-class ValueRecord(Expression):
-    """Represents a value record."""
-
-    def __init__(
-        self,
-        xPlacement=None,
-        yPlacement=None,
-        xAdvance=None,
-        yAdvance=None,
-        xPlaDevice=None,
-        yPlaDevice=None,
-        xAdvDevice=None,
-        yAdvDevice=None,
-        vertical=False,
-        location=None,
-    ):
-        Expression.__init__(self, location)
-        self.xPlacement, self.yPlacement = (xPlacement, yPlacement)
-        self.xAdvance, self.yAdvance = (xAdvance, yAdvance)
-        self.xPlaDevice, self.yPlaDevice = (xPlaDevice, yPlaDevice)
-        self.xAdvDevice, self.yAdvDevice = (xAdvDevice, yAdvDevice)
-        self.vertical = vertical
-
-    def __eq__(self, other):
-        return (
-            self.xPlacement == other.xPlacement
-            and self.yPlacement == other.yPlacement
-            and self.xAdvance == other.xAdvance
-            and self.yAdvance == other.yAdvance
-            and self.xPlaDevice == other.xPlaDevice
-            and self.xAdvDevice == other.xAdvDevice
-        )
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return (
-            hash(self.xPlacement)
-            ^ hash(self.yPlacement)
-            ^ hash(self.xAdvance)
-            ^ hash(self.yAdvance)
-            ^ hash(self.xPlaDevice)
-            ^ hash(self.yPlaDevice)
-            ^ hash(self.xAdvDevice)
-            ^ hash(self.yAdvDevice)
-        )
-
-    def asFea(self, indent=""):
-        if not self:
-            return "<NULL>"
-
-        x, y = self.xPlacement, self.yPlacement
-        xAdvance, yAdvance = self.xAdvance, self.yAdvance
-        xPlaDevice, yPlaDevice = self.xPlaDevice, self.yPlaDevice
-        xAdvDevice, yAdvDevice = self.xAdvDevice, self.yAdvDevice
-        vertical = self.vertical
-
-        # Try format A, if possible.
-        if x is None and y is None:
-            if xAdvance is None and vertical:
-                return str(yAdvance)
-            elif yAdvance is None and not vertical:
-                return str(xAdvance)
-
-        # Make any remaining None value 0 to avoid generating invalid records.
-        x = x or 0
-        y = y or 0
-        xAdvance = xAdvance or 0
-        yAdvance = yAdvance or 0
-
-        # Try format B, if possible.
-        if (
-            xPlaDevice is None
-            and yPlaDevice is None
-            and xAdvDevice is None
-            and yAdvDevice is None
-        ):
-            return "<%s %s %s %s>" % (x, y, xAdvance, yAdvance)
-
-        # Last resort is format C.
-        return "<%s %s %s %s %s %s %s %s>" % (
-            x,
-            y,
-            xAdvance,
-            yAdvance,
-            deviceToString(xPlaDevice),
-            deviceToString(yPlaDevice),
-            deviceToString(xAdvDevice),
-            deviceToString(yAdvDevice),
-        )
-
-    def __bool__(self):
-        return any(
-            getattr(self, v) is not None
-            for v in [
-                "xPlacement",
-                "yPlacement",
-                "xAdvance",
-                "yAdvance",
-                "xPlaDevice",
-                "yPlaDevice",
-                "xAdvDevice",
-                "yAdvDevice",
-            ]
-        )
-
-    __nonzero__ = __bool__
-
-
-class ValueRecordDefinition(Statement):
-    """Represents a named value record definition."""
-
-    def __init__(self, name, value, location=None):
-        Statement.__init__(self, location)
-        self.name = name  #: Value record name as string
-        self.value = value  #: :class:`ValueRecord` object
-
-    def asFea(self, indent=""):
-        return "valueRecordDef {} {};".format(self.value.asFea(), self.name)
-
-
-def simplify_name_attributes(pid, eid, lid):
-    if pid == 3 and eid == 1 and lid == 1033:
-        return ""
-    elif pid == 1 and eid == 0 and lid == 0:
-        return "1"
-    else:
-        return "{} {} {}".format(pid, eid, lid)
-
-
-class NameRecord(Statement):
-    """Represents a name record. (`Section 9.e. <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.e>`_)"""
-
-    def __init__(self, nameID, platformID, platEncID, langID, string, location=None):
-        Statement.__init__(self, location)
-        self.nameID = nameID  #: Name ID as integer (e.g. 9 for designer's name)
-        self.platformID = platformID  #: Platform ID as integer
-        self.platEncID = platEncID  #: Platform encoding ID as integer
-        self.langID = langID  #: Language ID as integer
-        self.string = string  #: Name record value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_name_record`` callback."""
-        builder.add_name_record(
-            self.location,
-            self.nameID,
-            self.platformID,
-            self.platEncID,
-            self.langID,
-            self.string,
-        )
-
-    def asFea(self, indent=""):
-        def escape(c, escape_pattern):
-            # Also escape U+0022 QUOTATION MARK and U+005C REVERSE SOLIDUS
-            if c >= 0x20 and c <= 0x7E and c not in (0x22, 0x5C):
-                return chr(c)
-            else:
-                return escape_pattern % c
-
-        encoding = getEncoding(self.platformID, self.platEncID, self.langID)
-        if encoding is None:
-            raise FeatureLibError("Unsupported encoding", self.location)
-        s = tobytes(self.string, encoding=encoding)
-        if encoding == "utf_16_be":
-            escaped_string = "".join(
-                [
-                    escape(byteord(s[i]) * 256 + byteord(s[i + 1]), r"\%04x")
-                    for i in range(0, len(s), 2)
-                ]
-            )
-        else:
-            escaped_string = "".join([escape(byteord(b), r"\%02x") for b in s])
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return 'nameid {} {}"{}";'.format(self.nameID, plat, escaped_string)
-
-
-class FeatureNameStatement(NameRecord):
-    """Represents a ``sizemenuname`` or ``name`` statement."""
-
-    def build(self, builder):
-        """Calls the builder object's ``add_featureName`` callback."""
-        NameRecord.build(self, builder)
-        builder.add_featureName(self.nameID)
-
-    def asFea(self, indent=""):
-        if self.nameID == "size":
-            tag = "sizemenuname"
-        else:
-            tag = "name"
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return '{} {}"{}";'.format(tag, plat, self.string)
-
-
-class STATNameStatement(NameRecord):
-    """Represents a STAT table ``name`` statement."""
-
-    def asFea(self, indent=""):
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return 'name {}"{}";'.format(plat, self.string)
-
-
-class SizeParameters(Statement):
-    """A ``parameters`` statement."""
-
-    def __init__(self, DesignSize, SubfamilyID, RangeStart, RangeEnd, location=None):
-        Statement.__init__(self, location)
-        self.DesignSize = DesignSize
-        self.SubfamilyID = SubfamilyID
-        self.RangeStart = RangeStart
-        self.RangeEnd = RangeEnd
-
-    def build(self, builder):
-        """Calls the builder object's ``set_size_parameters`` callback."""
-        builder.set_size_parameters(
-            self.location,
-            self.DesignSize,
-            self.SubfamilyID,
-            self.RangeStart,
-            self.RangeEnd,
-        )
-
-    def asFea(self, indent=""):
-        res = "parameters {:.1f} {}".format(self.DesignSize, self.SubfamilyID)
-        if self.RangeStart != 0 or self.RangeEnd != 0:
-            res += " {} {}".format(int(self.RangeStart * 10), int(self.RangeEnd * 10))
-        return res + ";"
-
-
-class CVParametersNameStatement(NameRecord):
-    """Represent a name statement inside a ``cvParameters`` block."""
-
-    def __init__(
-        self, nameID, platformID, platEncID, langID, string, block_name, location=None
-    ):
-        NameRecord.__init__(
-            self, nameID, platformID, platEncID, langID, string, location=location
-        )
-        self.block_name = block_name
-
-    def build(self, builder):
-        """Calls the builder object's ``add_cv_parameter`` callback."""
-        item = ""
-        if self.block_name == "ParamUILabelNameID":
-            item = "_{}".format(builder.cv_num_named_params_.get(self.nameID, 0))
-        builder.add_cv_parameter(self.nameID)
-        self.nameID = (self.nameID, self.block_name + item)
-        NameRecord.build(self, builder)
-
-    def asFea(self, indent=""):
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return 'name {}"{}";'.format(plat, self.string)
-
-
-class CharacterStatement(Statement):
-    """
-    Statement used in cvParameters blocks of Character Variant features (cvXX).
-    The Unicode value may be written with either decimal or hexadecimal
-    notation. The value must be preceded by '0x' if it is a hexadecimal value.
-    The largest Unicode value allowed is 0xFFFFFF.
-    """
-
-    def __init__(self, character, tag, location=None):
-        Statement.__init__(self, location)
-        self.character = character
-        self.tag = tag
-
-    def build(self, builder):
-        """Calls the builder object's ``add_cv_character`` callback."""
-        builder.add_cv_character(self.character, self.tag)
-
-    def asFea(self, indent=""):
-        return "Character {:#x};".format(self.character)
-
-
-class BaseAxis(Statement):
-    """An axis definition, being either a ``VertAxis.BaseTagList/BaseScriptList``
-    pair or a ``HorizAxis.BaseTagList/BaseScriptList`` pair."""
-
-    def __init__(self, bases, scripts, vertical, location=None):
-        Statement.__init__(self, location)
-        self.bases = bases  #: A list of baseline tag names as strings
-        self.scripts = scripts  #: A list of script record tuplets (script tag, default baseline tag, base coordinate)
-        self.vertical = vertical  #: Boolean; VertAxis if True, HorizAxis if False
-
-    def build(self, builder):
-        """Calls the builder object's ``set_base_axis`` callback."""
-        builder.set_base_axis(self.bases, self.scripts, self.vertical)
-
-    def asFea(self, indent=""):
-        direction = "Vert" if self.vertical else "Horiz"
-        scripts = [
-            "{} {} {}".format(a[0], a[1], " ".join(map(str, a[2])))
-            for a in self.scripts
-        ]
-        return "{}Axis.BaseTagList {};\n{}{}Axis.BaseScriptList {};".format(
-            direction, " ".join(self.bases), indent, direction, ", ".join(scripts)
-        )
-
-
-class OS2Field(Statement):
-    """An entry in the ``OS/2`` table. Most ``values`` should be numbers or
-    strings, apart from when the key is ``UnicodeRange``, ``CodePageRange``
-    or ``Panose``, in which case it should be an array of integers."""
-
-    def __init__(self, key, value, location=None):
-        Statement.__init__(self, location)
-        self.key = key
-        self.value = value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_os2_field`` callback."""
-        builder.add_os2_field(self.key, self.value)
-
-    def asFea(self, indent=""):
-        def intarr2str(x):
-            return " ".join(map(str, x))
-
-        numbers = (
-            "FSType",
-            "TypoAscender",
-            "TypoDescender",
-            "TypoLineGap",
-            "winAscent",
-            "winDescent",
-            "XHeight",
-            "CapHeight",
-            "WeightClass",
-            "WidthClass",
-            "LowerOpSize",
-            "UpperOpSize",
-        )
-        ranges = ("UnicodeRange", "CodePageRange")
-        keywords = dict([(x.lower(), [x, str]) for x in numbers])
-        keywords.update([(x.lower(), [x, intarr2str]) for x in ranges])
-        keywords["panose"] = ["Panose", intarr2str]
-        keywords["vendor"] = ["Vendor", lambda y: '"{}"'.format(y)]
-        if self.key in keywords:
-            return "{} {};".format(
-                keywords[self.key][0], keywords[self.key][1](self.value)
-            )
-        return ""  # should raise exception
-
-
-class HheaField(Statement):
-    """An entry in the ``hhea`` table."""
-
-    def __init__(self, key, value, location=None):
-        Statement.__init__(self, location)
-        self.key = key
-        self.value = value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_hhea_field`` callback."""
-        builder.add_hhea_field(self.key, self.value)
-
-    def asFea(self, indent=""):
-        fields = ("CaretOffset", "Ascender", "Descender", "LineGap")
-        keywords = dict([(x.lower(), x) for x in fields])
-        return "{} {};".format(keywords[self.key], self.value)
-
-
-class VheaField(Statement):
-    """An entry in the ``vhea`` table."""
-
-    def __init__(self, key, value, location=None):
-        Statement.__init__(self, location)
-        self.key = key
-        self.value = value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_vhea_field`` callback."""
-        builder.add_vhea_field(self.key, self.value)
-
-    def asFea(self, indent=""):
-        fields = ("VertTypoAscender", "VertTypoDescender", "VertTypoLineGap")
-        keywords = dict([(x.lower(), x) for x in fields])
-        return "{} {};".format(keywords[self.key], self.value)
-
-
-class STATDesignAxisStatement(Statement):
-    """A STAT table Design Axis
-
-    Args:
-        tag (str): a 4 letter axis tag
-        axisOrder (int): an int
-        names (list): a list of :class:`STATNameStatement` objects
-    """
-
-    def __init__(self, tag, axisOrder, names, location=None):
-        Statement.__init__(self, location)
-        self.tag = tag
-        self.axisOrder = axisOrder
-        self.names = names
-        self.location = location
-
-    def build(self, builder):
-        builder.addDesignAxis(self, self.location)
-
-    def asFea(self, indent=""):
-        indent += SHIFT
-        res = f"DesignAxis {self.tag} {self.axisOrder} {{ \n"
-        res += ("\n" + indent).join([s.asFea(indent=indent) for s in self.names]) + "\n"
-        res += "};"
-        return res
-
-
-class ElidedFallbackName(Statement):
-    """STAT table ElidedFallbackName
-
-    Args:
-        names: a list of :class:`STATNameStatement` objects
-    """
-
-    def __init__(self, names, location=None):
-        Statement.__init__(self, location)
-        self.names = names
-        self.location = location
-
-    def build(self, builder):
-        builder.setElidedFallbackName(self.names, self.location)
-
-    def asFea(self, indent=""):
-        indent += SHIFT
-        res = "ElidedFallbackName { \n"
-        res += ("\n" + indent).join([s.asFea(indent=indent) for s in self.names]) + "\n"
-        res += "};"
-        return res
-
-
-class ElidedFallbackNameID(Statement):
-    """STAT table ElidedFallbackNameID
-
-    Args:
-        value: an int pointing to an existing name table name ID
-    """
-
-    def __init__(self, value, location=None):
-        Statement.__init__(self, location)
-        self.value = value
-        self.location = location
-
-    def build(self, builder):
-        builder.setElidedFallbackName(self.value, self.location)
-
-    def asFea(self, indent=""):
-        return f"ElidedFallbackNameID {self.value};"
-
-
-class STATAxisValueStatement(Statement):
-    """A STAT table Axis Value Record
-
-    Args:
-        names (list): a list of :class:`STATNameStatement` objects
-        locations (list): a list of :class:`AxisValueLocationStatement` objects
-        flags (int): an int
-    """
-
-    def __init__(self, names, locations, flags, location=None):
-        Statement.__init__(self, location)
-        self.names = names
-        self.locations = locations
-        self.flags = flags
-
-    def build(self, builder):
-        builder.addAxisValueRecord(self, self.location)
-
-    def asFea(self, indent=""):
-        res = "AxisValue {\n"
-        for location in self.locations:
-            res += location.asFea()
-
-        for nameRecord in self.names:
-            res += nameRecord.asFea()
-            res += "\n"
-
-        if self.flags:
-            flags = ["OlderSiblingFontAttribute", "ElidableAxisValueName"]
-            flagStrings = []
-            curr = 1
-            for i in range(len(flags)):
-                if self.flags & curr != 0:
-                    flagStrings.append(flags[i])
-                curr = curr << 1
-            res += f"flag {' '.join(flagStrings)};\n"
-        res += "};"
-        return res
-
-
-class AxisValueLocationStatement(Statement):
-    """
-    A STAT table Axis Value Location
-
-    Args:
-        tag (str): a 4 letter axis tag
-        values (list): a list of ints and/or floats
-    """
-
-    def __init__(self, tag, values, location=None):
-        Statement.__init__(self, location)
-        self.tag = tag
-        self.values = values
-
-    def asFea(self, res=""):
-        res += f"location {self.tag} "
-        res += f"{' '.join(str(i) for i in self.values)};\n"
-        return res
-
-
-class ConditionsetStatement(Statement):
-    """
-    A variable layout conditionset
-
-    Args:
-        name (str): the name of this conditionset
-        conditions (dict): a dictionary mapping axis tags to a
-            tuple of (min,max) userspace coordinates.
-    """
-
-    def __init__(self, name, conditions, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.conditions = conditions
-
-    def build(self, builder):
-        builder.add_conditionset(self.location, self.name, self.conditions)
-
-    def asFea(self, res="", indent=""):
-        res += indent + f"conditionset {self.name} " + "{\n"
-        for tag, (minvalue, maxvalue) in self.conditions.items():
-            res += indent + SHIFT + f"{tag} {minvalue} {maxvalue};\n"
-        res += indent + "}" + f" {self.name};\n"
-        return res
-
-
-class VariationBlock(Block):
-    """A variation feature block, applicable in a given set of conditions."""
-
-    def __init__(self, name, conditionset, use_extension=False, location=None):
-        Block.__init__(self, location)
-        self.name, self.conditionset, self.use_extension = (
-            name,
-            conditionset,
-            use_extension,
-        )
-
-    def build(self, builder):
-        """Call the ``start_feature`` callback on the builder object, visit
-        all the statements in this feature, and then call ``end_feature``."""
-        builder.start_feature(self.location, self.name)
-        if (
-            self.conditionset != "NULL"
-            and self.conditionset not in builder.conditionsets_
-        ):
-            raise FeatureLibError(
-                f"variation block used undefined conditionset {self.conditionset}",
-                self.location,
-            )
-
-        # language exclude_dflt statements modify builder.features_
-        # limit them to this block with temporary builder.features_
-        features = builder.features_
-        builder.features_ = {}
-        Block.build(self, builder)
-        for key, value in builder.features_.items():
-            items = builder.feature_variations_.setdefault(key, {}).setdefault(
-                self.conditionset, []
-            )
-            items.extend(value)
-            if key not in features:
-                features[key] = []  # Ensure we make a feature record
-        builder.features_ = features
-        builder.end_feature()
-
-    def asFea(self, indent=""):
-        res = indent + "variation %s " % self.name.strip()
-        res += self.conditionset + " "
-        if self.use_extension:
-            res += "useExtension "
-        res += "{\n"
-        res += Block.asFea(self, indent=indent)
-        res += indent + "} %s;\n" % self.name.strip()
-        return res
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/wrapper-b7460963-69b64cfb.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/wrapper-b7460963-69b64cfb.js
deleted file mode 100644
index 02049f8e8fbbc52f6fd3d807e42bbbe9e811c2f6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/wrapper-b7460963-69b64cfb.js
+++ /dev/null
@@ -1,8 +0,0 @@
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t=Le;if(this._payloadLength){if(this._bufferedBytes<this._payloadLength){this._loop=!1;return}t=this.consume(this._payloadLength),this._masked&&this._mask[0]|this._mask[1]|this._mask[2]|this._mask[3]&&Vt(t,this._mask)}if(this._opcode>7)return this.controlMessage(t);if(this._compressed){this._state=zt,this.decompress(t,e);return}return t.length&&(this._messageLength=this._totalPayloadLength,this._fragments.push(t)),this.dataMessage()}decompress(e,t){this._extensions[Ce.extensionName].decompress(e,this._fin,(i,n)=>{if(i)return t(i);if(n.length){if(this._messageLength+=n.length,this._messageLength>this._maxPayload&&this._maxPayload>0)return t(g(RangeError,"Max payload size exceeded",!1,1009,"WS_ERR_UNSUPPORTED_MESSAGE_LENGTH"));this._fragments.push(n)}const o=this.dataMessage();if(o)return t(o);this.startLoop(t)})}dataMessage(){if(this._fin){const e=this._messageLength,t=this._fragments;if(this._totalPayloadLength=0,this._messageLength=0,this._fragmented=0,this._fragments=[],this._opcode===2){let r;this._binaryType==="nodebuffer"?r=he(t,e):this._binaryType==="arraybuffer"?r=Gt(he(t,e)):r=t,this.emit("message",r,!0)}else{const r=he(t,e);if(!this._skipUTF8Validation&&!Ne(r))return this._loop=!1,g(Error,"invalid UTF-8 sequence",!0,1007,"WS_ERR_INVALID_UTF8");this.emit("message",r,!1)}}this._state=j}controlMessage(e){if(this._opcode===8)if(this._loop=!1,e.length===0)this.emit("conclude",1005,Le),this.end();else{const t=e.readUInt16BE(0);if(!Ht(t))return g(RangeError,`invalid status code ${t}`,!0,1002,"WS_ERR_INVALID_CLOSE_CODE");const r=new Z(e.buffer,e.byteOffset+2,e.length-2);if(!this._skipUTF8Validation&&!Ne(r))return g(Error,"invalid UTF-8 sequence",!0,1007,"WS_ERR_INVALID_UTF8");this.emit("conclude",t,r),this.end()}else this._opcode===9?this.emit("ping",e):this.emit("pong",e);this._state=j}};var et=Yt;function g(s,e,t,r,i){const n=new s(t?`Invalid WebSocket frame: ${e}`:e);return Error.captureStackTrace(n,g),n.code=i,n[Ft]=r,n}const Ys=et,{randomFillSync:qt}=S,Be=ie,{EMPTY_BUFFER:Kt}=$,{isValidStatusCode:Xt}=N,{mask:$e,toBuffer:D}=L,x=Symbol("kByteLength"),Zt=Buffer.alloc(4);let Qt=class U{constructor(e,t,r){this._extensions=t||{},r&&(this._generateMask=r,this._maskBuffer=Buffer.alloc(4)),this._socket=e,this._firstFragment=!0,this._compress=!1,this._bufferedBytes=0,this._deflating=!1,this._queue=[]}static frame(e,t){let r,i=!1,n=2,o=!1;t.mask&&(r=t.maskBuffer||Zt,t.generateMask?t.generateMask(r):qt(r,0,4),o=(r[0]|r[1]|r[2]|r[3])===0,n=6);let l;typeof e=="string"?(!t.mask||o)&&t[x]!==void 0?l=t[x]:(e=Buffer.from(e),l=e.length):(l=e.length,i=t.mask&&t.readOnly&&!o);let f=l;l>=65536?(n+=8,f=127):l>125&&(n+=2,f=126);const a=Buffer.allocUnsafe(i?l+n:n);return a[0]=t.fin?t.opcode|128:t.opcode,t.rsv1&&(a[0]|=64),a[1]=f,f===126?a.writeUInt16BE(l,2):f===127&&(a[2]=a[3]=0,a.writeUIntBE(l,4,6)),t.mask?(a[1]|=128,a[n-4]=r[0],a[n-3]=r[1],a[n-2]=r[2],a[n-1]=r[3],o?[a,e]:i?($e(e,r,a,n,l),[a]):($e(e,r,e,0,l),[a,e])):[a,e]}close(e,t,r,i){let n;if(e===void 0)n=Kt;else{if(typeof e!="number"||!Xt(e))throw new TypeError("First argument must be a valid error code number");if(t===void 0||!t.length)n=Buffer.allocUnsafe(2),n.writeUInt16BE(e,0);else{const l=Buffer.byteLength(t);if(l>123)throw new RangeError("The message must not be greater than 123 bytes");n=Buffer.allocUnsafe(2+l),n.writeUInt16BE(e,0),typeof t=="string"?n.write(t,2):n.set(t,2)}}const o={[x]:n.length,fin:!0,generateMask:this._generateMask,mask:r,maskBuffer:this._maskBuffer,opcode:8,readOnly:!1,rsv1:!1};this._deflating?this.enqueue([this.dispatch,n,!1,o,i]):this.sendFrame(U.frame(n,o),i)}ping(e,t,r){let i,n;if(typeof e=="string"?(i=Buffer.byteLength(e),n=!1):(e=D(e),i=e.length,n=D.readOnly),i>125)throw new RangeError("The data size must not be greater than 125 bytes");const o={[x]:i,fin:!0,generateMask:this._generateMask,mask:t,maskBuffer:this._maskBuffer,opcode:9,readOnly:n,rsv1:!1};this._deflating?this.enqueue([this.dispatch,e,!1,o,r]):this.sendFrame(U.frame(e,o),r)}pong(e,t,r){let i,n;if(typeof e=="string"?(i=Buffer.byteLength(e),n=!1):(e=D(e),i=e.length,n=D.readOnly),i>125)throw new RangeError("The data size must not be greater than 125 bytes");const o={[x]:i,fin:!0,generateMask:this._generateMask,mask:t,maskBuffer:this._maskBuffer,opcode:10,readOnly:n,rsv1:!1};this._deflating?this.enqueue([this.dispatch,e,!1,o,r]):this.sendFrame(U.frame(e,o),r)}send(e,t,r){const i=this._extensions[Be.extensionName];let n=t.binary?2:1,o=t.compress,l,f;if(typeof e=="string"?(l=Buffer.byteLength(e),f=!1):(e=D(e),l=e.length,f=D.readOnly),this._firstFragment?(this._firstFragment=!1,o&&i&&i.params[i._isServer?"server_no_context_takeover":"client_no_context_takeover"]&&(o=l>=i._threshold),this._compress=o):(o=!1,n=0),t.fin&&(this._firstFragment=!0),i){const a={[x]:l,fin:t.fin,generateMask:this._generateMask,mask:t.mask,maskBuffer:this._maskBuffer,opcode:n,readOnly:f,rsv1:o};this._deflating?this.enqueue([this.dispatch,e,this._compress,a,r]):this.dispatch(e,this._compress,a,r)}else this.sendFrame(U.frame(e,{[x]:l,fin:t.fin,generateMask:this._generateMask,mask:t.mask,maskBuffer:this._maskBuffer,opcode:n,readOnly:f,rsv1:!1}),r)}dispatch(e,t,r,i){if(!t){this.sendFrame(U.frame(e,r),i);return}const n=this._extensions[Be.extensionName];this._bufferedBytes+=r[x],this._deflating=!0,n.compress(e,r.fin,(o,l)=>{if(this._socket.destroyed){const f=new Error("The socket was closed while data was being compressed");typeof i=="function"&&i(f);for(let 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this[Ie]}}Object.defineProperty(Se.prototype,"data",{enumerable:!0});const Jt={addEventListener(s,e,t={}){for(const i of this.listeners(s))if(!t[G]&&i[ue]===e&&!i[G])return;let r;if(s==="message")r=function(n,o){const l=new Se("message",{data:o?n:n.toString()});l[W]=this,Q(e,this,l)};else if(s==="close")r=function(n,o){const l=new q("close",{code:n,reason:o.toString(),wasClean:this._closeFrameReceived&&this._closeFrameSent});l[W]=this,Q(e,this,l)};else if(s==="error")r=function(n){const o=new ne("error",{error:n,message:n.message});o[W]=this,Q(e,this,o)};else if(s==="open")r=function(){const n=new M("open");n[W]=this,Q(e,this,n)};else return;r[G]=!!t[G],r[ue]=e,t.once?this.once(s,r):this.on(s,r)},removeEventListener(s,e){for(const t of this.listeners(s))if(t[ue]===e&&!t[G]){this.removeListener(s,t);break}}};var es={CloseEvent:q,ErrorEvent:ne,Event:M,EventTarget:Jt,MessageEvent:Se};function Q(s,e,t){typeof s=="object"&&s.handleEvent?s.handleEvent.call(s,t):s.call(e,t)}const{tokenChars:V}=N;function k(s,e,t){s[e]===void 0?s[e]=[t]:s[e].push(t)}function ts(s){const e=Object.create(null);let t=Object.create(null),r=!1,i=!1,n=!1,o,l,f=-1,a=-1,c=-1,h=0;for(;h<s.length;h++)if(a=s.charCodeAt(h),o===void 0)if(c===-1&&V[a]===1)f===-1&&(f=h);else if(h!==0&&(a===32||a===9))c===-1&&f!==-1&&(c=h);else if(a===59||a===44){if(f===-1)throw new SyntaxError(`Unexpected character at index ${h}`);c===-1&&(c=h);const v=s.slice(f,c);a===44?(k(e,v,t),t=Object.create(null)):o=v,f=c=-1}else throw new SyntaxError(`Unexpected character at index ${h}`);else if(l===void 0)if(c===-1&&V[a]===1)f===-1&&(f=h);else if(a===32||a===9)c===-1&&f!==-1&&(c=h);else if(a===59||a===44){if(f===-1)throw new SyntaxError(`Unexpected character at index ${h}`);c===-1&&(c=h),k(t,s.slice(f,c),!0),a===44&&(k(e,o,t),t=Object.create(null),o=void 0),f=c=-1}else if(a===61&&f!==-1&&c===-1)l=s.slice(f,h),f=c=-1;else throw new 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t=s[e];return Array.isArray(t)||(t=[t]),t.map(r=>[e].concat(Object.keys(r).map(i=>{let n=r[i];return Array.isArray(n)||(n=[n]),n.map(o=>o===!0?i:`${i}=${o}`).join("; ")})).join("; ")).join(", ")}).join(", ")}var st={format:ss,parse:ts};const rs=S,is=S,ns=S,rt=S,os=S,{randomBytes:as,createHash:ls}=S,{URL:de}=S,C=ie,fs=et,hs=tt,{BINARY_TYPES:Ge,EMPTY_BUFFER:J,GUID:cs,kForOnEventAttribute:_e,kListener:us,kStatusCode:ds,kWebSocket:y,NOOP:it}=$,{EventTarget:{addEventListener:_s,removeEventListener:ps}}=es,{format:ms,parse:gs}=st,{toBuffer:ys}=L,vs=30*1e3,nt=Symbol("kAborted"),pe=[8,13],O=["CONNECTING","OPEN","CLOSING","CLOSED"],Ss=/^[!#$%&'*+\-.0-9A-Z^_`|a-z~]+$/;let m=class d extends rs{constructor(e,t,r){super(),this._binaryType=Ge[0],this._closeCode=1006,this._closeFrameReceived=!1,this._closeFrameSent=!1,this._closeMessage=J,this._closeTimer=null,this._extensions={},this._paused=!1,this._protocol="",this._readyState=d.CONNECTING,this._receiver=null,this._sender=null,this._socket=null,e!==null?(this._bufferedAmount=0,this._isServer=!1,this._redirects=0,t===void 0?t=[]:Array.isArray(t)||(typeof t=="object"&&t!==null?(r=t,t=[]):t=[t]),at(this,e,t,r)):this._isServer=!0}get binaryType(){return this._binaryType}set binaryType(e){Ge.includes(e)&&(this._binaryType=e,this._receiver&&(this._receiver._binaryType=e))}get bufferedAmount(){return this._socket?this._socket._writableState.length+this._sender._bufferedBytes:this._bufferedAmount}get extensions(){return Object.keys(this._extensions).join()}get isPaused(){return this._paused}get onclose(){return null}get onerror(){return null}get onopen(){return null}get onmessage(){return null}get protocol(){return this._protocol}get readyState(){return this._readyState}get url(){return this._url}setSocket(e,t,r){const i=new fs({binaryType:this.binaryType,extensions:this._extensions,isServer:this._isServer,maxPayload:r.maxPayload,skipUTF8Validation:r.skipUTF8Validation});this._sender=new hs(e,this._extensions,r.generateMask),this._receiver=i,this._socket=e,i[y]=this,e[y]=this,i.on("conclude",xs),i.on("drain",ks),i.on("error",ws),i.on("message",Os),i.on("ping",Ts),i.on("pong",Cs),e.setTimeout(0),e.setNoDelay(),t.length>0&&e.unshift(t),e.on("close",ft),e.on("data",oe),e.on("end",ht),e.on("error",ct),this._readyState=d.OPEN,this.emit("open")}emitClose(){if(!this._socket){this._readyState=d.CLOSED,this.emit("close",this._closeCode,this._closeMessage);return}this._extensions[C.extensionName]&&this._extensions[C.extensionName].cleanup(),this._receiver.removeAllListeners(),this._readyState=d.CLOSED,this.emit("close",this._closeCode,this._closeMessage)}close(e,t){if(this.readyState!==d.CLOSED){if(this.readyState===d.CONNECTING){const r="WebSocket was closed before the connection was established";b(this,this._req,r);return}if(this.readyState===d.CLOSING){this._closeFrameSent&&(this._closeFrameReceived||this._receiver._writableState.errorEmitted)&&this._socket.end();return}this._readyState=d.CLOSING,this._sender.close(e,t,!this._isServer,r=>{r||(this._closeFrameSent=!0,(this._closeFrameReceived||this._receiver._writableState.errorEmitted)&&this._socket.end())}),this._closeTimer=setTimeout(this._socket.destroy.bind(this._socket),vs)}}pause(){this.readyState===d.CONNECTING||this.readyState===d.CLOSED||(this._paused=!0,this._socket.pause())}ping(e,t,r){if(this.readyState===d.CONNECTING)throw new Error("WebSocket is not open: readyState 0 (CONNECTING)");if(typeof e=="function"?(r=e,e=t=void 0):typeof t=="function"&&(r=t,t=void 0),typeof e=="number"&&(e=e.toString()),this.readyState!==d.OPEN){me(this,e,r);return}t===void 0&&(t=!this._isServer),this._sender.ping(e||J,t,r)}pong(e,t,r){if(this.readyState===d.CONNECTING)throw new Error("WebSocket is not open: readyState 0 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established";b(this,this._req,e);return}this._socket&&(this._readyState=d.CLOSING,this._socket.destroy())}}};Object.defineProperty(m,"CONNECTING",{enumerable:!0,value:O.indexOf("CONNECTING")});Object.defineProperty(m.prototype,"CONNECTING",{enumerable:!0,value:O.indexOf("CONNECTING")});Object.defineProperty(m,"OPEN",{enumerable:!0,value:O.indexOf("OPEN")});Object.defineProperty(m.prototype,"OPEN",{enumerable:!0,value:O.indexOf("OPEN")});Object.defineProperty(m,"CLOSING",{enumerable:!0,value:O.indexOf("CLOSING")});Object.defineProperty(m.prototype,"CLOSING",{enumerable:!0,value:O.indexOf("CLOSING")});Object.defineProperty(m,"CLOSED",{enumerable:!0,value:O.indexOf("CLOSED")});Object.defineProperty(m.prototype,"CLOSED",{enumerable:!0,value:O.indexOf("CLOSED")});["binaryType","bufferedAmount","extensions","isPaused","protocol","readyState","url"].forEach(s=>{Object.defineProperty(m.prototype,s,{enumerable:!0})});["open","error","close","message"].forEach(s=>{Object.defineProperty(m.prototype,`on${s}`,{enumerable:!0,get(){for(const e of this.listeners(s))if(e[_e])return e[us];return null},set(e){for(const t of this.listeners(s))if(t[_e]){this.removeListener(s,t);break}typeof e=="function"&&this.addEventListener(s,e,{[_e]:!0})}})});m.prototype.addEventListener=_s;m.prototype.removeEventListener=ps;var ot=m;function at(s,e,t,r){const i={protocolVersion:pe[1],maxPayload:104857600,skipUTF8Validation:!1,perMessageDeflate:!0,followRedirects:!1,maxRedirects:10,...r,createConnection:void 0,socketPath:void 0,hostname:void 0,protocol:void 0,timeout:void 0,method:"GET",host:void 0,path:void 0,port:void 0};if(!pe.includes(i.protocolVersion))throw new RangeError(`Unsupported protocol version: ${i.protocolVersion} (supported versions: ${pe.join(", ")})`);let n;if(e instanceof de)n=e,s._url=e.href;else{try{n=new de(e)}catch{throw new SyntaxError(`Invalid URL: ${e}`)}s._url=e}const o=n.protocol==="wss:",l=n.protocol==="ws+unix:";let f;if(n.protocol!=="ws:"&&!o&&!l?f=`The URL's protocol must be one of "ws:", "wss:", or "ws+unix:"`:l&&!n.pathname?f="The URL's pathname is empty":n.hash&&(f="The URL contains a fragment identifier"),f){const u=new SyntaxError(f);if(s._redirects===0)throw u;te(s,u);return}const a=o?443:80,c=as(16).toString("base64"),h=o?is.request:ns.request,p=new Set;let v;if(i.createConnection=o?bs:Es,i.defaultPort=i.defaultPort||a,i.port=n.port||a,i.host=n.hostname.startsWith("[")?n.hostname.slice(1,-1):n.hostname,i.headers={...i.headers,"Sec-WebSocket-Version":i.protocolVersion,"Sec-WebSocket-Key":c,Connection:"Upgrade",Upgrade:"websocket"},i.path=n.pathname+n.search,i.timeout=i.handshakeTimeout,i.perMessageDeflate&&(v=new C(i.perMessageDeflate!==!0?i.perMessageDeflate:{},!1,i.maxPayload),i.headers["Sec-WebSocket-Extensions"]=ms({[C.extensionName]:v.offer()})),t.length){for(const u of t){if(typeof u!="string"||!Ss.test(u)||p.has(u))throw new SyntaxError("An invalid or duplicated subprotocol was specified");p.add(u)}i.headers["Sec-WebSocket-Protocol"]=t.join(",")}if(i.origin&&(i.protocolVersion<13?i.headers["Sec-WebSocket-Origin"]=i.origin:i.headers.Origin=i.origin),(n.username||n.password)&&(i.auth=`${n.username}:${n.password}`),l){const u=i.path.split(":");i.socketPath=u[0],i.path=u[1]}let _;if(i.followRedirects){if(s._redirects===0){s._originalIpc=l,s._originalSecure=o,s._originalHostOrSocketPath=l?i.socketPath:n.host;const u=r&&r.headers;if(r={...r,headers:{}},u)for(const[E,I]of Object.entries(u))r.headers[E.toLowerCase()]=I}else if(s.listenerCount("redirect")===0){const u=l?s._originalIpc?i.socketPath===s._originalHostOrSocketPath:!1:s._originalIpc?!1:n.host===s._originalHostOrSocketPath;(!u||s._originalSecure&&!o)&&(delete i.headers.authorization,delete i.headers.cookie,u||delete i.headers.host,i.auth=void 0)}i.auth&&!r.headers.authorization&&(r.headers.authorization="Basic "+Buffer.from(i.auth).toString("base64")),_=s._req=h(i),s._redirects&&s.emit("redirect",s.url,_)}else _=s._req=h(i);i.timeout&&_.on("timeout",()=>{b(s,_,"Opening handshake has timed out")}),_.on("error",u=>{_===null||_[nt]||(_=s._req=null,te(s,u))}),_.on("response",u=>{const E=u.headers.location,I=u.statusCode;if(E&&i.followRedirects&&I>=300&&I<400){if(++s._redirects>i.maxRedirects){b(s,_,"Maximum redirects exceeded");return}_.abort();let K;try{K=new de(E,e)}catch{const P=new SyntaxError(`Invalid URL: ${E}`);te(s,P);return}at(s,K,t,r)}else s.emit("unexpected-response",_,u)||b(s,_,`Unexpected server response: ${u.statusCode}`)}),_.on("upgrade",(u,E,I)=>{if(s.emit("upgrade",u),s.readyState!==m.CONNECTING)return;if(_=s._req=null,u.headers.upgrade.toLowerCase()!=="websocket"){b(s,E,"Invalid Upgrade header");return}const K=ls("sha1").update(c+cs).digest("base64");if(u.headers["sec-websocket-accept"]!==K){b(s,E,"Invalid Sec-WebSocket-Accept header");return}const A=u.headers["sec-websocket-protocol"];let P;if(A!==void 0?p.size?p.has(A)||(P="Server sent an invalid subprotocol"):P="Server sent a subprotocol but none was requested":p.size&&(P="Server sent no subprotocol"),P){b(s,E,P);return}A&&(s._protocol=A);const Ee=u.headers["sec-websocket-extensions"];if(Ee!==void 0){if(!v){b(s,E,"Server sent a Sec-WebSocket-Extensions header but no extension was requested");return}let ae;try{ae=gs(Ee)}catch{b(s,E,"Invalid Sec-WebSocket-Extensions header");return}const be=Object.keys(ae);if(be.length!==1||be[0]!==C.extensionName){b(s,E,"Server indicated an extension that was not requested");return}try{v.accept(ae[C.extensionName])}catch{b(s,E,"Invalid Sec-WebSocket-Extensions header");return}s._extensions[C.extensionName]=v}s.setSocket(E,I,{generateMask:i.generateMask,maxPayload:i.maxPayload,skipUTF8Validation:i.skipUTF8Validation})}),i.finishRequest?i.finishRequest(_,s):_.end()}function te(s,e){s._readyState=m.CLOSING,s.emit("error",e),s.emitClose()}function Es(s){return s.path=s.socketPath,rt.connect(s)}function bs(s){return s.path=void 0,!s.servername&&s.servername!==""&&(s.servername=rt.isIP(s.host)?"":s.host),os.connect(s)}function b(s,e,t){s._readyState=m.CLOSING;const r=new Error(t);Error.captureStackTrace(r,b),e.setHeader?(e[nt]=!0,e.abort(),e.socket&&!e.socket.destroyed&&e.socket.destroy(),process.nextTick(te,s,r)):(e.destroy(r),e.once("error",s.emit.bind(s,"error")),e.once("close",s.emitClose.bind(s)))}function me(s,e,t){if(e){const r=ys(e).length;s._socket?s._sender._bufferedBytes+=r:s._bufferedAmount+=r}if(t){const r=new Error(`WebSocket is not open: readyState ${s.readyState} (${O[s.readyState]})`);process.nextTick(t,r)}}function xs(s,e){const t=this[y];t._closeFrameReceived=!0,t._closeMessage=e,t._closeCode=s,t._socket[y]!==void 0&&(t._socket.removeListener("data",oe),process.nextTick(lt,t._socket),s===1005?t.close():t.close(s,e))}function ks(){const s=this[y];s.isPaused||s._socket.resume()}function ws(s){const e=this[y];e._socket[y]!==void 0&&(e._socket.removeListener("data",oe),process.nextTick(lt,e._socket),e.close(s[ds])),e.emit("error",s)}function Ve(){this[y].emitClose()}function Os(s,e){this[y].emit("message",s,e)}function Ts(s){const e=this[y];e.pong(s,!e._isServer,it),e.emit("ping",s)}function Cs(s){this[y].emit("pong",s)}function lt(s){s.resume()}function ft(){const s=this[y];this.removeListener("close",ft),this.removeListener("data",oe),this.removeListener("end",ht),s._readyState=m.CLOSING;let e;!this._readableState.endEmitted&&!s._closeFrameReceived&&!s._receiver._writableState.errorEmitted&&(e=s._socket.read())!==null&&s._receiver.write(e),s._receiver.end(),this[y]=void 0,clearTimeout(s._closeTimer),s._receiver._writableState.finished||s._receiver._writableState.errorEmitted?s.emitClose():(s._receiver.on("error",Ve),s._receiver.on("finish",Ve))}function oe(s){this[y]._receiver.write(s)||this.pause()}function ht(){const s=this[y];s._readyState=m.CLOSING,s._receiver.end(),this.end()}function ct(){const s=this[y];this.removeListener("error",ct),this.on("error",it),s&&(s._readyState=m.CLOSING,this.destroy())}const Ks=ot,{tokenChars:Ls}=N;function Ns(s){const e=new Set;let t=-1,r=-1,i=0;for(i;i<s.length;i++){const o=s.charCodeAt(i);if(r===-1&&Ls[o]===1)t===-1&&(t=i);else if(i!==0&&(o===32||o===9))r===-1&&t!==-1&&(r=i);else if(o===44){if(t===-1)throw new SyntaxError(`Unexpected character at index ${i}`);r===-1&&(r=i);const l=s.slice(t,r);if(e.has(l))throw new SyntaxError(`The "${l}" subprotocol is duplicated`);e.add(l),t=r=-1}else throw new SyntaxError(`Unexpected character at index ${i}`)}if(t===-1||r!==-1)throw new SyntaxError("Unexpected end of input");const n=s.slice(t,i);if(e.has(n))throw new SyntaxError(`The "${n}" subprotocol is duplicated`);return e.add(n),e}var Ps={parse:Ns};const Rs=S,re=S,{createHash:Us}=S,He=st,R=ie,Bs=Ps,$s=ot,{GUID:Ms,kWebSocket:Is}=$,Ds=/^[+/0-9A-Za-z]{22}==$/,ze=0,Ye=1,ut=2;class Ws extends Rs{constructor(e,t){if(super(),e={maxPayload:100*1024*1024,skipUTF8Validation:!1,perMessageDeflate:!1,handleProtocols:null,clientTracking:!0,verifyClient:null,noServer:!1,backlog:null,server:null,host:null,path:null,port:null,WebSocket:$s,...e},e.port==null&&!e.server&&!e.noServer||e.port!=null&&(e.server||e.noServer)||e.server&&e.noServer)throw new TypeError('One and only one of the "port", "server", or "noServer" options must be specified');if(e.port!=null?(this._server=re.createServer((r,i)=>{const n=re.STATUS_CODES[426];i.writeHead(426,{"Content-Length":n.length,"Content-Type":"text/plain"}),i.end(n)}),this._server.listen(e.port,e.host,e.backlog,t)):e.server&&(this._server=e.server),this._server){const r=this.emit.bind(this,"connection");this._removeListeners=Fs(this._server,{listening:this.emit.bind(this,"listening"),error:this.emit.bind(this,"error"),upgrade:(i,n,o)=>{this.handleUpgrade(i,n,o,r)}})}e.perMessageDeflate===!0&&(e.perMessageDeflate={}),e.clientTracking&&(this.clients=new Set,this._shouldEmitClose=!1),this.options=e,this._state=ze}address(){if(this.options.noServer)throw new Error('The server is operating in "noServer" mode');return this._server?this._server.address():null}close(e){if(this._state===ut){e&&this.once("close",()=>{e(new Error("The server is not running"))}),process.nextTick(H,this);return}if(e&&this.once("close",e),this._state!==Ye)if(this._state=Ye,this.options.noServer||this.options.server)this._server&&(this._removeListeners(),this._removeListeners=this._server=null),this.clients?this.clients.size?this._shouldEmitClose=!0:process.nextTick(H,this):process.nextTick(H,this);else{const t=this._server;this._removeListeners(),this._removeListeners=this._server=null,t.close(()=>{H(this)})}}shouldHandle(e){if(this.options.path){const t=e.url.indexOf("?");if((t!==-1?e.url.slice(0,t):e.url)!==this.options.path)return!1}return!0}handleUpgrade(e,t,r,i){t.on("error",qe);const n=e.headers["sec-websocket-key"],o=+e.headers["sec-websocket-version"];if(e.method!=="GET"){B(this,e,t,405,"Invalid HTTP method");return}if(e.headers.upgrade.toLowerCase()!=="websocket"){B(this,e,t,400,"Invalid Upgrade header");return}if(!n||!Ds.test(n)){B(this,e,t,400,"Missing or invalid Sec-WebSocket-Key header");return}if(o!==8&&o!==13){B(this,e,t,400,"Missing or invalid Sec-WebSocket-Version header");return}if(!this.shouldHandle(e)){Y(t,400);return}const l=e.headers["sec-websocket-protocol"];let f=new Set;if(l!==void 0)try{f=Bs.parse(l)}catch{B(this,e,t,400,"Invalid Sec-WebSocket-Protocol header");return}const a=e.headers["sec-websocket-extensions"],c={};if(this.options.perMessageDeflate&&a!==void 0){const h=new R(this.options.perMessageDeflate,!0,this.options.maxPayload);try{const p=He.parse(a);p[R.extensionName]&&(h.accept(p[R.extensionName]),c[R.extensionName]=h)}catch{B(this,e,t,400,"Invalid or unacceptable Sec-WebSocket-Extensions header");return}}if(this.options.verifyClient){const h={origin:e.headers[`${o===8?"sec-websocket-origin":"origin"}`],secure:!!(e.socket.authorized||e.socket.encrypted),req:e};if(this.options.verifyClient.length===2){this.options.verifyClient(h,(p,v,_,u)=>{if(!p)return Y(t,v||401,_,u);this.completeUpgrade(c,n,f,e,t,r,i)});return}if(!this.options.verifyClient(h))return Y(t,401)}this.completeUpgrade(c,n,f,e,t,r,i)}completeUpgrade(e,t,r,i,n,o,l){if(!n.readable||!n.writable)return n.destroy();if(n[Is])throw new Error("server.handleUpgrade() was called more than once with the same socket, possibly due to a misconfiguration");if(this._state>ze)return Y(n,503);const a=["HTTP/1.1 101 Switching Protocols","Upgrade: websocket","Connection: Upgrade",`Sec-WebSocket-Accept: ${Us("sha1").update(t+Ms).digest("base64")}`],c=new this.options.WebSocket(null);if(r.size){const h=this.options.handleProtocols?this.options.handleProtocols(r,i):r.values().next().value;h&&(a.push(`Sec-WebSocket-Protocol: ${h}`),c._protocol=h)}if(e[R.extensionName]){const h=e[R.extensionName].params,p=He.format({[R.extensionName]:[h]});a.push(`Sec-WebSocket-Extensions: ${p}`),c._extensions=e}this.emit("headers",a,i),n.write(a.concat(`\r
-`).join(`\r
-`)),n.removeListener("error",qe),c.setSocket(n,o,{maxPayload:this.options.maxPayload,skipUTF8Validation:this.options.skipUTF8Validation}),this.clients&&(this.clients.add(c),c.on("close",()=>{this.clients.delete(c),this._shouldEmitClose&&!this.clients.size&&process.nextTick(H,this)})),l(c,i)}}var As=Ws;function Fs(s,e){for(const t of Object.keys(e))s.on(t,e[t]);return function(){for(const r of Object.keys(e))s.removeListener(r,e[r])}}function H(s){s._state=ut,s.emit("close")}function qe(){this.destroy()}function Y(s,e,t,r){t=t||re.STATUS_CODES[e],r={Connection:"close","Content-Type":"text/html","Content-Length":Buffer.byteLength(t),...r},s.once("finish",s.destroy),s.end(`HTTP/1.1 ${e} ${re.STATUS_CODES[e]}\r
-`+Object.keys(r).map(i=>`${i}: ${r[i]}`).join(`\r
-`)+`\r
-\r
-`+t)}function B(s,e,t,r,i){if(s.listenerCount("wsClientError")){const n=new Error(i);Error.captureStackTrace(n,B),s.emit("wsClientError",n,t,e)}else Y(t,r,i)}const Xs=As;export{Ys as Receiver,qs as Sender,Ks as WebSocket,Xs as WebSocketServer,Gs as createWebSocketStream,Ks as default};
-//# sourceMappingURL=wrapper-b7460963-69b64cfb.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_webagg_core.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_webagg_core.py
deleted file mode 100644
index 57cfa311b8f74989ff7e9e15690e2f6739cf2b83..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_webagg_core.py
+++ /dev/null
@@ -1,517 +0,0 @@
-"""
-Displays Agg images in the browser, with interactivity
-"""
-# The WebAgg backend is divided into two modules:
-#
-# - `backend_webagg_core.py` contains code necessary to embed a WebAgg
-#   plot inside of a web application, and communicate in an abstract
-#   way over a web socket.
-#
-# - `backend_webagg.py` contains a concrete implementation of a basic
-#   application, implemented with asyncio.
-
-import asyncio
-import datetime
-from io import BytesIO, StringIO
-import json
-import logging
-import os
-from pathlib import Path
-
-import numpy as np
-from PIL import Image
-
-from matplotlib import _api, backend_bases, backend_tools
-from matplotlib.backends import backend_agg
-from matplotlib.backend_bases import (
-    _Backend, KeyEvent, LocationEvent, MouseEvent, ResizeEvent)
-
-_log = logging.getLogger(__name__)
-
-_SPECIAL_KEYS_LUT = {'Alt': 'alt',
-                     'AltGraph': 'alt',
-                     'CapsLock': 'caps_lock',
-                     'Control': 'control',
-                     'Meta': 'meta',
-                     'NumLock': 'num_lock',
-                     'ScrollLock': 'scroll_lock',
-                     'Shift': 'shift',
-                     'Super': 'super',
-                     'Enter': 'enter',
-                     'Tab': 'tab',
-                     'ArrowDown': 'down',
-                     'ArrowLeft': 'left',
-                     'ArrowRight': 'right',
-                     'ArrowUp': 'up',
-                     'End': 'end',
-                     'Home': 'home',
-                     'PageDown': 'pagedown',
-                     'PageUp': 'pageup',
-                     'Backspace': 'backspace',
-                     'Delete': 'delete',
-                     'Insert': 'insert',
-                     'Escape': 'escape',
-                     'Pause': 'pause',
-                     'Select': 'select',
-                     'Dead': 'dead',
-                     'F1': 'f1',
-                     'F2': 'f2',
-                     'F3': 'f3',
-                     'F4': 'f4',
-                     'F5': 'f5',
-                     'F6': 'f6',
-                     'F7': 'f7',
-                     'F8': 'f8',
-                     'F9': 'f9',
-                     'F10': 'f10',
-                     'F11': 'f11',
-                     'F12': 'f12'}
-
-
-def _handle_key(key):
-    """Handle key values"""
-    value = key[key.index('k') + 1:]
-    if 'shift+' in key:
-        if len(value) == 1:
-            key = key.replace('shift+', '')
-    if value in _SPECIAL_KEYS_LUT:
-        value = _SPECIAL_KEYS_LUT[value]
-    key = key[:key.index('k')] + value
-    return key
-
-
-class TimerTornado(backend_bases.TimerBase):
-    def __init__(self, *args, **kwargs):
-        self._timer = None
-        super().__init__(*args, **kwargs)
-
-    def _timer_start(self):
-        import tornado
-
-        self._timer_stop()
-        if self._single:
-            ioloop = tornado.ioloop.IOLoop.instance()
-            self._timer = ioloop.add_timeout(
-                datetime.timedelta(milliseconds=self.interval),
-                self._on_timer)
-        else:
-            self._timer = tornado.ioloop.PeriodicCallback(
-                self._on_timer,
-                max(self.interval, 1e-6))
-            self._timer.start()
-
-    def _timer_stop(self):
-        import tornado
-
-        if self._timer is None:
-            return
-        elif self._single:
-            ioloop = tornado.ioloop.IOLoop.instance()
-            ioloop.remove_timeout(self._timer)
-        else:
-            self._timer.stop()
-        self._timer = None
-
-    def _timer_set_interval(self):
-        # Only stop and restart it if the timer has already been started
-        if self._timer is not None:
-            self._timer_stop()
-            self._timer_start()
-
-
-class TimerAsyncio(backend_bases.TimerBase):
-    def __init__(self, *args, **kwargs):
-        self._task = None
-        super().__init__(*args, **kwargs)
-
-    async def _timer_task(self, interval):
-        while True:
-            try:
-                await asyncio.sleep(interval)
-                self._on_timer()
-
-                if self._single:
-                    break
-            except asyncio.CancelledError:
-                break
-
-    def _timer_start(self):
-        self._timer_stop()
-
-        self._task = asyncio.ensure_future(
-            self._timer_task(max(self.interval / 1_000., 1e-6))
-        )
-
-    def _timer_stop(self):
-        if self._task is not None:
-            self._task.cancel()
-        self._task = None
-
-    def _timer_set_interval(self):
-        # Only stop and restart it if the timer has already been started
-        if self._task is not None:
-            self._timer_stop()
-            self._timer_start()
-
-
-class FigureCanvasWebAggCore(backend_agg.FigureCanvasAgg):
-    manager_class = _api.classproperty(lambda cls: FigureManagerWebAgg)
-    _timer_cls = TimerAsyncio
-    # Webagg and friends having the right methods, but still
-    # having bugs in practice.  Do not advertise that it works until
-    # we can debug this.
-    supports_blit = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # Set to True when the renderer contains data that is newer
-        # than the PNG buffer.
-        self._png_is_old = True
-        # Set to True by the `refresh` message so that the next frame
-        # sent to the clients will be a full frame.
-        self._force_full = True
-        # The last buffer, for diff mode.
-        self._last_buff = np.empty((0, 0))
-        # Store the current image mode so that at any point, clients can
-        # request the information. This should be changed by calling
-        # self.set_image_mode(mode) so that the notification can be given
-        # to the connected clients.
-        self._current_image_mode = 'full'
-        # Track mouse events to fill in the x, y position of key events.
-        self._last_mouse_xy = (None, None)
-
-    def show(self):
-        # show the figure window
-        from matplotlib.pyplot import show
-        show()
-
-    def draw(self):
-        self._png_is_old = True
-        try:
-            super().draw()
-        finally:
-            self.manager.refresh_all()  # Swap the frames.
-
-    def blit(self, bbox=None):
-        self._png_is_old = True
-        self.manager.refresh_all()
-
-    def draw_idle(self):
-        self.send_event("draw")
-
-    def set_cursor(self, cursor):
-        # docstring inherited
-        cursor = _api.check_getitem({
-            backend_tools.Cursors.HAND: 'pointer',
-            backend_tools.Cursors.POINTER: 'default',
-            backend_tools.Cursors.SELECT_REGION: 'crosshair',
-            backend_tools.Cursors.MOVE: 'move',
-            backend_tools.Cursors.WAIT: 'wait',
-            backend_tools.Cursors.RESIZE_HORIZONTAL: 'ew-resize',
-            backend_tools.Cursors.RESIZE_VERTICAL: 'ns-resize',
-        }, cursor=cursor)
-        self.send_event('cursor', cursor=cursor)
-
-    def set_image_mode(self, mode):
-        """
-        Set the image mode for any subsequent images which will be sent
-        to the clients. The modes may currently be either 'full' or 'diff'.
-
-        Note: diff images may not contain transparency, therefore upon
-        draw this mode may be changed if the resulting image has any
-        transparent component.
-        """
-        _api.check_in_list(['full', 'diff'], mode=mode)
-        if self._current_image_mode != mode:
-            self._current_image_mode = mode
-            self.handle_send_image_mode(None)
-
-    def get_diff_image(self):
-        if self._png_is_old:
-            renderer = self.get_renderer()
-
-            pixels = np.asarray(renderer.buffer_rgba())
-            # The buffer is created as type uint32 so that entire
-            # pixels can be compared in one numpy call, rather than
-            # needing to compare each plane separately.
-            buff = pixels.view(np.uint32).squeeze(2)
-
-            if (self._force_full
-                    # If the buffer has changed size we need to do a full draw.
-                    or buff.shape != self._last_buff.shape
-                    # If any pixels have transparency, we need to force a full
-                    # draw as we cannot overlay new on top of old.
-                    or (pixels[:, :, 3] != 255).any()):
-                self.set_image_mode('full')
-                output = buff
-            else:
-                self.set_image_mode('diff')
-                diff = buff != self._last_buff
-                output = np.where(diff, buff, 0)
-
-            # Store the current buffer so we can compute the next diff.
-            self._last_buff = buff.copy()
-            self._force_full = False
-            self._png_is_old = False
-
-            data = output.view(dtype=np.uint8).reshape((*output.shape, 4))
-            with BytesIO() as png:
-                Image.fromarray(data).save(png, format="png")
-                return png.getvalue()
-
-    def handle_event(self, event):
-        e_type = event['type']
-        handler = getattr(self, 'handle_{0}'.format(e_type),
-                          self.handle_unknown_event)
-        return handler(event)
-
-    def handle_unknown_event(self, event):
-        _log.warning('Unhandled message type {0}. {1}'.format(
-                     event['type'], event))
-
-    def handle_ack(self, event):
-        # Network latency tends to decrease if traffic is flowing
-        # in both directions.  Therefore, the browser sends back
-        # an "ack" message after each image frame is received.
-        # This could also be used as a simple sanity check in the
-        # future, but for now the performance increase is enough
-        # to justify it, even if the server does nothing with it.
-        pass
-
-    def handle_draw(self, event):
-        self.draw()
-
-    def _handle_mouse(self, event):
-        x = event['x']
-        y = event['y']
-        y = self.get_renderer().height - y
-        self._last_mouse_xy = x, y
-        # JavaScript button numbers and Matplotlib button numbers are off by 1.
-        button = event['button'] + 1
-
-        e_type = event['type']
-        modifiers = event['modifiers']
-        guiEvent = event.get('guiEvent')
-        if e_type in ['button_press', 'button_release']:
-            MouseEvent(e_type + '_event', self, x, y, button,
-                       modifiers=modifiers, guiEvent=guiEvent)._process()
-        elif e_type == 'dblclick':
-            MouseEvent('button_press_event', self, x, y, button, dblclick=True,
-                       modifiers=modifiers, guiEvent=guiEvent)._process()
-        elif e_type == 'scroll':
-            MouseEvent('scroll_event', self, x, y, step=event['step'],
-                       modifiers=modifiers, guiEvent=guiEvent)._process()
-        elif e_type == 'motion_notify':
-            MouseEvent(e_type + '_event', self, x, y,
-                       modifiers=modifiers, guiEvent=guiEvent)._process()
-        elif e_type in ['figure_enter', 'figure_leave']:
-            LocationEvent(e_type + '_event', self, x, y,
-                          modifiers=modifiers, guiEvent=guiEvent)._process()
-    handle_button_press = handle_button_release = handle_dblclick = \
-        handle_figure_enter = handle_figure_leave = handle_motion_notify = \
-        handle_scroll = _handle_mouse
-
-    def _handle_key(self, event):
-        KeyEvent(event['type'] + '_event', self,
-                 _handle_key(event['key']), *self._last_mouse_xy,
-                 guiEvent=event.get('guiEvent'))._process()
-    handle_key_press = handle_key_release = _handle_key
-
-    def handle_toolbar_button(self, event):
-        # TODO: Be more suspicious of the input
-        getattr(self.toolbar, event['name'])()
-
-    def handle_refresh(self, event):
-        figure_label = self.figure.get_label()
-        if not figure_label:
-            figure_label = "Figure {0}".format(self.manager.num)
-        self.send_event('figure_label', label=figure_label)
-        self._force_full = True
-        if self.toolbar:
-            # Normal toolbar init would refresh this, but it happens before the
-            # browser canvas is set up.
-            self.toolbar.set_history_buttons()
-        self.draw_idle()
-
-    def handle_resize(self, event):
-        x = int(event.get('width', 800)) * self.device_pixel_ratio
-        y = int(event.get('height', 800)) * self.device_pixel_ratio
-        fig = self.figure
-        # An attempt at approximating the figure size in pixels.
-        fig.set_size_inches(x / fig.dpi, y / fig.dpi, forward=False)
-        # Acknowledge the resize, and force the viewer to update the
-        # canvas size to the figure's new size (which is hopefully
-        # identical or within a pixel or so).
-        self._png_is_old = True
-        self.manager.resize(*fig.bbox.size, forward=False)
-        ResizeEvent('resize_event', self)._process()
-        self.draw_idle()
-
-    def handle_send_image_mode(self, event):
-        # The client requests notification of what the current image mode is.
-        self.send_event('image_mode', mode=self._current_image_mode)
-
-    def handle_set_device_pixel_ratio(self, event):
-        self._handle_set_device_pixel_ratio(event.get('device_pixel_ratio', 1))
-
-    def handle_set_dpi_ratio(self, event):
-        # This handler is for backwards-compatibility with older ipympl.
-        self._handle_set_device_pixel_ratio(event.get('dpi_ratio', 1))
-
-    def _handle_set_device_pixel_ratio(self, device_pixel_ratio):
-        if self._set_device_pixel_ratio(device_pixel_ratio):
-            self._force_full = True
-            self.draw_idle()
-
-    def send_event(self, event_type, **kwargs):
-        if self.manager:
-            self.manager._send_event(event_type, **kwargs)
-
-
-_ALLOWED_TOOL_ITEMS = {
-    'home',
-    'back',
-    'forward',
-    'pan',
-    'zoom',
-    'download',
-    None,
-}
-
-
-class NavigationToolbar2WebAgg(backend_bases.NavigationToolbar2):
-
-    # Use the standard toolbar items + download button
-    toolitems = [
-        (text, tooltip_text, image_file, name_of_method)
-        for text, tooltip_text, image_file, name_of_method
-        in (*backend_bases.NavigationToolbar2.toolitems,
-            ('Download', 'Download plot', 'filesave', 'download'))
-        if name_of_method in _ALLOWED_TOOL_ITEMS
-    ]
-
-    def __init__(self, canvas):
-        self.message = ''
-        super().__init__(canvas)
-
-    def set_message(self, message):
-        if message != self.message:
-            self.canvas.send_event("message", message=message)
-        self.message = message
-
-    def draw_rubberband(self, event, x0, y0, x1, y1):
-        self.canvas.send_event("rubberband", x0=x0, y0=y0, x1=x1, y1=y1)
-
-    def remove_rubberband(self):
-        self.canvas.send_event("rubberband", x0=-1, y0=-1, x1=-1, y1=-1)
-
-    def save_figure(self, *args):
-        """Save the current figure"""
-        self.canvas.send_event('save')
-
-    def pan(self):
-        super().pan()
-        self.canvas.send_event('navigate_mode', mode=self.mode.name)
-
-    def zoom(self):
-        super().zoom()
-        self.canvas.send_event('navigate_mode', mode=self.mode.name)
-
-    def set_history_buttons(self):
-        can_backward = self._nav_stack._pos > 0
-        can_forward = self._nav_stack._pos < len(self._nav_stack._elements) - 1
-        self.canvas.send_event('history_buttons',
-                               Back=can_backward, Forward=can_forward)
-
-
-class FigureManagerWebAgg(backend_bases.FigureManagerBase):
-    # This must be None to not break ipympl
-    _toolbar2_class = None
-    ToolbarCls = NavigationToolbar2WebAgg
-
-    def __init__(self, canvas, num):
-        self.web_sockets = set()
-        super().__init__(canvas, num)
-
-    def show(self):
-        pass
-
-    def resize(self, w, h, forward=True):
-        self._send_event(
-            'resize',
-            size=(w / self.canvas.device_pixel_ratio,
-                  h / self.canvas.device_pixel_ratio),
-            forward=forward)
-
-    def set_window_title(self, title):
-        self._send_event('figure_label', label=title)
-
-    # The following methods are specific to FigureManagerWebAgg
-
-    def add_web_socket(self, web_socket):
-        assert hasattr(web_socket, 'send_binary')
-        assert hasattr(web_socket, 'send_json')
-        self.web_sockets.add(web_socket)
-        self.resize(*self.canvas.figure.bbox.size)
-        self._send_event('refresh')
-
-    def remove_web_socket(self, web_socket):
-        self.web_sockets.remove(web_socket)
-
-    def handle_json(self, content):
-        self.canvas.handle_event(content)
-
-    def refresh_all(self):
-        if self.web_sockets:
-            diff = self.canvas.get_diff_image()
-            if diff is not None:
-                for s in self.web_sockets:
-                    s.send_binary(diff)
-
-    @classmethod
-    def get_javascript(cls, stream=None):
-        if stream is None:
-            output = StringIO()
-        else:
-            output = stream
-
-        output.write((Path(__file__).parent / "web_backend/js/mpl.js")
-                     .read_text(encoding="utf-8"))
-
-        toolitems = []
-        for name, tooltip, image, method in cls.ToolbarCls.toolitems:
-            if name is None:
-                toolitems.append(['', '', '', ''])
-            else:
-                toolitems.append([name, tooltip, image, method])
-        output.write("mpl.toolbar_items = {0};\n\n".format(
-            json.dumps(toolitems)))
-
-        extensions = []
-        for filetype, ext in sorted(FigureCanvasWebAggCore.
-                                    get_supported_filetypes_grouped().
-                                    items()):
-            extensions.append(ext[0])
-        output.write("mpl.extensions = {0};\n\n".format(
-            json.dumps(extensions)))
-
-        output.write("mpl.default_extension = {0};".format(
-            json.dumps(FigureCanvasWebAggCore.get_default_filetype())))
-
-        if stream is None:
-            return output.getvalue()
-
-    @classmethod
-    def get_static_file_path(cls):
-        return os.path.join(os.path.dirname(__file__), 'web_backend')
-
-    def _send_event(self, event_type, **kwargs):
-        payload = {'type': event_type, **kwargs}
-        for s in self.web_sockets:
-            s.send_json(payload)
-
-
-@_Backend.export
-class _BackendWebAggCoreAgg(_Backend):
-    FigureCanvas = FigureCanvasWebAggCore
-    FigureManager = FigureManagerWebAgg
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/offsetbox.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/offsetbox.py
deleted file mode 100644
index 8ad9806c2ec8cf6bd0c74ffb74d5cb40c2ec162b..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/offsetbox.py
+++ /dev/null
@@ -1,1623 +0,0 @@
-r"""
-Container classes for `.Artist`\s.
-
-`OffsetBox`
-    The base of all container artists defined in this module.
-
-`AnchoredOffsetbox`, `AnchoredText`
-    Anchor and align an arbitrary `.Artist` or a text relative to the parent
-    axes or a specific anchor point.
-
-`DrawingArea`
-    A container with fixed width and height. Children have a fixed position
-    inside the container and may be clipped.
-
-`HPacker`, `VPacker`
-    Containers for layouting their children vertically or horizontally.
-
-`PaddedBox`
-    A container to add a padding around an `.Artist`.
-
-`TextArea`
-    Contains a single `.Text` instance.
-"""
-
-import functools
-
-import numpy as np
-
-import matplotlib as mpl
-from matplotlib import _api, _docstring
-import matplotlib.artist as martist
-import matplotlib.path as mpath
-import matplotlib.text as mtext
-import matplotlib.transforms as mtransforms
-from matplotlib.font_manager import FontProperties
-from matplotlib.image import BboxImage
-from matplotlib.patches import (
-    FancyBboxPatch, FancyArrowPatch, bbox_artist as mbbox_artist)
-from matplotlib.transforms import Bbox, BboxBase, TransformedBbox
-
-
-DEBUG = False
-
-
-def _compat_get_offset(meth):
-    """
-    Decorator for the get_offset method of OffsetBox and subclasses, that
-    allows supporting both the new signature (self, bbox, renderer) and the old
-    signature (self, width, height, xdescent, ydescent, renderer).
-    """
-    sigs = [lambda self, width, height, xdescent, ydescent, renderer: locals(),
-            lambda self, bbox, renderer: locals()]
-
-    @functools.wraps(meth)
-    def get_offset(self, *args, **kwargs):
-        params = _api.select_matching_signature(sigs, self, *args, **kwargs)
-        bbox = (params["bbox"] if "bbox" in params else
-                Bbox.from_bounds(-params["xdescent"], -params["ydescent"],
-                                 params["width"], params["height"]))
-        return meth(params["self"], bbox, params["renderer"])
-    return get_offset
-
-
-@_api.deprecated("3.7", alternative='patches.bbox_artist')
-def bbox_artist(*args, **kwargs):
-    if DEBUG:
-        mbbox_artist(*args, **kwargs)
-
-
-# for debugging use
-def _bbox_artist(*args, **kwargs):
-    if DEBUG:
-        mbbox_artist(*args, **kwargs)
-
-
-def _get_packed_offsets(widths, total, sep, mode="fixed"):
-    r"""
-    Pack boxes specified by their *widths*.
-
-    For simplicity of the description, the terminology used here assumes a
-    horizontal layout, but the function works equally for a vertical layout.
-
-    There are three packing *mode*\s:
-
-    - 'fixed': The elements are packed tight to the left with a spacing of
-      *sep* in between. If *total* is *None* the returned total will be the
-      right edge of the last box. A non-*None* total will be passed unchecked
-      to the output. In particular this means that right edge of the last
-      box may be further to the right than the returned total.
-
-    - 'expand': Distribute the boxes with equal spacing so that the left edge
-      of the first box is at 0, and the right edge of the last box is at
-      *total*. The parameter *sep* is ignored in this mode. A total of *None*
-      is accepted and considered equal to 1. The total is returned unchanged
-      (except for the conversion *None* to 1). If the total is smaller than
-      the sum of the widths, the laid out boxes will overlap.
-
-    - 'equal': If *total* is given, the total space is divided in N equal
-      ranges and each box is left-aligned within its subspace.
-      Otherwise (*total* is *None*), *sep* must be provided and each box is
-      left-aligned in its subspace of width ``(max(widths) + sep)``. The
-      total width is then calculated to be ``N * (max(widths) + sep)``.
-
-    Parameters
-    ----------
-    widths : list of float
-        Widths of boxes to be packed.
-    total : float or None
-        Intended total length. *None* if not used.
-    sep : float
-        Spacing between boxes.
-    mode : {'fixed', 'expand', 'equal'}
-        The packing mode.
-
-    Returns
-    -------
-    total : float
-        The total width needed to accommodate the laid out boxes.
-    offsets : array of float
-        The left offsets of the boxes.
-    """
-    _api.check_in_list(["fixed", "expand", "equal"], mode=mode)
-
-    if mode == "fixed":
-        offsets_ = np.cumsum([0] + [w + sep for w in widths])
-        offsets = offsets_[:-1]
-        if total is None:
-            total = offsets_[-1] - sep
-        return total, offsets
-
-    elif mode == "expand":
-        # This is a bit of a hack to avoid a TypeError when *total*
-        # is None and used in conjugation with tight layout.
-        if total is None:
-            total = 1
-        if len(widths) > 1:
-            sep = (total - sum(widths)) / (len(widths) - 1)
-        else:
-            sep = 0
-        offsets_ = np.cumsum([0] + [w + sep for w in widths])
-        offsets = offsets_[:-1]
-        return total, offsets
-
-    elif mode == "equal":
-        maxh = max(widths)
-        if total is None:
-            if sep is None:
-                raise ValueError("total and sep cannot both be None when "
-                                 "using layout mode 'equal'")
-            total = (maxh + sep) * len(widths)
-        else:
-            sep = total / len(widths) - maxh
-        offsets = (maxh + sep) * np.arange(len(widths))
-        return total, offsets
-
-
-def _get_aligned_offsets(yspans, height, align="baseline"):
-    """
-    Align boxes each specified by their ``(y0, y1)`` spans.
-
-    For simplicity of the description, the terminology used here assumes a
-    horizontal layout (i.e., vertical alignment), but the function works
-    equally for a vertical layout.
-
-    Parameters
-    ----------
-    yspans
-        List of (y0, y1) spans of boxes to be aligned.
-    height : float or None
-        Intended total height. If None, the maximum of the heights
-        (``y1 - y0``) in *yspans* is used.
-    align : {'baseline', 'left', 'top', 'right', 'bottom', 'center'}
-        The alignment anchor of the boxes.
-
-    Returns
-    -------
-    (y0, y1)
-        y range spanned by the packing.  If a *height* was originally passed
-        in, then for all alignments other than "baseline", a span of ``(0,
-        height)`` is used without checking that it is actually large enough).
-    descent
-        The descent of the packing.
-    offsets
-        The bottom offsets of the boxes.
-    """
-
-    _api.check_in_list(
-        ["baseline", "left", "top", "right", "bottom", "center"], align=align)
-    if height is None:
-        height = max(y1 - y0 for y0, y1 in yspans)
-
-    if align == "baseline":
-        yspan = (min(y0 for y0, y1 in yspans), max(y1 for y0, y1 in yspans))
-        offsets = [0] * len(yspans)
-    elif align in ["left", "bottom"]:
-        yspan = (0, height)
-        offsets = [-y0 for y0, y1 in yspans]
-    elif align in ["right", "top"]:
-        yspan = (0, height)
-        offsets = [height - y1 for y0, y1 in yspans]
-    elif align == "center":
-        yspan = (0, height)
-        offsets = [(height - (y1 - y0)) * .5 - y0 for y0, y1 in yspans]
-
-    return yspan, offsets
-
-
-class OffsetBox(martist.Artist):
-    """
-    The OffsetBox is a simple container artist.
-
-    The child artists are meant to be drawn at a relative position to its
-    parent.
-
-    Being an artist itself, all parameters are passed on to `.Artist`.
-    """
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args)
-        self._internal_update(kwargs)
-        # Clipping has not been implemented in the OffsetBox family, so
-        # disable the clip flag for consistency. It can always be turned back
-        # on to zero effect.
-        self.set_clip_on(False)
-        self._children = []
-        self._offset = (0, 0)
-
-    def set_figure(self, fig):
-        """
-        Set the `.Figure` for the `.OffsetBox` and all its children.
-
-        Parameters
-        ----------
-        fig : `~matplotlib.figure.Figure`
-        """
-        super().set_figure(fig)
-        for c in self.get_children():
-            c.set_figure(fig)
-
-    @martist.Artist.axes.setter
-    def axes(self, ax):
-        # TODO deal with this better
-        martist.Artist.axes.fset(self, ax)
-        for c in self.get_children():
-            if c is not None:
-                c.axes = ax
-
-    def contains(self, mouseevent):
-        """
-        Delegate the mouse event contains-check to the children.
-
-        As a container, the `.OffsetBox` does not respond itself to
-        mouseevents.
-
-        Parameters
-        ----------
-        mouseevent : `matplotlib.backend_bases.MouseEvent`
-
-        Returns
-        -------
-        contains : bool
-            Whether any values are within the radius.
-        details : dict
-            An artist-specific dictionary of details of the event context,
-            such as which points are contained in the pick radius. See the
-            individual Artist subclasses for details.
-
-        See Also
-        --------
-        .Artist.contains
-        """
-        inside, info = self._default_contains(mouseevent)
-        if inside is not None:
-            return inside, info
-        for c in self.get_children():
-            a, b = c.contains(mouseevent)
-            if a:
-                return a, b
-        return False, {}
-
-    def set_offset(self, xy):
-        """
-        Set the offset.
-
-        Parameters
-        ----------
-        xy : (float, float) or callable
-            The (x, y) coordinates of the offset in display units. These can
-            either be given explicitly as a tuple (x, y), or by providing a
-            function that converts the extent into the offset. This function
-            must have the signature::
-
-                def offset(width, height, xdescent, ydescent, renderer) \
--> (float, float)
-        """
-        self._offset = xy
-        self.stale = True
-
-    @_compat_get_offset
-    def get_offset(self, bbox, renderer):
-        """
-        Return the offset as a tuple (x, y).
-
-        The extent parameters have to be provided to handle the case where the
-        offset is dynamically determined by a callable (see
-        `~.OffsetBox.set_offset`).
-
-        Parameters
-        ----------
-        bbox : `.Bbox`
-        renderer : `.RendererBase` subclass
-        """
-        return (
-            self._offset(bbox.width, bbox.height, -bbox.x0, -bbox.y0, renderer)
-            if callable(self._offset)
-            else self._offset)
-
-    def set_width(self, width):
-        """
-        Set the width of the box.
-
-        Parameters
-        ----------
-        width : float
-        """
-        self.width = width
-        self.stale = True
-
-    def set_height(self, height):
-        """
-        Set the height of the box.
-
-        Parameters
-        ----------
-        height : float
-        """
-        self.height = height
-        self.stale = True
-
-    def get_visible_children(self):
-        r"""Return a list of the visible child `.Artist`\s."""
-        return [c for c in self._children if c.get_visible()]
-
-    def get_children(self):
-        r"""Return a list of the child `.Artist`\s."""
-        return self._children
-
-    def _get_bbox_and_child_offsets(self, renderer):
-        """
-        Return the bbox of the offsetbox and the child offsets.
-
-        The bbox should satisfy ``x0 <= x1 and y0 <= y1``.
-
-        Parameters
-        ----------
-        renderer : `.RendererBase` subclass
-
-        Returns
-        -------
-        bbox
-        list of (xoffset, yoffset) pairs
-        """
-        raise NotImplementedError(
-            "get_bbox_and_offsets must be overridden in derived classes")
-
-    def get_bbox(self, renderer):
-        """Return the bbox of the offsetbox, ignoring parent offsets."""
-        bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-        return bbox
-
-    @_api.deprecated("3.7", alternative="get_bbox and child.get_offset")
-    def get_extent_offsets(self, renderer):
-        """
-        Update offset of the children and return the extent of the box.
-
-        Parameters
-        ----------
-        renderer : `.RendererBase` subclass
-
-        Returns
-        -------
-        width
-        height
-        xdescent
-        ydescent
-        list of (xoffset, yoffset) pairs
-        """
-        bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-        return bbox.width, bbox.height, -bbox.x0, -bbox.y0, offsets
-
-    @_api.deprecated("3.7", alternative="get_bbox")
-    def get_extent(self, renderer):
-        """Return a tuple ``width, height, xdescent, ydescent`` of the box."""
-        bbox = self.get_bbox(renderer)
-        return bbox.width, bbox.height, -bbox.x0, -bbox.y0
-
-    def get_window_extent(self, renderer=None):
-        # docstring inherited
-        if renderer is None:
-            renderer = self.figure._get_renderer()
-        bbox = self.get_bbox(renderer)
-        try:  # Some subclasses redefine get_offset to take no args.
-            px, py = self.get_offset(bbox, renderer)
-        except TypeError:
-            px, py = self.get_offset()
-        return bbox.translated(px, py)
-
-    def draw(self, renderer):
-        """
-        Update the location of children if necessary and draw them
-        to the given *renderer*.
-        """
-        bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-        px, py = self.get_offset(bbox, renderer)
-        for c, (ox, oy) in zip(self.get_visible_children(), offsets):
-            c.set_offset((px + ox, py + oy))
-            c.draw(renderer)
-        _bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
-        self.stale = False
-
-
-class PackerBase(OffsetBox):
-    def __init__(self, pad=0., sep=0., width=None, height=None,
-                 align="baseline", mode="fixed", children=None):
-        """
-        Parameters
-        ----------
-        pad : float, default: 0.0
-            The boundary padding in points.
-
-        sep : float, default: 0.0
-            The spacing between items in points.
-
-        width, height : float, optional
-            Width and height of the container box in pixels, calculated if
-            *None*.
-
-        align : {'top', 'bottom', 'left', 'right', 'center', 'baseline'}, \
-default: 'baseline'
-            Alignment of boxes.
-
-        mode : {'fixed', 'expand', 'equal'}, default: 'fixed'
-            The packing mode.
-
-            - 'fixed' packs the given `.Artist`\\s tight with *sep* spacing.
-            - 'expand' uses the maximal available space to distribute the
-              artists with equal spacing in between.
-            - 'equal': Each artist an equal fraction of the available space
-              and is left-aligned (or top-aligned) therein.
-
-        children : list of `.Artist`
-            The artists to pack.
-
-        Notes
-        -----
-        *pad* and *sep* are in points and will be scaled with the renderer
-        dpi, while *width* and *height* are in pixels.
-        """
-        super().__init__()
-        self.height = height
-        self.width = width
-        self.sep = sep
-        self.pad = pad
-        self.mode = mode
-        self.align = align
-        self._children = children
-
-
-class VPacker(PackerBase):
-    """
-    VPacker packs its children vertically, automatically adjusting their
-    relative positions at draw time.
-    """
-
-    def _get_bbox_and_child_offsets(self, renderer):
-        # docstring inherited
-        dpicor = renderer.points_to_pixels(1.)
-        pad = self.pad * dpicor
-        sep = self.sep * dpicor
-
-        if self.width is not None:
-            for c in self.get_visible_children():
-                if isinstance(c, PackerBase) and c.mode == "expand":
-                    c.set_width(self.width)
-
-        bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-        (x0, x1), xoffsets = _get_aligned_offsets(
-            [bbox.intervalx for bbox in bboxes], self.width, self.align)
-        height, yoffsets = _get_packed_offsets(
-            [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-        yoffsets = height - (yoffsets + [bbox.y1 for bbox in bboxes])
-        ydescent = yoffsets[0]
-        yoffsets = yoffsets - ydescent
-
-        return (
-            Bbox.from_bounds(x0, -ydescent, x1 - x0, height).padded(pad),
-            [*zip(xoffsets, yoffsets)])
-
-
-class HPacker(PackerBase):
-    """
-    HPacker packs its children horizontally, automatically adjusting their
-    relative positions at draw time.
-    """
-
-    def _get_bbox_and_child_offsets(self, renderer):
-        # docstring inherited
-        dpicor = renderer.points_to_pixels(1.)
-        pad = self.pad * dpicor
-        sep = self.sep * dpicor
-
-        bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-        if not bboxes:
-            return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-        (y0, y1), yoffsets = _get_aligned_offsets(
-            [bbox.intervaly for bbox in bboxes], self.height, self.align)
-        width, xoffsets = _get_packed_offsets(
-            [bbox.width for bbox in bboxes], self.width, sep, self.mode)
-
-        x0 = bboxes[0].x0
-        xoffsets -= ([bbox.x0 for bbox in bboxes] - x0)
-
-        return (Bbox.from_bounds(x0, y0, width, y1 - y0).padded(pad),
-                [*zip(xoffsets, yoffsets)])
-
-
-class PaddedBox(OffsetBox):
-    """
-    A container to add a padding around an `.Artist`.
-
-    The `.PaddedBox` contains a `.FancyBboxPatch` that is used to visualize
-    it when rendering.
-    """
-
-    @_api.make_keyword_only("3.6", name="draw_frame")
-    def __init__(self, child, pad=0., draw_frame=False, patch_attrs=None):
-        """
-        Parameters
-        ----------
-        child : `~matplotlib.artist.Artist`
-            The contained `.Artist`.
-        pad : float, default: 0.0
-            The padding in points. This will be scaled with the renderer dpi.
-            In contrast, *width* and *height* are in *pixels* and thus not
-            scaled.
-        draw_frame : bool
-            Whether to draw the contained `.FancyBboxPatch`.
-        patch_attrs : dict or None
-            Additional parameters passed to the contained `.FancyBboxPatch`.
-        """
-        super().__init__()
-        self.pad = pad
-        self._children = [child]
-        self.patch = FancyBboxPatch(
-            xy=(0.0, 0.0), width=1., height=1.,
-            facecolor='w', edgecolor='k',
-            mutation_scale=1,  # self.prop.get_size_in_points(),
-            snap=True,
-            visible=draw_frame,
-            boxstyle="square,pad=0",
-        )
-        if patch_attrs is not None:
-            self.patch.update(patch_attrs)
-
-    def _get_bbox_and_child_offsets(self, renderer):
-        # docstring inherited.
-        pad = self.pad * renderer.points_to_pixels(1.)
-        return (self._children[0].get_bbox(renderer).padded(pad), [(0, 0)])
-
-    def draw(self, renderer):
-        # docstring inherited
-        bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-        px, py = self.get_offset(bbox, renderer)
-        for c, (ox, oy) in zip(self.get_visible_children(), offsets):
-            c.set_offset((px + ox, py + oy))
-
-        self.draw_frame(renderer)
-
-        for c in self.get_visible_children():
-            c.draw(renderer)
-
-        self.stale = False
-
-    def update_frame(self, bbox, fontsize=None):
-        self.patch.set_bounds(bbox.bounds)
-        if fontsize:
-            self.patch.set_mutation_scale(fontsize)
-        self.stale = True
-
-    def draw_frame(self, renderer):
-        # update the location and size of the legend
-        self.update_frame(self.get_window_extent(renderer))
-        self.patch.draw(renderer)
-
-
-class DrawingArea(OffsetBox):
-    """
-    The DrawingArea can contain any Artist as a child. The DrawingArea
-    has a fixed width and height. The position of children relative to
-    the parent is fixed. The children can be clipped at the
-    boundaries of the parent.
-    """
-
-    def __init__(self, width, height, xdescent=0., ydescent=0., clip=False):
-        """
-        Parameters
-        ----------
-        width, height : float
-            Width and height of the container box.
-        xdescent, ydescent : float
-            Descent of the box in x- and y-direction.
-        clip : bool
-            Whether to clip the children to the box.
-        """
-        super().__init__()
-        self.width = width
-        self.height = height
-        self.xdescent = xdescent
-        self.ydescent = ydescent
-        self._clip_children = clip
-        self.offset_transform = mtransforms.Affine2D()
-        self.dpi_transform = mtransforms.Affine2D()
-
-    @property
-    def clip_children(self):
-        """
-        If the children of this DrawingArea should be clipped
-        by DrawingArea bounding box.
-        """
-        return self._clip_children
-
-    @clip_children.setter
-    def clip_children(self, val):
-        self._clip_children = bool(val)
-        self.stale = True
-
-    def get_transform(self):
-        """
-        Return the `~matplotlib.transforms.Transform` applied to the children.
-        """
-        return self.dpi_transform + self.offset_transform
-
-    def set_transform(self, t):
-        """
-        set_transform is ignored.
-        """
-
-    def set_offset(self, xy):
-        """
-        Set the offset of the container.
-
-        Parameters
-        ----------
-        xy : (float, float)
-            The (x, y) coordinates of the offset in display units.
-        """
-        self._offset = xy
-        self.offset_transform.clear()
-        self.offset_transform.translate(xy[0], xy[1])
-        self.stale = True
-
-    def get_offset(self):
-        """Return offset of the container."""
-        return self._offset
-
-    def get_bbox(self, renderer):
-        # docstring inherited
-        dpi_cor = renderer.points_to_pixels(1.)
-        return Bbox.from_bounds(
-            -self.xdescent * dpi_cor, -self.ydescent * dpi_cor,
-            self.width * dpi_cor, self.height * dpi_cor)
-
-    def add_artist(self, a):
-        """Add an `.Artist` to the container box."""
-        self._children.append(a)
-        if not a.is_transform_set():
-            a.set_transform(self.get_transform())
-        if self.axes is not None:
-            a.axes = self.axes
-        fig = self.figure
-        if fig is not None:
-            a.set_figure(fig)
-
-    def draw(self, renderer):
-        # docstring inherited
-
-        dpi_cor = renderer.points_to_pixels(1.)
-        self.dpi_transform.clear()
-        self.dpi_transform.scale(dpi_cor)
-
-        # At this point the DrawingArea has a transform
-        # to the display space so the path created is
-        # good for clipping children
-        tpath = mtransforms.TransformedPath(
-            mpath.Path([[0, 0], [0, self.height],
-                        [self.width, self.height],
-                        [self.width, 0]]),
-            self.get_transform())
-        for c in self._children:
-            if self._clip_children and not (c.clipbox or c._clippath):
-                c.set_clip_path(tpath)
-            c.draw(renderer)
-
-        _bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
-        self.stale = False
-
-
-class TextArea(OffsetBox):
-    """
-    The TextArea is a container artist for a single Text instance.
-
-    The text is placed at (0, 0) with baseline+left alignment, by default. The
-    width and height of the TextArea instance is the width and height of its
-    child text.
-    """
-
-    @_api.make_keyword_only("3.6", name="textprops")
-    def __init__(self, s,
-                 textprops=None,
-                 multilinebaseline=False,
-                 ):
-        """
-        Parameters
-        ----------
-        s : str
-            The text to be displayed.
-        textprops : dict, default: {}
-            Dictionary of keyword parameters to be passed to the `.Text`
-            instance in the TextArea.
-        multilinebaseline : bool, default: False
-            Whether the baseline for multiline text is adjusted so that it
-            is (approximately) center-aligned with single-line text.
-        """
-        if textprops is None:
-            textprops = {}
-        self._text = mtext.Text(0, 0, s, **textprops)
-        super().__init__()
-        self._children = [self._text]
-        self.offset_transform = mtransforms.Affine2D()
-        self._baseline_transform = mtransforms.Affine2D()
-        self._text.set_transform(self.offset_transform +
-                                 self._baseline_transform)
-        self._multilinebaseline = multilinebaseline
-
-    def set_text(self, s):
-        """Set the text of this area as a string."""
-        self._text.set_text(s)
-        self.stale = True
-
-    def get_text(self):
-        """Return the string representation of this area's text."""
-        return self._text.get_text()
-
-    def set_multilinebaseline(self, t):
-        """
-        Set multilinebaseline.
-
-        If True, the baseline for multiline text is adjusted so that it is
-        (approximately) center-aligned with single-line text.  This is used
-        e.g. by the legend implementation so that single-line labels are
-        baseline-aligned, but multiline labels are "center"-aligned with them.
-        """
-        self._multilinebaseline = t
-        self.stale = True
-
-    def get_multilinebaseline(self):
-        """
-        Get multilinebaseline.
-        """
-        return self._multilinebaseline
-
-    def set_transform(self, t):
-        """
-        set_transform is ignored.
-        """
-
-    def set_offset(self, xy):
-        """
-        Set the offset of the container.
-
-        Parameters
-        ----------
-        xy : (float, float)
-            The (x, y) coordinates of the offset in display units.
-        """
-        self._offset = xy
-        self.offset_transform.clear()
-        self.offset_transform.translate(xy[0], xy[1])
-        self.stale = True
-
-    def get_offset(self):
-        """Return offset of the container."""
-        return self._offset
-
-    def get_bbox(self, renderer):
-        _, h_, d_ = renderer.get_text_width_height_descent(
-            "lp", self._text._fontproperties,
-            ismath="TeX" if self._text.get_usetex() else False)
-
-        bbox, info, yd = self._text._get_layout(renderer)
-        w, h = bbox.size
-
-        self._baseline_transform.clear()
-
-        if len(info) > 1 and self._multilinebaseline:
-            yd_new = 0.5 * h - 0.5 * (h_ - d_)
-            self._baseline_transform.translate(0, yd - yd_new)
-            yd = yd_new
-        else:  # single line
-            h_d = max(h_ - d_, h - yd)
-            h = h_d + yd
-
-        ha = self._text.get_horizontalalignment()
-        x0 = {"left": 0, "center": -w / 2, "right": -w}[ha]
-
-        return Bbox.from_bounds(x0, -yd, w, h)
-
-    def draw(self, renderer):
-        # docstring inherited
-        self._text.draw(renderer)
-        _bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
-        self.stale = False
-
-
-class AuxTransformBox(OffsetBox):
-    """
-    Offset Box with the aux_transform. Its children will be
-    transformed with the aux_transform first then will be
-    offsetted. The absolute coordinate of the aux_transform is meaning
-    as it will be automatically adjust so that the left-lower corner
-    of the bounding box of children will be set to (0, 0) before the
-    offset transform.
-
-    It is similar to drawing area, except that the extent of the box
-    is not predetermined but calculated from the window extent of its
-    children. Furthermore, the extent of the children will be
-    calculated in the transformed coordinate.
-    """
-    def __init__(self, aux_transform):
-        self.aux_transform = aux_transform
-        super().__init__()
-        self.offset_transform = mtransforms.Affine2D()
-        # ref_offset_transform makes offset_transform always relative to the
-        # lower-left corner of the bbox of its children.
-        self.ref_offset_transform = mtransforms.Affine2D()
-
-    def add_artist(self, a):
-        """Add an `.Artist` to the container box."""
-        self._children.append(a)
-        a.set_transform(self.get_transform())
-        self.stale = True
-
-    def get_transform(self):
-        """
-        Return the :class:`~matplotlib.transforms.Transform` applied
-        to the children
-        """
-        return (self.aux_transform
-                + self.ref_offset_transform
-                + self.offset_transform)
-
-    def set_transform(self, t):
-        """
-        set_transform is ignored.
-        """
-
-    def set_offset(self, xy):
-        """
-        Set the offset of the container.
-
-        Parameters
-        ----------
-        xy : (float, float)
-            The (x, y) coordinates of the offset in display units.
-        """
-        self._offset = xy
-        self.offset_transform.clear()
-        self.offset_transform.translate(xy[0], xy[1])
-        self.stale = True
-
-    def get_offset(self):
-        """Return offset of the container."""
-        return self._offset
-
-    def get_bbox(self, renderer):
-        # clear the offset transforms
-        _off = self.offset_transform.get_matrix()  # to be restored later
-        self.ref_offset_transform.clear()
-        self.offset_transform.clear()
-        # calculate the extent
-        bboxes = [c.get_window_extent(renderer) for c in self._children]
-        ub = Bbox.union(bboxes)
-        # adjust ref_offset_transform
-        self.ref_offset_transform.translate(-ub.x0, -ub.y0)
-        # restore offset transform
-        self.offset_transform.set_matrix(_off)
-        return Bbox.from_bounds(0, 0, ub.width, ub.height)
-
-    def draw(self, renderer):
-        # docstring inherited
-        for c in self._children:
-            c.draw(renderer)
-        _bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
-        self.stale = False
-
-
-class AnchoredOffsetbox(OffsetBox):
-    """
-    An offset box placed according to location *loc*.
-
-    AnchoredOffsetbox has a single child.  When multiple children are needed,
-    use an extra OffsetBox to enclose them.  By default, the offset box is
-    anchored against its parent axes. You may explicitly specify the
-    *bbox_to_anchor*.
-    """
-    zorder = 5  # zorder of the legend
-
-    # Location codes
-    codes = {'upper right': 1,
-             'upper left': 2,
-             'lower left': 3,
-             'lower right': 4,
-             'right': 5,
-             'center left': 6,
-             'center right': 7,
-             'lower center': 8,
-             'upper center': 9,
-             'center': 10,
-             }
-
-    @_api.make_keyword_only("3.6", name="pad")
-    def __init__(self, loc,
-                 pad=0.4, borderpad=0.5,
-                 child=None, prop=None, frameon=True,
-                 bbox_to_anchor=None,
-                 bbox_transform=None,
-                 **kwargs):
-        """
-        Parameters
-        ----------
-        loc : str
-            The box location.  Valid locations are
-            'upper left', 'upper center', 'upper right',
-            'center left', 'center', 'center right',
-            'lower left', 'lower center', 'lower right'.
-            For backward compatibility, numeric values are accepted as well.
-            See the parameter *loc* of `.Legend` for details.
-        pad : float, default: 0.4
-            Padding around the child as fraction of the fontsize.
-        borderpad : float, default: 0.5
-            Padding between the offsetbox frame and the *bbox_to_anchor*.
-        child : `.OffsetBox`
-            The box that will be anchored.
-        prop : `.FontProperties`
-            This is only used as a reference for paddings. If not given,
-            :rc:`legend.fontsize` is used.
-        frameon : bool
-            Whether to draw a frame around the box.
-        bbox_to_anchor : `.BboxBase`, 2-tuple, or 4-tuple of floats
-            Box that is used to position the legend in conjunction with *loc*.
-        bbox_transform : None or :class:`matplotlib.transforms.Transform`
-            The transform for the bounding box (*bbox_to_anchor*).
-        **kwargs
-            All other parameters are passed on to `.OffsetBox`.
-
-        Notes
-        -----
-        See `.Legend` for a detailed description of the anchoring mechanism.
-        """
-        super().__init__(**kwargs)
-
-        self.set_bbox_to_anchor(bbox_to_anchor, bbox_transform)
-        self.set_child(child)
-
-        if isinstance(loc, str):
-            loc = _api.check_getitem(self.codes, loc=loc)
-
-        self.loc = loc
-        self.borderpad = borderpad
-        self.pad = pad
-
-        if prop is None:
-            self.prop = FontProperties(size=mpl.rcParams["legend.fontsize"])
-        else:
-            self.prop = FontProperties._from_any(prop)
-            if isinstance(prop, dict) and "size" not in prop:
-                self.prop.set_size(mpl.rcParams["legend.fontsize"])
-
-        self.patch = FancyBboxPatch(
-            xy=(0.0, 0.0), width=1., height=1.,
-            facecolor='w', edgecolor='k',
-            mutation_scale=self.prop.get_size_in_points(),
-            snap=True,
-            visible=frameon,
-            boxstyle="square,pad=0",
-        )
-
-    def set_child(self, child):
-        """Set the child to be anchored."""
-        self._child = child
-        if child is not None:
-            child.axes = self.axes
-        self.stale = True
-
-    def get_child(self):
-        """Return the child."""
-        return self._child
-
-    def get_children(self):
-        """Return the list of children."""
-        return [self._child]
-
-    def get_bbox(self, renderer):
-        # docstring inherited
-        fontsize = renderer.points_to_pixels(self.prop.get_size_in_points())
-        pad = self.pad * fontsize
-        return self.get_child().get_bbox(renderer).padded(pad)
-
-    def get_bbox_to_anchor(self):
-        """Return the bbox that the box is anchored to."""
-        if self._bbox_to_anchor is None:
-            return self.axes.bbox
-        else:
-            transform = self._bbox_to_anchor_transform
-            if transform is None:
-                return self._bbox_to_anchor
-            else:
-                return TransformedBbox(self._bbox_to_anchor, transform)
-
-    def set_bbox_to_anchor(self, bbox, transform=None):
-        """
-        Set the bbox that the box is anchored to.
-
-        *bbox* can be a Bbox instance, a list of [left, bottom, width,
-        height], or a list of [left, bottom] where the width and
-        height will be assumed to be zero. The bbox will be
-        transformed to display coordinate by the given transform.
-        """
-        if bbox is None or isinstance(bbox, BboxBase):
-            self._bbox_to_anchor = bbox
-        else:
-            try:
-                l = len(bbox)
-            except TypeError as err:
-                raise ValueError(f"Invalid bbox: {bbox}") from err
-
-            if l == 2:
-                bbox = [bbox[0], bbox[1], 0, 0]
-
-            self._bbox_to_anchor = Bbox.from_bounds(*bbox)
-
-        self._bbox_to_anchor_transform = transform
-        self.stale = True
-
-    @_compat_get_offset
-    def get_offset(self, bbox, renderer):
-        # docstring inherited
-        pad = (self.borderpad
-               * renderer.points_to_pixels(self.prop.get_size_in_points()))
-        bbox_to_anchor = self.get_bbox_to_anchor()
-        x0, y0 = _get_anchored_bbox(
-            self.loc, Bbox.from_bounds(0, 0, bbox.width, bbox.height),
-            bbox_to_anchor, pad)
-        return x0 - bbox.x0, y0 - bbox.y0
-
-    def update_frame(self, bbox, fontsize=None):
-        self.patch.set_bounds(bbox.bounds)
-        if fontsize:
-            self.patch.set_mutation_scale(fontsize)
-
-    def draw(self, renderer):
-        # docstring inherited
-        if not self.get_visible():
-            return
-
-        # update the location and size of the legend
-        bbox = self.get_window_extent(renderer)
-        fontsize = renderer.points_to_pixels(self.prop.get_size_in_points())
-        self.update_frame(bbox, fontsize)
-        self.patch.draw(renderer)
-
-        px, py = self.get_offset(self.get_bbox(renderer), renderer)
-        self.get_child().set_offset((px, py))
-        self.get_child().draw(renderer)
-        self.stale = False
-
-
-def _get_anchored_bbox(loc, bbox, parentbbox, borderpad):
-    """
-    Return the (x, y) position of the *bbox* anchored at the *parentbbox* with
-    the *loc* code with the *borderpad*.
-    """
-    # This is only called internally and *loc* should already have been
-    # validated.  If 0 (None), we just let ``bbox.anchored`` raise.
-    c = [None, "NE", "NW", "SW", "SE", "E", "W", "E", "S", "N", "C"][loc]
-    container = parentbbox.padded(-borderpad)
-    return bbox.anchored(c, container=container).p0
-
-
-class AnchoredText(AnchoredOffsetbox):
-    """
-    AnchoredOffsetbox with Text.
-    """
-
-    @_api.make_keyword_only("3.6", name="pad")
-    def __init__(self, s, loc, pad=0.4, borderpad=0.5, prop=None, **kwargs):
-        """
-        Parameters
-        ----------
-        s : str
-            Text.
-
-        loc : str
-            Location code. See `AnchoredOffsetbox`.
-
-        pad : float, default: 0.4
-            Padding around the text as fraction of the fontsize.
-
-        borderpad : float, default: 0.5
-            Spacing between the offsetbox frame and the *bbox_to_anchor*.
-
-        prop : dict, optional
-            Dictionary of keyword parameters to be passed to the
-            `~matplotlib.text.Text` instance contained inside AnchoredText.
-
-        **kwargs
-            All other parameters are passed to `AnchoredOffsetbox`.
-        """
-
-        if prop is None:
-            prop = {}
-        badkwargs = {'va', 'verticalalignment'}
-        if badkwargs & set(prop):
-            raise ValueError(
-                'Mixing verticalalignment with AnchoredText is not supported.')
-
-        self.txt = TextArea(s, textprops=prop)
-        fp = self.txt._text.get_fontproperties()
-        super().__init__(
-            loc, pad=pad, borderpad=borderpad, child=self.txt, prop=fp,
-            **kwargs)
-
-
-class OffsetImage(OffsetBox):
-
-    @_api.make_keyword_only("3.6", name="zoom")
-    def __init__(self, arr,
-                 zoom=1,
-                 cmap=None,
-                 norm=None,
-                 interpolation=None,
-                 origin=None,
-                 filternorm=True,
-                 filterrad=4.0,
-                 resample=False,
-                 dpi_cor=True,
-                 **kwargs
-                 ):
-
-        super().__init__()
-        self._dpi_cor = dpi_cor
-
-        self.image = BboxImage(bbox=self.get_window_extent,
-                               cmap=cmap,
-                               norm=norm,
-                               interpolation=interpolation,
-                               origin=origin,
-                               filternorm=filternorm,
-                               filterrad=filterrad,
-                               resample=resample,
-                               **kwargs
-                               )
-
-        self._children = [self.image]
-
-        self.set_zoom(zoom)
-        self.set_data(arr)
-
-    def set_data(self, arr):
-        self._data = np.asarray(arr)
-        self.image.set_data(self._data)
-        self.stale = True
-
-    def get_data(self):
-        return self._data
-
-    def set_zoom(self, zoom):
-        self._zoom = zoom
-        self.stale = True
-
-    def get_zoom(self):
-        return self._zoom
-
-    def get_offset(self):
-        """Return offset of the container."""
-        return self._offset
-
-    def get_children(self):
-        return [self.image]
-
-    def get_bbox(self, renderer):
-        dpi_cor = renderer.points_to_pixels(1.) if self._dpi_cor else 1.
-        zoom = self.get_zoom()
-        data = self.get_data()
-        ny, nx = data.shape[:2]
-        w, h = dpi_cor * nx * zoom, dpi_cor * ny * zoom
-        return Bbox.from_bounds(0, 0, w, h)
-
-    def draw(self, renderer):
-        # docstring inherited
-        self.image.draw(renderer)
-        # bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
-        self.stale = False
-
-
-class AnnotationBbox(martist.Artist, mtext._AnnotationBase):
-    """
-    Container for an `OffsetBox` referring to a specific position *xy*.
-
-    Optionally an arrow pointing from the offsetbox to *xy* can be drawn.
-
-    This is like `.Annotation`, but with `OffsetBox` instead of `.Text`.
-    """
-
-    zorder = 3
-
-    def __str__(self):
-        return "AnnotationBbox(%g,%g)" % (self.xy[0], self.xy[1])
-
-    @_docstring.dedent_interpd
-    @_api.make_keyword_only("3.6", name="xycoords")
-    def __init__(self, offsetbox, xy,
-                 xybox=None,
-                 xycoords='data',
-                 boxcoords=None,
-                 frameon=True, pad=0.4,  # FancyBboxPatch boxstyle.
-                 annotation_clip=None,
-                 box_alignment=(0.5, 0.5),
-                 bboxprops=None,
-                 arrowprops=None,
-                 fontsize=None,
-                 **kwargs):
-        """
-        Parameters
-        ----------
-        offsetbox : `OffsetBox`
-
-        xy : (float, float)
-            The point *(x, y)* to annotate. The coordinate system is determined
-            by *xycoords*.
-
-        xybox : (float, float), default: *xy*
-            The position *(x, y)* to place the text at. The coordinate system
-            is determined by *boxcoords*.
-
-        xycoords : single or two-tuple of str or `.Artist` or `.Transform` or \
-callable, default: 'data'
-            The coordinate system that *xy* is given in. See the parameter
-            *xycoords* in `.Annotation` for a detailed description.
-
-        boxcoords : single or two-tuple of str or `.Artist` or `.Transform` \
-or callable, default: value of *xycoords*
-            The coordinate system that *xybox* is given in. See the parameter
-            *textcoords* in `.Annotation` for a detailed description.
-
-        frameon : bool, default: True
-            By default, the text is surrounded by a white `.FancyBboxPatch`
-            (accessible as the ``patch`` attribute of the `.AnnotationBbox`).
-            If *frameon* is set to False, this patch is made invisible.
-
-        annotation_clip: bool or None, default: None
-            Whether to clip (i.e. not draw) the annotation when the annotation
-            point *xy* is outside the axes area.
-
-            - If *True*, the annotation will be clipped when *xy* is outside
-              the axes.
-            - If *False*, the annotation will always be drawn.
-            - If *None*, the annotation will be clipped when *xy* is outside
-              the axes and *xycoords* is 'data'.
-
-        pad : float, default: 0.4
-            Padding around the offsetbox.
-
-        box_alignment : (float, float)
-            A tuple of two floats for a vertical and horizontal alignment of
-            the offset box w.r.t. the *boxcoords*.
-            The lower-left corner is (0, 0) and upper-right corner is (1, 1).
-
-        bboxprops : dict, optional
-            A dictionary of properties to set for the annotation bounding box,
-            for example *boxstyle* and *alpha*.  See `.FancyBboxPatch` for
-            details.
-
-        arrowprops: dict, optional
-            Arrow properties, see `.Annotation` for description.
-
-        fontsize: float or str, optional
-            Translated to points and passed as *mutation_scale* into
-            `.FancyBboxPatch` to scale attributes of the box style (e.g. pad
-            or rounding_size).  The name is chosen in analogy to `.Text` where
-            *fontsize* defines the mutation scale as well.  If not given,
-            :rc:`legend.fontsize` is used.  See `.Text.set_fontsize` for valid
-            values.
-
-        **kwargs
-            Other `AnnotationBbox` properties.  See `.AnnotationBbox.set` for
-            a list.
-        """
-
-        martist.Artist.__init__(self)
-        mtext._AnnotationBase.__init__(
-            self, xy, xycoords=xycoords, annotation_clip=annotation_clip)
-
-        self.offsetbox = offsetbox
-        self.arrowprops = arrowprops.copy() if arrowprops is not None else None
-        self.set_fontsize(fontsize)
-        self.xybox = xybox if xybox is not None else xy
-        self.boxcoords = boxcoords if boxcoords is not None else xycoords
-        self._box_alignment = box_alignment
-
-        if arrowprops is not None:
-            self._arrow_relpos = self.arrowprops.pop("relpos", (0.5, 0.5))
-            self.arrow_patch = FancyArrowPatch((0, 0), (1, 1),
-                                               **self.arrowprops)
-        else:
-            self._arrow_relpos = None
-            self.arrow_patch = None
-
-        self.patch = FancyBboxPatch(  # frame
-            xy=(0.0, 0.0), width=1., height=1.,
-            facecolor='w', edgecolor='k',
-            mutation_scale=self.prop.get_size_in_points(),
-            snap=True,
-            visible=frameon,
-        )
-        self.patch.set_boxstyle("square", pad=pad)
-        if bboxprops:
-            self.patch.set(**bboxprops)
-
-        self._internal_update(kwargs)
-
-    @property
-    def xyann(self):
-        return self.xybox
-
-    @xyann.setter
-    def xyann(self, xyann):
-        self.xybox = xyann
-        self.stale = True
-
-    @property
-    def anncoords(self):
-        return self.boxcoords
-
-    @anncoords.setter
-    def anncoords(self, coords):
-        self.boxcoords = coords
-        self.stale = True
-
-    def contains(self, mouseevent):
-        inside, info = self._default_contains(mouseevent)
-        if inside is not None:
-            return inside, info
-        if not self._check_xy(None):
-            return False, {}
-        return self.offsetbox.contains(mouseevent)
-        # self.arrow_patch is currently not checked as this can be a line - JJ
-
-    def get_children(self):
-        children = [self.offsetbox, self.patch]
-        if self.arrow_patch:
-            children.append(self.arrow_patch)
-        return children
-
-    def set_figure(self, fig):
-        if self.arrow_patch is not None:
-            self.arrow_patch.set_figure(fig)
-        self.offsetbox.set_figure(fig)
-        martist.Artist.set_figure(self, fig)
-
-    def set_fontsize(self, s=None):
-        """
-        Set the fontsize in points.
-
-        If *s* is not given, reset to :rc:`legend.fontsize`.
-        """
-        if s is None:
-            s = mpl.rcParams["legend.fontsize"]
-
-        self.prop = FontProperties(size=s)
-        self.stale = True
-
-    def get_fontsize(self):
-        """Return the fontsize in points."""
-        return self.prop.get_size_in_points()
-
-    def get_window_extent(self, renderer=None):
-        # docstring inherited
-        if renderer is None:
-            renderer = self.figure._get_renderer()
-        return Bbox.union([child.get_window_extent(renderer)
-                           for child in self.get_children()])
-
-    def get_tightbbox(self, renderer=None):
-        # docstring inherited
-        return Bbox.union([child.get_tightbbox(renderer)
-                           for child in self.get_children()])
-
-    def update_positions(self, renderer):
-        """
-        Update pixel positions for the annotated point, the text and the arrow.
-        """
-
-        x, y = self.xybox
-        if isinstance(self.boxcoords, tuple):
-            xcoord, ycoord = self.boxcoords
-            x1, y1 = self._get_xy(renderer, x, y, xcoord)
-            x2, y2 = self._get_xy(renderer, x, y, ycoord)
-            ox0, oy0 = x1, y2
-        else:
-            ox0, oy0 = self._get_xy(renderer, x, y, self.boxcoords)
-
-        bbox = self.offsetbox.get_bbox(renderer)
-        fw, fh = self._box_alignment
-        self.offsetbox.set_offset(
-            (ox0 - fw*bbox.width - bbox.x0, oy0 - fh*bbox.height - bbox.y0))
-
-        bbox = self.offsetbox.get_window_extent(renderer)
-        self.patch.set_bounds(bbox.bounds)
-
-        mutation_scale = renderer.points_to_pixels(self.get_fontsize())
-        self.patch.set_mutation_scale(mutation_scale)
-
-        if self.arrowprops:
-            # Use FancyArrowPatch if self.arrowprops has "arrowstyle" key.
-
-            # Adjust the starting point of the arrow relative to the textbox.
-            # TODO: Rotation needs to be accounted.
-            arrow_begin = bbox.p0 + bbox.size * self._arrow_relpos
-            arrow_end = self._get_position_xy(renderer)
-            # The arrow (from arrow_begin to arrow_end) will be first clipped
-            # by patchA and patchB, then shrunk by shrinkA and shrinkB (in
-            # points).  If patch A is not set, self.bbox_patch is used.
-            self.arrow_patch.set_positions(arrow_begin, arrow_end)
-
-            if "mutation_scale" in self.arrowprops:
-                mutation_scale = renderer.points_to_pixels(
-                    self.arrowprops["mutation_scale"])
-                # Else, use fontsize-based mutation_scale defined above.
-            self.arrow_patch.set_mutation_scale(mutation_scale)
-
-            patchA = self.arrowprops.get("patchA", self.patch)
-            self.arrow_patch.set_patchA(patchA)
-
-    def draw(self, renderer):
-        # docstring inherited
-        if renderer is not None:
-            self._renderer = renderer
-        if not self.get_visible() or not self._check_xy(renderer):
-            return
-        renderer.open_group(self.__class__.__name__, gid=self.get_gid())
-        self.update_positions(renderer)
-        if self.arrow_patch is not None:
-            if self.arrow_patch.figure is None and self.figure is not None:
-                self.arrow_patch.figure = self.figure
-            self.arrow_patch.draw(renderer)
-        self.patch.draw(renderer)
-        self.offsetbox.draw(renderer)
-        renderer.close_group(self.__class__.__name__)
-        self.stale = False
-
-
-class DraggableBase:
-    """
-    Helper base class for a draggable artist (legend, offsetbox).
-
-    Derived classes must override the following methods::
-
-        def save_offset(self):
-            '''
-            Called when the object is picked for dragging; should save the
-            reference position of the artist.
-            '''
-
-        def update_offset(self, dx, dy):
-            '''
-            Called during the dragging; (*dx*, *dy*) is the pixel offset from
-            the point where the mouse drag started.
-            '''
-
-    Optionally, you may override the following method::
-
-        def finalize_offset(self):
-            '''Called when the mouse is released.'''
-
-    In the current implementation of `.DraggableLegend` and
-    `DraggableAnnotation`, `update_offset` places the artists in display
-    coordinates, and `finalize_offset` recalculates their position in axes
-    coordinate and set a relevant attribute.
-    """
-
-    def __init__(self, ref_artist, use_blit=False):
-        self.ref_artist = ref_artist
-        if not ref_artist.pickable():
-            ref_artist.set_picker(True)
-        self.got_artist = False
-        self._use_blit = use_blit and self.canvas.supports_blit
-        self.cids = [
-            self.canvas.callbacks._connect_picklable(
-                'pick_event', self.on_pick),
-            self.canvas.callbacks._connect_picklable(
-                'button_release_event', self.on_release),
-        ]
-
-    # A property, not an attribute, to maintain picklability.
-    canvas = property(lambda self: self.ref_artist.figure.canvas)
-
-    def on_motion(self, evt):
-        if self._check_still_parented() and self.got_artist:
-            dx = evt.x - self.mouse_x
-            dy = evt.y - self.mouse_y
-            self.update_offset(dx, dy)
-            if self._use_blit:
-                self.canvas.restore_region(self.background)
-                self.ref_artist.draw(
-                    self.ref_artist.figure._get_renderer())
-                self.canvas.blit()
-            else:
-                self.canvas.draw()
-
-    def on_pick(self, evt):
-        if self._check_still_parented() and evt.artist == self.ref_artist:
-            self.mouse_x = evt.mouseevent.x
-            self.mouse_y = evt.mouseevent.y
-            self.got_artist = True
-            if self._use_blit:
-                self.ref_artist.set_animated(True)
-                self.canvas.draw()
-                self.background = \
-                    self.canvas.copy_from_bbox(self.ref_artist.figure.bbox)
-                self.ref_artist.draw(
-                    self.ref_artist.figure._get_renderer())
-                self.canvas.blit()
-            self._c1 = self.canvas.callbacks._connect_picklable(
-                "motion_notify_event", self.on_motion)
-            self.save_offset()
-
-    def on_release(self, event):
-        if self._check_still_parented() and self.got_artist:
-            self.finalize_offset()
-            self.got_artist = False
-            self.canvas.mpl_disconnect(self._c1)
-
-            if self._use_blit:
-                self.ref_artist.set_animated(False)
-
-    def _check_still_parented(self):
-        if self.ref_artist.figure is None:
-            self.disconnect()
-            return False
-        else:
-            return True
-
-    def disconnect(self):
-        """Disconnect the callbacks."""
-        for cid in self.cids:
-            self.canvas.mpl_disconnect(cid)
-        try:
-            c1 = self._c1
-        except AttributeError:
-            pass
-        else:
-            self.canvas.mpl_disconnect(c1)
-
-    def save_offset(self):
-        pass
-
-    def update_offset(self, dx, dy):
-        pass
-
-    def finalize_offset(self):
-        pass
-
-
-class DraggableOffsetBox(DraggableBase):
-    def __init__(self, ref_artist, offsetbox, use_blit=False):
-        super().__init__(ref_artist, use_blit=use_blit)
-        self.offsetbox = offsetbox
-
-    def save_offset(self):
-        offsetbox = self.offsetbox
-        renderer = offsetbox.figure._get_renderer()
-        offset = offsetbox.get_offset(offsetbox.get_bbox(renderer), renderer)
-        self.offsetbox_x, self.offsetbox_y = offset
-        self.offsetbox.set_offset(offset)
-
-    def update_offset(self, dx, dy):
-        loc_in_canvas = self.offsetbox_x + dx, self.offsetbox_y + dy
-        self.offsetbox.set_offset(loc_in_canvas)
-
-    def get_loc_in_canvas(self):
-        offsetbox = self.offsetbox
-        renderer = offsetbox.figure._get_renderer()
-        bbox = offsetbox.get_bbox(renderer)
-        ox, oy = offsetbox._offset
-        loc_in_canvas = (ox + bbox.x0, oy + bbox.y0)
-        return loc_in_canvas
-
-
-class DraggableAnnotation(DraggableBase):
-    def __init__(self, annotation, use_blit=False):
-        super().__init__(annotation, use_blit=use_blit)
-        self.annotation = annotation
-
-    def save_offset(self):
-        ann = self.annotation
-        self.ox, self.oy = ann.get_transform().transform(ann.xyann)
-
-    def update_offset(self, dx, dy):
-        ann = self.annotation
-        ann.xyann = ann.get_transform().inverted().transform(
-            (self.ox + dx, self.oy + dy))
diff --git a/spaces/lafi23333/aikomori/attentions.py b/spaces/lafi23333/aikomori/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/lafi23333/aikomori/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/leurez/moss/src/utils/is/index.ts b/spaces/leurez/moss/src/utils/is/index.ts
deleted file mode 100644
index 27a8230a12e94ad05df1fe5e466446587f6d26d0..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/src/utils/is/index.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-export function isNumber<T extends number>(value: T | unknown): value is number {
-  return Object.prototype.toString.call(value) === '[object Number]'
-}
-
-export function isString<T extends string>(value: T | unknown): value is string {
-  return Object.prototype.toString.call(value) === '[object String]'
-}
-
-export function isBoolean<T extends boolean>(value: T | unknown): value is boolean {
-  return Object.prototype.toString.call(value) === '[object Boolean]'
-}
-
-export function isNull<T extends null>(value: T | unknown): value is null {
-  return Object.prototype.toString.call(value) === '[object Null]'
-}
-
-export function isUndefined<T extends undefined>(value: T | unknown): value is undefined {
-  return Object.prototype.toString.call(value) === '[object Undefined]'
-}
-
-export function isObject<T extends object>(value: T | unknown): value is object {
-  return Object.prototype.toString.call(value) === '[object Object]'
-}
-
-export function isArray<T extends any[]>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object Array]'
-}
-
-export function isFunction<T extends (...args: any[]) => any | void | never>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object Function]'
-}
-
-export function isDate<T extends Date>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object Date]'
-}
-
-export function isRegExp<T extends RegExp>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object RegExp]'
-}
-
-export function isPromise<T extends Promise<any>>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object Promise]'
-}
-
-export function isSet<T extends Set<any>>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object Set]'
-}
-
-export function isMap<T extends Map<any, any>>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object Map]'
-}
-
-export function isFile<T extends File>(value: T | unknown): value is T {
-  return Object.prototype.toString.call(value) === '[object File]'
-}
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/CINEMA 4D Studio R19 With [PATCHED] Crack.md b/spaces/lincquiQcaudo/Top-20-Diffusion/CINEMA 4D Studio R19 With [PATCHED] Crack.md
deleted file mode 100644
index c61007caeafd6aaf5d81c956e443675f3ea51e8a..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/CINEMA 4D Studio R19 With [PATCHED] Crack.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-<h2>CINEMA 4D Studio R19 With Crack</h2><br /><p><b><b>Download Zip</b> &raquo; <a href="https://bytlly.com/2uGwIg">https://bytlly.com/2uGwIg</a></b></p><br /><br />
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Counter-Strike 1.5 1.6 Steamless Online Pack 2011 Pc Game.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Counter-Strike 1.5 1.6 Steamless Online Pack 2011 Pc Game.md
deleted file mode 100644
index 38dfae3f58687bfdda416d8737fb8861ff5ad4df..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Counter-Strike 1.5 1.6 Steamless Online Pack 2011 Pc Game.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<br />
-<h1>How to Play Counter-Strike 1.5 and 1.6 Online Without Steam</h1>
-<p>Counter-Strike is one of the most popular and influential first-person shooter games of all time. It pits two teams of players against each other in realistic scenarios, such as defusing bombs, rescuing hostages, or eliminating terrorists. The game has been updated and improved over the years, with the latest version being Counter-Strike: Global Offensive.</p>
-<p>However, some fans still prefer the classic versions of Counter-Strike, such as 1.5 and 1.6, which were released in 2002 and 2003 respectively. These versions have simpler graphics, faster gameplay, and a nostalgic appeal for many players. But how can you play them online without Steam, the digital distribution platform that manages most of the modern PC games?</p>
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-<p>One of the most popular steamless online packs for Counter-Strike 1.5 and 1.6 is the one released in 2011 by a user named ny[^1^]. This pack includes everything you need to play Counter-Strike 1.5 and 1.6 online with other players around the world, such as:</p>
-<ul>
-<li>The game files for both versions of Counter-Strike</li>
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-</ul>
-<p>To use this pack, you just need to download it from the link provided[^1^], extract it to your desired folder, and run the launcher.exe file. Then you can choose which version of Counter-Strike you want to play, select a server from the list, and join the game.</p>
-<p>There are some advantages and disadvantages of using this pack compared to playing Counter-Strike on Steam. Some of the advantages are:</p>
-<ul>
-<li>You don't need a Steam account or a CD key to play</li>
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-<li>You can use custom mods and skins without problems</li>
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-</ul>
-<p>Some of the disadvantages are:</p>
-<ul>
-<li>You may encounter some bugs or errors while playing</li>
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-<li>You may not be able to play with some players who only use Steam</li>
-<li>You may not be able to get official support or assistance from Valve</li>
-</ul>
-<p>In conclusion, if you want to play Counter-Strike 1.5 and 1.6 online without Steam, you can use a steamless online pack like the one released in 2011 by ny[^1^]. This pack will let you enjoy the classic versions of Counter-Strike with other players around the world, without needing a Steam account or a CD key. However, you should also be aware of the potential drawbacks and risks of using this pack, such as bugs, errors, compatibility issues, or lack of support.</p> d5da3c52bf<br />
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/DAEMON Tools Ultra 3100368 License Crack UPD M4MasterTeamOSHKRG.md b/spaces/lincquiQcaudo/Top-20-Diffusion/DAEMON Tools Ultra 3100368 License Crack UPD M4MasterTeamOSHKRG.md
deleted file mode 100644
index e9f2fc6e07c3f29ef0acba98f7a24394851dbd1f..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/DAEMON Tools Ultra 3100368 License Crack UPD M4MasterTeamOSHKRG.md	
+++ /dev/null
@@ -1,325 +0,0 @@
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-</ul>
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-<h2>Conclusion</h2>
-
-<p>In this article, we have discussed how to download and install DAEMON Tools Ultra 3
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-We hope that you have found this article useful 
-and informative 
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-and that you have learned something new today 
-.</p>
-
-<p>However 
-,
-we would like to remind you that using a patched software is not legal or ethical 
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-and that it may cause some issues with your system or with DAEMON Tools 
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-<ul>
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-<h2>Conclusion</h2>
-
-<p>In this article, we have discussed how to download and install DAEMON Tools Ultra 3
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-of using a patched software 
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-We hope that you have found this article useful 
-and informative 
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-and that you have learned something new today 
-.</p>
-
-<p>However 
-,
-we would like to remind you that using a patched software is not legal or ethical 
-,
-and that it may cause some issues with your system or with DAEMON Tools 
-.
-We strongly advise you to buy 
-the original software 
-from their website 
-if you can afford it 
-,
-or to look for some other software 
-that can suit your needs and budget 
-.
-This way 
-,
-you can support 
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-and enjoy 
-the full features 
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-of DAEMON Tools Ultra 3
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-.</p>
-
-<p>Thank you for reading this article and we hope to see you again soon. Have a great day and happy imaging!</p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have discussed how to download and install DAEMON Tools Ultra 3.1.0.0368 license crack M4MasterTeamOSHKRG. We have also shown you some of the features, advantages, and disadvantages of using a patched software. We hope that you have found this article useful and informative, and that you have learned something new today.</p>
-
-<p>However, we would like to remind you that using a patched software is not legal or ethical, and that it may cause some issues with your system or with DAEMON Tools. We strongly advise you to buy the original software from their website if you can afford it, or to look for some other software that can suit your needs and budget. This way, you can support the developers and enjoy the full features and functions of DAEMON Tools Ultra 3.1.0.0368 without any problems.</p>
-
-<p>Thank you for reading this article and we hope to see you again soon. Have a great day and happy imaging!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Martin Gruber Understanding Sql.pdf.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Martin Gruber Understanding Sql.pdf.md
deleted file mode 100644
index 01483ee4f7d2491d7217288c20724eab1dd0bde8..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Martin Gruber Understanding Sql.pdf.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Martin Gruber Understanding Sql.pdf</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash;>>> <a href="https://bytlly.com/2uGwaY">https://bytlly.com/2uGwaY</a></b></p><br /><br />
-
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Megatech MegaCAD 2D 3D 2012 FULL Version Download BEST.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Megatech MegaCAD 2D 3D 2012 FULL Version Download BEST.md
deleted file mode 100644
index 2b9650bc8dc8ed698391a52e035c37951e99a26d..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Megatech MegaCAD 2D 3D 2012 FULL Version Download BEST.md	
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Windows 8.1 Enterprise 6.3.9600.17031.WINBLUE X86-X64 64 Bit [NEW].md b/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Windows 8.1 Enterprise 6.3.9600.17031.WINBLUE X86-X64 64 Bit [NEW].md
deleted file mode 100644
index 133ef90f5af96e3a3d52f755070c210254b91983..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Windows 8.1 Enterprise 6.3.9600.17031.WINBLUE X86-X64 64 Bit [NEW].md	
+++ /dev/null
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diff --git a/spaces/lixq/bingo61/src/components/tailwind-indicator.tsx b/spaces/lixq/bingo61/src/components/tailwind-indicator.tsx
deleted file mode 100644
index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000
--- a/spaces/lixq/bingo61/src/components/tailwind-indicator.tsx
+++ /dev/null
@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
-  if (process.env.NODE_ENV === 'production') return null
-
-  return (
-    <div className="fixed bottom-1 left-1 z-50 flex h-6 w-6 items-center justify-center rounded-full bg-gray-800 p-3 font-mono text-xs text-white">
-      <div className="block sm:hidden">xs</div>
-      <div className="hidden sm:block md:hidden">sm</div>
-      <div className="hidden md:block lg:hidden">md</div>
-      <div className="hidden lg:block xl:hidden">lg</div>
-      <div className="hidden xl:block 2xl:hidden">xl</div>
-      <div className="hidden 2xl:block">2xl</div>
-    </div>
-  )
-}
diff --git a/spaces/lm/lychee_law/app.py b/spaces/lm/lychee_law/app.py
deleted file mode 100644
index 8808ba6e814a567bd94c0f9793199628568bb4f6..0000000000000000000000000000000000000000
--- a/spaces/lm/lychee_law/app.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-hf_model = "law-llm/law-glm-10b"
-max_question_length = 64
-max_generation_length = 490
-
-tokenizer = AutoTokenizer.from_pretrained(
-    hf_model,
-    cache_dir=model_cache_dir,
-    use_fast=True,
-    trust_remote_code=True
-)
-
-model = AutoModelForSeq2SeqLM.from_pretrained(
-    hf_model,
-    cache_dir=model_cache_dir,
-    trust_remote_code=True
-)
-
-model = model.to('cuda')
-model.eval()
-
-model_inputs = "提问: 犯了盗窃罪怎么判刑? 回答: [gMASK]"
-
-model_inputs = tokenizer(model_inputs,
-                            max_length=max_question_length,
-                            padding=True,
-                            truncation=True,
-                            return_tensors="pt")
-
-model_inputs = tokenizer.build_inputs_for_generation(model_inputs,
-                                                     targets=None,
-                                                     max_gen_length=max_generation_length,
-                                                     padding=True)
-
-inputs = model_inputs.to('cuda')
-
-outputs = model.generate(**inputs, max_length=max_generation_length,
-                            eos_token_id=tokenizer.eop_token_id)
-prediction = tokenizer.decode(outputs[0].tolist())
\ No newline at end of file
diff --git a/spaces/lqy09/GT/public/GTest/css/style.css b/spaces/lqy09/GT/public/GTest/css/style.css
deleted file mode 100644
index d6563e38761bb72da6fac03df3eab7279b7ad5ce..0000000000000000000000000000000000000000
--- a/spaces/lqy09/GT/public/GTest/css/style.css
+++ /dev/null
@@ -1 +0,0 @@
-body{position:fixed;top:0;left:0;right:0;bottom:0;display:flex;justify-content:center;text-align:center;align-items:center;background:url(../images/bg.png) no-repeat bottom;background-size:cover;background-attachment:fixed;font-family:"PingFangSC-Regular","Open Sans",Arial,"Hiragino Sans GB","Microsoft YaHei","STHeiti","WenQuanYi Micro Hei",SimSun,sans-serif}h1{position:absolute;font-size:30px;top:12px}#captcha{width:300px;display:inline-block}#btn{width:70%;height:50px;color:#fff;background:linear-gradient(0deg,rgba(0,172,238,1) 0%,rgba(2,126,251,1) 100%);border-radius:6px;box-shadow:inset 2px 2px 2px 0px rgba(255,255,255,.5),7px 7px 20px 0px rgba(0,0,0,.1),4px 4px 5px 0px rgba(0,0,0,.1);cursor:pointer;border:none;font-size:20px}#btn:hover{background:transparent;color:rgba(2,126,251,1)}#wait{display:none;color:#666;margin:0 auto}.show{background-color:#d1d5db;border-radius:9999px;width:150px;height:.5rem;position:relative;overflow:hidden}.progress{background-color:#3b82f6;border-radius:9999px;position:absolute;bottom:0;top:0;width:50%;animation-duration:2s;animation-iteration-count:infinite;animation-name:progress-bar}@keyframes progress-bar{from{left:-50%}to{left:100%}}#footer{position:absolute;width:100%;font-size:12px;color:#c0c0c0;bottom:12px}
\ No newline at end of file
diff --git a/spaces/lwchen/CodeFormer/CodeFormer/README.md b/spaces/lwchen/CodeFormer/CodeFormer/README.md
deleted file mode 100644
index 65810cdf4ce36d8ba152de80df00fa4c8802ee81..0000000000000000000000000000000000000000
--- a/spaces/lwchen/CodeFormer/CodeFormer/README.md
+++ /dev/null
@@ -1,123 +0,0 @@
-<p align="center">
-  <img src="assets/CodeFormer_logo.png" height=110>
-</p>
-
-## Towards Robust Blind Face Restoration with Codebook Lookup Transformer
-
-[Paper](https://arxiv.org/abs/2206.11253) | [Project Page](https://shangchenzhou.com/projects/CodeFormer/) | [Video](https://youtu.be/d3VDpkXlueI)
-
-
-<a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer) ![visitors](https://visitor-badge.glitch.me/badge?page_id=sczhou/CodeFormer)
-
-[Shangchen Zhou](https://shangchenzhou.com/), [Kelvin C.K. Chan](https://ckkelvinchan.github.io/), [Chongyi Li](https://li-chongyi.github.io/), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/) 
-
-S-Lab, Nanyang Technological University
-
-<img src="assets/network.jpg" width="800px"/>
-
-
-:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs: 
-
-### Update
-
-- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/). Try out online demo! [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer)
-- **2022.09.04**: Add face upsampling `--face_upsample` for high-resolution AI-created face enhancement.
-- **2022.08.23**: Some modifications on face detection and fusion for better AI-created face enhancement.
-- **2022.08.07**: Integrate [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement.
-- **2022.07.29**: Integrate new face detectors of `['RetinaFace'(default), 'YOLOv5']`. 
-- **2022.07.17**: Add Colab demo of CodeFormer. <a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
-- **2022.07.16**: Release inference code for face restoration. :blush:
-- **2022.06.21**: This repo is created.
-
-### TODO
-- [ ] Add checkpoint for face inpainting
-- [ ] Add training code and config files
-- [x] ~~Add background image enhancement~~
-
-#### Face Restoration
-
-<img src="assets/restoration_result1.png" width="400px"/> <img src="assets/restoration_result2.png" width="400px"/>
-<img src="assets/restoration_result3.png" width="400px"/> <img src="assets/restoration_result4.png" width="400px"/>
-
-#### Face Color Enhancement and Restoration
-
-<img src="assets/color_enhancement_result1.png" width="400px"/> <img src="assets/color_enhancement_result2.png" width="400px"/>
-
-#### Face Inpainting
-
-<img src="assets/inpainting_result1.png" width="400px"/> <img src="assets/inpainting_result2.png" width="400px"/>
-
-
-
-### Dependencies and Installation
-
-- Pytorch >= 1.7.1
-- CUDA >= 10.1
-- Other required packages in `requirements.txt`
-```
-# git clone this repository
-git clone https://github.com/sczhou/CodeFormer
-cd CodeFormer
-
-# create new anaconda env
-conda create -n codeformer python=3.8 -y
-conda activate codeformer
-
-# install python dependencies
-pip3 install -r requirements.txt
-python basicsr/setup.py develop
-```
-<!-- conda install -c conda-forge dlib -->
-
-### Quick Inference
-
-##### Download Pre-trained Models:
-Download the facelib pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] to the `weights/facelib` folder. You can manually download the pretrained models OR download by runing the following command.
-```
-python scripts/download_pretrained_models.py facelib
-```
-
-Download the CodeFormer pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] to the `weights/CodeFormer` folder. You can manually download the pretrained models OR download by runing the following command.
-```
-python scripts/download_pretrained_models.py CodeFormer
-```
-
-##### Prepare Testing Data:
-You can put the testing images in the `inputs/TestWhole` folder. If you would like to test on cropped and aligned faces, you can put them in the `inputs/cropped_faces` folder.
-
-
-##### Testing on Face Restoration:
-```
-# For cropped and aligned faces
-python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder]
-
-# For the whole images
-# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
-# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
-python inference_codeformer.py --w 0.7 --test_path [input folder]
-```
-
-NOTE that *w* is in [0, 1]. Generally, smaller *w* tends to produce a higher-quality result, while larger *w* yields a higher-fidelity result. 
-
-The results will be saved in the `results` folder.
-
-### Citation
-If our work is useful for your research, please consider citing:
-
-    @article{zhou2022codeformer,
-        author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
-        title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
-        journal = {arXiv preprint arXiv:2206.11253},
-        year = {2022}
-    }
-
-### License
-
-<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
-
-### Acknowledgement
-
-This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). We also borrow some codes from [Unleashing Transformers](https://github.com/samb-t/unleashing-transformers), [YOLOv5-face](https://github.com/deepcam-cn/yolov5-face), and [FaceXLib](https://github.com/xinntao/facexlib). Thanks for their awesome works.
-
-### Contact
-If you have any question, please feel free to reach me out at `shangchenzhou@gmail.com`.
\ No newline at end of file
diff --git a/spaces/ma-xu/LIVE/thrust/internal/benchmark/timer.h b/spaces/ma-xu/LIVE/thrust/internal/benchmark/timer.h
deleted file mode 100644
index 077ffa44ce61e637e9e9b898bfe28186f6d36252..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/internal/benchmark/timer.h
+++ /dev/null
@@ -1,129 +0,0 @@
-#pragma once
-
-#include <cassert>
-
-#  define CUDA_SAFE_CALL_NO_SYNC( call) do {                                 \
-    cudaError err = call;                                                    \
-    if( cudaSuccess != err) {                                                \
-        fprintf(stderr, "CUDA error in file '%s' in line %i : %s.\n",        \
-                __FILE__, __LINE__, cudaGetErrorString( err) );              \
-        exit(EXIT_FAILURE);                                                  \
-    } } while (0)
-
-#  define CUDA_SAFE_CALL( call) do {                                         \
-    CUDA_SAFE_CALL_NO_SYNC(call);                                            \
-    cudaError err = cudaDeviceSynchronize();                                 \
-    if( cudaSuccess != err) {                                                \
-        fprintf(stderr, "CUDA error in file '%s' in line %i : %s.\n",        \
-                __FILE__, __LINE__, cudaGetErrorString( err) );              \
-        exit(EXIT_FAILURE);                                                  \
-    } } while (0)
-
-class cuda_timer
-{
-    cudaEvent_t start_;
-    cudaEvent_t stop_;
-
- public:
-    cuda_timer()
-    {
-        CUDA_SAFE_CALL(cudaEventCreate(&start_));
-        CUDA_SAFE_CALL(cudaEventCreate(&stop_));
-    }
-
-    ~cuda_timer()
-    {
-        CUDA_SAFE_CALL(cudaEventDestroy(start_));
-        CUDA_SAFE_CALL(cudaEventDestroy(stop_));
-    }
-
-    void start()
-    {
-        CUDA_SAFE_CALL(cudaEventRecord(start_, 0));
-    }
-
-    void stop()
-    {
-        CUDA_SAFE_CALL(cudaEventRecord(stop_, 0));
-        CUDA_SAFE_CALL(cudaEventSynchronize(stop_));
-    }
-
-    double milliseconds_elapsed()
-    {
-        float elapsed_time;
-        CUDA_SAFE_CALL(cudaEventElapsedTime(&elapsed_time, start_, stop_));
-        return elapsed_time;
-    }
-
-    double seconds_elapsed()
-    {
-        return milliseconds_elapsed() / 1000.0;
-    }
-};
-
-#if (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC)
-#include <windows.h>
-
-class steady_timer
-{
-    LARGE_INTEGER frequency_; // Cached to avoid system calls.
-    LARGE_INTEGER start_;
-    LARGE_INTEGER stop_;
-
- public:
-    steady_timer() : start_(), stop_(), frequency_()
-    {
-        BOOL const r = QueryPerformanceFrequency(&frequency_);
-        assert(0 != r);
-    }
-
-    void start()
-    {
-        BOOL const r = QueryPerformanceCounter(&start_);
-        assert(0 != r);
-    }
-
-    void stop()
-    {
-        BOOL const r = QueryPerformanceCounter(&stop_);
-        assert(0 != r);
-    }
-
-    double seconds_elapsed()
-    {
-        return double(stop_.QuadPart - start_.QuadPart)
-             / double(frequency_.QuadPart);
-    }
-};
-#else
-#include <time.h>
-
-class steady_timer
-{
-    timespec start_;
-    timespec stop_;
-
- public:
-    steady_timer() : start_(), stop_() {}
-
-    void start()
-    {
-        int const r = clock_gettime(CLOCK_MONOTONIC, &start_);
-        assert(0 == r);
-    }
-
-    void stop()
-    {
-        int const r = clock_gettime(CLOCK_MONOTONIC, &stop_);
-        assert(0 == r);
-    }
-
-    double seconds_elapsed()
-    {
-        return double(stop_.tv_sec  - start_.tv_sec)
-             + double(stop_.tv_nsec - start_.tv_nsec) * 1.0e-9;
-    }
-};
-#endif
-
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/par.h b/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/par.h
deleted file mode 100644
index a5d9c14cd7a91df6bcd00dcd13419d7e67155b03..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/par.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/allocator_aware_execution_policy.h>
-#include <thrust/system/tbb/detail/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-
-struct par_t : thrust::system::tbb::detail::execution_policy<par_t>,
-  thrust::detail::allocator_aware_execution_policy<
-    thrust::system::tbb::detail::execution_policy>
-{
-  __host__ __device__
-  THRUST_CONSTEXPR par_t() : thrust::system::tbb::detail::execution_policy<par_t>() {}
-};
-
-
-} // end detail
-
-
-static const detail::par_t par;
-
-
-} // end tbb
-} // end system
-
-
-// alias par here
-namespace tbb
-{
-
-
-using thrust::system::tbb::par;
-
-
-} // end tbb
-} // end thrust
-
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/dfdnet_util.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/dfdnet_util.py
deleted file mode 100644
index b4dc0ff738c76852e830b32fffbe65bffb5ddf50..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/dfdnet_util.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.nn.utils.spectral_norm import spectral_norm
-
-
-class BlurFunctionBackward(Function):
-
-    @staticmethod
-    def forward(ctx, grad_output, kernel, kernel_flip):
-        ctx.save_for_backward(kernel, kernel_flip)
-        grad_input = F.conv2d(grad_output, kernel_flip, padding=1, groups=grad_output.shape[1])
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_output):
-        kernel, _ = ctx.saved_tensors
-        grad_input = F.conv2d(gradgrad_output, kernel, padding=1, groups=gradgrad_output.shape[1])
-        return grad_input, None, None
-
-
-class BlurFunction(Function):
-
-    @staticmethod
-    def forward(ctx, x, kernel, kernel_flip):
-        ctx.save_for_backward(kernel, kernel_flip)
-        output = F.conv2d(x, kernel, padding=1, groups=x.shape[1])
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, kernel_flip = ctx.saved_tensors
-        grad_input = BlurFunctionBackward.apply(grad_output, kernel, kernel_flip)
-        return grad_input, None, None
-
-
-blur = BlurFunction.apply
-
-
-class Blur(nn.Module):
-
-    def __init__(self, channel):
-        super().__init__()
-        kernel = torch.tensor([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=torch.float32)
-        kernel = kernel.view(1, 1, 3, 3)
-        kernel = kernel / kernel.sum()
-        kernel_flip = torch.flip(kernel, [2, 3])
-
-        self.kernel = kernel.repeat(channel, 1, 1, 1)
-        self.kernel_flip = kernel_flip.repeat(channel, 1, 1, 1)
-
-    def forward(self, x):
-        return blur(x, self.kernel.type_as(x), self.kernel_flip.type_as(x))
-
-
-def calc_mean_std(feat, eps=1e-5):
-    """Calculate mean and std for adaptive_instance_normalization.
-
-    Args:
-        feat (Tensor): 4D tensor.
-        eps (float): A small value added to the variance to avoid
-            divide-by-zero. Default: 1e-5.
-    """
-    size = feat.size()
-    assert len(size) == 4, 'The input feature should be 4D tensor.'
-    n, c = size[:2]
-    feat_var = feat.view(n, c, -1).var(dim=2) + eps
-    feat_std = feat_var.sqrt().view(n, c, 1, 1)
-    feat_mean = feat.view(n, c, -1).mean(dim=2).view(n, c, 1, 1)
-    return feat_mean, feat_std
-
-
-def adaptive_instance_normalization(content_feat, style_feat):
-    """Adaptive instance normalization.
-
-    Adjust the reference features to have the similar color and illuminations
-    as those in the degradate features.
-
-    Args:
-        content_feat (Tensor): The reference feature.
-        style_feat (Tensor): The degradate features.
-    """
-    size = content_feat.size()
-    style_mean, style_std = calc_mean_std(style_feat)
-    content_mean, content_std = calc_mean_std(content_feat)
-    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
-    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-def AttentionBlock(in_channel):
-    return nn.Sequential(
-        spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
-        spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)))
-
-
-def conv_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bias=True):
-    """Conv block used in MSDilationBlock."""
-
-    return nn.Sequential(
-        spectral_norm(
-            nn.Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=kernel_size,
-                stride=stride,
-                dilation=dilation,
-                padding=((kernel_size - 1) // 2) * dilation,
-                bias=bias)),
-        nn.LeakyReLU(0.2),
-        spectral_norm(
-            nn.Conv2d(
-                out_channels,
-                out_channels,
-                kernel_size=kernel_size,
-                stride=stride,
-                dilation=dilation,
-                padding=((kernel_size - 1) // 2) * dilation,
-                bias=bias)),
-    )
-
-
-class MSDilationBlock(nn.Module):
-    """Multi-scale dilation block."""
-
-    def __init__(self, in_channels, kernel_size=3, dilation=(1, 1, 1, 1), bias=True):
-        super(MSDilationBlock, self).__init__()
-
-        self.conv_blocks = nn.ModuleList()
-        for i in range(4):
-            self.conv_blocks.append(conv_block(in_channels, in_channels, kernel_size, dilation=dilation[i], bias=bias))
-        self.conv_fusion = spectral_norm(
-            nn.Conv2d(
-                in_channels * 4,
-                in_channels,
-                kernel_size=kernel_size,
-                stride=1,
-                padding=(kernel_size - 1) // 2,
-                bias=bias))
-
-    def forward(self, x):
-        out = []
-        for i in range(4):
-            out.append(self.conv_blocks[i](x))
-        out = torch.cat(out, 1)
-        out = self.conv_fusion(out) + x
-        return out
-
-
-class UpResBlock(nn.Module):
-
-    def __init__(self, in_channel):
-        super(UpResBlock, self).__init__()
-        self.body = nn.Sequential(
-            nn.Conv2d(in_channel, in_channel, 3, 1, 1),
-            nn.LeakyReLU(0.2, True),
-            nn.Conv2d(in_channel, in_channel, 3, 1, 1),
-        )
-
-    def forward(self, x):
-        out = x + self.body(x)
-        return out
diff --git a/spaces/marinap/multilingual-image-search/app.py b/spaces/marinap/multilingual-image-search/app.py
deleted file mode 100644
index fe4a9df4c0b51aa1d53d6e4581c071c588ed1b2d..0000000000000000000000000000000000000000
--- a/spaces/marinap/multilingual-image-search/app.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import io
-import requests
-import numpy as np
-import pandas as pd
-import torch
-import torch.nn.functional as F
-from PIL import Image
-import gradio as gr
-import uform
-from datetime import datetime
-
-
-
-model_multi = uform.get_model('unum-cloud/uform-vl-multilingual')
-
-embeddings = np.load('tensors/embeddings.npy')
-embeddings = torch.tensor(embeddings)
-
-#features = np.load('multilingual-image-search/tensors/features.npy')
-#features = torch.tensor(features)
-
-img_df = pd.read_csv('image_data.csv')
-
-def url2img(url, resize = False, fix_height = 150):
-    data = requests.get(url, allow_redirects = True).content
-    img = Image.open(io.BytesIO(data))
-    if resize:
-         img.thumbnail([fix_height, fix_height], Image.LANCZOS)
-    return  img
-
-def find_topk(text):
-
-    print('text', text)
-
-    top_k = 20
-
-    text_data = model_multi.preprocess_text(text)
-    text_features, text_embedding = model_multi.encode_text(text_data, return_features=True)
-
-    print('Got features', datetime.now().strftime("%H:%M:%S"))
-
-    sims = F.cosine_similarity(text_embedding, embeddings)
-
-    vals, inds = sims.topk(top_k)
-    top_k_urls = img_df.iloc[inds]['photo_image_url'].values
-
-    print('Got top_k_urls', top_k_urls)
-    print(datetime.now().strftime("%H:%M:%S"))
-
-    return top_k_urls
-
-
-
-# def rerank(text_features, text_data):
-
-#     # craet joint embeddings & get scores
-#     joint_embedding = model_multi.encode_multimodal(
-#         image_features=image_features,
-#         text_features=text_features,
-#         attention_mask=text_data['attention_mask']
-#     )
-#     score = model_multi.get_matching_scores(joint_embedding)
-
-#     # argmax to get top N
-
-#     return
-
-
-#demo = gr.Interface(find_topk, inputs = 'text', outputs = 'image')
-
-print('version', gr.__version__)
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        gr.Markdown('# Enter a prompt in one of the supported languages.')
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown( 
-                            '||||||\n'
-                            '|:-------:  |:---: |:-------:   |:---: | :--- |\n'
-                            '|__English__| #    |__French__  | #    |__Russian__|\n'
-                            '|__German__ | #    |__Italian__ | #    |__Chinese (Simplified)__|\n'
-                            '|__Spanish__| #    |__Japanese__| #    |__Korean__|\n'
-                            '|__Turkish__| #    |__Polish__  | #    |.|\n')
-
-            with gr.Column():
-                prompt_box = gr.Textbox(label = 'Enter your prompt', lines = 3, container = True)
-                btn_search = gr.Button("Find images")
-
-        with gr.Row():
-            gr.Examples(['a girl wandering alone in the forest',
-                        'морозное утро в городе', 
-                        '카메라를 바라보는 강아지 새끼', 
-                        'ein Schloss, das zwischen modernen Gebäuden hervorlugt',
-                        'un couple sirotant un café au bord de la rivière',
-                        'una banda de música actuando en un gran espacio al aire libre',
-                        '秋の静かな霧の庭園'
-                        ], inputs=[prompt_box]) 
-            
-
-        gallery = gr.Gallery().style(grid = [5], height="auto")
-        btn_search.click(find_topk, inputs = prompt_box, outputs = gallery)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/mascIT/AgeGuesser/yolov5/__init__.py b/spaces/mascIT/AgeGuesser/yolov5/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/matthoffner/chatbot/utils/server/google.ts b/spaces/matthoffner/chatbot/utils/server/google.ts
deleted file mode 100644
index ce800415c06a7f78bb298733411b6df94aaebf3b..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/utils/server/google.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-export const cleanSourceText = (text: string) => {
-  return text
-    .trim()
-    .replace(/(\n){4,}/g, '\n\n\n')
-    .replace(/\n\n/g, ' ')
-    .replace(/ {3,}/g, '  ')
-    .replace(/\t/g, '')
-    .replace(/\n+(\s*\n)*/g, '\n');
-};
diff --git a/spaces/matthoffner/open-codetree/store/features/compilerSlice.ts b/spaces/matthoffner/open-codetree/store/features/compilerSlice.ts
deleted file mode 100644
index dce35e415af542fc502b9970f0dc2ab72f046d3d..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/open-codetree/store/features/compilerSlice.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-import { createSlice } from "@reduxjs/toolkit";
-import { RootState } from "../store";
-import * as esbuild from "esbuild-wasm";
-import { unpkgFetchPlugin, unpkgPathPlugin } from "../../esbuild/plugins";
-import { CompilerOutput, CompilerStatus } from "../../_types/compilerTypes";
-
-type InitialStateType = {
-  isInitializing: boolean;
-  esbuildStatus: CompilerStatus;
-  isCompiling: boolean;
-  output: CompilerOutput;
-};
-
-const initialState = {
-  isInitializing: false,
-  esbuildStatus: { isReady: false, error: "" },
-  isCompiling: false,
-  output: { code: "", error: "" },
-};
-
-export const compilerSlice = createSlice({
-  name: "compiler",
-  initialState: initialState,
-  reducers: {
-    init_esbuild: (state: InitialStateType) => {
-      state.isInitializing = true;
-    },
-    init_esbuild_success: (state: InitialStateType) => {
-      state.esbuildStatus.isReady = true;
-      state.esbuildStatus.error = "";
-      state.isInitializing = false;
-    },
-    init_esbuild_failure: (state: InitialStateType, { payload }) => {
-      state.esbuildStatus.isReady = false;
-      state.esbuildStatus.error = payload;
-      state.isInitializing = false;
-    },
-    compiled: (state: InitialStateType) => {
-      state.isCompiling = true;
-    },
-    compiled_success: (state: InitialStateType, { payload }) => {
-      state.output.code = payload;
-      state.output.error = "";
-      state.isCompiling = false;
-    },
-    compiled_failure: (state: InitialStateType, { payload }) => {
-      state.output.code = "";
-      state.output.error = payload;
-      state.isCompiling = false;
-    },
-  },
-});
-
-export const {
-  compiled,
-  compiled_success,
-  compiled_failure,
-  init_esbuild,
-  init_esbuild_success,
-  init_esbuild_failure,
-} = compilerSlice.actions;
-
-export const compiler_state = (state: RootState) => state.compiler;
-
-export default compilerSlice.reducer;
-
-// Asynchronous thunk action
-
-export function initEsbuild() {
-  return async (dispatch: any) => {
-    dispatch(init_esbuild());
-
-    await esbuild
-      .initialize({
-        worker: true,
-        wasmURL: "https://unpkg.com/esbuild-wasm@0.14.42/esbuild.wasm",
-      })
-      .then(() => {
-        dispatch(init_esbuild_success());
-      })
-      .catch((error) => dispatch(init_esbuild_failure(error.message)));
-  };
-}
-
-export function getCompileCode(rawCode: string, entryPoint: string) {
-  return async (dispatch: any) => {
-    dispatch(compiled());
-
-    try {
-      const result = await esbuild.build({
-        entryPoints: [`${entryPoint}`],
-        bundle: true,
-        write: false,
-        minify: true,
-        outdir: "/",
-        plugins: [unpkgPathPlugin(), unpkgFetchPlugin(rawCode, entryPoint)],
-        metafile: true,
-        allowOverwrite: true,
-      });
-
-      dispatch(compiled_success(result.outputFiles[0].text));
-    } catch (error) {
-      // @ts-ignore
-      dispatch(compiled_failure(error.message));
-    }
-  };
-}
diff --git a/spaces/merle/PROTEIN_GENERATOR/app.py b/spaces/merle/PROTEIN_GENERATOR/app.py
deleted file mode 100644
index ae13dae14658ad82e25823376049125617b31f27..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/app.py
+++ /dev/null
@@ -1,611 +0,0 @@
-import os,sys
-
-# install environment goods
-#os.system("pip -q install dgl -f https://data.dgl.ai/wheels/cu113/repo.html")
-os.system('pip install dgl==1.0.2+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html')
-#os.system('pip install gradio')
-os.environ["DGLBACKEND"] = "pytorch"
-#os.system(f'pip install -r ./PROTEIN_GENERATOR/requirements.txt')
-print('Modules installed')
-
-os.system('pip install --force gradio==3.36.1')
-os.system('pip install gradio_client==0.2.7')
-
-os.environ["DGLBACKEND"] = "pytorch"
-
-if not os.path.exists('./SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'):
-    print('Downloading model weights 1')
-    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt')
-    print('Successfully Downloaded')
-
-if not os.path.exists('./SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'):
-    print('Downloading model weights 2')
-    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt')
-    print('Successfully Downloaded')
-
-import numpy as np
-import gradio as gr
-import py3Dmol
-from io import StringIO
-import json
-import secrets
-import copy
-import matplotlib.pyplot as plt
-from utils.sampler import HuggingFace_sampler
-from utils.parsers_inference import parse_pdb
-from model.util import writepdb
-from utils.inpainting_util import *
-
-
-plt.rcParams.update({'font.size': 13})
-
-with open('./tmp/args.json','r') as f:
-    args = json.load(f)
-
-# manually set checkpoint to load
-args['checkpoint'] = None
-args['dump_trb'] = False
-args['dump_args'] = True
-args['save_best_plddt'] = True
-args['T'] = 25
-args['strand_bias'] = 0.0
-args['loop_bias'] = 0.0
-args['helix_bias'] = 0.0
-
-
-
-def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bias, 
-                    secondary_structure, aa_bias, aa_bias_potential, 
-                    #target_charge, target_ph, charge_potential, 
-                    num_steps, noise, hydrophobic_target_score, hydrophobic_potential,
-                    contigs, pssm, seq_mask, str_mask, rewrite_pdb):
-    
-    dssp_checkpoint = './SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'
-    og_checkpoint = './SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'
-
-    model_args = copy.deepcopy(args)
-
-    # make sampler
-    S = HuggingFace_sampler(args=model_args)
-
-    # get random prefix 
-    S.out_prefix = './tmp/'+secrets.token_hex(nbytes=10).upper()
-
-    # set args
-    S.args['checkpoint'] = None
-    S.args['dump_trb'] = False
-    S.args['dump_args'] = True
-    S.args['save_best_plddt'] = True
-    S.args['T'] = 20
-    S.args['strand_bias'] = 0.0
-    S.args['loop_bias'] = 0.0
-    S.args['helix_bias'] = 0.0
-    S.args['potentials'] = None
-    S.args['potential_scale'] = None
-    S.args['aa_composition'] = None
-
-
-    # get sequence if entered and make sure all chars are valid
-    alt_aa_dict = {'B':['D','N'],'J':['I','L'],'U':['C'],'Z':['E','Q'],'O':['K']}
-    if sequence not in ['',None]:
-        L = len(sequence)
-        aa_seq = []
-        for aa in sequence.upper():
-            if aa in alt_aa_dict.keys():
-                aa_seq.append(np.random.choice(alt_aa_dict[aa]))
-            else:
-                aa_seq.append(aa)
-
-        S.args['sequence'] = aa_seq
-    elif contigs not in ['',None]:
-        S.args['contigs'] = [contigs]
-    else:
-        S.args['contigs'] = [f'{seq_len}']
-        L = int(seq_len)
-    
-    print('DEBUG: ',rewrite_pdb)
-    if rewrite_pdb not in ['',None]:
-        S.args['pdb'] = rewrite_pdb.name
-
-    if seq_mask not in ['',None]:
-        S.args['inpaint_seq'] = [seq_mask]
-    if str_mask not in ['',None]:
-        S.args['inpaint_str'] = [str_mask]
-
-    if secondary_structure in ['',None]:
-        secondary_structure = None
-    else:
-        secondary_structure = ''.join(['E' if x == 'S' else x for x in secondary_structure])
-        if L < len(secondary_structure):
-            secondary_structure = secondary_structure[:len(sequence)]
-        elif L == len(secondary_structure):
-            pass
-        else:
-            dseq = L - len(secondary_structure)
-            secondary_structure += secondary_structure[-1]*dseq
-    
-
-    # potentials
-    potential_list = []
-    potential_bias_list = []
-
-    if aa_bias not in ['',None]:
-        potential_list.append('aa_bias')
-        S.args['aa_composition'] = aa_bias
-        if aa_bias_potential in ['',None]:
-            aa_bias_potential = 3
-        potential_bias_list.append(str(aa_bias_potential))
-    '''
-    if target_charge not in ['',None]:
-        potential_list.append('charge')
-        if charge_potential in ['',None]:
-            charge_potential = 1
-        potential_bias_list.append(str(charge_potential))
-        S.args['target_charge'] = float(target_charge)
-        if target_ph in ['',None]:
-            target_ph = 7.4
-        S.args['target_pH'] = float(target_ph)
-    '''
-    
-    if hydrophobic_target_score not in ['',None]:
-        potential_list.append('hydrophobic')
-        S.args['hydrophobic_score'] = float(hydrophobic_target_score)
-        if hydrophobic_potential in ['',None]:
-            hydrophobic_potential = 3
-        potential_bias_list.append(str(hydrophobic_potential))
-    
-    if pssm not in ['',None]:
-        potential_list.append('PSSM')
-        potential_bias_list.append('5')
-        S.args['PSSM'] = pssm.name
-        
-
-    if len(potential_list) > 0:
-        S.args['potentials'] = ','.join(potential_list)
-        S.args['potential_scale'] = ','.join(potential_bias_list)
-
-
-    # normalise secondary_structure bias from range 0-0.3
-    S.args['secondary_structure'] = secondary_structure
-    S.args['helix_bias'] = helix_bias
-    S.args['strand_bias'] = strand_bias
-    S.args['loop_bias'] = loop_bias
-    
-    # set T
-    if num_steps in ['',None]:
-        S.args['T'] = 20
-    else:
-        S.args['T'] = int(num_steps)
-
-    # noise
-    if 'normal' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [0]
-        S.args['sample_distribution_gmm_variances'] = [1]
-    elif 'gmm2' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [-1,1]
-        S.args['sample_distribution_gmm_variances'] = [1,1]
-    elif 'gmm3' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [-1,0,1]
-        S.args['sample_distribution_gmm_variances'] = [1,1,1]
-
-
-
-    if secondary_structure not in ['',None] or helix_bias+strand_bias+loop_bias > 0:
-        S.args['checkpoint'] = dssp_checkpoint
-        S.args['d_t1d'] = 29
-        print('using dssp checkpoint')
-    else:
-        S.args['checkpoint'] = og_checkpoint
-        S.args['d_t1d'] = 24
-        print('using og checkpoint')
-    
-
-    for k,v in S.args.items():
-        print(f"{k} --> {v}")
-    
-    # init S
-    S.model_init()
-    S.diffuser_init()
-    S.setup()
-
-    # sampling loop
-    plddt_data = []
-    for j in range(S.max_t):
-        output_seq, output_pdb, plddt = S.take_step_get_outputs(j)
-        plddt_data.append(plddt)
-        yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
-    
-    output_seq, output_pdb, plddt = S.get_outputs()
-
-    yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
-
-def get_plddt_plot(plddt_data, max_t):
-    x = [i+1 for i in range(len(plddt_data))]
-    fig, ax = plt.subplots(figsize=(15,6))
-    ax.plot(x,plddt_data,color='#661dbf', linewidth=3,marker='o')
-    ax.set_xticks([i+1 for i in range(max_t)])
-    ax.set_yticks([(i+1)/10 for i in range(10)])
-    ax.set_ylim([0,1])
-    ax.set_ylabel('model confidence (plddt)')
-    ax.set_xlabel('diffusion steps (t)')
-    return fig
-
-def display_pdb(path_to_pdb):
-    '''
-        #function to display pdb in py3dmol
-    '''
-    pdb = open(path_to_pdb, "r").read()
-    
-    view = py3Dmol.view(width=500, height=500)
-    view.addModel(pdb, "pdb")
-    view.setStyle({'model': -1}, {"cartoon": {'colorscheme':{'prop':'b','gradient':'roygb','min':0,'max':1}}})#'linear', 'min': 0, 'max': 1, 'colors': ["#ff9ef0","#a903fc",]}}}) 
-    view.zoomTo()
-    output = view._make_html().replace("'", '"')
-    print(view._make_html())
-    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""  # do not use ' in this input
-    
-    return f"""<iframe height="500px" width="100%"  name="result" allow="midi; geolocation; microphone; camera;
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-                            allow-scripts allow-same-origin allow-popups
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-
-'''
-    return f"""<iframe  style="width: 100%; height:700px" name="result" allow="midi; geolocation; microphone; camera; 
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
-                            allow-scripts allow-same-origin allow-popups 
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-'''
-
-
-
-# MOTIF SCAFFOLDING
-def get_motif_preview(pdb_id, contigs):
-    '''
-        #function to display selected motif in py3dmol
-    '''
-    input_pdb = fetch_pdb(pdb_id=pdb_id.lower())
-
-    # rewrite pdb
-    parse = parse_pdb(input_pdb)
-    #output_name = './rewrite_'+input_pdb.split('/')[-1]
-    #writepdb(output_name, torch.tensor(parse_og['xyz']),torch.tensor(parse_og['seq']))
-    #parse = parse_pdb(output_name)
-    output_name = input_pdb
-
-    pdb = open(output_name, "r").read()
-    view = py3Dmol.view(width=500, height=500)
-    view.addModel(pdb, "pdb")
-
-    if contigs in ['',0]:
-        contigs = ['0']
-    else:
-        contigs = [contigs]
-
-    print('DEBUG: ',contigs)
-    
-    pdb_map = get_mappings(ContigMap(parse,contigs))
-    print('DEBUG: ',pdb_map)
-    print('DEBUG: ',pdb_map['con_ref_idx0'])
-    roi = [x[1]-1 for x in pdb_map['con_ref_pdb_idx']]
-
-    colormap = {0:'#D3D3D3', 1:'#F74CFF'}
-    colors = {i+1: colormap[1] if i in roi else colormap[0] for i in range(parse['xyz'].shape[0])}
-    view.setStyle({"cartoon": {"colorscheme": {"prop": "resi", "map": colors}}})
-    view.zoomTo()
-    output = view._make_html().replace("'", '"')
-    print(view._make_html())
-    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""  # do not use ' in this input
-    
-    return f"""<iframe height="500px" width="100%"  name="result" allow="midi; geolocation; microphone; camera;
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-                            allow-scripts allow-same-origin allow-popups
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>""", output_name
-
-def fetch_pdb(pdb_id=None):
-    if pdb_id is None or pdb_id == "":
-        return None
-    else:
-        os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_id}.pdb")
-        return f"{pdb_id}.pdb"
-
-# MSA AND PSSM GUIDANCE
-def save_pssm(file_upload):
-    filename = file_upload.name
-    orig_name = file_upload.orig_name
-    if filename.split('.')[-1] in ['fasta', 'a3m']:
-        return msa_to_pssm(file_upload)
-    return filename
-
-def msa_to_pssm(msa_file):
-    # Define the lookup table for converting amino acids to indices
-    aa_to_index = {'A': 0, 'R': 1, 'N': 2, 'D': 3, 'C': 4, 'Q': 5, 'E': 6, 'G': 7, 'H': 8, 'I': 9, 'L': 10,
-                'K': 11, 'M': 12, 'F': 13, 'P': 14, 'S': 15, 'T': 16, 'W': 17, 'Y': 18, 'V': 19, 'X': 20, '-': 21}
-    # Open the FASTA file and read the sequences
-    records = list(SeqIO.parse(msa_file.name, "fasta"))
-
-    assert len(records) >= 1, "MSA must contain more than one protein sequecne."
-
-    first_seq = str(records[0].seq)
-    aligned_seqs = [first_seq]
-    # print(aligned_seqs)
-    # Perform sequence alignment using the Needleman-Wunsch algorithm
-    aligner = Align.PairwiseAligner()
-    aligner.open_gap_score = -0.7
-    aligner.extend_gap_score = -0.3
-    for record in records[1:]:
-        alignment = aligner.align(first_seq, str(record.seq))[0]
-        alignment = alignment.format().split("\n")
-        al1 = alignment[0]
-        al2 = alignment[2]
-        al1_fin = ""
-        al2_fin = ""
-        percent_gap = al2.count('-')/ len(al2)
-        if percent_gap > 0.4:
-            continue
-        for i in range(len(al1)):
-            if al1[i] != '-':
-                al1_fin += al1[i]
-                al2_fin += al2[i]
-        aligned_seqs.append(str(al2_fin))
-    # Get the length of the aligned sequences
-    aligned_seq_length = len(first_seq)
-    # Initialize the position scoring matrix
-    matrix = np.zeros((22, aligned_seq_length))
-    # Iterate through the aligned sequences and count the amino acids at each position
-    for seq in aligned_seqs:
-        #print(seq)
-        for i in range(aligned_seq_length):
-            if i == len(seq):
-                break
-            amino_acid = seq[i]
-            if amino_acid.upper() not in aa_to_index.keys():
-                continue
-            else:
-                aa_index = aa_to_index[amino_acid.upper()]
-            matrix[aa_index, i] += 1
-    # Normalize the counts to get the frequency of each amino acid at each position
-    matrix /= len(aligned_seqs)
-    print(len(aligned_seqs))
-    matrix[20:,]=0
-
-    outdir = ".".join(msa_file.name.split('.')[:-1]) + ".csv"
-    np.savetxt(outdir, matrix[:21,:].T, delimiter=",")
-    return outdir
-
-def get_pssm(fasta_msa, input_pssm):
-
-    if input_pssm not in ['',None]:
-        outdir = input_pssm.name
-    else:
-        outdir = save_pssm(fasta_msa)
-
-    pssm = np.loadtxt(outdir, delimiter=",", dtype=float)
-    fig, ax = plt.subplots(figsize=(15,6))
-    plt.imshow(torch.permute(torch.tensor(pssm),(1,0)))
-
-    return fig, outdir
-
-
-#toggle options
-def toggle_seq_input(choice):
-    if choice == "protein length":
-        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
-    elif choice == "custom sequence":
-        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
-
-def toggle_secondary_structure(choice):
-    if choice == "sliders":
-        return gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=False, value=None)
-    elif choice == "explicit":
-        return gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=True, value=None)
-
-
-# Define the Gradio interface
-with gr.Blocks(theme='ParityError/Interstellar') as demo:
-    
-    gr.Markdown(f"""# Protein Generation via Diffusion in Sequence Space""")
-
-    with gr.Row():
-        with gr.Column(min_width=500):
-            gr.Markdown(f"""
-                    ## How does it work?\n
-                    --- [PREPRINT](https://biorxiv.org/content/10.1101/2023.05.08.539766v1) ---
-                    Protein sequence and structure co-generation is a long outstanding problem in the field of protein design. By implementing [ddpm](https://arxiv.org/abs/2006.11239) style diffusion over protein seqeuence space we generate protein sequence and structure pairs. Starting with [RoseTTAFold](https://www.science.org/doi/10.1126/science.abj8754), a protein structure prediction network, we finetuned it to predict sequence and structure given a partially noised sequence. By applying losses to both the predicted sequence and structure the model is forced to generate meaningful pairs. Diffusing in sequence space makes it easy to implement potentials to guide the diffusive process toward particular amino acid composition, net charge, and more! Furthermore, you can sample proteins from a family of sequences or even train a small sequence to function classifier to guide generation toward desired sequences.
-                    ![fig1](http://files.ipd.uw.edu/pub/sequence_diffusion/figs/diffusion_landscape.png)
-                    
-                    ## How to use it?\n
-                    A user can either design a custom input sequence to diffuse from or specify a length below. To scaffold a sequence use the following format where X represent residues to diffuse: XXXXXXXXSCIENCESCIENCEXXXXXXXXXXXXXXXXXXX. You can even design a protein with your name XXXXXXXXXXXXNAMEHEREXXXXXXXXXXXXX!
-                    
-                    ### Acknowledgements\n
-                    Thank you to Simon Dürr and the Hugging Face team for setting us up with a community GPU grant!
-                    """)
-            
-        gr.Markdown("""
-        ## Model in Action
-        ![gif1](http://files.ipd.uw.edu/pub/sequence_diffusion/figs/seqdiff_anim_720p.gif)
-        """)
-
-    with gr.Row().style(equal_height=False):
-        with gr.Column():
-            with gr.Tabs():
-                with gr.TabItem("Inputs"):
-                    gr.Markdown("""## INPUTS""")
-                    gr.Markdown("""#### Start Sequence
-                                Specify the protein length for complete unconditional generation, or scaffold a motif (or your name) using the custom sequence input""")
-                    seq_opt = gr.Radio(["protein length","custom sequence"], label="How would you like to specify the starting sequence?", value='protein length')
-
-                    sequence = gr.Textbox(label="custom sequence", lines=1, placeholder='AMINO ACIDS: A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y\n  MASK TOKEN: X', visible=False)
-                    seq_len = gr.Slider(minimum=5.0, maximum=250.0, label="protein length", value=100, visible=True)
-                    
-                    seq_opt.change(fn=toggle_seq_input,
-                                            inputs=[seq_opt],
-                                            outputs=[seq_len, sequence],
-                                            queue=False)
-
-                    gr.Markdown("""### Optional Parameters""")
-                    with gr.Accordion(label='Secondary Structure',open=True):
-                        gr.Markdown("""Try changing the sliders or inputing explicit secondary structure conditioning for each residue""")
-                        sec_str_opt = gr.Radio(["sliders","explicit"], label="How would you like to specify secondary structure?", value='sliders')
-
-                        secondary_structure = gr.Textbox(label="secondary structure", lines=1, placeholder='HELIX = H  STRAND = S  LOOP = L  MASK = X(must be the same length as input sequence)', visible=False)
-                        
-                        with gr.Column():
-                            helix_bias = gr.Slider(minimum=0.0, maximum=0.05, label="helix bias", visible=True)
-                            strand_bias = gr.Slider(minimum=0.0, maximum=0.05, label="strand bias", visible=True)
-                            loop_bias = gr.Slider(minimum=0.0, maximum=0.20, label="loop bias", visible=True)
-                    
-                        sec_str_opt.change(fn=toggle_secondary_structure,
-                                                inputs=[sec_str_opt],
-                                                outputs=[helix_bias,strand_bias,loop_bias,secondary_structure],
-                                                queue=False)
-                        
-                    with gr.Accordion(label='Amino Acid Compositional Bias',open=False):
-                        gr.Markdown("""Bias sequence composition for particular amino acids by specifying the one letter code followed by the fraction to bias. This can be input as a list for example: W0.2,E0.1""")
-                        with gr.Row():
-                            aa_bias = gr.Textbox(label="aa bias", lines=1, placeholder='specify one letter AA and fraction to bias, for example W0.1 or M0.1,K0.1' )
-                            aa_bias_potential = gr.Textbox(label="aa bias scale", lines=1, placeholder='AA Bias potential scale (recomended range 1.0-5.0)')
-                    
-                    '''
-                    with gr.Accordion(label='Charge Bias',open=False):
-                        gr.Markdown("""Bias for a specified net charge at a particular pH using the boxes below""")
-                        with gr.Row():
-                            target_charge = gr.Textbox(label="net charge", lines=1, placeholder='net charge to target')
-                            target_ph = gr.Textbox(label="pH", lines=1, placeholder='pH at which net charge is desired')
-                            charge_potential = gr.Textbox(label="charge potential scale", lines=1, placeholder='charge potential scale (recomended range 1.0-5.0)')
-                    '''
-
-                    with gr.Accordion(label='Hydrophobic Bias',open=False):
-                        gr.Markdown("""Bias for or against hydrophobic composition, to get more soluble proteins, bias away with a negative target score (ex. -5)""")
-                        with gr.Row():
-                            hydrophobic_target_score = gr.Textbox(label="hydrophobic score", lines=1, placeholder='hydrophobic score to target (negative score is good for solublility)')
-                            hydrophobic_potential = gr.Textbox(label="hydrophobic potential scale", lines=1, placeholder='hydrophobic potential scale (recomended range 1.0-2.0)')
-                    
-                    with gr.Accordion(label='Diffusion Params',open=False):
-                        gr.Markdown("""Increasing T to more steps can be helpful for harder design challenges, sampling from different distributions can change the sequence and structural composition""")
-                        with gr.Row():
-                            num_steps = gr.Textbox(label="T", lines=1, placeholder='number of diffusion steps (25 or less will speed things up)')
-                            noise = gr.Dropdown(['normal','gmm2 [-1,1]','gmm3 [-1,0,1]'], label='noise type', value='normal') 
-                
-                with gr.TabItem("Motif Selection"):
-
-                    gr.Markdown("""### Motif Selection Preview""")
-                    gr.Markdown('Contigs explained: to grab residues (seq and str) on a pdb chain you will provide the chain letter followed by a range of residues as indexed in the pdb file for example (A3-10) is the syntax to select residues 3-10 on chain A (the chain always needs to be specified). To add diffused residues to either side of this motif you can specify a range or discrete value without a chain letter infront. To add 15 residues before the motif and 20-30 residues (randomly sampled) after use the following syntax: 15,A3-10,20-30 commas are used to separate regions selected from the pdb and designed (diffused) resiudes which will be added. ')
-                    pdb_id_code = gr.Textbox(label="PDB ID", lines=1, placeholder='INPUT PDB ID TO FETCH (ex. 1DPX)', visible=True)
-                    contigs = gr.Textbox(label="contigs", lines=1, placeholder='specify contigs to grab particular residues from pdb ()', visible=True)
-                    gr.Markdown('Using the same contig syntax, seq or str of input motif residues can be masked, allowing the model to hold strucutre fixed and design sequence or vice-versa')
-                    with gr.Row():
-                        seq_mask = gr.Textbox(label='seq mask',lines=1,placeholder='input residues to mask sequence')
-                        str_mask = gr.Textbox(label='str mask',lines=1,placeholder='input residues to mask structure')
-                    preview_viewer = gr.HTML()
-                    rewrite_pdb = gr.File(label='PDB file')
-                    preview_btn = gr.Button("Preview Motif")
-
-                with gr.TabItem("MSA to PSSM"):
-                    gr.Markdown("""### MSA to PSSM Generation""")
-                    gr.Markdown('input either an MSA or PSSM to guide the model toward generating samples within your family of interest')
-                    with gr.Row():
-                        fasta_msa = gr.File(label='MSA')
-                        input_pssm = gr.File(label='PSSM (.csv)')
-                    pssm = gr.File(label='Generated PSSM')
-                    pssm_view = gr.Plot(label='PSSM Viewer')
-                    pssm_gen_btn = gr.Button("Generate PSSM")
-
-
-            btn = gr.Button("GENERATE")
-
-        #with gr.Row():
-        with gr.Column():
-            gr.Markdown("""## OUTPUTS""")
-            gr.Markdown("""#### Confidence score for generated structure at each timestep""")
-            plddt_plot = gr.Plot(label='plddt at step t')
-            gr.Markdown("""#### Output protein sequnece""")
-            output_seq = gr.Textbox(label="sequence")
-            gr.Markdown("""#### Download PDB file""")
-            output_pdb = gr.File(label="PDB file")
-            gr.Markdown("""#### Structure viewer""")
-            output_viewer = gr.HTML()
-
-    gr.Markdown("""### Don't know where to get started? Click on an example below to try it out!""")
-    gr.Examples(
-        [["","125",0.0,0.0,0.2,"","","","20","normal",'','','',None,'','',None],
-         ["","100",0.0,0.0,0.0,"","W0.2","2","20","normal",'','','',None,'','',None],
-         ["","100",0.0,0.0,0.0,
-        "XXHHHHHHHHHXXXXXXXHHHHHHHHHXXXXXXXHHHHHHHHXXXXSSSSSSSSSSSXXXXXXXXSSSSSSSSSSSSXXXXXXXSSSSSSSSSXXXXXXX",
-                "","","25","normal",'','','',None,'','',None],
-         ["XXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXXXXXPEPSEQXXXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXX",
-          "",0.0,0.0,0.0,"","","","25","normal",'','','',None,'','',None],
-         ["","",0.0,0.0,0.0,"","","","25","normal",'','',
-          '9,D10-11,8,D20-20,4,D25-35,65,D101-101,2,D104-105,8,D114-116,15,D132-138,6,D145-145,2,D148-148,12,D161-161,3',
-          './tmp/PSSM_lysozyme.csv',
-          'D25-25,D27-31,D33-35,D132-137',
-          'D26-26','./tmp/150l.pdb']
-         
-         ],
-        inputs=[sequence, 
-                seq_len, 
-                helix_bias, 
-                strand_bias, 
-                loop_bias, 
-                secondary_structure, 
-                aa_bias, 
-                aa_bias_potential,
-                #target_charge, 
-                #target_ph, 
-                #charge_potential, 
-                num_steps, 
-                noise, 
-                hydrophobic_target_score, 
-                hydrophobic_potential,
-                contigs,
-                pssm,
-                seq_mask,
-                str_mask,
-                rewrite_pdb],
-        outputs=[output_seq,
-                 output_pdb,
-                 output_viewer,
-                 plddt_plot],
-        fn=protein_diffusion_model,
-        ) 
-    
-    preview_btn.click(get_motif_preview,[pdb_id_code, contigs],[preview_viewer, rewrite_pdb])
-
-    pssm_gen_btn.click(get_pssm,[fasta_msa,input_pssm],[pssm_view, pssm])
-
-    btn.click(protein_diffusion_model, 
-                [sequence, 
-                 seq_len, 
-                 helix_bias, 
-                 strand_bias, 
-                 loop_bias, 
-                 secondary_structure, 
-                 aa_bias, 
-                 aa_bias_potential, 
-                 #target_charge, 
-                 #target_ph, 
-                 #charge_potential, 
-                 num_steps, 
-                 noise, 
-                 hydrophobic_target_score, 
-                 hydrophobic_potential,
-                 contigs,
-                 pssm,
-                 seq_mask, 
-                 str_mask,
-                 rewrite_pdb], 
-                [output_seq,
-                 output_pdb,
-                 output_viewer,
-                 plddt_plot])
-
-demo.queue()
-demo.launch(debug=True)
-
-
-
diff --git a/spaces/merve/anonymization/public/third_party/topojson-client.js b/spaces/merve/anonymization/public/third_party/topojson-client.js
deleted file mode 100644
index 728070f185d11aa72b3f78ab88037275614fe89b..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/public/third_party/topojson-client.js
+++ /dev/null
@@ -1,2 +0,0 @@
-// https://github.com/topojson/topojson-client v3.0.1 Copyright 2019 Mike Bostock
-!function(e,r){"object"==typeof exports&&"undefined"!=typeof module?r(exports):"function"==typeof define&&define.amd?define(["exports"],r):r((e=e||self).topojson=e.topojson||{})}(this,function(e){"use strict";function r(e){return e}function t(e){if(null==e)return r;var t,n,o=e.scale[0],a=e.scale[1],i=e.translate[0],c=e.translate[1];return function(e,r){r||(t=n=0);var u=2,f=e.length,s=new Array(f);for(s[0]=(t+=e[0])*o+i,s[1]=(n+=e[1])*a+c;u<f;)s[u]=e[u],++u;return s}}function n(e){var r,n=t(e.transform),o=1/0,a=o,i=-o,c=-o;function u(e){(e=n(e))[0]<o&&(o=e[0]),e[0]>i&&(i=e[0]),e[1]<a&&(a=e[1]),e[1]>c&&(c=e[1])}function f(e){switch(e.type){case"GeometryCollection":e.geometries.forEach(f);break;case"Point":u(e.coordinates);break;case"MultiPoint":e.coordinates.forEach(u)}}for(r in e.arcs.forEach(function(e){for(var r,t=-1,u=e.length;++t<u;)(r=n(e[t],t))[0]<o&&(o=r[0]),r[0]>i&&(i=r[0]),r[1]<a&&(a=r[1]),r[1]>c&&(c=r[1])}),e.objects)f(e.objects[r]);return[o,a,i,c]}function o(e,r){var t=r.id,n=r.bbox,o=null==r.properties?{}:r.properties,i=a(e,r);return null==t&&null==n?{type:"Feature",properties:o,geometry:i}:null==n?{type:"Feature",id:t,properties:o,geometry:i}:{type:"Feature",id:t,bbox:n,properties:o,geometry:i}}function a(e,r){var n=t(e.transform),o=e.arcs;function a(e,r){r.length&&r.pop();for(var t=o[e<0?~e:e],a=0,i=t.length;a<i;++a)r.push(n(t[a],a));e<0&&function(e,r){for(var t,n=e.length,o=n-r;o<--n;)t=e[o],e[o++]=e[n],e[n]=t}(r,i)}function i(e){return n(e)}function c(e){for(var r=[],t=0,n=e.length;t<n;++t)a(e[t],r);return r.length<2&&r.push(r[0]),r}function u(e){for(var r=c(e);r.length<4;)r.push(r[0]);return r}function f(e){return e.map(u)}return function e(r){var t,n=r.type;switch(n){case"GeometryCollection":return{type:n,geometries:r.geometries.map(e)};case"Point":t=i(r.coordinates);break;case"MultiPoint":t=r.coordinates.map(i);break;case"LineString":t=c(r.arcs);break;case"MultiLineString":t=r.arcs.map(c);break;case"Polygon":t=f(r.arcs);break;case"MultiPolygon":t=r.arcs.map(f);break;default:return null}return{type:n,coordinates:t}}(r)}function i(e,r){var t={},n={},o={},a=[],i=-1;function c(e,r){for(var n in e){var o=e[n];delete r[o.start],delete o.start,delete o.end,o.forEach(function(e){t[e<0?~e:e]=1}),a.push(o)}}return r.forEach(function(t,n){var o,a=e.arcs[t<0?~t:t];a.length<3&&!a[1][0]&&!a[1][1]&&(o=r[++i],r[i]=t,r[n]=o)}),r.forEach(function(r){var t,a,i=function(r){var t,n=e.arcs[r<0?~r:r],o=n[0];e.transform?(t=[0,0],n.forEach(function(e){t[0]+=e[0],t[1]+=e[1]})):t=n[n.length-1];return r<0?[t,o]:[o,t]}(r),c=i[0],u=i[1];if(t=o[c])if(delete o[t.end],t.push(r),t.end=u,a=n[u]){delete n[a.start];var f=a===t?t:t.concat(a);n[f.start=t.start]=o[f.end=a.end]=f}else n[t.start]=o[t.end]=t;else if(t=n[u])if(delete n[t.start],t.unshift(r),t.start=c,a=o[c]){delete o[a.end];var s=a===t?t:a.concat(t);n[s.start=a.start]=o[s.end=t.end]=s}else n[t.start]=o[t.end]=t;else n[(t=[r]).start=c]=o[t.end=u]=t}),c(o,n),c(n,o),r.forEach(function(e){t[e<0?~e:e]||a.push([e])}),a}function c(e,r,t){var n,o,a;if(arguments.length>1)n=function(e,r,t){var n,o=[],a=[];function i(e){var r=e<0?~e:e;(a[r]||(a[r]=[])).push({i:e,g:n})}function c(e){e.forEach(i)}function u(e){e.forEach(c)}return function e(r){switch(n=r,r.type){case"GeometryCollection":r.geometries.forEach(e);break;case"LineString":c(r.arcs);break;case"MultiLineString":case"Polygon":u(r.arcs);break;case"MultiPolygon":!function(e){e.forEach(u)}(r.arcs)}}(r),a.forEach(null==t?function(e){o.push(e[0].i)}:function(e){t(e[0].g,e[e.length-1].g)&&o.push(e[0].i)}),o}(0,r,t);else for(o=0,n=new Array(a=e.arcs.length);o<a;++o)n[o]=o;return{type:"MultiLineString",arcs:i(e,n)}}function u(e,r){var t={},n=[],o=[];function c(e){e.forEach(function(r){r.forEach(function(r){(t[r=r<0?~r:r]||(t[r]=[])).push(e)})}),n.push(e)}function u(r){return function(e){for(var r,t=-1,n=e.length,o=e[n-1],a=0;++t<n;)r=o,o=e[t],a+=r[0]*o[1]-r[1]*o[0];return Math.abs(a)}(a(e,{type:"Polygon",arcs:[r]}).coordinates[0])}return r.forEach(function e(r){switch(r.type){case"GeometryCollection":r.geometries.forEach(e);break;case"Polygon":c(r.arcs);break;case"MultiPolygon":r.arcs.forEach(c)}}),n.forEach(function(e){if(!e._){var r=[],n=[e];for(e._=1,o.push(r);e=n.pop();)r.push(e),e.forEach(function(e){e.forEach(function(e){t[e<0?~e:e].forEach(function(e){e._||(e._=1,n.push(e))})})})}}),n.forEach(function(e){delete e._}),{type:"MultiPolygon",arcs:o.map(function(r){var n,o=[];if(r.forEach(function(e){e.forEach(function(e){e.forEach(function(e){t[e<0?~e:e].length<2&&o.push(e)})})}),(n=(o=i(e,o)).length)>1)for(var 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a={LineString:n,MultiLineString:o,Polygon:o,MultiPolygon:function(e,r){e.forEach(function(e){o(e,r)})}};for(var i in e.forEach(function e(r,t){"GeometryCollection"===r.type?r.geometries.forEach(function(r){e(r,t)}):r.type in a&&a[r.type](r.arcs,t)}),r)for(var c=r[i],u=c.length,s=0;s<u;++s)for(var l=s+1;l<u;++l){var h,p=c[s],g=c[l];(h=t[p])[i=f(h,g)]!==g&&h.splice(i,0,g),(h=t[g])[i=f(h,p)]!==p&&h.splice(i,0,p)}return t},e.quantize=function(e,r){if(e.transform)throw new Error("already quantized");if(r&&r.scale)u=e.bbox;else{if(!((t=Math.floor(r))>=2))throw new Error("n must be ≥2");var t,o=(u=e.bbox||n(e))[0],a=u[1],i=u[2],c=u[3];r={scale:[i-o?(i-o)/(t-1):1,c-a?(c-a)/(t-1):1],translate:[o,a]}}var u,f,l=s(r),h=e.objects,p={};function g(e){return l(e)}function y(e){var r;switch(e.type){case"GeometryCollection":r={type:"GeometryCollection",geometries:e.geometries.map(y)};break;case"Point":r={type:"Point",coordinates:g(e.coordinates)};break;case"MultiPoint":r={type:"MultiPoint",coordinates:e.coordinates.map(g)};break;default:return e}return null!=e.id&&(r.id=e.id),null!=e.bbox&&(r.bbox=e.bbox),null!=e.properties&&(r.properties=e.properties),r}for(f in h)p[f]=y(h[f]);return{type:"Topology",bbox:u,transform:r,objects:p,arcs:e.arcs.map(function(e){var r,t=0,n=1,o=e.length,a=new Array(o);for(a[0]=l(e[0],0);++t<o;)((r=l(e[t],t))[0]||r[1])&&(a[n++]=r);return 1===n&&(a[n++]=[0,0]),a.length=n,a})}},e.transform=t,e.untransform=s,Object.defineProperty(e,"__esModule",{value:!0})});
\ No newline at end of file
diff --git a/spaces/merve/fill-in-the-blank/public/anonymization/make-sliders.js b/spaces/merve/fill-in-the-blank/public/anonymization/make-sliders.js
deleted file mode 100644
index 72f6dfd7c96d6c74cfb35db5854f06b668bf3d46..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/public/anonymization/make-sliders.js
+++ /dev/null
@@ -1,139 +0,0 @@
-window.makeSliders = function(){
-  var rv = {
-    population: 144,
-    headsProb: .5,
-  }
-
-  rv.updateHeadsProb = (headsProb) => {
-    rv.headsProb = headsProb
-    updateSliderPos()
-
-
-    estimates.updateEstimates()
-    estimates.render()
-  }
-
-  rv.updatePopulation = (population) => {
-    rv.population = population
-    updateSliderPos()
-
-
-    var scale = d3.clamp(0, 13 / Math.sqrt(population), 1)
-    sel.studentGroup.st({
-      transformOrigin: 'top',
-      transformOrigin: c.width/2 + 'px ' + 160 + 'px',
-      transform: `scale(${scale})`
-    })
-
-    estimates.updateEstimates()
-    estimates.render()
-
-    sel.student.classed('inactive',(d, i) => i >= population)
-  }
-
-  rv.updatePopulationSlider = (val) => {
-    rv.updatePopulation(val)
-  }
-
-  rv.updateNoiseSlider = (val) => {
-    rv.updateHeadsProb(val)
-  }
-
-  var updateSliderPos = (function(){
-    var width = d3.clamp(50, window.innerWidth/2 - 40, 145)
-    var height = 30
-    var color = '#007276'
-
-    var sliderVals = {
-      population: {
-        key: 'population',
-        textFn: d => rv.population + ' students' ,
-        r: [144, 756],
-        v: 144,
-        stepFn: d => rv.updatePopulation(Math.round(d.v/2)*2),
-      },
-      headsProb: {
-        key: 'headsProb',
-        textFn: d => d3.format('.1%')(rv.headsProb) + ' chance of heads',
-        r: [.2, .5],
-        v: .5,
-        stepFn: d => rv.updateHeadsProb(d.v),
-      }
-    }
-    var sliders = [sliderVals.headsProb, sliderVals.population, sliderVals.headsProb]
-    sliders.forEach(d => {
-      d.s = d3.scaleLinear().domain(d.r).range([0, width])
-    })
-
-    var sliderSel = d3.selectAll('.slide-container-population,.slide-container-heads-prob').html('')
-      .data(sliders)
-      .classed('slider', true)
-      .st({
-        display: 'inline-block',
-        width: width,
-        paddingRight: (d, i) => i == 1 ? 40 : 0,
-        marginTop: 20,
-      })
-
-    var textSel = sliderSel.append('div.slider-label-container')
-      .st({marginBottom: -5})
-
-    var svgSel = sliderSel.append('svg').at({width, height})
-      .on('click', function(d){
-        d.v = d.s.invert(d3.mouse(this)[0])
-        d.stepFn(d)
-      })
-      .st({
-        cursor: 'pointer'
-      })
-      .append('g').translate(height/2, 1)
-    svgSel.append('rect').at({width, height, y: -height/2, fill: 'rgba(0,0,0,0)'})
-
-    svgSel.append('path').at({
-      d: `M 0 -.5 H ${width}`, 
-      stroke: color,
-      strokeWidth: 1
-    })
-
-    var leftPathSel = svgSel.append('path').at({
-      d: `M 0 -.5 H ${width}`, 
-      stroke: color,
-      strokeWidth: 3
-    })
-
-
-    var drag = d3.drag()
-      .on('drag', function(d){
-        var x = d3.mouse(this)[0]
-        d.v = d3.clamp(d3.min(d.r), d.s.invert(x), d3.max(d.r))
-        d.stepFn(d)
-      })
-
-    var rectSel = svgSel.append('rect')
-      .at({
-        width: height/2 - 1,
-        height: height/2 - 1,
-        stroke: color, 
-        strokeWidth: 3,
-        fill: '#fff',
-      })
-      .translate([-height/4, -height/4])
-      .call(drag)
-
-    return isDrag => {
-      rectSel.at({x: d => Math.round(d.s(rv[d.key]))})
-      textSel.text(d => d.textFn(d))
-
-      leftPathSel.at({d: d => `M 0 -.5 H ${d.s(rv[d.key])}`})
-    }
-  })()
-  updateSliderPos()
-
-
-  return rv
-}
-
-
-
-
-if (window.init) window.init()
\ No newline at end of file
diff --git a/spaces/merve/fill-in-the-blank/source/measuring-diversity/sliders.js b/spaces/merve/fill-in-the-blank/source/measuring-diversity/sliders.js
deleted file mode 100644
index 13b03fa080fe5d1c2db81ef456242c0d856b0a0f..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/measuring-diversity/sliders.js
+++ /dev/null
@@ -1,206 +0,0 @@
-window.highlightColor = '#bf0bbf'
-
-window.makeSliders = function(metrics, sets, c, selectSet, drawRow, onRender){
-
-  var width = 180
-  var height = 30
-  var color = '#000'
-
-  var xScale = d3.scaleLinear().range([0, width]).domain([0, 1])
-    .clamp(1)
-
-  var sliderSel = c.svg.appendMany('g', metrics)
-    .translate((d, i) => [-c.margin.left -10 , 130*i + 30])
-    .on('click', function(d){
-      d.target = xScale.invert(d3.mouse(this)[0])
-      render()
-    })
-    .classed('slider', true)
-    .st({cursor: 'pointer'})
-
-  var textSel = sliderSel.append('text.slider-label-container')
-    .at({y: -20, fontWeight: 500, textAnchor: 'middle', x: 180/2})
-
-  sliderSel.append('rect')
-    .at({width, height, y: -height/2, fill: 'rgba(0,0,0,0)'})
-
-  sliderSel.append('path').at({
-    d: `M 0 -.5 H ${width}`, 
-    stroke: color,
-    strokeWidth: 1
-  })
-
-  var leftPathSel = sliderSel.append('path').at({
-    d: `M 0 -.5 H ${width}`, 
-    stroke: color,
-    strokeWidth: 3
-  })
-
-  var drag = d3.drag()
-    .on('drag', function(d){
-      var x = d3.mouse(this)[0]
-      d.target = xScale.invert(x)
-      render()
-    })
-
-  var circleSel = sliderSel.append('circle').call(drag)
-    .at({r: 7, stroke: '#000'})
-
-
-  var exSel = c.svg.append('g').translate([-c.margin.left -10, 400])
-    .st({fontSize: 13})
-
-  var curY = 0
-  exSel.append('g')
-    .append('text').text('The selected set is...')
-
-  var selectedSetG = exSel.append('g.selected').translate([-10, curY += 15])
-    .datum(sets[0])
-    .call(drawRow)
-
-  selectedSetG.select('.no-stroke').classed('selected', 1)
-
-  curY += 25
-  var exMetrics = exSel.appendMany('g', metrics)
-    .translate(() => curY +=22, 1)
-    .append('text').html(d => '10% small, 10% more than target')
-
-  curY += 10
-  var exMeanDiff = exSel.append('text').translate(() => curY +=22, 1)
-    .at({textAnchor: 'end', x: 190})
-  var exMaxDiff = exSel.append('text').translate(() => curY +=22, 1)
-    .at({textAnchor: 'end', x: 190})
-
-
-  // Make histogram data
-  sliderSel.each(function(metric){
-    var countKey = metric.key + '_count'
-    sets.forEach(set => {
-      var v = d3.sum(set, d => d[metric.field] == metric.key)
-      set[countKey] = v / set.length
-    })
-
-    var byCountKey = d3.nestBy(sets, d => d[countKey])
-
-    d3.range(.1, 1, .1).forEach(i => {
-      if (byCountKey.some(d => d.key*100 == Math.round(i*100))) return
-
-      var rv = []
-      rv.key = i 
-      byCountKey.push(rv)
-    })
-
-    byCountKey.forEach(d => {
-      d.metric = metric
-      d.key = +d.key
-    })
-
-    var countSel = d3.select(this).append('g.histogram').lower()
-      .translate(30, 1)
-      .appendMany('g', byCountKey)
-      .translate(d => xScale.clamp(0)(d.key - .05), 0)
-    xScale.clamp(1)
-
-    countSel.append('text')
-      // .text(d => '10')
-      .at({fontSize: 11, opacity: .7, y: -8, textAnchor: 'middle', x: 9.5})
-      .text(d => d.key*100)
-
-    countSel.append('path')
-      .at({d: 'M 9.5 -18 V -30', stroke: '#ccc'})
-
-    countSel
-      .appendMany('rect.histogram-set', d => d)
-      .at({width: 16, height: 4, x: 1.5, y: (d, i) => i*6})
-      // .on('mouseover', selectSet)
-  })
-  var histogramSetSel = sliderSel.selectAll('rect.histogram-set')
-    .st({cursor: 'default'})
-
-  var axisSel = sliderSel.selectAll('.histogram text')
-
-
-  var pinkSel = sliderSel.append('g')
-    .at({r: 4, fill: highlightColor})
-    .st({pointerEvents: 'none', opacity:0})
-  pinkSel.append('path').at({stroke: highlightColor, d: 'M .5 0 V 15'})
-  pinkSel.append('text').at({y: 30, textAnchor: 'middle'})
-  pinkSel.append('text.score').at({y: 50, textAnchor: 'middle'})
-
-
-  function render(){
-    circleSel.at({cx: d => xScale(d.target)})
-    // circleSel.at({cx: d => xScale(d.target)})
-    textSel.text(d => (d.str + ' Target: ').replace('s ', ' ') + pctFmt(d.target))
-
-    axisSel
-      .classed('selected', false)
-      // .text(function(d){
-      //   var str = Math.round(100*Math.abs(d.key - d.metric.target))
-
-      //   if (d.some(e => e.selected)){
-      //     d3.select(this).classed('selected', 1)
-      //     // str = str + '%'
-      //   }
-
-      //   return str
-      // })
-
-    leftPathSel.at({d: d => `M 0 -.5 H ${xScale(d.target)}`})
-    metrics.forEach(d => {
-      d.scoreScale = d3.scaleLinear()
-        .domain([-.1, d.target, 1.1])
-        .range([0, 1, 0])
-    })
-    histogramSetSel.st({fill: d => d === sets.selected ? highlightColor: '#bbb'})
-
-    if (onRender) onRender()
-
-    var shapes = sets.selected
-
-    var metricVals = metrics.map(m => {
-      return d3.sum(shapes, (d, i) => shapes[i][m.field] == m.key)/shapes.length
-    })
-
-    pinkSel.translate((d, i) => xScale(metricVals[i]), 0)
-    pinkSel.select('text').text((d, i) => pctFmt(metricVals[i]))
-    pinkSel.select('.score').text((d, i) => 'Difference: ' + Math.round(shapes.score[i]*100))
-
-
-    selectedSetG.html('')
-      .datum(sets.selected)
-      .call(drawRow)
-
-    selectedSetG.select('.no-stroke').classed('selected', 1)
-
-    exMetrics
-      .html((d, i) => {
-        var target = d.target
-        var actual = sets.selected[d.key + '_count']
-        var diff = sets.selected.score[i]
-
-        var str = d.str.replace('ls', 'l').replace('ns', 'n').toLowerCase()
-
-        return `
-          ${pctFmt(actual)}
-          ${str}, 
-          ${pctFmt(diff)} 
-          ${actual < target ? 'less' : 'more'} than target
-        `
-      })
-      .at({textAnchor: 'end', x: 190})
-
-    exMeanDiff
-      .text('Mean Difference: ' + d3.format('.2%')(sets.selected['Utilitarian']/100))
-
-    exMaxDiff
-      .text('Max Difference: ' + measures[1].ppFn(sets.selected['score']).replace('%', '.00%'))
-
-  }
-
-  return {render}
-}
-
-
-// window.initColumns('#columns-height', metrics1, measures)
-// window.initColumns('#columns-height-disagree', metrics2, measures2)
diff --git a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/watch-files.js b/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/watch-files.js
deleted file mode 100644
index 25d1fcfe5b17fa1e63323e0389379264463572af..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/watch-files.js
+++ /dev/null
@@ -1,88 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-
-!(function(){
-  function watchFile(path){
-    var lastStr = ''
-
-    console.log(path)
-    function check(){
-      d3.text(path + '?' + Math.random(), (err, nextStr) => {
-        if (err){
-          console.log(err)
-          return check()
-        }
-
-        if (nextStr == lastStr) return
-        lastStr = nextStr
-
-        if (path.includes('.js')){
-          // console.log('js', new Date())
-          Function(nextStr.replace('\n', ';').replace('\n', ';'))()
-        }
-
-        if (path.includes('.css')){
-          // console.log('css', new Date())
-
-          Array.from(document.querySelectorAll('link'))
-            .filter(d => d.href.includes(path) || d.href.includes('__hs_placeholder'))
-            .filter((d, i) => i == 0)
-            .forEach(d => d.href = path + '?' + Math.random())
-
-          throw 'up'
-        }
-      })
-
-      if (python_settings.isDev) setTimeout(check, 100)
-    }
-    check()
-  }
-
-  ;[
-    '../spearman-compare/list.css', 
-    'style.css', 
-    '../two-sentences/init-scatter.js', 
-    '../two-sentences/init-util.js', 
-    '../two-sentences/init-pair.js', 
-    'init.js'
-  ].forEach(filename => {
-    var root = document.currentScript.src.replace('watch-files.js', '').split('?')[0]
-    var path = root + filename
-    console.log(filename)
-
-    if (python_settings.isDev){
-      watchFile(path)
-    } else {
-      
-      if (path.includes('.js')){
-        var node = document.createElement('script')
-        node.setAttribute('src', path)
-        document.body.appendChild(node)
-      }
-
-      if (path.includes('.css')){
-        Array.from(document.querySelectorAll('link'))
-          .filter(d => d.href.includes(path) || d.href.includes('__hs_placeholder'))
-          .filter((d, i) => i == 0)
-          .forEach(d => d.href = path + '?' + Math.random())
-      }
-    }
-  })
-})()
-
-
-
diff --git a/spaces/merve/uncertainty-calibration/source/fill-in-the-blank/data/cachekey2filename.js b/spaces/merve/uncertainty-calibration/source/fill-in-the-blank/data/cachekey2filename.js
deleted file mode 100644
index 85df2a5b1806c3853f4e12ab05b430af77c800f9..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/source/fill-in-the-blank/data/cachekey2filename.js
+++ /dev/null
@@ -1,19 +0,0 @@
-window.cacheKey2filename = {
-  "{\"tokens\":[101,2000,2022,2030,2025,2000,2022,29623,2008,2003,1996,3160,29628,102]}embed_group_top": "tokens-101-2000-2022-2030-2025-2000-2022-29623-2008-2003-1996-3160-29628-102-embed-group-top.json",
-  "{\"sentence\":\"In New York, they like to buy [MASK].\"}embed": "sentence-in-new-york-they-like-to-buy-mask-embed.json",
-  "{\"sentence\":\"Elsie was born in the year of [MASK].\"}embed": "sentence-elsie-was-born-in-the-year-of-mask-embed.json",
-  "{\"sentence\":\"Jim worked as a [MASK].\"}embed": "sentence-jim-worked-as-a-mask-embed.json",
-  "{\"sentence\":\"The new nurse was named [MASK].\"}embed": "sentence-the-new-nurse-was-named-mask-embed.json",
-  "{\"sentence\":\"The doctor performed CPR even though [MASK] knew it was too late.\"}embed_zari_cda": "sentence-the-doctor-performed-cpr-even-though-mask-knew-it-was-too-late-embed-zari-cda.json",
-  "{\"sentence\":\"In 1908, he was employed as a [MASK].\"}embed": "sentence-in-1908-he-was-employed-as-a-mask-embed.json",
-  "{\"sentence\":\"Jane worked as a [MASK].\"}embed": "sentence-jane-worked-as-a-mask-embed.json",
-  "{\"sentence\":\"In Texas, they like to buy [MASK].\"}embed": "sentence-in-texas-they-like-to-buy-mask-embed.json",
-  "{\"sentence\":\"Lauren was born in the year of [MASK].\"}embed": "sentence-lauren-was-born-in-the-year-of-mask-embed.json",
-  "{\"sentence\":\"The new doctor was named [MASK].\"}embed": "sentence-the-new-doctor-was-named-mask-embed.json",
-  "{\"sentence\":\"The nurse performed CPR even though [MASK] knew it was too late.\"}embed_zari_cda": "sentence-the-nurse-performed-cpr-even-though-mask-knew-it-was-too-late-embed-zari-cda.json",
-  "{\"sentence\":\"In 1908, she was employed as a [MASK].\"}embed": "sentence-in-1908-she-was-employed-as-a-mask-embed.json",
-  "{\"sentence\":\"In 2018, he was employed as a [MASK].\"}embed": "sentence-in-2018-he-was-employed-as-a-mask-embed.json",
-  "{\"sentence\":\"In 2018, she was employed as a [MASK].\"}embed": "sentence-in-2018-she-was-employed-as-a-mask-embed.json",
-  "{\"tokens\":[101,1999,2047,2259,29623,2027,2066,2000,4965,2477,29625,102]}embed_group_top": "tokens-101-1999-2047-2259-29623-2027-2066-2000-4965-2477-29625-102-embed-group-top.json",
-  "{\"tokens\":[101,1999,3146,29623,2027,2066,2000,4965,2477,29625,102]}embed_group_top": "tokens-101-1999-3146-29623-2027-2066-2000-4965-2477-29625-102-embed-group-top.json"
-}
\ No newline at end of file
diff --git a/spaces/miesnerjacob/Multi-task-NLP/README.md b/spaces/miesnerjacob/Multi-task-NLP/README.md
deleted file mode 100644
index 6093c5ae13af47fb1d72f5f6283e4ebc2baf9f10..0000000000000000000000000000000000000000
--- a/spaces/miesnerjacob/Multi-task-NLP/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-title: Multi Task NLP
-emoji: 🤖👽🔥💯
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-python_version: 3.9
-pinned: false
----
-
-# Multi-task NLP
-
-This application is a celebration of open-source and the power that programmers have been granted today by those who 
-give back to the community. This tool was constructed using Streamlit, Huggingface Transformers, 
-Transformers-Interpret, NLTK, Spacy, amongst other open-source Python libraries and models.
-
-You can access a live version of this app here: https://huggingface.co/spaces/miesnerjacob/Multi-task-NLP
diff --git a/spaces/mmDigital/therapy-bot/README.md b/spaces/mmDigital/therapy-bot/README.md
deleted file mode 100644
index 8f307d90d3d50c557fcdedaf037c78bb5e0d2655..0000000000000000000000000000000000000000
--- a/spaces/mmDigital/therapy-bot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Therapy Bot
-emoji: 🌖
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/utils/misc.py b/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/utils/misc.py
deleted file mode 100644
index a22d0a978c9cd89595c6e7c900885e1c148844b1..0000000000000000000000000000000000000000
--- a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/utils/misc.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-from typing import List, Optional
-
-import torch
-import torch.distributed as dist
-import torchvision
-from torch import Tensor
-
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(
-            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-        ).to(torch.int64)
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(
-            img, (0, padding[2], 0, padding[1], 0, padding[0])
-        )
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(
-            m, (0, padding[2], 0, padding[1]), "constant", 1
-        )
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-def get_gt_binary_masks(gt_semseg):
-    mask_ids = torch.unique(gt_semseg)
-    gt_masks = []
-    for id in mask_ids:
-        if id != 255:
-            gt_masks.append(gt_semseg == id)
-    gt_masks = torch.stack(gt_masks).float()
-    return gt_masks
diff --git a/spaces/mrdbourke/foodvision_big_video/model.py b/spaces/mrdbourke/foodvision_big_video/model.py
deleted file mode 100644
index 2060a8a6ae4f6692cc634c067a876cb1daea285b..0000000000000000000000000000000000000000
--- a/spaces/mrdbourke/foodvision_big_video/model.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-def create_effnetb2_model(num_classes:int=3, # default output classes = 3 (pizza, steak, sushi)
-                          seed:int=42):
-  # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
-  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-  transforms = weights.transforms()
-  model = torchvision.models.efficientnet_b2(weights=weights)
-
-  # 4. Freeze all layers in the base model
-  for param in model.parameters():
-    param.requires_grad = False
-
-  # 5. Change classifier head with random seed for reproducibility
-  torch.manual_seed(seed)
-  model.classifier = nn.Sequential(
-      nn.Dropout(p=0.3, inplace=True),
-      nn.Linear(in_features=1408, out_features=num_classes)
-  )
-
-  return model, transforms
diff --git a/spaces/mrm8488/llama-2-7b-chat-cpp/app.py b/spaces/mrm8488/llama-2-7b-chat-cpp/app.py
deleted file mode 100644
index 1860bb749ff9805ef3ac7886656a6f236f837695..0000000000000000000000000000000000000000
--- a/spaces/mrm8488/llama-2-7b-chat-cpp/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os
-import gradio as gr
-import copy
-import time
-import llama_cpp
-from llama_cpp import Llama
-from huggingface_hub import hf_hub_download  
-
-
-llm = Llama(
-    model_path=hf_hub_download(
-        repo_id=os.environ.get("REPO_ID", "TheBloke/Llama-2-7B-Chat-GGML"),
-        filename=os.environ.get("MODEL_FILE", "llama-2-7b-chat.ggmlv3.q5_0.bin"),
-    ),
-    n_ctx=2048,
-    n_gpu_layers=50, # change n_gpu_layers if you have more or less VRAM 
-) 
-
-history = []
-
-system_prompt = """
-You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
-"""
-
-system_prompt_es = """
-Eres un asistente útil, respetuoso y honest. Responde siemore en español de la manera más útil posible.
-"""
-
-
-def generate_text(message, history):
-    temp = ""
-    input_prompt = f"[INST] <<SYS>>\n{system_prompt_es}\n<</SYS>>\n\n "
-    for interaction in history:
-        input_prompt = input_prompt + str(interaction[0]) + " [/INST] " + str(interaction[1]) + " </s><s> [INST] "
-
-    input_prompt = input_prompt + str(message) + " [/INST] "
-
-    output = llm(
-        input_prompt,
-        temperature=0.15,
-        top_p=0.1,
-        top_k=40, 
-        repeat_penalty=1.1,
-        max_tokens=1024,
-        stop=[
-            "<|prompter|>",
-            "<|endoftext|>",
-            "<|endoftext|> \n",
-            "ASSISTANT:",
-            "USER:",
-            "SYSTEM:",
-        ],
-        stream=True,
-    )
-    for out in output:
-        stream = copy.deepcopy(out)
-        temp += stream["choices"][0]["text"]
-        yield temp
-
-    history = ["init", input_prompt]
-
-
-demo = gr.ChatInterface(
-    generate_text,
-    title="llama-2 🦙 7B chat cpp on GPU",
-    description="Running LLM with https://github.com/abetlen/llama-cpp-python",
-    examples=["Cúenta cosas interesantes sobre la Región de Murcia", "¿Qué es una Chirigota?", "Si tengo 8 manzanas, compro una y vendo 7, ¿cuántas me quedan?"],
-    cache_examples=True,
-    retry_btn=None,
-    undo_btn="Delete Previous",
-    clear_btn="Clear",
-)
-demo.queue(concurrency_count=1, max_size=5)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/mrneuralnet/P-DFD/dataset/abstract_dataset.py b/spaces/mrneuralnet/P-DFD/dataset/abstract_dataset.py
deleted file mode 100644
index 5b029f8c2b3d0a41c383abfd235ceb41e028e8fc..0000000000000000000000000000000000000000
--- a/spaces/mrneuralnet/P-DFD/dataset/abstract_dataset.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import cv2
-import torch
-import numpy as np
-from torchvision.datasets import VisionDataset
-import albumentations
-from albumentations import Compose
-from albumentations.pytorch.transforms import ToTensorV2
-
-
-class AbstractDataset(VisionDataset):
-    def __init__(self, cfg, seed=2022, transforms=None, transform=None, target_transform=None):
-        super(AbstractDataset, self).__init__(cfg['root'], transforms=transforms,
-                                              transform=transform, target_transform=target_transform)
-        # fix for re-production
-        np.random.seed(seed)
-
-        self.images = list()
-        self.targets = list()
-        self.split = cfg['split']
-        if self.transforms is None:
-            self.transforms = Compose(
-                [getattr(albumentations, _['name'])(**_['params']) for _ in cfg['transforms']] +
-                [ToTensorV2()]
-            )
-
-    def __len__(self):
-        return len(self.images)
-
-    def __getitem__(self, index):
-        path = self.images[index]
-        tgt = self.targets[index]
-        return path, tgt
-
-    def load_item(self, items):
-        images = list()
-        for item in items:
-            img = cv2.imread(item)
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            image = self.transforms(image=img)['image']
-            images.append(image)
-        return torch.stack(images, dim=0)
diff --git a/spaces/mrrandom123/mattmdjaga-segformer_b2_clothes/README.md b/spaces/mrrandom123/mattmdjaga-segformer_b2_clothes/README.md
deleted file mode 100644
index 99d0b60a615aca44b1d3dcc122277ec2bcf32887..0000000000000000000000000000000000000000
--- a/spaces/mrrandom123/mattmdjaga-segformer_b2_clothes/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mattmdjaga-segformer B2 Clothes
-emoji: 🔥
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mshkdm/VToonify/vtoonify/model/raft/alt_cuda_corr/correlation.cpp b/spaces/mshkdm/VToonify/vtoonify/model/raft/alt_cuda_corr/correlation.cpp
deleted file mode 100644
index b01584d19edb99e7feec5f2e4c51169a1ed208db..0000000000000000000000000000000000000000
--- a/spaces/mshkdm/VToonify/vtoonify/model/raft/alt_cuda_corr/correlation.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-#include <torch/extension.h>
-#include <vector>
-
-// CUDA forward declarations
-std::vector<torch::Tensor> corr_cuda_forward(
-    torch::Tensor fmap1,
-    torch::Tensor fmap2,
-    torch::Tensor coords,
-    int radius);
-
-std::vector<torch::Tensor> corr_cuda_backward(
-  torch::Tensor fmap1,
-  torch::Tensor fmap2,
-  torch::Tensor coords,
-  torch::Tensor corr_grad,
-  int radius);
-
-// C++ interface
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-std::vector<torch::Tensor> corr_forward(
-    torch::Tensor fmap1,
-    torch::Tensor fmap2,
-    torch::Tensor coords,
-    int radius) {
-  CHECK_INPUT(fmap1);
-  CHECK_INPUT(fmap2);
-  CHECK_INPUT(coords);
-
-  return corr_cuda_forward(fmap1, fmap2, coords, radius);
-}
-
-
-std::vector<torch::Tensor> corr_backward(
-    torch::Tensor fmap1,
-    torch::Tensor fmap2,
-    torch::Tensor coords,
-    torch::Tensor corr_grad,
-    int radius) {
-  CHECK_INPUT(fmap1);
-  CHECK_INPUT(fmap2);
-  CHECK_INPUT(coords);
-  CHECK_INPUT(corr_grad);
-
-  return corr_cuda_backward(fmap1, fmap2, coords, corr_grad, radius);
-}
-
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("forward", &corr_forward, "CORR forward");
-  m.def("backward", &corr_backward, "CORR backward");
-}
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/evaluation/__init__.py b/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/evaluation/__init__.py
deleted file mode 100644
index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/evaluation/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/transformer/transformer_encoder.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/transformer/transformer_encoder.py
deleted file mode 100644
index f007776a6f3b7e6731edc01d95aa24eed255d0e8..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/transformer/transformer_encoder.py
+++ /dev/null
@@ -1,341 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from typing import Dict, List, Optional
-
-import torch
-import torch.nn as nn
-from fairseq import utils
-from fairseq.distributed import fsdp_wrap
-from fairseq.models import FairseqEncoder
-from fairseq.modules import (
-    FairseqDropout,
-    LayerDropModuleList,
-    LayerNorm,
-    PositionalEmbedding,
-    SinusoidalPositionalEmbedding,
-)
-from fairseq.modules import transformer_layer
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
-from torch import Tensor
-from fairseq.models.transformer import (
-    TransformerConfig,
-)
-
-
-# rewrite name for backward compatibility in `make_generation_fast_`
-def module_name_fordropout(module_name: str) -> str:
-    if module_name == 'TransformerEncoderBase':
-        return 'TransformerEncoder'
-    else:
-        return module_name
-
-
-class TransformerEncoderBase(FairseqEncoder):
-    """
-    Transformer encoder consisting of *cfg.encoder.layers* layers. Each layer
-    is a :class:`TransformerEncoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): encoding dictionary
-        embed_tokens (torch.nn.Embedding): input embedding
-    """
-
-    def __init__(self, cfg, dictionary, embed_tokens):
-        self.cfg = cfg
-        super().__init__(dictionary)
-        self.register_buffer("version", torch.Tensor([3]))
-
-        self.dropout_module = FairseqDropout(
-            cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__)
-        )
-        self.encoder_layerdrop = cfg.encoder.layerdrop
-
-        embed_dim = embed_tokens.embedding_dim
-        self.padding_idx = embed_tokens.padding_idx
-        self.max_source_positions = cfg.max_source_positions
-
-        self.embed_tokens = embed_tokens
-
-        self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim)
-
-        self.embed_positions = (
-            PositionalEmbedding(
-                cfg.max_source_positions,
-                embed_dim,
-                self.padding_idx,
-                learned=cfg.encoder.learned_pos,
-            )
-            if not cfg.no_token_positional_embeddings
-            else None
-        )
-        if cfg.layernorm_embedding:
-            self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export)
-        else:
-            self.layernorm_embedding = None
-
-        if not cfg.adaptive_input and cfg.quant_noise.pq > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(embed_dim, embed_dim, bias=False),
-                cfg.quant_noise.pq,
-                cfg.quant_noise.pq_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        if self.encoder_layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.encoder_layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-        self.layers.extend(
-            [self.build_encoder_layer(cfg) for i in range(cfg.encoder.layers)]
-        )
-        self.num_layers = len(self.layers)
-
-        if cfg.encoder.normalize_before:
-            self.layer_norm = LayerNorm(embed_dim, export=cfg.export)
-        else:
-            self.layer_norm = None
-
-    def build_encoder_layer(self, cfg):
-        layer = transformer_layer.TransformerEncoderLayerBase(cfg)
-        checkpoint = cfg.checkpoint_activations
-        if checkpoint:
-            offload_to_cpu = cfg.offload_activations
-            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
-        # if we are checkpointing, enforce that FSDP always wraps the
-        # checkpointed layer, regardless of layer size
-        min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0
-        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
-        return layer
-
-    def forward_embedding(
-        self, src_tokens, token_embedding: Optional[torch.Tensor] = None
-    ):
-        # embed tokens and positions
-        if token_embedding is None:
-            token_embedding = self.embed_tokens(src_tokens)
-        x = embed = self.embed_scale * token_embedding
-        if self.embed_positions is not None:
-            x = embed + self.embed_positions(src_tokens)
-        if self.layernorm_embedding is not None:
-            x = self.layernorm_embedding(x)
-        x = self.dropout_module(x)
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-        return x, embed
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        return self.forward_scriptable(
-            src_tokens, src_lengths, return_all_hiddens, token_embeddings
-        )
-
-    # TorchScript doesn't support super() method so that the scriptable Subclass
-    # can't access the base class model in Torchscript.
-    # Current workaround is to add a helper function with different name and
-    # call the helper function from scriptable Subclass.
-    def forward_scriptable(
-        self,
-        src_tokens,
-        src_lengths: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        # compute padding mask
-        encoder_padding_mask = src_tokens.eq(self.padding_idx)
-        has_pads = src_tokens.device.type == "xla" or encoder_padding_mask.any()
-
-        x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings)
-
-        # account for padding while computing the representation
-        if has_pads:
-            x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        encoder_states = []
-
-        if return_all_hiddens:
-            encoder_states.append(x)
-
-        # encoder layers
-        for layer in self.layers:
-            x = layer(
-                x, encoder_padding_mask=encoder_padding_mask if has_pads else None
-            )
-            if return_all_hiddens:
-                assert encoder_states is not None
-                encoder_states.append(x)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
-        # `forward` so we use a dictionary instead.
-        # TorchScript does not support mixed values so the values are all lists.
-        # The empty list is equivalent to None.
-        src_lengths = src_tokens.ne(self.padding_idx).sum(dim=1, dtype=torch.int32).reshape(-1, 1).contiguous()
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [encoder_padding_mask],  # B x T
-            "encoder_embedding": [encoder_embedding],  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [src_lengths],
-        }
-
-    @torch.jit.export
-    def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
-        """
-        Reorder encoder output according to *new_order*.
-
-        Args:
-            encoder_out: output from the ``forward()`` method
-            new_order (LongTensor): desired order
-
-        Returns:
-            *encoder_out* rearranged according to *new_order*
-        """
-        if len(encoder_out["encoder_out"]) == 0:
-            new_encoder_out = []
-        else:
-            new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
-        if len(encoder_out["encoder_padding_mask"]) == 0:
-            new_encoder_padding_mask = []
-        else:
-            new_encoder_padding_mask = [
-                encoder_out["encoder_padding_mask"][0].index_select(0, new_order)
-            ]
-        if len(encoder_out["encoder_embedding"]) == 0:
-            new_encoder_embedding = []
-        else:
-            new_encoder_embedding = [
-                encoder_out["encoder_embedding"][0].index_select(0, new_order)
-            ]
-
-        if len(encoder_out["src_tokens"]) == 0:
-            src_tokens = []
-        else:
-            src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)]
-
-        if len(encoder_out["src_lengths"]) == 0:
-            src_lengths = []
-        else:
-            src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)]
-
-        encoder_states = encoder_out["encoder_states"]
-        if len(encoder_states) > 0:
-            for idx, state in enumerate(encoder_states):
-                encoder_states[idx] = state.index_select(1, new_order)
-
-        return {
-            "encoder_out": new_encoder_out,  # T x B x C
-            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
-            "encoder_embedding": new_encoder_embedding,  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": src_tokens,  # B x T
-            "src_lengths": src_lengths,  # B x 1
-        }
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        if self.embed_positions is None:
-            return self.max_source_positions
-        return min(self.max_source_positions, self.embed_positions.max_positions)
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
-            weights_key = "{}.embed_positions.weights".format(name)
-            if weights_key in state_dict:
-                print("deleting {0}".format(weights_key))
-                del state_dict[weights_key]
-            state_dict[
-                "{}.embed_positions._float_tensor".format(name)
-            ] = torch.FloatTensor(1)
-        for i in range(self.num_layers):
-            # update layer norms
-            self.layers[i].upgrade_state_dict_named(
-                state_dict, "{}.layers.{}".format(name, i)
-            )
-
-        version_key = "{}.version".format(name)
-        if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2:
-            # earlier checkpoints did not normalize after the stack of layers
-            self.layer_norm = None
-            self.normalize = False
-            state_dict[version_key] = torch.Tensor([1])
-        return state_dict
-
-
-class TransformerEncoder(TransformerEncoderBase):
-    def __init__(self, args, dictionary, embed_tokens):
-        self.args = args
-        super().__init__(
-            TransformerConfig.from_namespace(args),
-            dictionary,
-            embed_tokens,
-        )
-
-    def build_encoder_layer(self, args):
-        return super().build_encoder_layer(
-            TransformerConfig.from_namespace(args),
-        )
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/wav2vec/wav2vec2.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/wav2vec/wav2vec2.py
deleted file mode 100644
index 714fd3ab50443b8d15715b1cf5abd4eb517298c4..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/wav2vec/wav2vec2.py
+++ /dev/null
@@ -1,1016 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-from typing import List, Tuple
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.data.data_utils import compute_mask_indices
-from fairseq.dataclass import ChoiceEnum, FairseqDataclass
-from fairseq.models import BaseFairseqModel, register_model
-from fairseq.modules import (
-    Fp32GroupNorm,
-    Fp32LayerNorm,
-    GradMultiply,
-    GumbelVectorQuantizer,
-    LayerNorm,
-    MultiheadAttention,
-    SamePad,
-    TransposeLast,
-)
-from fairseq.modules.transformer_sentence_encoder import init_bert_params
-from fairseq.utils import buffered_arange, index_put, is_xla_tensor
-
-
-EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"])
-MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"])
-
-
-@dataclass
-class Wav2Vec2Config(FairseqDataclass):
-    extractor_mode: EXTRACTOR_MODE_CHOICES = field(
-        default="default",
-        metadata={
-            "help": "mode for feature extractor. default has a single group norm with d "
-            "groups in the first conv block, whereas layer_norm has layer norms in "
-            "every block (meant to use with normalize=True)"
-        },
-    )
-    encoder_layers: int = field(
-        default=12, metadata={"help": "num encoder layers in the transformer"}
-    )
-    encoder_embed_dim: int = field(
-        default=768, metadata={"help": "encoder embedding dimension"}
-    )
-    encoder_ffn_embed_dim: int = field(
-        default=3072, metadata={"help": "encoder embedding dimension for FFN"}
-    )
-    encoder_attention_heads: int = field(
-        default=12, metadata={"help": "num encoder attention heads"}
-    )
-    activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
-        default="gelu", metadata={"help": "activation function to use"}
-    )
-
-    # dropouts
-    dropout: float = field(
-        default=0.1, metadata={"help": "dropout probability for the transformer"}
-    )
-    attention_dropout: float = field(
-        default=0.1, metadata={"help": "dropout probability for attention weights"}
-    )
-    activation_dropout: float = field(
-        default=0.0, metadata={"help": "dropout probability after activation in FFN"}
-    )
-    encoder_layerdrop: float = field(
-        default=0.0, metadata={"help": "probability of dropping a tarnsformer layer"}
-    )
-    dropout_input: float = field(
-        default=0.0,
-        metadata={"help": "dropout to apply to the input (after feat extr)"},
-    )
-    dropout_features: float = field(
-        default=0.0,
-        metadata={"help": "dropout to apply to the features (after feat extr)"},
-    )
-
-    final_dim: int = field(
-        default=0,
-        metadata={
-            "help": "project final representations and targets to this many dimensions."
-            "set to encoder_embed_dim is <= 0"
-        },
-    )
-    layer_norm_first: bool = field(
-        default=False, metadata={"help": "apply layernorm first in the transformer"}
-    )
-    conv_feature_layers: str = field(
-        default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]",
-        metadata={
-            "help": "string describing convolutional feature extraction layers in form of a python list that contains "
-            "[(dim, kernel_size, stride), ...]"
-        },
-    )
-    conv_bias: bool = field(
-        default=False, metadata={"help": "include bias in conv encoder"}
-    )
-    logit_temp: float = field(
-        default=0.1, metadata={"help": "temperature to divide logits by"}
-    )
-    quantize_targets: bool = field(
-        default=False, metadata={"help": "use quantized targets"}
-    )
-    quantize_input: bool = field(
-        default=False, metadata={"help": "use quantized inputs"}
-    )
-    same_quantizer: bool = field(
-        default=False, metadata={"help": "use same quantizer for inputs and targets"}
-    )
-    target_glu: bool = field(
-        default=False, metadata={"help": "adds projection + glu to targets"}
-    )
-    feature_grad_mult: float = field(
-        default=1.0, metadata={"help": "multiply feature extractor var grads by this"}
-    )
-    quantizer_depth: int = field(
-        default=1,
-        metadata={"help": "number of quantizer layers"},
-    )
-    quantizer_factor: int = field(
-        default=3,
-        metadata={
-            "help": "dimensionality increase for inner quantizer layers (if depth > 1)"
-        },
-    )
-    latent_vars: int = field(
-        default=320,
-        metadata={"help": "number of latent variables V in each group of the codebook"},
-    )
-    latent_groups: int = field(
-        default=2,
-        metadata={"help": "number of groups G of latent variables in the codebook"},
-    )
-    latent_dim: int = field(
-        default=0,
-        metadata={
-            "help": "if > 0, uses this dimensionality for latent variables. "
-            "otherwise uses final_dim / latent_groups"
-        },
-    )
-
-    # masking
-    mask_length: int = field(default=10, metadata={"help": "mask length"})
-    mask_prob: float = field(
-        default=0.65, metadata={"help": "probability of replacing a token with mask"}
-    )
-    mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
-        default="static", metadata={"help": "how to choose mask length"}
-    )
-    mask_other: float = field(
-        default=0,
-        metadata={
-            "help": "secondary mask argument (used for more complex distributions), "
-            "see help in compute_mask_indices"
-        },
-    )
-    no_mask_overlap: bool = field(
-        default=False, metadata={"help": "whether to allow masks to overlap"}
-    )
-    mask_min_space: int = field(
-        default=1,
-        metadata={"help": "min space between spans (if no overlap is enabled)"},
-    )
-
-    # channel masking
-    mask_channel_length: int = field(
-        default=10, metadata={"help": "length of the mask for features (channels)"}
-    )
-    mask_channel_prob: float = field(
-        default=0.0, metadata={"help": "probability of replacing a feature with 0"}
-    )
-    mask_channel_before: bool = False
-    mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
-        default="static",
-        metadata={"help": "how to choose mask length for channel masking"},
-    )
-    mask_channel_other: float = field(
-        default=0,
-        metadata={
-            "help": "secondary mask argument (used for more complex distributions), "
-            "see help in compute_mask_indicesh"
-        },
-    )
-    no_mask_channel_overlap: bool = field(
-        default=False, metadata={"help": "whether to allow channel masks to overlap"}
-    )
-    mask_channel_min_space: int = field(
-        default=1,
-        metadata={"help": "min space between spans (if no overlap is enabled)"},
-    )
-
-    # negative selection
-    num_negatives: int = field(
-        default=100,
-        metadata={"help": "number of negative examples from the same sample"},
-    )
-    negatives_from_everywhere: bool = field(
-        default=False,
-        metadata={"help": "sample negatives from everywhere, not just masked states"},
-    )
-    cross_sample_negatives: int = field(
-        default=0, metadata={"help": "number of negative examples from the any sample"}
-    )
-    codebook_negatives: int = field(
-        default=0, metadata={"help": "number of negative examples codebook"}
-    )
-
-    # positional embeddings
-    conv_pos: int = field(
-        default=128,
-        metadata={"help": "number of filters for convolutional positional embeddings"},
-    )
-    conv_pos_groups: int = field(
-        default=16,
-        metadata={"help": "number of groups for convolutional positional embedding"},
-    )
-
-    latent_temp: Tuple[float, float, float] = field(
-        default=(2, 0.5, 0.999995),
-        metadata={
-            "help": "temperature for latent variable sampling. "
-            "can be tuple of 3 values (start, end, decay)"
-        },
-    )
-
-
-@register_model("wav2vec2", dataclass=Wav2Vec2Config)
-class Wav2Vec2Model(BaseFairseqModel):
-    def __init__(self, cfg: Wav2Vec2Config):
-        super().__init__()
-        self.cfg = cfg
-
-        feature_enc_layers = eval(cfg.conv_feature_layers)
-        self.embed = feature_enc_layers[-1][0]
-
-        self.feature_extractor = ConvFeatureExtractionModel(
-            conv_layers=feature_enc_layers,
-            dropout=0.0,
-            mode=cfg.extractor_mode,
-            conv_bias=cfg.conv_bias,
-        )
-
-        self.post_extract_proj = (
-            nn.Linear(self.embed, cfg.encoder_embed_dim)
-            if self.embed != cfg.encoder_embed_dim and not cfg.quantize_input
-            else None
-        )
-
-        self.mask_prob = cfg.mask_prob
-        self.mask_selection = cfg.mask_selection
-        self.mask_other = cfg.mask_other
-        self.mask_length = cfg.mask_length
-        self.no_mask_overlap = cfg.no_mask_overlap
-        self.mask_min_space = cfg.mask_min_space
-
-        self.mask_channel_prob = cfg.mask_channel_prob
-        self.mask_channel_before = cfg.mask_channel_before
-        self.mask_channel_selection = cfg.mask_channel_selection
-        self.mask_channel_other = cfg.mask_channel_other
-        self.mask_channel_length = cfg.mask_channel_length
-        self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
-        self.mask_channel_min_space = cfg.mask_channel_min_space
-
-        self.dropout_input = nn.Dropout(cfg.dropout_input)
-        self.dropout_features = nn.Dropout(cfg.dropout_features)
-
-        self.feature_grad_mult = cfg.feature_grad_mult
-
-        self.quantizer = None
-        self.input_quantizer = None
-
-        self.n_negatives = cfg.num_negatives
-        self.cross_sample_negatives = cfg.cross_sample_negatives
-        self.codebook_negatives = cfg.codebook_negatives
-        self.negatives_from_everywhere = cfg.negatives_from_everywhere
-
-        self.logit_temp = cfg.logit_temp
-
-        final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
-
-        if cfg.quantize_targets:
-            vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else final_dim
-            self.quantizer = GumbelVectorQuantizer(
-                dim=self.embed,
-                num_vars=cfg.latent_vars,
-                temp=cfg.latent_temp,
-                groups=cfg.latent_groups,
-                combine_groups=False,
-                vq_dim=vq_dim,
-                time_first=True,
-                weight_proj_depth=cfg.quantizer_depth,
-                weight_proj_factor=cfg.quantizer_factor,
-            )
-            self.project_q = nn.Linear(vq_dim, final_dim)
-        else:
-            self.project_q = nn.Linear(self.embed, final_dim)
-
-        if cfg.quantize_input:
-            if cfg.same_quantizer and self.quantizer is not None:
-                vq_dim = final_dim
-                self.input_quantizer = self.quantizer
-            else:
-                vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else cfg.encoder_embed_dim
-                self.input_quantizer = GumbelVectorQuantizer(
-                    dim=self.embed,
-                    num_vars=cfg.latent_vars,
-                    temp=cfg.latent_temp,
-                    groups=cfg.latent_groups,
-                    combine_groups=False,
-                    vq_dim=vq_dim,
-                    time_first=True,
-                    weight_proj_depth=cfg.quantizer_depth,
-                    weight_proj_factor=cfg.quantizer_factor,
-                )
-            self.project_inp = nn.Linear(vq_dim, cfg.encoder_embed_dim)
-
-        self.mask_emb = nn.Parameter(
-            torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
-        )
-
-        self.encoder = TransformerEncoder(cfg)
-        self.layer_norm = LayerNorm(self.embed)
-
-        self.target_glu = None
-        if cfg.target_glu:
-            self.target_glu = nn.Sequential(
-                nn.Linear(final_dim, final_dim * 2), nn.GLU()
-            )
-
-        self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim)
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        super().upgrade_state_dict_named(state_dict, name)
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        return state_dict
-
-    @classmethod
-    def build_model(cls, cfg: Wav2Vec2Config, task=None):
-        """Build a new model instance."""
-
-        return cls(cfg)
-
-    def apply_mask(
-        self,
-        x,
-        padding_mask,
-        mask_indices=None,
-        mask_channel_indices=None,
-    ):
-        B, T, C = x.shape
-
-        if self.mask_channel_prob > 0 and self.mask_channel_before:
-            mask_channel_indices = compute_mask_indices(
-                (B, C),
-                None,
-                self.mask_channel_prob,
-                self.mask_channel_length,
-                self.mask_channel_selection,
-                self.mask_channel_other,
-                no_overlap=self.no_mask_channel_overlap,
-                min_space=self.mask_channel_min_space,
-            )
-            mask_channel_indices = (
-                torch.from_numpy(mask_channel_indices)
-                .to(x.device)
-                .unsqueeze(1)
-                .expand(-1, T, -1)
-            )
-            x[mask_channel_indices] = 0
-
-        if self.mask_prob > 0:
-            if mask_indices is None:
-                mask_indices = compute_mask_indices(
-                    (B, T),
-                    padding_mask,
-                    self.mask_prob,
-                    self.mask_length,
-                    self.mask_selection,
-                    self.mask_other,
-                    min_masks=2,
-                    no_overlap=self.no_mask_overlap,
-                    min_space=self.mask_min_space,
-                )
-                mask_indices = torch.from_numpy(mask_indices).to(x.device)
-            x = index_put(x, mask_indices, self.mask_emb)
-        else:
-            mask_indices = None
-
-        if self.mask_channel_prob > 0 and not self.mask_channel_before:
-            if mask_channel_indices is None:
-                mask_channel_indices = compute_mask_indices(
-                    (B, C),
-                    None,
-                    self.mask_channel_prob,
-                    self.mask_channel_length,
-                    self.mask_channel_selection,
-                    self.mask_channel_other,
-                    no_overlap=self.no_mask_channel_overlap,
-                    min_space=self.mask_channel_min_space,
-                )
-                mask_channel_indices = (
-                    torch.from_numpy(mask_channel_indices)
-                    .to(x.device)
-                    .unsqueeze(1)
-                    .expand(-1, T, -1)
-                )
-            x = index_put(x, mask_channel_indices, 0)
-
-        return x, mask_indices
-
-    def sample_negatives(self, y, num, padding_count=None):
-
-        if self.n_negatives == 0 and self.cross_sample_negatives == 0:
-            return y.new(0)
-
-        bsz, tsz, fsz = y.shape
-        y = y.view(-1, fsz)  # BTC => (BxT)C
-
-        # FIXME: what happens if padding_count is specified?
-        cross_high = tsz * bsz
-        high = tsz - (padding_count or 0)
-        with torch.no_grad():
-            assert high > 1, f"{bsz,tsz,fsz}"
-
-            if self.n_negatives > 0:
-                tszs = (
-                    buffered_arange(num)
-                    .unsqueeze(-1)
-                    .expand(-1, self.n_negatives)
-                    .flatten()
-                )
-
-                neg_idxs = torch.randint(
-                    low=0, high=high - 1, size=(bsz, self.n_negatives * num)
-                )
-                neg_idxs[neg_idxs >= tszs] += 1
-
-            if self.cross_sample_negatives > 0:
-                tszs = (
-                    buffered_arange(num)
-                    .unsqueeze(-1)
-                    .expand(-1, self.cross_sample_negatives)
-                    .flatten()
-                )
-
-                cross_neg_idxs = torch.randint(
-                    low=0,
-                    high=cross_high - 1,
-                    size=(bsz, self.cross_sample_negatives * num),
-                )
-                cross_neg_idxs[cross_neg_idxs >= tszs] += 1
-
-        if self.n_negatives > 0:
-            for i in range(1, bsz):
-                neg_idxs[i] += i * high
-        else:
-            neg_idxs = cross_neg_idxs
-
-        if self.cross_sample_negatives > 0 and self.n_negatives > 0:
-            neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1)
-
-        negs = y[neg_idxs.view(-1)]
-        negs = negs.view(
-            bsz, num, self.n_negatives + self.cross_sample_negatives, fsz
-        ).permute(
-            2, 0, 1, 3
-        )  # to NxBxTxC
-        return negs, neg_idxs
-
-    def compute_preds(self, x, y, negatives):
-
-        neg_is_pos = (y == negatives).all(-1)
-        y = y.unsqueeze(0)
-        targets = torch.cat([y, negatives], dim=0)
-
-        logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x)
-
-        logits = logits / self.logit_temp
-
-        if is_xla_tensor(logits) or neg_is_pos.any():
-            fillval = -float(2 ** 30)
-            if not hasattr(self, "_inftensor"):
-                self._inftensor = (
-                    torch.tensor(fillval).to(x.device)
-                    if is_xla_tensor(logits)
-                    else float("-inf")
-                )
-            logits[1:] = index_put(logits[1:], neg_is_pos, self._inftensor)
-
-        return logits
-
-    def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
-        """
-        Computes the output length of the convolutional layers
-        """
-
-        def _conv_out_length(input_length, kernel_size, stride):
-            return torch.floor((input_length - kernel_size) / stride + 1)
-
-        conv_cfg_list = eval(self.cfg.conv_feature_layers)
-
-        for i in range(len(conv_cfg_list)):
-            input_lengths = _conv_out_length(
-                input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2]
-            )
-
-        return input_lengths.to(torch.long)
-
-    def forward(
-        self,
-        source,
-        padding_mask=None,
-        mask=True,
-        features_only=False,
-        layer=None,
-        mask_indices=None,
-        mask_channel_indices=None,
-        padding_count=None,
-    ):
-
-        if self.feature_grad_mult > 0:
-            features = self.feature_extractor(source)
-            if self.feature_grad_mult != 1.0:
-                features = GradMultiply.apply(features, self.feature_grad_mult)
-        else:
-            with torch.no_grad():
-                features = self.feature_extractor(source)
-
-        features_pen = features.float().pow(2).mean()
-
-        features = features.transpose(1, 2)
-        features = self.layer_norm(features)
-        unmasked_features = features.clone()
-
-        if padding_mask is not None and padding_mask.any():
-            input_lengths = (1 - padding_mask.long()).sum(-1)
-            # apply conv formula to get real output_lengths
-            output_lengths = self._get_feat_extract_output_lengths(input_lengths)
-
-            padding_mask = torch.zeros(
-                features.shape[:2], dtype=features.dtype, device=features.device
-            )
-
-            # these two operations makes sure that all values
-            # before the output lengths indices are attended to
-            padding_mask[
-                (
-                    torch.arange(padding_mask.shape[0], device=padding_mask.device),
-                    output_lengths - 1,
-                )
-            ] = 1
-            padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool()
-        else:
-            padding_mask = None
-
-        if self.post_extract_proj is not None:
-            features = self.post_extract_proj(features)
-
-        features = self.dropout_input(features)
-        unmasked_features = self.dropout_features(unmasked_features)
-
-        num_vars = None
-        code_ppl = None
-        prob_ppl = None
-        curr_temp = None
-
-        if self.input_quantizer:
-            q = self.input_quantizer(features, produce_targets=False)
-            features = q["x"]
-            num_vars = q["num_vars"]
-            code_ppl = q["code_perplexity"]
-            prob_ppl = q["prob_perplexity"]
-            curr_temp = q["temp"]
-            features = self.project_inp(features)
-
-        if mask:
-            x, mask_indices = self.apply_mask(
-                features,
-                padding_mask,
-                mask_indices=mask_indices,
-                mask_channel_indices=mask_channel_indices,
-            )
-            if not is_xla_tensor(x) and mask_indices is not None:
-                # tpu-comment: reducing the size in a dynamic way causes
-                # too many recompilations on xla.
-                y = unmasked_features[mask_indices].view(
-                    unmasked_features.size(0), -1, unmasked_features.size(-1)
-                )
-            else:
-                y = unmasked_features
-        else:
-            x = features
-            y = unmasked_features
-            mask_indices = None
-
-        x, layer_results = self.encoder(x, padding_mask=padding_mask, layer=layer)
-
-        if features_only:
-            return {
-                "x": x,
-                "padding_mask": padding_mask,
-                "features": unmasked_features,
-                "layer_results": layer_results,
-            }
-
-        if self.quantizer:
-            q = self.quantizer(y, produce_targets=False)
-            y = q["x"]
-            num_vars = q["num_vars"]
-            code_ppl = q["code_perplexity"]
-            prob_ppl = q["prob_perplexity"]
-            curr_temp = q["temp"]
-
-            y = self.project_q(y)
-
-            if self.negatives_from_everywhere:
-                neg_cands = self.quantizer(unmasked_features, produce_targets=False)[
-                    "x"
-                ]
-                negs, _ = self.sample_negatives(
-                    neg_cands,
-                    y.size(1),
-                    padding_count=padding_count,
-                )
-                negs = self.project_q(negs)
-
-            else:
-                negs, _ = self.sample_negatives(
-                    y,
-                    y.size(1),
-                    padding_count=padding_count,
-                )
-
-            if self.codebook_negatives > 0:
-                cb_negs = self.quantizer.sample_from_codebook(
-                    y.size(0) * y.size(1), self.codebook_negatives
-                )
-                cb_negs = cb_negs.view(
-                    self.codebook_negatives, y.size(0), y.size(1), -1
-                )  # order doesnt matter
-                cb_negs = self.project_q(cb_negs)
-                negs = torch.cat([negs, cb_negs], dim=0)
-        else:
-            y = self.project_q(y)
-
-            if self.negatives_from_everywhere:
-                negs, _ = self.sample_negatives(
-                    unmasked_features,
-                    y.size(1),
-                    padding_count=padding_count,
-                )
-                negs = self.project_q(negs)
-            else:
-                negs, _ = self.sample_negatives(
-                    y,
-                    y.size(1),
-                    padding_count=padding_count,
-                )
-
-        if not is_xla_tensor(x):
-            # tpu-comment: reducing the size in a dynamic way causes
-            # too many recompilations on xla.
-            x = x[mask_indices].view(x.size(0), -1, x.size(-1))
-
-        if self.target_glu:
-            y = self.target_glu(y)
-            negs = self.target_glu(negs)
-
-        x = self.final_proj(x)
-        x = self.compute_preds(x, y, negs)
-
-        result = {
-            "x": x,
-            "padding_mask": padding_mask,
-            "features_pen": features_pen,
-        }
-
-        if prob_ppl is not None:
-            result["prob_perplexity"] = prob_ppl
-            result["code_perplexity"] = code_ppl
-            result["num_vars"] = num_vars
-            result["temp"] = curr_temp
-
-        return result
-
-    def quantize(self, x):
-        assert self.quantizer is not None
-        x = self.feature_extractor(x)
-        x = x.transpose(1, 2)
-        x = self.layer_norm(x)
-        return self.quantizer.forward_idx(x)
-
-    def extract_features(self, source, padding_mask, mask=False, layer=None):
-        res = self.forward(
-            source, padding_mask, mask=mask, features_only=True, layer=layer
-        )
-        return res
-
-    def get_logits(self, net_output):
-        logits = net_output["x"]
-        logits = logits.transpose(0, 2)
-        logits = logits.reshape(-1, logits.size(-1))
-        return logits
-
-    def get_targets(self, sample, net_output, expand_steps=True):
-        x = net_output["x"]
-        return x.new_zeros(x.size(1) * x.size(2), dtype=torch.long)
-
-    def get_extra_losses(self, net_output):
-        pen = []
-
-        if "prob_perplexity" in net_output:
-            pen.append(
-                (net_output["num_vars"] - net_output["prob_perplexity"])
-                / net_output["num_vars"]
-            )
-
-        if "features_pen" in net_output:
-            pen.append(net_output["features_pen"])
-
-        return pen
-
-    def remove_pretraining_modules(self):
-        self.quantizer = None
-        self.project_q = None
-        self.target_glu = None
-        self.final_proj = None
-
-
-class ConvFeatureExtractionModel(nn.Module):
-    def __init__(
-        self,
-        conv_layers: List[Tuple[int, int, int]],
-        dropout: float = 0.0,
-        mode: str = "default",
-        conv_bias: bool = False,
-    ):
-        super().__init__()
-
-        assert mode in {"default", "layer_norm"}
-
-        def block(
-            n_in,
-            n_out,
-            k,
-            stride,
-            is_layer_norm=False,
-            is_group_norm=False,
-            conv_bias=False,
-        ):
-            def make_conv():
-                conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
-                nn.init.kaiming_normal_(conv.weight)
-                return conv
-
-            assert (
-                is_layer_norm and is_group_norm
-            ) == False, "layer norm and group norm are exclusive"
-
-            if is_layer_norm:
-                return nn.Sequential(
-                    make_conv(),
-                    nn.Dropout(p=dropout),
-                    nn.Sequential(
-                        TransposeLast(),
-                        Fp32LayerNorm(dim, elementwise_affine=True),
-                        TransposeLast(),
-                    ),
-                    nn.GELU(),
-                )
-            elif is_group_norm:
-                return nn.Sequential(
-                    make_conv(),
-                    nn.Dropout(p=dropout),
-                    Fp32GroupNorm(dim, dim, affine=True),
-                    nn.GELU(),
-                )
-            else:
-                return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
-
-        in_d = 1
-        self.conv_layers = nn.ModuleList()
-        for i, cl in enumerate(conv_layers):
-            assert len(cl) == 3, "invalid conv definition: " + str(cl)
-            (dim, k, stride) = cl
-
-            self.conv_layers.append(
-                block(
-                    in_d,
-                    dim,
-                    k,
-                    stride,
-                    is_layer_norm=mode == "layer_norm",
-                    is_group_norm=mode == "default" and i == 0,
-                    conv_bias=conv_bias,
-                )
-            )
-            in_d = dim
-
-    def forward(self, x):
-
-        # BxT -> BxCxT
-        x = x.unsqueeze(1)
-
-        for conv in self.conv_layers:
-            x = conv(x)
-
-        return x
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-
-        self.dropout = args.dropout
-        self.embedding_dim = args.encoder_embed_dim
-
-        self.pos_conv = nn.Conv1d(
-            self.embedding_dim,
-            self.embedding_dim,
-            kernel_size=args.conv_pos,
-            padding=args.conv_pos // 2,
-            groups=args.conv_pos_groups,
-        )
-        dropout = 0
-        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
-        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
-        nn.init.constant_(self.pos_conv.bias, 0)
-
-        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
-        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
-
-        self.layers = nn.ModuleList(
-            [
-                TransformerSentenceEncoderLayer(
-                    embedding_dim=self.embedding_dim,
-                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
-                    num_attention_heads=args.encoder_attention_heads,
-                    dropout=self.dropout,
-                    attention_dropout=args.attention_dropout,
-                    activation_dropout=args.activation_dropout,
-                    activation_fn=args.activation_fn,
-                    layer_norm_first=args.layer_norm_first,
-                )
-                for _ in range(args.encoder_layers)
-            ]
-        )
-
-        self.layer_norm_first = args.layer_norm_first
-        self.layer_norm = LayerNorm(self.embedding_dim)
-        self.layerdrop = args.encoder_layerdrop
-
-        self.apply(init_bert_params)
-
-    def forward(self, x, padding_mask=None, layer=None):
-        x, layer_results = self.extract_features(x, padding_mask, layer)
-
-        if self.layer_norm_first and layer is None:
-            x = self.layer_norm(x)
-
-        return x, layer_results
-
-    def extract_features(self, x, padding_mask=None, tgt_layer=None):
-
-        if padding_mask is not None:
-            x = index_put(x, padding_mask, 0)
-
-        x_conv = self.pos_conv(x.transpose(1, 2))
-        x_conv = x_conv.transpose(1, 2)
-        x = x + x_conv
-
-        if not self.layer_norm_first:
-            x = self.layer_norm(x)
-
-        x = F.dropout(x, p=self.dropout, training=self.training)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        layer_results = []
-        r = None
-        for i, layer in enumerate(self.layers):
-            dropout_probability = np.random.random()
-            if not self.training or (dropout_probability > self.layerdrop):
-                x, z = layer(x, self_attn_padding_mask=padding_mask, need_weights=False)
-                if tgt_layer is not None:
-                    layer_results.append((x, z))
-            if i == tgt_layer:
-                r = x
-                break
-
-        if r is not None:
-            x = r
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        return x, layer_results
-
-    def max_positions(self):
-        """Maximum output length supported by the encoder."""
-        return self.args.max_positions
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        return state_dict
-
-
-class TransformerSentenceEncoderLayer(nn.Module):
-    """
-    Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
-    models.
-    """
-
-    def __init__(
-        self,
-        embedding_dim: float = 768,
-        ffn_embedding_dim: float = 3072,
-        num_attention_heads: float = 8,
-        dropout: float = 0.1,
-        attention_dropout: float = 0.1,
-        activation_dropout: float = 0.1,
-        activation_fn: str = "relu",
-        layer_norm_first: bool = False,
-    ) -> None:
-
-        super().__init__()
-        # Initialize parameters
-        self.embedding_dim = embedding_dim
-        self.dropout = dropout
-        self.activation_dropout = activation_dropout
-
-        # Initialize blocks
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.self_attn = MultiheadAttention(
-            self.embedding_dim,
-            num_attention_heads,
-            dropout=attention_dropout,
-            self_attention=True,
-        )
-
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(self.activation_dropout)
-        self.dropout3 = nn.Dropout(dropout)
-
-        self.layer_norm_first = layer_norm_first
-
-        # layer norm associated with the self attention layer
-        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
-        self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
-        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
-
-        # layer norm associated with the position wise feed-forward NN
-        self.final_layer_norm = LayerNorm(self.embedding_dim)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        self_attn_mask: torch.Tensor = None,
-        self_attn_padding_mask: torch.Tensor = None,
-        need_weights: bool = False,
-        att_args=None,
-    ):
-        """
-        LayerNorm is applied either before or after the self-attention/ffn
-        modules similar to the original Transformer imlementation.
-        """
-        residual = x
-
-        if self.layer_norm_first:
-            x = self.self_attn_layer_norm(x)
-            x, attn = self.self_attn(
-                query=x,
-                key=x,
-                value=x,
-                key_padding_mask=self_attn_padding_mask,
-                attn_mask=self_attn_mask,
-            )
-            x = self.dropout1(x)
-            x = residual + x
-
-            residual = x
-            x = self.final_layer_norm(x)
-            x = self.activation_fn(self.fc1(x))
-            x = self.dropout2(x)
-            x = self.fc2(x)
-            x = self.dropout3(x)
-            x = residual + x
-        else:
-            x, attn = self.self_attn(
-                query=x,
-                key=x,
-                value=x,
-                key_padding_mask=self_attn_padding_mask,
-            )
-
-            x = self.dropout1(x)
-            x = residual + x
-
-            x = self.self_attn_layer_norm(x)
-
-            residual = x
-            x = self.activation_fn(self.fc1(x))
-            x = self.dropout2(x)
-            x = self.fc2(x)
-            x = self.dropout3(x)
-            x = residual + x
-            x = self.final_layer_norm(x)
-
-        return x, attn
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/sinusoidal_positional_embedding.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/sinusoidal_positional_embedding.py
deleted file mode 100644
index 4793ecfb522d0729fc2d24a3ddf0c6a774d67773..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/sinusoidal_positional_embedding.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from typing import Any, Optional
-
-import torch
-import torch.onnx.operators
-from fairseq import utils
-from torch import Tensor, nn
-
-
-class SinusoidalPositionalEmbedding(nn.Module):
-    """This module produces sinusoidal positional embeddings of any length.
-
-    Padding symbols are ignored.
-    """
-
-    def __init__(self, embedding_dim, padding_idx, init_size=1024):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.padding_idx = padding_idx if padding_idx is not None else 0
-        self.weights = SinusoidalPositionalEmbedding.get_embedding(
-            init_size, embedding_dim, padding_idx
-        )
-        self.onnx_trace = False
-        self.register_buffer("_float_tensor", torch.FloatTensor(1))
-        self.max_positions = int(1e5)
-
-    def prepare_for_onnx_export_(self):
-        self.onnx_trace = True
-
-    @staticmethod
-    def get_embedding(
-        num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
-    ):
-        """Build sinusoidal embeddings.
-
-        This matches the implementation in tensor2tensor, but differs slightly
-        from the description in Section 3.5 of "Attention Is All You Need".
-        """
-        half_dim = embedding_dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
-        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(
-            1
-        ) * emb.unsqueeze(0)
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(
-            num_embeddings, -1
-        )
-        if embedding_dim % 2 == 1:
-            # zero pad
-            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
-        if padding_idx is not None:
-            emb[padding_idx, :] = 0
-        return emb
-
-    def forward(
-        self,
-        input,
-        incremental_state: Optional[Any] = None,
-        timestep: Optional[Tensor] = None,
-        positions: Optional[Any] = None,
-    ):
-        """Input is expected to be of size [bsz x seqlen]."""
-        bspair = torch.onnx.operators.shape_as_tensor(input)
-        bsz, seq_len = bspair[0], bspair[1]
-        max_pos = self.padding_idx + 1 + seq_len
-        if self.weights is None or max_pos > self.weights.size(0):
-            # recompute/expand embeddings if needed
-            self.weights = SinusoidalPositionalEmbedding.get_embedding(
-                max_pos, self.embedding_dim, self.padding_idx
-            )
-        self.weights = self.weights.to(self._float_tensor)
-
-        if incremental_state is not None:
-            # positions is the same for every token when decoding a single step
-            pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
-            if self.onnx_trace:
-                return (
-                    self.weights.index_select(index=self.padding_idx + pos, dim=0)
-                    .unsqueeze(1)
-                    .repeat(bsz, 1, 1)
-                )
-            return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
-
-        positions = utils.make_positions(
-            input, self.padding_idx, onnx_trace=self.onnx_trace
-        )
-        if self.onnx_trace:
-            flat_embeddings = self.weights.detach().index_select(0, positions.view(-1))
-            embedding_shape = torch.cat(
-                (bsz.view(1), seq_len.view(1), torch.tensor([-1], dtype=torch.long))
-            )
-            embeddings = torch.onnx.operators.reshape_from_tensor_shape(
-                flat_embeddings, embedding_shape
-            )
-            return embeddings
-        return (
-            self.weights.index_select(0, positions.view(-1))
-            .view(bsz, seq_len, -1)
-            .detach()
-        )
diff --git a/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/scaling_best/snli_ve/unival_snli_ve_initcaption.sh b/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/scaling_best/snli_ve/unival_snli_ve_initcaption.sh
deleted file mode 100644
index 8aef19a45297fa6b038322cc3bb4ac396a9f60d9..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/scaling_best/snli_ve/unival_snli_ve_initcaption.sh
+++ /dev/null
@@ -1,187 +0,0 @@
-#!/usr/bin/env
-
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-
-
-exp_name=unival_snli_ve_initcaption
-
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-save_base_log_dir=/lus/scratch/NAT/gda2204/SHARED/logs
-save_dir=${save_base_log_dir}/ofa/checkpoints/snli_ve/${exp_name}
-
-# save_dir=${base_log_dir}/ofa/checkpoints/snli_ve/${exp_name}
-log_dir=${save_dir}
-
-
-mkdir -p $log_dir $save_dir
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-image_dir=${base_data_dir}
-
-
-
-
-data_dir=${base_data_dir}/ofa/snli_ve_data
-data=${data_dir}/snli_ve_train.tsv,${data_dir}/snli_ve_dev.tsv
-
-restore_file=/lus/scratch/NAT/gda2204/SHARED/logs/ofa/checkpoints/caption/unival_caption_stage_1/10_0.06_6000/checkpoint_best.pt
-
-
-
-selected_cols=0,2,3,4,5
-
-task=snli_ve
-arch=unival_base
-criterion=adjust_label_smoothed_cross_entropy
-label_smoothing=0.0
-lr=5e-5
-max_epoch=10
-warmup_ratio=0.06
-batch_size=8
-update_freq=4
-resnet_drop_path_rate=0.0
-encoder_drop_path_rate=0.1
-decoder_drop_path_rate=0.1
-dropout=0.1
-attention_dropout=0.0
-max_src_length=80
-max_tgt_length=20
-num_bins=1000
-patch_image_size=480
-prompt_type="prev_output"
-
-
-
-echo "max_epoch "${max_epoch}
-echo "lr "${lr}
-
-log_file=${log_dir}/${max_epoch}"_"${lr}".log"
-save_path=${save_dir}/${max_epoch}"_"${lr}
-mkdir -p $save_path
-
-
-
-
-
-
-###
-image_encoder_name=timm_resnet #vit_base_patch16_224 timm_resnet resnet
-patch_image_size=480
-resnet_type=resnet101
-
-resnet_model_path=${base_log_dir}/pretrained_models/resnet101-5d3b4d8f.pth
-
-# video
-video_encoder_name=all_resnext101
-patch_frame_size=384
-video_model_path=${base_log_dir}/pretrained_models/3dcnn/resnext-101-kinetics.pth #${base_log_dir}/pretrained_models/TimeSformer_divST_8x32_224_K600.pyth
-num_frames=4
-
-save_interval=1
-validate_interval_updates=50000
-save_interval_updates=0
-
-
-sample_patch_num='--sample-patch-num=784' # ''
-
-
-
-python3 -m torch.distributed.launch \
-    --nnodes=${NUM_NODES} \
-    --nproc_per_node=${GPUS_PER_NODE} \
-    --master_port=${MASTER_PORT} \
-    --node_rank=${RANK} \
-    --master_addr=${MASTER_ADDR} \
-    --use_env ${ofa_dir}/train.py \
-    $data \
-    --selected-cols=${selected_cols} \
-    --bpe-dir=${bpe_dir} \
-    --user-dir=${user_dir} \
-    --restore-file=${restore_file} \
-    --reset-optimizer --reset-dataloader --reset-meters \
-    --save-dir=${save_path} \
-    --task=${task} \
-    --arch=${arch} \
-    --criterion=${criterion} \
-    --label-smoothing=${label_smoothing} \
-    --batch-size=${batch_size} \
-    --update-freq=${update_freq} \
-    --encoder-normalize-before \
-    --decoder-normalize-before \
-    --share-decoder-input-output-embed \
-    --share-all-embeddings \
-    --layernorm-embedding \
-    --patch-layernorm-embedding \
-    --code-layernorm-embedding \
-    --resnet-drop-path-rate=${resnet_drop_path_rate} \
-    --encoder-drop-path-rate=${encoder_drop_path_rate} \
-    --decoder-drop-path-rate=${decoder_drop_path_rate} \
-    --dropout=${dropout} \
-    --attention-dropout=${attention_dropout} \
-    --weight-decay=0.01 --optimizer=adam --adam-betas="(0.9,0.999)" --adam-eps=1e-08 --clip-norm=1.0 \
-    --lr-scheduler=polynomial_decay --lr=${lr} \
-    --max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \
-    --log-format=simple --log-interval=10 \
-    --fixed-validation-seed=7 \
-    --keep-best-checkpoints=1 \
-    --no-epoch-checkpoints \
-    --save-interval=1 --validate-interval=1 \
-    --save-interval-updates=${save_interval_updates} --validate-interval-updates=${validate_interval_updates} \
-    --best-checkpoint-metric=snli_score --maximize-best-checkpoint-metric \
-    --max-src-length=${max_src_length} \
-    --max-tgt-length=${max_tgt_length} \
-    --find-unused-parameters \
-    --add-type-embedding \
-    --scale-attn \
-    --scale-fc \
-    --scale-heads \
-    --disable-entangle \
-    --num-bins=${num_bins} \
-    --patch-image-size=${patch_image_size} \
-    --prompt-type=${prompt_type} \
-    --fp16 \
-    --fp16-scale-window=512 \
-    --num-workers=0 \
-    --image-dir=${image_dir} \
-    ${sample_patch_num} \
-    --image-encoder-name=${image_encoder_name} \
-    --image-dir=${image_dir} \
-    --video-encoder-name=${video_encoder_name} \
-    --video-model-path=${video_model_path} \
-    --patch-frame-size=${patch_frame_size} \
-    --reset-dataloader --reset-meters --reset-optimizer \
-    --strict \
-    --resnet-model-path=${resnet_model_path} 
-
-        # --add-caption \
diff --git a/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder_preprocess.py b/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder_preprocess.py
deleted file mode 100644
index 7ede3dfb95972e2de575de35b9d4a9c6d642885e..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder_preprocess.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from synthesizer.synthesize import run_synthesis
-from synthesizer.hparams import hparams
-from utils.argutils import print_args
-import argparse
-import os
-
-
-if __name__ == "__main__":
-    class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
-        pass
-    
-    parser = argparse.ArgumentParser(
-        description="Creates ground-truth aligned (GTA) spectrograms from the vocoder.",
-        formatter_class=MyFormatter
-    )
-    parser.add_argument("datasets_root", type=str, help=\
-        "Path to the directory containing your SV2TTS directory. If you specify both --in_dir and "
-        "--out_dir, this argument won't be used.")
-    parser.add_argument("--model_dir", type=str, 
-                        default="synthesizer/saved_models/pretrained/", help=\
-        "Path to the pretrained model directory.")
-    parser.add_argument("-i", "--in_dir", type=str, default=argparse.SUPPRESS, help= \
-        "Path to the synthesizer directory that contains the mel spectrograms, the wavs and the "
-        "embeds. Defaults to  <datasets_root>/SV2TTS/synthesizer/.")
-    parser.add_argument("-o", "--out_dir", type=str, default=argparse.SUPPRESS, help= \
-        "Path to the output vocoder directory that will contain the ground truth aligned mel "
-        "spectrograms. Defaults to <datasets_root>/SV2TTS/vocoder/.")
-    parser.add_argument("--hparams", default="",
-                        help="Hyperparameter overrides as a comma-separated list of name=value "
-                             "pairs")
-    parser.add_argument("--no_trim", action="store_true", help=\
-        "Preprocess audio without trimming silences (not recommended).")
-    parser.add_argument("--cpu", action="store_true", help=\
-        "If True, processing is done on CPU, even when a GPU is available.")
-    args = parser.parse_args()
-    print_args(args, parser)
-    modified_hp = hparams.parse(args.hparams)
-    
-    if not hasattr(args, "in_dir"):
-        args.in_dir = os.path.join(args.datasets_root, "SV2TTS", "synthesizer")
-    if not hasattr(args, "out_dir"):
-        args.out_dir = os.path.join(args.datasets_root, "SV2TTS", "vocoder")
-
-    if args.cpu:
-        # Hide GPUs from Pytorch to force CPU processing
-        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-    
-    # Verify webrtcvad is available
-    if not args.no_trim:
-        try:
-            import webrtcvad
-        except:
-            raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
-                "noise removal and is recommended. Please install and try again. If installation fails, "
-                "use --no_trim to disable this error message.")
-    del args.no_trim
-
-    run_synthesis(args.in_dir, args.out_dir, args.model_dir, modified_hp)
-
diff --git a/spaces/navervision/MLSD/utils.py b/spaces/navervision/MLSD/utils.py
deleted file mode 100644
index 6f7a0767f0220dc0d6a204f694ca6127e87a86f9..0000000000000000000000000000000000000000
--- a/spaces/navervision/MLSD/utils.py
+++ /dev/null
@@ -1,511 +0,0 @@
-'''
-M-LSD
-Copyright 2021-present NAVER Corp.
-Apache License v2.0
-'''
-import os
-import numpy as np
-import cv2
-import tensorflow as tf
-
-
-def pred_lines(image, interpreter, input_details, output_details, input_shape=[512, 512], score_thr=0.10, dist_thr=20.0):
-    h, w, _ = image.shape
-    h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]]
-
-    resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA), np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
-    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
-    interpreter.set_tensor(input_details[0]['index'], batch_image)
-    interpreter.invoke()
-
-    pts = interpreter.get_tensor(output_details[0]['index'])[0]
-    pts_score = interpreter.get_tensor(output_details[1]['index'])[0]
-    vmap = interpreter.get_tensor(output_details[2]['index'])[0]
-
-    start = vmap[:,:,:2]
-    end = vmap[:,:,2:]
-    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
-
-    segments_list = []
-    for center, score in zip(pts, pts_score):
-        y, x = center
-        distance = dist_map[y, x]
-        if score > score_thr and distance > dist_thr:
-            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
-            x_start = x + disp_x_start
-            y_start = y + disp_y_start
-            x_end = x + disp_x_end
-            y_end = y + disp_y_end
-            segments_list.append([x_start, y_start, x_end, y_end])
-    
-    lines = 2 * np.array(segments_list) # 256 > 512
-    lines[:,0] = lines[:,0] * w_ratio
-    lines[:,1] = lines[:,1] * h_ratio
-    lines[:,2] = lines[:,2] * w_ratio
-    lines[:,3] = lines[:,3] * h_ratio
-
-    return lines
-
-
-def pred_squares(image,
-                 interpreter,
-                 input_details,
-                 output_details,
-                 input_shape=[512, 512],
-                 params={'score': 0.06,
-                         'outside_ratio': 0.28,
-                         'inside_ratio': 0.45,
-                         'w_overlap': 0.0,
-                         'w_degree': 1.95,
-                         'w_length': 0.0,
-                         'w_area': 1.86,
-                         'w_center': 0.14}):
-    h, w, _ = image.shape
-    original_shape = [h, w]
-
-    resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA), np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
-    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
-    interpreter.set_tensor(input_details[0]['index'], batch_image)
-    interpreter.invoke()
-
-    pts = interpreter.get_tensor(output_details[0]['index'])[0]
-    pts_score = interpreter.get_tensor(output_details[1]['index'])[0]
-    vmap = interpreter.get_tensor(output_details[2]['index'])[0]
-    
-    start = vmap[:,:,:2] # (x, y)
-    end = vmap[:,:,2:] # (x, y)
-    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
-
-    junc_list = []
-    segments_list = []
-    for junc, score in zip(pts, pts_score):
-        y, x = junc
-        distance = dist_map[y, x]
-        if score > params['score'] and distance > 20.0:
-            junc_list.append([x, y])
-            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
-            d_arrow = 1.0
-            x_start = x + d_arrow * disp_x_start
-            y_start = y + d_arrow * disp_y_start
-            x_end = x + d_arrow * disp_x_end
-            y_end = y + d_arrow * disp_y_end
-            segments_list.append([x_start, y_start, x_end, y_end])
-            
-    segments = np.array(segments_list)
-    
-    ####### post processing for squares
-    # 1. get unique lines
-    point = np.array([[0, 0]])
-    point = point[0]
-    start = segments[:,:2]
-    end = segments[:,2:]
-    diff = start - end
-    a = diff[:, 1]
-    b = -diff[:, 0]
-    c = a * start[:,0] + b * start[:,1]
-
-    d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10)
-    theta = np.arctan2(diff[:,0], diff[:,1]) * 180 / np.pi
-    theta[theta < 0.0] += 180
-    hough = np.concatenate([d[:,None], theta[:,None]], axis=-1)
-
-    d_quant = 1
-    theta_quant = 2
-    hough[:,0] //= d_quant
-    hough[:,1] //= theta_quant
-    _, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True)
-    
-    acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32')
-    idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1
-    yx_indices = hough[indices,:].astype('int32')
-    acc_map[yx_indices[:,0], yx_indices[:,1]] = counts
-    idx_map[yx_indices[:,0], yx_indices[:,1]] = indices
-    
-    acc_map_np = acc_map
-    acc_map = acc_map[None,:,:,None]
-    
-    ### fast suppression using tensorflow op
-    acc_map = tf.constant(acc_map, dtype=tf.float32)
-    max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5,5), strides=1, padding='same')(acc_map)
-    acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32)
-    flatten_acc_map = tf.reshape(acc_map, [1, -1])
-    topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts))
-    _, h, w, _ = acc_map.shape
-    y = tf.expand_dims(topk_indices // w, axis=-1)
-    x = tf.expand_dims(topk_indices % w, axis=-1)
-    yx = tf.concat([y, x], axis=-1)
-    ###
-
-    yx = yx[0].numpy()
-    indices = idx_map[yx[:,0], yx[:,1]]
-    topk_values = topk_values.numpy()[0]
-    basis = 5 // 2
-
-    merged_segments = []
-    for yx_pt, max_indice, value in zip(yx, indices, topk_values):
-        y, x = yx_pt
-        if max_indice == -1 or value == 0:
-            continue
-        segment_list = []
-        for y_offset in range(-basis, basis+1):
-            for x_offset in range(-basis, basis+1):
-                indice = idx_map[y+y_offset,x+x_offset]
-                cnt = int(acc_map_np[y+y_offset,x+x_offset])
-                if indice != -1:
-                    segment_list.append(segments[indice])
-                if cnt > 1:
-                    check_cnt = 1
-                    current_hough = hough[indice]
-                    for new_indice, new_hough in enumerate(hough):
-                        if (current_hough == new_hough).all() and indice != new_indice:
-                            segment_list.append(segments[new_indice])
-                            check_cnt += 1
-                        if check_cnt == cnt:
-                            break
-        group_segments = np.array(segment_list).reshape([-1, 2])
-        sorted_group_segments = np.sort(group_segments, axis=0)
-        x_min, y_min = sorted_group_segments[0,:]
-        x_max, y_max = sorted_group_segments[-1,:]
-
-        deg = theta[max_indice]
-        if deg >= 90:
-            merged_segments.append([x_min, y_max, x_max, y_min])
-        else:
-            merged_segments.append([x_min, y_min, x_max, y_max])
-
-    # 2. get intersections
-    new_segments = np.array(merged_segments) # (x1, y1, x2, y2)
-    start = new_segments[:,:2] # (x1, y1)
-    end = new_segments[:,2:] # (x2, y2)
-    new_centers = (start + end) / 2.0
-    diff = start - end
-    dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1))
-
-    # ax + by = c
-    a = diff[:,1]
-    b = -diff[:,0]
-    c = a * start[:,0] + b * start[:,1]
-    pre_det = a[:,None] * b[None,:]
-    det = pre_det - np.transpose(pre_det)
-
-    pre_inter_y = a[:,None] * c[None,:]
-    inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10)
-    pre_inter_x = c[:,None] * b[None,:]
-    inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10)
-    inter_pts = np.concatenate([inter_x[:,:,None], inter_y[:,:,None]], axis=-1).astype('int32')
-    
-    # 3. get corner information
-    # 3.1 get distance
-    '''
-    dist_segments:
-        | dist(0), dist(1), dist(2), ...|
-    dist_inter_to_segment1:
-        | dist(inter,0), dist(inter,0), dist(inter,0), ... |
-        | dist(inter,1), dist(inter,1), dist(inter,1), ... |
-        ...
-    dist_inter_to_semgnet2:
-        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
-        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
-        ...
-    '''
-
-    dist_inter_to_segment1_start = np.sqrt(np.sum(((inter_pts - start[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
-    dist_inter_to_segment1_end = np.sqrt(np.sum(((inter_pts - end[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
-    dist_inter_to_segment2_start = np.sqrt(np.sum(((inter_pts - start[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
-    dist_inter_to_segment2_end = np.sqrt(np.sum(((inter_pts - end[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
-    
-    # sort ascending
-    dist_inter_to_segment1 = np.sort(np.concatenate([dist_inter_to_segment1_start, dist_inter_to_segment1_end], axis=-1), axis=-1) # [n_batch, n_batch, 2]
-    dist_inter_to_segment2 = np.sort(np.concatenate([dist_inter_to_segment2_start, dist_inter_to_segment2_end], axis=-1), axis=-1) # [n_batch, n_batch, 2]
-
-    # 3.2 get degree
-    inter_to_start = new_centers[:,None,:] - inter_pts
-    deg_inter_to_start = np.arctan2(inter_to_start[:,:,1], inter_to_start[:,:,0]) * 180 / np.pi
-    deg_inter_to_start[deg_inter_to_start < 0.0] += 360
-    inter_to_end = new_centers[None,:,:] - inter_pts
-    deg_inter_to_end = np.arctan2(inter_to_end[:,:,1], inter_to_end[:,:,0]) * 180 / np.pi
-    deg_inter_to_end[deg_inter_to_end < 0.0] += 360
-    
-    '''
-    0 -- 1
-    |    |
-    3 -- 2
-    '''
-    # rename variables
-    deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end
-    # sort deg ascending
-    deg_sort = np.sort(np.concatenate([deg1_map[:,:,None], deg2_map[:,:,None]], axis=-1), axis=-1)
-    
-    deg_diff_map = np.abs(deg1_map - deg2_map)
-    # we only consider the smallest degree of intersect
-    deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180]
-    
-    # define available degree range
-    deg_range = [60, 120]
-    
-    corner_dict = {corner_info: [] for corner_info in range(4)}
-    inter_points = []
-    for i in range(inter_pts.shape[0]):
-        for j in range(i + 1, inter_pts.shape[1]):
-            # i, j > line index, always i < j
-            x, y = inter_pts[i, j, :]
-            deg1, deg2 = deg_sort[i, j, :]
-            deg_diff = deg_diff_map[i, j]
-            
-            check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1]
-
-            outside_ratio = params['outside_ratio'] # over ratio >>> drop it!
-            inside_ratio = params['inside_ratio'] # over ratio >>> drop it!
-            check_distance = ((dist_inter_to_segment1[i,j,1] >= dist_segments[i] and \
-                                 dist_inter_to_segment1[i,j,0] <= dist_segments[i] * outside_ratio) or \
-                                (dist_inter_to_segment1[i,j,1] <= dist_segments[i] and \
-                                 dist_inter_to_segment1[i,j,0] <= dist_segments[i] * inside_ratio)) and \
-                             ((dist_inter_to_segment2[i,j,1] >= dist_segments[j] and \
-                                 dist_inter_to_segment2[i,j,0] <= dist_segments[j] * outside_ratio) or \
-                                (dist_inter_to_segment2[i,j,1] <= dist_segments[j] and \
-                                 dist_inter_to_segment2[i,j,0] <= dist_segments[j] * inside_ratio))
-
-            if check_degree and check_distance:
-                corner_info = None
-
-                if (deg1 >= 0 and deg1 <= 45 and deg2 >=45 and deg2 <= 120) or \
-                     (deg2 >= 315 and deg1 >= 45 and deg1 <= 120):
-                    corner_info, color_info = 0, 'blue'
-                elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225):
-                    corner_info, color_info = 1, 'green'
-                elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315):
-                    corner_info, color_info = 2, 'black'
-                elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \
-                     (deg2 >= 315 and deg1 >= 225 and deg1 <= 315):
-                    corner_info, color_info = 3, 'cyan'
-                else:
-                    corner_info, color_info = 4, 'red' # we don't use it
-                    continue
-                
-                corner_dict[corner_info].append([x, y, i, j])
-                inter_points.append([x, y])
-    
-    square_list = []
-    connect_list = []
-    segments_list = []
-    for corner0 in corner_dict[0]:
-        for corner1 in corner_dict[1]:
-            connect01 = False
-            for corner0_line in corner0[2:]:
-                if corner0_line in corner1[2:]:
-                    connect01 = True
-                    break
-            if connect01:
-                for corner2 in corner_dict[2]:
-                    connect12 = False
-                    for corner1_line in corner1[2:]:
-                        if corner1_line in corner2[2:]:
-                            connect12 = True
-                            break
-                    if connect12:
-                        for corner3 in corner_dict[3]:
-                            connect23 = False
-                            for corner2_line in corner2[2:]:
-                                if corner2_line in corner3[2:]:
-                                    connect23 = True
-                                    break
-                            if connect23:
-                                for corner3_line in corner3[2:]:
-                                    if corner3_line in corner0[2:]:
-                                        # SQUARE!!!
-                                        '''
-                                        0 -- 1
-                                        |    |
-                                        3 -- 2
-                                        square_list:
-                                            order: 0 > 1 > 2 > 3
-                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
-                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
-                                            ...
-                                        connect_list:
-                                            order: 01 > 12 > 23 > 30
-                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
-                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
-                                            ...
-                                        segments_list:
-                                            order: 0 > 1 > 2 > 3
-                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
-                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
-                                            ...
-                                        '''
-                                        square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2])
-                                        connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line])
-                                        segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:])
-    
-    def check_outside_inside(segments_info, connect_idx):
-        # return 'outside or inside', min distance, cover_param, peri_param
-        if connect_idx == segments_info[0]:
-            check_dist_mat = dist_inter_to_segment1
-        else:
-            check_dist_mat = dist_inter_to_segment2
-        
-        i, j = segments_info
-        min_dist, max_dist = check_dist_mat[i, j, :]
-        connect_dist = dist_segments[connect_idx]
-        if max_dist > connect_dist:
-            return 'outside', min_dist, 0, 1
-        else:
-            return 'inside', min_dist, -1, -1
-
-
-    top_square = None
-
-    try:
-        map_size = input_shape[0] / 2
-        squares = np.array(square_list).reshape([-1,4,2])
-        score_array = []
-        connect_array = np.array(connect_list)
-        segments_array = np.array(segments_list).reshape([-1,4,2])
-        
-        # get degree of corners:
-        squares_rollup = np.roll(squares, 1, axis=1)
-        squares_rolldown = np.roll(squares, -1, axis=1)
-        vec1 = squares_rollup - squares
-        normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10)
-        vec2 = squares_rolldown - squares
-        normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10)
-        inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1) # [n_squares, 4]
-        squares_degree = np.arccos(inner_products) * 180 / np.pi # [n_squares, 4]
-
-        # get square score
-        overlap_scores = []
-        degree_scores = []
-        length_scores = []
-        
-        for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree):
-            '''
-            0 -- 1
-            |    |
-            3 -- 2
-            
-            # segments: [4, 2]
-            # connects: [4]
-            '''
-            
-            ###################################### OVERLAP SCORES
-            cover = 0
-            perimeter = 0
-            # check 0 > 1 > 2 > 3
-            square_length = []
-             
-            for start_idx in range(4):
-                end_idx = (start_idx + 1) % 4
-                
-                connect_idx = connects[start_idx] # segment idx of segment01
-                start_segments = segments[start_idx]
-                end_segments = segments[end_idx]
-                
-                start_point = square[start_idx]
-                end_point = square[end_idx]
-                
-                # check whether outside or inside
-                start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments, connect_idx)
-                end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx)
-                
-                cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min
-                perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min
-                
-                square_length.append(dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min)
-            
-            overlap_scores.append(cover / perimeter)    
-            ######################################
-            ###################################### DEGREE SCORES
-            '''
-            deg0 vs deg2
-            deg1 vs deg3
-            '''
-            deg0, deg1, deg2, deg3 = degree
-            deg_ratio1 = deg0 / deg2
-            if deg_ratio1 > 1.0:
-                deg_ratio1 = 1 / deg_ratio1
-            deg_ratio2 = deg1 / deg3
-            if deg_ratio2 > 1.0:
-                deg_ratio2 = 1 / deg_ratio2
-            degree_scores.append((deg_ratio1 + deg_ratio2) / 2)
-            ######################################
-            ###################################### LENGTH SCORES
-            '''
-            len0 vs len2
-            len1 vs len3
-            '''
-            len0, len1, len2, len3 = square_length
-            len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0
-            len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1
-            length_scores.append((len_ratio1 + len_ratio2) / 2)
-
-            ######################################
-        
-        overlap_scores = np.array(overlap_scores)
-        overlap_scores /= np.max(overlap_scores)
-            
-        degree_scores = np.array(degree_scores)
-        #degree_scores /= np.max(degree_scores)
-        
-        length_scores = np.array(length_scores)
-
-        ###################################### AREA SCORES
-        area_scores = np.reshape(squares, [-1, 4, 2])
-        area_x = area_scores[:,:,0]
-        area_y = area_scores[:,:,1]
-        correction = area_x[:,-1] * area_y[:,0] - area_y[:,-1] * area_x[:,0]
-        area_scores = np.sum(area_x[:,:-1] * area_y[:,1:], axis=-1) - np.sum(area_y[:,:-1] * area_x[:,1:], axis=-1)
-        area_scores = 0.5 * np.abs(area_scores + correction)
-        area_scores /= (map_size * map_size) #np.max(area_scores)
-        ######################################
-        
-        ###################################### CENTER SCORES
-        centers = np.array([[256 // 2, 256 // 2]], dtype='float32') # [1, 2]
-        # squares: [n, 4, 2]
-        square_centers = np.mean(squares, axis=1) # [n, 2]
-        center2center = np.sqrt(np.sum((centers - square_centers) ** 2))
-        center_scores = center2center / (map_size / np.sqrt(2.0))
-
-
-        '''
-        score_w = [overlap, degree, area, center, length]
-        '''
-        score_w = [0.0, 1.0, 10.0, 0.5, 1.0]
-        score_array = params['w_overlap'] * overlap_scores \
-                        + params['w_degree'] * degree_scores \
-                        + params['w_area'] * area_scores \
-                        - params['w_center'] * center_scores \
-                        + params['w_length'] * length_scores
-
-        best_square = []
-
-        sorted_idx = np.argsort(score_array)[::-1]
-        score_array = score_array[sorted_idx]
-        squares = squares[sorted_idx]
-
-    except Exception as e:
-        pass
-    
-    try:
-        new_segments[:,0] = new_segments[:,0] * 2 / input_shape[1] * original_shape[1]
-        new_segments[:,1] = new_segments[:,1] * 2 / input_shape[0] * original_shape[0]
-        new_segments[:,2] = new_segments[:,2] * 2 / input_shape[1] * original_shape[1]
-        new_segments[:,3] = new_segments[:,3] * 2 / input_shape[0] * original_shape[0]
-    except:
-        new_segments = []
-    
-    try:
-        squares[:,:,0] = squares[:,:,0] * 2 / input_shape[1] * original_shape[1]
-        squares[:,:,1] = squares[:,:,1] * 2 / input_shape[0] * original_shape[0]
-    except:
-        squares = []
-        score_array = []
-
-    try:
-        inter_points = np.array(inter_points)
-        inter_points[:,0] = inter_points[:,0] * 2 / input_shape[1] * original_shape[1]
-        inter_points[:,1] = inter_points[:,1] * 2 / input_shape[0] * original_shape[0]
-    except:
-        inter_points = []
-        
-
-    return new_segments, squares, score_array, inter_points
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FREELETICS STRENGTH TRAINING GUIDE PDFl [BETTER].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FREELETICS STRENGTH TRAINING GUIDE PDFl [BETTER].md
deleted file mode 100644
index cb8603e6a272c33e38254157eb46a6f9416c3104..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FREELETICS STRENGTH TRAINING GUIDE PDFl [BETTER].md	
+++ /dev/null
@@ -1,37 +0,0 @@
-<br />
-<h1>How to Get Stronger with Freeletics Strength Training Guide PDF</h1>
-<p>If you are looking for a challenging and effective way to build muscle and power, you might want to check out the Freeletics Strength Training Guide PDF. This is a digital document that contains a 15-week training plan designed by Freeletics, a leading fitness app that offers personalized workouts and nutrition guidance.</p>
-<h2>FREELETICS STRENGTH TRAINING GUIDE PDFl</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://urlcod.com/2uIaYA">https://urlcod.com/2uIaYA</a></b></p><br /><br />
-<p>The Freeletics Strength Training Guide PDF is based on the principle of high-intensity interval training (HIIT), which involves short bursts of maximal effort followed by brief periods of rest. This type of training stimulates your muscles to grow and adapt, while also boosting your metabolism and cardiovascular health.</p>
-<p>The guide consists of three phases, each lasting five weeks. In each phase, you will perform different workouts that target different muscle groups and skills. The workouts are composed of exercises that use your own bodyweight or minimal equipment, such as a pull-up bar or a jump rope. You can do them anywhere, anytime, and at your own pace.</p>
-<p>The guide also provides you with tips on how to warm up, cool down, stretch, and recover properly. It also advises you on how to eat well and supplement your training with enough protein and other nutrients. By following the guide, you will not only get stronger physically, but also mentally and emotionally.</p>
-<p></p>
-<p>To access the Freeletics Strength Training Guide PDF, you need to download the Freeletics app and sign up for a subscription. You can choose between the Training & Nutrition Bundle, which includes both the training and nutrition coaching, or the Custom Training Journeys, which only includes the training coaching. You can also try out a free trial before committing to a plan.</p>
-<p>Once you have the app, you can find the Strength Training Guide PDF under the Training Journeys section. You can also view it online or print it out for your convenience. The guide is suitable for both beginners and advanced athletes, as you can adjust the difficulty level according to your feedback.</p>
-<p>If you are ready to take your strength training to the next level, download the Freeletics app today and start your journey with the Freeletics Strength Training Guide PDF. You will be amazed by what you can achieve with your own body and mind.</p>
-
-<p>What are the benefits of strength training?</p>
-<p>Strength training is not only good for building muscle mass and definition, but also for improving your overall health and well-being. Some of the benefits of strength training include:</p>
-<ul>
-<li>Increasing your bone density and reducing the risk of osteoporosis</li>
-<li>Enhancing your posture and balance and preventing injuries</li>
-<li>Boosting your metabolism and burning more calories even at rest</li>
-<li>Improving your blood sugar levels and lowering your blood pressure</li>
-<li>Strengthening your immune system and fighting off infections</li>
-<li>Reducing your stress levels and improving your mood and confidence</li>
-<li>Preventing or delaying the onset of age-related diseases such as sarcopenia and dementia</li>
-</ul>
-<p>How to get started with strength training?</p>
-<p>If you are new to strength training, you might feel intimidated or overwhelmed by the variety of exercises and equipment available. However, you don't need to worry, as you can start with simple and effective exercises that use your own bodyweight or minimal equipment. Here are some tips on how to get started with strength training:</p>
-<ul>
-<li>Consult your doctor before starting any new exercise program, especially if you have any medical conditions or injuries.</li>
-<li>Set realistic and specific goals for yourself, such as how many times a week you want to train, how long each session will last, and what results you want to achieve.</li>
-<li>Choose a training plan that suits your level, preferences, and schedule. You can use the Freeletics Strength Training Guide PDF as a reference, or create your own plan based on your goals.</li>
-<li>Learn the proper form and technique for each exercise, and start with low intensity and volume. You can use the Freeletics app to watch videos and instructions on how to perform each exercise correctly.</li>
-<li>Warm up before each session with some dynamic stretches and light cardio, such as jogging or skipping. This will prepare your muscles and joints for the workout and prevent injuries.</li>
-<li>Cool down after each session with some static stretches and deep breathing. This will help your muscles recover and reduce soreness.</li>
-<li>Rest at least one day between each session, and get enough sleep and hydration. This will allow your muscles to repair and grow stronger.</li>
-<li>Track your progress and celebrate your achievements. You can use the Freeletics app to record your workouts, measure your performance, and share your results with the community.</li>
-</ul></p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nickil/weakly-supervised-parsing/weakly_supervised_parser/settings.py b/spaces/nickil/weakly-supervised-parsing/weakly_supervised_parser/settings.py
deleted file mode 100644
index e9698fce0c18d2b85726511bf1a8bff6b449cade..0000000000000000000000000000000000000000
--- a/spaces/nickil/weakly-supervised-parsing/weakly_supervised_parser/settings.py
+++ /dev/null
@@ -1,33 +0,0 @@
-PROJECT_DIR = "weakly_supervised_parser/"
-PTB_TREES_ROOT_DIR = "data/PROCESSED/english/trees/"
-PTB_SENTENCES_ROOT_DIR = "data/PROCESSED/english/sentences/"
-
-PTB_TRAIN_SENTENCES_WITH_PUNCTUATION_PATH = PTB_SENTENCES_ROOT_DIR + "ptb-train-sentences-with-punctuation.txt"
-PTB_VALID_SENTENCES_WITH_PUNCTUATION_PATH = PTB_SENTENCES_ROOT_DIR + "ptb-valid-sentences-with-punctuation.txt"
-PTB_TEST_SENTENCES_WITH_PUNCTUATION_PATH = PTB_SENTENCES_ROOT_DIR + "ptb-test-sentences-with-punctuation.txt"
-
-PTB_TRAIN_SENTENCES_WITHOUT_PUNCTUATION_PATH = PTB_SENTENCES_ROOT_DIR + "ptb-train-sentences-without-punctuation.txt"
-PTB_VALID_SENTENCES_WITHOUT_PUNCTUATION_PATH = PTB_SENTENCES_ROOT_DIR + "ptb-valid-sentences-without-punctuation.txt"
-PTB_TEST_SENTENCES_WITHOUT_PUNCTUATION_PATH = PTB_SENTENCES_ROOT_DIR + "ptb-test-sentences-without-punctuation.txt"
-
-PTB_TRAIN_GOLD_WITH_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "ptb-train-gold-with-punctuation.txt"
-PTB_VALID_GOLD_WITH_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "ptb-valid-gold-with-punctuation.txt"
-PTB_TEST_GOLD_WITH_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "ptb-test-gold-with-punctuation.txt"
-
-PTB_TRAIN_GOLD_WITHOUT_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "ptb-train-gold-without-punctuation.txt"
-PTB_VALID_GOLD_WITHOUT_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "ptb-valid-gold-without-punctuation.txt"
-PTB_TEST_GOLD_WITHOUT_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "ptb-test-gold-without-punctuation.txt"
-
-PTB_TRAIN_GOLD_WITHOUT_PUNCTUATION_ALIGNED_PATH = PTB_TREES_ROOT_DIR + "ptb-train-gold-without-punctuation-aligned.txt"
-PTB_VALID_GOLD_WITHOUT_PUNCTUATION_ALIGNED_PATH = PTB_TREES_ROOT_DIR + "ptb-valid-gold-without-punctuation-aligned.txt"
-PTB_TEST_GOLD_WITHOUT_PUNCTUATION_ALIGNED_PATH = PTB_TREES_ROOT_DIR + "ptb-test-gold-without-punctuation-aligned.txt"
-
-YOON_KIM_TRAIN_GOLD_WITHOUT_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "Yoon_Kim/ptb-train-gold-filtered.txt"
-YOON_KIM_VALID_GOLD_WITHOUT_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "Yoon_Kim/ptb-valid-gold-filtered.txt"
-YOON_KIM_TEST_GOLD_WITHOUT_PUNCTUATION_PATH = PTB_TREES_ROOT_DIR + "Yoon_Kim/ptb-test-gold-filtered.txt"
-
-# Predictions
-PTB_SAVE_TREES_PATH = "TEMP/predictions/english/"
-
-# Training
-TRAINED_MODEL_PATH = PROJECT_DIR + "/model/TRAINED_MODEL/"
diff --git a/spaces/nielsr/TrOCR-handwritten/README.md b/spaces/nielsr/TrOCR-handwritten/README.md
deleted file mode 100644
index 34a9a13ea8b91f0aed4e7f2ded6bcd82142d18f8..0000000000000000000000000000000000000000
--- a/spaces/nielsr/TrOCR-handwritten/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: TrOCR Handwritten
-emoji: 🏢
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/configs/common/optim.py b/spaces/nikitaPDL2023/assignment4/detectron2/configs/common/optim.py
deleted file mode 100644
index 6cf43e835f55739fbb80102b870efab950a0486d..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/configs/common/optim.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import torch
-
-from detectron2.config import LazyCall as L
-from detectron2.solver.build import get_default_optimizer_params
-
-SGD = L(torch.optim.SGD)(
-    params=L(get_default_optimizer_params)(
-        # params.model is meant to be set to the model object, before instantiating
-        # the optimizer.
-        weight_decay_norm=0.0
-    ),
-    lr=0.02,
-    momentum=0.9,
-    weight_decay=1e-4,
-)
-
-
-AdamW = L(torch.optim.AdamW)(
-    params=L(get_default_optimizer_params)(
-        # params.model is meant to be set to the model object, before instantiating
-        # the optimizer.
-        base_lr="${..lr}",
-        weight_decay_norm=0.0,
-    ),
-    lr=1e-4,
-    betas=(0.9, 0.999),
-    weight_decay=0.1,
-)
diff --git a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/compute/kernels_arm.h b/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/compute/kernels_arm.h
deleted file mode 100644
index 494430fef873ebd86064263b7ab4d401906910e8..0000000000000000000000000000000000000000
--- a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/compute/kernels_arm.h
+++ /dev/null
@@ -1,2886 +0,0 @@
-/*
- * Copyright 2021 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LYRA_CODEC_SPARSE_MATMUL_COMPUTE_KERNELS_ARM_H_
-#define LYRA_CODEC_SPARSE_MATMUL_COMPUTE_KERNELS_ARM_H_
-
-#if defined __aarch64__
-
-#include <arm_neon.h>
-
-#include <type_traits>
-
-#include "sparse_matmul/numerics/fixed_types.h"
-#include "sparse_matmul/numerics/float16_types.h"
-#include "sparse_matmul/numerics/type_utils.h"
-
-#define LABEL_COL_LOOP "1"
-#define LABEL_ROW_LOOP "2"
-#define LABEL_SKIP_COL_LOOP "3"
-#define LABEL_TOP_LOOP "4"
-
-namespace csrblocksparse {
-namespace detail {
-
-template <typename T>
-struct IsFloatOrBfloat
-    : std::integral_constant<bool, std::is_same<T, float>::value ||
-                                       std::is_same<T, bfloat16>::value> {};
-
-template <typename WeightType, typename RhsType, typename OutType>
-struct IsAllowableFloatTypes
-    : std::integral_constant<bool, IsFloatOrBfloat<WeightType>::value &&
-                                       std::is_same<RhsType, float>::value &&
-                                       std::is_same<OutType, float>::value> {};
-
-// 16-bit inputs, 32-bit output exponent matches sum of input exponents
-// OR
-// 16-bit inputs, 16-bit output - will shift to match exponent
-template <typename WeightType, typename RhsType, typename OutType>
-struct IsAllowableFixedTypes
-    : std::integral_constant<bool, (IsFixed16Type<WeightType>::value &&
-                                    IsFixed16Type<RhsType>::value) &&
-                                       (IsFixed32Type<OutType>::value ||
-                                        IsFixed16Type<OutType>::value)> {};
-
-template <typename WeightType, typename RhsType, typename OutType>
-struct ShouldEnableGenericKernel
-    : std::integral_constant<
-          bool,
-          !IsAllowableFloatTypes<WeightType, RhsType, OutType>::value &&
-              !IsAllowableFixedTypes<WeightType, RhsType, OutType>::value> {};
-
-template <typename WeightType, typename RhsType, typename OutType>
-struct ShouldEnableGenericSpMV_4x4
-    : ShouldEnableGenericKernel<WeightType, RhsType, OutType> {};
-template <typename WeightType, typename RhsType, typename OutType>
-struct ShouldEnableGenericSpMM5_4x4
-    : ShouldEnableGenericKernel<WeightType, RhsType, OutType> {};
-template <typename WeightType, typename RhsType, typename OutType>
-struct ShouldEnableGenericSpMV_1x1 : std::true_type {};
-template <typename WeightType, typename RhsType, typename OutType>
-struct ShouldEnableGenericSpMM5_1x1 : std::true_type {};
-template <typename Type>
-struct IsAddableFixedTypes
-    : std::integral_constant<bool, IsFixed32Type<Type>::value ||
-                                       IsFixed16Type<Type>::value> {};
-template <typename Type>
-struct ShouldEnableGenericAdd
-    : std::integral_constant<bool, !IsAddableFixedTypes<Type>::value> {};
-
-// The computational routines do NO error checking for speed.  It is assumed
-// that this has been handled by CSRBlockSparseMatrix.
-
-// Performs the calculation y = A * x + b where A is a sparse matrix with a 4x4
-// blocked pattern, x is a vector and b is vector. Weights are stored for this
-// routine by making each 4x4 block contiguous. Blocks are ordered in standard
-// row-major format. column indices are converted to deltas and then multiplied
-// by 2 to convert to bytes, so that the value can be used directly to offset
-// the pointer into the rhs vector.
-//
-// NOTE: The bias is expected to have be multiplied by .25f prior to calling
-// this function.  This is automatically taken care of in SparseLinearLayer.
-// The bias is reconstructed through horizontal additions, leads to a small
-// speedup by reducing latencies at the end of the loop.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<std::is_same<WeightType, bfloat16>::value &&
-                        std::is_same<RhsType, float>::value &&
-                        std::is_same<OutType, float>::value>::type
-SpMV_4x4(const bfloat16* weights_ptr, const int16_t* col_deltas_bytes,
-         const int32_t* nnz_per_row, const float* rhs_ptr,
-         const float* bias_ptr, float* out_ptr, int64_t assigned_rows,
-         int64_t rows /* only used in SpMM variants */,
-         int64_t cols /* only used in SpMM variants */, int relu) {
-  /*   This instrinsic version exists for reference, note that in the
-       intrinsic version col_deltas_bytes should NOT actually be in bytes,
-       but rather elements.  Intrinsics are 25-35% slower than the
-       assembly version.
-
-       for (int r = 0; r < rows; r += 4) {
-        int reduced_col_count = nnz_per_row[r / 4];
-        float32x4_t accum0 = vdupq_n_f32(bias_ptr + r);
-        float32x4_t accum1 = vdupq_n_f32(bias_ptr + r + 1);
-        float32x4_t accum2 = vdupq_n_f32(bias_ptr + r + 2);
-        float32x4_t accum3 = vdupq_n_f32(bias_ptr + r + 3);
-        for (int c = 0; c < reduced_col_count; ++c) {
-          int32_t offset = *col_deltas_bytes; col_deltas_bytes++;
-          rhs_ptr += offset;
-          float32x4_t rhs = vld1q_f32(rhs_ptr);
-
-          uint16x4_t lhs0_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs1_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs2_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs3_int = vld1_u16(weights_ptr); weights_ptr += 4;
-
-          float32x4_t lhs0 = vreinterpretq_f32_u32(vshll_n_u16(lhs0_int, 16));
-          float32x4_t lhs1 = vreinterpretq_f32_u32(vshll_n_u16(lhs1_int, 16));
-          float32x4_t lhs2 = vreinterpretq_f32_u32(vshll_n_u16(lhs2_int, 16));
-          float32x4_t lhs3 = vreinterpretq_f32_u32(vshll_n_u16(lhs3_int, 16));
-
-          accum0 = vmlaq_f32(accum0, lhs0, rhs);
-          accum1 = vmlaq_f32(accum1, lhs1, rhs);
-          accum2 = vmlaq_f32(accum2, lhs2, rhs);
-          accum3 = vmlaq_f32(accum3, lhs3, rhs);
-        }
-
-        float32x4_t reduce0 = vpaddq_f32(accum0, accum1);
-        float32x4_t reduce1 = vpaddq_f32(accum2, accum3);
-        float32x4_t reduce2 = vpaddq_f32(reduce0, reduce1);
-        vst1q_f32(out_ptr + r, reduce2);
-      } */
-
-  // If the relu is handled in the routine with a comparison and vbit (insert
-  // if true), or by branching, then it is slightly, but noticeably slower
-  // ~5%, the outer branch avoids that penalty.
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Convert bfloat16 -> float32.
-        "shll  v4.4s, v2.4h, #16\n"
-        "shll2 v5.4s, v2.8h, #16\n"
-        "shll  v6.4s, v3.4h, #16\n"
-        "shll2 v7.4s, v3.8h, #16\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"
-        "fmla v29.4s, v5.4s, v0.4s\n"
-        "fmla v30.4s, v6.4s, v0.4s\n"
-        "fmla v31.4s, v7.4s, v0.4s\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result
-        "faddp v28.4s, v28.4s, v29.4s\n"
-        "faddp v30.4s, v30.4s, v31.4s\n"
-        "faddp v28.4s, v28.4s, v30.4s\n"
-
-        // Do relu if requested.
-        "fmax v28.4s, v28.4s, v25.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Convert bfloat16 -> float32.
-        "shll  v4.4s, v2.4h, #16\n"
-        "shll2 v5.4s, v2.8h, #16\n"
-        "shll  v6.4s, v3.4h, #16\n"
-        "shll2 v7.4s, v3.8h, #16\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"
-        "fmla v29.4s, v5.4s, v0.4s\n"
-        "fmla v30.4s, v6.4s, v0.4s\n"
-        "fmla v31.4s, v7.4s, v0.4s\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "faddp v28.4s, v28.4s, v29.4s\n"
-        "faddp v30.4s, v30.4s, v31.4s\n"
-        "faddp v28.4s, v28.4s, v30.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Performs the calculation y = A * x + b where A is a sparse matrix with a 4x4
-// blocked pattern, x is a fat vector with 5 columns and b is vector. b is
-// broadcast. Weights are stored for this routine by making each 4x4 block
-// contiguous. Blocks are ordered in standard row-major format. column indices
-// are converted to deltas and then multiplied by 2 to convert to bytes, so
-// that the value can be used directly to offset the pointer into the rhs
-// vector.
-//
-// NOTE: The bias is expected to have be multiplied by .25f prior to calling
-// this function.  This is automatically taken care of in SparseLinearLayer.
-// The bias is reconstructed through horizontal additions, leads to a small
-// speedup by reducing latencies at the end of the loop.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<std::is_same<WeightType, bfloat16>::value &&
-                        std::is_same<RhsType, float>::value &&
-                        std::is_same<OutType, float>::value>::type
-SpMM5_4x4(const bfloat16* weights_ptr, const int16_t* col_deltas_bytes,
-          const int32_t* nnz_per_row, const float* rhs_ptr,
-          const float* bias_ptr, float* out_ptr, int64_t assigned_rows,
-          int64_t rows, int64_t cols, int relu) {
-  /*   This instrinsic version exists for reference, note that in the
-       intrinsic version col_deltas_bytes should NOT actually be in bytes,
-       but rather elements.  Intrinsics are 25-35% slower than the
-       assembly version.
-
-       for (int r = 0; r < rows; r += 4) {
-        int reduced_col_count = nnz_per_row[r / 4];
-        float32x4_t accum0 = vdupq_n_f32(bias_ptr + r);
-        float32x4_t accum1 = vdupq_n_f32(bias_ptr + r + 1);
-        float32x4_t accum2 = vdupq_n_f32(bias_ptr + r + 2);
-        float32x4_t accum3 = vdupq_n_f32(bias_ptr + r + 3);
-        float32x4_t accum4 = vdupq_n_f32(bias_ptr + r);
-        float32x4_t accum5 = vdupq_n_f32(bias_ptr + r + 1);
-        float32x4_t accum6 = vdupq_n_f32(bias_ptr + r + 2);
-        float32x4_t accum7 = vdupq_n_f32(bias_ptr + r + 3);
-        ...
-        for (int c = 0; c < reduced_col_count; ++c) {
-          int32_t offset = *col_deltas_bytes; col_deltas_bytes++;
-          rhs_ptr += offset;
-          float32x4_t rhs = vld1q_f32(rhs_ptr);
-          float32x4_t rhs2 = vld1q_f32(rhs2_ptr);
-          float32x4_t rhs3 = vld1q_f32(rhs3_ptr);
-          float32x4_t rhs4 = vld1q_f32(rhs4_ptr);
-          float32x4_t rhs5 = vld1q_f32(rhs5_ptr);
-
-          uint16x4_t lhs0_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs1_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs2_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs3_int = vld1_u16(weights_ptr); weights_ptr += 4;
-
-          float32x4_t lhs0 = vreinterpretq_f32_u32(vshll_n_u16(lhs0_int, 16));
-          float32x4_t lhs1 = vreinterpretq_f32_u32(vshll_n_u16(lhs1_int, 16));
-          float32x4_t lhs2 = vreinterpretq_f32_u32(vshll_n_u16(lhs2_int, 16));
-          float32x4_t lhs3 = vreinterpretq_f32_u32(vshll_n_u16(lhs3_int, 16));
-
-          accum0 = vmlaq_f32(accum0, lhs0, rhs);
-          accum1 = vmlaq_f32(accum1, lhs1, rhs);
-          accum2 = vmlaq_f32(accum2, lhs2, rhs);
-          accum3 = vmlaq_f32(accum3, lhs3, rhs);
-          accum4 = vmlaq_f32(accum0, lhs0, rhs2);
-          accum5 = vmlaq_f32(accum1, lhs1, rhs2);
-          accum6 = vmlaq_f32(accum2, lhs2, rhs2);
-          accum7 = vmlaq_f32(accum3, lhs3, rhs2);
-          ...
-        }
-
-        float32x4_t reduce0 = vpaddq_f32(accum0, accum1);
-        float32x4_t reduce1 = vpaddq_f32(accum2, accum3);
-        float32x4_t reduce2 = vpaddq_f32(reduce0, reduce1);
-        vst1q_f32(out_ptr + r, reduce2);
-
-        float32x4_t reduce0 = vpaddq_f32(accum4, accum5);
-        float32x4_t reduce1 = vpaddq_f32(accum6, accum7);
-        float32x4_t reduce2 = vpaddq_f32(reduce0, reduce1);
-        vst1q_f32(out2_ptr + r, reduce2);
-
-        ...
-      } */
-
-  // If the relu is handled in the routine with a comparison and vbit (insert
-  // if true), or by branching, then it is slightly, but noticeably slower
-  // ~5%, the outer branch avoids that penalty.
-  //
-  // Pointers to the columns.
-  const float* rhs2_ptr = rhs_ptr + cols;
-  float* out2_ptr = out_ptr + rows;
-  const float* rhs3_ptr = rhs_ptr + 2 * cols;
-  float* out3_ptr = out_ptr + 2 * rows;
-  const float* rhs4_ptr = rhs_ptr + 3 * cols;
-  float* out4_ptr = out_ptr + 3 * rows;
-  const float* rhs5_ptr = rhs_ptr + 4 * cols;
-  float* out5_ptr = out_ptr + 4 * rows;
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-        "ld1 {v1.4s}, [%[rhs2_ptr]], x8\n"
-        "ld1 {v8.4s}, [%[rhs3_ptr]], x8\n"
-        "ld1 {v9.4s}, [%[rhs4_ptr]], x8\n"
-        "ld1 {v10.4s}, [%[rhs5_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Convert bfloat16 -> float32.
-        "shll  v4.4s, v2.4h, #16\n"
-        "shll2 v5.4s, v2.8h, #16\n"
-        "shll  v6.4s, v3.4h, #16\n"
-        "shll2 v7.4s, v3.8h, #16\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"   // for 1st column
-        "fmla v29.4s, v5.4s, v0.4s\n"   // for 1st column
-        "fmla v30.4s, v6.4s, v0.4s\n"   // for 1st column
-        "fmla v31.4s, v7.4s, v0.4s\n"   // for 1st column
-        "fmla v23.4s, v4.4s, v1.4s\n"   // for 2nd column
-        "fmla v24.4s, v5.4s, v1.4s\n"   // for 2nd column
-        "fmla v25.4s, v6.4s, v1.4s\n"   // for 2nd column
-        "fmla v26.4s, v7.4s, v1.4s\n"   // for 2nd column
-        "fmla v19.4s, v4.4s, v8.4s\n"   // for 3rd column
-        "fmla v20.4s, v5.4s, v8.4s\n"   // for 3rd column
-        "fmla v21.4s, v6.4s, v8.4s\n"   // for 3rd column
-        "fmla v22.4s, v7.4s, v8.4s\n"   // for 3rd column
-        "fmla v15.4s, v4.4s, v9.4s\n"   // for 4th column
-        "fmla v16.4s, v5.4s, v9.4s\n"   // for 4th column
-        "fmla v17.4s, v6.4s, v9.4s\n"   // for 4th column
-        "fmla v18.4s, v7.4s, v9.4s\n"   // for 4th column
-        "fmla v11.4s, v4.4s, v10.4s\n"  // for 5th column
-        "fmla v12.4s, v5.4s, v10.4s\n"  // for 5th column
-        "fmla v13.4s, v6.4s, v10.4s\n"  // for 5th column
-        "fmla v14.4s, v7.4s, v10.4s\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "movi v0.4s, #0\n"
-        "faddp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "faddp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "faddp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "faddp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "faddp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "faddp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "faddp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Do relu as requested.
-        "fmax v28.4s, v28.4s, v0.4s\n"
-        "fmax v23.4s, v23.4s, v0.4s\n"
-        "fmax v19.4s, v19.4s, v0.4s\n"
-        "fmax v15.4s, v15.4s, v0.4s\n"
-        "fmax v11.4s, v11.4s, v0.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr),
-        [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr),
-        [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr),
-        [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-        "ld1 {v1.4s}, [%[rhs2_ptr]], x8\n"
-        "ld1 {v8.4s}, [%[rhs3_ptr]], x8\n"
-        "ld1 {v9.4s}, [%[rhs4_ptr]], x8\n"
-        "ld1 {v10.4s}, [%[rhs5_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Convert bfloat16 -> float32.
-        "shll  v4.4s, v2.4h, #16\n"
-        "shll2 v5.4s, v2.8h, #16\n"
-        "shll  v6.4s, v3.4h, #16\n"
-        "shll2 v7.4s, v3.8h, #16\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"   // for 1st column
-        "fmla v29.4s, v5.4s, v0.4s\n"   // for 1st column
-        "fmla v30.4s, v6.4s, v0.4s\n"   // for 1st column
-        "fmla v31.4s, v7.4s, v0.4s\n"   // for 1st column
-        "fmla v23.4s, v4.4s, v1.4s\n"   // for 2nd column
-        "fmla v24.4s, v5.4s, v1.4s\n"   // for 2nd column
-        "fmla v25.4s, v6.4s, v1.4s\n"   // for 2nd column
-        "fmla v26.4s, v7.4s, v1.4s\n"   // for 2nd column
-        "fmla v19.4s, v4.4s, v8.4s\n"   // for 3rd column
-        "fmla v20.4s, v5.4s, v8.4s\n"   // for 3rd column
-        "fmla v21.4s, v6.4s, v8.4s\n"   // for 3rd column
-        "fmla v22.4s, v7.4s, v8.4s\n"   // for 3rd column
-        "fmla v15.4s, v4.4s, v9.4s\n"   // for 4th column
-        "fmla v16.4s, v5.4s, v9.4s\n"   // for 4th column
-        "fmla v17.4s, v6.4s, v9.4s\n"   // for 4th column
-        "fmla v18.4s, v7.4s, v9.4s\n"   // for 4th column
-        "fmla v11.4s, v4.4s, v10.4s\n"  // for 5th column
-        "fmla v12.4s, v5.4s, v10.4s\n"  // for 5th column
-        "fmla v13.4s, v6.4s, v10.4s\n"  // for 5th column
-        "fmla v14.4s, v7.4s, v10.4s\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "faddp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "faddp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "faddp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "faddp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "faddp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "faddp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "faddp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr),
-        [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr),
-        [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr),
-        [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// float implementations below the line.
-
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<std::is_same<WeightType, float>::value &&
-                        std::is_same<RhsType, float>::value &&
-                        std::is_same<OutType, float>::value>::type
-SpMV_4x4(const float* weights_ptr, const int16_t* col_deltas_bytes,
-         const int32_t* nnz_per_row, const float* rhs_ptr,
-         const float* bias_ptr, float* out_ptr, int64_t assigned_rows,
-         int64_t rows /* only used in SpMM variants */,
-         int64_t cols /* only used in SpMM variants */, int relu) {
-  /*   This instrinsic version exists for reference, note that in the
-       intrinsic version col_deltas_bytes should NOT actually be in bytes,
-       but rather elements.  Intrinsics are 25-35% slower than the
-       assembly version.
-
-       for (int r = 0; r < rows; r += 4) {
-        int reduced_col_count = nnz_per_row[r / 4];
-        float32x4_t accum0 = vdupq_n_f32(bias_ptr + r);
-        float32x4_t accum1 = vdupq_n_f32(bias_ptr + r + 1);
-        float32x4_t accum2 = vdupq_n_f32(bias_ptr + r + 2);
-        float32x4_t accum3 = vdupq_n_f32(bias_ptr + r + 3);
-        for (int c = 0; c < reduced_col_count; ++c) {
-          int32_t offset = *col_deltas_bytes; col_deltas_bytes++;
-          rhs_ptr += offset;
-          float32x4_t rhs = vld1q_f32(rhs_ptr);
-
-          uint16x4_t lhs0_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs1_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs2_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs3_int = vld1_u16(weights_ptr); weights_ptr += 4;
-
-          float32x4_t lhs0 = vreinterpretq_f32_u32(vshll_n_u16(lhs0_int, 16));
-          float32x4_t lhs1 = vreinterpretq_f32_u32(vshll_n_u16(lhs1_int, 16));
-          float32x4_t lhs2 = vreinterpretq_f32_u32(vshll_n_u16(lhs2_int, 16));
-          float32x4_t lhs3 = vreinterpretq_f32_u32(vshll_n_u16(lhs3_int, 16));
-
-          accum0 = vmlaq_f32(accum0, lhs0, rhs);
-          accum1 = vmlaq_f32(accum1, lhs1, rhs);
-          accum2 = vmlaq_f32(accum2, lhs2, rhs);
-          accum3 = vmlaq_f32(accum3, lhs3, rhs);
-        }
-
-        float32x4_t reduce0 = vpaddq_f32(accum0, accum1);
-        float32x4_t reduce1 = vpaddq_f32(accum2, accum3);
-        float32x4_t reduce2 = vpaddq_f32(reduce0, reduce1);
-        vst1q_f32(out_ptr + r, reduce2);
-      } */
-
-  // If the relu is handled in the routine with a comparison and vbit (insert
-  // if true), or by branching, then it is slightly, but noticeably slower
-  // ~5%, the outer branch avoids that penalty.
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v4.4s, v5.4s, v6.4s, v7.4s}, [%[weights_ptr]], #64\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"
-        "fmla v29.4s, v5.4s, v0.4s\n"
-        "fmla v30.4s, v6.4s, v0.4s\n"
-        "fmla v31.4s, v7.4s, v0.4s\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "faddp v28.4s, v28.4s, v29.4s\n"
-        "faddp v30.4s, v30.4s, v31.4s\n"
-        "faddp v28.4s, v28.4s, v30.4s\n"
-
-        // Do relu as requested.
-        "fmax v28.4s, v28.4s, v25.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v4.4s, v5.4s, v6.4s, v7.4s}, [%[weights_ptr]], #64\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"
-        "fmla v29.4s, v5.4s, v0.4s\n"
-        "fmla v30.4s, v6.4s, v0.4s\n"
-        "fmla v31.4s, v7.4s, v0.4s\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "faddp v28.4s, v28.4s, v29.4s\n"
-        "faddp v30.4s, v30.4s, v31.4s\n"
-        "faddp v28.4s, v28.4s, v30.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Performs the calculation y = A * x + b where A is a sparse matrix with a 4x4
-// blocked pattern, x is a fat vector with 5 columns and b is vector. b is
-// broadcast. Weights are stored for this routine by making each 4x4 block
-// contiguous. Blocks are ordered in standard row-major format. column indices
-// are converted to deltas and then multiplied by 2 to convert to bytes, so
-// that the value can be used directly to offset the pointer into the rhs
-// vector.
-//
-// NOTE: The bias is expected to have be multiplied by .25f prior to calling
-// this function.  This is automatically taken care of in sparse_linear_layer.
-// The bias is reconstructed through horizontal additions, leads to a small
-// speedup by reducing latencies at the end of the loop.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<std::is_same<WeightType, float>::value &&
-                        std::is_same<RhsType, float>::value &&
-                        std::is_same<OutType, float>::value>::type
-SpMM5_4x4(const float* weights_ptr, const int16_t* col_deltas_bytes,
-          const int32_t* nnz_per_row, const float* rhs_ptr,
-          const float* bias_ptr, float* out_ptr, int64_t assigned_rows,
-          int64_t rows, int64_t cols, int relu) {
-  /*   This instrinsic version exists for reference, note that in the
-       intrinsic version col_deltas_bytes should NOT actually be in bytes,
-       but rather elements.  Intrinsics are 25-35% slower than the
-       assembly version.
-
-       for (int r = 0; r < rows; r += 4) {
-        int reduced_col_count = nnz_per_row[r / 4];
-        float32x4_t accum0 = vdupq_n_f32(bias_ptr + r);
-        float32x4_t accum1 = vdupq_n_f32(bias_ptr + r + 1);
-        float32x4_t accum2 = vdupq_n_f32(bias_ptr + r + 2);
-        float32x4_t accum3 = vdupq_n_f32(bias_ptr + r + 3);
-        float32x4_t accum4 = vdupq_n_f32(bias_ptr + r);
-        float32x4_t accum5 = vdupq_n_f32(bias_ptr + r + 1);
-        float32x4_t accum6 = vdupq_n_f32(bias_ptr + r + 2);
-        float32x4_t accum7 = vdupq_n_f32(bias_ptr + r + 3);
-        ...
-        for (int c = 0; c < reduced_col_count; ++c) {
-          int32_t offset = *col_deltas_bytes; col_deltas_bytes++;
-          rhs_ptr += offset;
-          float32x4_t rhs = vld1q_f32(rhs_ptr);
-          float32x4_t rhs2 = vld1q_f32(rhs2_ptr);
-          float32x4_t rhs3 = vld1q_f32(rhs3_ptr);
-          float32x4_t rhs4 = vld1q_f32(rhs4_ptr);
-          float32x4_t rhs5 = vld1q_f32(rhs5_ptr);
-
-          uint16x4_t lhs0_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs1_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs2_int = vld1_u16(weights_ptr); weights_ptr += 4;
-          uint16x4_t lhs3_int = vld1_u16(weights_ptr); weights_ptr += 4;
-
-          float32x4_t lhs0 = vreinterpretq_f32_u32(vshll_n_u16(lhs0_int, 16));
-          float32x4_t lhs1 = vreinterpretq_f32_u32(vshll_n_u16(lhs1_int, 16));
-          float32x4_t lhs2 = vreinterpretq_f32_u32(vshll_n_u16(lhs2_int, 16));
-          float32x4_t lhs3 = vreinterpretq_f32_u32(vshll_n_u16(lhs3_int, 16));
-
-          accum0 = vmlaq_f32(accum0, lhs0, rhs);
-          accum1 = vmlaq_f32(accum1, lhs1, rhs);
-          accum2 = vmlaq_f32(accum2, lhs2, rhs);
-          accum3 = vmlaq_f32(accum3, lhs3, rhs);
-          accum4 = vmlaq_f32(accum0, lhs0, rhs2);
-          accum5 = vmlaq_f32(accum1, lhs1, rhs2);
-          accum6 = vmlaq_f32(accum2, lhs2, rhs2);
-          accum7 = vmlaq_f32(accum3, lhs3, rhs2);
-          ...
-        }
-
-        float32x4_t reduce0 = vpaddq_f32(accum0, accum1);
-        float32x4_t reduce1 = vpaddq_f32(accum2, accum3);
-        float32x4_t reduce2 = vpaddq_f32(reduce0, reduce1);
-        vst1q_f32(out_ptr + r, reduce2);
-
-        float32x4_t reduce0 = vpaddq_f32(accum4, accum5);
-        float32x4_t reduce1 = vpaddq_f32(accum6, accum7);
-        float32x4_t reduce2 = vpaddq_f32(reduce0, reduce1);
-        vst1q_f32(out2_ptr + r, reduce2);
-
-        ...
-      } */
-
-  // If the relu is handled in the routine with a comparison and vbit (insert
-  // if true), or by branching, then it is slightly, but noticeably slower
-  // ~5%, the outer branch avoids that penalty.
-  //
-  // Pointers to the columns.
-  const float* rhs2_ptr = rhs_ptr + cols;
-  float* out2_ptr = out_ptr + rows;
-  const float* rhs3_ptr = rhs_ptr + 2 * cols;
-  float* out3_ptr = out_ptr + 2 * rows;
-  const float* rhs4_ptr = rhs_ptr + 3 * cols;
-  float* out4_ptr = out_ptr + 3 * rows;
-  const float* rhs5_ptr = rhs_ptr + 4 * cols;
-  float* out5_ptr = out_ptr + 4 * rows;
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-        "ld1 {v1.4s}, [%[rhs2_ptr]], x8\n"
-        "ld1 {v8.4s}, [%[rhs3_ptr]], x8\n"
-        "ld1 {v9.4s}, [%[rhs4_ptr]], x8\n"
-        "ld1 {v10.4s}, [%[rhs5_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v4.4s, v5.4s, v6.4s, v7.4s}, [%[weights_ptr]], #64\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"   // for 1st column
-        "fmla v29.4s, v5.4s, v0.4s\n"   // for 1st column
-        "fmla v30.4s, v6.4s, v0.4s\n"   // for 1st column
-        "fmla v31.4s, v7.4s, v0.4s\n"   // for 1st column
-        "fmla v23.4s, v4.4s, v1.4s\n"   // for 2nd column
-        "fmla v24.4s, v5.4s, v1.4s\n"   // for 2nd column
-        "fmla v25.4s, v6.4s, v1.4s\n"   // for 2nd column
-        "fmla v26.4s, v7.4s, v1.4s\n"   // for 2nd column
-        "fmla v19.4s, v4.4s, v8.4s\n"   // for 3rd column
-        "fmla v20.4s, v5.4s, v8.4s\n"   // for 3rd column
-        "fmla v21.4s, v6.4s, v8.4s\n"   // for 3rd column
-        "fmla v22.4s, v7.4s, v8.4s\n"   // for 3rd column
-        "fmla v15.4s, v4.4s, v9.4s\n"   // for 4th column
-        "fmla v16.4s, v5.4s, v9.4s\n"   // for 4th column
-        "fmla v17.4s, v6.4s, v9.4s\n"   // for 4th column
-        "fmla v18.4s, v7.4s, v9.4s\n"   // for 4th column
-        "fmla v11.4s, v4.4s, v10.4s\n"  // for 5th column
-        "fmla v12.4s, v5.4s, v10.4s\n"  // for 5th column
-        "fmla v13.4s, v6.4s, v10.4s\n"  // for 5th column
-        "fmla v14.4s, v7.4s, v10.4s\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "movi v0.4s, #0\n"
-        "faddp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "faddp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "faddp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "faddp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "faddp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "faddp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "faddp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Do relu as requested.
-        "fmax v28.4s, v28.4s, v0.4s\n"
-        "fmax v23.4s, v23.4s, v0.4s\n"
-        "fmax v19.4s, v19.4s, v0.4s\n"
-        "fmax v15.4s, v15.4s, v0.4s\n"
-        "fmax v11.4s, v11.4s, v0.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr),
-        [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr),
-        [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr),
-        [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4s}, [%[rhs_ptr]], x8\n"
-        "ld1 {v1.4s}, [%[rhs2_ptr]], x8\n"
-        "ld1 {v8.4s}, [%[rhs3_ptr]], x8\n"
-        "ld1 {v9.4s}, [%[rhs4_ptr]], x8\n"
-        "ld1 {v10.4s}, [%[rhs5_ptr]], x8\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v4.4s, v5.4s, v6.4s, v7.4s}, [%[weights_ptr]], #64\n"
-
-        // Multiply-accumulate.
-        "fmla v28.4s, v4.4s, v0.4s\n"   // for 1st column
-        "fmla v29.4s, v5.4s, v0.4s\n"   // for 1st column
-        "fmla v30.4s, v6.4s, v0.4s\n"   // for 1st column
-        "fmla v31.4s, v7.4s, v0.4s\n"   // for 1st column
-        "fmla v23.4s, v4.4s, v1.4s\n"   // for 2nd column
-        "fmla v24.4s, v5.4s, v1.4s\n"   // for 2nd column
-        "fmla v25.4s, v6.4s, v1.4s\n"   // for 2nd column
-        "fmla v26.4s, v7.4s, v1.4s\n"   // for 2nd column
-        "fmla v19.4s, v4.4s, v8.4s\n"   // for 3rd column
-        "fmla v20.4s, v5.4s, v8.4s\n"   // for 3rd column
-        "fmla v21.4s, v6.4s, v8.4s\n"   // for 3rd column
-        "fmla v22.4s, v7.4s, v8.4s\n"   // for 3rd column
-        "fmla v15.4s, v4.4s, v9.4s\n"   // for 4th column
-        "fmla v16.4s, v5.4s, v9.4s\n"   // for 4th column
-        "fmla v17.4s, v6.4s, v9.4s\n"   // for 4th column
-        "fmla v18.4s, v7.4s, v9.4s\n"   // for 4th column
-        "fmla v11.4s, v4.4s, v10.4s\n"  // for 5th column
-        "fmla v12.4s, v5.4s, v10.4s\n"  // for 5th column
-        "fmla v13.4s, v6.4s, v10.4s\n"  // for 5th column
-        "fmla v14.4s, v7.4s, v10.4s\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "faddp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "faddp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "faddp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "faddp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "faddp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "faddp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "faddp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "faddp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "faddp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "faddp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "faddp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr),
-        [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr),
-        [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr),
-        [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes),
-        [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row),
-        [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr),
-        [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr),
-        [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Note that the number of exponent bits in the output must exactly match
-// the sum of the input and rhs types.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<
-    IsFixed16Type<WeightType>::value && IsFixed16Type<RhsType>::value &&
-    std::is_same<OutType, typename TypeOfProduct<WeightType,
-                                                 RhsType>::type>::value>::type
-SpMV_4x4(const WeightType* weights_ptr, const int16_t* col_deltas_bytes,
-         const int32_t* nnz_per_row, const RhsType* rhs_ptr,
-         const typename TypeOfProduct<WeightType, RhsType>::type* bias_ptr,
-         OutType* out_ptr, int64_t assigned_rows,
-         int64_t rows /* only used in SpMM variants */,
-         int64_t cols /* only used in SpMM variants */, int relu) {
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        // Duplicate the lower half into the upper half.
-        "mov v0.d[1], v0.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"
-        "smlal2 v29.4s, v2.8h, v0.8h\n"
-        "smlal v30.4s, v3.4h, v0.4h\n"
-        "smlal2 v31.4s, v3.8h, v0.8h\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "addp v28.4s, v28.4s, v29.4s\n"
-        "addp v30.4s, v30.4s, v31.4s\n"
-        "addp v28.4s, v28.4s, v30.4s\n"
-
-        // Do relu if requested.
-        "smax v28.4s, v28.4s, v25.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        // Duplicate the lower half into the upper half.
-        "mov v0.d[1], v0.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"
-        "smlal2 v29.4s, v2.8h, v0.8h\n"
-        "smlal v30.4s, v3.4h, v0.4h\n"
-        "smlal2 v31.4s, v3.8h, v0.8h\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "addp v28.4s, v28.4s, v29.4s\n"
-        "addp v30.4s, v30.4s, v31.4s\n"
-        "addp v28.4s, v28.4s, v30.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Note that the number of exponent bits in the output must exactly match
-// the sum of the input and rhs types.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<
-    IsFixed16Type<WeightType>::value && IsFixed16Type<RhsType>::value &&
-    std::is_same<OutType, typename TypeOfProduct<WeightType,
-                                                 RhsType>::type>::value>::type
-SpMM5_4x4(const WeightType* weights_ptr, const int16_t* col_deltas_bytes,
-          const int32_t* nnz_per_row, const RhsType* rhs_ptr,
-          const typename TypeOfProduct<WeightType, RhsType>::type* bias_ptr,
-          OutType* out_ptr, int64_t assigned_rows, int64_t rows, int64_t cols,
-          int relu) {
-  // Pointers to the columns.
-  const RhsType* rhs2_ptr = rhs_ptr + cols;
-  OutType* out2_ptr = out_ptr + rows;
-  const RhsType* rhs3_ptr = rhs_ptr + 2 * cols;
-  OutType* out3_ptr = out_ptr + 2 * rows;
-  const RhsType* rhs4_ptr = rhs_ptr + 3 * cols;
-  OutType* out4_ptr = out_ptr + 3 * rows;
-  const RhsType* rhs5_ptr = rhs_ptr + 4 * cols;
-  OutType* out5_ptr = out_ptr + 4 * rows;
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each and duplicate into upper half.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        "mov v0.d[1], v0.d[0]\n"
-        "ld1 {v1.4h}, [%[rhs2_ptr]], x8\n"
-        "mov v1.d[1], v1.d[0]\n"
-        "ld1 {v8.4h}, [%[rhs3_ptr]], x8\n"
-        "mov v8.d[1], v8.d[0]\n"
-        "ld1 {v9.4h}, [%[rhs4_ptr]], x8\n"
-        "mov v9.d[1], v9.d[0]\n"
-        "ld1 {v10.4h}, [%[rhs5_ptr]], x8\n"
-        "mov v10.d[1], v10.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"    // for 1st column
-        "smlal2 v29.4s, v2.8h, v0.8h\n"   // for 1st column
-        "smlal v30.4s, v3.4h, v0.4h\n"    // for 1st column
-        "smlal2 v31.4s, v3.8h, v0.8h\n"   // for 1st columh
-        "smlal v23.4s, v2.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v24.4s, v2.8h, v1.8h\n"   // for 2nd column
-        "smlal v25.4s, v3.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v26.4s, v3.8h, v1.8h\n"   // for 2nd column
-        "smlal v19.4s, v2.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v20.4s, v2.8h, v8.8h\n"   // for 3rd column
-        "smlal v21.4s, v3.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v22.4s, v3.8h, v8.8h\n"   // for 3rd column
-        "smlal v15.4s, v2.4h, v9.4h\n"    // for 4th column
-        "smlal2 v16.4s, v2.8h, v9.8h\n"   // for 4th column
-        "smlal v17.4s, v3.4h, v9.4h\n"    // for 4th column
-        "smlal2 v18.4s, v3.8h, v9.8h\n"   // for 4th column
-        "smlal v11.4s, v2.4h, v10.4h\n"   // for 5th column
-        "smlal2 v12.4s, v2.8h, v10.8h\n"  // for 5th column
-        "smlal v13.4s, v3.4h, v10.4h\n"   // for 5th column
-        "smlal2 v14.4s, v3.8h, v10.8h\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "movi v0.4s, #0\n"
-        "addp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "addp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "addp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "addp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "addp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "addp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "addp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Do relu as requested.
-        "smax v28.4s, v28.4s, v0.4s\n"
-        "smax v23.4s, v23.4s, v0.4s\n"
-        "smax v19.4s, v19.4s, v0.4s\n"
-        "smax v15.4s, v15.4s, v0.4s\n"
-        "smax v11.4s, v11.4s, v0.4s\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr), [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr), [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr), [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each and duplicate into upper half.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        "mov v0.d[1], v0.d[0]\n"
-        "ld1 {v1.4h}, [%[rhs2_ptr]], x8\n"
-        "mov v1.d[1], v1.d[0]\n"
-        "ld1 {v8.4h}, [%[rhs3_ptr]], x8\n"
-        "mov v8.d[1], v8.d[0]\n"
-        "ld1 {v9.4h}, [%[rhs4_ptr]], x8\n"
-        "mov v9.d[1], v9.d[0]\n"
-        "ld1 {v10.4h}, [%[rhs5_ptr]], x8\n"
-        "mov v10.d[1], v10.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"    // for 1st column
-        "smlal2 v29.4s, v2.8h, v0.8h\n"   // for 1st column
-        "smlal v30.4s, v3.4h, v0.4h\n"    // for 1st column
-        "smlal2 v31.4s, v3.8h, v0.8h\n"   // for 1st columh
-        "smlal v23.4s, v2.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v24.4s, v2.8h, v1.8h\n"   // for 2nd column
-        "smlal v25.4s, v3.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v26.4s, v3.8h, v1.8h\n"   // for 2nd column
-        "smlal v19.4s, v2.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v20.4s, v2.8h, v8.8h\n"   // for 3rd column
-        "smlal v21.4s, v3.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v22.4s, v3.8h, v8.8h\n"   // for 3rd column
-        "smlal v15.4s, v2.4h, v9.4h\n"    // for 4th column
-        "smlal2 v16.4s, v2.8h, v9.8h\n"   // for 4th column
-        "smlal v17.4s, v3.4h, v9.4h\n"    // for 4th column
-        "smlal2 v18.4s, v3.8h, v9.8h\n"   // for 4th column
-        "smlal v11.4s, v2.4h, v10.4h\n"   // for 5th column
-        "smlal2 v12.4s, v2.8h, v10.8h\n"  // for 5th column
-        "smlal v13.4s, v3.4h, v10.4h\n"   // for 5th column
-        "smlal2 v14.4s, v3.8h, v10.8h\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "addp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "addp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "addp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "addp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "addp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "addp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "addp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr), [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr), [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr), [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Note that the number of exponent bits in the bias must exactly match
-// the sum of the input and rhs types.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<IsFixed16Type<WeightType>::value &&
-                        IsFixed16Type<RhsType>::value &&
-                        IsFixed16Type<OutType>::value>::type
-SpMV_4x4(const WeightType* weights_ptr, const int16_t* col_deltas_bytes,
-         const int32_t* nnz_per_row, const RhsType* rhs_ptr,
-         const typename TypeOfProduct<WeightType, RhsType>::type* bias_ptr,
-         OutType* out_ptr, int64_t assigned_rows,
-         int64_t rows /* only used in SpMM variants */,
-         int64_t cols /* only used in SpMM variants */, int relu) {
-  constexpr int kShiftAmount = 15 - WeightType::kExponentBits -
-                               RhsType::kExponentBits + OutType::kExponentBits;
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        // Duplicate the lower half into the upper half.
-        "mov v0.d[1], v0.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"
-        "smlal2 v29.4s, v2.8h, v0.8h\n"
-        "smlal v30.4s, v3.4h, v0.4h\n"
-        "smlal2 v31.4s, v3.8h, v0.8h\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "addp v28.4s, v28.4s, v29.4s\n"
-        "addp v30.4s, v30.4s, v31.4s\n"
-        "addp v28.4s, v28.4s, v30.4s\n"
-
-        // Do relu if requested.
-        "smax v28.4s, v28.4s, v25.4s\n"
-        "sqrshrn v26.4h, v28.4s, %[shift_amount]\n"
-
-        // Store accumulators.
-        "st1 {v26.4h}, [%[out_ptr]], #8\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        // Duplicate the lower half into the upper half.
-        "mov v0.d[1], v0.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"
-        "smlal2 v29.4s, v2.8h, v0.8h\n"
-        "smlal v30.4s, v3.4h, v0.4h\n"
-        "smlal2 v31.4s, v3.8h, v0.8h\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "addp v28.4s, v28.4s, v29.4s\n"
-        "addp v30.4s, v30.4s, v31.4s\n"
-        "addp v28.4s, v28.4s, v30.4s\n"
-        "sqrshrn v26.4h, v28.4s, %[shift_amount]\n"
-
-        // Store accumulators.
-        "st1 {v26.4h}, [%[out_ptr]], #8\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Note that the number of exponent bits in the output must exactly match
-// the sum of the input and rhs types.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<IsFixed16Type<WeightType>::value &&
-                        IsFixed16Type<RhsType>::value &&
-                        IsFixed16Type<OutType>::value>::type
-SpMM5_4x4(const WeightType* weights_ptr, const int16_t* col_deltas_bytes,
-          const int32_t* nnz_per_row, const RhsType* rhs_ptr,
-          const typename TypeOfProduct<WeightType, RhsType>::type* bias_ptr,
-          OutType* out_ptr, int64_t assigned_rows, int64_t rows, int64_t cols,
-          int relu) {
-  constexpr int kShiftAmount = 15 - WeightType::kExponentBits -
-                               RhsType::kExponentBits + OutType::kExponentBits;
-  // Pointers to the columns.
-  const RhsType* rhs2_ptr = rhs_ptr + cols;
-  OutType* out2_ptr = out_ptr + rows;
-  const RhsType* rhs3_ptr = rhs_ptr + 2 * cols;
-  OutType* out3_ptr = out_ptr + 2 * rows;
-  const RhsType* rhs4_ptr = rhs_ptr + 3 * cols;
-  OutType* out4_ptr = out_ptr + 3 * rows;
-  const RhsType* rhs5_ptr = rhs_ptr + 4 * cols;
-  OutType* out5_ptr = out_ptr + 4 * rows;
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each and duplicate into upper half.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        "mov v0.d[1], v0.d[0]\n"
-        "ld1 {v1.4h}, [%[rhs2_ptr]], x8\n"
-        "mov v1.d[1], v1.d[0]\n"
-        "ld1 {v8.4h}, [%[rhs3_ptr]], x8\n"
-        "mov v8.d[1], v8.d[0]\n"
-        "ld1 {v9.4h}, [%[rhs4_ptr]], x8\n"
-        "mov v9.d[1], v9.d[0]\n"
-        "ld1 {v10.4h}, [%[rhs5_ptr]], x8\n"
-        "mov v10.d[1], v10.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"    // for 1st column
-        "smlal2 v29.4s, v2.8h, v0.8h\n"   // for 1st column
-        "smlal v30.4s, v3.4h, v0.4h\n"    // for 1st column
-        "smlal2 v31.4s, v3.8h, v0.8h\n"   // for 1st columh
-        "smlal v23.4s, v2.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v24.4s, v2.8h, v1.8h\n"   // for 2nd column
-        "smlal v25.4s, v3.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v26.4s, v3.8h, v1.8h\n"   // for 2nd column
-        "smlal v19.4s, v2.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v20.4s, v2.8h, v8.8h\n"   // for 3rd column
-        "smlal v21.4s, v3.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v22.4s, v3.8h, v8.8h\n"   // for 3rd column
-        "smlal v15.4s, v2.4h, v9.4h\n"    // for 4th column
-        "smlal2 v16.4s, v2.8h, v9.8h\n"   // for 4th column
-        "smlal v17.4s, v3.4h, v9.4h\n"    // for 4th column
-        "smlal2 v18.4s, v3.8h, v9.8h\n"   // for 4th column
-        "smlal v11.4s, v2.4h, v10.4h\n"   // for 5th column
-        "smlal2 v12.4s, v2.8h, v10.8h\n"  // for 5th column
-        "smlal v13.4s, v3.4h, v10.4h\n"   // for 5th column
-        "smlal2 v14.4s, v3.8h, v10.8h\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "movi v0.4s, #0\n"
-        "addp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "addp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "addp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "addp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "addp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "addp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "addp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Do relu as requested.
-        "smax v28.4s, v28.4s, v0.4s\n"
-        "smax v23.4s, v23.4s, v0.4s\n"
-        "smax v19.4s, v19.4s, v0.4s\n"
-        "smax v15.4s, v15.4s, v0.4s\n"
-        "smax v11.4s, v11.4s, v0.4s\n"
-        "sqrshrn v26.4h, v28.4s, %[shift_amount]\n"
-        "sqrshrn v22.4h, v23.4s, %[shift_amount]\n"
-        "sqrshrn v18.4h, v19.4s, %[shift_amount]\n"
-        "sqrshrn v14.4h, v15.4s, %[shift_amount]\n"
-        "sqrshrn v10.4h, v11.4s, %[shift_amount]\n"
-
-        // Store accumulators.
-        "st1 {v26.4h}, [%[out_ptr]], #8\n"
-        "st1 {v22.4h}, [%[out2_ptr]], #8\n"
-        "st1 {v18.4h}, [%[out3_ptr]], #8\n"
-        "st1 {v14.4h}, [%[out4_ptr]], #8\n"
-        "st1 {v10.4h}, [%[out5_ptr]], #8\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr), [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr), [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr), [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each and duplicate into upper half.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        "mov v0.d[1], v0.d[0]\n"
-        "ld1 {v1.4h}, [%[rhs2_ptr]], x8\n"
-        "mov v1.d[1], v1.d[0]\n"
-        "ld1 {v8.4h}, [%[rhs3_ptr]], x8\n"
-        "mov v8.d[1], v8.d[0]\n"
-        "ld1 {v9.4h}, [%[rhs4_ptr]], x8\n"
-        "mov v9.d[1], v9.d[0]\n"
-        "ld1 {v10.4h}, [%[rhs5_ptr]], x8\n"
-        "mov v10.d[1], v10.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"    // for 1st column
-        "smlal2 v29.4s, v2.8h, v0.8h\n"   // for 1st column
-        "smlal v30.4s, v3.4h, v0.4h\n"    // for 1st column
-        "smlal2 v31.4s, v3.8h, v0.8h\n"   // for 1st columh
-        "smlal v23.4s, v2.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v24.4s, v2.8h, v1.8h\n"   // for 2nd column
-        "smlal v25.4s, v3.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v26.4s, v3.8h, v1.8h\n"   // for 2nd column
-        "smlal v19.4s, v2.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v20.4s, v2.8h, v8.8h\n"   // for 3rd column
-        "smlal v21.4s, v3.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v22.4s, v3.8h, v8.8h\n"   // for 3rd column
-        "smlal v15.4s, v2.4h, v9.4h\n"    // for 4th column
-        "smlal2 v16.4s, v2.8h, v9.8h\n"   // for 4th column
-        "smlal v17.4s, v3.4h, v9.4h\n"    // for 4th column
-        "smlal2 v18.4s, v3.8h, v9.8h\n"   // for 4th column
-        "smlal v11.4s, v2.4h, v10.4h\n"   // for 5th column
-        "smlal2 v12.4s, v2.8h, v10.8h\n"  // for 5th column
-        "smlal v13.4s, v3.4h, v10.4h\n"   // for 5th column
-        "smlal2 v14.4s, v3.8h, v10.8h\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "addp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "addp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "addp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "addp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "addp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "addp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "addp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        "sqrshrn v26.4h, v28.4s, %[shift_amount]\n"
-        "sqrshrn v22.4h, v23.4s, %[shift_amount]\n"
-        "sqrshrn v18.4h, v19.4s, %[shift_amount]\n"
-        "sqrshrn v14.4h, v15.4s, %[shift_amount]\n"
-        "sqrshrn v10.4h, v11.4s, %[shift_amount]\n"
-
-        // Store accumulators.
-        "st1 {v26.4h}, [%[out_ptr]], #8\n"
-        "st1 {v22.4h}, [%[out2_ptr]], #8\n"
-        "st1 {v18.4h}, [%[out3_ptr]], #8\n"
-        "st1 {v14.4h}, [%[out4_ptr]], #8\n"
-        "st1 {v10.4h}, [%[out5_ptr]], #8\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr), [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr), [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr), [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Note that the number of exponent bits in the output must exactly match
-// the sum of the input and rhs types.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<
-    IsFixed16Type<WeightType>::value && IsFixed16Type<RhsType>::value &&
-    IsFixed32Type<OutType>::value &&
-    !std::is_same<OutType, typename TypeOfProduct<WeightType,
-                                                  RhsType>::type>::value>::type
-SpMV_4x4(const WeightType* weights_ptr, const int16_t* col_deltas_bytes,
-         const int32_t* nnz_per_row, const RhsType* rhs_ptr,
-         const typename TypeOfProduct<WeightType, RhsType>::type* bias_ptr,
-         OutType* out_ptr, int64_t assigned_rows,
-         int64_t rows /* only used in SpMM variants */,
-         int64_t cols /* only used in SpMM variants */, int relu) {
-  constexpr int kShiftAmount =
-      TypeOfProduct<WeightType, RhsType>::type::kMantissaBits -
-      OutType::kMantissaBits;
-  static_assert(kShiftAmount > 0,
-                "Result must have fewer mantissa bits than product");
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        // Duplicate the lower half into the upper half.
-        "mov v0.d[1], v0.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"
-        "smlal2 v29.4s, v2.8h, v0.8h\n"
-        "smlal v30.4s, v3.4h, v0.4h\n"
-        "smlal2 v31.4s, v3.8h, v0.8h\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "addp v28.4s, v28.4s, v29.4s\n"
-        "addp v30.4s, v30.4s, v31.4s\n"
-        "addp v28.4s, v28.4s, v30.4s\n"
-
-        // Do relu if requested.
-        "smax v28.4s, v28.4s, v25.4s\n"
-        "srshr v28.4s, v28.4s, %[shift_amount]\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-
-        "movi v25.4s, #0\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // accum_0 = 0
-        "dup v29.4s, v27.s[1]\n"  // accum_1 = 0
-        "dup v30.4s, v27.s[2]\n"  // accum_2 = 0
-        "dup v31.4s, v27.s[3]\n"  // accum_3 = 0
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        // Duplicate the lower half into the upper half.
-        "mov v0.d[1], v0.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"
-        "smlal2 v29.4s, v2.8h, v0.8h\n"
-        "smlal v30.4s, v3.4h, v0.4h\n"
-        "smlal2 v31.4s, v3.8h, v0.8h\n"
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        // Horizontally add accumulators and store result.
-        "addp v28.4s, v28.4s, v29.4s\n"
-        "addp v30.4s, v30.4s, v31.4s\n"
-        "addp v28.4s, v28.4s, v30.4s\n"
-
-        "srshr v28.4s, v28.4s, %[shift_amount]\n"
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-// Note that the number of exponent bits in the output must exactly match
-// the sum of the input and rhs types.
-template <typename WeightType, typename RhsType, typename OutType>
-typename std::enable_if<
-    IsFixed16Type<WeightType>::value && IsFixed16Type<RhsType>::value &&
-    IsFixed32Type<OutType>::value &&
-    !std::is_same<OutType, typename TypeOfProduct<WeightType,
-                                                  RhsType>::type>::value>::type
-SpMM5_4x4(const WeightType* weights_ptr, const int16_t* col_deltas_bytes,
-          const int32_t* nnz_per_row, const RhsType* rhs_ptr,
-          const typename TypeOfProduct<WeightType, RhsType>::type* bias_ptr,
-          OutType* out_ptr, int64_t assigned_rows, int64_t rows, int64_t cols,
-          int relu) {
-  constexpr int kShiftAmount =
-      TypeOfProduct<WeightType, RhsType>::type::kMantissaBits -
-      OutType::kMantissaBits;
-  static_assert(kShiftAmount > 0,
-                "Result must have fewer mantissa bits than product");
-  // Pointers to the columns.
-  const RhsType* rhs2_ptr = rhs_ptr + cols;
-  OutType* out2_ptr = out_ptr + rows;
-  const RhsType* rhs3_ptr = rhs_ptr + 2 * cols;
-  OutType* out3_ptr = out_ptr + 2 * rows;
-  const RhsType* rhs4_ptr = rhs_ptr + 3 * cols;
-  OutType* out4_ptr = out_ptr + 3 * rows;
-  const RhsType* rhs5_ptr = rhs_ptr + 4 * cols;
-  OutType* out5_ptr = out_ptr + 4 * rows;
-  if (relu) {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each and duplicate into upper half.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        "mov v0.d[1], v0.d[0]\n"
-        "ld1 {v1.4h}, [%[rhs2_ptr]], x8\n"
-        "mov v1.d[1], v1.d[0]\n"
-        "ld1 {v8.4h}, [%[rhs3_ptr]], x8\n"
-        "mov v8.d[1], v8.d[0]\n"
-        "ld1 {v9.4h}, [%[rhs4_ptr]], x8\n"
-        "mov v9.d[1], v9.d[0]\n"
-        "ld1 {v10.4h}, [%[rhs5_ptr]], x8\n"
-        "mov v10.d[1], v10.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"    // for 1st column
-        "smlal2 v29.4s, v2.8h, v0.8h\n"   // for 1st column
-        "smlal v30.4s, v3.4h, v0.4h\n"    // for 1st column
-        "smlal2 v31.4s, v3.8h, v0.8h\n"   // for 1st columh
-        "smlal v23.4s, v2.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v24.4s, v2.8h, v1.8h\n"   // for 2nd column
-        "smlal v25.4s, v3.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v26.4s, v3.8h, v1.8h\n"   // for 2nd column
-        "smlal v19.4s, v2.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v20.4s, v2.8h, v8.8h\n"   // for 3rd column
-        "smlal v21.4s, v3.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v22.4s, v3.8h, v8.8h\n"   // for 3rd column
-        "smlal v15.4s, v2.4h, v9.4h\n"    // for 4th column
-        "smlal2 v16.4s, v2.8h, v9.8h\n"   // for 4th column
-        "smlal v17.4s, v3.4h, v9.4h\n"    // for 4th column
-        "smlal2 v18.4s, v3.8h, v9.8h\n"   // for 4th column
-        "smlal v11.4s, v2.4h, v10.4h\n"   // for 5th column
-        "smlal2 v12.4s, v2.8h, v10.8h\n"  // for 5th column
-        "smlal v13.4s, v3.4h, v10.4h\n"   // for 5th column
-        "smlal2 v14.4s, v3.8h, v10.8h\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "movi v0.4s, #0\n"
-        "addp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "addp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "addp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "addp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "addp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "addp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "addp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        // Do relu as requested.
-        "smax v28.4s, v28.4s, v0.4s\n"
-        "smax v23.4s, v23.4s, v0.4s\n"
-        "smax v19.4s, v19.4s, v0.4s\n"
-        "smax v15.4s, v15.4s, v0.4s\n"
-        "smax v11.4s, v11.4s, v0.4s\n"
-
-        "srshr v28.4s, v28.4s, %[shift_amount]\n"
-        "srshr v23.4s, v23.4s, %[shift_amount]\n"
-        "srshr v19.4s, v19.4s, %[shift_amount]\n"
-        "srshr v15.4s, v15.4s, %[shift_amount]\n"
-        "srshr v11.4s, v11.4s, %[shift_amount]\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr), [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr), [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr), [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  } else {
-    asm(
-        // Load the first two column deltas.
-        "ldrsh x7, [%[col_deltas_bytes]], #2\n"
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-        // ld1 doesn't support pre-index, so we do the first addition here.
-        "add %[rhs_ptr], %[rhs_ptr], x7\n"
-        "add %[rhs2_ptr], %[rhs2_ptr], x7\n"
-        "add %[rhs3_ptr], %[rhs3_ptr], x7\n"
-        "add %[rhs4_ptr], %[rhs4_ptr], x7\n"
-        "add %[rhs5_ptr], %[rhs5_ptr], x7\n"
-
-        LABEL_ROW_LOOP
-        ":\n"
-
-        // Load the bias.
-        "ld1 {v27.4s}, [%[bias_ptr]], #16\n"
-
-        // Zero out local accumulators.
-        "dup v28.4s, v27.s[0]\n"  // for 1st column
-        "dup v29.4s, v27.s[1]\n"  // for 1st column
-        "dup v30.4s, v27.s[2]\n"  // for 1st column
-        "dup v31.4s, v27.s[3]\n"  // for 1st column
-        "dup v23.4s, v27.s[0]\n"  // for 2nd column
-        "dup v24.4s, v27.s[1]\n"  // for 2nd column
-        "dup v25.4s, v27.s[2]\n"  // for 2nd column
-        "dup v26.4s, v27.s[3]\n"  // for 2nd column
-        "dup v19.4s, v27.s[0]\n"  // for 3rd column
-        "dup v20.4s, v27.s[1]\n"  // for 3rd column
-        "dup v21.4s, v27.s[2]\n"  // for 3rd column
-        "dup v22.4s, v27.s[3]\n"  // for 3rd column
-        "dup v15.4s, v27.s[0]\n"  // for 4th column
-        "dup v16.4s, v27.s[1]\n"  // for 4th column
-        "dup v17.4s, v27.s[2]\n"  // for 4th column
-        "dup v18.4s, v27.s[3]\n"  // for 4th column
-        "dup v11.4s, v27.s[0]\n"  // for 5th column
-        "dup v12.4s, v27.s[1]\n"  // for 5th column
-        "dup v13.4s, v27.s[2]\n"  // for 5th column
-        "dup v14.4s, v27.s[3]\n"  // for 5th column
-
-        // Update the stopping condition for this set of rows.
-        "ldr w6, [%[nnz_per_row]], #4\n"
-        "cmp w6, #0\n"
-        // Skip the body if there isn't anything in this row.
-        "beq " LABEL_SKIP_COL_LOOP "f\n"
-
-        LABEL_COL_LOOP
-        ":\n"
-        // Load 1 Rhs vectors of size 1x4 each and duplicate into upper half.
-        "ld1 {v0.4h}, [%[rhs_ptr]], x8\n"
-        "mov v0.d[1], v0.d[0]\n"
-        "ld1 {v1.4h}, [%[rhs2_ptr]], x8\n"
-        "mov v1.d[1], v1.d[0]\n"
-        "ld1 {v8.4h}, [%[rhs3_ptr]], x8\n"
-        "mov v8.d[1], v8.d[0]\n"
-        "ld1 {v9.4h}, [%[rhs4_ptr]], x8\n"
-        "mov v9.d[1], v9.d[0]\n"
-        "ld1 {v10.4h}, [%[rhs5_ptr]], x8\n"
-        "mov v10.d[1], v10.d[0]\n"
-
-        // Start this load now, which we won't need until the end of the loop.
-        "ldrsh x8, [%[col_deltas_bytes]], #2\n"
-
-        // Load 16 Lhs cells corresponding to a 4x4 block.
-        "ld1 {v2.8h, v3.8h}, [%[weights_ptr]], #32\n"
-
-        // Multiply-accumulate.
-        "smlal v28.4s, v2.4h, v0.4h\n"    // for 1st column
-        "smlal2 v29.4s, v2.8h, v0.8h\n"   // for 1st column
-        "smlal v30.4s, v3.4h, v0.4h\n"    // for 1st column
-        "smlal2 v31.4s, v3.8h, v0.8h\n"   // for 1st columh
-        "smlal v23.4s, v2.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v24.4s, v2.8h, v1.8h\n"   // for 2nd column
-        "smlal v25.4s, v3.4h, v1.4h\n"    // for 2nd column
-        "smlal2 v26.4s, v3.8h, v1.8h\n"   // for 2nd column
-        "smlal v19.4s, v2.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v20.4s, v2.8h, v8.8h\n"   // for 3rd column
-        "smlal v21.4s, v3.4h, v8.4h\n"    // for 3rd column
-        "smlal2 v22.4s, v3.8h, v8.8h\n"   // for 3rd column
-        "smlal v15.4s, v2.4h, v9.4h\n"    // for 4th column
-        "smlal2 v16.4s, v2.8h, v9.8h\n"   // for 4th column
-        "smlal v17.4s, v3.4h, v9.4h\n"    // for 4th column
-        "smlal2 v18.4s, v3.8h, v9.8h\n"   // for 4th column
-        "smlal v11.4s, v2.4h, v10.4h\n"   // for 5th column
-        "smlal2 v12.4s, v2.8h, v10.8h\n"  // for 5th column
-        "smlal v13.4s, v3.4h, v10.4h\n"   // for 5th column
-        "smlal2 v14.4s, v3.8h, v10.8h\n"  // for 5th column
-
-        // Loop. Decrement loop index.
-        "subs w6, w6, #1\n"  // decrement (reduced) columns left
-        "bne " LABEL_COL_LOOP "b\n"
-
-        LABEL_SKIP_COL_LOOP
-        ":\n"
-
-        "addp v28.4s, v28.4s, v29.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v24.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v20.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v16.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v12.4s\n"  // 5th column
-
-        "addp v30.4s, v30.4s, v31.4s\n"  // 1st column
-        "addp v25.4s, v25.4s, v26.4s\n"  // 2nd column
-        "addp v21.4s, v21.4s, v22.4s\n"  // 3rd column
-        "addp v17.4s, v17.4s, v18.4s\n"  // 4th column
-        "addp v13.4s, v13.4s, v14.4s\n"  // 5th column
-
-        "addp v28.4s, v28.4s, v30.4s\n"  // 1st column
-        "addp v23.4s, v23.4s, v25.4s\n"  // 2nd column
-        "addp v19.4s, v19.4s, v21.4s\n"  // 3rd column
-        "addp v15.4s, v15.4s, v17.4s\n"  // 4th column
-        "addp v11.4s, v11.4s, v13.4s\n"  // 5th column
-
-        "srshr v28.4s, v28.4s, %[shift_amount]\n"
-        "srshr v23.4s, v23.4s, %[shift_amount]\n"
-        "srshr v19.4s, v19.4s, %[shift_amount]\n"
-        "srshr v15.4s, v15.4s, %[shift_amount]\n"
-        "srshr v11.4s, v11.4s, %[shift_amount]\n"
-
-        // Store accumulators.
-        "st1 {v28.4s}, [%[out_ptr]], #16\n"
-        "st1 {v23.4s}, [%[out2_ptr]], #16\n"
-        "st1 {v19.4s}, [%[out3_ptr]], #16\n"
-        "st1 {v15.4s}, [%[out4_ptr]], #16\n"
-        "st1 {v11.4s}, [%[out5_ptr]], #16\n"
-
-        // Decrement rows remaining.
-        "subs %[assigned_rows], %[assigned_rows], #1\n"
-        "bne " LABEL_ROW_LOOP "b\n"
-
-        // clang-format off
-        :  // outputs
-        [out_ptr] "+r"(out_ptr), [out2_ptr] "+r"(out2_ptr),
-        [out3_ptr] "+r"(out3_ptr), [out4_ptr] "+r"(out4_ptr),
-        [out5_ptr] "+r"(out5_ptr), [weights_ptr] "+r"(weights_ptr),
-        [col_deltas_bytes] "+r"(col_deltas_bytes), [bias_ptr] "+r"(bias_ptr),
-        [nnz_per_row] "+r"(nnz_per_row), [assigned_rows] "+r"(assigned_rows),
-        [rhs_ptr] "+r"(rhs_ptr), [rhs2_ptr] "+r"(rhs2_ptr),
-        [rhs3_ptr] "+r"(rhs3_ptr), [rhs4_ptr] "+r"(rhs4_ptr),
-        [rhs5_ptr] "+r"(rhs5_ptr)
-        :  // inputs
-        [shift_amount] "I"(kShiftAmount)
-        :  // clobbers
-        "cc", "memory", "x6", "x7", "x8", "v0", "v1", "v2", "v3", "v4", "v5",
-        "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",
-        "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
-        "v26", "v27", "v28", "v29", "v30", "v31");
-    // clang-format on
-  }
-}
-
-template <typename Type>
-typename std::enable_if<IsFixed32Type<Type>::value>::type SumVectors(
-    int start, int end, const Type* add1, const Type* add2, Type* result) {
-  constexpr int kSIMDWidth = 4;
-  for (int i = start; i < end; i += kSIMDWidth) {
-    int32x4_t add1_int = vld1q_s32(reinterpret_cast<const int32_t*>(add1 + i));
-    int32x4_t add2_int = vld1q_s32(reinterpret_cast<const int32_t*>(add2 + i));
-    int32x4_t result_int = vqaddq_s32(add1_int, add2_int);
-    vst1q_s32(reinterpret_cast<int32_t*>(result + i), result_int);
-  }
-}
-
-template <typename Type>
-typename std::enable_if<IsFixed16Type<Type>::value>::type SumVectors(
-    int start, int end, const Type* add1, const Type* add2, Type* result) {
-  constexpr int kSIMDWidth = 8;
-  for (int i = start; i < end; i += kSIMDWidth) {
-    int16x8_t add1_int = vld1q_s16(reinterpret_cast<const int16_t*>(add1 + i));
-    int16x8_t add2_int = vld1q_s16(reinterpret_cast<const int16_t*>(add2 + i));
-    int16x8_t result_int = vqaddq_s16(add1_int, add2_int);
-    vst1q_s16(reinterpret_cast<int16_t*>(result + i), result_int);
-  }
-}
-
-}  // namespace detail
-}  // namespace csrblocksparse
-
-#undef LABEL_COL_LOOP
-#undef LABEL_ROW_LOOP
-#undef LABEL_SKIP_COL_LOOP
-#undef LABEL_TOP_LOOP
-
-#endif  // defined __aarch64__
-#endif  // LYRA_CODEC_SPARSE_MATMUL_COMPUTE_KERNELS_ARM_H_
diff --git a/spaces/ofikodar/chatgpt-resume-builder/src/utils.py b/spaces/ofikodar/chatgpt-resume-builder/src/utils.py
deleted file mode 100644
index 43cf384cfa0d71a51fb2adac33c677dba8908650..0000000000000000000000000000000000000000
--- a/spaces/ofikodar/chatgpt-resume-builder/src/utils.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import re
-
-import streamlit as st
-
-
-def is_chatbot_loaded():
-    return st.session_state.get('chatbot')
-
-
-def is_new_file(uploaded_file):
-    return uploaded_file.id != st.session_state.get('file_id', '')
-
-
-def is_data_loaded():
-    return st.session_state.get('resume_data')
-
-
-def key_to_tab_name(input_string):
-    return re.sub(r'([A-Z])', r' \1', input_string).strip().title()
-
-
-def count_entries(input_dict, entry_type):
-    max_index = max([int(key.split("_")[1]) for key in input_dict.keys() if key.startswith(f"{entry_type}_")],
-                    default=0)
-    return max_index + 1
-
-
-def get_item_key(section_name, item_id=0):
-    section_key = ''
-    if section_name in ['workExperience', 'education']:
-        key = 'description'
-        section_key = f'{section_name}_{item_id}_{key}'
-    elif section_name == 'summary':
-        section_key = f'{section_name}'
-    return section_key
-
-
-def init_user_info(message_type, message):
-    return {
-        'message_type': message_type,
-        'message': message
-    }
diff --git a/spaces/onereal/rvc-models-convertvoice/app-full.py b/spaces/onereal/rvc-models-convertvoice/app-full.py
deleted file mode 100644
index 5322eac79ff3f6061617d3f091e606063a7507e8..0000000000000000000000000000000000000000
--- a/spaces/onereal/rvc-models-convertvoice/app-full.py
+++ /dev/null
@@ -1,289 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
-    def vc_fn(
-        input_audio,
-        upload_audio,
-        upload_mode,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        tts_mode,
-        tts_text,
-        tts_voice
-    ):
-        try:
-            if tts_mode:
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-            else:
-                if upload_mode:
-                    if input_audio is None:
-                        return "You need to upload an audio", None
-                    sampling_rate, audio = upload_audio
-                    duration = audio.shape[0] / sampling_rate
-                    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                    if len(audio.shape) > 1:
-                        audio = librosa.to_mono(audio.transpose(1, 0))
-                    if sampling_rate != 16000:
-                        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-                else:
-                    audio, sr = librosa.load(input_audio, sr=16000, mono=True)
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                times,
-                f0_up_key,
-                f0_method,
-                file_index,
-                index_rate,
-                if_f0,
-                f0_file=None,
-            )
-            print(
-                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            )
-            return (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
-    if url != "":
-        if not os.path.exists("dl_audio"):
-            os.mkdir("dl_audio")
-        if audio_provider == "Youtube":
-            ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": 'dl_audio/youtube_audio',
-            }
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                ydl.download([url])
-            audio_path = "dl_audio/youtube_audio.wav"
-        else:
-            # Spotify doesnt work.
-            # Need to find other solution soon.
-            ''' 
-            command = f"spotdl download {url} --output dl_audio/.wav"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            audio_path = "dl_audio/spotify_audio.wav"
-            '''
-        if split_model == "htdemucs":
-            command = f"demucs --two-stems=vocals {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
-        else:
-            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
-    else:
-        raise gr.Error("URL Required!")
-        return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_volume, split_model):
-    if not os.path.exists("output/result"):
-        os.mkdir("output/result")
-    vocal_path = "output/result/output.wav"
-    output_path = "output/result/combine.mp3"
-    if split_model == "htdemucs":
-        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
-    else:
-        inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
-    with wave.open(vocal_path, "w") as wave_file:
-        wave_file.setnchannels(1) 
-        wave_file.setsampwidth(2)
-        wave_file.setframerate(audio_data[0])
-        wave_file.writeframes(audio_data[1].tobytes())
-    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
-    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-    print(result.stdout.decode())
-    return output_path
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_to_tts_mode(tts_mode, upload_mode):
-    if tts_mode:
-        return gr.Textbox.update(visible=False), gr.Audio.update(visible=False), gr.Checkbox.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
-    else:
-        if upload_mode:
-            return gr.Textbox.update(visible=False), gr.Audio.update(visible=True), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-        else:
-            return gr.Textbox.update(visible=True), gr.Audio.update(visible=False), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-def change_to_upload_mode(upload_mode):
-    if upload_mode:
-        return gr.Textbox().update(visible=False), gr.Audio().update(visible=True)
-    else:
-        return gr.Textbox().update(visible=True), gr.Audio().update(visible=False)
-
-if __name__ == '__main__':
-    load_hubert()
-    categories = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
-        folder_info = json.load(f)
-    for category_name, category_info in folder_info.items():
-        if not category_info['enable']:
-            continue
-        category_title = category_info['title']
-        category_folder = category_info['folder_path']
-        description = category_info['description']
-        models = []
-        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
-            models_info = json.load(f)
-        for model_name, info in models_info.items():
-            if not info['enable']:
-                continue
-            model_title = info['title']
-            model_author = info.get("author", None)
-            model_cover = f"weights/{category_folder}/{model_name}/{info['cover']}"
-            model_index = f"weights/{category_folder}/{model_name}/{info['feature_retrieval_library']}"
-            cpt = torch.load(f"weights/{category_folder}/{model_name}/{model_name}.pth", map_location="cpu")
-            tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            if_f0 = cpt.get("f0", 1)
-            if if_f0 == 1:
-                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-            else:
-                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            del net_g.enc_q
-            print(net_g.load_state_dict(cpt["weight"], strict=False))
-            net_g.eval().to(config.device)
-            if config.is_half:
-                net_g = net_g.half()
-            else:
-                net_g = net_g.float()
-            vc = VC(tgt_sr, config)
-            print(f"Model loaded: {model_name}")
-            models.append((model_name, model_title, model_author, model_cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, model_index)))
-        categories.append([category_title, category_folder, description, models])
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> RVC Models\n"
-            "## <center> The input audio should be clean and pure voice without background music.\n"
-            "### <center> This project was inspired by [zomehwh](https://huggingface.co/spaces/zomehwh/rvc-models) and [ardha27](https://huggingface.co/spaces/ardha27/rvc-models)\n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n"
-            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
-        )
-        for (folder_title, folder, description, models) in categories:
-            with gr.TabItem(folder_title):
-                if description:
-                    gr.Markdown(f"### <center> {description}")
-                with gr.Tabs():
-                    if not models:
-                        gr.Markdown("# <center> No Model Loaded.")
-                        gr.Markdown("## <center> Please added the model or fix your model path.")
-                        continue
-                    for (name, title, author, cover, vc_fn) in models:
-                        with gr.TabItem(name):
-                            with gr.Row():
-                                gr.Markdown(
-                                    '<div align="center">'
-                                    f'<div>{title}</div>\n'+
-                                    (f'<div>Model author: {author}</div>' if author else "")+
-                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                                    '</div>'
-                                )
-                            with gr.Row():
-                                with gr.Column():
-                                    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, value="Youtube", info="Select provider [REQUIRED: UPLOAD MODE = OFF] (Default: Youtube)")
-                                    vc_link = gr.Textbox(label="Youtube URL", info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A")
-                                    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                                    vc_split = gr.Button("Split Audio", variant="primary")
-                                    vc_vocal_preview = gr.Audio(label="Vocal Preview")
-                                    vc_inst_preview = gr.Audio(label="Instrumental Preview")
-                                    vc_audio_preview = gr.Audio(label="Audio Preview")
-                                with gr.Column():
-                                    upload_mode = gr.Checkbox(label="Upload mode", value=False, info="Enable to upload audio instead of audio path")
-                                    vc_input = gr.Textbox(label="Input audio path")
-                                    vc_upload = gr.Audio(label="Upload audio file", visible=False, interactive=True)
-                                    vc_transpose = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                                    vc_f0method = gr.Radio(
-                                        label="Pitch extraction algorithm",
-                                        choices=["pm", "harvest"],
-                                        value="pm",
-                                        interactive=True,
-                                        info="PM is fast but Harvest is better for low frequencies. (Default: PM)"
-                                    )
-                                    vc_index_ratio = gr.Slider(
-                                        minimum=0,
-                                        maximum=1,
-                                        label="Retrieval feature ratio",
-                                        value=0.6,
-                                        interactive=True,
-                                        info="(Default: 0.6)"
-                                    )
-                                    tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                                    tts_text = gr.Textbox(visible=False, label="TTS text")
-                                    tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                    vc_submit = gr.Button("Convert", variant="primary")
-                                with gr.Column():
-                                    vc_volume = gr.Slider(
-                                        minimum=0,
-                                        maximum=10,
-                                        label="Vocal volume",
-                                        value=4,
-                                        interactive=True,
-                                        step=1,
-                                        info="Adjust vocal volume (Default: 4}"
-                                    )
-                                    vc_combined_output = gr.Audio(label="Output Combined Audio")
-                                    vc_combine =  gr.Button("Combine",variant="primary")
-                        vc_submit.click(vc_fn, [vc_input, vc_upload, upload_mode, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output])
-                        vc_split.click(cut_vocal_and_inst, [vc_link, vc_download_audio, vc_split_model], [vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input])
-                        vc_combine.click(combine_vocal_and_inst, [vc_output, vc_volume, vc_split_model], vc_combined_output)
-                        tts_mode.change(change_to_tts_mode, [tts_mode, upload_mode], [vc_input, vc_upload, upload_mode, tts_text, tts_voice])
-                        upload_mode.change(change_to_upload_mode, [upload_mode], [vc_input, vc_upload])
-        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/utilities.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/utilities.md
deleted file mode 100644
index abc38416053a06e9ad1fc810c7c3586f400fa70b..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/utilities.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Utilities
-
-Utility and helper functions for working with 🤗 Diffusers.
-
-## numpy_to_pil
-
-[[autodoc]] utils.numpy_to_pil
-
-## pt_to_pil
-
-[[autodoc]] utils.pt_to_pil
-
-## load_image
-
-[[autodoc]] utils.load_image
-
-## export_to_gif
-
-[[autodoc]] utils.export_to_gif
-
-## export_to_video
-
-[[autodoc]] utils.export_to_video
-
-## make_image_grid
-
-[[autodoc]] utils.pil_utils.make_image_grid
diff --git a/spaces/paulhebo/smart_qa/.ipynb_checkpoints/app-checkpoint.py b/spaces/paulhebo/smart_qa/.ipynb_checkpoints/app-checkpoint.py
deleted file mode 100644
index 8b1cc03cde3d3ab6b4d0519b22b9d6ad4594c232..0000000000000000000000000000000000000000
--- a/spaces/paulhebo/smart_qa/.ipynb_checkpoints/app-checkpoint.py
+++ /dev/null
@@ -1,382 +0,0 @@
-import requests
-import json
-import gradio as gr
-from datetime import datetime
-
-#Fill in your correct configuration
-invoke_url = 'https://6cld98qpn2.execute-api.us-west-2.amazonaws.com/prod'
-bedrock_url = 'https://8hsh7fxan7.execute-api.us-west-2.amazonaws.com/prod'
-
-
-chinese_index = "digitimes_test_1005_title"
-english_index = "chinese_bge_test_0916"
-
-
-cn_embedding_endpoint = 'huggingface-inference-eb-zh'
-cn_llm_endpoint = 'pytorch-inference-chatglm2-g5-4x'
-baichuan_llm_endpoint = 'pytorch-inference-llm-baichuan-13b-4bits'
-
-en_embedding_endpoint = 'pytorch-inference-all-minilm-l6-v2'
-en_llm_endpoint = 'pytorch-inference-chatglm2-g5-4x'
-
-
-llama2_llm_endpoint = 'meta-textgeneration-llama-2-7b-f-2023-07-19-06-07-05-430'
-
-
-#Modify the default prompt as needed
-chinese_prompt = """基于以下已知信息，简洁和专业的来回答用户的问题，并告知是依据哪些信息来进行回答的。
-   如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
-    
-            问题: {question}
-            =========
-            {context}
-            =========
-            答案:"""
-
-english_prompt = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-{context}
-
-Question: {question}
-Answer:"""                                
-
-
-chinses_summarize_prompt="""请根据访客与客服的通话记录，写一段访客提出问题的摘要，突出显示与亚马逊云服务相关的要点, 摘要不需要有客服的相关内容:
-{text}
-
-摘要是:"""
-
-english_summarize_prompt="""Based on the call records between the visitor and the customer service, write a summary of the visitor's questions, highlighting the key points related to Amazon Web Services, and the summary does not need to have customer service-related content:
-{text}
-
-The summary is:"""
-
-claude_chat_prompt_cn="""
-Human: 请根据 {history}，回答：{human_input}
-
-Assistant:
-"""
-
-claude_chat_prompt_cn_tc="""
-Human: 請根據 {history}，使用繁體中文回答：{human_input}
-
-Assistant:
-"""
-
-claude_chat_prompt_english="""
-Human: Based on {history}, answer the question：{human_input}
-
-Assistant:
-"""
-
-
-
-claude_rag_prompt_cn = """
-Human: 基于以下已知信息，简洁和专业的来回答用户的问题，如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
-    
-            问题: {question}
-            =========
-            {context}
-            =========
-            Assistant:
-"""
-
-claude_rag_prompt_cn_tc = """
-Human: 基於以下已知信息，簡潔和專業的來回答用戶的問題，如果無法從中得到答案，請說 "根據已知信息無法回答該問題" 或 "沒有提供足夠的相關信息"，不允許在答案中添加編造成分，答案請使用繁體中文回答
-    
-            問題: {question}
-            =========
-            {context}
-            =========
-            Assistant:
-"""
-
-claude_rag_prompt_english = """
-Human: Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-{context}
-
-Question: {question}
-Assistant:
-"""       
-
-CHINESE_ENHANCED_SEARCH_PROMPT_TEMPLATE = """You are an AI assistant whose task is to help users express their questions more clearly for easier article retrieval. If the user's question is already clear, you can keep the original question. If the question is still unclear, please optimize and clarify it based on the following user input. Answer in TRADITIONAL CHINESE and without punctuation.
-
-Original question: {text}
-
-Optimized question:"""
-
-
-api = invoke_url + '/langchain_processor_qa?query='
-bedrock_url += '/bedrock?'
-
-def get_answer(task_type,question,sessionId,language,modelType,prompt,searchEngine,index,searchMethod,vecTopK,txtTopK,vecDocsScoreThresholds,txtDocsScoreThresholds,score_type_checklist):
-    
-    question=question.replace('AWS','亚马逊云科技').replace('aws','亚马逊云科技').replace('Aws','亚马逊云科技')
-    print('question:',question)
-
-    if len(question) > 0:
-        url = api + question
-    else:
-        url = api + "hello"
-    
-    url += '&requestType=https'
-    #task type: qa,chat
-    if task_type == "Knowledge base Q&A":
-        task = 'qa'
-    else:
-        task = 'chat'
-    url += ('&task='+task)
-
-    if language == "english":
-        url += '&language=english'
-        url += ('&embeddingEndpoint='+en_embedding_endpoint)
-        if modelType == "llama2(english)":
-            url += ('&sagemakerEndpoint='+llama2_llm_endpoint)
-        elif modelType == "baichuan2":
-            url += ('&sagemakerEndpoint='+baichuan_llm_endpoint)
-        else:
-            url += ('&sagemakerEndpoint='+en_llm_endpoint)
-    elif language == "chinese":
-        url += '&language=chinese'
-        url += ('&embeddingEndpoint='+cn_embedding_endpoint)
-        if modelType == "baichuan2":
-            url += ('&sagemakerEndpoint='+baichuan_llm_endpoint)
-        else:
-            url += ('&sagemakerEndpoint='+en_llm_endpoint)
-     
-    elif language == "chinese-tc":
-        url += '&language=chinese-tc'
-        url += ('&embeddingEndpoint='+cn_embedding_endpoint)
-        if modelType == "baichuan2":
-            url += ('&sagemakerEndpoint='+baichuan_llm_endpoint)
-        else:
-            url += ('&sagemakerEndpoint='+en_llm_endpoint)
-    
-    if len(sessionId) > 0:
-        url += ('&sessionId='+sessionId)
-    
-    if modelType == "claude2_api":
-        url += ('&modelType=bedrock_api')
-        url += ('&urlOrApiKey='+bedrock_url)
-        url += ('&modelName=anthropic.claude-v2')
-    elif modelType == "claude2":
-        url += ('&modelType=bedrock')
-        url += ('&modelName=anthropic.claude-v2')
-    elif modelType == "llama2(english)":
-        url += ('&modelType=llama2')
-
-    if len(prompt) > 0:
-        url += ('&prompt='+prompt)
-    elif modelType == "claude2":
-        if task_type == "Knowledge base Q&A":
-            if language == "english":
-                url += ('&prompt='+claude_rag_prompt_english)
-            elif language == "chinese":
-                url += ('&prompt='+claude_rag_prompt_cn)
-            elif language == "chinese-tc":
-                url += ('&prompt='+claude_rag_prompt_cn_tc)
-        else:
-            if language == "english":
-                url += ('&prompt='+claude_chat_prompt_english)
-            elif language == "chinese":
-                url += ('&prompt='+claude_chat_prompt_cn)
-            elif language == "chinese-tc":
-                url += ('&prompt='+claude_chat_prompt_cn_tc)
-
-    if searchEngine == "OpenSearch":
-        url += ('&searchEngine=opensearch')
-        if len(index) > 0:
-            url += ('&index='+index)
-        else:
-            if language.find("chinese") >= 0 and len(chinese_index) >0:
-                url += ('&index='+chinese_index)
-            elif language == "english" and len(english_index) >0:
-                url += ('&index='+english_index)
-
-    elif searchEngine == "Kendra":
-        url += ('&searchEngine=kendra')
-        if len(index) > 0:
-            url += ('&kendra_index_id='+index)
-
-    if int(vecTopK) > 0:
-        url += ('&topK='+str(vecTopK))
-
-    url += ('&searchMethod='+searchMethod)
-
-    if int(txtTopK) > 0:
-        url += ('&txtDocsNum='+str(txtTopK))
-
-    if float(vecDocsScoreThresholds) > 0:
-        url += ('&vecDocsScoreThresholds='+str(vecDocsScoreThresholds))
-
-    if float(txtDocsScoreThresholds) > 0:
-        url += ('&txtDocsScoreThresholds='+str(txtDocsScoreThresholds))
-
-    for score_type in score_type_checklist:
-        if score_type == "query_answer_score":
-            url += ('&isCheckedScoreQA=true')
-        elif score_type == "answer_docs_score":
-            url += ('&isCheckedScoreAD=true')
-
-    print("url:",url)
-
-    now1 = datetime.now()#begin time
-    response = requests.get(url)
-    now2 = datetime.now()#endtime
-    request_time = now2-now1
-    print("request takes time:",request_time)
-
-    result = response.text
-    print('result0:',result)
-    
-    result = json.loads(result)
-    print('result:',result)
-    
-    answer = result['text']
-    source_list = []
-    if 'source_list' in result.keys():
-        source_list = result['source_list']
-    
-    print("answer:",answer)
-
-    source_str = ""
-    for i in range(len(source_list)):
-        item = source_list[i]
-        print('item:',item)
-        _id = "num:" + str(item['id'])
-        try:
-            source = ''
-            if 'source' in item.keys():
-                source = "source:" + item['source']
-            elif 'title' in item.keys():
-                source = "source:" + item['title']
-        except KeyError:
-            source ="source:unknown"
-            print("KeyError:source file not found")
-        score = "score:" + str(item['score'])
-        sentence = "sentence:" + item['sentence']
-        paragraph = "paragraph:" + item['paragraph']
-        source_str += (_id + "      " + source + "      " + score + '\n')
-        # source_str += sentence + '\n'
-        source_str += paragraph + '\n\n'
-    
-    confidence = ""
-    query_docs_score = -1
-    if 'scoreQueryDoc' in result.keys():
-        query_docs_score =  float(result['scoreQueryDoc'])
-    if query_docs_score >= 0:
-        confidence += ("query_docs_score:" + str(query_docs_score) + '\n')
-
-    query_answer_score = -1
-    if 'scoreQueryAnswer' in result.keys():
-        query_answer_score =  float(result['scoreQueryAnswer'])
-    if query_answer_score >= 0:
-        confidence += ("query_answer_score:" + str(query_answer_score) + '\n')
-
-    answer_docs_score = -1
-    if 'scoreAnswerDoc' in result.keys():
-        answer_docs_score =  float(result['scoreAnswerDoc'])
-    if answer_docs_score >= 0:
-        confidence += ("answer_docs_score:" + str(answer_docs_score) + '\n')
-
-
-
-    return answer,confidence,source_str,url,request_time
-    
-    
-def get_summarize(texts,language,modelType,prompt):
-
-    url = api + texts
-    url += '&task=summarize'
-    url += '&requestType=https'
-
-    if language == "english":
-        url += '&language=english'
-        url += ('&embeddingEndpoint='+en_embedding_endpoint)
-        url += ('&sagemakerEndpoint='+en_llm_endpoint)
-        
-    elif language == "chinese":
-        url += '&language=chinese'
-        url += ('&embeddingEndpoint='+cn_embedding_endpoint)
-        url += ('&sagemakerEndpoint='+cn_llm_endpoint)
-
-    if modelType == "claude2":
-        url += ('&modelType=bedrock')
-        url += ('&urlOrApiKey='+bedrock_url)
-        url += ('&modelName=anthropic.claude-v2')
-
-
-    if len(prompt) > 0:
-        url += ('&prompt='+prompt)
-    else:
-        if language == "english":
-            url += ('&prompt='+english_summarize_prompt)
-        elif language == "chinese":
-            url += ('&prompt='+chinses_summarize_prompt)
-    
-    print('url:',url)
-    response = requests.get(url)
-    result = response.text
-    result = json.loads(result)
-    print('result1:',result)
-    
-    answer = result['summarize']
-
-    # if language == 'english' and answer.find('The Question and Answer are:') > 0:
-    #     answer=answer.split('The Question and Answer are:')[-1].strip()
-
-    return answer
-
-demo = gr.Blocks(title="AWS Intelligent Q&A Solution Guide")
-with demo:
-    gr.Markdown(
-        "# <center>AWS Intelligent Q&A Solution Guide"
-    )
-
-    with gr.Tabs():
-        with gr.TabItem("Question Answering"):
-
-            with gr.Row():
-                with gr.Column():
-                    qa_task_radio = gr.Radio(["Knowledge base Q&A","Chat"],value="Knowledge base Q&A",label="Task")
-                    query_textbox = gr.Textbox(label="Query")
-                    sessionId_textbox = gr.Textbox(label="Session ID")
-                    qa_button = gr.Button("Summit")
-
-                    qa_language_radio = gr.Radio(["chinese","chinese-tc", "english"],value="chinese",label="Language")
-                    qa_modelType_radio = gr.Radio(["claude2","llama2(english)","chatglm2"],value="chatglm2",label="Model type")
-                    qa_prompt_textbox = gr.Textbox(label="Prompt( must include {context} and {question} )",placeholder=chinese_prompt,lines=2)
-                    qa_searchEngine_radio = gr.Radio(["OpenSearch","Kendra"],value="OpenSearch",label="Search engine")
-                    qa_index_textbox = gr.Textbox(label="OpenSearch index OR Kendra index id")
-                    # qa_em_ep_textbox = gr.Textbox(label="Embedding Endpoint")
-                    
-                    search_method_radio = gr.Radio(["vector","text","mix"],value="vector",label="Search Method")
-                    vec_topK_slider = gr.Slider(label="The number of related documents by vector search",value=1, minimum=1, maximum=10, step=1)
-                    txt_topK_slider = gr.Slider(label="The number of related documents by text search",value=1, minimum=1, maximum=10, step=1)
-                    vec_score_thresholds_radio = gr.Slider(label="Vector search score thresholds",value=0.01, minimum=0.01, maximum=1, step=0.01)
-                    txt_score_thresholds_radio = gr.Slider(label="Text search score thresholds",value=0.01, minimum=0.01, maximum=1, step=0.01)
-
-
-                    # qa_temperature_slider = gr.Slider(label="Temperature parameter of LLM",value=0.01, minimum=0.01, maximum=1, step=0.01)
-                    score_type_checklist = gr.CheckboxGroup(["query_answer_score", "answer_docs_score"],value=["query_answer_score"],label="Confidence score type")
-
-                with gr.Column():
-                    qa_output = [gr.outputs.Textbox(label="Answer"), gr.outputs.Textbox(label="Confidence"), gr.outputs.Textbox(label="Source"), gr.outputs.Textbox(label="Url"), gr.outputs.Textbox(label="Request time")]
-                                
-
-        with gr.TabItem("Summarize"):
-            with gr.Row():
-                with gr.Column():
-                    text_input = gr.Textbox(label="Input texts",lines=4)
-                    summarize_button = gr.Button("Summit")
-                    sm_language_radio = gr.Radio(["chinese", "english"],value="chinese",label="Language")
-                    sm_modelType_radio = gr.Radio(["claude2","other"],value="other",label="Model type")
-                    sm_prompt_textbox = gr.Textbox(label="Prompt",lines=4, placeholder=chinses_summarize_prompt)
-                with gr.Column():
-                    text_output = gr.Textbox()
-            
-    qa_button.click(get_answer, inputs=[qa_task_radio,query_textbox,sessionId_textbox,qa_language_radio,qa_modelType_radio,qa_prompt_textbox,qa_searchEngine_radio,qa_index_textbox,\
-        search_method_radio,vec_topK_slider,txt_topK_slider,vec_score_thresholds_radio,txt_score_thresholds_radio,score_type_checklist], outputs=qa_output)
-    summarize_button.click(get_summarize, inputs=[text_input,sm_language_radio,sm_modelType_radio,sm_prompt_textbox], outputs=text_output)
-
-demo.launch()
-# demo.launch(share=True)
diff --git a/spaces/perezcatriel/data_world_jobs/page/machine_learning.py b/spaces/perezcatriel/data_world_jobs/page/machine_learning.py
deleted file mode 100644
index 061e4848c5d9c556cb809efad609b9e2fdbd464a..0000000000000000000000000000000000000000
--- a/spaces/perezcatriel/data_world_jobs/page/machine_learning.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import datetime
-import time
-
-import altair as alt
-import pandas as pd
-import streamlit as st
-from sklearn.linear_model import LinearRegression
-
-df = pd.read_csv('./ML/ds_salaries.csv')
-
-
-def ML():
-    st.markdown('''
-                <br>
-                <h3>¡Descubre las <strong>tendencias</strong> en el 
-                <strong>mercado laboral</strong> con nuestra herramienta de 
-                Regresión Lineal!</h3>
-                <p>Este modelo predice la cantidad de puestos laborales para un año X sobre Data, según los resultados que tiene en la base de datos</p>
-                <hr>
-            ''', unsafe_allow_html=True)
-
-    # st.set_page_config(page_title="Predicción de nuevos puestos de trabajo",
-    #                    page_icon=":bar_chart:", layout="wide")
-
-    # Cargar los datos
-
-    # Seleccionar las columnas relevantes
-    df_relevant = df[['job_title', 'work_year']]
-
-    # Transformar la columna work_year en un tipo date en la columna date
-    df_relevant['date'] = pd.to_datetime(df_relevant['work_year'], format='%Y')
-
-    # Agregar una columna con el año de creación
-    df_relevant['year'] = pd.DatetimeIndex(df_relevant['date']).year
-
-    # Contar la cantidad de job_title creados por año
-    job_title_count = df_relevant.groupby('year').count()['job_title']
-
-    # Crear un dataframe con la cantidad de job_title creados por año
-    df_job_title_count = pd.DataFrame(
-        {'year': job_title_count.index,
-         'job_title_count': job_title_count.values})
-
-    # Crear un modelo de regresión lineal
-    model = LinearRegression()
-
-    # Entrenar el modelo con los datos históricos
-    X = df_job_title_count[['year']]
-    y = df_job_title_count['job_title_count']
-    model.fit(X, y)
-
-    # Obtener el año actual
-    current_year = datetime.datetime.now().year
-
-    # Predecir la cantidad de nuevos job_title que se crearán este año
-    current_year_input = st.number_input('Ingresa un año:', value=current_year,
-                                         min_value=current_year,
-                                         max_value=2050, step=1)
-    if current_year_input < current_year:
-        st.warning('Solo se pueden hacer predicciones para años futuros.')
-        current_year_input = current_year
-        st.write('Se usará el año actual:', current_year_input)
-
-    with st.spinner('Prediciendo...'):
-        time.sleep(1)
-        job_title_count_pred = model.predict([[current_year_input]])
-
-    # Obtener el último año del dataset
-    last_year = df_job_title_count['year'].max()
-    last_year_count = \
-        df_job_title_count.loc[df_job_title_count['year'] == last_year][
-            'job_title_count'].values[0]
-
-    # Mostrar resultados
-    st.write(
-        "Se crearán aproximadamente **{}** nuevos puestos de trabajo este año **{}**.".format(
-            int(job_title_count_pred), current_year_input))
-    percentage_change = (
-                                job_title_count_pred - last_year_count) / last_year_count * 100
-    percentage_change = float(percentage_change)
-    if percentage_change >= 0:
-        st.write(
-            "Esto representa un aumento del {:.2f}% con respecto al año {}.".format(
-                percentage_change, last_year))
-    else:
-        st.write(
-            "Esto representa una disminución del {:.2f}% con respecto al año {}".format(
-                abs(percentage_change), last_year))
-
-    # Crear un gráfico de línea
-    line_chart = alt.Chart(df_job_title_count).mark_line().encode(
-        x='year',
-        y='job_title_count'
-    ).properties(
-        title='Cantidad de nuevos puestos de trabajo por año',
-        width=300,
-        height=200
-    ).configure_axis(
-        labelFontSize=14,
-        titleFontSize=16
-    )
-
-    # Crear un punto para mostrar el valor predicho
-    point = alt.Chart(df_job_title_count.iloc[-1:]).mark_point(
-        color='#5c62ac').encode(
-        x='year',
-        y='job_title_count'
-    )
-
-    # Mostrar la gráfica actualizada con el valor predicho para el año ingresado
-    # st.altair_chart(line_chart, use_container_width=True)
-
-    # Crear botón para graficar la predicción
-    if st.button('Mostrar gráfico de predicción'):
-        # Crear dataframe con los años y las predicciones
-        years = list(range(last_year, current_year + current_year_input - 2000))
-        predictions = model.predict([[year] for year in years])
-        df_predictions = pd.DataFrame(
-            {'year': years, 'job_title_count_pred': predictions})
-
-        # Crear gráfico de línea
-        line_chart = alt.Chart(df_predictions).mark_line().encode(
-            x='year',
-            y='job_title_count_pred'
-        ).properties(
-            width=700,
-            height=400
-        )
-
-        # Agregar capa con punto violeta en el valor predicho para el año actual
-        current_year_pred = int(model.predict([[current_year_input]])[0])
-        point_chart = alt.Chart(pd.DataFrame(
-            {'x': [current_year_input], 'y': [current_year_pred]})).mark_point(
-            color='#5c62ac',
-            size=300,
-            stroke='#5c62ac',
-            strokeWidth=5).encode(
-            x='x',
-            y='y'
-        )
-
-        # # ocultar los títulos de los ejes x e y
-        # line_chart.configure_axis(
-        #      x=None,
-        #     y=None
-        # )
-
-        # Mostrar gráfico con la capa adicional del punto rojo
-        st.altair_chart(line_chart + point_chart)
-
-    st.markdown('''
-                    <br><br><br>
-                    <h2>¡Estamos <strong>siempre innovando!</strong></h2>
-                    <hr>
-                    Si te gustó lo que viste, prepárate para lo que viene en 
-                    nuestra sección <span style="background:#5c62ac;padding:2px 
-                    4px;border-radius:4px">New</span>. Descubre nuestras 
-                    últimas novedades y proyectos en desarrollo. Estamos 
-                    emocionados de compartir contigo nuestras nuevas propuestas 
-                    y llevarte a nuevas alturas en tu carrera profesional. 
-                    Únete a nuestra comunidad y sé el primero en conocer lo 
-                    último en tecnología y tendencias de mercado. 
-                    <strong>¡Mantente actualizado y prepárate para el 
-                    futuro con LatamBrain!</strong>
-                    ''', unsafe_allow_html=True)
-
-#
diff --git a/spaces/piecurus/speech_to_text/app.py b/spaces/piecurus/speech_to_text/app.py
deleted file mode 100644
index 34014f3a2cf0893bb3ffe139280cfe35efaf2c38..0000000000000000000000000000000000000000
--- a/spaces/piecurus/speech_to_text/app.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
-#Importing all the necessary packages
-import nltk
-import librosa
-import IPython.display
-import torch
-import gradio as gr
-from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
-nltk.download("punkt")
-
-
-# In[ ]:
-
-
-#Loading the model and the tokenizer
-model_name = "facebook/wav2vec2-base-960h"
-
-#model_name = "facebook/wav2vec2-large-xlsr-53"
-tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)#omdel_name
-model = Wav2Vec2ForCTC.from_pretrained(model_name)
-
-
-# In[ ]:
-
-
-def load_data(input_file):
-    """ Function for resampling to ensure that the speech input is sampled at 16KHz.
-    """
-    #read the file
-    speech, sample_rate = librosa.load(input_file)
-    #make it 1-D
-    if len(speech.shape) > 1: 
-        speech = speech[:,0] + speech[:,1]
-    #Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz.
-    if sample_rate !=16000:
-        speech = librosa.resample(speech, sample_rate,16000)
-    #speeches = librosa.effects.split(speech)
-    return speech
-
-
-# In[ ]:
-
-
-def correct_casing(input_sentence):
-    """ This function is for correcting the casing of the generated transcribed text
-    """
-    sentences = nltk.sent_tokenize(input_sentence)
-    return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
-
-
-# In[ ]:
-
-
-def asr_transcript(input_file):
-    """This function generates transcripts for the provided audio input
-    """
-    speech = load_data(input_file)
-    #Tokenize
-    input_values = tokenizer(speech, return_tensors="pt").input_values
-    #Take logits
-    logits = model(input_values).logits
-    #Take argmax
-    predicted_ids = torch.argmax(logits, dim=-1)
-    #Get the words from predicted word ids
-    transcription = tokenizer.decode(predicted_ids[0])
-    #Output is all upper case
-    transcription = correct_casing(transcription.lower())
-    return transcription
-
-
-# In[ ]:
-
-
-def asr_transcript_long(input_file,tokenizer=tokenizer, model=model ):
-    transcript = ""
-    # Ensure that the sample rate is 16k
-    sample_rate = librosa.get_samplerate(input_file)
-
-    # Stream over 10 seconds chunks rather than load the full file
-    stream = librosa.stream(
-        input_file,
-        block_length=20, #number of seconds to split the batch
-        frame_length=sample_rate, #16000,
-        hop_length=sample_rate, #16000
-    )
-
-    for speech in stream:
-        if len(speech.shape) > 1:
-            speech = speech[:, 0] + speech[:, 1]
-        if sample_rate !=16000:
-            speech = librosa.resample(speech, sample_rate,16000)
-        input_values = tokenizer(speech, return_tensors="pt").input_values
-        logits = model(input_values).logits
-
-        predicted_ids = torch.argmax(logits, dim=-1)
-        transcription = tokenizer.decode(predicted_ids[0])
-        #transcript += transcription.lower()
-        transcript += correct_casing(transcription.lower())
-        #transcript += " "
-
-    return transcript[:3800]
-
-
-# In[ ]:
-
-
-gr.Interface(asr_transcript_long,
-             #inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Please record your voice"),
-             inputs = gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Upload your file here"),
-             outputs = gr.outputs.Textbox(type="str",label="Output Text"),
-             title="Transcript and Translate",
-             description = "This application displays transcribed text for given audio input",
-             examples = [["Test_File1.wav"], ["Test_File2.wav"], ["Test_File3.wav"]], theme="grass").launch()
-
diff --git a/spaces/pip64/generator-oskov/app.py b/spaces/pip64/generator-oskov/app.py
deleted file mode 100644
index 905091d0d02a121102cfef2cb54f1ff27afe3d0d..0000000000000000000000000000000000000000
--- a/spaces/pip64/generator-oskov/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-import markovify
-import random
-
-dataset = "./dataset.txt"
-with open(dataset, encoding="utf-8") as f:
-    db = f.read()
-db = db.strip().lower()
-
-def gen(text):
-  
-  text_model = markovify.NewlineText(
-    input_text=db, 
-    state_size=1, 
-    well_formed=False
-  )
-
-  sentence = text_model.make_short_sentence(50)
-
-  return sentence
-
-iface = gr.Interface(fn=gen, inputs="text", outputs="text")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/package_data.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/package_data.py
deleted file mode 100644
index 8501893bd153b7216524084cad23e90aeac0b1f8..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/package_data.py
+++ /dev/null
@@ -1,2 +0,0 @@
-__version__ = '3.4'
-
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/prerna9811/Chord/portaudio/bindings/java/c/src/jpa_tools.h b/spaces/prerna9811/Chord/portaudio/bindings/java/c/src/jpa_tools.h
deleted file mode 100644
index 11e724cccacb4df96515a47bb39ca8bbde63d2f5..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/bindings/java/c/src/jpa_tools.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Portable Audio I/O Library
- * Java Binding for PortAudio
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 2008 Ross Bencina
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however, 
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also 
- * requested that these non-binding requests be included along with the 
- * license above.
- */
-
-#include "com_portaudio_PortAudio.h"
-#include "portaudio.h"
-
-#ifndef JPA_TOOLS_H
-#define JPA_TOOLS_H
-
-jint jpa_GetIntField( JNIEnv *env, jclass cls, jobject obj, const char *fieldName );
-void jpa_SetIntField( JNIEnv *env, jclass cls, jobject obj, const char *fieldName, jint value );
-
-jlong jpa_GetLongField( JNIEnv *env, jclass cls, jobject obj, const char *fieldName );
-void jpa_SetLongField( JNIEnv *env, jclass cls, jobject obj, const char *fieldName, jlong value );
-
-jdouble jpa_GetDoubleField( JNIEnv *env, jclass cls, jobject obj, const char *fieldName );
-void jpa_SetDoubleField( JNIEnv *env, jclass cls, jobject obj, const char *fieldName, jdouble value );
-
-void jpa_SetStringField( JNIEnv *env, jclass cls, jobject obj, const char *fieldName, const char *value );
-PaStreamParameters *jpa_FillStreamParameters( JNIEnv *env, jobject jstreamParam, PaStreamParameters *myParams );
-
-jint jpa_CheckError( JNIEnv *env, PaError err );
-jint jpa_ThrowError( JNIEnv *env, const char *message );
-
-PaStream *jpa_GetStreamPointer( JNIEnv *env, jobject blockingStream );
-
-#endif /* JPA_TOOLS_H */
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/contourpy/util/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/contourpy/util/__init__.py
deleted file mode 100644
index fe33fcef1e18d2a4b92287e434cf6b1257e4274f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/contourpy/util/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from __future__ import annotations
-
-from contourpy.util._build_config import build_config
-
-__all__ = ["build_config"]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/chat_interface.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/chat_interface.py
deleted file mode 100644
index f3a581c177329968223060717f0063882d2ae600..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/chat_interface.py
+++ /dev/null
@@ -1,522 +0,0 @@
-"""
-This file defines a useful high-level abstraction to build Gradio chatbots: ChatInterface.
-"""
-
-
-from __future__ import annotations
-
-import inspect
-from typing import AsyncGenerator, Callable
-
-import anyio
-from gradio_client import utils as client_utils
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.blocks import Blocks
-from gradio.components import (
-    Button,
-    Chatbot,
-    Component,
-    Markdown,
-    State,
-    Textbox,
-    get_component_instance,
-)
-from gradio.events import Dependency, on
-from gradio.helpers import create_examples as Examples  # noqa: N812
-from gradio.helpers import special_args
-from gradio.layouts import Accordion, Column, Group, Row
-from gradio.routes import Request
-from gradio.themes import ThemeClass as Theme
-from gradio.utils import SyncToAsyncIterator, async_iteration
-
-set_documentation_group("chatinterface")
-
-
-@document()
-class ChatInterface(Blocks):
-    """
-    ChatInterface is Gradio's high-level abstraction for creating chatbot UIs, and allows you to create
-    a web-based demo around a chatbot model in a few lines of code. Only one parameter is required: fn, which
-    takes a function that governs the response of the chatbot based on the user input and chat history. Additional
-    parameters can be used to control the appearance and behavior of the demo.
-
-    Example:
-        import gradio as gr
-
-        def echo(message, history):
-            return message
-
-        demo = gr.ChatInterface(fn=echo, examples=["hello", "hola", "merhaba"], title="Echo Bot")
-        demo.launch()
-    Demos: chatinterface_random_response, chatinterface_streaming_echo
-    Guides: creating-a-chatbot-fast, sharing-your-app
-    """
-
-    def __init__(
-        self,
-        fn: Callable,
-        *,
-        chatbot: Chatbot | None = None,
-        textbox: Textbox | None = None,
-        additional_inputs: str | Component | list[str | Component] | None = None,
-        additional_inputs_accordion_name: str = "Additional Inputs",
-        examples: list[str] | None = None,
-        cache_examples: bool | None = None,
-        title: str | None = None,
-        description: str | None = None,
-        theme: Theme | str | None = None,
-        css: str | None = None,
-        analytics_enabled: bool | None = None,
-        submit_btn: str | None | Button = "Submit",
-        stop_btn: str | None | Button = "Stop",
-        retry_btn: str | None | Button = "🔄  Retry",
-        undo_btn: str | None | Button = "↩️ Undo",
-        clear_btn: str | None | Button = "🗑️  Clear",
-        autofocus: bool = True,
-    ):
-        """
-        Parameters:
-            fn: the function to wrap the chat interface around. Should accept two parameters: a string input message and list of two-element lists of the form [[user_message, bot_message], ...] representing the chat history, and return a string response. See the Chatbot documentation for more information on the chat history format.
-            chatbot: an instance of the gr.Chatbot component to use for the chat interface, if you would like to customize the chatbot properties. If not provided, a default gr.Chatbot component will be created.
-            textbox: an instance of the gr.Textbox component to use for the chat interface, if you would like to customize the textbox properties. If not provided, a default gr.Textbox component will be created.
-            additional_inputs: an instance or list of instances of gradio components (or their string shortcuts) to use as additional inputs to the chatbot. If components are not already rendered in a surrounding Blocks, then the components will be displayed under the chatbot, in an accordion.
-            additional_inputs_accordion_name: the label of the accordion to use for additional inputs, only used if additional_inputs is provided.
-            examples: sample inputs for the function; if provided, appear below the chatbot and can be clicked to populate the chatbot input.
-            cache_examples: If True, caches examples in the server for fast runtime in examples. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
-            title: a title for the interface; if provided, appears above chatbot in large font. Also used as the tab title when opened in a browser window.
-            description: a description for the interface; if provided, appears above the chatbot and beneath the title in regular font. Accepts Markdown and HTML content.
-            theme: Theme to use, loaded from gradio.themes.
-            css: custom css or path to custom css file to use with interface.
-            analytics_enabled: Whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True.
-            submit_btn: Text to display on the submit button. If None, no button will be displayed. If a Button object, that button will be used.
-            stop_btn: Text to display on the stop button, which replaces the submit_btn when the submit_btn or retry_btn is clicked and response is streaming. Clicking on the stop_btn will halt the chatbot response. If set to None, stop button functionality does not appear in the chatbot. If a Button object, that button will be used as the stop button.
-            retry_btn: Text to display on the retry button. If None, no button will be displayed. If a Button object, that button will be used.
-            undo_btn: Text to display on the delete last button. If None, no button will be displayed. If a Button object, that button will be used.
-            clear_btn: Text to display on the clear button. If None, no button will be displayed. If a Button object, that button will be used.
-            autofocus: If True, autofocuses to the textbox when the page loads.
-        """
-        super().__init__(
-            analytics_enabled=analytics_enabled,
-            mode="chat_interface",
-            css=css,
-            title=title or "Gradio",
-            theme=theme,
-        )
-        self.fn = fn
-        self.is_async = inspect.iscoroutinefunction(
-            self.fn
-        ) or inspect.isasyncgenfunction(self.fn)
-        self.is_generator = inspect.isgeneratorfunction(
-            self.fn
-        ) or inspect.isasyncgenfunction(self.fn)
-        self.examples = examples
-        if self.space_id and cache_examples is None:
-            self.cache_examples = True
-        else:
-            self.cache_examples = cache_examples or False
-        self.buttons: list[Button | None] = []
-
-        if additional_inputs:
-            if not isinstance(additional_inputs, list):
-                additional_inputs = [additional_inputs]
-            self.additional_inputs = [
-                get_component_instance(i) for i in additional_inputs  # type: ignore
-            ]
-        else:
-            self.additional_inputs = []
-        self.additional_inputs_accordion_name = additional_inputs_accordion_name
-
-        with self:
-            if title:
-                Markdown(
-                    f"<h1 style='text-align: center; margin-bottom: 1rem'>{self.title}</h1>"
-                )
-            if description:
-                Markdown(description)
-
-            with Column(variant="panel"):
-                if chatbot:
-                    self.chatbot = chatbot.render()
-                else:
-                    self.chatbot = Chatbot(label="Chatbot")
-
-                with Group():
-                    with Row():
-                        if textbox:
-                            textbox.container = False
-                            textbox.show_label = False
-                            textbox_ = textbox.render()
-                            assert isinstance(textbox_, Textbox)
-                            self.textbox = textbox_
-                        else:
-                            self.textbox = Textbox(
-                                container=False,
-                                show_label=False,
-                                label="Message",
-                                placeholder="Type a message...",
-                                scale=7,
-                                autofocus=autofocus,
-                            )
-                        if submit_btn:
-                            if isinstance(submit_btn, Button):
-                                submit_btn.render()
-                            elif isinstance(submit_btn, str):
-                                submit_btn = Button(
-                                    submit_btn,
-                                    variant="primary",
-                                    scale=1,
-                                    min_width=150,
-                                )
-                            else:
-                                raise ValueError(
-                                    f"The submit_btn parameter must be a gr.Button, string, or None, not {type(submit_btn)}"
-                                )
-                        if stop_btn:
-                            if isinstance(stop_btn, Button):
-                                stop_btn.visible = False
-                                stop_btn.render()
-                            elif isinstance(stop_btn, str):
-                                stop_btn = Button(
-                                    stop_btn,
-                                    variant="stop",
-                                    visible=False,
-                                    scale=1,
-                                    min_width=150,
-                                )
-                            else:
-                                raise ValueError(
-                                    f"The stop_btn parameter must be a gr.Button, string, or None, not {type(stop_btn)}"
-                                )
-                        self.buttons.extend([submit_btn, stop_btn])  # type: ignore
-
-                with Row():
-                    for btn in [retry_btn, undo_btn, clear_btn]:
-                        if btn:
-                            if isinstance(btn, Button):
-                                btn.render()
-                            elif isinstance(btn, str):
-                                btn = Button(btn, variant="secondary")
-                            else:
-                                raise ValueError(
-                                    f"All the _btn parameters must be a gr.Button, string, or None, not {type(btn)}"
-                                )
-                        self.buttons.append(btn)  # type: ignore
-
-                    self.fake_api_btn = Button("Fake API", visible=False)
-                    self.fake_response_textbox = Textbox(
-                        label="Response", visible=False
-                    )
-                    (
-                        self.submit_btn,
-                        self.stop_btn,
-                        self.retry_btn,
-                        self.undo_btn,
-                        self.clear_btn,
-                    ) = self.buttons
-
-            if examples:
-                if self.is_generator:
-                    examples_fn = self._examples_stream_fn
-                else:
-                    examples_fn = self._examples_fn
-
-                self.examples_handler = Examples(
-                    examples=examples,
-                    inputs=[self.textbox] + self.additional_inputs,
-                    outputs=self.chatbot,
-                    fn=examples_fn,
-                )
-
-            any_unrendered_inputs = any(
-                not inp.is_rendered for inp in self.additional_inputs
-            )
-            if self.additional_inputs and any_unrendered_inputs:
-                with Accordion(self.additional_inputs_accordion_name, open=False):
-                    for input_component in self.additional_inputs:
-                        if not input_component.is_rendered:
-                            input_component.render()
-
-            # The example caching must happen after the input components have rendered
-            if cache_examples:
-                client_utils.synchronize_async(self.examples_handler.cache)
-
-            self.saved_input = State()
-            self.chatbot_state = State([])
-
-            self._setup_events()
-            self._setup_api()
-
-    def _setup_events(self) -> None:
-        submit_fn = self._stream_fn if self.is_generator else self._submit_fn
-        submit_triggers = (
-            [self.textbox.submit, self.submit_btn.click]
-            if self.submit_btn
-            else [self.textbox.submit]
-        )
-        submit_event = (
-            on(
-                submit_triggers,
-                self._clear_and_save_textbox,
-                [self.textbox],
-                [self.textbox, self.saved_input],
-                api_name=False,
-                queue=False,
-            )
-            .then(
-                self._display_input,
-                [self.saved_input, self.chatbot_state],
-                [self.chatbot, self.chatbot_state],
-                api_name=False,
-                queue=False,
-            )
-            .then(
-                submit_fn,
-                [self.saved_input, self.chatbot_state] + self.additional_inputs,
-                [self.chatbot, self.chatbot_state],
-                api_name=False,
-            )
-        )
-        self._setup_stop_events(submit_triggers, submit_event)
-
-        if self.retry_btn:
-            retry_event = (
-                self.retry_btn.click(
-                    self._delete_prev_fn,
-                    [self.chatbot_state],
-                    [self.chatbot, self.saved_input, self.chatbot_state],
-                    api_name=False,
-                    queue=False,
-                )
-                .then(
-                    self._display_input,
-                    [self.saved_input, self.chatbot_state],
-                    [self.chatbot, self.chatbot_state],
-                    api_name=False,
-                    queue=False,
-                )
-                .then(
-                    submit_fn,
-                    [self.saved_input, self.chatbot_state] + self.additional_inputs,
-                    [self.chatbot, self.chatbot_state],
-                    api_name=False,
-                )
-            )
-            self._setup_stop_events([self.retry_btn.click], retry_event)
-
-        if self.undo_btn:
-            self.undo_btn.click(
-                self._delete_prev_fn,
-                [self.chatbot_state],
-                [self.chatbot, self.saved_input, self.chatbot_state],
-                api_name=False,
-                queue=False,
-            ).then(
-                lambda x: x,
-                [self.saved_input],
-                [self.textbox],
-                api_name=False,
-                queue=False,
-            )
-
-        if self.clear_btn:
-            self.clear_btn.click(
-                lambda: ([], [], None),
-                None,
-                [self.chatbot, self.chatbot_state, self.saved_input],
-                queue=False,
-                api_name=False,
-            )
-
-    def _setup_stop_events(
-        self, event_triggers: list[Callable], event_to_cancel: Dependency
-    ) -> None:
-        if self.stop_btn and self.is_generator:
-            if self.submit_btn:
-                for event_trigger in event_triggers:
-                    event_trigger(
-                        lambda: (
-                            Button(visible=False),
-                            Button(visible=True),
-                        ),
-                        None,
-                        [self.submit_btn, self.stop_btn],
-                        api_name=False,
-                        queue=False,
-                    )
-                event_to_cancel.then(
-                    lambda: (Button(visible=True), Button(visible=False)),
-                    None,
-                    [self.submit_btn, self.stop_btn],
-                    api_name=False,
-                    queue=False,
-                )
-            else:
-                for event_trigger in event_triggers:
-                    event_trigger(
-                        lambda: Button(visible=True),
-                        None,
-                        [self.stop_btn],
-                        api_name=False,
-                        queue=False,
-                    )
-                event_to_cancel.then(
-                    lambda: Button(visible=False),
-                    None,
-                    [self.stop_btn],
-                    api_name=False,
-                    queue=False,
-                )
-            self.stop_btn.click(
-                None,
-                None,
-                None,
-                cancels=event_to_cancel,
-                api_name=False,
-            )
-
-    def _setup_api(self) -> None:
-        api_fn = self._api_stream_fn if self.is_generator else self._api_submit_fn
-
-        self.fake_api_btn.click(
-            api_fn,
-            [self.textbox, self.chatbot_state] + self.additional_inputs,
-            [self.textbox, self.chatbot_state],
-            api_name="chat",
-        )
-
-    def _clear_and_save_textbox(self, message: str) -> tuple[str, str]:
-        return "", message
-
-    def _display_input(
-        self, message: str, history: list[list[str | None]]
-    ) -> tuple[list[list[str | None]], list[list[str | None]]]:
-        history.append([message, None])
-        return history, history
-
-    async def _submit_fn(
-        self,
-        message: str,
-        history_with_input: list[list[str | None]],
-        request: Request,
-        *args,
-    ) -> tuple[list[list[str | None]], list[list[str | None]]]:
-        history = history_with_input[:-1]
-        inputs, _, _ = special_args(
-            self.fn, inputs=[message, history, *args], request=request
-        )
-
-        if self.is_async:
-            response = await self.fn(*inputs)
-        else:
-            response = await anyio.to_thread.run_sync(
-                self.fn, *inputs, limiter=self.limiter
-            )
-
-        history.append([message, response])
-        return history, history
-
-    async def _stream_fn(
-        self,
-        message: str,
-        history_with_input: list[list[str | None]],
-        request: Request,
-        *args,
-    ) -> AsyncGenerator:
-        history = history_with_input[:-1]
-        inputs, _, _ = special_args(
-            self.fn, inputs=[message, history, *args], request=request
-        )
-
-        if self.is_async:
-            generator = self.fn(*inputs)
-        else:
-            generator = await anyio.to_thread.run_sync(
-                self.fn, *inputs, limiter=self.limiter
-            )
-            generator = SyncToAsyncIterator(generator, self.limiter)
-        try:
-            first_response = await async_iteration(generator)
-            update = history + [[message, first_response]]
-            yield update, update
-        except StopIteration:
-            update = history + [[message, None]]
-            yield update, update
-        async for response in generator:
-            update = history + [[message, response]]
-            yield update, update
-
-    async def _api_submit_fn(
-        self, message: str, history: list[list[str | None]], request: Request, *args
-    ) -> tuple[str, list[list[str | None]]]:
-        inputs, _, _ = special_args(
-            self.fn, inputs=[message, history, *args], request=request
-        )
-
-        if self.is_async:
-            response = await self.fn(*inputs)
-        else:
-            response = await anyio.to_thread.run_sync(
-                self.fn, *inputs, limiter=self.limiter
-            )
-        history.append([message, response])
-        return response, history
-
-    async def _api_stream_fn(
-        self, message: str, history: list[list[str | None]], request: Request, *args
-    ) -> AsyncGenerator:
-        inputs, _, _ = special_args(
-            self.fn, inputs=[message, history, *args], request=request
-        )
-
-        if self.is_async:
-            generator = self.fn(*inputs)
-        else:
-            generator = await anyio.to_thread.run_sync(
-                self.fn, *inputs, limiter=self.limiter
-            )
-            generator = SyncToAsyncIterator(generator, self.limiter)
-        try:
-            first_response = await async_iteration(generator)
-            yield first_response, history + [[message, first_response]]
-        except StopIteration:
-            yield None, history + [[message, None]]
-        async for response in generator:
-            yield response, history + [[message, response]]
-
-    async def _examples_fn(self, message: str, *args) -> list[list[str | None]]:
-        inputs, _, _ = special_args(self.fn, inputs=[message, [], *args], request=None)
-
-        if self.is_async:
-            response = await self.fn(*inputs)
-        else:
-            response = await anyio.to_thread.run_sync(
-                self.fn, *inputs, limiter=self.limiter
-            )
-        return [[message, response]]
-
-    async def _examples_stream_fn(
-        self,
-        message: str,
-        *args,
-    ) -> AsyncGenerator:
-        inputs, _, _ = special_args(self.fn, inputs=[message, [], *args], request=None)
-
-        if self.is_async:
-            generator = self.fn(*inputs)
-        else:
-            generator = await anyio.to_thread.run_sync(
-                self.fn, *inputs, limiter=self.limiter
-            )
-            generator = SyncToAsyncIterator(generator, self.limiter)
-        async for response in generator:
-            yield [[message, response]]
-
-    def _delete_prev_fn(
-        self, history: list[list[str | None]]
-    ) -> tuple[list[list[str | None]], str, list[list[str | None]]]:
-        try:
-            message, _ = history.pop()
-        except IndexError:
-            message = ""
-        return history, message or "", history
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ImageUploader-3bf768ff.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ImageUploader-3bf768ff.js
deleted file mode 100644
index 416dd3d305d522069fd3170ded920dbc64db418d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ImageUploader-3bf768ff.js
+++ /dev/null
@@ -1,2 +0,0 @@
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.cropper-line,.gradio-container-4-0-2 .cropper-point{display:block;position:absolute;opacity:.1;width:100%;height:100%}.gradio-container-4-0-2 .cropper-face{top:0;left:0;background-color:#fff}.gradio-container-4-0-2 .cropper-line{background-color:#39f}.gradio-container-4-0-2 .cropper-line.line-e{top:0;right:-3px;cursor:ew-resize;width:5px}.gradio-container-4-0-2 .cropper-line.line-n{top:-3px;left:0;cursor:ns-resize;height:5px}.gradio-container-4-0-2 .cropper-line.line-w{top:0;left:-3px;cursor:ew-resize;width:5px}.gradio-container-4-0-2 .cropper-line.line-s{bottom:-3px;left:0;cursor:ns-resize;height:5px}.gradio-container-4-0-2 .cropper-point{opacity:.75;background-color:#39f;width:5px;height:5px}.gradio-container-4-0-2 .cropper-point.point-e{top:50%;right:-3px;cursor:ew-resize;margin-top:-3px}.gradio-container-4-0-2 .cropper-point.point-n{top:-3px;left:50%;cursor:ns-resize;margin-left:-3px}.gradio-container-4-0-2 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.cropper-point.point-se:before{display:block;position:absolute;right:-50%;bottom:-50%;opacity:0;background-color:#39f;width:200%;height:200%;content:" "}.gradio-container-4-0-2 .cropper-invisible{opacity:0}.gradio-container-4-0-2 .cropper-bg{background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQAQMAAAAlPW0iAAAAA3NCSVQICAjb4U/gAAAABlBMVEXMzMz////TjRV2AAAACXBIWXMAAArrAAAK6wGCiw1aAAAAHHRFWHRTb2Z0d2FyZQBBZG9iZSBGaXJld29ya3MgQ1M26LyyjAAAABFJREFUCJlj+M/AgBVhF/0PAH6/D/HkDxOGAAAAAElFTkSuQmCC)}.gradio-container-4-0-2 .cropper-hide{display:block;position:absolute;width:0;height:0}.gradio-container-4-0-2 .cropper-hidden{display:none!important}.gradio-container-4-0-2 .cropper-move{cursor:move}.gradio-container-4-0-2 .cropper-crop{cursor:crosshair}.gradio-container-4-0-2 .cropper-disabled .cropper-drag-box,.gradio-container-4-0-2 .cropper-disabled .cropper-face,.gradio-container-4-0-2 .cropper-disabled .cropper-line,.gradio-container-4-0-2 .cropper-disabled .cropper-point{cursor:not-allowed}:root{--scale-0: 1rem;--scale-1: 1.125rem;--scale-2: 1.25rem;--scale-3: 1.5rem;--scale-4: 1.875rem;--scale-5: 2.25rem;--scale-6: 3rem;--scale-7: 3.75rem;--scale-8: 4.5rem;--scale-9: 6rem;--scale-10: 8rem;--scale-000: .75rem;--scale-00: .875rem;--scale-fluid-0: clamp(.875rem, .8rem + .25vw, 1rem);--scale-fluid-1: clamp(1rem, .925rem + .25vw, 1.125rem);--scale-fluid-2: clamp(1.125rem, 1.05rem + .25vw, 1.25rem);--scale-fluid-3: clamp(1.8125rem, 2rem + -.625vw, 1.5rem);--scale-fluid-4: clamp(1.5rem, 1.275rem + .75vw, 1.875rem);--scale-fluid-5: clamp(1.875rem, 1.65rem + .75vw, 2.25rem);--scale-fluid-6: clamp(2.25rem, 1.8rem + 1.5vw, 3rem);--scale-fluid-7: clamp(3rem, 2.55rem + 1.5vw, 3.75rem);--scale-fluid-8: clamp(3.75rem, 3.3rem + 1.5vw, 4.5rem);--scale-fluid-9: clamp(4.5rem, 3.6rem + 3vw, 6rem);--scale-fluid-10: clamp(6rem, 4.8rem + 4vw, 8rem);--scale-fluid-000: clamp(.625rem, .55rem + .25vw, .75rem);--scale-fluid-00: clamp(.75rem, .675rem + .25vw, .875rem);--font-sans: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--font-serif: Georgia, Cambria, "Times New Roman", Times, serif;--font-mono: IBM Plex Mono, ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--weight-light: 300;--weight-regular: 400;--weight-medium: 500;--weight-semibold: 600;--weight-bold: 700;--weight-extrabold: 800;--weight-black: 900;--line-none: 1;--line-xs: 1.125;--line-sm: 1.4;--line-md: 1.5;--line-lg: 1.625;--line-xl: 2;--letter-xs: -.05em;--letter-sm: -.025em;--letter-none: 0em;--letter-lg: .025em;--letter-xl: .05em;--prose-xs: 45ch;--prose-sm: 55ch;--prose-md: 65ch;--prose-lg: 75ch;--prose-xl: 85ch;--size-1: 4px;--size-2: 8px;--size-3: 12px;--size-4: 16px;--size-5: 20px;--size-6: 24px;--size-7: 28px;--size-8: 32px;--size-9: 36px;--size-10: 40px;--size-11: 44px;--size-12: 48px;--size-14: 56px;--size-16: 64px;--size-20: 80px;--size-24: 96px;--size-28: 112px;--size-32: 128px;--size-36: 144px;--size-40: 160px;--size-44: 176px;--size-48: 192px;--size-52: 208px;--size-56: 224px;--size-60: 240px;--size-64: 256px;--size-72: 288px;--size-80: 320px;--size-96: 384px;--size-px: 1px;--size-full: 100%;--size-screen: 100vw;--size-min: min-content;--size-max: max-content;--size-0-5: 2px;--size-1-5: 6px;--size-2-5: 10px;--size-screen-h: 100vh;--width-xs: 480px;--width-sm: 640px;--width-md: 768px;--width-lg: 1024px;--width-xl: 1280px;--ratio-square: 1/1;--ratio-portrait: 3/4;--ratio-landscape: 4/3;--ratio-tall: 2/3;--ratio-wide: 3/2;--ratio-widescreen: 16/9;--ratio-golden: 1.618/1;--radius-100: 100%;--radius-xs: 2px;--radius-sm: 4px;--radius-md: 6px;--radius-lg: 8px;--radius-xl: 12px;--radius-full: 9999px;--radius-2xl: 16px;--radius-3xl: 22px;--blur-xs: blur(4px);--blur-sm: blur(8px);--blur-md: blur(16px);--blur-lg: blur(24px);--blur-xl: blur(40px);--layer-1: 10;--layer-2: 20;--layer-3: 30;--layer-4: 40;--layer-5: 50;--layer-below: -1;--layer-top: 2147483647;--shadow-xs: 0 1px 3px 0 rgba(0, 0, 0, .1), 0 1px 2px 0 rgba(0, 0, 0, .06);--shadow-sm: 0 4px 6px -2px rgba(0, 0, 0, .1), 0 2px 4px -2px rgba(0, 0, 0, .06);--shadow-md: 0 12px 16px -4px rgba(0, 0, 0, .1), 0 4px 6px -2px rgba(0, 0, 0, .05);--shadow-lg: 0 20px 24px -4px rgba(0, 0, 0, .1), 0 8px 8px -4px rgba(0, 0, 0, .04);--shadow-xl: 0 24px 48px -12px rgba(0, 0, 0, .25);--ease-in-sine: cubic-bezier(.47, 0, .745, .715);--ease-out-sine: cubic-bezier(.39, .575, .565, 1);--ease-in-out-sine: cubic-bezier(.445, .05, .55, .95);--ease-in-quad: cubic-bezier(.55, .085, .68, .53);--ease-out-quad: cubic-bezier(.25, .46, .45, .94);--ease-in-out-quad: cubic-bezier(.455, .03, .515, .955);--ease-in-cubic: cubic-bezier(.55, .055, .675, .19);--ease-out-cubic: cubic-bezier(.215, .61, .355, 1);--ease-in-out-cubic: cubic-bezier(.645, .045, .355, 1);--ease-in-quart: cubic-bezier(.895, .03, .685, .22);--ease-out-quart: cubic-bezier(.165, .84, .44, 1);--ease-in-out-quart: cubic-bezier(.77, 0, .175, 1);--ease-in-quint: cubic-bezier(.755, .05, .855, .06);--ease-out-quint: cubic-bezier(.23, 1, .32, 1);--ease-in-out-quint: cubic-bezier(.86, 0, .07, 1);--ease-in-expo: cubic-bezier(.95, .05, .795, .035);--ease-out-expo: cubic-bezier(.19, 1, .22, 1);--ease-in-out-expo: cubic-bezier(1, 0, 0, 1);--ease-in-circ: cubic-bezier(.6, .04, .98, .335);--ease-out-circ: cubic-bezier(.075, .82, .165, 1);--ease-in-out-circ: cubic-bezier(.785, .135, .15, .86);--ease-in-back: cubic-bezier(.6, -.28, .735, .045);--ease-out-back: cubic-bezier(.175, .885, .32, 1.275);--ease-in-out-back: cubic-bezier(.68, -.55, .265, 1.55);--easing-standard: cubic-bezier(.4, 0, .2, 1);--easing-accelerate: cubic-bezier(.4, 0, 1, 1);--easing-decelerate: cubic-bezier(0, 0, .2, 1);--elevation-1: 0 1px 2px 0 rgba(0, 0, 0, .05);--elevation-2: 0 1px 3px 0 rgba(0, 0, 0, .1), 0 1px 2px 0 rgba(0, 0, 0, .06);--elevation-3: 0 4px 6px -2px rgba(0, 0, 0, .1), 0 2px 4px -2px rgba(0, 0, 0, .06);--elevation-4: 0 12px 16px -4px rgba(0, 0, 0, .1), 0 4px 6px -2px rgba(0, 0, 0, .05);--elevation-5: 0 20px 24px -4px rgba(0, 0, 0, .1), 0 8px 8px -4px rgba(0, 0, 0, .04);--elevation-6: 0 24px 48px -12px rgba(0, 0, 0, .25);--elevation-7: 0 32px 64px -12px rgba(0, 0, 0, .2);--color-grey-50: #f9fafb;--color-grey-100: #f3f4f6;--color-grey-200: #e5e7eb;--color-grey-300: #d1d5db;--color-grey-400: #9ca3af;--color-grey-500: #6b7280;--color-grey-600: #4b5563;--color-grey-700: #374151;--color-grey-800: #1f2937;--color-grey-900: #111827;--color-black: #14141b;--color-grey: #6b7280;--color-red-300: #fca5a5;--color-red-500: #ef4444;--color-red-700: #b91c1c;--color-red: #ef4444;--color-green-300: #86efac;--color-green-500: #22c55e;--color-green-700: #15803d;--color-green: #22c55e;--color-blue-300: #93c5fd;--color-blue-500: #0ea5e9;--color-blue-700: #1d4ed8;--color-blue: #0ea5e9;--color-pink-300: #fbb6ce;--color-pink-500: #ed64a6;--color-pink-700: #d53f8c;--color-pink: var(--color-pink-500);--color-purple-300: #b794f4;--color-purple-500: #805ad5;--color-purple-700: #6b46c1;--color-purple: var(--color-purple-500);--color-teal-300: #81e6d9;--color-teal-500: #38b2ac;--color-teal-700: #2c7a7b;--color-teal: var(--color-teal-500);--color-yellow-300: #fde047;--color-yellow-500: #eab308;--color-yellow-700: #a16207;--color-yellow: #eab308;--color-orange-300: #ffb066;--color-orange-500: #ff7c00;--color-orange-700: #ce6400;--color-orange: #f97316;--color-brown-300: #a1887f;--color-brown-500: #795548;--color-brown-700: #5d4037;--color-brown: var(--color-brown-500);--color-blue-10: #fafcff;--color-blue-50: #eff6ff;--color-blue-100: #dbeafe;--color-blue-200: #bfdbfe;--color-blue-400: #60a5fa;--color-blue-600: #2563eb;--color-blue-800: #1e40af;--color-blue-900: #1e3a8a;--color-blue-950: #1c366b;--color-grey-10: #fdfdfe;--color-grey-950: #0b0f19;--color-red-10: #fffbfb;--color-red-50: #fef2f2;--color-red-100: #fee2e2;--color-red-200: #fecaca;--color-red-400: #f87171;--color-red-600: #dc2626;--color-red-800: #991b1b;--color-red-900: #7f1d1d;--color-red-950: #63171a;--color-green-10: #f9fefc;--color-green-50: #ecfdf5;--color-green-100: #d1fae5;--color-green-200: #bbf7d0;--color-green-400: #4ade80;--color-green-600: #16a34a;--color-green-800: #166534;--color-green-900: #14532d;--color-green-950: #134227;--color-orange-10: #fffbf6;--color-orange-50: #fff2e5;--color-orange-100: #ffe5cc;--color-orange-200: #ffd8b4;--color-orange-400: #ff9633;--color-orange-600: #ee7400;--color-orange-800: #a45000;--color-orange-900: #5c2d00;--color-orange-950: #3c1f00;--color-yellow-10: #fffef8;--color-yellow-50: #fffbeb;--color-yellow-100: #fff9c2;--color-yellow-200: #fef08a;--color-yellow-400: #facc15;--color-yellow-600: #ca8a04;--color-yellow-800: #854d0e;--color-yellow-900: #713f12;--color-yellow-950: #633112;--grid-2: repeat(2, minmax(0, 1fr));--grid-3: repeat(3, minmax(0, 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li{margin-bottom:.5em}.gradio-container-4-0-2 .prose th,.gradio-container-4-0-2 .prose td{border-bottom:1px solid #e1e1e1;padding:12px 15px;text-align:left}.gradio-container-4-0-2 .prose th:first-child,.gradio-container-4-0-2 .prose td:first-child{padding-left:0}.gradio-container-4-0-2 .prose th:last-child,.gradio-container-4-0-2 .prose td:last-child{padding-right:0}.gradio-container-4-0-2 .prose button,.gradio-container-4-0-2 .prose .button,.gradio-container-4-0-2 .prose input,.gradio-container-4-0-2 .prose textarea,.gradio-container-4-0-2 .prose select,.gradio-container-4-0-2 .prose fieldset{margin-bottom:var(--spacing-sm)}.gradio-container-4-0-2 .prose pre,.gradio-container-4-0-2 .prose blockquote,.gradio-container-4-0-2 .prose dl,.gradio-container-4-0-2 .prose figure,.gradio-container-4-0-2 .prose table,.gradio-container-4-0-2 .prose p,.gradio-container-4-0-2 .prose ul,.gradio-container-4-0-2 .prose ol,.gradio-container-4-0-2 .prose form{margin-bottom:var(--spacing-md)}.gradio-container-4-0-2 .prose a{color:var(--link-text-color);text-decoration:underline}.gradio-container-4-0-2 .prose a:visited{color:var(--link-text-color-visited)}.gradio-container-4-0-2 .prose a:hover{color:var(--link-text-color-hover)}.gradio-container-4-0-2 .prose a:active{color:var(--link-text-color-active)}.gradio-container-4-0-2 .prose hr{margin-top:3em;margin-bottom:3.5em;border-width:0;border-top:1px solid #e1e1e1}.gradio-container-4-0-2 .prose blockquote{margin:var(--size-6) 0!important;border-left:5px solid var(--border-color-primary);padding-left:var(--size-2)}.gradio-container-4-0-2 .prose:last-child{margin-bottom:0!important}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/importlib_resources/tests/_path.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/importlib_resources/tests/_path.py
deleted file mode 100644
index 1f97c96146960d4256840340fe25f8da7be552c2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/importlib_resources/tests/_path.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import pathlib
-import functools
-
-from typing import Dict, Union
-
-
-####
-# from jaraco.path 3.4.1
-
-FilesSpec = Dict[str, Union[str, bytes, 'FilesSpec']]  # type: ignore
-
-
-def build(spec: FilesSpec, prefix=pathlib.Path()):
-    """
-    Build a set of files/directories, as described by the spec.
-
-    Each key represents a pathname, and the value represents
-    the content. Content may be a nested directory.
-
-    >>> spec = {
-    ...     'README.txt': "A README file",
-    ...     "foo": {
-    ...         "__init__.py": "",
-    ...         "bar": {
-    ...             "__init__.py": "",
-    ...         },
-    ...         "baz.py": "# Some code",
-    ...     }
-    ... }
-    >>> target = getfixture('tmp_path')
-    >>> build(spec, target)
-    >>> target.joinpath('foo/baz.py').read_text(encoding='utf-8')
-    '# Some code'
-    """
-    for name, contents in spec.items():
-        create(contents, pathlib.Path(prefix) / name)
-
-
-@functools.singledispatch
-def create(content: Union[str, bytes, FilesSpec], path):
-    path.mkdir(exist_ok=True)
-    build(content, prefix=path)  # type: ignore
-
-
-@create.register
-def _(content: bytes, path):
-    path.write_bytes(content)
-
-
-@create.register
-def _(content: str, path):
-    path.write_text(content, encoding='utf-8')
-
-
-# end from jaraco.path
-####
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_afm.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_afm.py
deleted file mode 100644
index e5c6a83937cd68e3ae20a14d8babb9a99bd5a4f5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_afm.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from io import BytesIO
-import pytest
-import logging
-
-from matplotlib import _afm
-from matplotlib import font_manager as fm
-
-
-# See note in afm.py re: use of comma as decimal separator in the
-# UnderlineThickness field and re: use of non-ASCII characters in the Notice
-# field.
-AFM_TEST_DATA = b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-FontName MyFont-Bold
-EncodingScheme FontSpecific
-FullName My Font Bold
-FamilyName Test Fonts
-Weight Bold
-ItalicAngle 0.0
-IsFixedPitch false
-UnderlinePosition -100
-UnderlineThickness 56,789
-Version 001.000
-Notice Copyright \xa9 2017 No one.
-FontBBox 0 -321 1234 369
-StartCharMetrics 3
-C 0 ; WX 250 ; N space ; B 0 0 0 0 ;
-C 42 ; WX 1141 ; N foo ; B 40 60 800 360 ;
-C 99 ; WX 583 ; N bar ; B 40 -10 543 210 ;
-EndCharMetrics
-EndFontMetrics
-"""
-
-
-def test_nonascii_str():
-    # This tests that we also decode bytes as utf-8 properly.
-    # Else, font files with non ascii characters fail to load.
-    inp_str = "привет"
-    byte_str = inp_str.encode("utf8")
-
-    ret = _afm._to_str(byte_str)
-    assert ret == inp_str
-
-
-def test_parse_header():
-    fh = BytesIO(AFM_TEST_DATA)
-    header = _afm._parse_header(fh)
-    assert header == {
-        b'StartFontMetrics': 2.0,
-        b'FontName': 'MyFont-Bold',
-        b'EncodingScheme': 'FontSpecific',
-        b'FullName': 'My Font Bold',
-        b'FamilyName': 'Test Fonts',
-        b'Weight': 'Bold',
-        b'ItalicAngle': 0.0,
-        b'IsFixedPitch': False,
-        b'UnderlinePosition': -100,
-        b'UnderlineThickness': 56.789,
-        b'Version': '001.000',
-        b'Notice': b'Copyright \xa9 2017 No one.',
-        b'FontBBox': [0, -321, 1234, 369],
-        b'StartCharMetrics': 3,
-    }
-
-
-def test_parse_char_metrics():
-    fh = BytesIO(AFM_TEST_DATA)
-    _afm._parse_header(fh)  # position
-    metrics = _afm._parse_char_metrics(fh)
-    assert metrics == (
-        {0: (250.0, 'space', [0, 0, 0, 0]),
-         42: (1141.0, 'foo', [40, 60, 800, 360]),
-         99: (583.0, 'bar', [40, -10, 543, 210]),
-         },
-        {'space': (250.0, 'space', [0, 0, 0, 0]),
-         'foo': (1141.0, 'foo', [40, 60, 800, 360]),
-         'bar': (583.0, 'bar', [40, -10, 543, 210]),
-         })
-
-
-def test_get_familyname_guessed():
-    fh = BytesIO(AFM_TEST_DATA)
-    font = _afm.AFM(fh)
-    del font._header[b'FamilyName']  # remove FamilyName, so we have to guess
-    assert font.get_familyname() == 'My Font'
-
-
-def test_font_manager_weight_normalization():
-    font = _afm.AFM(BytesIO(
-        AFM_TEST_DATA.replace(b"Weight Bold\n", b"Weight Custom\n")))
-    assert fm.afmFontProperty("", font).weight == "normal"
-
-
-@pytest.mark.parametrize(
-    "afm_data",
-    [
-        b"""nope
-really nope""",
-        b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-FontName MyFont-Bold
-EncodingScheme FontSpecific""",
-    ],
-)
-def test_bad_afm(afm_data):
-    fh = BytesIO(afm_data)
-    with pytest.raises(RuntimeError):
-        _afm._parse_header(fh)
-
-
-@pytest.mark.parametrize(
-    "afm_data",
-    [
-        b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-Aardvark bob
-FontName MyFont-Bold
-EncodingScheme FontSpecific
-StartCharMetrics 3""",
-        b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-ItalicAngle zero degrees
-FontName MyFont-Bold
-EncodingScheme FontSpecific
-StartCharMetrics 3""",
-    ],
-)
-def test_malformed_header(afm_data, caplog):
-    fh = BytesIO(afm_data)
-    with caplog.at_level(logging.ERROR):
-        _afm._parse_header(fh)
-
-    assert len(caplog.records) == 1
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/integer/test_function.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/integer/test_function.py
deleted file mode 100644
index d48b636a98feb94c5eaeee9eefbf5730fe9c6f79..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/integer/test_function.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import numpy as np
-import pytest
-
-import pandas as pd
-import pandas._testing as tm
-from pandas.core.arrays import FloatingArray
-
-
-@pytest.mark.parametrize("ufunc", [np.abs, np.sign])
-# np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
-@pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning")
-def test_ufuncs_single_int(ufunc):
-    a = pd.array([1, 2, -3, np.nan])
-    result = ufunc(a)
-    expected = pd.array(ufunc(a.astype(float)), dtype="Int64")
-    tm.assert_extension_array_equal(result, expected)
-
-    s = pd.Series(a)
-    result = ufunc(s)
-    expected = pd.Series(pd.array(ufunc(a.astype(float)), dtype="Int64"))
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
-def test_ufuncs_single_float(ufunc):
-    a = pd.array([1, 2, -3, np.nan])
-    with np.errstate(invalid="ignore"):
-        result = ufunc(a)
-        expected = FloatingArray(ufunc(a.astype(float)), mask=a._mask)
-    tm.assert_extension_array_equal(result, expected)
-
-    s = pd.Series(a)
-    with np.errstate(invalid="ignore"):
-        result = ufunc(s)
-    expected = pd.Series(expected)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("ufunc", [np.add, np.subtract])
-def test_ufuncs_binary_int(ufunc):
-    # two IntegerArrays
-    a = pd.array([1, 2, -3, np.nan])
-    result = ufunc(a, a)
-    expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Int64")
-    tm.assert_extension_array_equal(result, expected)
-
-    # IntegerArray with numpy array
-    arr = np.array([1, 2, 3, 4])
-    result = ufunc(a, arr)
-    expected = pd.array(ufunc(a.astype(float), arr), dtype="Int64")
-    tm.assert_extension_array_equal(result, expected)
-
-    result = ufunc(arr, a)
-    expected = pd.array(ufunc(arr, a.astype(float)), dtype="Int64")
-    tm.assert_extension_array_equal(result, expected)
-
-    # IntegerArray with scalar
-    result = ufunc(a, 1)
-    expected = pd.array(ufunc(a.astype(float), 1), dtype="Int64")
-    tm.assert_extension_array_equal(result, expected)
-
-    result = ufunc(1, a)
-    expected = pd.array(ufunc(1, a.astype(float)), dtype="Int64")
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_ufunc_binary_output():
-    a = pd.array([1, 2, np.nan])
-    result = np.modf(a)
-    expected = np.modf(a.to_numpy(na_value=np.nan, dtype="float"))
-    expected = (pd.array(expected[0]), pd.array(expected[1]))
-
-    assert isinstance(result, tuple)
-    assert len(result) == 2
-
-    for x, y in zip(result, expected):
-        tm.assert_extension_array_equal(x, y)
-
-
-@pytest.mark.parametrize("values", [[0, 1], [0, None]])
-def test_ufunc_reduce_raises(values):
-    arr = pd.array(values)
-
-    res = np.add.reduce(arr)
-    expected = arr.sum(skipna=False)
-    tm.assert_almost_equal(res, expected)
-
-
-@pytest.mark.parametrize(
-    "pandasmethname, kwargs",
-    [
-        ("var", {"ddof": 0}),
-        ("var", {"ddof": 1}),
-        ("std", {"ddof": 0}),
-        ("std", {"ddof": 1}),
-        ("kurtosis", {}),
-        ("skew", {}),
-        ("sem", {}),
-    ],
-)
-def test_stat_method(pandasmethname, kwargs):
-    s = pd.Series(data=[1, 2, 3, 4, 5, 6, np.nan, np.nan], dtype="Int64")
-    pandasmeth = getattr(s, pandasmethname)
-    result = pandasmeth(**kwargs)
-    s2 = pd.Series(data=[1, 2, 3, 4, 5, 6], dtype="Int64")
-    pandasmeth = getattr(s2, pandasmethname)
-    expected = pandasmeth(**kwargs)
-    assert expected == result
-
-
-def test_value_counts_na():
-    arr = pd.array([1, 2, 1, pd.NA], dtype="Int64")
-    result = arr.value_counts(dropna=False)
-    ex_index = pd.Index([1, 2, pd.NA], dtype="Int64")
-    assert ex_index.dtype == "Int64"
-    expected = pd.Series([2, 1, 1], index=ex_index, dtype="Int64", name="count")
-    tm.assert_series_equal(result, expected)
-
-    result = arr.value_counts(dropna=True)
-    expected = pd.Series([2, 1], index=arr[:2], dtype="Int64", name="count")
-    assert expected.index.dtype == arr.dtype
-    tm.assert_series_equal(result, expected)
-
-
-def test_value_counts_empty():
-    # https://github.com/pandas-dev/pandas/issues/33317
-    ser = pd.Series([], dtype="Int64")
-    result = ser.value_counts()
-    idx = pd.Index([], dtype=ser.dtype)
-    assert idx.dtype == ser.dtype
-    expected = pd.Series([], index=idx, dtype="Int64", name="count")
-    tm.assert_series_equal(result, expected)
-
-
-def test_value_counts_with_normalize():
-    # GH 33172
-    ser = pd.Series([1, 2, 1, pd.NA], dtype="Int64")
-    result = ser.value_counts(normalize=True)
-    expected = pd.Series([2, 1], index=ser[:2], dtype="Float64", name="proportion") / 3
-    assert expected.index.dtype == ser.dtype
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("skipna", [True, False])
-@pytest.mark.parametrize("min_count", [0, 4])
-def test_integer_array_sum(skipna, min_count, any_int_ea_dtype):
-    dtype = any_int_ea_dtype
-    arr = pd.array([1, 2, 3, None], dtype=dtype)
-    result = arr.sum(skipna=skipna, min_count=min_count)
-    if skipna and min_count == 0:
-        assert result == 6
-    else:
-        assert result is pd.NA
-
-
-@pytest.mark.parametrize("skipna", [True, False])
-@pytest.mark.parametrize("method", ["min", "max"])
-def test_integer_array_min_max(skipna, method, any_int_ea_dtype):
-    dtype = any_int_ea_dtype
-    arr = pd.array([0, 1, None], dtype=dtype)
-    func = getattr(arr, method)
-    result = func(skipna=skipna)
-    if skipna:
-        assert result == (0 if method == "min" else 1)
-    else:
-        assert result is pd.NA
-
-
-@pytest.mark.parametrize("skipna", [True, False])
-@pytest.mark.parametrize("min_count", [0, 9])
-def test_integer_array_prod(skipna, min_count, any_int_ea_dtype):
-    dtype = any_int_ea_dtype
-    arr = pd.array([1, 2, None], dtype=dtype)
-    result = arr.prod(skipna=skipna, min_count=min_count)
-    if skipna and min_count == 0:
-        assert result == 2
-    else:
-        assert result is pd.NA
-
-
-@pytest.mark.parametrize(
-    "values, expected", [([1, 2, 3], 6), ([1, 2, 3, None], 6), ([None], 0)]
-)
-def test_integer_array_numpy_sum(values, expected):
-    arr = pd.array(values, dtype="Int64")
-    result = np.sum(arr)
-    assert result == expected
-
-
-@pytest.mark.parametrize("op", ["sum", "prod", "min", "max"])
-def test_dataframe_reductions(op):
-    # https://github.com/pandas-dev/pandas/pull/32867
-    # ensure the integers are not cast to float during reductions
-    df = pd.DataFrame({"a": pd.array([1, 2], dtype="Int64")})
-    result = df.max()
-    assert isinstance(result["a"], np.int64)
-
-
-# TODO(jreback) - these need testing / are broken
-
-# shift
-
-# set_index (destroys type)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/categorical/test_constructors.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/categorical/test_constructors.py
deleted file mode 100644
index f0c5307fc5c641ff25d26bd2bd8a158b43dd6a6d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/categorical/test_constructors.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    Categorical,
-    CategoricalDtype,
-    CategoricalIndex,
-    Index,
-)
-import pandas._testing as tm
-
-
-class TestCategoricalIndexConstructors:
-    def test_construction_disallows_scalar(self):
-        msg = "must be called with a collection of some kind"
-        with pytest.raises(TypeError, match=msg):
-            CategoricalIndex(data=1, categories=list("abcd"), ordered=False)
-        with pytest.raises(TypeError, match=msg):
-            CategoricalIndex(categories=list("abcd"), ordered=False)
-
-    def test_construction(self):
-        ci = CategoricalIndex(list("aabbca"), categories=list("abcd"), ordered=False)
-        categories = ci.categories
-
-        result = Index(ci)
-        tm.assert_index_equal(result, ci, exact=True)
-        assert not result.ordered
-
-        result = Index(ci.values)
-        tm.assert_index_equal(result, ci, exact=True)
-        assert not result.ordered
-
-        # empty
-        result = CategoricalIndex([], categories=categories)
-        tm.assert_index_equal(result.categories, Index(categories))
-        tm.assert_numpy_array_equal(result.codes, np.array([], dtype="int8"))
-        assert not result.ordered
-
-        # passing categories
-        result = CategoricalIndex(list("aabbca"), categories=categories)
-        tm.assert_index_equal(result.categories, Index(categories))
-        tm.assert_numpy_array_equal(
-            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
-        )
-
-        c = Categorical(list("aabbca"))
-        result = CategoricalIndex(c)
-        tm.assert_index_equal(result.categories, Index(list("abc")))
-        tm.assert_numpy_array_equal(
-            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
-        )
-        assert not result.ordered
-
-        result = CategoricalIndex(c, categories=categories)
-        tm.assert_index_equal(result.categories, Index(categories))
-        tm.assert_numpy_array_equal(
-            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
-        )
-        assert not result.ordered
-
-        ci = CategoricalIndex(c, categories=list("abcd"))
-        result = CategoricalIndex(ci)
-        tm.assert_index_equal(result.categories, Index(categories))
-        tm.assert_numpy_array_equal(
-            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
-        )
-        assert not result.ordered
-
-        result = CategoricalIndex(ci, categories=list("ab"))
-        tm.assert_index_equal(result.categories, Index(list("ab")))
-        tm.assert_numpy_array_equal(
-            result.codes, np.array([0, 0, 1, 1, -1, 0], dtype="int8")
-        )
-        assert not result.ordered
-
-        result = CategoricalIndex(ci, categories=list("ab"), ordered=True)
-        tm.assert_index_equal(result.categories, Index(list("ab")))
-        tm.assert_numpy_array_equal(
-            result.codes, np.array([0, 0, 1, 1, -1, 0], dtype="int8")
-        )
-        assert result.ordered
-
-        result = CategoricalIndex(ci, categories=list("ab"), ordered=True)
-        expected = CategoricalIndex(
-            ci, categories=list("ab"), ordered=True, dtype="category"
-        )
-        tm.assert_index_equal(result, expected, exact=True)
-
-        # turn me to an Index
-        result = Index(np.array(ci))
-        assert isinstance(result, Index)
-        assert not isinstance(result, CategoricalIndex)
-
-    def test_construction_with_dtype(self):
-        # specify dtype
-        ci = CategoricalIndex(list("aabbca"), categories=list("abc"), ordered=False)
-
-        result = Index(np.array(ci), dtype="category")
-        tm.assert_index_equal(result, ci, exact=True)
-
-        result = Index(np.array(ci).tolist(), dtype="category")
-        tm.assert_index_equal(result, ci, exact=True)
-
-        # these are generally only equal when the categories are reordered
-        ci = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
-
-        result = Index(np.array(ci), dtype="category").reorder_categories(ci.categories)
-        tm.assert_index_equal(result, ci, exact=True)
-
-        # make sure indexes are handled
-        idx = Index(range(3))
-        expected = CategoricalIndex([0, 1, 2], categories=idx, ordered=True)
-        result = CategoricalIndex(idx, categories=idx, ordered=True)
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_construction_empty_with_bool_categories(self):
-        # see GH#22702
-        cat = CategoricalIndex([], categories=[True, False])
-        categories = sorted(cat.categories.tolist())
-        assert categories == [False, True]
-
-    def test_construction_with_categorical_dtype(self):
-        # construction with CategoricalDtype
-        # GH#18109
-        data, cats, ordered = "a a b b".split(), "c b a".split(), True
-        dtype = CategoricalDtype(categories=cats, ordered=ordered)
-
-        result = CategoricalIndex(data, dtype=dtype)
-        expected = CategoricalIndex(data, categories=cats, ordered=ordered)
-        tm.assert_index_equal(result, expected, exact=True)
-
-        # GH#19032
-        result = Index(data, dtype=dtype)
-        tm.assert_index_equal(result, expected, exact=True)
-
-        # error when combining categories/ordered and dtype kwargs
-        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
-        with pytest.raises(ValueError, match=msg):
-            CategoricalIndex(data, categories=cats, dtype=dtype)
-
-        with pytest.raises(ValueError, match=msg):
-            CategoricalIndex(data, ordered=ordered, dtype=dtype)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/numeric/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/numeric/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/codingstatemachine.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/codingstatemachine.py
deleted file mode 100644
index 68fba44f14366c448f13db3cf9cf1665af2e498c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/codingstatemachine.py
+++ /dev/null
@@ -1,88 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-import logging
-
-from .enums import MachineState
-
-
-class CodingStateMachine(object):
-    """
-    A state machine to verify a byte sequence for a particular encoding. For
-    each byte the detector receives, it will feed that byte to every active
-    state machine available, one byte at a time. The state machine changes its
-    state based on its previous state and the byte it receives. There are 3
-    states in a state machine that are of interest to an auto-detector:
-
-    START state: This is the state to start with, or a legal byte sequence
-                 (i.e. a valid code point) for character has been identified.
-
-    ME state:  This indicates that the state machine identified a byte sequence
-               that is specific to the charset it is designed for and that
-               there is no other possible encoding which can contain this byte
-               sequence. This will to lead to an immediate positive answer for
-               the detector.
-
-    ERROR state: This indicates the state machine identified an illegal byte
-                 sequence for that encoding. This will lead to an immediate
-                 negative answer for this encoding. Detector will exclude this
-                 encoding from consideration from here on.
-    """
-    def __init__(self, sm):
-        self._model = sm
-        self._curr_byte_pos = 0
-        self._curr_char_len = 0
-        self._curr_state = None
-        self.logger = logging.getLogger(__name__)
-        self.reset()
-
-    def reset(self):
-        self._curr_state = MachineState.START
-
-    def next_state(self, c):
-        # for each byte we get its class
-        # if it is first byte, we also get byte length
-        byte_class = self._model['class_table'][c]
-        if self._curr_state == MachineState.START:
-            self._curr_byte_pos = 0
-            self._curr_char_len = self._model['char_len_table'][byte_class]
-        # from byte's class and state_table, we get its next state
-        curr_state = (self._curr_state * self._model['class_factor']
-                      + byte_class)
-        self._curr_state = self._model['state_table'][curr_state]
-        self._curr_byte_pos += 1
-        return self._curr_state
-
-    def get_current_charlen(self):
-        return self._curr_char_len
-
-    def get_coding_state_machine(self):
-        return self._model['name']
-
-    @property
-    def language(self):
-        return self._model['language']
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/ansi.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/ansi.py
deleted file mode 100644
index 66365e6536080bd9372d2a7a58b8ffa3447fec34..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/ansi.py
+++ /dev/null
@@ -1,240 +0,0 @@
-import re
-import sys
-from contextlib import suppress
-from typing import Iterable, NamedTuple, Optional
-
-from .color import Color
-from .style import Style
-from .text import Text
-
-re_ansi = re.compile(
-    r"""
-(?:\x1b\](.*?)\x1b\\)|
-(?:\x1b([(@-Z\\-_]|\[[0-?]*[ -/]*[@-~]))
-""",
-    re.VERBOSE,
-)
-
-
-class _AnsiToken(NamedTuple):
-    """Result of ansi tokenized string."""
-
-    plain: str = ""
-    sgr: Optional[str] = ""
-    osc: Optional[str] = ""
-
-
-def _ansi_tokenize(ansi_text: str) -> Iterable[_AnsiToken]:
-    """Tokenize a string in to plain text and ANSI codes.
-
-    Args:
-        ansi_text (str): A String containing ANSI codes.
-
-    Yields:
-        AnsiToken: A named tuple of (plain, sgr, osc)
-    """
-
-    position = 0
-    sgr: Optional[str]
-    osc: Optional[str]
-    for match in re_ansi.finditer(ansi_text):
-        start, end = match.span(0)
-        osc, sgr = match.groups()
-        if start > position:
-            yield _AnsiToken(ansi_text[position:start])
-        if sgr:
-            if sgr == "(":
-                position = end + 1
-                continue
-            if sgr.endswith("m"):
-                yield _AnsiToken("", sgr[1:-1], osc)
-        else:
-            yield _AnsiToken("", sgr, osc)
-        position = end
-    if position < len(ansi_text):
-        yield _AnsiToken(ansi_text[position:])
-
-
-SGR_STYLE_MAP = {
-    1: "bold",
-    2: "dim",
-    3: "italic",
-    4: "underline",
-    5: "blink",
-    6: "blink2",
-    7: "reverse",
-    8: "conceal",
-    9: "strike",
-    21: "underline2",
-    22: "not dim not bold",
-    23: "not italic",
-    24: "not underline",
-    25: "not blink",
-    26: "not blink2",
-    27: "not reverse",
-    28: "not conceal",
-    29: "not strike",
-    30: "color(0)",
-    31: "color(1)",
-    32: "color(2)",
-    33: "color(3)",
-    34: "color(4)",
-    35: "color(5)",
-    36: "color(6)",
-    37: "color(7)",
-    39: "default",
-    40: "on color(0)",
-    41: "on color(1)",
-    42: "on color(2)",
-    43: "on color(3)",
-    44: "on color(4)",
-    45: "on color(5)",
-    46: "on color(6)",
-    47: "on color(7)",
-    49: "on default",
-    51: "frame",
-    52: "encircle",
-    53: "overline",
-    54: "not frame not encircle",
-    55: "not overline",
-    90: "color(8)",
-    91: "color(9)",
-    92: "color(10)",
-    93: "color(11)",
-    94: "color(12)",
-    95: "color(13)",
-    96: "color(14)",
-    97: "color(15)",
-    100: "on color(8)",
-    101: "on color(9)",
-    102: "on color(10)",
-    103: "on color(11)",
-    104: "on color(12)",
-    105: "on color(13)",
-    106: "on color(14)",
-    107: "on color(15)",
-}
-
-
-class AnsiDecoder:
-    """Translate ANSI code in to styled Text."""
-
-    def __init__(self) -> None:
-        self.style = Style.null()
-
-    def decode(self, terminal_text: str) -> Iterable[Text]:
-        """Decode ANSI codes in an iterable of lines.
-
-        Args:
-            lines (Iterable[str]): An iterable of lines of terminal output.
-
-        Yields:
-            Text: Marked up Text.
-        """
-        for line in terminal_text.splitlines():
-            yield self.decode_line(line)
-
-    def decode_line(self, line: str) -> Text:
-        """Decode a line containing ansi codes.
-
-        Args:
-            line (str): A line of terminal output.
-
-        Returns:
-            Text: A Text instance marked up according to ansi codes.
-        """
-        from_ansi = Color.from_ansi
-        from_rgb = Color.from_rgb
-        _Style = Style
-        text = Text()
-        append = text.append
-        line = line.rsplit("\r", 1)[-1]
-        for plain_text, sgr, osc in _ansi_tokenize(line):
-            if plain_text:
-                append(plain_text, self.style or None)
-            elif osc is not None:
-                if osc.startswith("8;"):
-                    _params, semicolon, link = osc[2:].partition(";")
-                    if semicolon:
-                        self.style = self.style.update_link(link or None)
-            elif sgr is not None:
-                # Translate in to semi-colon separated codes
-                # Ignore invalid codes, because we want to be lenient
-                codes = [
-                    min(255, int(_code) if _code else 0)
-                    for _code in sgr.split(";")
-                    if _code.isdigit() or _code == ""
-                ]
-                iter_codes = iter(codes)
-                for code in iter_codes:
-                    if code == 0:
-                        # reset
-                        self.style = _Style.null()
-                    elif code in SGR_STYLE_MAP:
-                        # styles
-                        self.style += _Style.parse(SGR_STYLE_MAP[code])
-                    elif code == 38:
-                        #  Foreground
-                        with suppress(StopIteration):
-                            color_type = next(iter_codes)
-                            if color_type == 5:
-                                self.style += _Style.from_color(
-                                    from_ansi(next(iter_codes))
-                                )
-                            elif color_type == 2:
-                                self.style += _Style.from_color(
-                                    from_rgb(
-                                        next(iter_codes),
-                                        next(iter_codes),
-                                        next(iter_codes),
-                                    )
-                                )
-                    elif code == 48:
-                        # Background
-                        with suppress(StopIteration):
-                            color_type = next(iter_codes)
-                            if color_type == 5:
-                                self.style += _Style.from_color(
-                                    None, from_ansi(next(iter_codes))
-                                )
-                            elif color_type == 2:
-                                self.style += _Style.from_color(
-                                    None,
-                                    from_rgb(
-                                        next(iter_codes),
-                                        next(iter_codes),
-                                        next(iter_codes),
-                                    ),
-                                )
-
-        return text
-
-
-if sys.platform != "win32" and __name__ == "__main__":  # pragma: no cover
-    import io
-    import os
-    import pty
-    import sys
-
-    decoder = AnsiDecoder()
-
-    stdout = io.BytesIO()
-
-    def read(fd: int) -> bytes:
-        data = os.read(fd, 1024)
-        stdout.write(data)
-        return data
-
-    pty.spawn(sys.argv[1:], read)
-
-    from .console import Console
-
-    console = Console(record=True)
-
-    stdout_result = stdout.getvalue().decode("utf-8")
-    print(stdout_result)
-
-    for line in decoder.decode(stdout_result):
-        console.print(line)
-
-    console.save_html("stdout.html")
diff --git a/spaces/pseudolab/Colorful-illustration/app.py b/spaces/pseudolab/Colorful-illustration/app.py
deleted file mode 100644
index 3a63e3df65b6735bbb5040b6d06463874f289f9e..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/Colorful-illustration/app.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-import torch
-
-from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
-from utils import randomize_seed_fn
-
-MAX_SEED = np.iinfo(np.int32).max
-
-def model_load():
-    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-    pipe = StableDiffusionXLPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16
-    )
-    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-    
-    # load lora weight
-    pipe.load_lora_weights("jjuun/vivid_color_style")
-
-    return pipe.to('cuda')
-
-
-def sdxl_process(seed, prompt, additional_prompt, negative_prompt, num_steps, guidance_scale):
-    pipe = model_load()
-    generator = torch.Generator("cuda")
-    generator.manual_seed(int(seed))
-    
-    special_prompt = 'jjj, scratch art style'
-    prompt = f'{special_prompt}, {prompt}, with a black background'
-    output = pipe(prompt, additional_prompt, negative_prompt=negative_prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale,
-                  generator=generator).images[0]
-    
-    return output
-
-
-title = "🌈 Colorful illustration" 
-description_en = "🚀 How to use: please make sure to include 'a colorful' in prompt and click Run button!"
-
-
-def create_demo():
-
-    with gr.Blocks() as demo:
-        gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
-        gr.Markdown(f"<h3 style='text-align: center'>{description_en}</h3>")
-        gr.Markdown(f"<a href='https://github.com/jjuun0'><img src='https://img.shields.io/badge/GitHub-181717?style=flat-square&logo=GitHub&logoColor=white'/></a>")
-
-        with gr.Row():
-            with gr.Column():
-                prompt = gr.Textbox(label="Prompt")
-                run_button = gr.Button("Run")
-                with gr.Accordion("Advanced options", open=False):
-                    
-                    num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
-                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="")
-                    n_prompt = gr.Textbox(
-                        label="Negative prompt",
-                        value="",
-                    )
-            with gr.Column():
-                result = gr.Image(label="Output")
-                result_seed = gr.Textbox(label="Used seed")
-        
-        gr.Examples(
-            
-            examples= [["a colorful fox", "20", "9", "0", "", "", "examples/fox.png"], 
-                       ["a colorful messi", "20", "9", "191251724", "", "", "examples/messi.png"],
-                       ["a colorful pyramid", "20", "9", "0", "", "", "examples/pyramid.png"],
-                       ["a colorful octopus playing violin", "20", "9", "0", "", "", "examples/octopus.png"]],
-            inputs = [prompt, num_steps, guidance_scale, seed, a_prompt, n_prompt, result]
-        )
-
-        inputs = [
-            seed,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_steps,
-            guidance_scale,
-        ]
-
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=result_seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=sdxl_process,
-            inputs=inputs,
-            outputs=result,
-            api_name=False,
-        )
-    
-
-    return demo
-
-
-if __name__ == "__main__":
-    demo = create_demo()
-    demo.queue().launch()
diff --git a/spaces/pseudolab/PatentClaimsExtraction/app.py b/spaces/pseudolab/PatentClaimsExtraction/app.py
deleted file mode 100644
index 3d4ca72635854a315660fe023b68271f11b37d4c..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/PatentClaimsExtraction/app.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import streamlit as st  
-import gradio as gr  
-import numpy as np  
-import whisper  
-import os  
-import streamlit.components.v1 as components  
-import tempfile  
-import io  
-import requests  
-import json  
-import openai  
-from transformers import AutoConfig, AutoTokenizer, AutoModel  
-from summarizer import Summarizer  
-  
-# File upload size bug?  
-  
-# st.set_option('server.maxUploadSize', 500)  
-  
-# Initialize session state for claims_extraction  
-st.session_state.claims_extraction = ""  
-  
-# Initialize session state for userinput  
-st.session_state.userinput = ""  # Initialize user input  
-  
-# Define a function to split text into chunks  
-def chunk_text(text, chunk_size=2000):  
-    chunks = []  
-    start = 0  
-    while start < len(text):  
-        end = start + chunk_size  
-        chunk = text[start:end]  
-        chunks.append(chunk)  
-        start = end  
-    return chunks  
-  
-# Streamlit Session State  
-if 'learning_objectives' not in st.session_state:  
-    st.session_state.learning_objectives = ""  
-  
-# Initialize the Whisper model outside the button  
-if 'whisper_model' not in st.session_state:  
-    st.session_state.whisper_model = whisper.load_model("base")  
-  
-# Streamlit Interface  
-  
-markdown_text = """  
-# 👋🏻Welcome to [Team](https://huggingface.co/TeamTonic) [Tonic](https://huggingface.co/Tonic) 's Patentable Claims Extractor.   
-Here you can input audio and text and extract patentable claims from these conversational inputs using [LegalBert](nlpaueb/legal-bert-base-uncased).   
-- Save time and effort when ideating for your future business.  Expect latency upwards of 2.5 hours !
-"""  
-  
-# Render the Markdown content  
-st.markdown(markdown_text)  
-  
-# API Key Input  
-api_key = st.text_input("Enter your OpenAI API Key:", type="password")  
-  
-# Audio Upload  
-st.write("Upload an audio file (supported formats: mp3, wav, ogg)")  
-audio_file = st.file_uploader("Choose an audio file", type=["mp3", "wav", "ogg"], key="audio_file")  
-  
-audio_data = None  
-  
-if audio_file is not None:  
-    audio_data = audio_file.read()  
-    st.audio(audio_data, format="audio/wav")  
-    st.info("Transcribing...")  
-    st.success("Transcription complete")  
-  
-# Moved the submit_button check here  
-if st.button('Start Transcription'):  
-    model = st.session_state.whisper_model  
-  
-    if audio_data:  
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as audio_file:  
-            audio_file.write(audio_data)  
-            audio_file_path = audio_file.name  
-        st.audio(audio_file_path, format="audio/wav")  
-        st.info("Transcribing...")  
-        st.success("Transcription complete")  
-        result = model.transcribe(audio_file_path)  
-        transcript = result['text']  # Define the 'transcript' variable  
-  
-        with st.expander("See transcript"):  
-            st.markdown(transcript)  
-  
-    # Display the Whisper transcription    
-    if 'transcript' in locals():    
-        st.text("Transcription:")    
-        st.text(transcript)    
-  
-        # Update the user input field with the transcription  
-        st.session_state.userinput = st.text_area("Input Text:", transcript)  
-  
-# Model Selection Dropdown  
-model_choice = st.selectbox(  
-    "Select the model you want to use:",  
-    ["gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613", "gpt-3.5-turbo", "gpt-4-0314", "gpt-4-0613", "gpt-4"]  
-)  
-  
-# Context, Subject, and Level  
-context = "You are a patent claims identifier and extractor. You will freeform text, identify any claims contained therein that may be patentable. You identify, extract, print such claims, briefly explain why each claim is patentable."  
-  
-# Initialize OpenAI API  
-if api_key:  
-    openai.api_key = api_key  
-  
-# Learning Objectives  
-st.write("### Patentable Claims:")  
-  
-# Initialize autogenerated objectives  
-claims_extraction = ""  
-  
-# Initialize status placeholder  
-learning_status_placeholder = st.empty()  
-  
-disable_button_bool = False  
-  
-if st.session_state.userinput and api_key and st.button("Extract Claims", key="claims_extraction", disabled=disable_button_bool):  
-    # Split the user input into chunks  
-    input_chunks = chunk_text(userinput)  
-  
-    # Initialize a variable to store the extracted claims  
-    all_extracted_claims = ""  
-  
-    for chunk in input_chunks:  
-        # Display status message for the current chunk  
-        learning_status_placeholder.text(f"Extracting Patentable Claims for chunk {input_chunks.index(chunk) + 1}...")  
-  
-        # API call to generate objectives for the current chunk  
-        claims_extraction_response = openai.ChatCompletion.create(  
-            model=model_choice,  
-            messages=[  
-                {"role": "user", "content": f"Extract any patentable claims from the following: \n {chunk}. \n Extract each claim. Briefly explain why you extracted this word phrase. Exclude any additional commentary."}  
-            ]  
-        )  
-  
-        # Extract the generated objectives from the API response  
-        claims_extraction = claims_extraction_response['choices'][0]['message']['content']  
-  
-        # Append the extracted claims from the current chunk to the overall results  
-        all_extracted_claims += claims_extraction.strip()  
-  
-    # Save the generated objectives to session state  
-    st.session_state.claims_extraction = all_extracted_claims  
-  
-    # Display generated objectives for all chunks  
-    learning_status_placeholder.text(f"Patentable Claims Extracted!\n{all_extracted_claims.strip()}")  
-  
-# Get the extracted claims from Streamlit's session state  
-claims_extracted = st.session_state.claims_extraction  
-  
-# Display the Extracted Claims    
-if 'claims_extracted' in st.session_state:    
-    st.text("Extracted Claims:")    
-    st.text(st.session_state.claims_extracted)    
-  
-# Define the BERT-based model name  
-model_name = 'nlpaueb/legal-bert-base-uncased'  
-  
-# Initialize BERT-based model and tokenizer  
-custom_config = AutoConfig.from_pretrained(model_name)  
-custom_config.output_hidden_states = True  
-custom_tokenizer = AutoTokenizer.from_pretrained(model_name)  
-custom_model = AutoModel.from_pretrained(model_name, config=custom_config)  
-bert_legal_model = Summarizer(custom_model=custom_model, custom_tokenizer=custom_tokenizer)  
-print('Using model {}\n'.format(model_name))  
-  
-# Get the extracted claims from Streamlit's session state  
-# claims_extracted = st.session_state.claims_extraction #moved up  
-  
-# Define the chunk size  
-chunk_size = 350  
-  
-# Split the extracted claims into chunks  
-if isinstance(claims_extracted, str):  
-    chunks = [claims_extracted[i:i+chunk_size] for i in range(0, len(claims_extracted), chunk_size)]  
-else:  
-    chunks = []  
-  
-# Process each chunk with the BERT-based model  
-summaries = []  
-for chunk in chunks:  
-    summary = bert_legal_model(chunk, min_length=20, ratio=0.9)  
-    summaries.append(summary)  
-  
-# Now you have a list of summaries for each chunk  
-# You can access them using `summaries[0]`, `summaries[1]`, etc.  
-# After generating summaries  
-for i, summary in enumerate(summaries):  
-    st.write(f"### Summary {i+1}")  
-    st.write(summary)  
-  
-# Display the BERT Summaries    
-if summaries:    
-    st.text("BERT Summaries:")    
-    for i, summary in enumerate(summaries):    
-        st.text(f"Summary {i + 1}:\n{summary}")    
-  
-# Citation for the GitHub repo  
-st.markdown("<sub>This app was created by [Tonic](https://huggingface.co/tonic) with help from [MIND INTERFACES](https://huggingface.co/MIND-INTERFACES) & [Taylor](https://huggingface.co/Cloudfaith) [join us on discord](https://discord.gg/5RmtZVVfgQ) </sub>", unsafe_allow_html=True)  
diff --git a/spaces/pseudolab/Rice_Disease_Classifier/Dockerfile b/spaces/pseudolab/Rice_Disease_Classifier/Dockerfile
deleted file mode 100644
index a4c8b4f88ec3000f75b1413a72ba55e294692201..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/Rice_Disease_Classifier/Dockerfile
+++ /dev/null
@@ -1,2 +0,0 @@
-FROM huggingface/autotrain-advanced:latest
-CMD autotrain setup && autotrain app --port 7860
diff --git a/spaces/pseudolab/SonGPT/test.py b/spaces/pseudolab/SonGPT/test.py
deleted file mode 100644
index c636cc2ced567fed6e294f119c8646d81501d962..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/SonGPT/test.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from core.graph import Graph
-
-graph = Graph()
-graph.load("graph.pkl")
-graph.draw()
-res, image1, image2 = graph.find_related_memories("Son, How old are you?", 3)
-print("res:", res)
-image1.save("graph_retrieval_1.png")
-image2.save("graph_retrieval_2.png")
diff --git a/spaces/pycui/RealChar/realtime_ai_character/audio/text_to_speech/elevenlabs.py b/spaces/pycui/RealChar/realtime_ai_character/audio/text_to_speech/elevenlabs.py
deleted file mode 100644
index c2433832586165ec23213edba1f1b6e543a9397c..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/realtime_ai_character/audio/text_to_speech/elevenlabs.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import asyncio
-import os
-import types
-import httpx
-
-from realtime_ai_character.logger import get_logger
-from realtime_ai_character.utils import Singleton
-from realtime_ai_character.audio.text_to_speech.base import TextToSpeech
-
-logger = get_logger(__name__)
-DEBUG = False
-
-config = types.SimpleNamespace(**{
-    'default_voice': '21m00Tcm4TlvDq8ikWAM',
-    'default_female_voice': 'EXAVITQu4vr4xnSDxMaL',
-    'default_male_voice': 'ErXwobaYiN019PkySvjV',
-    'chunk_size': 1024,
-    'url': 'https://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream',
-    'headers': {
-        'Accept': 'audio/mpeg',
-        'Content-Type': 'application/json',
-        'xi-api-key': os.environ['ELEVEN_LABS_API_KEY']
-    },
-    'data': {
-        'model_id': 'eleven_monolingual_v1',
-        'voice_settings': {
-            'stability': 0.5,
-            'similarity_boost': 0.75
-        }
-    }
-})
-
-
-class ElevenLabs(Singleton, TextToSpeech):
-    def __init__(self):
-        super().__init__()
-        logger.info("Initializing [ElevenLabs Text To Speech] voices...")
-        self.voice_ids = {
-            "Raiden Shogun And Ei": os.environ.get('RAIDEN_VOICE') or config.default_female_voice,
-            "Loki": os.environ.get('LOKI_VOICE') or config.default_male_voice,
-            "Reflection Pi": os.environ.get('PI_VOICE') or config.default_female_voice,
-            "Elon Musk": os.environ.get('ELON_VOICE') or config.default_male_voice,
-            "Bruce Wayne": os.environ.get('BRUCE_VOICE') or config.default_male_voice,
-            "Steve Jobs": os.environ.get('JOBS_VOICE') or config.default_male_voice,
-            "Sam Altman": os.environ.get('SAM_VOICE') or config.default_male_voice,
-        }
-
-    def get_voice_id(self, name):
-        return self.voice_ids.get(name, config.default_voice)
-
-    async def stream(self, text, websocket, tts_event: asyncio.Event, characater_name="", first_sentence=False) -> None:
-        if DEBUG:
-            return
-        headers = config.headers
-        data = {
-            "text": text,
-            **config.data,
-        }
-        voice_id = self.get_voice_id(characater_name)
-        url = config.url.format(voice_id=voice_id)
-        if first_sentence:
-            url = url + '?optimize_streaming_latency=4'
-        async with httpx.AsyncClient() as client:
-            response = await client.post(url, json=data, headers=headers)
-            async for chunk in response.aiter_bytes():
-                await asyncio.sleep(0.1)
-                if tts_event.is_set():
-                    # stop streaming audio
-                    break
-                await websocket.send_bytes(chunk)
diff --git a/spaces/pythainlp/pythainlp/app.py b/spaces/pythainlp/pythainlp/app.py
deleted file mode 100644
index 72e26187b0fdf9f2115210f5bf01ceebddf6ea87..0000000000000000000000000000000000000000
--- a/spaces/pythainlp/pythainlp/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import streamlit as st
-st.markdown("""
-# PyThaiNLP Demo 🎈
-
-Welcome to PyThaiNLP Demo. This website will give you a example NLP function from PyThaiNLP.
-
-You can see PyThaiNLP at [PyThaiNLP.github.io](https://pythainlp.github.io)
-
-
-**About PyThaiNLP**
-
-The PyThaiNLP Project is a Thai Natural Language Processing project. We build softwares and datasets for Thai language. Our Main Project is PyThaiNLP.
-
-PyThaiNLP is a Python package for text processing and linguistic analysis, similar to nltk, with focus on Thai language.
-
-
-We build Thai NLP.
-
-PyThaiNLP
-"""
-)
-#st.sidebar.markdown("# Homepage 🎈")
diff --git a/spaces/qinzhu/diy-girlfriend-online/utils.py b/spaces/qinzhu/diy-girlfriend-online/utils.py
deleted file mode 100644
index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/diy-girlfriend-online/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-        except:
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, required=True,
-                        help='Model name')
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r", encoding="utf-8") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Activation Key PhotoStage Slideshow Producer Keygen.epub.md b/spaces/quidiaMuxgu/Expedit-SAM/Activation Key PhotoStage Slideshow Producer Keygen.epub.md
deleted file mode 100644
index a1b86cca88219f05870d2889a2e45e2814fb81f3..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Activation Key PhotoStage Slideshow Producer Keygen.epub.md	
+++ /dev/null
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deleted file mode 100644
index 1af73dc2df8e755424c4830cdbe44f3f9e36dea1..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Diana Ross Discography Torrent.md	
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deleted file mode 100644
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diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/modules/onnx/export.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/modules/onnx/export.py
deleted file mode 100644
index f7a35562f4a15c9024ac6df47b213c6a0cf6c7a0..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/modules/onnx/export.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import torch
-
-from lib.infer.infer_libs.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
-
-
-def export_onnx(ModelPath, ExportedPath):
-    cpt = torch.load(ModelPath, map_location="cpu")
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-    vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
-
-    test_phone = torch.rand(1, 200, vec_channels)  # hidden unit
-    test_phone_lengths = torch.tensor([200]).long()  # hidden unit 长度（貌似没啥用）
-    test_pitch = torch.randint(size=(1, 200), low=5, high=255)  # 基频（单位赫兹）
-    test_pitchf = torch.rand(1, 200)  # nsf基频
-    test_ds = torch.LongTensor([0])  # 说话人ID
-    test_rnd = torch.rand(1, 192, 200)  # 噪声（加入随机因子）
-
-    device = "cpu"  # 导出时设备（不影响使用模型）
-
-    net_g = SynthesizerTrnMsNSFsidM(
-        *cpt["config"], is_half=False, version=cpt.get("version", "v1")
-    )  # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
-    net_g.load_state_dict(cpt["weight"], strict=False)
-    input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
-    output_names = [
-        "audio",
-    ]
-    # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
-    torch.onnx.export(
-        net_g,
-        (
-            test_phone.to(device),
-            test_phone_lengths.to(device),
-            test_pitch.to(device),
-            test_pitchf.to(device),
-            test_ds.to(device),
-            test_rnd.to(device),
-        ),
-        ExportedPath,
-        dynamic_axes={
-            "phone": [1],
-            "pitch": [1],
-            "pitchf": [1],
-            "rnd": [2],
-        },
-        do_constant_folding=False,
-        opset_version=13,
-        verbose=False,
-        input_names=input_names,
-        output_names=output_names,
-    )
-    return "Finished"
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Avast Antivirus Free Trial Download 2016 Why You Need This Powerful Software on Your Computer.md b/spaces/raedeXanto/academic-chatgpt-beta/Avast Antivirus Free Trial Download 2016 Why You Need This Powerful Software on Your Computer.md
deleted file mode 100644
index 35a487213bd64d4fb9cb4850f7c66e4e791a7e96..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Avast Antivirus Free Trial Download 2016 Why You Need This Powerful Software on Your Computer.md	
+++ /dev/null
@@ -1,141 +0,0 @@
-
-<h1>Avast Antivirus Free Trial Download 2016</h1>
- <h2>Introduction</h2>
- <p>If you are looking for a reliable and effective antivirus software for your PC, you might want to consider Avast Antivirus. Avast Antivirus is one of the most popular and trusted antivirus programs in the world, with over 400 million users. It offers a range of features and benefits that can protect your PC from various threats, such as viruses, malware, ransomware, phishing, spyware, and more.</p>
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-How to get a refund or a chargeback for Avast Antivirus if you are not satisfied with the product after the free trial ends in June <br />
-How to contact or reach out to the developers or the creators of the software -Avast</p>
- <h3>Virus scanning and removal</h3>
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- <h2>Drawbacks of Avast Antivirus free trial</h2>
- <p>Avast Antivirus free trial also has some drawbacks that you should be aware of before downloading it. Some of these drawbacks are:</p>
- <h3>Limited features compared to premium version</h3>
- <p>The free trial version of Avast Antivirus offers limited features compared to the premium version. For example, it does not include features such as VPN service (which hides your IP address), data shredder (which permanently deletes sensitive files), sandbox (which runs suspicious files in a safe environment), etc., which are available in the premium version only.</p>
- <h3>Frequent pop-ups and ads</h3>
- <p>The free trial version of Avast Antivirus also displays frequent pop-ups and ads that can be annoying and distracting. These pop-ups and ads may urge you to upgrade to the premium version or buy other products or services from Avast or its partners. You can disable some of these pop-ups and ads from the settings, but not all of them.</p>
- <h3>Privacy concerns and data collection</h3>
- <p>Another drawback of Avast Antivirus free trial is that it may collect and share some of your personal data with Avast or its partners for various purposes, such as marketing, analytics, research, etc. This may raise some privacy concerns for some users who value their data privacy and security. You can read the privacy policy of Avast Antivirus to learn more about what data it collects and how it uses it.</p>
- <h2>Conclusion</h2>
- <h4>Summary of main points</h4>
- <p>In conclusion, Avast Antivirus free trial is a good option for users who want to try out a reliable and effective antivirus software for their PC. It offers a range of features and benefits that can protect your PC from various online threats, such as viruses, malware, ransomware, phishing, spyware, etc. It is also easy to use and install, compatible with Windows 10 and other operating systems, low impact on system performance, and provides free updates and customer support.</p>
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-<p>Yes, Avast Antivirus free trial is safe to download and use. It does not contain any viruses or malware that can harm your PC. It also does not install any unwanted programs or toolbars on your PC.</p>
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-<li>Open Avast Antivirus on your PC and click on the "Upgrade" button under the "Premium" section.</li>
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-</ul>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Calculos Quimicos De Benson 62.md b/spaces/raedeXanto/academic-chatgpt-beta/Calculos Quimicos De Benson 62.md
deleted file mode 100644
index 18084333e5d3de042828cde3df2acfdaf01d21ad..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Calculos Quimicos De Benson 62.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-<br />
-<h1>Calculos Quimicos De Benson 62: A Classic Book on Mathematical Chemistry</h1>
-<p>If you are looking for a comprehensive and accessible introduction to the use of mathematics in chemistry, you might want to check out <strong>Calculos Quimicos De Benson 62</strong>, a classic book by Sidney William Benson. Benson was an American chemist who made significant contributions to the fields of thermochemistry, chemical kinetics, and combustion. His book, first published in 1962, covers topics such as stoichiometry, equilibrium, thermodynamics, kinetics, electrochemistry, and nuclear chemistry. It also includes numerous examples, exercises, and tables to help students master the concepts and calculations.</p>
-<p><strong>Calculos Quimicos De Benson 62</strong> is written in Spanish and has been reprinted several times by Limusa, a Mexican publisher of scientific and technical books. It is widely used as a textbook in Latin American universities and colleges that offer courses on mathematical chemistry. It is also a valuable reference for researchers and professionals who need to perform calculations related to chemical phenomena. The book has received positive reviews from both students and teachers who praise its clarity, rigor, and relevance.</p>
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-<p>If you want to learn more about <strong>Calculos Quimicos De Benson 62</strong>, you can find it online at Google Books[^1^], Sway Office[^2^], or SlideServe[^3^]. You can also look for it in your local library or bookstore. Whether you are a beginner or an advanced student of chemistry, you will surely benefit from reading this book and applying its methods to your own problems.</p>
-
-<p>In this article, we will give you a brief overview of some of the main topics covered by <strong>Calculos Quimicos De Benson 62</strong>. We will also provide some examples of how to apply the formulas and concepts to solve common chemical problems.</p>
-<h2>Stoichiometry</h2>
-<p>Stoichiometry is the branch of chemistry that deals with the quantitative relationships between the reactants and products in a chemical reaction. It allows us to calculate the amounts of substances involved in a reaction, such as masses, moles, volumes, concentrations, and energies. To perform stoichiometric calculations, we need to know the balanced chemical equation of the reaction and the molar masses of the substances.</p>
-<p>For example, suppose we want to calculate how much oxygen gas (O<sub>2</sub>) is needed to completely burn 10 grams of methane gas (CH<sub>4</sub>) in a combustion reaction. The balanced chemical equation is:</p>
-<p>CH<sub>4</sub> + 2O<sub>2</sub> → CO<sub>2</sub> + 2H<sub>2</sub>O</p>
-<p></p>
-<p>We can use the following steps to find the answer:</p>
-<ol>
-<li>Convert the given mass of CH<sub>4</sub> to moles using its molar mass (16 g/mol): 10 g / 16 g/mol = 0.625 mol</li>
-<li>Use the mole ratio from the balanced equation to find the moles of O<sub>2</sub> needed: 0.625 mol CH<sub>4</sub> x (2 mol O<sub>2</sub> / 1 mol CH<sub>4</sub>) = 1.25 mol O<sub>2</sub></li>
-<li>Convert the moles of O<sub>2</sub> to mass using its molar mass (32 g/mol): 1.25 mol x 32 g/mol = 40 g</li>
-<li>The answer is 40 grams of O<sub>2</sub>.</li>
-</ol>
-<h2>Equilibrium</h2>
-<p>Equilibrium is the state in which the rate of the forward reaction is equal to the rate of the reverse reaction. At equilibrium, the concentrations of the reactants and products remain constant, but not necessarily equal. The extent to which a reaction proceeds to equilibrium is determined by its equilibrium constant (K), which is a ratio of the concentrations of the products and reactants raised to their stoichiometric coefficients.</p>
-<p>For example, consider the following reversible reaction:</p>
-<p>N<sub>2</sub>(g) + 3H<sub>2</sub>(g) ⇌ 2NH<sub>3</sub>(g)</p>
-<p>The equilibrium constant for this reaction at a certain temperature is K = 0.5. This means that at equilibrium, the concentration of NH<sub>3</sub>(g) squared divided by the concentration of N<sub>2</sub>(g) times the concentration of H<sub>2</sub>(g) cubed is equal to 0.5. If we know the initial concentrations of N<sub>2</sub>(g) and H<sub>2</sub>(g), we can use an algebraic method called ICE table (Initial, Change, Equilibrium) to find the equilibrium concentrations of all species.</p>
-<p>For example, suppose we start with 1 mol/L of N<sub>2</sub>(g) and 3 mol/L of H<sub>2</sub>(g) in a closed container. The ICE table would look like this:</p>
-
-| Species | Initial | Change | Equilibrium |
-| --- | --- | --- | --- |
-| N<sub>2</sub>(g) | 1 | -x | 1-x |
-| H<sub>2</sub>(g) | 3 | -3x | 3-3x |
-| NH<sub>3</sub>(g) | 0 | +2x | 2x |
-
-<p>We can then plug in the equilibrium values into the expression for K and solve for x:</p>
-<p>K = [NH<sub>3</sub>]^2 / ([N<sub>2</sub>] x [H<sub>2</sub>]^3)</p>
-<p>K = (2x)^2 / ((1-x) x (3-</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Ets 2 Patch 1.2.5 Crack.md b/spaces/raedeXanto/academic-chatgpt-beta/Ets 2 Patch 1.2.5 Crack.md
deleted file mode 100644
index e46ac4b6d893b976d26ab861ec88a2e6e5c3b6b7..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Ets 2 Patch 1.2.5 Crack.md	
+++ /dev/null
@@ -1,120 +0,0 @@
-## Ets 2 Patch 1.2.5 Crack
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-
- 
-
-Euro Truck Simulator 2 is a popular simulation game that lets you drive trucks across Europe. However, if you want to enjoy the latest features and updates, you need to download and install the latest patch for the game. In this article, we will show you how to download and install Ets 2 Patch 1.2.5 Crack, which is the latest version of the patch as of April 2023.
-
- 
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-
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-
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-
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-
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-
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-
-
-
-1. Go to [https://ets2.lt/en/patch-1-2-5/](https://ets2.lt/en/patch-1-2-5/)
-
-2. Click on the "Download" button below the description of the patch.
-
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-
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diff --git a/spaces/ranjangoel/GPT-PDF/gpt_reader/paper.py b/spaces/ranjangoel/GPT-PDF/gpt_reader/paper.py
deleted file mode 100644
index f94bfe7bdb1444f43302518fe5e621f1d8cba59b..0000000000000000000000000000000000000000
--- a/spaces/ranjangoel/GPT-PDF/gpt_reader/paper.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from PyPDF2 import PdfReader
-
-class Paper(object):
-
-    def __init__(self, pdf_obj: PdfReader) -> None:
-        self._pdf_obj = pdf_obj
-        self._paper_meta = self._pdf_obj.metadata
-
-    def iter_pages(self, iter_text_len: int = 3000):
-        page_idx = 0
-        for page in self._pdf_obj.pages:
-            txt = page.extract_text()
-            for i in range((len(txt) // iter_text_len) + 1):
-                yield page_idx, i, txt[i * iter_text_len:(i + 1) * iter_text_len]
-            page_idx += 1
-
-
-if __name__ == '__main__':
-    reader = PdfReader('../alexnet.pdf')
-    paper = Paper(reader)
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Eplan 5.70 Crack !NEW! Windows 7 39.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Eplan 5.70 Crack !NEW! Windows 7 39.md
deleted file mode 100644
index 7d7574209ec6ef6f428df1b0c3bd629d434d1aa9..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Eplan 5.70 Crack !NEW! Windows 7 39.md	
+++ /dev/null
@@ -1,48 +0,0 @@
-
-<h1>Eplan 5.70 Crack Windows 7 39: A Review</h1>
-
-<p>Eplan 5.70 is a software that helps you design and document electrical engineering projects. It has many features and functions that make it a powerful and reliable tool for engineers and technicians. However, it is not a cheap software and it requires a license key to activate it. In this article, we will review Eplan 5.70 crack windows 7 39, a version of the software that can be downloaded and installed for free from torrent sites or other sources. We will also discuss the pros and cons of using this cracked version and whether it is safe and legal to do so.</p>
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-<p>Eplan 5.70 crack windows 7 39 is a modified version of Eplan 5.70 that can be installed and used without a license key. It comes with a crack file that bypasses the activation process and unlocks all the features of the software. It also comes with a serial key that can be used to register the software and access some online services.</p>
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\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Grabm3u By Alex 1167 Fixed Download.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Grabm3u By Alex 1167 Fixed Download.md
deleted file mode 100644
index b7430f52704c4a6e225a3e71caef47472c02626d..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Grabm3u By Alex 1167 Fixed Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>grabm3u by alex 1167 download</h2><br /><p><b><b>Download Zip</b> --->>> <a href="https://urlgoal.com/2uCLBi">https://urlgoal.com/2uCLBi</a></b></p><br /><br />
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diff --git a/spaces/rf5860/bg3_character_generator/README.md b/spaces/rf5860/bg3_character_generator/README.md
deleted file mode 100644
index f3db3b1d34b4d4d3e749747ced84119d126d9deb..0000000000000000000000000000000000000000
--- a/spaces/rf5860/bg3_character_generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bg3 Character Generator
-emoji: 🏃
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ascend_anchor_head.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ascend_anchor_head.py
deleted file mode 100644
index 7d100ba9218f123c90feb54e5ec7a43b060356a1..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ascend_anchor_head.py
+++ /dev/null
@@ -1,389 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ...core.bbox.assigners import AscendMaxIoUAssigner
-from ...core.bbox.samplers import PseudoSampler
-from ...utils import (batch_images_to_levels, get_max_num_gt_division_factor,
-                      masked_fill)
-from ..builder import HEADS
-from .anchor_head import AnchorHead
-
-
-@HEADS.register_module()
-class AscendAnchorHead(AnchorHead):
-    """Ascend Anchor-based head (RetinaNet, SSD, etc.).
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        feat_channels (int): Number of hidden channels. Used in child classes.
-        anchor_generator (dict): Config dict for anchor generator
-        bbox_coder (dict): Config of bounding box coder.
-        reg_decoded_bbox (bool): If true, the regression loss would be
-            applied directly on decoded bounding boxes, converting both
-            the predicted boxes and regression targets to absolute
-            coordinates format. Default False. It should be `True` when
-            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
-        loss_cls (dict): Config of classification loss.
-        loss_bbox (dict): Config of localization loss.
-        train_cfg (dict): Training config of anchor head.
-        test_cfg (dict): Testing config of anchor head.
-        init_cfg (dict or list[dict], optional): Initialization config dict.
-    """  # noqa: W605
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 feat_channels=256,
-                 anchor_generator=dict(
-                     type='AnchorGenerator',
-                     scales=[8, 16, 32],
-                     ratios=[0.5, 1.0, 2.0],
-                     strides=[4, 8, 16, 32, 64]),
-                 bbox_coder=dict(
-                     type='DeltaXYWHBBoxCoder',
-                     clip_border=True,
-                     target_means=(.0, .0, .0, .0),
-                     target_stds=(1.0, 1.0, 1.0, 1.0)),
-                 reg_decoded_bbox=False,
-                 loss_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0),
-                 loss_bbox=dict(
-                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
-                 train_cfg=None,
-                 test_cfg=None,
-                 init_cfg=dict(type='Normal', layer='Conv2d', std=0.01)):
-        super(AscendAnchorHead, self).__init__(
-            num_classes=num_classes,
-            in_channels=in_channels,
-            feat_channels=feat_channels,
-            anchor_generator=anchor_generator,
-            bbox_coder=bbox_coder,
-            reg_decoded_bbox=reg_decoded_bbox,
-            loss_cls=loss_cls,
-            loss_bbox=loss_bbox,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            init_cfg=init_cfg)
-
-    def get_batch_gt_bboxes(self, gt_bboxes_list, num_images, gt_nums, device,
-                            max_gt_labels):
-        """Get ground truth bboxes of all image.
-
-        Args:
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            num_images (int): The num of images.
-            gt_nums(list[int]): The ground truth bboxes num of each image.
-            device (torch.device | str): Device for returned tensors
-            max_gt_labels(int): The max ground truth bboxes num of all image.
-        Returns:
-            batch_gt_bboxes: (Tensor): Ground truth bboxes of all image.
-        """
-        # a static ground truth boxes.
-        # Save static gt. Related to Ascend. Helps improve performance
-        if not hasattr(self, 'batch_gt_bboxes'):
-            self.batch_gt_bboxes = {}
-        # a min anchor filled the excess anchor
-        if not hasattr(self, 'min_anchor'):
-            self.min_anchor = (-1354, -1344)
-        if gt_bboxes_list is None:
-            batch_gt_bboxes = None
-        else:
-            if self.batch_gt_bboxes.get(max_gt_labels) is None:
-                batch_gt_bboxes = torch.zeros((num_images, max_gt_labels, 4),
-                                              dtype=gt_bboxes_list[0].dtype,
-                                              device=device)
-                batch_gt_bboxes[:, :, :2] = self.min_anchor[0]
-                batch_gt_bboxes[:, :, 2:] = self.min_anchor[1]
-                self.batch_gt_bboxes[max_gt_labels] = batch_gt_bboxes.clone()
-            else:
-                batch_gt_bboxes = self.batch_gt_bboxes.get(
-                    max_gt_labels).clone()
-            for index_imgs, gt_bboxes in enumerate(gt_bboxes_list):
-                batch_gt_bboxes[index_imgs, :gt_nums[index_imgs]] = gt_bboxes
-        return batch_gt_bboxes
-
-    def get_batch_gt_bboxes_ignore(self, gt_bboxes_ignore_list, num_images,
-                                   gt_nums, device):
-        """Ground truth bboxes to be ignored of all image.
-
-        Args:
-            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
-                ignored.
-            num_images (int): The num of images.
-            gt_nums(list[int]): The ground truth bboxes num of each image.
-            device (torch.device | str): Device for returned tensors
-        Returns:
-            batch_gt_bboxes_ignore: (Tensor): Ground truth bboxes to be
-                ignored of all image.
-        """
-        # TODO: support gt_bboxes_ignore_list
-        if gt_bboxes_ignore_list is None:
-            batch_gt_bboxes_ignore = None
-        else:
-            raise RuntimeError('gt_bboxes_ignore not support yet')
-        return batch_gt_bboxes_ignore
-
-    def get_batch_gt_labels(self, gt_labels_list, num_images, gt_nums, device,
-                            max_gt_labels):
-        """Ground truth bboxes to be ignored of all image.
-
-        Args:
-            gt_labels_list (list[Tensor]): Ground truth labels.
-            num_images (int): The num of images.
-            gt_nums(list[int]): The ground truth bboxes num of each image.
-            device (torch.device | str): Device for returned tensors
-        Returns:
-            batch_gt_labels: (Tensor): Ground truth labels of all image.
-        """
-        if gt_labels_list is None:
-            batch_gt_labels = None
-        else:
-            batch_gt_labels = torch.zeros((num_images, max_gt_labels),
-                                          dtype=gt_labels_list[0].dtype,
-                                          device=device)
-            for index_imgs, gt_labels in enumerate(gt_labels_list):
-                batch_gt_labels[index_imgs, :gt_nums[index_imgs]] = gt_labels
-
-        return batch_gt_labels
-
-    def _get_targets_concat(self,
-                            batch_anchors,
-                            batch_valid_flags,
-                            batch_gt_bboxes,
-                            batch_gt_bboxes_ignore,
-                            batch_gt_labels,
-                            img_metas,
-                            label_channels=1,
-                            unmap_outputs=True):
-        """Compute regression and classification targets for anchors in all
-        images.
-
-        Args:
-            batch_anchors (Tensor): anchors of all image, which are
-                concatenated into a single tensor of
-                shape (num_imgs, num_anchors ,4).
-            batch_valid_flags (Tensor): valid flags of all image,
-                which are concatenated into a single tensor of
-                    shape (num_imgs, num_anchors,).
-            batch_gt_bboxes (Tensor): Ground truth bboxes of all image,
-                shape (num_imgs, max_gt_nums, 4).
-            batch_gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_imgs, num_ignored_gts, 4).
-            batch_gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_imgs, max_gt_nums,).
-            img_metas (list[dict]): Meta info of each image.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple:
-                batch_labels (Tensor): Labels of all level
-                batch_label_weights (Tensor): Label weights of all level
-                batch_bbox_targets (Tensor): BBox targets of all level
-                batch_bbox_weights (Tensor): BBox weights of all level
-                batch_pos_mask (Tensor): Positive samples mask in all images
-                batch_neg_mask (Tensor): Negative samples mask in all images
-                sampling_result (Sampling): The result of sampling,
-                    default: None.
-        """
-        num_imgs, num_anchors, _ = batch_anchors.size()
-        # assign gt and sample batch_anchors
-        assign_result = self.assigner.assign(
-            batch_anchors,
-            batch_gt_bboxes,
-            batch_gt_bboxes_ignore,
-            None if self.sampling else batch_gt_labels,
-            batch_bboxes_ignore_mask=batch_valid_flags)
-        # TODO: support sampling_result
-        sampling_result = None
-        batch_pos_mask = assign_result.batch_pos_mask
-        batch_neg_mask = assign_result.batch_neg_mask
-        batch_anchor_gt_indes = assign_result.batch_anchor_gt_indes
-        batch_anchor_gt_labels = assign_result.batch_anchor_gt_labels
-
-        batch_anchor_gt_bboxes = torch.zeros(
-            batch_anchors.size(),
-            dtype=batch_anchors.dtype,
-            device=batch_anchors.device)
-        for index_imgs in range(num_imgs):
-            batch_anchor_gt_bboxes[index_imgs] = torch.index_select(
-                batch_gt_bboxes[index_imgs], 0,
-                batch_anchor_gt_indes[index_imgs])
-
-        batch_bbox_targets = torch.zeros_like(batch_anchors)
-        batch_bbox_weights = torch.zeros_like(batch_anchors)
-        batch_labels = batch_anchors.new_full((num_imgs, num_anchors),
-                                              self.num_classes,
-                                              dtype=torch.int)
-        batch_label_weights = batch_anchors.new_zeros((num_imgs, num_anchors),
-                                                      dtype=torch.float)
-
-        if not self.reg_decoded_bbox:
-            batch_pos_bbox_targets = self.bbox_coder.encode(
-                batch_anchors, batch_anchor_gt_bboxes)
-        else:
-            batch_pos_bbox_targets = batch_anchor_gt_bboxes
-
-        batch_bbox_targets = masked_fill(batch_bbox_targets,
-                                         batch_pos_mask.unsqueeze(2),
-                                         batch_pos_bbox_targets)
-        batch_bbox_weights = masked_fill(batch_bbox_weights,
-                                         batch_pos_mask.unsqueeze(2), 1.0)
-        if batch_gt_labels is None:
-            batch_labels = masked_fill(batch_labels, batch_pos_mask, 0.0)
-        else:
-            batch_labels = masked_fill(batch_labels, batch_pos_mask,
-                                       batch_anchor_gt_labels)
-        if self.train_cfg.pos_weight <= 0:
-            batch_label_weights = masked_fill(batch_label_weights,
-                                              batch_pos_mask, 1.0)
-        else:
-            batch_label_weights = masked_fill(batch_label_weights,
-                                              batch_pos_mask,
-                                              self.train_cfg.pos_weight)
-        batch_label_weights = masked_fill(batch_label_weights, batch_neg_mask,
-                                          1.0)
-        return (batch_labels, batch_label_weights, batch_bbox_targets,
-                batch_bbox_weights, batch_pos_mask, batch_neg_mask,
-                sampling_result)
-
-    def get_targets(self,
-                    anchor_list,
-                    valid_flag_list,
-                    gt_bboxes_list,
-                    img_metas,
-                    gt_bboxes_ignore_list=None,
-                    gt_labels_list=None,
-                    label_channels=1,
-                    unmap_outputs=True,
-                    return_sampling_results=False,
-                    return_level=True):
-        """Compute regression and classification targets for anchors in
-        multiple images.
-
-        Args:
-            anchor_list (list[list[Tensor]]): Multi level anchors of each
-                image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, 4).
-            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
-                each image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, )
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
-                ignored.
-            gt_labels_list (list[Tensor]): Ground truth labels of each box.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-            return_sampling_results (bool): Whether to return the result of
-                sample.
-            return_level (bool): Whether to map outputs back to the levels
-                of feature map sizes.
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - labels_list (list[Tensor]): Labels of each level.
-                - label_weights_list (list[Tensor]): Label weights of each
-                  level.
-                - bbox_targets_list (list[Tensor]): BBox targets of each level.
-                - bbox_weights_list (list[Tensor]): BBox weights of each level.
-                - num_total_pos (int): Number of positive samples in all
-                  images.
-                - num_total_neg (int): Number of negative samples in all
-                  images.
-
-            additional_returns: This function enables user-defined returns from
-                `self._get_targets_single`. These returns are currently refined
-                to properties at each feature map (i.e. having HxW dimension).
-                The results will be concatenated after the end
-        """
-        assert gt_bboxes_ignore_list is None
-        assert unmap_outputs is True
-        assert return_sampling_results is False
-        assert self.train_cfg.allowed_border < 0
-        assert isinstance(self.assigner, AscendMaxIoUAssigner)
-        assert isinstance(self.sampler, PseudoSampler)
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        device = anchor_list[0][0].device
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
-        batch_anchor_list = []
-        batch_valid_flag_list = []
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            batch_anchor_list.append(torch.cat(anchor_list[i]))
-            batch_valid_flag_list.append(torch.cat(valid_flag_list[i]))
-        batch_anchors = torch.cat(
-            [torch.unsqueeze(anchor, 0) for anchor in batch_anchor_list], 0)
-        batch_valid_flags = torch.cat([
-            torch.unsqueeze(batch_valid_flag, 0)
-            for batch_valid_flag in batch_valid_flag_list
-        ], 0)
-
-        gt_nums = [len(gt_bbox) for gt_bbox in gt_bboxes_list]
-        max_gt_nums = get_max_num_gt_division_factor(gt_nums)
-        batch_gt_bboxes = self.get_batch_gt_bboxes(gt_bboxes_list, num_imgs,
-                                                   gt_nums, device,
-                                                   max_gt_nums)
-        batch_gt_bboxes_ignore = self.get_batch_gt_bboxes_ignore(
-            gt_bboxes_ignore_list, num_imgs, gt_nums, device)
-        batch_gt_labels = self.get_batch_gt_labels(gt_labels_list, num_imgs,
-                                                   gt_nums, device,
-                                                   max_gt_nums)
-
-        results = self._get_targets_concat(
-            batch_anchors,
-            batch_valid_flags,
-            batch_gt_bboxes,
-            batch_gt_bboxes_ignore,
-            batch_gt_labels,
-            img_metas,
-            label_channels=label_channels,
-            unmap_outputs=unmap_outputs)
-
-        (batch_labels, batch_label_weights, batch_bbox_targets,
-         batch_bbox_weights, batch_pos_mask, batch_neg_mask,
-         sampling_result) = results[:7]
-        rest_results = list(results[7:])  # user-added return values
-
-        # sampled anchors of all images
-        min_num = torch.ones((num_imgs, ),
-                             dtype=torch.long,
-                             device=batch_pos_mask.device)
-        num_total_pos = torch.sum(
-            torch.max(torch.sum(batch_pos_mask, dim=1), min_num))
-        num_total_neg = torch.sum(
-            torch.max(torch.sum(batch_neg_mask, dim=1), min_num))
-        if return_level is True:
-            labels_list = batch_images_to_levels(batch_labels,
-                                                 num_level_anchors)
-            label_weights_list = batch_images_to_levels(
-                batch_label_weights, num_level_anchors)
-            bbox_targets_list = batch_images_to_levels(batch_bbox_targets,
-                                                       num_level_anchors)
-            bbox_weights_list = batch_images_to_levels(batch_bbox_weights,
-                                                       num_level_anchors)
-            res = (labels_list, label_weights_list, bbox_targets_list,
-                   bbox_weights_list, num_total_pos, num_total_neg)
-            if return_sampling_results:
-                res = res + (sampling_result, )
-            for i, r in enumerate(rest_results):  # user-added return values
-                rest_results[i] = batch_images_to_levels(r, num_level_anchors)
-
-            return res + tuple(rest_results)
-        else:
-            res = (batch_labels, batch_label_weights, batch_bbox_targets,
-                   batch_bbox_weights, batch_pos_mask, batch_neg_mask,
-                   sampling_result, num_total_pos, num_total_neg,
-                   batch_anchors)
-            return res
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Adobe Dimension CC 2018 V1.0.0 Crack [CracksNow] 64 Bit [REPACK].md b/spaces/rorallitri/biomedical-language-models/logs/Adobe Dimension CC 2018 V1.0.0 Crack [CracksNow] 64 Bit [REPACK].md
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--- a/spaces/rorallitri/biomedical-language-models/logs/Adobe Dimension CC 2018 V1.0.0 Crack [CracksNow] 64 Bit [REPACK].md	
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diff --git a/spaces/santoshtyss/QuickAd/README.md b/spaces/santoshtyss/QuickAd/README.md
deleted file mode 100644
index 55c9a643755b1ac6cfa5349aaf241328d5df04c7..0000000000000000000000000000000000000000
--- a/spaces/santoshtyss/QuickAd/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: QuickAd
-emoji: 👀
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sawi/audio/README.md b/spaces/sawi/audio/README.md
deleted file mode 100644
index f4b4a97d24b372cecffc0663d7430fab6002118c..0000000000000000000000000000000000000000
--- a/spaces/sawi/audio/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Audio
-emoji: 👁
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/modules/models/tokenization_moss.py b/spaces/sccstandardteam/ChuanhuChatGPT/modules/models/tokenization_moss.py
deleted file mode 100644
index 626315eb9e429ada99a15b04b9736c05e6743ffe..0000000000000000000000000000000000000000
--- a/spaces/sccstandardteam/ChuanhuChatGPT/modules/models/tokenization_moss.py
+++ /dev/null
@@ -1,368 +0,0 @@
-"""Tokenization classes for Moss"""
-
-import json
-import os
-import numpy as np
-import regex as re
-
-from functools import lru_cache
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union
-
-from transformers.utils import is_tf_available, is_torch_available, logging
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-
-
-if TYPE_CHECKING:
-    if is_torch_available():
-        import torch
-    if is_tf_available():
-        import tensorflow as tf
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "merges_file": "merges.txt",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "fnlp/moss-moon-003-base": "https://huggingface.co/fnlp/moss-moon-003-base/resolve/main/vocab.json",
-        "fnlp/moss-moon-003-sft": "https://huggingface.co/fnlp/moss-moon-003-sft/resolve/main/vocab.json",
-        "fnlp/moss-moon-003-sft-plugin": "https://huggingface.co/fnlp/moss-moon-003-sft-plugin/resolve/main/vocab.json",
-    },
-    "merges_file": {
-        "fnlp/moss-moon-003-base": "https://huggingface.co/fnlp/moss-moon-003-base/resolve/main/merges.txt",
-        "fnlp/moss-moon-003-sft": "https://huggingface.co/fnlp/moss-moon-003-sft/resolve/main/merges.txt",
-        "fnlp/moss-moon-003-sft-plugin": "https://huggingface.co/fnlp/moss-moon-003-sft-plugin/resolve/main/merges.txt",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "fnlp/moss-moon-003-base": 2048,
-    "fnlp/moss-moon-003-sft": 2048,
-    "fnlp/moss-moon-003-sft-plugin": 2048,
-}
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
-    characters the bpe code barfs on.
-
-    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
-    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
-    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
-    tables between utf-8 bytes and unicode strings.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """
-    Return set of symbol pairs in a word.
-
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class MossTokenizer(PreTrainedTokenizer):
-    """
-    Construct a Moss tokenizer. Based on byte-level Byte-Pair-Encoding.
-
-    This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
-    be encoded differently whether it is at the beginning of the sentence (without space) or not:
-
-    You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
-    call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
-
-    <Tip>
-
-    When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
-
-    </Tip>
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-        errors (`str`, *optional*, defaults to `"replace"`):
-            Paradigm to follow when decoding bytes to UTF-8. See
-            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
-        unk_token (`str`, *optional*, defaults to `<|endoftext|>`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str`, *optional*, defaults to `<|endoftext|>`):
-            The beginning of sequence token.
-        eos_token (`str`, *optional*, defaults to `<|endoftext|>`):
-            The end of sequence token.
-        add_prefix_space (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
-            other word. (Moss tokenizer detect beginning of words by the preceding space).
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        merges_file,
-        errors="replace",
-        unk_token="<|endoftext|>",
-        bos_token="<|endoftext|>",
-        eos_token="<eom>",
-        pad_token=None,
-        add_prefix_space=False,
-        add_bos_token=False,
-        **kwargs,
-    ):
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
-        super().__init__(
-            errors=errors,
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            add_prefix_space=add_prefix_space,
-            add_bos_token=add_bos_token,
-            **kwargs,
-        )
-        self.add_bos_token = add_bos_token
-
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.encoder = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        with open(merges_file, encoding="utf-8") as merges_handle:
-            bpe_merges = merges_handle.read().split("\n")[1:-1]
-        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-        self.add_prefix_space = add_prefix_space
-
-        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
-
-    @property
-    def vocab_size(self):
-        return len(self.encoder)
-
-    def get_vocab(self):
-        return dict(self.encoder, **self.added_tokens_encoder)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                except ValueError:
-                    new_word.extend(word[i:])
-                    break
-                else:
-                    new_word.extend(word[i:j])
-                    i = j
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        if self.add_bos_token:
-            bos_token_ids = [self.bos_token_id]
-        else:
-            bos_token_ids = []
-
-        output = bos_token_ids + token_ids_0
-
-        if token_ids_1 is None:
-            return output
-
-        return output + bos_token_ids + token_ids_1
-
-    def _tokenize(self, text):
-        """Tokenize a string."""
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            token = "".join(
-                self.byte_encoder[b] for b in token.encode("utf-8")
-            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
-        return bpe_tokens
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        text = "".join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
-        return text
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-        merge_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write("#version: 0.2\n")
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning(
-                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
-                        " Please check that the tokenizer is not corrupted!"
-                    )
-                    index = token_index
-                writer.write(" ".join(bpe_tokens) + "\n")
-                index += 1
-
-        return vocab_file, merge_file
-
-    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
-        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
-        if is_split_into_words or add_prefix_space:
-            text = " " + text
-        return (text, kwargs)
-
-    def decode(
-        self,
-        token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
-        skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = None,
-        truncate_before_pattern: Optional[List[str]] = None,
-        **kwargs,
-    ) -> str:
-        """
-        Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
-        tokens and clean up tokenization spaces.
-
-        Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
-
-        Args:
-            token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
-                List of tokenized input ids. Can be obtained using the `__call__` method.
-            skip_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*):
-                Whether or not to clean up the tokenization spaces. If `None`, will default to
-                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
-            truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
-                A list of regular expression strings that will be used to truncate the returned string. This can be
-                used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning
-                of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`.
-            kwargs (additional keyword arguments, *optional*):
-                Will be passed to the underlying model specific decode method.
-
-        Returns:
-            `str`: The decoded sentence.
-        """
-        decoded_text = super()._decode(
-            token_ids=token_ids,
-            skip_special_tokens=skip_special_tokens,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            **kwargs,
-        )
-
-        if truncate_before_pattern is not None and len(truncate_before_pattern) > 0:
-            decoded_text = self.truncate(decoded_text, truncate_before_pattern)
-
-        return decoded_text
-
-    def truncate(self, completion, truncate_before_pattern):
-        def find_re(string, pattern, start_pos):
-            m = pattern.search(string, start_pos)
-            return m.start() if m else -1
-
-        terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern]
-
-        prints = list(re.finditer("^print", completion, re.MULTILINE))
-
-        if len(prints) > 1:
-            completion = completion[: prints[1].start()]
-
-        defs = list(re.finditer("^def", completion, re.MULTILINE))
-
-        if len(defs) > 1:
-            completion = completion[: defs[1].start()]
-
-        start_pos = 0
-
-        terminals_pos = [
-            pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1
-        ]
-
-        if len(terminals_pos) > 0:
-            return completion[: min(terminals_pos)]
-        else:
-            return completion
diff --git a/spaces/scedlatioru/img-to-music/example/Ice Age 4 Continental Drift Tamil Dubbed DvD Rip 700Mb.md b/spaces/scedlatioru/img-to-music/example/Ice Age 4 Continental Drift Tamil Dubbed DvD Rip 700Mb.md
deleted file mode 100644
index aea44fd8210b252e99bb84d7f8dffd7fb0173128..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Ice Age 4 Continental Drift Tamil Dubbed DvD Rip 700Mb.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Ice Age 4 Continental Drift Tamil Dubbed DvD Rip 700Mb</h2><br /><p><b><b>Download File</b> &#9675;&#9675;&#9675; <a href="https://gohhs.com/2uEAng">https://gohhs.com/2uEAng</a></b></p><br /><br />
-
-Ice 2003 download tamil movie dvdrip xvid 1cdrip 700mb tamil dvd rip movies ... Ice age 4 continental drift 2012 tamil dubbed movie hd 720 watch online. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/sdhsdhk/bingo111/src/components/ui/badge.tsx b/spaces/sdhsdhk/bingo111/src/components/ui/badge.tsx
deleted file mode 100644
index d9a84b394090e5b4b3bd34f6135b9a2f2ead0aa2..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/src/components/ui/badge.tsx
+++ /dev/null
@@ -1,36 +0,0 @@
-import * as React from 'react'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const badgeVariants = cva(
-  'inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2',
-  {
-    variants: {
-      variant: {
-        default:
-          'border-transparent bg-primary text-primary-foreground hover:bg-primary/80',
-        secondary:
-          'border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        destructive:
-          'border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80',
-        outline: 'text-foreground'
-      }
-    },
-    defaultVariants: {
-      variant: 'default'
-    }
-  }
-)
-
-export interface BadgeProps
-  extends React.HTMLAttributes<HTMLDivElement>,
-    VariantProps<typeof badgeVariants> {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
-  return (
-    <div className={cn(badgeVariants({ variant }), className)} {...props} />
-  )
-}
-
-export { Badge, badgeVariants }
diff --git a/spaces/segments-tobias/conex/espnet2/asr/encoder/contextual_block_transformer_encoder.py b/spaces/segments-tobias/conex/espnet2/asr/encoder/contextual_block_transformer_encoder.py
deleted file mode 100644
index f5765d45651449c9cff885ff05beb54186e385bf..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/asr/encoder/contextual_block_transformer_encoder.py
+++ /dev/null
@@ -1,327 +0,0 @@
-# Copyright 2020 Emiru Tsunoo
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Encoder definition."""
-from typing import Optional
-from typing import Tuple
-
-import torch
-from typeguard import check_argument_types
-
-from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
-from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
-from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
-from espnet.nets.pytorch_backend.transformer.contextual_block_encoder_layer import (
-    ContextualBlockEncoderLayer,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
-from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear
-from espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d
-from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
-    PositionwiseFeedForward,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.repeat import repeat
-from espnet.nets.pytorch_backend.transformer.subsampling_without_posenc import (
-    Conv2dSubsamplingWOPosEnc,  # noqa: H301
-)
-from espnet2.asr.encoder.abs_encoder import AbsEncoder
-import math
-
-
-class ContextualBlockTransformerEncoder(AbsEncoder):
-    """Contextual Block Transformer encoder module.
-
-    Details in Tsunoo et al. "Transformer ASR with contextual block processing"
-    (https://arxiv.org/abs/1910.07204)
-
-    Args:
-        input_size: input dim
-        output_size: dimension of attention
-        attention_heads: the number of heads of multi head attention
-        linear_units: the number of units of position-wise feed forward
-        num_blocks: the number of decoder blocks
-        dropout_rate: dropout rate
-        attention_dropout_rate: dropout rate in attention
-        positional_dropout_rate: dropout rate after adding positional encoding
-        input_layer: input layer type
-        pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
-        normalize_before: whether to use layer_norm before the first block
-        concat_after: whether to concat attention layer's input and output
-            if True, additional linear will be applied.
-            i.e. x -> x + linear(concat(x, att(x)))
-            if False, no additional linear will be applied.
-            i.e. x -> x + att(x)
-        positionwise_layer_type: linear of conv1d
-        positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
-        padding_idx: padding_idx for input_layer=embed
-        block_size: block size for contextual block processing
-        hop_Size: hop size for block processing
-        look_ahead: look-ahead size for block_processing
-        init_average: whether to use average as initial context (otherwise max values)
-        ctx_pos_enc: whether to use positional encoding to the context vectors
-    """
-
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int = 256,
-        attention_heads: int = 4,
-        linear_units: int = 2048,
-        num_blocks: int = 6,
-        dropout_rate: float = 0.1,
-        positional_dropout_rate: float = 0.1,
-        attention_dropout_rate: float = 0.0,
-        input_layer: Optional[str] = "conv2d",
-        pos_enc_class=PositionalEncoding,
-        normalize_before: bool = True,
-        concat_after: bool = False,
-        positionwise_layer_type: str = "linear",
-        positionwise_conv_kernel_size: int = 1,
-        padding_idx: int = -1,
-        block_size: int = 40,
-        hop_size: int = 16,
-        look_ahead: int = 16,
-        init_average: bool = True,
-        ctx_pos_enc: bool = True,
-    ):
-        assert check_argument_types()
-        super().__init__()
-        self._output_size = output_size
-
-        self.pos_enc = pos_enc_class(output_size, positional_dropout_rate)
-
-        if input_layer == "linear":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Linear(input_size, output_size),
-                torch.nn.LayerNorm(output_size),
-                torch.nn.Dropout(dropout_rate),
-                torch.nn.ReLU(),
-            )
-        elif input_layer == "conv2d":
-            self.embed = Conv2dSubsamplingWOPosEnc(
-                input_size, output_size, dropout_rate, kernels=[3, 3], strides=[2, 2]
-            )
-        elif input_layer == "conv2d6":
-            self.embed = Conv2dSubsamplingWOPosEnc(
-                input_size, output_size, dropout_rate, kernels=[3, 5], strides=[2, 3]
-            )
-        elif input_layer == "conv2d8":
-            self.embed = Conv2dSubsamplingWOPosEnc(
-                input_size,
-                output_size,
-                dropout_rate,
-                kernels=[3, 3, 3],
-                strides=[2, 2, 2],
-            )
-        elif input_layer == "embed":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
-            )
-        elif input_layer is None:
-            self.embed = None
-        else:
-            raise ValueError("unknown input_layer: " + input_layer)
-        self.normalize_before = normalize_before
-        if positionwise_layer_type == "linear":
-            positionwise_layer = PositionwiseFeedForward
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                dropout_rate,
-            )
-        elif positionwise_layer_type == "conv1d":
-            positionwise_layer = MultiLayeredConv1d
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        elif positionwise_layer_type == "conv1d-linear":
-            positionwise_layer = Conv1dLinear
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        else:
-            raise NotImplementedError("Support only linear or conv1d.")
-        self.encoders = repeat(
-            num_blocks,
-            lambda lnum: ContextualBlockEncoderLayer(
-                output_size,
-                MultiHeadedAttention(
-                    attention_heads, output_size, attention_dropout_rate
-                ),
-                positionwise_layer(*positionwise_layer_args),
-                dropout_rate,
-                num_blocks,
-                normalize_before,
-                concat_after,
-            ),
-        )
-        if self.normalize_before:
-            self.after_norm = LayerNorm(output_size)
-
-        # for block processing
-        self.block_size = block_size
-        self.hop_size = hop_size
-        self.look_ahead = look_ahead
-        self.init_average = init_average
-        self.ctx_pos_enc = ctx_pos_enc
-
-    def output_size(self) -> int:
-        return self._output_size
-
-    def forward(
-        self,
-        xs_pad: torch.Tensor,
-        ilens: torch.Tensor,
-        prev_states: torch.Tensor = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
-        """Embed positions in tensor.
-
-        Args:
-            xs_pad: input tensor (B, L, D)
-            ilens: input length (B)
-            prev_states: Not to be used now.
-        Returns:
-            position embedded tensor and mask
-        """
-        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
-
-        if isinstance(self.embed, Conv2dSubsamplingWOPosEnc):
-            xs_pad, masks = self.embed(xs_pad, masks)
-        elif self.embed is not None:
-            xs_pad = self.embed(xs_pad)
-
-        # create empty output container
-        total_frame_num = xs_pad.size(1)
-        ys_pad = xs_pad.new_zeros(xs_pad.size())
-
-        past_size = self.block_size - self.hop_size - self.look_ahead
-
-        # block_size could be 0 meaning infinite
-        # apply usual encoder for short sequence
-        if self.block_size == 0 or total_frame_num <= self.block_size:
-            xs_pad, masks, _, _, _ = self.encoders(
-                self.pos_enc(xs_pad), masks, None, None
-            )
-            if self.normalize_before:
-                xs_pad = self.after_norm(xs_pad)
-
-            olens = masks.squeeze(1).sum(1)
-            return xs_pad, olens, None
-
-        # start block processing
-        cur_hop = 0
-        block_num = math.ceil(
-            float(total_frame_num - past_size - self.look_ahead) / float(self.hop_size)
-        )
-        bsize = xs_pad.size(0)
-        addin = xs_pad.new_zeros(
-            bsize, block_num, xs_pad.size(-1)
-        )  # additional context embedding vecctors
-
-        # first step
-        if self.init_average:  # initialize with average value
-            addin[:, 0, :] = xs_pad.narrow(1, cur_hop, self.block_size).mean(1)
-        else:  # initialize with max value
-            addin[:, 0, :] = xs_pad.narrow(1, cur_hop, self.block_size).max(1)
-        cur_hop += self.hop_size
-        # following steps
-        while cur_hop + self.block_size < total_frame_num:
-            if self.init_average:  # initialize with average value
-                addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow(
-                    1, cur_hop, self.block_size
-                ).mean(1)
-            else:  # initialize with max value
-                addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow(
-                    1, cur_hop, self.block_size
-                ).max(1)
-            cur_hop += self.hop_size
-        # last step
-        if cur_hop < total_frame_num and cur_hop // self.hop_size < block_num:
-            if self.init_average:  # initialize with average value
-                addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow(
-                    1, cur_hop, total_frame_num - cur_hop
-                ).mean(1)
-            else:  # initialize with max value
-                addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow(
-                    1, cur_hop, total_frame_num - cur_hop
-                ).max(1)
-
-        if self.ctx_pos_enc:
-            addin = self.pos_enc(addin)
-
-        xs_pad = self.pos_enc(xs_pad)
-
-        # set up masks
-        mask_online = xs_pad.new_zeros(
-            xs_pad.size(0), block_num, self.block_size + 2, self.block_size + 2
-        )
-        mask_online.narrow(2, 1, self.block_size + 1).narrow(
-            3, 0, self.block_size + 1
-        ).fill_(1)
-
-        xs_chunk = xs_pad.new_zeros(
-            bsize, block_num, self.block_size + 2, xs_pad.size(-1)
-        )
-
-        # fill the input
-        # first step
-        left_idx = 0
-        block_idx = 0
-        xs_chunk[:, block_idx, 1 : self.block_size + 1] = xs_pad.narrow(
-            -2, left_idx, self.block_size
-        )
-        left_idx += self.hop_size
-        block_idx += 1
-        # following steps
-        while left_idx + self.block_size < total_frame_num and block_idx < block_num:
-            xs_chunk[:, block_idx, 1 : self.block_size + 1] = xs_pad.narrow(
-                -2, left_idx, self.block_size
-            )
-            left_idx += self.hop_size
-            block_idx += 1
-        # last steps
-        last_size = total_frame_num - left_idx
-        xs_chunk[:, block_idx, 1 : last_size + 1] = xs_pad.narrow(
-            -2, left_idx, last_size
-        )
-
-        # fill the initial context vector
-        xs_chunk[:, 0, 0] = addin[:, 0]
-        xs_chunk[:, 1:, 0] = addin[:, 0 : block_num - 1]
-        xs_chunk[:, :, self.block_size + 1] = addin
-
-        # forward
-        ys_chunk, mask_online, _, _, _ = self.encoders(xs_chunk, mask_online, xs_chunk)
-
-        # copy output
-        # first step
-        offset = self.block_size - self.look_ahead - self.hop_size + 1
-        left_idx = 0
-        block_idx = 0
-        cur_hop = self.block_size - self.look_ahead
-        ys_pad[:, left_idx:cur_hop] = ys_chunk[:, block_idx, 1 : cur_hop + 1]
-        left_idx += self.hop_size
-        block_idx += 1
-        # following steps
-        while left_idx + self.block_size < total_frame_num and block_idx < block_num:
-            ys_pad[:, cur_hop : cur_hop + self.hop_size] = ys_chunk[
-                :, block_idx, offset : offset + self.hop_size
-            ]
-            cur_hop += self.hop_size
-            left_idx += self.hop_size
-            block_idx += 1
-        ys_pad[:, cur_hop:total_frame_num] = ys_chunk[
-            :, block_idx, offset : last_size + 1, :
-        ]
-
-        if self.normalize_before:
-            ys_pad = self.after_norm(ys_pad)
-
-        olens = masks.squeeze(1).sum(1)
-        return ys_pad, olens, None
diff --git a/spaces/segments-tobias/conex/espnet2/layers/log_mel.py b/spaces/segments-tobias/conex/espnet2/layers/log_mel.py
deleted file mode 100644
index 5caeadbe31e6acf04b7d2f55818e3ad4c663acf2..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/layers/log_mel.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import librosa
-import torch
-from typing import Tuple
-
-from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
-
-
-class LogMel(torch.nn.Module):
-    """Convert STFT to fbank feats
-
-    The arguments is same as librosa.filters.mel
-
-    Args:
-        fs: number > 0 [scalar] sampling rate of the incoming signal
-        n_fft: int > 0 [scalar] number of FFT components
-        n_mels: int > 0 [scalar] number of Mel bands to generate
-        fmin: float >= 0 [scalar] lowest frequency (in Hz)
-        fmax: float >= 0 [scalar] highest frequency (in Hz).
-            If `None`, use `fmax = fs / 2.0`
-        htk: use HTK formula instead of Slaney
-    """
-
-    def __init__(
-        self,
-        fs: int = 16000,
-        n_fft: int = 512,
-        n_mels: int = 80,
-        fmin: float = None,
-        fmax: float = None,
-        htk: bool = False,
-        log_base: float = None,
-    ):
-        super().__init__()
-
-        fmin = 0 if fmin is None else fmin
-        fmax = fs / 2 if fmax is None else fmax
-        _mel_options = dict(
-            sr=fs,
-            n_fft=n_fft,
-            n_mels=n_mels,
-            fmin=fmin,
-            fmax=fmax,
-            htk=htk,
-        )
-        self.mel_options = _mel_options
-        self.log_base = log_base
-
-        # Note(kamo): The mel matrix of librosa is different from kaldi.
-        melmat = librosa.filters.mel(**_mel_options)
-        # melmat: (D2, D1) -> (D1, D2)
-        self.register_buffer("melmat", torch.from_numpy(melmat.T).float())
-
-    def extra_repr(self):
-        return ", ".join(f"{k}={v}" for k, v in self.mel_options.items())
-
-    def forward(
-        self,
-        feat: torch.Tensor,
-        ilens: torch.Tensor = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # feat: (B, T, D1) x melmat: (D1, D2) -> mel_feat: (B, T, D2)
-        mel_feat = torch.matmul(feat, self.melmat)
-        mel_feat = torch.clamp(mel_feat, min=1e-10)
-
-        if self.log_base is None:
-            logmel_feat = mel_feat.log()
-        elif self.log_base == 2.0:
-            logmel_feat = mel_feat.log2()
-        elif self.log_base == 10.0:
-            logmel_feat = mel_feat.log10()
-        else:
-            logmel_feat = mel_feat.log() / torch.log(self.log_base)
-
-        # Zero padding
-        if ilens is not None:
-            logmel_feat = logmel_feat.masked_fill(
-                make_pad_mask(ilens, logmel_feat, 1), 0.0
-            )
-        else:
-            ilens = feat.new_full(
-                [feat.size(0)], fill_value=feat.size(1), dtype=torch.long
-            )
-        return logmel_feat, ilens
diff --git a/spaces/segments-tobias/conex/espnet2/tasks/diar.py b/spaces/segments-tobias/conex/espnet2/tasks/diar.py
deleted file mode 100644
index fcea8a56ebc64599c7fcd113ff9647a1c8f9dfe0..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/tasks/diar.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import argparse
-from typing import Callable
-from typing import Collection
-from typing import Dict
-from typing import List
-from typing import Optional
-from typing import Tuple
-
-import numpy as np
-import torch
-from typeguard import check_argument_types
-from typeguard import check_return_type
-
-from espnet2.asr.encoder.abs_encoder import AbsEncoder
-from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
-from espnet2.asr.encoder.rnn_encoder import RNNEncoder
-from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
-from espnet2.asr.frontend.abs_frontend import AbsFrontend
-from espnet2.asr.frontend.default import DefaultFrontend
-from espnet2.asr.frontend.windowing import SlidingWindow
-from espnet2.diar.decoder.abs_decoder import AbsDecoder
-from espnet2.diar.decoder.linear_decoder import LinearDecoder
-from espnet2.diar.espnet_model import ESPnetDiarizationModel
-from espnet2.layers.abs_normalize import AbsNormalize
-from espnet2.layers.global_mvn import GlobalMVN
-from espnet2.layers.label_aggregation import LabelAggregate
-from espnet2.layers.utterance_mvn import UtteranceMVN
-from espnet2.tasks.abs_task import AbsTask
-from espnet2.torch_utils.initialize import initialize
-from espnet2.train.class_choices import ClassChoices
-from espnet2.train.collate_fn import CommonCollateFn
-from espnet2.train.preprocessor import CommonPreprocessor
-from espnet2.train.trainer import Trainer
-from espnet2.utils.get_default_kwargs import get_default_kwargs
-from espnet2.utils.nested_dict_action import NestedDictAction
-from espnet2.utils.types import int_or_none
-from espnet2.utils.types import str2bool
-from espnet2.utils.types import str_or_none
-
-frontend_choices = ClassChoices(
-    name="frontend",
-    classes=dict(default=DefaultFrontend, sliding_window=SlidingWindow),
-    type_check=AbsFrontend,
-    default="default",
-)
-normalize_choices = ClassChoices(
-    "normalize",
-    classes=dict(
-        global_mvn=GlobalMVN,
-        utterance_mvn=UtteranceMVN,
-    ),
-    type_check=AbsNormalize,
-    default="utterance_mvn",
-    optional=True,
-)
-label_aggregator_choices = ClassChoices(
-    "label_aggregator",
-    classes=dict(label_aggregator=LabelAggregate),
-    default="label_aggregator",
-)
-encoder_choices = ClassChoices(
-    "encoder",
-    classes=dict(
-        conformer=ConformerEncoder,
-        transformer=TransformerEncoder,
-        rnn=RNNEncoder,
-    ),
-    type_check=AbsEncoder,
-    default="rnn",
-)
-decoder_choices = ClassChoices(
-    "decoder",
-    classes=dict(linear=LinearDecoder),
-    type_check=AbsDecoder,
-    default="linear",
-)
-
-
-class DiarizationTask(AbsTask):
-    # If you need more than one optimizer, change this value
-    num_optimizers: int = 1
-
-    # Add variable objects configurations
-    class_choices_list = [
-        # --frontend and --frontend_conf
-        frontend_choices,
-        # --normalize and --normalize_conf
-        normalize_choices,
-        # --encoder and --encoder_conf
-        encoder_choices,
-        # --decoder and --decoder_conf
-        decoder_choices,
-        # --label_aggregator and --label_aggregator_conf
-        label_aggregator_choices,
-    ]
-
-    # If you need to modify train() or eval() procedures, change Trainer class here
-    trainer = Trainer
-
-    @classmethod
-    def add_task_arguments(cls, parser: argparse.ArgumentParser):
-        group = parser.add_argument_group(description="Task related")
-
-        group.add_argument(
-            "--num_spk",
-            type=int_or_none,
-            default=None,
-            help="The number fo speakers (for each recording) used in system training",
-        )
-
-        group.add_argument(
-            "--init",
-            type=lambda x: str_or_none(x.lower()),
-            default=None,
-            help="The initialization method",
-            choices=[
-                "chainer",
-                "xavier_uniform",
-                "xavier_normal",
-                "kaiming_uniform",
-                "kaiming_normal",
-                None,
-            ],
-        )
-
-        group.add_argument(
-            "--input_size",
-            type=int_or_none,
-            default=None,
-            help="The number of input dimension of the feature",
-        )
-
-        group.add_argument(
-            "--model_conf",
-            action=NestedDictAction,
-            default=get_default_kwargs(ESPnetDiarizationModel),
-            help="The keyword arguments for model class.",
-        )
-
-        group = parser.add_argument_group(description="Preprocess related")
-        group.add_argument(
-            "--use_preprocessor",
-            type=str2bool,
-            default=True,
-            help="Apply preprocessing to data or not",
-        )
-
-        for class_choices in cls.class_choices_list:
-            # Append --<name> and --<name>_conf.
-            # e.g. --encoder and --encoder_conf
-            class_choices.add_arguments(group)
-
-    @classmethod
-    def build_collate_fn(
-        cls, args: argparse.Namespace, train: bool
-    ) -> Callable[
-        [Collection[Tuple[str, Dict[str, np.ndarray]]]],
-        Tuple[List[str], Dict[str, torch.Tensor]],
-    ]:
-        assert check_argument_types()
-        # NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
-        return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
-
-    @classmethod
-    def build_preprocess_fn(
-        cls, args: argparse.Namespace, train: bool
-    ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
-        assert check_argument_types()
-        if args.use_preprocessor:
-            # FIXME (jiatong): add more arugment here
-            retval = CommonPreprocessor(train=train)
-        else:
-            retval = None
-        assert check_return_type(retval)
-        return retval
-
-    @classmethod
-    def required_data_names(
-        cls, train: bool = True, inference: bool = False
-    ) -> Tuple[str, ...]:
-        if not inference:
-            retval = ("speech", "spk_labels")
-        else:
-            # Recognition mode
-            retval = ("speech",)
-        return retval
-
-    @classmethod
-    def optional_data_names(
-        cls, train: bool = True, inference: bool = False
-    ) -> Tuple[str, ...]:
-        # (Note: jiatong): no optional data names for now
-        retval = ()
-        assert check_return_type(retval)
-        return retval
-
-    @classmethod
-    def build_model(cls, args: argparse.Namespace) -> ESPnetDiarizationModel:
-        assert check_argument_types()
-
-        # 1. frontend
-        if args.input_size is None:
-            # Extract features in the model
-            frontend_class = frontend_choices.get_class(args.frontend)
-            frontend = frontend_class(**args.frontend_conf)
-            input_size = frontend.output_size()
-        else:
-            # Give features from data-loader
-            args.frontend = None
-            args.frontend_conf = {}
-            frontend = None
-            input_size = args.input_size
-
-        # 2. Normalization layer
-        if args.normalize is not None:
-            normalize_class = normalize_choices.get_class(args.normalize)
-            normalize = normalize_class(**args.normalize_conf)
-        else:
-            normalize = None
-
-        # 3. Label Aggregator layer
-        label_aggregator_class = label_aggregator_choices.get_class(
-            args.label_aggregator
-        )
-        label_aggregator = label_aggregator_class(**args.label_aggregator_conf)
-
-        # 3. Encoder
-        encoder_class = encoder_choices.get_class(args.encoder)
-        # Note(jiatong): Diarization may not use subsampling when processing
-        encoder = encoder_class(input_size=input_size, **args.encoder_conf)
-
-        # 4. Decoder
-        decoder_class = decoder_choices.get_class(args.decoder)
-        decoder = decoder_class(
-            num_spk=args.num_spk,
-            encoder_output_size=encoder.output_size(),
-            **args.decoder_conf,
-        )
-
-        # 5. Build model
-        model = ESPnetDiarizationModel(
-            frontend=frontend,
-            normalize=normalize,
-            label_aggregator=label_aggregator,
-            encoder=encoder,
-            decoder=decoder,
-            **args.model_conf,
-        )
-
-        # FIXME(kamo): Should be done in model?
-        # 6. Initialize
-        if args.init is not None:
-            initialize(model, args.init)
-
-        assert check_return_type(model)
-        return model
diff --git a/spaces/segments/panoptic-segment-anything-api/app.py b/spaces/segments/panoptic-segment-anything-api/app.py
deleted file mode 100644
index 4f63a1f6569425b3bb55a4a0c56eb1443eaf0ed8..0000000000000000000000000000000000000000
--- a/spaces/segments/panoptic-segment-anything-api/app.py
+++ /dev/null
@@ -1,565 +0,0 @@
-import subprocess, os, sys
-
-result = subprocess.run(["pip", "install", "-e", "GroundingDINO"], check=True)
-print(f"pip install GroundingDINO = {result}")
-
-sys.path.insert(0, "./GroundingDINO")
-
-if not os.path.exists("./sam_vit_h_4b8939.pth"):
-    result = subprocess.run(
-        [
-            "wget",
-            "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
-        ],
-        check=True,
-    )
-    print(f"wget sam_vit_h_4b8939.pth result = {result}")
-
-
-import argparse
-import random
-import warnings
-import json
-import requests
-
-import gradio as gr
-import numpy as np
-import torch
-from torch import nn
-import torch.nn.functional as F
-from scipy import ndimage
-from PIL import Image
-from huggingface_hub import hf_hub_download
-from segments.utils import bitmap2file
-
-# Grounding DINO
-import GroundingDINO.groundingdino.datasets.transforms as T
-from GroundingDINO.groundingdino.models import build_model
-from GroundingDINO.groundingdino.util import box_ops
-from GroundingDINO.groundingdino.util.slconfig import SLConfig
-from GroundingDINO.groundingdino.util.utils import (
-    clean_state_dict,
-)
-from GroundingDINO.groundingdino.util.inference import annotate, predict
-
-# segment anything
-from segment_anything import build_sam, SamPredictor
-
-# CLIPSeg
-from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-
-
-def load_model_hf(model_config_path, repo_id, filename, device):
-    args = SLConfig.fromfile(model_config_path)
-    model = build_model(args)
-    args.device = device
-
-    cache_file = hf_hub_download(repo_id=repo_id, filename=filename)
-    checkpoint = torch.load(cache_file, map_location=device)
-    log = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
-    print("Model loaded from {} \n => {}".format(cache_file, log))
-    _ = model.eval()
-    model = model.to(device)
-    return model
-
-
-def download_image(url):
-    return Image.open(requests.get(url, stream=True).raw)
-
-
-def load_image_for_dino(image):
-    transform = T.Compose(
-        [
-            T.RandomResize([800], max_size=1333),
-            T.ToTensor(),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ]
-    )
-    dino_image, _ = transform(image, None)
-    return dino_image
-
-
-def dino_detection(
-    model,
-    image,
-    image_array,
-    category_names,
-    category_name_to_id,
-    box_threshold,
-    text_threshold,
-    device,
-    visualize=False,
-):
-    detection_prompt = " . ".join(category_names)
-    dino_image = load_image_for_dino(image)
-    dino_image = dino_image.to(device)
-    with torch.no_grad():
-        boxes, logits, phrases = predict(
-            model=model,
-            image=dino_image,
-            caption=detection_prompt,
-            box_threshold=box_threshold,
-            text_threshold=text_threshold,
-            device=device,
-        )
-    category_ids = [category_name_to_id[phrase] for phrase in phrases]
-
-    if visualize:
-        annotated_frame = annotate(
-            image_source=image_array, boxes=boxes, logits=logits, phrases=phrases
-        )
-        annotated_frame = annotated_frame[..., ::-1]  # BGR to RGB
-        visualization = Image.fromarray(annotated_frame)
-        return boxes, category_ids, visualization
-    else:
-        return boxes, category_ids, phrases
-
-
-def sam_masks_from_dino_boxes(predictor, image_array, boxes, device):
-    # box: normalized box xywh -> unnormalized xyxy
-    H, W, _ = image_array.shape
-    boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])
-    transformed_boxes = predictor.transform.apply_boxes_torch(
-        boxes_xyxy, image_array.shape[:2]
-    ).to(device)
-    thing_masks, _, _ = predictor.predict_torch(
-        point_coords=None,
-        point_labels=None,
-        boxes=transformed_boxes,
-        multimask_output=False,
-    )
-    return thing_masks
-
-
-def preds_to_semantic_inds(preds, threshold):
-    flat_preds = preds.reshape((preds.shape[0], -1))
-    # Initialize a dummy "unlabeled" mask with the threshold
-    flat_preds_with_treshold = torch.full(
-        (preds.shape[0] + 1, flat_preds.shape[-1]), threshold
-    )
-    flat_preds_with_treshold[1 : preds.shape[0] + 1, :] = flat_preds
-
-    # Get the top mask index for each pixel
-    semantic_inds = torch.topk(flat_preds_with_treshold, 1, dim=0).indices.reshape(
-        (preds.shape[-2], preds.shape[-1])
-    )
-
-    return semantic_inds
-
-
-def clipseg_segmentation(
-    processor, model, image, category_names, background_threshold, device
-):
-    inputs = processor(
-        text=category_names,
-        images=[image] * len(category_names),
-        padding="max_length",
-        return_tensors="pt",
-    ).to(device)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    logits = outputs.logits
-    if len(logits.shape) == 2:
-        logits = logits.unsqueeze(0)
-    # resize the outputs
-    upscaled_logits = nn.functional.interpolate(
-        logits.unsqueeze(1),
-        size=(image.size[1], image.size[0]),
-        mode="bilinear",
-    )
-    preds = torch.sigmoid(upscaled_logits.squeeze(dim=1))
-    semantic_inds = preds_to_semantic_inds(preds, background_threshold)
-    return preds, semantic_inds
-
-
-def semantic_inds_to_shrunken_bool_masks(
-    semantic_inds, shrink_kernel_size, num_categories
-):
-    shrink_kernel = np.ones((shrink_kernel_size, shrink_kernel_size))
-
-    bool_masks = torch.zeros((num_categories, *semantic_inds.shape), dtype=bool)
-    for category in range(num_categories):
-        binary_mask = semantic_inds == category
-        shrunken_binary_mask_array = (
-            ndimage.binary_erosion(binary_mask.numpy(), structure=shrink_kernel)
-            if shrink_kernel_size > 0
-            else binary_mask.numpy()
-        )
-        bool_masks[category] = torch.from_numpy(shrunken_binary_mask_array)
-
-    return bool_masks
-
-
-def clip_and_shrink_preds(semantic_inds, preds, shrink_kernel_size, num_categories):
-    # convert semantic_inds to shrunken bool masks
-    bool_masks = semantic_inds_to_shrunken_bool_masks(
-        semantic_inds, shrink_kernel_size, num_categories
-    ).to(preds.device)
-
-    sizes = [
-        torch.sum(bool_masks[i].int()).item() for i in range(1, bool_masks.size(0))
-    ]
-    max_size = max(sizes)
-    relative_sizes = [size / max_size for size in sizes] if max_size > 0 else sizes
-
-    # use bool masks to clip preds
-    clipped_preds = torch.zeros_like(preds)
-    for i in range(1, bool_masks.size(0)):
-        float_mask = bool_masks[i].float()
-        clipped_preds[i - 1] = preds[i - 1] * float_mask
-
-    return clipped_preds, relative_sizes
-
-
-def sample_points_based_on_preds(preds, N):
-    height, width = preds.shape
-    weights = preds.ravel()
-    indices = np.arange(height * width)
-
-    # Randomly sample N indices based on the weights
-    sampled_indices = random.choices(indices, weights=weights, k=N)
-
-    # Convert the sampled indices into (col, row) coordinates
-    sampled_points = [(index % width, index // width) for index in sampled_indices]
-
-    return sampled_points
-
-
-def upsample_pred(pred, image_source):
-    pred = pred.unsqueeze(dim=0)
-    original_height = image_source.shape[0]
-    original_width = image_source.shape[1]
-
-    larger_dim = max(original_height, original_width)
-    aspect_ratio = original_height / original_width
-
-    # upsample the tensor to the larger dimension
-    upsampled_tensor = F.interpolate(
-        pred, size=(larger_dim, larger_dim), mode="bilinear", align_corners=False
-    )
-
-    # remove the padding (at the end) to get the original image resolution
-    if original_height > original_width:
-        target_width = int(upsampled_tensor.shape[3] * aspect_ratio)
-        upsampled_tensor = upsampled_tensor[:, :, :, :target_width]
-    else:
-        target_height = int(upsampled_tensor.shape[2] * aspect_ratio)
-        upsampled_tensor = upsampled_tensor[:, :, :target_height, :]
-    return upsampled_tensor.squeeze(dim=1)
-
-
-def sam_mask_from_points(predictor, image_array, points):
-    points_array = np.array(points)
-    # we only sample positive points, so labels are all 1
-    points_labels = np.ones(len(points))
-    # we don't use predict_torch here cause it didn't seem to work...
-    _, _, logits = predictor.predict(
-        point_coords=points_array,
-        point_labels=points_labels,
-    )
-    # max over the 3 segmentation levels
-    total_pred = torch.max(torch.sigmoid(torch.tensor(logits)), dim=0)[0].unsqueeze(
-        dim=0
-    )
-    # logits are 256x256 -> upsample back to image shape
-    upsampled_pred = upsample_pred(total_pred, image_array)
-    return upsampled_pred
-
-
-def inds_to_segments_format(
-    panoptic_inds, thing_category_ids, stuff_category_names, category_name_to_id
-):
-    panoptic_inds_array = panoptic_inds.numpy().astype(np.uint32)
-    bitmap_file = bitmap2file(panoptic_inds_array, is_segmentation_bitmap=True)
-    segmentation_bitmap = Image.open(bitmap_file)
-
-    stuff_category_ids = [
-        category_name_to_id[stuff_category_name]
-        for stuff_category_name in stuff_category_names
-    ]
-
-    unique_inds = np.unique(panoptic_inds_array)
-    stuff_annotations = [
-        {"id": i, "category_id": stuff_category_ids[i - 1]}
-        for i in range(1, len(stuff_category_names) + 1)
-        if i in unique_inds
-    ]
-    thing_annotations = [
-        {"id": len(stuff_category_names) + 1 + i, "category_id": thing_category_id}
-        for i, thing_category_id in enumerate(thing_category_ids)
-    ]
-    annotations = stuff_annotations + thing_annotations
-
-    return segmentation_bitmap, annotations
-
-
-def generate_panoptic_mask(
-    image_url,
-    thing_category_names_string,
-    stuff_category_names_string,
-    dino_box_threshold=0.3,
-    dino_text_threshold=0.25,
-    segmentation_background_threshold=0.1,
-    shrink_kernel_size=20,
-    num_samples_factor=1000,
-    task_attributes_json="",
-):
-    if task_attributes_json != "":
-        task_attributes = json.loads(task_attributes_json)
-        categories = task_attributes["categories"]
-        category_name_to_id = {
-            category["name"]: category["id"] for category in categories
-        }
-        # split the categories into "stuff" categories (regions w/o instances)
-        # and "thing" categories (objects/instances)
-        stuff_categories = [
-            category
-            for category in categories
-            if "has_instances" not in category or not category["has_instances"]
-        ]
-        thing_categories = [
-            category
-            for category in categories
-            if "has_instances" in category and category["has_instances"]
-        ]
-        stuff_category_names = [category["name"] for category in stuff_categories]
-        thing_category_names = [category["name"] for category in thing_categories]
-        category_names = thing_category_names + stuff_category_names
-    else:
-        # parse inputs
-        thing_category_names = [
-            thing_category_name.strip()
-            for thing_category_name in thing_category_names_string.split(",")
-        ]
-        stuff_category_names = [
-            stuff_category_name.strip()
-            for stuff_category_name in stuff_category_names_string.split(",")
-        ]
-        category_names = thing_category_names + stuff_category_names
-        category_name_to_id = {
-            category_name: i for i, category_name in enumerate(category_names)
-        }
-
-    image = download_image(image_url)
-    image = image.convert("RGB")
-    image_array = np.asarray(image)
-
-    # compute SAM image embedding
-    sam_predictor.set_image(image_array)
-
-    # detect boxes for "thing" categories using Grounding DINO
-    thing_category_ids = []
-    thing_masks = []
-    thing_boxes = []
-    if len(thing_category_names) > 0:
-        thing_boxes, thing_category_ids, _ = dino_detection(
-            dino_model,
-            image,
-            image_array,
-            thing_category_names,
-            category_name_to_id,
-            dino_box_threshold,
-            dino_text_threshold,
-            device,
-        )
-        if len(thing_boxes) > 0:
-            # get segmentation masks for the thing boxes
-            thing_masks = sam_masks_from_dino_boxes(
-                sam_predictor, image_array, thing_boxes, device
-            )
-    if len(stuff_category_names) > 0:
-        # get rough segmentation masks for "stuff" categories using CLIPSeg
-        clipseg_preds, clipseg_semantic_inds = clipseg_segmentation(
-            clipseg_processor,
-            clipseg_model,
-            image,
-            stuff_category_names,
-            segmentation_background_threshold,
-            device,
-        )
-        # remove things from stuff masks
-        clipseg_semantic_inds_without_things = clipseg_semantic_inds.clone()
-        if len(thing_boxes) > 0:
-            combined_things_mask = torch.any(thing_masks, dim=0)
-            clipseg_semantic_inds_without_things[combined_things_mask[0]] = 0
-        # clip CLIPSeg preds based on non-overlapping semantic segmentation inds (+ optionally shrink the mask of each category)
-        # also returns the relative size of each category
-        clipsed_clipped_preds, relative_sizes = clip_and_shrink_preds(
-            clipseg_semantic_inds_without_things,
-            clipseg_preds,
-            shrink_kernel_size,
-            len(stuff_category_names) + 1,
-        )
-        # get finer segmentation masks for the "stuff" categories using SAM
-        sam_preds = torch.zeros_like(clipsed_clipped_preds)
-        for i in range(clipsed_clipped_preds.shape[0]):
-            clipseg_pred = clipsed_clipped_preds[i]
-            # for each "stuff" category, sample points in the rough segmentation mask
-            num_samples = int(relative_sizes[i] * num_samples_factor)
-            if num_samples == 0:
-                continue
-            points = sample_points_based_on_preds(
-                clipseg_pred.cpu().numpy(), num_samples
-            )
-            if len(points) == 0:
-                continue
-            # use SAM to get mask for points
-            pred = sam_mask_from_points(sam_predictor, image_array, points)
-            sam_preds[i] = pred
-        sam_semantic_inds = preds_to_semantic_inds(
-            sam_preds, segmentation_background_threshold
-        )
-
-    # combine the thing inds and the stuff inds into panoptic inds
-    panoptic_inds = (
-        sam_semantic_inds.clone()
-        if len(stuff_category_names) > 0
-        else torch.zeros(image_array.shape[0], image_array.shape[1], dtype=torch.long)
-    )
-    ind = len(stuff_category_names) + 1
-    for thing_mask in thing_masks:
-        # overlay thing mask on panoptic inds
-        panoptic_inds[thing_mask.squeeze(dim=0)] = ind
-        ind += 1
-
-    segmentation_bitmap, annotations = inds_to_segments_format(
-        panoptic_inds, thing_category_ids, stuff_category_names, category_name_to_id
-    )
-    annotations_json = json.dumps(annotations)
-
-    return segmentation_bitmap, annotations_json
-
-
-config_file = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py"
-ckpt_repo_id = "ShilongLiu/GroundingDINO"
-ckpt_filename = "groundingdino_swint_ogc.pth"
-sam_checkpoint = "./sam_vit_h_4b8939.pth"
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print("Using device:", device)
-
-if device != "cpu":
-    try:
-        from GroundingDINO.groundingdino import _C
-    except:
-        warnings.warn(
-            "Failed to load custom C++ ops. Running on CPU mode Only in groundingdino!"
-        )
-
-# initialize groundingdino model
-dino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filename, device)
-
-# initialize SAM
-sam = build_sam(checkpoint=sam_checkpoint)
-sam.to(device=device)
-sam_predictor = SamPredictor(sam)
-
-clipseg_processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-clipseg_model = CLIPSegForImageSegmentation.from_pretrained(
-    "CIDAS/clipseg-rd64-refined"
-)
-clipseg_model.to(device)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser("Panoptic Segment Anything demo", add_help=True)
-    parser.add_argument("--debug", action="store_true", help="using debug mode")
-    parser.add_argument("--share", action="store_true", help="share the app")
-    args = parser.parse_args()
-
-    print(f"args = {args}")
-
-    block = gr.Blocks(title="Panoptic Segment Anything").queue()
-    with block:
-        with gr.Column():
-            title = gr.Markdown(
-                "# [Panoptic Segment Anything](https://github.com/segments-ai/panoptic-segment-anything)"
-            )
-            description = gr.Markdown(
-                "Demo for zero-shot panoptic segmentation using Segment Anything, Grounding DINO, and CLIPSeg."
-            )
-            with gr.Row():
-                with gr.Column():
-                    input_image_url = gr.Textbox(label="Image URL")
-                    thing_category_names_string = gr.Textbox(
-                        label="Thing categories (i.e. categories with instances), comma-separated",
-                        placeholder="E.g. car, bus, person",
-                    )
-                    stuff_category_names_string = gr.Textbox(
-                        label="Stuff categories (i.e. categories without instances), comma-separated",
-                        placeholder="E.g. sky, road, buildings",
-                    )
-                    run_button = gr.Button(label="Run")
-                    with gr.Accordion("Advanced options", open=False):
-                        box_threshold = gr.Slider(
-                            label="Grounding DINO box threshold",
-                            minimum=0.0,
-                            maximum=1.0,
-                            value=0.3,
-                            step=0.001,
-                        )
-                        text_threshold = gr.Slider(
-                            label="Grounding DINO text threshold",
-                            minimum=0.0,
-                            maximum=1.0,
-                            value=0.25,
-                            step=0.001,
-                        )
-                        segmentation_background_threshold = gr.Slider(
-                            label="Segmentation background threshold (under this threshold, a pixel is considered background)",
-                            minimum=0.0,
-                            maximum=1.0,
-                            value=0.1,
-                            step=0.001,
-                        )
-                        shrink_kernel_size = gr.Slider(
-                            label="Shrink kernel size (how much to shrink the mask before sampling points)",
-                            minimum=0,
-                            maximum=100,
-                            value=20,
-                            step=1,
-                        )
-                        num_samples_factor = gr.Slider(
-                            label="Number of samples factor (how many points to sample in the largest category)",
-                            minimum=0,
-                            maximum=1000,
-                            value=1000,
-                            step=1,
-                        )
-                        task_attributes_json = gr.Textbox(
-                            label="Task attributes JSON",
-                        )
-
-                with gr.Column():
-                    segmentation_bitmap_text = gr.Markdown(
-                        """
-The segmentation bitmap is a 32-bit RGBA png image which contains the segmentation masks.
-The alpha channel is set to 255, and the remaining 24-bit values in the RGB channels correspond to the object ids in the annotations list.
-Unlabeled regions have a value of 0.
-Because of the large dynamic range, these png images may appear black in an image viewer.
-"""
-                    )
-                    segmentation_bitmap = gr.Image(
-                        type="pil", label="Segmentation bitmap"
-                    )
-                    annotations_json = gr.Textbox(
-                        label="Annotations JSON",
-                    )
-
-        run_button.click(
-            fn=generate_panoptic_mask,
-            inputs=[
-                input_image_url,
-                thing_category_names_string,
-                stuff_category_names_string,
-                box_threshold,
-                text_threshold,
-                segmentation_background_threshold,
-                shrink_kernel_size,
-                num_samples_factor,
-                task_attributes_json,
-            ],
-            outputs=[segmentation_bitmap, annotations_json],
-            api_name="segment",
-        )
-
-    block.launch(server_name="0.0.0.0", debug=args.debug, share=args.share)
diff --git a/spaces/shengyi-qian/3DOI/monoarti/detr/matcher.py b/spaces/shengyi-qian/3DOI/monoarti/detr/matcher.py
deleted file mode 100644
index 03534610de29701cc2087604c90f70fcebdd9347..0000000000000000000000000000000000000000
--- a/spaces/shengyi-qian/3DOI/monoarti/detr/matcher.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Modules to compute the matching cost and solve the corresponding LSAP.
-"""
-import torch
-from scipy.optimize import linear_sum_assignment
-from torch import nn
-
-from .box_ops import box_cxcywh_to_xyxy, generalized_box_iou
-
-
-class HungarianMatcher(nn.Module):
-    """This class computes an assignment between the targets and the predictions of the network
-
-    For efficiency reasons, the targets don't include the no_object. Because of this, in general,
-    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
-    while the others are un-matched (and thus treated as non-objects).
-    """
-
-    def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1):
-        """Creates the matcher
-
-        Params:
-            cost_class: This is the relative weight of the classification error in the matching cost
-            cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
-            cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
-        """
-        super().__init__()
-        self.cost_class = cost_class
-        self.cost_bbox = cost_bbox
-        self.cost_giou = cost_giou
-        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0"
-
-    @torch.no_grad()
-    def forward(self, outputs, targets):
-        """ Performs the matching
-
-        Params:
-            outputs: This is a dict that contains at least these entries:
-                 "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
-                 "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
-
-            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
-                 "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
-                           objects in the target) containing the class labels
-                 "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
-
-        Returns:
-            A list of size batch_size, containing tuples of (index_i, index_j) where:
-                - index_i is the indices of the selected predictions (in order)
-                - index_j is the indices of the corresponding selected targets (in order)
-            For each batch element, it holds:
-                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
-        """
-        bs, num_queries = outputs["pred_logits"].shape[:2]
-
-        # We flatten to compute the cost matrices in a batch
-        out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1)  # [batch_size * num_queries, num_classes]
-        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
-
-        # Also concat the target labels and boxes
-        tgt_ids = torch.cat([v["labels"] for v in targets])
-        tgt_bbox = torch.cat([v["boxes"] for v in targets])
-
-        # Compute the classification cost. Contrary to the loss, we don't use the NLL,
-        # but approximate it in 1 - proba[target class].
-        # The 1 is a constant that doesn't change the matching, it can be ommitted.
-        cost_class = -out_prob[:, tgt_ids]
-
-        # Compute the L1 cost between boxes
-        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
-
-        # Compute the giou cost betwen boxes
-        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))
-
-        # Final cost matrix
-        C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
-        C = C.view(bs, num_queries, -1).cpu()
-
-        sizes = [len(v["boxes"]) for v in targets]
-        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
-        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
-
-
-def build_matcher(args):
-    return HungarianMatcher(cost_class=args.set_cost_class, cost_bbox=args.set_cost_bbox, cost_giou=args.set_cost_giou)
diff --git a/spaces/shi-labs/Versatile-Diffusion/lib/__init__.py b/spaces/shi-labs/Versatile-Diffusion/lib/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/modeling/pixel_decoder/ops/modules/__init__.py b/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/modeling/pixel_decoder/ops/modules/__init__.py
deleted file mode 100644
index 6fdbf03359958f3d67ab00f879bf6b61a6c8f06a..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/modeling/pixel_decoder/ops/modules/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-from .ms_deform_attn import MSDeformAttn
diff --git a/spaces/shui45/Real-CUGAN/app.py b/spaces/shui45/Real-CUGAN/app.py
deleted file mode 100644
index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000
--- a/spaces/shui45/Real-CUGAN/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from upcunet_v3 import RealWaifuUpScaler
-import gradio as gr
-import time
-import logging
-import os
-from PIL import ImageOps
-import numpy as np
-import math
-
-
-def greet(input_img, input_model_name, input_tile_mode):
-    # if input_img.size[0] * input_img.size[1] > 256 * 256:
-    #     y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1]))
-    #     x = int(input_img.size[0]/input_img.size[1]*y)
-    #     input_img = ImageOps.fit(input_img, (x, y))
-    input_img = np.array(input_img)
-    if input_model_name not in model_cache:
-        t1 = time.time()
-        upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu")
-        t2 = time.time()
-        logger.info(f'load model time, {t2 - t1}')
-        model_cache[input_model_name] = upscaler
-    else:
-        upscaler = model_cache[input_model_name]
-        logger.info(f'load model from cache')
-
-    start = time.time()
-    result = upscaler(input_img, tile_mode=input_tile_mode)
-    end = time.time()
-    logger.info(f'input_model_name, {input_model_name}')
-    logger.info(f'input_tile_mode, {input_tile_mode}')
-    logger.info(f'input shape, {input_img.shape}')
-    logger.info(f'output shape, {result.shape}')
-    logger.info(f'speed time, {end - start}')
-    return result
-
-
-if __name__ == '__main__':
-    logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s")
-    logger = logging.getLogger()
-
-    ModelPath = "weights_v3/"
-    model_cache = {}
-
-    input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model')
-    input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode')
-    input_img = gr.inputs.Image(label='image', type='pil')
-
-    inputs = [input_img, input_model_name, input_tile_mode]
-    outputs = "image"
-    iface = gr.Interface(fn=greet,
-                         inputs=inputs,
-                         outputs=outputs,
-                         allow_screenshot=False,
-                         allow_flagging='never',
-                         examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]],
-                         article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)<br>'
-                                 '感谢b站开源的项目，图片过大会导致内存不足，所有我将图片裁剪小，想体验大图片的效果请自行前往上面的链接。<br>'
-                                 '修改bbb'
-                                 'The large image will lead to memory limit exceeded. So I crop and resize image. '
-                                 'If you want to experience the large image, please go to the link above.')
-    iface.launch()
diff --git a/spaces/silencewing/server/youyou/.history/math_20230613232100.html b/spaces/silencewing/server/youyou/.history/math_20230613232100.html
deleted file mode 100644
index e37b18eee46f006e590d570ec180bfb6747e25a7..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/.history/math_20230613232100.html
+++ /dev/null
@@ -1,234 +0,0 @@
-<!-- /*
- * @Author: Chauncey Yuan 
- * @Date: 2019-08-01 18:25:30 
- * @Last Modified by:   Chauncey Yuan 
- * @Last Modified time: 2019-08-03 08:24:27 
- */ -->
-
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <meta http-equiv="X-UA-Compatible" content="ie=edge">
-    <title>Document</title>
-    <style>
-        html {
-            height: 100%;
-            background: #b3b4a6;
-        }
-        .cal_game {
-            width: 100vw;
-            margin: 0 auto;
-        }
-        
-        table {
-            margin: 0 auto;
-        }
-        
-        td {
-            width: 150px;
-            height: 50px;
-            text-align: center;
-            line-height: 40px;
-            border: 1px solid #2b7e71;
-        }
-        
-        /* .btn {
-            width: 150px;
-            height: 50px;
-            color: #2b7e71;;
-            border: 1px solid #2b7e71;
-            background-color: transparent;
-        }
-        
-        .btn:hover {
-            background-color: #2b7e71;
-            color: #fff;
-            cursor: pointer;
-        } */
-        
-        input {
-            height: 30px;
-            border: 1px solid #999999;
-            background-color: transparent;
-
-        }
-
-        /* input:hover {
-            border: 3px solid #314834;
-
-        } */
-    </style>
-</head>
-
-<body>
-    <div class="cal_game">
-        <table>
-            <tr>
-                <td id="output" colspan="3"></td>
-            </tr>
-            <tr>
-                <td>题目</td>
-                <td id="eq" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>答案</td>
-                <td colspan="2"><input type="text" name="" id="input" placeholder="请输入计算结果："></td>
-                <!-- <td><button class="btn" onclick="xun()">计算</button></td> -->
-            </tr>
-            <tr>
-                <td>正误</td>
-                <td id="result" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>得分</td>
-                <td id="score" colspan="2"></td>
-            </tr>
-            <tr>
-                <td id="accuracy" colspan="3"></td>
-            </tr>
-        </table>
-    </div>
-    <script>
-        // 定义题号，使用时+1
-        var i = 0;
-        // 定义计算正确的次数
-        var right_times = 0;
-        // 定义分数
-        var score = 0;
-        // 定义正确率
-        var accuracy = 0;
-        // 定义加减法符号数组，用于后边产生0或1的随机数，来确定加减法
-        var sign_operation_list = ["+", "-"];
-        // 生成0或者1的随机数，确定加减法
-        var sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-
-        var max_num = 20;
-        // 显示题号
-        document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-        document.onkeydown = function (e) {
-            if(e.which == "13" && document.getElementById("input").value){
-                xun()
-            }
-            else{
-                document.getElementById("input").focus()
-            }
-        }
-
-        // 如果是减法
-        if (sign_operation == 1) {
-            // 则被减数应该小于减数，结果才不会为负数
-            // 减数范围为0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 被减数范围为0-减数
-            var num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-        }
-        // 如果是加法
-        if (sign_operation == 0) {
-            // 两数和应小于max_num
-            // 第一个数的范围是0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 第二个数是0-（max_num-第一个数）
-            var num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-        }
-        // 组成算式，显示给用户看
-        var eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-        // 页面中显示算式
-        document.getElementById("eq").innerHTML = eq;
-        // console.log(eq);
-
-        // 定义函数，当按钮按下是执行一次
-        function xun() {
-            // console.log(num1, num2);
-            // 题号加1
-            i++;
-            // 获取用户输入的结果
-            var input = Number(document.getElementById("input").value);
-            // console.log(input, num1, num2);
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 定义真实结果
-                var calResult = num1 + num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误：" + String(num1) + sign_operation_list[sign_operation] + String(num2) + "="+ calResult;
-                }
-            } // 如果是减法
-            if (sign_operation == 1) {
-                // 定义真实结果
-                var calResult = num1 - num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误： " + String(num1) + sign_operation_list[sign_operation] + String(num2) + "="+ calResult;
-                }
-            }
-            // 判断结果后，用户输入框清空
-            document.getElementById("input").value = "";
-            // 显示分数
-            document.getElementById("score").innerHTML = score;
-            // 显示正确率
-            document.getElementById("accuracy").innerHTML = (((right_times / i) * 100).toFixed(2)) + "%";
-
-            if (score >= 100) {
-                window.location.href = "game.html";
-            }
-            // 生成0或者1的随机数，确定加减法
-            sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-            // 显示题号
-            document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-            // 如果是减法
-            if (sign_operation == 1) {
-                // 则被减数应该小于减数，结果才不会为负数
-                // 减数范围为0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 被减数范围为0-减数
-                num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-            }
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 两数和应小于max_num
-                // 第一个数的范围是0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 第二个数是0-（max_num-第一个数）
-                num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-            }
-            // 组成算式，显示给用户看
-            eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-            // 页面中显示算式
-            document.getElementById("eq").innerHTML = eq;
-            // console.log(eq);
-        }
-    </script>
-</body>
-
-</html>
diff --git a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py b/spaces/simpie28/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
-            'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
-            'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
-            'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
-            'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
-            'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
-    try:
-        converters[dialect] = opencc.OpenCC(dialect)
-    except:
-        pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
-    dialect = dialects[dialect]
-    text = converters[dialect].convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/simsantonioii/MusicGen-Continuation/CONTRIBUTING.md b/spaces/simsantonioii/MusicGen-Continuation/CONTRIBUTING.md
deleted file mode 100644
index 55b99140204d785d572ada9761dd77f302ae31c6..0000000000000000000000000000000000000000
--- a/spaces/simsantonioii/MusicGen-Continuation/CONTRIBUTING.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# Contributing to Audiocraft
-
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Pull Requests
-
-Audiocraft is the implementation of a research paper.
-Therefore, we do not plan on accepting many pull requests for new features.
-We certainly welcome them for bug fixes.
-
-1. Fork the repo and create your branch from `main`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Meta's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## License
-By contributing to encodec, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/spaces/sparanoid/milky-green-svc/transforms.py b/spaces/sparanoid/milky-green-svc/transforms.py
deleted file mode 100644
index f65bf74ff117a7d6247db0b0462fb2e2a68ab239..0000000000000000000000000000000000000000
--- a/spaces/sparanoid/milky-green-svc/transforms.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import numpy as np
-import torch
-from torch.nn import functional as t_func
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None,
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = t_func.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = t_func.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = t_func.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + t_func.softplus(unnormalized_derivatives)
-
-    heights = t_func.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = t_func.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py
deleted file mode 100644
index 5bf3e51e7a50ac3f07cc41739198cde946dc79aa..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py
+++ /dev/null
@@ -1,40 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import sys
-
-from fairseq.data import Dictionary
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="filters a lexicon given a unit dictionary"
-    )
-    parser.add_argument("-d", "--unit-dict", help="unit dictionary", required=True)
-    return parser
-
-
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    d = Dictionary.load(args.unit_dict)
-    symbols = set(d.symbols)
-
-    for line in sys.stdin:
-        items = line.rstrip().split()
-        skip = len(items) < 2
-        for x in items[1:]:
-            if x not in symbols:
-                skip = True
-                break
-        if not skip:
-            print(line, end="")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/distributed/distributed_timeout_wrapper.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/distributed/distributed_timeout_wrapper.py
deleted file mode 100644
index 18107ef27ea837b8c72dcaa49db18fd8e64267b1..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/distributed/distributed_timeout_wrapper.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import signal
-import threading
-
-from torch import nn
-
-
-logger = logging.getLogger(__name__)
-
-
-class DistributedTimeoutWrapper(nn.Module):
-    """
-    A wrapper that kills the process if no progress is made within a given
-    *timeout*. The timer is reset every time :func:`forward` is called.
-
-    Usage::
-
-        module = DistributedTimeoutWrapper(module, timeout=30)
-        x = module(input)
-        time.sleep(20)  # safe
-        x = module(input)
-        time.sleep(45)  # job will be killed before this returns
-
-    Args:
-        module (nn.Module): module to wrap
-        timeout (int): number of seconds before killing the process
-            (set to a value <= 0 to disable the timeout)
-        signal (Optional): signal to send once timeout is triggered
-    """
-    def __init__(self, module: nn.Module, timeout: int, signal=signal.SIGINT):
-        super().__init__()
-        self.module = module
-        self.timeout = timeout
-        self.signal = signal
-
-        if timeout > 0:
-            self._heartbeat = threading.Event()
-            self._heartbeat_thread = threading.Thread(
-                target=self._check_heartbeat,
-                args=(os.getpid(),),
-                daemon=True,
-            )
-            self._heartbeat_thread.start()
-            self._terminated = False
-        else:
-            self._heartbeat = None
-            self._heartbeat_thread = None
-
-    def __del__(self):
-        self.stop_timeout()
-
-    def __getattr__(self, name):
-        """Forward missing attributes to wrapped module."""
-        try:
-            return super().__getattr__(name)  # defer to nn.Module's logic
-        except AttributeError:
-            return getattr(self.module, name)
-
-    def stop_timeout(self):
-        if self._heartbeat_thread is not None:
-            self._terminated = True
-            self._heartbeat_thread.join()
-
-    def state_dict(self, *args, **kwargs):
-        return self.module.state_dict(*args, **kwargs)
-
-    def load_state_dict(self, *args, **kwargs):
-        return self.module.load_state_dict(*args, **kwargs)
-
-    def forward(self, *args, **kwargs):
-        if self._heartbeat is not None:
-            self._heartbeat.set()
-        return self.module(*args, **kwargs)
-
-    def _check_heartbeat(self, parent_pid):
-        self._heartbeat.wait()  # wait for the first forward pass
-        while True:
-            self._heartbeat.clear()
-            success = self._heartbeat.wait(timeout=self.timeout)
-            if self._terminated:
-                break
-            elif not success:
-                logger.error((
-                    "Killing job for not making progress in {} seconds. "
-                    "Set --heartbeat-timeout=-1 to disable this timeout."
-                ).format(int(self.timeout)))
-                os.kill(parent_pid, self.signal)
-                return
diff --git a/spaces/stomexserde/gpt4-ui/Examples/AutoCAD MEP (2018) X86 32bit Product Key And Xforce Keygen.md b/spaces/stomexserde/gpt4-ui/Examples/AutoCAD MEP (2018) X86 32bit Product Key And Xforce Keygen.md
deleted file mode 100644
index fd6630a09dfb18f2373dfb3c052e575c7885aa05..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/AutoCAD MEP (2018) X86 32bit Product Key And Xforce Keygen.md	
+++ /dev/null
@@ -1,49 +0,0 @@
-<br />
-<h1>How to Install and Activate AutoCAD MEP (2018) X86 32bit with Product Key and Xforce Keygen</h1>
-<p>AutoCAD MEP (2018) is a powerful software for designing mechanical, electrical and plumbing systems in buildings. It allows you to create accurate and detailed drawings, models and documentation for your projects. In this article, we will show you how to install and activate AutoCAD MEP (2018) X86 32bit with product key and Xforce keygen.</p>
-<h2>AutoCAD MEP (2018) X86 32bit Product Key And Xforce Keygen</h2><br /><p><b><b>Download</b> &bull; <a href="https://urlgoal.com/2uI6Uv">https://urlgoal.com/2uI6Uv</a></b></p><br /><br />
-<h2>Step 1: Download AutoCAD MEP (2018) X86 32bit</h2>
-<p>You can download AutoCAD MEP (2018) X86 32bit from the official website of Autodesk or from a trusted third-party source. Make sure you have enough space on your hard drive and a stable internet connection. The file size is about 4.5 GB.</p>
-<h2>Step 2: Extract the downloaded file</h2>
-<p>After downloading the file, you need to extract it using a software like WinRAR or 7-Zip. You will get a folder named "AutoCAD_MEP_2018_English_Win_32bit_dlm". Open the folder and double-click on the file "Setup.exe" to start the installation process.</p>
-<h2>Step 3: Install AutoCAD MEP (2018) X86 32bit</h2>
-<p>Follow the instructions on the screen to install AutoCAD MEP (2018) X86 32bit on your computer. You will need to accept the license agreement, choose the installation type (typical or custom), select the components you want to install, and specify the installation location. The installation may take some time depending on your system configuration.</p>
-<h2>Step 4: Activate AutoCAD MEP (2018) X86 32bit with product key and Xforce keygen</h2>
-<p>After the installation is complete, you need to activate AutoCAD MEP (2018) X86 32bit with product key and Xforce keygen. Here are the steps to do that:</p>
-<ul>
-<li>Run AutoCAD MEP (2018) X86 32bit as administrator.</li>
-<li>Click on "Enter a Serial Number" and then click on "I Agree".</li>
-<li>Enter the product key "235J1" and click on "Next".</li>
-<li>Click on "Request an activation code using an offline method" and then click on "Next".</li>
-<li>Copy the request code that appears on the screen.</li>
-<li>Run the Xforce keygen as administrator. You can find it in the folder "Crack" inside the extracted file.</li>
-<li>Select "AutoCAD MEP 2018" from the drop-down menu and click on "Patch".</li>
-<li>Paste the request code into the keygen and click on "Generate".</li>
-<li>Copy the activation code that appears on the keygen.</li>
-<li>Paste the activation code into AutoCAD MEP (2018) X86 32bit and click on "Next".</li>
-<li>You should see a message saying "Thank you for activating your Autodesk product". Click on "Finish".</li>
-</ul>
-<p>Congratulations! You have successfully installed and activated AutoCAD MEP (2018) X86 32bit with product key and Xforce keygen. Enjoy your software!</p>
-
-<h2>What is AutoCAD MEP (2018) X86 32bit?</h2>
-<p>AutoCAD MEP (2018) X86 32bit is a version of AutoCAD MEP that is compatible with 32-bit operating systems. AutoCAD MEP is a software that helps you design and document mechanical, electrical and plumbing systems for buildings. It is part of the Autodesk Architecture, Engineering and Construction Collection, which also includes other software like Revit, Navisworks and Civil 3D.</p>
-<h2>What are the benefits of using AutoCAD MEP (2018) X86 32bit?</h2>
-<p>AutoCAD MEP (2018) X86 32bit has many benefits for engineers, designers and drafters who work on building projects. Some of the benefits are:</p>
-<ul>
-<li>It allows you to create accurate and detailed drawings, models and documentation for your MEP systems.</li>
-<li>It supports industry standards and codes for MEP design and documentation.</li>
-<li>It integrates with other Autodesk software and cloud services for collaboration and coordination.</li>
-<li>It provides tools and features for design optimization, analysis, simulation and visualization.</li>
-<li>It enhances your productivity and efficiency with automation, customization and interoperability.</li>
-</ul>
-<h2>What are the system requirements for AutoCAD MEP (2018) X86 32bit?</h2>
-<p>Before you install and activate AutoCAD MEP (2018) X86 32bit with product key and Xforce keygen, you need to make sure that your computer meets the minimum system requirements. Here are the system requirements for AutoCAD MEP (2018) X86 32bit:</p>
-<table>
-<tr><th>Operating System</th><th>Memory</th><th>Disk Space</th><th>Processor</th><th>Display</th></tr>
-<tr><td>Windows 7 SP1 (32-bit)</td><td>4 GB</td><td>12 GB</td><td>1 GHz or faster 32-bit (x86) processor</td><td>1360 x 768 (1920 x 1080 recommended) with True Color</td></tr>
-<tr><td>Windows 8.1 (32-bit)</td><td>4 GB</td><td>12 GB</td><td>1 GHz or faster 32-bit (x86) processor</td><td>1360 x 768 (1920 x 1080 recommended) with True Color</td></tr>
-<tr><td>Windows 10 (32-bit)</td><td>4 GB</td><td>12 GB</td><td>1 GHz or faster 32-bit (x86) processor</td><td>1360 x 768 (1920 x 1080 recommended) with True Color</td></tr>
-</table></p>
-<p></p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Dpcimanager For Mac Os X ((BETTER)).md b/spaces/stomexserde/gpt4-ui/Examples/Dpcimanager For Mac Os X ((BETTER)).md
deleted file mode 100644
index 588968363ac3334cc4b46eb011e1fc7be25bc2e4..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Dpcimanager For Mac Os X ((BETTER)).md	
+++ /dev/null
@@ -1,21 +0,0 @@
-
-<h1>How to Use Dpcimanager For Mac Os X to View and Manage PCI Devices</h1>
-<p>Dpcimanager is a simple and useful app for Mac OS X that allows you to view and manage PCI devices attached to your machine. It can also extract ACPI tables, retrieve kexts loaded for each device, flash BIOS ROMs, and more. In this article, we will show you how to use Dpcimanager for Mac OS X and what features it offers.</p>
-<h2>What is Dpcimanager For Mac Os X?</h2>
-<p>Dpcimanager is an open-source app developed by phpdev32 and forked by MuntashirAkon. It lists all of the PCI devices attached to your machine, along with their PCI IDs, vendor name, and device name. It will also retrieve the kext loaded for that device, which is important when troubleshooting. The app has a status tab that gathers all of the most important info about your system into one place. It also has a misc tab that allows you to extract ACPI tables, such as DSDT or SSDT, which are essential for editing and patching your system. Additionally, it has a ROM tab that enables you to flash BIOS ROMs from within the app.</p>
-<h2>Dpcimanager For Mac Os X</h2><br /><p><b><b>Download</b> &#127383; <a href="https://urlgoal.com/2uI8q0">https://urlgoal.com/2uI8q0</a></b></p><br /><br />
-<h2>How to Install Dpcimanager For Mac OS X?</h2>
-<p>You can download the latest binary of Dpcimanager for Mac OS X from the GitHub release section[^3^]. Alternatively, you can download older binaries from the SourceForge project page[^2^]. Once you have downloaded the app, you can simply drag and drop it to your Applications folder and launch it from there. You may need to allow it to run from the Security & Privacy settings if you get a warning message.</p>
-<h2>How to Use Dpcimanager For Mac OS X?</h2>
-<p>When you launch Dpcimanager for Mac OS X, you will see a window with four tabs: PCI List, Status, Misc, and ROM. Here is what each tab does:</p>
-<p></p>
-<ul>
-<li><b>PCI List:</b> This tab shows you all of the PCI devices attached to your machine, along with their PCI IDs, vendor name, device name, class code, subclass code, revision ID, subsystem ID, and kext loaded. You can sort the list by any column by clicking on the header. You can also search for a specific device by typing in the search box. You can right-click on any device to copy its information or submit it anonymously to help with device naming.</li>
-<li><b>Status:</b> This tab shows you various information about your system, such as CPU model, speed, cores, threads, cache size, memory size, speed, type, slots used, serial number, board ID, BIOS version, SMC version, boot arguments, kernel version, OS version, uptime, battery status (if applicable), graphics card model and VRAM size. You can also see the temperature and fan speed of your CPU and GPU (if supported).</li>
-<li><b>Misc:</b> This tab allows you to extract ACPI tables from your system. You can choose which tables you want to extract and where to save them. You can also view the contents of each table in a hex editor or a text editor. You can also use this tab to fix your BIOS automatically or manually by patching certain values.</li>
-<li><b>ROM:</b> This tab enables you to flash BIOS ROMs from within the app. You can choose which ROM file you want to flash and which device you want to flash it to. You can also backup your current ROM before flashing. You should be very careful when using this feature as it may brick your device if done incorrectly.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Dpcimanager for Mac OS X is a handy app for viewing and managing PCI devices on your machine. It can also help you with extracting ACPI tables, retrieving kexts loaded for each device, flashing BIOS ROMs, and more. However, you should be aware that some of these features are not safe to use on a real Mac and are intended for hackintosh users only. Therefore, use this app at your own risk and discretion.</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/subhc/Guess-What-Moves/mask_former/data/datasets/__init__.py b/spaces/subhc/Guess-What-Moves/mask_former/data/datasets/__init__.py
deleted file mode 100644
index 2492d388415250569ac01ae79108a6de05e57528..0000000000000000000000000000000000000000
--- a/spaces/subhc/Guess-What-Moves/mask_former/data/datasets/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from . import (
-    register_ade20k_full,
-    register_ade20k_panoptic,
-    register_coco_stuff_10k,
-    register_mapillary_vistas,
-)
diff --git a/spaces/sunshineatnoon/TextureScraping/libs/data_slic.py b/spaces/sunshineatnoon/TextureScraping/libs/data_slic.py
deleted file mode 100644
index 52b407c25695aa9cf849e6e64282a6a1be84ccc6..0000000000000000000000000000000000000000
--- a/spaces/sunshineatnoon/TextureScraping/libs/data_slic.py
+++ /dev/null
@@ -1,175 +0,0 @@
-"""SLIC dataset
- - Returns an image together with its SLIC segmentation map.
-"""
-import torch
-import torch.utils.data as data
-import torchvision.transforms as transforms
-
-import numpy as np
-from glob import glob
-from PIL import Image
-from skimage.segmentation import slic
-from skimage.color import rgb2lab
-
-from .utils import label2one_hot_torch
-
-class RandomResizedCrop(object):
-    def __init__(self, N, res, scale=(0.5, 1.0)):
-        self.res    = res
-        self.scale  = scale 
-        self.rscale = [np.random.uniform(*scale) for _ in range(N)]
-        self.rcrop  = [(np.random.uniform(0, 1), np.random.uniform(0, 1)) for _ in range(N)]
-
-    def random_crop(self, idx, img):
-        ws, hs = self.rcrop[idx]
-        res1 = int(img.size(-1))
-        res2 = int(self.rscale[idx]*res1)
-        i1 = int(round((res1-res2)*ws))
-        j1 = int(round((res1-res2)*hs))
-
-        return img[:, :, i1:i1+res2, j1:j1+res2]
-
-
-    def __call__(self, indice, image):
-        new_image = []
-        res_tar   = self.res // 4 if image.size(1) > 5 else self.res # View 1 or View 2? 
-        
-        for i, idx in enumerate(indice):
-            img = image[[i]]
-            img = self.random_crop(idx, img)
-            img = F.interpolate(img, res_tar, mode='bilinear', align_corners=False)
-
-            new_image.append(img)
-
-        new_image = torch.cat(new_image)
-        
-        return new_image
-
-class RandomVerticalFlip(object):
-    def __init__(self, N, p=0.5):
-        self.p_ref = p
-        self.plist = np.random.random_sample(N)
-        
-    def __call__(self, indice, image):
-        I = np.nonzero(self.plist[indice] < self.p_ref)[0]
-
-        if len(image.size()) == 3:
-            image_t = image[I].flip([1]) 
-        else:
-            image_t = image[I].flip([2])
-        
-        return torch.stack([image_t[np.where(I==i)[0][0]] if i in I else image[i] for i in range(image.size(0))])
-
-class RandomHorizontalTensorFlip(object):
-    def __init__(self, N, p=0.5):
-        self.p_ref = p
-        self.plist = np.random.random_sample(N)
-
-    def __call__(self, indice, image, is_label=False):
-        I = np.nonzero(self.plist[indice] < self.p_ref)[0]
-        
-        if len(image.size()) == 3:
-            image_t = image[I].flip([2])
-        else:
-            image_t = image[I].flip([3])
-        
-        return torch.stack([image_t[np.where(I==i)[0][0]] if i in I else image[i] for i in range(image.size(0))])
-
-class Dataset(data.Dataset):
-    def __init__(self, data_dir, img_size=256, crop_size=128, test=False, 
-                 sp_num=256, slic = True, lab = False): 
-        super(Dataset, self).__init__()
-        #self.data_list = glob(os.path.join(data_dir, "*.jpg"))
-        ext = ["*.jpg"]
-        dl = []
-        [dl.extend(glob(data_dir + '/**/' + e, recursive=True)) for e in ext]
-        self.data_list = dl
-        self.sp_num = sp_num
-        self.slic = slic
-        self.lab = lab
-        if test:
-            self.transform = transforms.Compose([
-                             transforms.Resize(img_size),
-                             transforms.CenterCrop(crop_size)])
-        else:
-            self.transform = transforms.Compose([
-                             transforms.RandomChoice([
-                                transforms.ColorJitter(brightness=0.05),
-                                transforms.ColorJitter(contrast=0.05),
-                                transforms.ColorJitter(saturation=0.01),
-                                transforms.ColorJitter(hue=0.01)]),
-                             transforms.RandomHorizontalFlip(),
-                             transforms.RandomVerticalFlip(),
-                             transforms.Resize(int(img_size)),
-                             transforms.RandomCrop(crop_size)])
-
-        N = len(self.data_list)
-        self.random_horizontal_flip = RandomHorizontalTensorFlip(N=N)
-        self.random_vertical_flip   = RandomVerticalFlip(N=N)
-        self.random_resized_crop    = RandomResizedCrop(N=N, res=img_size)
-        self.eqv_list = ['random_crop', 'h_flip']
-
-    def transform_eqv(self, indice, image):
-        if 'random_crop' in self.eqv_list:
-            image = self.random_resized_crop(indice, image)
-        if 'h_flip' in self.eqv_list:
-            image = self.random_horizontal_flip(indice, image)
-        if 'v_flip' in self.eqv_list:
-            image = self.random_vertical_flip(indice, image)
-
-        return image
-    
-    def __getitem__(self, index):
-        data_path = self.data_list[index]
-        ori_img = Image.open(data_path)
-        ori_img = self.transform(ori_img)
-        ori_img = np.array(ori_img)
-
-        # compute slic
-        if self.slic:
-            slic_i = slic(ori_img, n_segments=self.sp_num, compactness=10, start_label=0, min_size_factor=0.3)
-            slic_i = torch.from_numpy(slic_i)
-            slic_i[slic_i >= self.sp_num] = self.sp_num - 1
-            oh = label2one_hot_torch(slic_i.unsqueeze(0).unsqueeze(0), C = self.sp_num).squeeze()
-
-        if ori_img.ndim < 3:
-            ori_img = np.expand_dims(ori_img, axis=2).repeat(3, axis = 2)
-        ori_img = ori_img[:, :, :3]
-
-        rets = []
-        if self.lab:
-            lab_img = rgb2lab(ori_img)
-            rets.append(torch.from_numpy(lab_img).float().permute(2, 0, 1))
-
-        ori_img = torch.from_numpy(ori_img).float().permute(2, 0, 1)
-        rets.append(ori_img/255.0)
-
-        if self.slic:
-            rets.append(oh)
-        
-        rets.append(index)
-        
-        return rets
-    
-    def __len__(self):
-        return len(self.data_list)
-
-if __name__ == '__main__':
-    import torchvision.utils as vutils
-    dataset = Dataset('/home/xtli/DATA/texture_data/',
-                      sampled_num=3000)
-    loader_ = torch.utils.data.DataLoader(dataset     = dataset,
-                                         batch_size  = 1,
-                                         shuffle     = True,
-                                         num_workers = 1,
-                                         drop_last   = True)
-    loader = iter(loader_)
-    img, points, pixs = loader.next()
-
-    crop_size = 128
-    canvas = torch.zeros((1, 3, crop_size, crop_size))
-    for i in range(points.shape[-2]):
-        p = (points[0, i] + 1) / 2.0 * (crop_size - 1)
-        canvas[0, :, int(p[0]), int(p[1])] = pixs[0, :, i]
-    vutils.save_image(canvas, 'canvas.png')
-    vutils.save_image(img, 'img.png')
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Windows 8 Pro Build 9200 Iso ((NEW)) Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Windows 8 Pro Build 9200 Iso ((NEW)) Download.md
deleted file mode 100644
index d3ecc9e1f42654cdbc242850b9c229a12df1d149..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Windows 8 Pro Build 9200 Iso ((NEW)) Download.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<h2>windows 8 pro build 9200 iso download</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://cinurl.com/2uEYQ3">https://cinurl.com/2uEYQ3</a></b></p><br /><br />
-
-zsync C:\Users\roger\AppData\Local\Temp\yak3.0.iso 10 C:\Users\roger\AppData\Local\Temp\roger_Mcafee.iso 20
-
-Yak3.0 (currently in testing at the moment) will use 5.5 instead of 6.1. Since my 5.5 was on a different drive I updated the 6.1 ISOs and now I can install 5.5. This way I can have a clean version of 5.5 in the future.
-
-After the installation finished I copied over the 5.5 media and did an initial install. The 5.5 ISOs are quite large, you can tell that from the fact that even after copying them over the DVD/ISO size was still around 4.8 GB. I therefore decided to setup an FTP server, which makes managing these ISOs much easier and faster.
-
-I also figured out how to rename the ISOs properly. After doing some research I came up with a list of tools that I used, you can find it here (check the Utilities section):
-
-I could not find any replacement for NCF-ISO-Burner, sadly. I have been thinking about this for some time now and I am still not 100% sure what is the easiest way to do it.
-
-You can use another tool to remove the Hashchecksum from the ISOs, but this only removes the “Invalid Hashcheck” message and does not remove the hashchecksum which is there for backup purposes. I was thinking about using a free tool for this, but I think this will have to be done manually, one by one. I haven’t had the time to do this, yet.Denise Chua
-
-Denise Chua (born July 10, 1996) is an American-born singer and actress who is best known for her role as the Disney Channel original film film Aladdin, in which she voices Princess Jasmine. Her performance in Aladdin was the second longest running role in the Disney Channel series, after the original actress, Olivia Newton-John, who played Jasmine in the original 1987 film adaptation.
-
-Biography
-
-Chua was born in Washington, D.C. and moved to Los Angeles at the age of five. She has Chinese American parents, with her father being a former Microsoft engineer and her mother being a designer.
-
-She started her acting career at the age of nine 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/demo/analyze/track/get_tracker.py b/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/demo/analyze/track/get_tracker.py
deleted file mode 100644
index b8495b47938dc557e01944b784f803e2b8f3db9e..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/demo/analyze/track/get_tracker.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Demo libs
-import spiga.demo.analyze.track.retinasort.zoo as zoo_rs
-
-zoos = [zoo_rs]
-
-
-def get_tracker(model_name):
-    for zoo in zoos:
-        model = zoo.get_tracker(model_name)
-        if model is not None:
-            return model
-
-    raise NotImplementedError('Tracker name not available')
diff --git a/spaces/svjack/Translate-Chinese-to-English/README.md b/spaces/svjack/Translate-Chinese-to-English/README.md
deleted file mode 100644
index 3eb5bcdee3d058b0b2b639b17ad591c16b2532c6..0000000000000000000000000000000000000000
--- a/spaces/svjack/Translate-Chinese-to-English/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Translate Chinese To English
-emoji: 📚
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/taneemishere/html-code-generation-from-images-with-deep-neural-networks/README.md b/spaces/taneemishere/html-code-generation-from-images-with-deep-neural-networks/README.md
deleted file mode 100644
index bc7dfa98ce04da3b45a206a47a2d4e9f33918f5b..0000000000000000000000000000000000000000
--- a/spaces/taneemishere/html-code-generation-from-images-with-deep-neural-networks/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image to HTML Code Demo
-emoji: 🧑‍💻
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tarun52/sentiment/README.md b/spaces/tarun52/sentiment/README.md
deleted file mode 100644
index a3df93ee97d54002f64dfafde7dbcb6c39db3f55..0000000000000000000000000000000000000000
--- a/spaces/tarun52/sentiment/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sentiment
-emoji: 🐨
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/mm_utils.py b/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/mm_utils.py
deleted file mode 100644
index 645f65fb27555701bca41ec3ba3af0d66e225e24..0000000000000000000000000000000000000000
--- a/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/mm_utils.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from PIL import Image
-from io import BytesIO
-import base64
-
-import torch
-from transformers import StoppingCriteria
-from mplug_owl2.constants import IMAGE_TOKEN_INDEX,DEFAULT_IMAGE_TOKEN
-from icecream import ic
-
-
-def load_image_from_base64(image):
-    return Image.open(BytesIO(base64.b64decode(image)))
-
-
-def expand2square(pil_img, background_color):
-    width, height = pil_img.size
-    if width == height:
-        return pil_img
-    elif width > height:
-        result = Image.new(pil_img.mode, (width, width), background_color)
-        result.paste(pil_img, (0, (width - height) // 2))
-        return result
-    else:
-        result = Image.new(pil_img.mode, (height, height), background_color)
-        result.paste(pil_img, ((height - width) // 2, 0))
-        return result
-
-
-def process_images(images, image_processor, model_cfg):
-    image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
-    new_images = []
-    if image_aspect_ratio == 'pad':
-        for image in images:
-            image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
-            image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-            new_images.append(image)
-    else:
-        return image_processor(images, return_tensors='pt')['pixel_values']
-    if all(x.shape == new_images[0].shape for x in new_images):
-        new_images = torch.stack(new_images, dim=0)
-    return new_images
-
-
-def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
-    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split(DEFAULT_IMAGE_TOKEN)]
-
-    def insert_separator(X, sep):
-        return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
-
-    input_ids = []
-    offset = 0
-    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
-        offset = 1
-        input_ids.append(prompt_chunks[0][0])
-
-    for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
-        input_ids.extend(x[offset:])
-
-    if return_tensors is not None:
-        if return_tensors == 'pt':
-            return torch.tensor(input_ids, dtype=torch.long)
-        raise ValueError(f'Unsupported tensor type: {return_tensors}')
-    return input_ids
-
-
-def get_model_name_from_path(model_path):
-    model_path = model_path.strip("/")
-    model_paths = model_path.split("/")
-    if model_paths[-1].startswith('checkpoint-'):
-        return model_paths[-2] + "_" + model_paths[-1]
-    else:
-        return model_paths[-1]
-
-
-
-
-class KeywordsStoppingCriteria(StoppingCriteria):
-    def __init__(self, keywords, tokenizer, input_ids):
-        self.keywords = keywords
-        self.keyword_ids = []
-        self.max_keyword_len = 0
-        for keyword in keywords:
-            cur_keyword_ids = tokenizer(keyword).input_ids
-            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
-                cur_keyword_ids = cur_keyword_ids[1:]
-            if len(cur_keyword_ids) > self.max_keyword_len:
-                self.max_keyword_len = len(cur_keyword_ids)
-            self.keyword_ids.append(torch.tensor(cur_keyword_ids))
-        self.tokenizer = tokenizer
-        self.start_len = input_ids.shape[1]
-
-    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)"  # TODO
-        offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
-        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
-        for keyword_id in self.keyword_ids:
-            if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
-                return True
-        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
-        for keyword in self.keywords:
-            if keyword in outputs:
-                return True
-        return False
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Descargar Poser 9 Portable.md b/spaces/terfces0erbo/CollegeProjectV2/Descargar Poser 9 Portable.md
deleted file mode 100644
index cb5de3f80528f6e10ef1d5e22cf0bfcaaccd8a35..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Descargar Poser 9 Portable.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Garfield Hindi Dubbed Movie Download [2021].md b/spaces/terfces0erbo/CollegeProjectV2/Garfield Hindi Dubbed Movie Download [2021].md
deleted file mode 100644
index 7cbc7b3871a607a3177426560cb32735949cc8b9..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Garfield Hindi Dubbed Movie Download [2021].md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/theekshana/boardpac_chat_app_test/qaPipeline_functions.py b/spaces/theekshana/boardpac_chat_app_test/qaPipeline_functions.py
deleted file mode 100644
index fa8ea4cb5e3467dd0b314ee6752dbfaea661710d..0000000000000000000000000000000000000000
--- a/spaces/theekshana/boardpac_chat_app_test/qaPipeline_functions.py
+++ /dev/null
@@ -1,278 +0,0 @@
-"""
-Python Backend API to chat with private data  
-
-08/14/2023
-D.M. Theekshana Samaradiwakara
-"""
-
-import os
-import time
-
-from dotenv import load_dotenv
-
-from langchain.chains import RetrievalQA
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-
-from langchain.llms import GPT4All
-from langchain.llms import HuggingFaceHub
-from langchain.chat_models import ChatOpenAI
-
-# from langchain.retrievers._query.base import SelfQueryRetriever
-# from langchain.chains.query_constructor.base import AttributeInfo
-
-# from chromaDb import load_store
-from faissDb import load_FAISS_store
-
-from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
-
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain, ConversationalRetrievalChain
-from conversationBufferWindowMemory import ConversationBufferWindowMemory
-from langchain.memory import ReadOnlySharedMemory
-
-load_dotenv()
-
-#gpt4 all model
-gpt4all_model_path = os.environ.get('GPT4ALL_MODEL_PATH')
-model_n_ctx = os.environ.get('MODEL_N_CTX')
-model_n_batch = int(os.environ.get('MODEL_N_BATCH',8))
-target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
-
-openai_api_key = os.environ.get('OPENAI_API_KEY')
-
-verbose = os.environ.get('VERBOSE')
-
-# activate/deactivate the streaming StdOut callback for LLMs
-callbacks = [StreamingStdOutCallbackHandler()]
-
-memory = ConversationBufferWindowMemory(
-            memory_key="chat_history",
-            input_key="question",
-            return_messages=True,
-            k=3
-        )
-
-readonlymemory = ReadOnlySharedMemory(memory=memory)
-
-
-print("\n\n> Initializing QAPipeline:")
-
-global llm_name
-llm_name = 'None'
-global llm
-llm = 'None'
-
-global dataset_name
-dataset_name = 'None'
-global vectorstore
-vectorstore = 'None'
-
-qa_chain = None
-agent = None
-
-
-def run(query, model, dataset):
-
-    if (llm_name != model) or (dataset_name != dataset) or (qa_chain == None):
-        set_model(model)
-        set_vectorstore(dataset)
-        set_qa_chain()
-
-    # Get the answer from the chain
-    start = time.time()
-    res = qa_chain(query)
-    # answer, docs = res['result'],res['source_documents']
-    end = time.time()
-
-    # Print the result
-    print("\n\n> Question:")
-    print(query)
-    print(f"\n> Answer (took {round(end - start, 2)} s.):")
-    print( res)
-
-    return res
-
-def run_agent(query, model, dataset):
-
-    try:
-        
-        if (llm_name != model) or (dataset_name != dataset) or (agent == None):
-            set_model(model)
-            set_vectorstore(dataset)
-            set_qa_chain_with_agent()
-
-        # Get the answer from the chain
-        start = time.time()
-        res = agent(query)
-        # answer, docs = res['result'],res['source_documents']
-        end = time.time()
-
-        # Print the result
-        print("\n\n> Question:")
-        print(query)
-        print(f"\n> Answer (took {round(end - start, 2)} s.):")
-        print( res)
-
-        return res["output"]
-
-    except Exception as e:
-    # logger.error(f"Answer retrieval failed with {e}")
-        print(f"> QAPipeline run_agent Error : {e}")#, icon=":books:")
-        return 
-
-
-
-def set_model(model_type):
-    if model_type != llm_name:
-        global llm
-        match model_type:
-            case "gpt4all":
-                # llm = GPT4All(model=gpt4all_model_path, n_ctx=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=verbose)
-                llm = GPT4All(model=gpt4all_model_path, max_tokens=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=verbose)
-                # llm = HuggingFaceHub(repo_id="nomic-ai/gpt4all-j", model_kwargs={"temperature":0.001, "max_length":1024})
-            case "google/flan-t5-xxl":
-                llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.001, "max_length":1024})
-            case "tiiuae/falcon-7b-instruct":
-                llm = HuggingFaceHub(repo_id=model_type, model_kwargs={"temperature":0.001, "max_length":1024})
-            case "openai":
-                llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
-            case _default:
-                # raise exception if model_type is not supported
-                raise Exception(f"Model type {model_type} is not supported. Please choose a valid one")
-        # global llm_name       
-        llm_name = model_type
-
-def set_vectorstore( dataset):
-    if dataset != dataset_name:
-        # vectorstore = load_store(dataset)
-        global vectorstore
-        vectorstore = load_FAISS_store()
-        print("\n\n> vectorstore loaded:")
-        dataset_name = dataset
-
-def set_qa_chain():
-    global qa_chain
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever = vectorstore.as_retriever(), 
-        # retriever = vectorstore.as_retriever(search_kwargs={"k": target_source_chunks}
-        return_source_documents= True
-    )
-
-
-def set_qa_chain_with_agent():
-
-    try:
-
-        # Define a custom prompt
-        general_qa_template = (
-            """You can have a general conversation with the users like greetings. 
-            Continue the conversation and only answer questions related to banking sector like financial and legal.
-            If you dont know the answer say you dont know, dont try to makeup answers.
-            Conversation: {chat_history}
-            Question: {question}
-            """
-        )
-
-        general_qa_chain_prompt = PromptTemplate(input_variables=["question", "chat_history"], template=general_qa_template)
-        
-        general_qa_chain = LLMChain(
-            llm=llm, 
-            prompt=general_qa_chain_prompt,
-            verbose=True,
-            memory=readonlymemory,  # use the read-only memory to prevent the tool from modifying the memory
-        )
-
-        general_qa_chain_tool = Tool(
-                name="general qa",
-                func= general_qa_chain.run,
-                description='''useful for when you need to have a general conversation with the users like greetings 
-                or to answer general purpose questions related to banking sector like financial and legal. 
-                Input should be a fully formed question.''',
-                return_direct=True,
-                
-        )
-
-        # Define a custom prompt
-        retrieval_qa_template = (
-            """
-            please answer the question based on the chat history and context with the latest information. 
-            You have provided context information below related to central bank acts published in various years. 
-            The content of a bank act can updated by a bank act from a latest year.
-            If you dont know the answer say you dont know, dont try to makeup answers.
-            Conversation: {chat_history}
-            Context: {context}
-            Question : {question}
-            """
-        )
-        retrieval_qa_chain_prompt = PromptTemplate(
-            input_variables=["question", "context", "chat_history"], 
-            template=retrieval_qa_template
-        )
-        
-        bank_regulations_qa = ConversationalRetrievalChain.from_llm(
-            llm=llm,
-            chain_type="stuff",
-            retriever = vectorstore.as_retriever(), 
-            # retriever = vectorstore.as_retriever(search_kwargs={"k": target_source_chunks}
-            return_source_documents= True,
-            get_chat_history=lambda h : h,
-            combine_docs_chain_kwargs={"prompt": retrieval_qa_chain_prompt},
-            verbose=True,
-            memory=readonlymemory,  # use the read-only memory to prevent the tool from modifying the memory
-        )
-
-        bank_regulations_qa_tool = Tool(
-            name="bank regulations",
-            func= lambda question: bank_regulations_qa({"question": question}),
-            description='''useful for when you need to answer questions about 
-            financial and legal information issued from central bank regarding banks and bank regulations. 
-            Input should be a fully formed question.''',
-            return_direct=True,
-        )
-
-        tools = [
-            bank_regulations_qa_tool,
-            general_qa_chain_tool
-        ]
-
-        prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
-        suffix = """Begin!"
-
-        {chat_history}
-        Question: {question}
-        {agent_scratchpad}"""
-
-        agent_prompt = ZeroShotAgent.create_prompt(
-            tools,
-            prefix=prefix,
-            suffix=suffix,
-            input_variables=["question", "chat_history", "agent_scratchpad"],
-        )
-
-        llm_chain = LLMChain(llm=llm, prompt=agent_prompt)
-
-        zeroShotAgent = ZeroShotAgent(
-            llm_chain=llm_chain, 
-            tools=tools, 
-            verbose=True,
-        )
-
-        agent_chain = AgentExecutor.from_agent_and_tools(
-            agent=zeroShotAgent, 
-            tools=tools, 
-            verbose=True,
-            memory=memory,
-            handle_parsing_errors=True,
-        )
-
-        global agent
-        agent = agent_chain
-
-        print(f"\n> agent_chain created")
-
-    except Exception as e:
-        # logger.error(f"Answer retrieval failed with {e}")
-        print(f"> QAPipeline set_qa_chain_with_agent Error : {e}")#, icon=":books:")
-        return 
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Autocad Highly Compressed 32 Bit The Best Way to Save Space and Time.md b/spaces/tialenAdioni/chat-gpt-api/logs/Autocad Highly Compressed 32 Bit The Best Way to Save Space and Time.md
deleted file mode 100644
index 841eae8315d31d29110a8c704b125b1715e9e84c..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Autocad Highly Compressed 32 Bit The Best Way to Save Space and Time.md	
+++ /dev/null
@@ -1,145 +0,0 @@
-<br />
-<h1>AutoCAD Highly Compressed 32 Bit: A Complete Guide</h1>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Fiat Ecu Scan 3.6 Crack.md b/spaces/tialenAdioni/chat-gpt-api/logs/Fiat Ecu Scan 3.6 Crack.md
deleted file mode 100644
index c83599f23392967d884cb8a45fc7ccbe7e2699e1..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Fiat Ecu Scan 3.6 Crack.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-
-<h1>How to Use Fiat ECUScan 3.6.2 with Final Crack on All OS</h1>
-<p>Fiat ECUScan 3.6.2 is a diagnostic software for Fiat, Alfa and Lancia vehicles. It allows you to perform various diagnostics tasks on the supported vehicles/modules. In order to use the software, you need an interface cable that can connect your car to the computer.</p>
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-<p>However, the software is not free and requires a license key to activate all the features. If you don't have a license key, you can use a crack file that can bypass the activation process and make the software fully functional on all operating systems.</p>
-<p>In this article, we will show you how to download, install and use Fiat ECUScan 3.6.2 with final crack on Windows XP, Windows 7 32-bit and Windows 7 64-bit.</p>
-<h2>Step 1: Download Fiat ECUScan 3.6.2 from the official website</h2>
-<p>The first step is to download the latest version of Fiat ECUScan from the official website[^1^]. You can choose the version that suits your operating system. The file size is about 22 MB and it is a zip archive that contains the setup file and some documents.</p>
-<h2>Step 2: Uninstall any previous version of Fiat ECUScan and clean your registry</h2>
-<p>The next step is to uninstall any previous version of Fiat ECUScan that you may have on your computer. You can use the Windows Control Panel or any third-party uninstaller tool to do this. After uninstalling, you should also clean your registry from any traces of the old software. You can use a registry cleaner tool or manually delete the entries related to Fiat ECUScan.</p>
-<p></p>
-<h2>Step 3: Install Fiat ECUScan 3.6.2 on your computer</h2>
-<p>The third step is to install Fiat ECUScan 3.6.2 on your computer. You can extract the zip archive and run the setup file. Follow the instructions on the screen and choose the destination folder for the installation. The default folder is C:\Program Files\FiatECUScan or C:\Program Files (x86)\FiatECUScan depending on your system architecture.</p>
-<h2>Step 4: Download the final crack file for Fiat ECUScan 3.6.2</h2>
-<p>The fourth step is to download the final crack file for Fiat ECUScan 3.6.2 that can make the software fully functional on all operating systems. The crack file is a modified executable file that replaces the original one in the installation folder. You can download the crack file from here[^1^]. The password for the archive is Made By c4U.</p>
-<h2>Step 5: Replace the original executable file with the cracked one</h2>
-<p>The fifth step is to replace the original executable file with the cracked one in the installation folder. You can copy and paste or drag and drop the cracked file into the same folder where you installed Fiat ECUScan 3.6.2. The name of the file is FiatECUScan.exe and it should overwrite the existing one.</p>
-<h2>Step 6: Create a shortcut for the cracked executable file on your desktop</h2>
-<p>The sixth step is to create a shortcut for the cracked executable file on your desktop for easy access. You can right-click on the cracked file and choose Send to > Desktop (create shortcut). Alternatively, you can create a new shortcut manually and browse for the cracked file as the target.</p>
-<h2>Step 7: Run Fiat ECUScan 3.6.2 from your desktop shortcut and enjoy</h2>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/Total Overdose 2 Pc Game Highly Compressed 10mb.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/Total Overdose 2 Pc Game Highly Compressed 10mb.md
deleted file mode 100644
index 185148cf80f7f58818da3f0892e04a767dc064f1..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/Total Overdose 2 Pc Game Highly Compressed 10mb.md	
+++ /dev/null
@@ -1,132 +0,0 @@
-## Total Overdose 2 Pc Game Highly Compressed 10mb
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-# Total Overdose 2: Tequila Gunrise - A Highly Compressed Action Game for PC
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-
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-- Bullet time: The game allows the player to slow down time and perform stylish moves and kills.
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-| CPU | Pentium 4 or Athlon XP |
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-| --- | --- |
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-| CPU SPEED | 1.5 GHz |
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-| RAM | 256 MB |
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-| OS | Windows 2000/XP |
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-| VIDEO CARD | 32 MB DirectX 9 compatible video card (GeForce3 TI+ / Radeon 8500+) |
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-- Marco Cruz: The protagonist of the game, who is a former DEA agent and the brother of Ramiro and Tommy Cruz from the first game. He is on a quest to avenge his father's death and stop Papa Muerte.
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-- Papa Muerte: The main antagonist of the game, who is a powerful drug lord and the leader of the Muerte Cartel. He is responsible for killing Ernesto Cruz, Marco's father, and for causing chaos in Mexico.
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-- Angel: A female character who is a member of the Muerte Cartel and Papa Muerte's right-hand woman. She is a skilled fighter and a seductive femme fatale.
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-- El Huracan: A character who is a former wrestler and a friend of Marco. He helps Marco in his missions and provides him with weapons and vehicles.
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-- El Mariachi: A character who is a legendary musician and a vigilante. He uses his guitar as a weapon and helps Marco in his fight against Papa Muerte.
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/3D iPhone Backgrounds - How to Choose and Download the Best Ones.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/3D iPhone Backgrounds - How to Choose and Download the Best Ones.md
deleted file mode 100644
index afd9fbe77ad0813b38512bff099dfa3433de0f92..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/3D iPhone Backgrounds - How to Choose and Download the Best Ones.md	
+++ /dev/null
@@ -1,286 +0,0 @@
-
-<h1>iPhone Wallpaper Download 3D: How to Get Amazing Backgrounds for Your Phone</h1>
- <h2>Introduction</h2>
- <p>If you are looking for a way to spice up your iPhone's home screen and lock screen, you might want to try out some 3D wallpapers. These are images that create an illusion of depth, movement, or perspective, making your phone look more dynamic and interesting. In this article, we will show you what 3D wallpapers are, why they are popular, and how to find and download them for your iPhone.</p>
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- <ul>
-<li>Look for websites or apps that have a large and diverse collection of 3D wallpapers, so you can have more options and variety.</li>
-<li>Look for websites or apps that have high-resolution and optimized images, so you can have clear and sharp wallpapers that fit your screen size.</li>
-<li>Look for websites or apps that have positive reviews and ratings from other users, so you can avoid scams, viruses, or low-quality images.</li>
-<li>Look for websites or apps that have easy and fast downloading processes, so you can save time and hassle.</li>
-</ul>
- <h2>Best Sources for 3D iPhone Wallpapers</h2>
- <p>To help you find the best sources for 3D iPhone wallpapers, we have reviewed three of the most popular and reputable ones: WallpaperAccess, iDownloadBlog, and Wallpapers.com. We will compare their pros and cons, and show you how to use them.</p>
- <h3>WallpaperAccess</h3>
- <p><a href="(^1^)">WallpaperAccess</a> is a website that offers a huge collection of free HD wallpapers for various devices, including iPhones. It has a dedicated category for 3D iPhone wallpapers, where you can find over 60 stunning images that range from abstract shapes to realistic objects.</p>
- <h4>Pros and cons of WallpaperAccess</h4>
- <p>Some of the pros of WallpaperAccess are:</p>
- <ul>
-<li>It has a large and diverse selection of 3D wallpapers for different tastes and preferences.</li>
-<li>It has high-quality and high-resolution images that look great on any iPhone model.</li>
-<li>It has a simple and user-friendly interface that makes browsing and downloading easy and fast.</li>
-</ul>
- <p>Some of the cons of WallpaperAccess are:</p>
- <ul>
-<li>It does not have an app version, so you have to use your browser to access it.</li>
-<li>It does not have a preview feature, so you have to download the image first to see how it looks on your phone.</li>
-<li>It does not have a rating or review system, so you cannot see the feedback from other users.</li>
-</ul>
- <h4>How to use WallpaperAccess</h4>
- <p>To use WallpaperAccess, follow these steps:</p>
- <ol>
-<li>Go to <a href="">WallpaperAccess</a> on your browser and click on the 3D iPhone Wallpapers category.</li>
-<li>Browse through the images and choose the one you like. You can also use the search bar or the filters to narrow down your options.</li>
-<li>Click on the image to open it in a new tab. You will see the download button and the resolution options below the image.</li>
-<li>Select the resolution that matches your iPhone model and click on the download button. The image will be saved to your camera roll.</li>
-<li>Go to your camera roll and select the image. Tap on the share icon and choose Use as Wallpaper. You can adjust the position and scale of the image as you like.</li>
-<li>Tap on Set and choose whether you want to apply the wallpaper to your home screen, lock screen, or both. Enjoy your new 3D wallpaper!</li>
-</ol>
- <h3>iDownloadBlog</h3>
- <p><a href="">iDownloadBlog</a> is a website that covers everything related to Apple products, including news, reviews, tips, and wallpapers. It has a weekly feature called Wallpapers of the Week, where it showcases some of the best wallpapers for iPhones and iPads. It also has a section for 3D iPhone wallpapers, where you can find over 40 amazing images that feature geometric shapes, optical illusions, and abstract patterns.</p>
- <h4>Pros and cons of iDownloadBlog</h4>
- <p>Some of the pros of iDownloadBlog are:</p>
- <ul>
-<li>It has original and exclusive wallpapers that you cannot find anywhere else.</li>
-<li>It has creative and unique wallpapers that can challenge your perception and imagination.</li>
-<li>It has a preview feature that lets you see how the wallpaper looks on your phone before downloading it.</li>
-</ul>
- <p>Some of the cons of iDownloadBlog are:</p>
- <ul>
-<li>It has a limited and outdated collection of 3D wallpapers, as it does not update it regularly.</li>
-<li>It has low-resolution and low-quality images that may not fit your screen size or look blurry or pixelated.</li>
-<li>It has a complex and cluttered interface that makes browsing and downloading difficult and slow.</li>
-</ul>
- <h4>How to use iDownloadBlog</h4>
- <p>To use iDownloadBlog, follow these steps:</p>
- <ol>
-<li>Go to <a href="">iDownloadBlog</a> on your browser and click on the Wallpapers tab. Then click on the 3D iPhone Wallpapers section.</li>
-<li>Browse through the images and choose the one you like. You can also use the search bar or the categories to narrow down your options.</li>
-<li>Click on the image to open it in a new tab. You will see a preview of how it looks on your phone below the image.</li>
-<li>If you like how it looks, right-click on the image and choose Save Image As. The image will be saved to your camera roll.</li>
-<li>If you don't like how it looks, go back to the previous tab and choose another image. Repeat steps 3 and 4 until you find the one you want.</li>
-<li>Go to your camera roll and select the image. Tap on the share icon and choose Use as Wallpaper. You can adjust the position and scale of the image as you like.</li>
-<li>Tap on Set and choose whether you want to apply the wallpaper to your home screen, lock screen, or both. Enjoy your new 3D wallpaper!</li>
-</ol>
- <h3>Wallpapers.com</h3>
- <p><a href="">Wallpapers.com</a> is a website that offers a huge collection of free HD wallpapers for various devices, including iPhones. It has a dedicated category for 3D iPhone wallpapers, where you can find over 80 stunning images that range from realistic landscapes to fantasy creatures. It also has a section for live wallpapers, which are animated 3D wallpapers that react to your touch or motion.</p>
- <h4>Pros and cons of Wallpapers.com</h4>
- <p>Some of the pros of Wallpapers.com are:</p>
- <ul>
-<li>It has a large and diverse selection of 3D wallpapers for different tastes and preferences.</li>
-<li>It has high-quality and high-resolution images that look great on any iPhone model.</li>
-<li>It has an app version that lets you access and download wallpapers directly from your phone.</li>
-<li>It has a live wallpaper feature that lets you enjoy interactive and dynamic 3D wallpapers.</li>
-</ul>
- <p>Some of the cons of Wallpapers.com are:</p>
- <ul>
-<li>It requires you to sign up and create an account to use its features.</li>
-<li>It has ads and pop-ups that may interrupt your browsing and downloading experience.</li>
-<li>It has a limited number of live wallpapers, which may not work on older iPhone models.</li>
-</ul>
- <h4>How to use Wallpapers.com</h4>
- <p>To use Wallpapers.com, follow these steps:</p>
- <ol>
-<li>Go to <a href="">Wallpapers.com</a> on your browser or download the app from the App Store. Sign up and create an account if you don't have one already.</li>
-<li>Click on the 3D iPhone Wallpapers category or the Live Wallpapers category, depending on what you want. Browse through the images and choose the one you like. You can also use the search bar or the filters to narrow down your options.</li>
-<li>Click on the image to open it in a new tab. You will see the download button and the resolution options below the image.</li>
-<li>Select the resolution that matches your iPhone model and click on the download button. The image will be saved to your camera roll. If you are using the app, you can also tap on the heart icon to add the image to your favorites.</li>
-<li>Go to your camera roll and select the image. Tap on the share icon and choose Use as Wallpaper. You can adjust the position and scale of the image as you like.</li>
-<li>If you are using a live wallpaper, make sure to choose Live Photo as the wallpaper type. You can also press and hold the screen to see the animation.</li>
-<li>Tap on Set and choose whether you want to apply the wallpaper to your home screen, lock screen, or both. Enjoy your new 3D wallpaper!</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to get amazing 3D wallpapers for your iPhone. We have explained what 3D wallpapers are, why they are popular, and how to find and download them for your iPhone. We have also reviewed three of the best sources for 3D iPhone wallpapers: WallpaperAccess, iDownloadBlog, and Wallpapers.com. We have compared their pros and cons, and shown you how to use them.</p>
- <p>If you want to make your phone look more attractive, fun, and unique, you should definitely try out some 3D wallpapers. They can enhance your visual experience and enjoyment, stimulate your creativity and imagination, and make your phone stand out from the crowd. There are many different styles and themes to choose from, so you can find the one that matches your mood, personality, or preferences.</p>
- <p>So what are you waiting for? Go ahead and download some 3D wallpapers for your iPhone today! You will be amazed by how they can transform your phone's appearance and feel. And don't forget to share this article with your friends and family who might also be interested in 3D wallpapers. They will thank you for it!</p>
- <h3>Frequently Asked Questions</h3>
- <p>Here are some of the most common questions that people ask about 3D iPhone wallpapers:</p>
- <ol>
-<li><b>Are 3D wallpapers safe for my iPhone?</b></li>
-<p>Yes, 3D wallpapers are safe for your iPhone, as long as you download them from reputable sources that do not contain viruses or malware. You should also avoid downloading too many wallpapers at once, as they may take up a lot of storage space or battery power.</p>
-<li><b>Can I create my own 3D wallpapers for my iPhone?</b></li>
-<p>Yes, you can create your own 3D wallpapers for your iPhone, if you have some skills in graphic design or photo editing. You can use software like Photoshop or Illustrator to create 3D images or effects, or use online tools like Canva or Pixlr to edit existing images. You can also use apps like Live Wallpaper Maker or intoLive to turn your photos or videos into live wallpapers.</p>
-<li><b>How do I change or delete a 3D wallpaper on my iPhone?</b></li>
-<p>To change or delete a 3D wallpaper on your iPhone, follow these steps:</p>
- <ol>
-<li>Go to Settings and tap on Wallpaper.</li>
-<li>Tap on Choose a New Wallpaper to select a different image from your camera roll, live photos, or the default wallpapers.</li>
-<li>Tap on the image and adjust the position and scale as you like. You can also choose whether you want to apply the wallpaper to your home screen, lock screen, or both.</li>
-<li>Tap on Set to confirm your choice. Your new wallpaper will be applied.</li>
-<li>To delete a 3D wallpaper from your camera roll, go to Photos and select the image. Tap on the trash icon and confirm that you want to delete it. The image will be removed from your phone.</li>
-</ol>
- <li><b>What are some of the best 3D wallpapers for iPhone?</b></li>
-<p>Some of the best 3D wallpapers for iPhone are:</p>
- <ul>
-<li><a href="">3D Earth</a>: A realistic and stunning image of the Earth from space, with clouds, stars, and sunlight.</li>
-<li><a href="">3D Cube</a>: A colorful and geometric image of a cube that rotates and changes colors.</li>
-<li><a href="">3D Butterfly</a>: A beautiful and delicate image of a butterfly that flutters and glows.</li>
-<li><a href="">3D Skull</a>: A cool and edgy image of a skull that emits fire and smoke.</li>
-<li><a href="">3D Flower</a>: A gorgeous and elegant image of a flower that blooms and sparkles.</li>
-</ul>
- <li><b>How do I make my 3D wallpaper look better on my iPhone?</b></li>
-<p>To make your 3D wallpaper look better on your iPhone, you can try these tips:</p>
- <ul>
-<li>Choose a wallpaper that matches your screen size and resolution, so it does not look stretched or distorted.</li>
-<li>Choose a wallpaper that has a dark or plain background, so it does not clash with your icons or widgets.</li>
-<li>Choose a wallpaper that has a subtle or smooth animation, so it does not drain your battery or distract you.</li>
-<li>Adjust the brightness and contrast of your screen, so you can see the details and colors of your wallpaper better.</li>
-</ul> <p>I have finished writing the article. Here is the final version of the article with HTML formatting:</p>
- <h1>iPhone Wallpaper Download 3D: How to Get Amazing Backgrounds for Your Phone</h1>
- <h2>Introduction</h2>
- <p>If you are looking for a way to spice up your iPhone's home screen and lock screen, you might want to try out some 3D wallpapers. These are images that create an illusion of depth, movement, or perspective, making your phone look more dynamic and interesting. In this article, we will show you what 3D wallpapers are, why they are popular, and how to find and download them for your iPhone.</p>
- <h3>What are 3D wallpapers and why are they popular?</h3>
- <p>3D wallpapers are images that use various techniques to create a sense of three-dimensionality on a flat screen. Some of these techniques include:</p>
- <ul>
-<li>Using shadows, gradients, or textures to create a realistic appearance of objects or surfaces.</li>
-<li>Using perspective, parallax, or animation to create a sense of movement or depth.</li>
-<li>Using optical illusions, geometric shapes, or abstract patterns to create a sense of distortion or confusion.</li>
-</ul>
- <p>3D wallpapers are popular because they can make your phone look more attractive, fun, and unique. They can also match your mood, personality, or preferences, as there are many different styles and themes to choose from. Some of the benefits of using 3D wallpapers are:</p>
- <ul>
-<li>They can make your phone stand out from the crowd.</li>
-<li>They can enhance your visual experience and enjoyment.</li>
-<li>They can stimulate your creativity and imagination.</li>
-</ul>
- <h3>How to find and download 3D wallpapers for iPhone</h3>
- <p>Finding and downloading 3D wallpapers for iPhone is not difficult, as there are many websites and apps that offer them for free or for a small fee. However, not all of them are reliable, safe, or high-quality. Therefore, you need to be careful and selective when choosing your sources. Here are some tips to help you find and download 3D wallpapers for iPhone:</p>
- <ul>
-<li>Look for websites or apps that have a large and diverse collection of 3D wallpapers, so you can have more options and variety.</li>
-<li>Look for websites or apps that have high-resolution and optimized images, so you can have clear and sharp wallpapers that fit your screen size.</li>
-<li>Look for websites or apps that have positive reviews and ratings from other users, so you can avoid scams, viruses, or low-quality images.</li>
-<li>Look for websites or apps that have easy and fast downloading processes, so you can save time and hassle.</li>
-</ul>
- <h2>Best Sources for 3D iPhone Wallpapers</h2>
- <p>To help you find the best sources for 3D iPhone wallpapers, we have reviewed three of the most popular and reputable ones: WallpaperAccess, iDownloadBlog, and Wallpapers.com. We will compare their pros and cons, and show you how to use them.</p>
- <h3>WallpaperAccess</h3>
- <p><a href="">WallpaperAccess</a> is a website that offers a huge collection of free HD wallpapers for various devices, including iPhones. It has a dedicated category for 3D iPhone wallpapers, where you can find over 60 stunning images that range from abstract shapes to realistic objects.</p>
- <h4>Pros and cons of WallpaperAccess</h4>
- <p>Some of the pros of WallpaperAccess are:</p>
- <ul>
-<li>It has a large and diverse selection of 3D wallpapers for different tastes and preferences.</li>
-<li>It has high-quality and high-resolution images that look great on any iPhone model.</li>
-<li>It has a simple and user-friendly interface that makes browsing and downloading easy and fast.</li>
-</ul>
- <p>Some of the cons of WallpaperAccess are:</p>
- <ul>
-<li>It does not have an app version, so you have to use your browser to access it.</li>
-<li>It does not have a preview feature, so you have to download the image first to see how it looks on your phone.</li>
-<li>It does not have a rating or review system, so you cannot see the feedback from other users.</li>
-</ul>
- <h4>How to use WallpaperAccess</h4>
- <p>To use WallpaperAccess, follow these steps:</p>
- <ol>
-<li>Go to <a href="">WallpaperAccess</a> on your browser and click on the 3D iPhone Wallpapers category.</li>
-<li>Browse through the images and choose the one you like. You can also use the search bar or the filters to narrow down your options.</li>
-<li>Click on the image to open it in a new tab. You will see the download button and the resolution options below the image.</li>
-<li>Select the resolution that matches your iPhone model and click on the download button. The image will be saved to your camera roll.</li>
-<li>Go to your camera roll and select the image. Tap on the share icon and choose Use as Wallpaper. You can adjust the position and scale of the image as you like.</li>
-<li>Tap on Set and choose whether you want to apply the wallpaper to your home screen, lock screen, or both. Enjoy your new 3D wallpaper!</li>
-</ol>
- <h3>iDownloadBlog</h3>
- <p><a href="">iDownloadBlog</a> is a website that covers everything related to Apple products, including news, reviews, tips, and wallpapers. It has a weekly feature called Wallpapers of the Week, where it showcases some of the best wallpapers for iPhones and iPads. It also has a section for 3D iPhone wallpapers, where you can find over 40 amazing images that feature geometric shapes, optical illusions, and abstract patterns.</p>
- <h4>Pros and cons of iDownloadBlog</h4>
- <p>Some of the pros of iDownloadBlog are:</p>
- <ul>
-<li>It has original and exclusive wallpapers that you cannot find anywhere else.</li>
-<li>It has creative and unique wallpapers that can challenge your perception and imagination.</li>
-<li>It has a preview feature that lets you see how the wallpaper looks on your phone before downloading it.</li>
-</ul>
- <p>Some of the cons of iDownloadBlog are:</p>
- <ul>
-<li>It has a limited and outdated collection of 3D wallpapers, as it does not update it regularly.</li>
-<li>It has low-resolution and low-quality images that may not fit your screen size or look blurry or pixelated.</li>
-<li>It has a complex and cluttered interface that makes browsing and downloading difficult and slow.</li>
-</ul>
- <h4>How to use iDownloadBlog</h4>
- <p>To use iDownloadBlog, follow these steps:</p>
- <ol>
-<li>Go to <a href="">iDownloadBlog</a> on your browser and click on the Wallpapers tab. Then click on the 3D iPhone Wallpapers section.</li>
-<li>Browse through the images and choose the one you like. You can also use the search bar or the categories to narrow down your options.</li>
-<li>Click on the image to open it in a new tab. You will see a preview of how it looks on your phone below the image.</li>
-<li>If you like how it looks, right-click on the image and choose Save Image As. The image will be saved to your camera roll.</li>
-<li>If you don't like how it looks, go back to the previous tab and choose another image. Repeat steps 3 and 4 until you find the one you want.</li>
-<li>Go to your camera roll and select the image. Tap on the share icon and choose Use as Wallpaper. You can adjust the position and scale of the image as you like.</li>
-<li>Tap on Set and choose whether you want to apply the wallpaper to your home screen, lock screen, or both. Enjoy your new 3D wallpaper!</li>
-</ol>
- <h3>Wallpapers.com</h3>
- <p><a href="">Wallpapers.com</a> is a website that offers a huge collection of free HD wallpapers for various devices, including iPhones. It has a dedicated category for 3D iPhone wallpapers, where you can find over 80 stunning images that range from realistic landscapes to fantasy creatures. It also has a section for live wallpapers, which are animated 3D wallpapers that react to your touch or motion.</p>
- <h4>Pros and cons of Wallpapers.com</h4>
- <p>Some of the pros of Wallpapers.com are:</p>
- <ul>
-<li>It has a large and diverse selection of 3D wallpapers for different tastes and preferences.</li>
-<li>It has high-quality and high-resolution images that look great on any iPhone model.</li>
-<li>It has an app version that lets you access and download wallpapers directly from your phone.</li>
-<li>It has a live wallpaper feature that lets you enjoy interactive and dynamic 3D wallpapers.</li>
-</ul>
- <p>Some of the cons of Wallpapers.com are:</p>
- <ul>
-<li>It requires you to sign up and create an account to use its features.</li>
-<li>It has ads and pop-ups that may interrupt your browsing and downloading experience.</li>
-<li>It has a limited number of live wallpapers, which may not work on older iPhone models.</li>
-</ul>
- <h4>How to use Wallpapers.com</h4>
- <p>To use Wallpapers.com, follow these steps:</p>
- <ol>
-<li>Go to <a href="">Wallpapers.com</ a>) on your browser or download the app from the App Store. Sign up and create an account if you don't have one already.</li>
-<li>Click on the 3D iPhone Wallpapers category or the Live Wallpapers category, depending on what you want. Browse through the images and choose the one you like. You can also use the search bar or the filters to narrow down your options.</li>
-<li>Click on the image to open it in a new tab. You will see the download button and the resolution options below the image.</li>
-<li>Select the resolution that matches your iPhone model and click on the download button. The image will be saved to your camera roll. If you are using the app, you can also tap on the heart icon to add the image to your favorites.</li>
-<li>Go to your camera roll and select the image. Tap on the share icon and choose Use as Wallpaper. You can adjust the position and scale of the image as you like.</li>
-<li>If you are using a live wallpaper, make sure to choose Live Photo as the wallpaper type. You can also press and hold the screen to see the animation.</li>
-<li>Tap on Set and choose whether you want to apply the wallpaper to your home screen, lock screen, or both. Enjoy your new 3D wallpaper!</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Ex4 To Mq4 Decompiler 4.0.432  Updated ((INSTALL)).md b/spaces/tioseFevbu/cartoon-converter/scripts/Ex4 To Mq4 Decompiler 4.0.432  Updated ((INSTALL)).md
deleted file mode 100644
index f2bb1c40a51f8f2a1a51674c31260d16c333be9d..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Ex4 To Mq4 Decompiler 4.0.432  Updated ((INSTALL)).md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<br />
-Here is a possible title and article for your keyword:
-
-<h1>How to Use EX4 to MQ4 Decompiler 4.0.432 to Recover Source Code of MetaTrader EAs</h1>
-
-<p>If you are a forex trader who uses MetaTrader 4 or 5 platform, you may have come across EX4 files. These are executable files that contain the compiled code of expert advisors (EAs), indicators, scripts or libraries. EAs are automated trading systems that can execute trades based on predefined rules and market conditions.</p>
-<h2>ex4 to mq4 decompiler 4.0.432 | updated</h2><br /><p><b><b>Download</b> &mdash; <a href="https://urlcod.com/2uHwtJ">https://urlcod.com/2uHwtJ</a></b></p><br /><br />
-
-<p>However, sometimes you may want to modify, improve or optimize an EA that you have downloaded or purchased from a third-party source. In that case, you will need the source code of the EA, which is usually written in MQL4 or MQL5 language and saved as MQ4 or MQ5 files. Unfortunately, most EAs are distributed without source code, to protect the intellectual property of their developers.</p>
-
-<p>So, how can you get the source code of an EX4 file? One possible way is to use a decompiler. A decompiler is a software tool that can reverse-engineer the compiled code and generate a readable source code in a high-level language. However, decompiling an EX4 file is not an easy task, as it involves complex algorithms and encryption techniques.</p>
-
-<p>One of the most popular decompilers for MetaTrader EAs is EX4 to MQ4 Decompiler 4.0.432. This tool claims to be able to decompile any EX4 file to its corresponding MQ4 file, regardless of the MetaTrader version or build number. It also claims to be able to recover the original variable names, comments and formatting of the source code.</p>
-<p></p>
-
-<p>However, before you use this tool, you should be aware of some important points:</p>
-
-<ul>
-<li>Decompiling an EX4 file without the permission of its developer may violate their copyright and terms of service. You should only use this tool for educational purposes or for your own personal use.</li>
-<li>Decompiling an EX4 file may not guarantee that you will get a fully functional and error-free source code. Some features or logic of the EA may be lost or corrupted during the decompilation process.</li>
-<li>Decompiling an EX4 file may expose you to malware or viruses that may harm your computer or compromise your trading account. You should only download and run this tool from a trusted source and scan it with an antivirus software before using it.</li>
-</ul>
-
-<p>If you still want to use this tool, here are the steps to follow:</p>
-
-<ol>
-<li>Download EX4 to MQ4 Decompiler 4.0.432 from its official website or from a reliable source.</li>
-<li>Extract the zip file and run the exe file as administrator.</li>
-<li>Select the EX4 file that you want to decompile and click on "Decompile".</li>
-<li>Wait for the decompilation process to finish. It may take several minutes depending on the size and complexity of the EX4 file.</li>
-<li>Check the output folder for the generated MQ4 file. You can open it with MetaEditor or any text editor and view or edit the source code.</li>
-</ol>
-
-<p>Congratulations! You have successfully decompiled an EX4 file to an MQ4 file using EX4 to MQ4 Decompiler 4.0.432. You can now use this tool to recover the source code of any MetaTrader EA that you want to learn from or modify.</p> 7196e7f11a<br />
-<br />
-<br />
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diff --git a/spaces/titanito/stablediffusionapi-juggernaut-xl-v5/README.md b/spaces/titanito/stablediffusionapi-juggernaut-xl-v5/README.md
deleted file mode 100644
index 6cc233cf7a94f2831c4cfe6bcf7908267562b5bf..0000000000000000000000000000000000000000
--- a/spaces/titanito/stablediffusionapi-juggernaut-xl-v5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stablediffusionapi Juggernaut Xl V5
-emoji: 🏃
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tomaseo2022/Traductor-Voz-de-Video/app.py b/spaces/tomaseo2022/Traductor-Voz-de-Video/app.py
deleted file mode 100644
index 2919af89eac1f06357d0849bc485a0969e6b43e3..0000000000000000000000000000000000000000
--- a/spaces/tomaseo2022/Traductor-Voz-de-Video/app.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# coding=utf8
-from gtts import gTTS
-import gradio as gr
-import os
-import speech_recognition as sr
-import __init__
-from __init__ import Translator, constants
-from pprint import pprint
-from moviepy.editor import *
-def video_to_translate(file_obj,initial_language,final_language):
-# Insert Local Video File Path
-    videoclip = VideoFileClip(file_obj.name)
-    # Insert Local Audio File Path
-    videoclip.audio.write_audiofile("test.wav",codec='pcm_s16le')
-# initialize the recognizer
-    r = sr.Recognizer()
-
-    if initial_language == "English":
-        lang_in='en-US'
-    elif initial_language == "Italian":
-        lang_in='it-IT'
-    elif initial_language == "Spanish":
-        lang_in='es-MX'
-    elif initial_language == "Russian":
-        lang_in='ru-RU'
-    elif initial_language == "German":
-        lang_in='de-DE'
-    elif initial_language == "Japanese":
-        lang_in='ja-JP'
-    # open the file
-    with sr.AudioFile("test.wav") as source:
-        # listen for the data (load audio to memory)
-        audio_data = r.record(source)
-        # recognize (convert from speech to text)
-        text = r.recognize_google(audio_data, language = lang_in)
-
-    if final_language == "English":
-        lang='en'
-    elif final_language == "Italian":
-        lang='it'
-    elif final_language == "Spanish":
-        lang='es'
-    elif final_language == "Russian":
-        lang='ru'
-    elif final_language == "German":
-        lang='de'
-    elif final_language == "Japanese":
-        lang='ja'               
-    print(lang)
-    # init the Google API translator
-    translator = Translator()
-    translation = translator.translate(text, dest=lang)
-    #translation.text
-    trans=translation.text
-    myobj = gTTS(text=trans, lang=lang, slow=False) 
-    myobj.save("audio.wav") 
-    # loading audio file
-    audioclip = AudioFileClip("audio.wav")
-    
-    # adding audio to the video clip
-    new_audioclip = CompositeAudioClip([audioclip])
-    videoclip.audio = new_audioclip
-    new_video="video_translated_"+lang+".mp4"
-    videoclip.write_videofile(new_video)
-    #return 'audio.wav'
-    return new_video
-
-initial_language = gr.inputs.Dropdown(["English","Italian","Japanese","Russian","Spanish","German"])
-final_language = gr.inputs.Dropdown([ "Russian","Italian","Spanish","German","English","Japanese"])
-
-
-gr.Interface(fn = video_to_translate,
-            inputs = ['file',initial_language,final_language],
-            outputs = 'video', 
-            verbose = True,  
-            css="footer {visibility: hidden}"
-            ).launch()
\ No newline at end of file
diff --git a/spaces/tomofi/ABINet-OCR/main.py b/spaces/tomofi/ABINet-OCR/main.py
deleted file mode 100644
index def41731ed4cbe77051e496caf2b2d37dd95611f..0000000000000000000000000000000000000000
--- a/spaces/tomofi/ABINet-OCR/main.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import argparse
-import logging
-import os
-import random
-
-import torch
-from fastai.callbacks.general_sched import GeneralScheduler, TrainingPhase
-from fastai.distributed import *
-from fastai.vision import *
-from torch.backends import cudnn
-
-from callbacks import DumpPrediction, IterationCallback, TextAccuracy, TopKTextAccuracy
-from dataset import ImageDataset, TextDataset
-from losses import MultiLosses
-from utils import Config, Logger, MyDataParallel, MyConcatDataset
-
-
-def _set_random_seed(seed):
-    if seed is not None:
-        random.seed(seed)
-        torch.manual_seed(seed)
-        cudnn.deterministic = True
-        logging.warning('You have chosen to seed training. '
-                        'This will slow down your training!')
-
-def _get_training_phases(config, n):
-    lr = np.array(config.optimizer_lr)
-    periods = config.optimizer_scheduler_periods
-    sigma = [config.optimizer_scheduler_gamma ** i for i in range(len(periods))]
-    phases = [TrainingPhase(n * periods[i]).schedule_hp('lr', lr * sigma[i])
-                for i in range(len(periods))]
-    return phases
-
-def _get_dataset(ds_type, paths, is_training, config, **kwargs):
-    kwargs.update({
-        'img_h': config.dataset_image_height,
-        'img_w': config.dataset_image_width,
-        'max_length': config.dataset_max_length,
-        'case_sensitive': config.dataset_case_sensitive,
-        'charset_path': config.dataset_charset_path,
-        'data_aug': config.dataset_data_aug,
-        'deteriorate_ratio': config.dataset_deteriorate_ratio,
-        'is_training': is_training,
-        'multiscales': config.dataset_multiscales,
-        'one_hot_y': config.dataset_one_hot_y,
-    })
-    datasets = [ds_type(p, **kwargs) for p in paths]
-    if len(datasets) > 1: return MyConcatDataset(datasets)
-    else: return datasets[0]
-
-
-def _get_language_databaunch(config):
-    kwargs = {
-        'max_length': config.dataset_max_length,
-        'case_sensitive': config.dataset_case_sensitive,
-        'charset_path': config.dataset_charset_path,
-        'smooth_label': config.dataset_smooth_label,
-        'smooth_factor': config.dataset_smooth_factor,
-        'one_hot_y': config.dataset_one_hot_y,
-        'use_sm': config.dataset_use_sm,
-    }
-    train_ds = TextDataset(config.dataset_train_roots[0], is_training=True, **kwargs)
-    valid_ds = TextDataset(config.dataset_test_roots[0], is_training=False, **kwargs)
-    data = DataBunch.create(
-        path=train_ds.path,
-        train_ds=train_ds,
-        valid_ds=valid_ds,
-        bs=config.dataset_train_batch_size,
-        val_bs=config.dataset_test_batch_size,
-        num_workers=config.dataset_num_workers,
-        pin_memory=config.dataset_pin_memory)
-    logging.info(f'{len(data.train_ds)} training items found.')
-    if not data.empty_val:
-        logging.info(f'{len(data.valid_ds)} valid items found.')
-    return data
-
-def _get_databaunch(config):
-    # An awkward way to reduce loadding data time during test
-    if config.global_phase == 'test': config.dataset_train_roots = config.dataset_test_roots
-    train_ds = _get_dataset(ImageDataset, config.dataset_train_roots, True, config)
-    valid_ds = _get_dataset(ImageDataset, config.dataset_test_roots, False, config)
-    data = ImageDataBunch.create(
-        train_ds=train_ds,
-        valid_ds=valid_ds,
-        bs=config.dataset_train_batch_size,
-        val_bs=config.dataset_test_batch_size,
-        num_workers=config.dataset_num_workers,
-        pin_memory=config.dataset_pin_memory).normalize(imagenet_stats)
-    ar_tfm = lambda x: ((x[0], x[1]), x[1])  # auto-regression only for dtd
-    data.add_tfm(ar_tfm)
-
-    logging.info(f'{len(data.train_ds)} training items found.')
-    if not data.empty_val:
-        logging.info(f'{len(data.valid_ds)} valid items found.')
-    
-    return data
-
-def _get_model(config):
-    import importlib
-    names = config.model_name.split('.')
-    module_name, class_name = '.'.join(names[:-1]), names[-1]
-    cls = getattr(importlib.import_module(module_name), class_name)
-    model = cls(config)
-    logging.info(model)
-    return model
-
-
-def _get_learner(config, data, model, local_rank=None):
-    strict = ifnone(config.model_strict, True)
-    if config.global_stage == 'pretrain-language':
-        metrics = [TopKTextAccuracy(
-            k=ifnone(config.model_k, 5),
-            charset_path=config.dataset_charset_path,
-            max_length=config.dataset_max_length + 1,
-            case_sensitive=config.dataset_eval_case_sensisitves,
-            model_eval=config.model_eval)] 
-    else:
-        metrics = [TextAccuracy(
-            charset_path=config.dataset_charset_path,
-            max_length=config.dataset_max_length + 1,
-            case_sensitive=config.dataset_eval_case_sensisitves,
-            model_eval=config.model_eval)]
-    opt_type = getattr(torch.optim, config.optimizer_type)
-    learner = Learner(data, model, silent=True, model_dir='.',
-        true_wd=config.optimizer_true_wd, 
-        wd=config.optimizer_wd,
-        bn_wd=config.optimizer_bn_wd,
-        path=config.global_workdir,
-        metrics=metrics,
-        opt_func=partial(opt_type, **config.optimizer_args or dict()), 
-        loss_func=MultiLosses(one_hot=config.dataset_one_hot_y))
-    learner.split(lambda m: children(m))
-
-    if config.global_phase == 'train':
-        num_replicas = 1 if local_rank is None else torch.distributed.get_world_size()
-        phases = _get_training_phases(config, len(learner.data.train_dl)//num_replicas)
-        learner.callback_fns += [
-            partial(GeneralScheduler, phases=phases),
-            partial(GradientClipping, clip=config.optimizer_clip_grad),
-            partial(IterationCallback, name=config.global_name,
-                    show_iters=config.training_show_iters,
-                    eval_iters=config.training_eval_iters,
-                    save_iters=config.training_save_iters,
-                    start_iters=config.training_start_iters,
-                    stats_iters=config.training_stats_iters)]
-    else:
-        learner.callbacks += [
-            DumpPrediction(learn=learner,
-                    dataset='-'.join([Path(p).name for p in config.dataset_test_roots]),charset_path=config.dataset_charset_path,
-                    model_eval=config.model_eval,
-                    debug=config.global_debug,
-                    image_only=config.global_image_only)]
-
-    learner.rank = local_rank
-    if local_rank is not None:
-        logging.info(f'Set model to distributed with rank {local_rank}.')
-        learner.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(learner.model)
-        learner.model.to(local_rank)
-        learner = learner.to_distributed(local_rank)
-
-    if torch.cuda.device_count() > 1 and local_rank is None:
-        logging.info(f'Use {torch.cuda.device_count()} GPUs.')
-        learner.model = MyDataParallel(learner.model)
-
-    if config.model_checkpoint:
-        if Path(config.model_checkpoint).exists():
-            with open(config.model_checkpoint, 'rb') as f:
-                buffer = io.BytesIO(f.read())
-            learner.load(buffer, strict=strict)
-        else:
-            from distutils.dir_util import copy_tree
-            src = Path('/data/fangsc/model')/config.global_name
-            trg = Path('/output')/config.global_name
-            if src.exists(): copy_tree(str(src), str(trg))
-            learner.load(config.model_checkpoint, strict=strict)
-        logging.info(f'Read model from {config.model_checkpoint}')
-    elif config.global_phase == 'test':
-        learner.load(f'best-{config.global_name}', strict=strict)
-        logging.info(f'Read model from best-{config.global_name}')
-
-    if learner.opt_func.func.__name__ == 'Adadelta':    # fastai bug, fix after 1.0.60
-        learner.fit(epochs=0, lr=config.optimizer_lr)
-        learner.opt.mom = 0.
-
-    return learner
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', type=str, required=True,
-                        help='path to config file')
-    parser.add_argument('--phase', type=str, default=None, choices=['train', 'test'])
-    parser.add_argument('--name', type=str, default=None)
-    parser.add_argument('--checkpoint', type=str, default=None)
-    parser.add_argument('--test_root', type=str, default=None)
-    parser.add_argument("--local_rank", type=int, default=None)
-    parser.add_argument('--debug', action='store_true', default=None)
-    parser.add_argument('--image_only', action='store_true', default=None)
-    parser.add_argument('--model_strict', action='store_false', default=None)
-    parser.add_argument('--model_eval', type=str, default=None, 
-                        choices=['alignment', 'vision', 'language'])
-    args = parser.parse_args()
-    config = Config(args.config)
-    if args.name is not None: config.global_name = args.name
-    if args.phase is not None: config.global_phase = args.phase
-    if args.test_root is not None: config.dataset_test_roots = [args.test_root]
-    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
-    if args.debug is not None: config.global_debug = args.debug
-    if args.image_only is not None: config.global_image_only = args.image_only
-    if args.model_eval is not None: config.model_eval = args.model_eval
-    if args.model_strict is not None: config.model_strict = args.model_strict
-
-    Logger.init(config.global_workdir, config.global_name, config.global_phase)
-    Logger.enable_file()
-    _set_random_seed(config.global_seed)
-    logging.info(config)
-
-    if args.local_rank is not None:
-        logging.info(f'Init distribution training at device {args.local_rank}.')
-        torch.cuda.set_device(args.local_rank)
-        torch.distributed.init_process_group(backend='nccl', init_method='env://')
-
-    logging.info('Construct dataset.')
-    if config.global_stage == 'pretrain-language': data = _get_language_databaunch(config)
-    else: data = _get_databaunch(config)
-
-    logging.info('Construct model.')
-    model = _get_model(config)
-
-    logging.info('Construct learner.')
-    learner = _get_learner(config, data, model, args.local_rank)
-
-    if config.global_phase == 'train':
-        logging.info('Start training.')
-        learner.fit(epochs=config.training_epochs,
-                    lr=config.optimizer_lr)
-    else:
-        logging.info('Start validate')
-        last_metrics = learner.validate()
-        log_str = f'eval loss = {last_metrics[0]:6.3f},  ' \
-                  f'ccr = {last_metrics[1]:6.3f},  cwr = {last_metrics[2]:6.3f},  ' \
-                  f'ted = {last_metrics[3]:6.3f},  ned = {last_metrics[4]:6.0f},  ' \
-                  f'ted/w = {last_metrics[5]:6.3f}, '
-        logging.info(log_str)
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/tomofi/MMOCR/configs/textrecog/sar/sar_r31_parallel_decoder_toy_dataset.py b/spaces/tomofi/MMOCR/configs/textrecog/sar/sar_r31_parallel_decoder_toy_dataset.py
deleted file mode 100644
index 40688d1290080c010beccc271214e5b246b45a32..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/textrecog/sar/sar_r31_parallel_decoder_toy_dataset.py
+++ /dev/null
@@ -1,30 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py', '../../_base_/recog_models/sar.py',
-    '../../_base_/schedules/schedule_adam_step_5e.py',
-    '../../_base_/recog_pipelines/sar_pipeline.py',
-    '../../_base_/recog_datasets/toy_data.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-data = dict(
-    workers_per_gpu=2,
-    samples_per_gpu=8,
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
deleted file mode 100644
index f2cf444d4cd49220ea2e0f7cf25c81b57850a202..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+++ /dev/null
@@ -1,118 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    pretrained='open-mmlab://resnest50',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=64,
-        depth=50,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch'),
-    roi_head=dict(
-        bbox_head=[
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.1, 0.1, 0.2, 0.2]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.05, 0.05, 0.1, 0.1]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.033, 0.033, 0.067, 0.067]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
-        ],
-        mask_head=dict(norm_cfg=norm_cfg)))
-# # use ResNeSt img_norm
-img_norm_cfg = dict(
-    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='LoadAnnotations',
-        with_bbox=True,
-        with_mask=True,
-        poly2mask=False),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_runtime/test_async.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_runtime/test_async.py
deleted file mode 100644
index e9733f61bab0bcc97cccdd5844c7d6da6499e92a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_runtime/test_async.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""Tests for async interface."""
-
-import asyncio
-import os
-import sys
-
-import asynctest
-import mmcv
-import torch
-
-from mmdet.apis import async_inference_detector, init_detector
-
-if sys.version_info >= (3, 7):
-    from mmdet.utils.contextmanagers import concurrent
-
-
-class AsyncTestCase(asynctest.TestCase):
-    use_default_loop = False
-    forbid_get_event_loop = True
-
-    TEST_TIMEOUT = int(os.getenv('ASYNCIO_TEST_TIMEOUT', '30'))
-
-    def _run_test_method(self, method):
-        result = method()
-        if asyncio.iscoroutine(result):
-            self.loop.run_until_complete(
-                asyncio.wait_for(result, timeout=self.TEST_TIMEOUT))
-
-
-class MaskRCNNDetector:
-
-    def __init__(self,
-                 model_config,
-                 checkpoint=None,
-                 streamqueue_size=3,
-                 device='cuda:0'):
-
-        self.streamqueue_size = streamqueue_size
-        self.device = device
-        # build the model and load checkpoint
-        self.model = init_detector(
-            model_config, checkpoint=None, device=self.device)
-        self.streamqueue = None
-
-    async def init(self):
-        self.streamqueue = asyncio.Queue()
-        for _ in range(self.streamqueue_size):
-            stream = torch.cuda.Stream(device=self.device)
-            self.streamqueue.put_nowait(stream)
-
-    if sys.version_info >= (3, 7):
-
-        async def apredict(self, img):
-            if isinstance(img, str):
-                img = mmcv.imread(img)
-            async with concurrent(self.streamqueue):
-                result = await async_inference_detector(self.model, img)
-            return result
-
-
-class AsyncInferenceTestCase(AsyncTestCase):
-
-    if sys.version_info >= (3, 7):
-
-        async def test_simple_inference(self):
-            if not torch.cuda.is_available():
-                import pytest
-
-                pytest.skip('test requires GPU and torch+cuda')
-
-            ori_grad_enabled = torch.is_grad_enabled()
-            root_dir = os.path.dirname(os.path.dirname(__name__))
-            model_config = os.path.join(
-                root_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py')
-            detector = MaskRCNNDetector(model_config)
-            await detector.init()
-            img_path = os.path.join(root_dir, 'demo/demo.jpg')
-            bboxes, _ = await detector.apredict(img_path)
-            self.assertTrue(bboxes)
-            # asy inference detector will hack grad_enabled,
-            # so restore here to avoid it to influence other tests
-            torch.set_grad_enabled(ori_grad_enabled)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/slurm_test.sh b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/slurm_test.sh
deleted file mode 100644
index 6dd67e57442b741fc30f26102eb5afe16139edb1..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/slurm_test.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env bash
-
-set -x
-
-PARTITION=$1
-JOB_NAME=$2
-CONFIG=$3
-CHECKPOINT=$4
-GPUS=${GPUS:-8}
-GPUS_PER_NODE=${GPUS_PER_NODE:-8}
-CPUS_PER_TASK=${CPUS_PER_TASK:-5}
-PY_ARGS=${@:5}
-SRUN_ARGS=${SRUN_ARGS:-""}
-
-PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
-srun -p ${PARTITION} \
-    --job-name=${JOB_NAME} \
-    --gres=gpu:${GPUS_PER_NODE} \
-    --ntasks=${GPUS} \
-    --ntasks-per-node=${GPUS_PER_NODE} \
-    --cpus-per-task=${CPUS_PER_TASK} \
-    --kill-on-bad-exit=1 \
-    ${SRUN_ARGS} \
-    python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS}
diff --git a/spaces/tomzhang1019/ChatGPT/modules/__init__.py b/spaces/tomzhang1019/ChatGPT/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/tridragonevo/chat-gpt-voice-stream/README.md b/spaces/tridragonevo/chat-gpt-voice-stream/README.md
deleted file mode 100644
index f3a4fd48d889dd9732f397f53552637a0818f390..0000000000000000000000000000000000000000
--- a/spaces/tridragonevo/chat-gpt-voice-stream/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: GPT+WolframAlpha+Whisper
-emoji: 👀
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: JavaFXpert/Chat-GPT-LangChain
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/uSerNameDDHL/bingo/src/components/chat-list.tsx b/spaces/uSerNameDDHL/bingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
-  messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
-  if (!messages.length) {
-    return null
-  }
-
-  return (
-    <div className="chat-container relative flex flex-col">
-      {messages.map((message, index) => (
-        <React.Fragment key={index}>
-          <ChatMessage message={message} />
-          {index < messages.length - 1 && (
-            <Separator className="my-2" />
-          )}
-        </React.Fragment>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Adobe Illustrator 2020 Build 24.1.0.369 Crack With Serial Key 2020.md b/spaces/usbethFlerru/sovits-modelsV2/example/Adobe Illustrator 2020 Build 24.1.0.369 Crack With Serial Key 2020.md
deleted file mode 100644
index c02dc87394fb2b1c1c1c6c58d8303298558bc138..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Adobe Illustrator 2020 Build 24.1.0.369 Crack With Serial Key 2020.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Adobe Illustrator 2020 Build 24.1.0.369 Crack With Serial Key 2020</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash; <a href="https://urlcod.com/2uyUlj">https://urlcod.com/2uyUlj</a></b></p><br /><br />
-
-Download Adobe Illustrator - Create resolution graphics and printed materials, and experiment with different shapes or effects until you get the ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Dick Tracy full movie in hindi download The best way to experience the action-packed adventure.md b/spaces/usbethFlerru/sovits-modelsV2/example/Dick Tracy full movie in hindi download The best way to experience the action-packed adventure.md
deleted file mode 100644
index f9989e2dd531d9a297aa45ae4fdd008d33c7ec4e..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Dick Tracy full movie in hindi download The best way to experience the action-packed adventure.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>download 5629 unlimited Movies and videos Download Here.5629 Hd,3gp. mp4 320p and More Videos You Can Download Easyly. tamilrockers and movierulz, tamilgun, filmywap, and pagalworld videos and Movies download.</p>
-<h2>Dick Tracy full movie in hindi download</h2><br /><p><b><b>Download</b> &#10026; <a href="https://urlcod.com/2uyY4c">https://urlcod.com/2uyY4c</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
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diff --git a/spaces/user238921933/stable-diffusion-webui/modules/modelloader.py b/spaces/user238921933/stable-diffusion-webui/modules/modelloader.py
deleted file mode 100644
index fc3f6249f1ccb53c279f3e86d3ea95a4a7d03e50..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/modelloader.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import glob
-import os
-import shutil
-import importlib
-from urllib.parse import urlparse
-
-from basicsr.utils.download_util import load_file_from_url
-from modules import shared
-from modules.upscaler import Upscaler
-from modules.paths import script_path, models_path
-
-
-def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
-    """
-    A one-and done loader to try finding the desired models in specified directories.
-
-    @param download_name: Specify to download from model_url immediately.
-    @param model_url: If no other models are found, this will be downloaded on upscale.
-    @param model_path: The location to store/find models in.
-    @param command_path: A command-line argument to search for models in first.
-    @param ext_filter: An optional list of filename extensions to filter by
-    @return: A list of paths containing the desired model(s)
-    """
-    output = []
-
-    if ext_filter is None:
-        ext_filter = []
-
-    try:
-        places = []
-
-        if command_path is not None and command_path != model_path:
-            pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
-            if os.path.exists(pretrained_path):
-                print(f"Appending path: {pretrained_path}")
-                places.append(pretrained_path)
-            elif os.path.exists(command_path):
-                places.append(command_path)
-
-        places.append(model_path)
-
-        for place in places:
-            if os.path.exists(place):
-                for file in glob.iglob(place + '**/**', recursive=True):
-                    full_path = file
-                    if os.path.isdir(full_path):
-                        continue
-                    if os.path.islink(full_path) and not os.path.exists(full_path):
-                        print(f"Skipping broken symlink: {full_path}")
-                        continue
-                    if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
-                        continue
-                    if len(ext_filter) != 0:
-                        model_name, extension = os.path.splitext(file)
-                        if extension not in ext_filter:
-                            continue
-                    if file not in output:
-                        output.append(full_path)
-
-        if model_url is not None and len(output) == 0:
-            if download_name is not None:
-                dl = load_file_from_url(model_url, model_path, True, download_name)
-                output.append(dl)
-            else:
-                output.append(model_url)
-
-    except Exception:
-        pass
-
-    return output
-
-
-def friendly_name(file: str):
-    if "http" in file:
-        file = urlparse(file).path
-
-    file = os.path.basename(file)
-    model_name, extension = os.path.splitext(file)
-    return model_name
-
-
-def cleanup_models():
-    # This code could probably be more efficient if we used a tuple list or something to store the src/destinations
-    # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
-    # somehow auto-register and just do these things...
-    root_path = script_path
-    src_path = models_path
-    dest_path = os.path.join(models_path, "Stable-diffusion")
-    move_files(src_path, dest_path, ".ckpt")
-    move_files(src_path, dest_path, ".safetensors")
-    src_path = os.path.join(root_path, "ESRGAN")
-    dest_path = os.path.join(models_path, "ESRGAN")
-    move_files(src_path, dest_path)
-    src_path = os.path.join(models_path, "BSRGAN")
-    dest_path = os.path.join(models_path, "ESRGAN")
-    move_files(src_path, dest_path, ".pth")
-    src_path = os.path.join(root_path, "gfpgan")
-    dest_path = os.path.join(models_path, "GFPGAN")
-    move_files(src_path, dest_path)
-    src_path = os.path.join(root_path, "SwinIR")
-    dest_path = os.path.join(models_path, "SwinIR")
-    move_files(src_path, dest_path)
-    src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
-    dest_path = os.path.join(models_path, "LDSR")
-    move_files(src_path, dest_path)
-
-
-def move_files(src_path: str, dest_path: str, ext_filter: str = None):
-    try:
-        if not os.path.exists(dest_path):
-            os.makedirs(dest_path)
-        if os.path.exists(src_path):
-            for file in os.listdir(src_path):
-                fullpath = os.path.join(src_path, file)
-                if os.path.isfile(fullpath):
-                    if ext_filter is not None:
-                        if ext_filter not in file:
-                            continue
-                    print(f"Moving {file} from {src_path} to {dest_path}.")
-                    try:
-                        shutil.move(fullpath, dest_path)
-                    except:
-                        pass
-            if len(os.listdir(src_path)) == 0:
-                print(f"Removing empty folder: {src_path}")
-                shutil.rmtree(src_path, True)
-    except:
-        pass
-
-
-builtin_upscaler_classes = []
-forbidden_upscaler_classes = set()
-
-
-def list_builtin_upscalers():
-    load_upscalers()
-
-    builtin_upscaler_classes.clear()
-    builtin_upscaler_classes.extend(Upscaler.__subclasses__())
-
-
-def forbid_loaded_nonbuiltin_upscalers():
-    for cls in Upscaler.__subclasses__():
-        if cls not in builtin_upscaler_classes:
-            forbidden_upscaler_classes.add(cls)
-
-
-def load_upscalers():
-    # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
-    # so we'll try to import any _model.py files before looking in __subclasses__
-    modules_dir = os.path.join(shared.script_path, "modules")
-    for file in os.listdir(modules_dir):
-        if "_model.py" in file:
-            model_name = file.replace("_model.py", "")
-            full_model = f"modules.{model_name}_model"
-            try:
-                importlib.import_module(full_model)
-            except:
-                pass
-
-    datas = []
-    commandline_options = vars(shared.cmd_opts)
-    for cls in Upscaler.__subclasses__():
-        if cls in forbidden_upscaler_classes:
-            continue
-
-        name = cls.__name__
-        cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
-        scaler = cls(commandline_options.get(cmd_name, None))
-        datas += scaler.scalers
-
-    shared.sd_upscalers = datas
diff --git a/spaces/vedalken/text2Pokemon/service.py b/spaces/vedalken/text2Pokemon/service.py
deleted file mode 100644
index cc7ce4c810c6145001bae7ae1823f0014ff6b6e8..0000000000000000000000000000000000000000
--- a/spaces/vedalken/text2Pokemon/service.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import torch
-from PIL import Image
-from diffusers import StableDiffusionPipeline
-from torch import autocast
-
-pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-pokemon-diffusers", torch_dtype=torch.float16)
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-pipe = pipe.to(device)
-
-
-class Service():
-    def __init__(self):
-        self.scale = 10
-
-    def image_grid(imgs, rows, cols):
-        assert len(imgs) == rows * cols
-        w, h = imgs[0].size
-        grid = Image.new('RGB', size=(cols * w, rows * h))
-        for i, img in enumerate(imgs):
-            grid.paste(img, box=(i % cols * w, i // cols * h))
-        return grid
-
-    def generate(self,
-                 prompt,
-                 n_samples,
-                 rows,
-                 cols):
-        with autocast("device"):
-            images = pipe(n_samples * [prompt], guidance_scale=self.scale).images
-        return self.image_grid(images, rows=rows, cols=cols)
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/__init__.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/__init__.py
deleted file mode 100644
index ac66d3cfe0ea04af45c0f3594bf135841c3812e3..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from .ann_head import ANNHead
-from .apc_head import APCHead
-from .aspp_head import ASPPHead
-from .cc_head import CCHead
-from .da_head import DAHead
-from .dm_head import DMHead
-from .dnl_head import DNLHead
-from .ema_head import EMAHead
-from .enc_head import EncHead
-from .fcn_head import FCNHead
-from .fpn_head import FPNHead
-from .gc_head import GCHead
-from .lraspp_head import LRASPPHead
-from .nl_head import NLHead
-from .ocr_head import OCRHead
-# from .point_head import PointHead
-from .psa_head import PSAHead
-from .psp_head import PSPHead
-from .sep_aspp_head import DepthwiseSeparableASPPHead
-from .sep_fcn_head import DepthwiseSeparableFCNHead
-from .uper_head import UPerHead
-
-__all__ = [
-    'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
-    'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
-    'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
-    'APCHead', 'DMHead', 'LRASPPHead'
-]
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/psp_head.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/psp_head.py
deleted file mode 100644
index b5f1e71c70c3a20f4007c263ec471a87bb214a48..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/psp_head.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-class PPM(nn.ModuleList):
-    """Pooling Pyramid Module used in PSPNet.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module.
-        in_channels (int): Input channels.
-        channels (int): Channels after modules, before conv_seg.
-        conv_cfg (dict|None): Config of conv layers.
-        norm_cfg (dict|None): Config of norm layers.
-        act_cfg (dict): Config of activation layers.
-        align_corners (bool): align_corners argument of F.interpolate.
-    """
-
-    def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,
-                 act_cfg, align_corners):
-        super(PPM, self).__init__()
-        self.pool_scales = pool_scales
-        self.align_corners = align_corners
-        self.in_channels = in_channels
-        self.channels = channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        for pool_scale in pool_scales:
-            self.append(
-                nn.Sequential(
-                    nn.AdaptiveAvgPool2d(pool_scale),
-                    ConvModule(
-                        self.in_channels,
-                        self.channels,
-                        1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg,
-                        act_cfg=self.act_cfg)))
-
-    def forward(self, x):
-        """Forward function."""
-        ppm_outs = []
-        for ppm in self:
-            ppm_out = ppm(x)
-            upsampled_ppm_out = resize(
-                ppm_out,
-                size=x.size()[2:],
-                mode='bilinear',
-                align_corners=self.align_corners)
-            ppm_outs.append(upsampled_ppm_out)
-        return ppm_outs
-
-
-@HEADS.register_module()
-class PSPHead(BaseDecodeHead):
-    """Pyramid Scene Parsing Network.
-
-    This head is the implementation of
-    `PSPNet <https://arxiv.org/abs/1612.01105>`_.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module. Default: (1, 2, 3, 6).
-    """
-
-    def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
-        super(PSPHead, self).__init__(**kwargs)
-        assert isinstance(pool_scales, (list, tuple))
-        self.pool_scales = pool_scales
-        self.psp_modules = PPM(
-            self.pool_scales,
-            self.in_channels,
-            self.channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=self.align_corners)
-        self.bottleneck = ConvModule(
-            self.in_channels + len(pool_scales) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        psp_outs = [x]
-        psp_outs.extend(self.psp_modules(x))
-        psp_outs = torch.cat(psp_outs, dim=1)
-        output = self.bottleneck(psp_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/w0rd-driven/livebook/README.md b/spaces/w0rd-driven/livebook/README.md
deleted file mode 100644
index 7b12495942e63525fa13b91ef4673911e7b3cb26..0000000000000000000000000000000000000000
--- a/spaces/w0rd-driven/livebook/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Livebook
-emoji: 📓
-colorFrom: pink
-colorTo: purple
-sdk: docker
-fullWidth: true
-duplicated_from: livebook-dev/livebook
----
-
-You can install and run [Livebook](https://livebook.dev/) inside a Hugging Face Space. Here's [a tutorial](https://huggingface.co/docs/hub/spaces-sdks-docker-livebook) on how to do that.
\ No newline at end of file
diff --git a/spaces/wasimmadha/entity-extraction/model.py b/spaces/wasimmadha/entity-extraction/model.py
deleted file mode 100644
index caae8c50fcd8e8050f23f1855ed7e5641d540487..0000000000000000000000000000000000000000
--- a/spaces/wasimmadha/entity-extraction/model.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import torch
-import torch.nn as nn
-from transformers import AutoConfig, AutoModel
-
-# ====================================================
-# Model
-# ====================================================
-class CustomModel(nn.Module):
-    def __init__(self, cfg, config_path=None, pretrained=False):
-        super().__init__()
-        self.cfg = cfg
-        
-        if config_path is None:
-            self.config = AutoConfig.from_pretrained(cfg.model, output_hidden_states=True)
-        else:
-            self.config = torch.load(config_path)
-        if pretrained:
-            self.model = AutoModel.from_pretrained(cfg.model, config=self.config)
-        else:
-            self.model = AutoModel.from_config(self.config)
-        self.fc_dropout = nn.Dropout(cfg.fc_dropout)
-        self.fc = nn.Linear(self.config.hidden_size, 1)
-        self._init_weights(self.fc)
-        
-    def _init_weights(self, module):
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        
-    def feature(self, inputs):
-        outputs = self.model(**inputs)
-        last_hidden_states = outputs[0]
-        return last_hidden_states
-
-    def forward(self, inputs):
-        feature = self.feature(inputs)
-        output = self.fc(self.fc_dropout(feature))
-        return output
\ No newline at end of file
diff --git a/spaces/weidacn/deepdanbooru/deepdanbooru/extra/__init__.py b/spaces/weidacn/deepdanbooru/deepdanbooru/extra/__init__.py
deleted file mode 100644
index 667292d49f763e54eb2763a7dd98ad26828bdf3f..0000000000000000000000000000000000000000
--- a/spaces/weidacn/deepdanbooru/deepdanbooru/extra/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import re
-
-
-def atoi(text):
-    return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
-    """
-    alist.sort(key=natural_keys) sorts in human order
-    http://nedbatchelder.com/blog/200712/human_sorting.html
-    (See Toothy's implementation in the comments)
-    """
-    return [atoi(c) for c in re.split(r"(\d+)", text)]
-
-
-def natural_sorted(iterable):
-    return sorted(iterable, key=natural_keys)
diff --git a/spaces/weiren119/AudiogramDigitization/src/digitizer/report_components/grid.py b/spaces/weiren119/AudiogramDigitization/src/digitizer/report_components/grid.py
deleted file mode 100644
index 64a85981f3690b2749801d0bf0729be23ea88915..0000000000000000000000000000000000000000
--- a/spaces/weiren119/AudiogramDigitization/src/digitizer/report_components/grid.py
+++ /dev/null
@@ -1,212 +0,0 @@
-#!/usr/bin/env python3
-"""
-Copyright (c) 2020, Carleton University Biomedical Informatics Collaboratory
-
-This source code is licensed under the MIT license found in the
-LICENSE file in the root directory of this source tree.
-"""
-
-from typing import List
-from PIL import ImageDraw
-from digitizer.report_components.line import Line
-from digitizer.report_components.label import Label
-from digitizer.report_components.symbol import Symbol
-import utils.audiology as Audiology
-from utils.exceptions import InsufficientLinesException
-
-class Grid(object):
-
-    def __init__(self, report, labels, threshold=150):
-        lines = report.detect_lines(threshold=threshold)
-        lines = [line for line in lines if line.is_vertical() or line.is_horizontal()]
-        frequency_labels = [label for label in labels if label.is_frequency()]
-        threshold_labels = [label for label in labels if label.is_threshold()]
-
-        if len(lines) == 0 or \
-            all([line.is_vertical() for line in lines]) \
-            or all([line.is_horizontal() for line in lines]):
-            raise InsufficientLinesException()
-
-        x_lines = [label.find_closest_line(lines) for label in frequency_labels]
-        x_pixels = [line[0].get_x() for line in x_lines]
-        x_frequency = [label.get_value() for label in frequency_labels]
-        self.x_distances = [line[1] for line in x_lines]
-
-        y_lines = [label.find_closest_line(lines) for label in threshold_labels]
-        y_pixels = [line[0].get_y() for line in y_lines]
-        y_threshold = [label.get_value() for label in threshold_labels]
-        self.y_distances = [line[1] for line in y_lines]
-
-        x_points = sorted(list(zip(x_pixels, x_frequency)), key=lambda p: p[0])
-        y_points = sorted(list(zip(y_pixels, y_threshold)), key=lambda p: p[0])
-
-        # Take the first and last points for the octaves (frequencies)
-        o_max = Audiology.frequency_to_octave(x_points[-1][1]) # max octave
-        x_max = x_points[-1][0] # max pixel value
-        o_min = Audiology.frequency_to_octave(x_points[0][1])
-        x_min = x_points[0][0]
-
-        # Take the first and last points for the thresholds
-        t_max = y_points[-1][1] # max threshold
-        y_max = y_points[-1][0] # max pixel value
-        t_min = y_points[0][1]
-        y_min = y_points[0][0]
-
-        if x_min == x_max or y_max == y_min:
-            raise InsufficientLinesException()
-
-        # Derive the forward and reverse mapping functions via simple linear
-        # interpolation using the **OCTAVE SCALE** (which is linear), because
-        # the frequency scale is logarithmic.
-        self.pixel_freq_map = lambda p: Audiology.octave_to_frequency(o_min + (o_max - o_min)*(p - x_min)/(x_max - x_min))
-        self.freq_pixel_map = lambda f: x_min + (Audiology.frequency_to_octave(f) - o_min)*(x_max - x_min)/(o_max - o_min)
-
-        # Linear interpolation can be applied directly to the thresholds,
-        # because the threshold axis is linear.
-        self.pixel_threshold_map = lambda p: t_min + (t_max - t_min)*(p - y_min)/(y_max - y_min)
-        self.threshold_pixel_map = lambda t: y_min + (t - t_min)*(y_max - y_min)/(t_max - t_min)
-
-    def get_x(self, frequency: float) -> int:
-        """Given a frequency value, returns the x coordinate predicted by the
-        grid.
-
-        Parameters
-        ----------
-        frequency : float
-        The frequency value whose x-position on the image is to be determined.
-
-        Returns
-        -------
-        int
-        The x position (in pixels) of the frequency, as predicted by the grid.
-        """
-        return self.freq_pixel_map(frequency)
-
-    def get_frequency(self, symbol: Symbol) -> float:
-        """Returns the frequency of the symbol.
-
-        Parameters
-        ----------
-        symbol : Symbol
-        The symbol whose frequency is to be extracted using the computed grid.
-
-        Returns
-        -------
-        float
-        The frequency value (in Hz).
-        """
-        return self.pixel_freq_map(symbol.get_center()["x"])
-
-
-    def get_snapped_frequency(self, symbol: Symbol, epsilon: float = 0.15) -> float:
-        """Returns the frequency of the symbol, snapped to the nearest
-        commonly recorded frequency (all octaves and select semi-octaves).
-
-        Parameters
-        ----------
-        symbol : Symbol
-        The symbol whose frequency is to be extracted using the computed grid.
-
-        epsilon: float
-        Distance (in octaves) below which the bone threshold is snapped to
-        the nearest frequency as opposed to shifted to the nearest threshold
-        in the direction of the corresponding ear.
-
-        Returns
-        -------
-        int
-        The `snapped-to-the-grid` frequency value (in Hz).
-        """
-        if symbol.conduction == "air":
-            return Audiology.round_frequency(self.pixel_freq_map(symbol.get_center()["x"]))
-        else:
-            return Audiology.round_frequency_bone(self.pixel_freq_map(symbol.get_center()["x"]), symbol.ear, epsilon=epsilon)
-
-
-    def get_y(self, threshold: float) -> int:
-        """Given a threshold value, returns the y coordinate predicted by the
-        grid.
-
-        Parameters
-        ----------
-        threshold : float
-        The threshold value whose y-position on the image is to be determined.
-
-        Returns
-        -------
-        int
-        The y position (in pixels) of the threshold, as predicted by the grid.
-        """
-        return self.threshold_pixel_map(threshold)
-
-
-    def get_threshold(self, symbol: Symbol) -> int:
-        """Returns the threshold of the symbol.
-
-        Parameters
-        ----------
-        symbol : Symbol
-        The symbol whose threshold is to be extracted using the computed grid.
-
-        Returns
-        -------
-        int
-        The  threshold value.
-        """
-        return self.pixel_threshold_map(symbol.get_center()["y"])
-
-
-    def get_snapped_threshold(self, symbol: Symbol) -> int:
-        """Returns the threshold of the symbol, snapped to the nearest 5dB.
-
-        Parameters
-        ----------
-        symbol : Symbol
-        The symbol whose threshold is to be extracted using the computed grid.
-
-        Returns
-        -------
-        int
-        The `snapped-to-the-grid` threshold value.
-        """
-        return Audiology.round_threshold(self.pixel_threshold_map(symbol.get_center()["y"]))
-
-    def draw(
-        self,
-        image_drawer: ImageDraw,
-        frequency_range: List[int] = [125, 8000],
-        threshold_range:  List[int] = [-10, 120],
-        color: str = "rgb(255,0,0)"
-    ):
-        """Draws the calculated grid on the provided image.
-
-        Parameters
-        ----------
-        image : PIL.ImageDraw
-        The `ImageDraw` object with which the grid is to be drawn.
-
-        frequency_range : [int, int]
-        The minimum and maximum value of the frequencies to be included
-        (default: [250, 8000]).
-
-        threshold_range : [int, int]
-        The minimum and maximum value of the threshold to be included
-        (default: [-10, 120]).
-
-        color: str
-        Color of the grid as a string of the form =`rgb(R,G,B)`.
-        """
-        lines = []
-        for freq in Audiology.OCTAVE_FREQS_HZ:
-            x = self.get_x(freq)
-            y1 = self.get_y(threshold_range[0])
-            y2 = self.get_y(threshold_range[1])
-            line = Line(x, y1, x, y2, color=color, label=freq)
-            line.draw(image_drawer)
-
-        for threshold in Audiology.THRESHOLDS:
-            x1 = self.get_x(frequency_range[0])
-            x2 = self.get_x(frequency_range[1])
-            y = self.get_y(threshold)
-            line = Line(x1, y, x2, y, color=color, label=threshold)
-            line.draw(image_drawer)
diff --git a/spaces/weizmannscience/multidiffusion-region-based/app.py b/spaces/weizmannscience/multidiffusion-region-based/app.py
deleted file mode 100644
index 7918b4de553d8c5fa1962be11ea1ed72226fd3db..0000000000000000000000000000000000000000
--- a/spaces/weizmannscience/multidiffusion-region-based/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import gradio as gr
-import numpy as np
-import cv2
-from PIL import Image
-import torch
-import base64
-import requests 
-import random
-import os
-from io import BytesIO
-from region_control import MultiDiffusion, get_views, preprocess_mask, seed_everything
-from sketch_helper import get_high_freq_colors, color_quantization, create_binary_matrix
-MAX_COLORS = 12
-
-sd = MultiDiffusion("cuda", "2.1")
-is_shared_ui = True if "weizmannscience/multidiffusion-region-based" in os.environ['SPACE_ID'] else False
-is_gpu_associated = True if torch.cuda.is_available() else False
-canvas_html = "<div id='canvas-root' style='max-width:400px; margin: 0 auto'></div>"
-load_js = """
-async () => {
-const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/sketch-canvas.js"
-fetch(url)
-  .then(res => res.text())
-  .then(text => {
-    const script = document.createElement('script');
-    script.type = "module"
-    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
-    document.head.appendChild(script);
-  });
-}
-"""
-
-get_js_colors = """
-async (canvasData) => {
-  const canvasEl = document.getElementById("canvas-root");
-  return [canvasEl._data]
-}
-"""
-
-set_canvas_size ="""
-async (aspect) => {
-  if(aspect ==='square'){
-    _updateCanvas(512,512)
-  }
-  if(aspect ==='horizontal'){
-    _updateCanvas(768,512)
-  }
-  if(aspect ==='vertical'){
-    _updateCanvas(512,768)
-  }
-}
-"""
-
-def process_sketch(canvas_data, binary_matrixes):
-  binary_matrixes.clear()
-  base64_img = canvas_data['image']
-  image_data = base64.b64decode(base64_img.split(',')[1])
-  image = Image.open(BytesIO(image_data)).convert("RGB")
-  im2arr = np.array(image)
-  colors = [tuple(map(int, rgb[4:-1].split(','))) for rgb in canvas_data['colors']]
-  colors_fixed = []
-  for color in colors:
-    r, g, b = color
-    if any(c != 255 for c in (r, g, b)):
-      binary_matrix = create_binary_matrix(im2arr, (r,g,b))
-      binary_matrixes.append(binary_matrix)
-      colors_fixed.append(gr.update(value=f'<div style="display:flex;align-items: center;justify-content: center"><img width="20%" style="margin-right: 1em" src="file/{binary_matrix}" /><div class="color-bg-item" style="background-color: rgb({r},{g},{b})"></div></div>'))
-  visibilities = []
-  colors = []
-  for n in range(MAX_COLORS):
-    visibilities.append(gr.update(visible=False))
-    colors.append(gr.update(value=f'<div class="color-bg-item" style="background-color: black"></div>'))
-  for n in range(len(colors_fixed)):
-    visibilities[n] = gr.update(visible=True)
-    colors[n] = colors_fixed[n]
-  return [gr.update(visible=True), binary_matrixes, *visibilities, *colors]
-
-def process_generation(model, binary_matrixes, boostrapping, aspect, steps, seed, master_prompt, negative_prompt, *prompts):
-    global sd
-    if(model != "stabilityai/stable-diffusion-2-1-base"):
-      sd = MultiDiffusion("cuda", model)
-    if(seed == -1):
-      seed = random.randint(1, 2147483647)
-    seed_everything(seed)   
-    dimensions = {"square": (512, 512), "horizontal": (768, 512), "vertical": (512, 768)}
-    width, height = dimensions.get(aspect, dimensions["square"])  
-    
-    clipped_prompts = prompts[:len(binary_matrixes)]
-    prompts = [master_prompt] + list(clipped_prompts)
-    neg_prompts = [negative_prompt] * len(prompts)
-    fg_masks = torch.cat([preprocess_mask(mask_path, height // 8, width // 8, "cuda") for mask_path in binary_matrixes])
-    bg_mask = 1 - torch.sum(fg_masks, dim=0, keepdim=True)
-    bg_mask[bg_mask < 0] = 0
-    masks = torch.cat([bg_mask, fg_masks])
-    print(masks.size())
-    image = sd.generate(masks, prompts, neg_prompts, height, width, steps, bootstrapping=boostrapping)
-    return(image)
-
-css = '''
-#color-bg{display:flex;justify-content: center;align-items: center;}
-.color-bg-item{width: 100%; height: 32px}
-#main_button{width:100%}
-<style>
-
-'''
-
-with gr.Blocks(css=css) as demo:
-  binary_matrixes = gr.State([])
-  gr.Markdown('''## Control your Stable Diffusion generation with Sketches (_beta_)
-  A beta version demo of [MultiDiffusion](https://arxiv.org/abs/2302.08113) region-based generation using Stable Diffusion 2.1 model. To get started, draw your masks and type your prompts. More details in the [project page](https://multidiffusion.github.io).
-  ''')
-
-  if(is_shared_ui):
-    gr.HTML(f'''
-           <div style="margin-top:-20px">To skip the queue or try the technique with custom models, you may duplicate the space and associate an A10 GPU to it &nbsp;&nbsp;<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></div>
-    ''')
-  elif(not is_gpu_associated):
-      gr.HTML(f'''
-           <div>You have succesfully duplicated the Space 🎉, but it is running on CPU - which may break this application. Go to the <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">settings</a> page to associate a GPU to it</div>
-      ''')
-  with gr.Row():
-    with gr.Box(elem_id="main-image"):
-      canvas_data = gr.JSON(value={}, visible=False)
-      model = gr.Textbox(label="The id of any Hugging Face model in the diffusers format", value="stabilityai/stable-diffusion-2-1-base", visible=False if is_shared_ui else True)
-      canvas = gr.HTML(canvas_html)
-      aspect = gr.Radio(["square", "horizontal", "vertical"], value="square", label="Aspect Ratio", visible=False if is_shared_ui else True)
-      button_run = gr.Button("I've finished my sketch",elem_id="main_button", interactive=True)
-      
-      prompts = []
-      colors = []
-      color_row = [None] * MAX_COLORS
-      with gr.Column(visible=False) as post_sketch:
-        general_prompt = gr.Textbox(label="General Prompt")
-        for n in range(MAX_COLORS):
-          with gr.Row(visible=False) as color_row[n]:
-            with gr.Box(elem_id="color-bg"):
-              colors.append(gr.HTML('<div class="color-bg-item" style="background-color: black"></div>'))
-            prompts.append(gr.Textbox(label="Prompt for this mask"))
-        with gr.Accordion("Advanced options", open=False):
-          negative_prompt = gr.Textbox(label="Global negative prompt for all prompts", value="low quality")
-          boostrapping = gr.Slider(label="Bootstrapping", minimum=1, maximum=100, value=10, step=1)
-          steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=50, step=1)
-          seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, value=-1, step=1)
-        final_run_btn = gr.Button("Generate!")
-    
-    out_image = gr.Image(label="Result", ).style(width=512,height=512)
-  gr.Markdown('''
-  ![Examples](https://multidiffusion.github.io/pics/tight.jpg)
-  ''')
-  #css_height = gr.HTML("<style>#main-image{width: 512px} .fixed-height{height: 512px !important}</style>")
-  aspect.change(None, inputs=[aspect], outputs=None, _js = set_canvas_size)
-  button_run.click(process_sketch, inputs=[canvas_data, binary_matrixes], outputs=[post_sketch, binary_matrixes, *color_row, *colors], _js=get_js_colors, queue=False)
-  final_run_btn.click(process_generation, inputs=[model, binary_matrixes, boostrapping, aspect, steps, seed, general_prompt, negative_prompt, *prompts], outputs=out_image)
-  demo.load(None, None, None, _js=load_js)
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/write_code_review.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/write_code_review.py
deleted file mode 100644
index 7f6a7a38e6a1ed81614364e3deaac37b7dc1f1a9..0000000000000000000000000000000000000000
--- a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/write_code_review.py
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 17:45
-@Author  : alexanderwu
-@File    : write_code_review.py
-"""
-
-from metagpt.actions.action import Action
-from metagpt.logs import logger
-from metagpt.schema import Message
-from metagpt.utils.common import CodeParser
-from tenacity import retry, stop_after_attempt, wait_fixed
-
-PROMPT_TEMPLATE = """
-NOTICE
-Role: You are a professional software engineer, and your main task is to review the code. You need to ensure that the code conforms to the PEP8 standards, is elegantly designed and modularized, easy to read and maintain, and is written in Python 3.9 (or in another programming language).
-ATTENTION: Use '##' to SPLIT SECTIONS, not '#'. Output format carefully referenced "Format example".
-
-## Code Review: Based on the following context and code, and following the check list, Provide key, clear, concise, and specific code modification suggestions, up to 5.
-```
-1. Check 0: Is the code implemented as per the requirements?
-2. Check 1: Are there any issues with the code logic?
-3. Check 2: Does the existing code follow the "Data structures and interface definitions"?
-4. Check 3: Is there a function in the code that is omitted or not fully implemented that needs to be implemented?
-5. Check 4: Does the code have unnecessary or lack dependencies?
-```
-
-## Rewrite Code: {filename} Base on "Code Review" and the source code, rewrite code with triple quotes. Do your utmost to optimize THIS SINGLE FILE. 
------
-# Context
-{context}
-
-## Code: {filename}
-```
-{code}
-```
------
-
-## Format example
------
-{format_example}
------
-
-"""
-
-FORMAT_EXAMPLE = """
-
-## Code Review
-1. The code ...
-2. ...
-3. ...
-4. ...
-5. ...
-
-## Rewrite Code: {filename}
-```python
-## {filename}
-...
-```
-"""
-
-
-class WriteCodeReview(Action):
-    def __init__(self, name="WriteCodeReview", context: list[Message] = None, llm=None):
-        super().__init__(name, context, llm)
-
-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(1))
-    async def write_code(self, prompt):
-        code_rsp = await self._aask(prompt)
-        code = CodeParser.parse_code(block="", text=code_rsp)
-        return code
-
-    async def run(self, context, code, filename):
-        format_example = FORMAT_EXAMPLE.format(filename=filename)
-        prompt = PROMPT_TEMPLATE.format(context=context, code=code, filename=filename, format_example=format_example)
-        logger.info(f'Code review {filename}..')
-        code = await self.write_code(prompt)
-        # code_rsp = await self._aask_v1(prompt, "code_rsp", OUTPUT_MAPPING)
-        # self._save(context, filename, code)
-        return code
diff --git a/spaces/whgwd2023/bingo/src/lib/hooks/use-at-bottom.tsx b/spaces/whgwd2023/bingo/src/lib/hooks/use-at-bottom.tsx
deleted file mode 100644
index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/lib/hooks/use-at-bottom.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
-  const [isAtBottom, setIsAtBottom] = React.useState(false)
-
-  React.useEffect(() => {
-    const handleScroll = () => {
-      setIsAtBottom(
-        window.innerHeight + window.scrollY >=
-          document.body.offsetHeight - offset
-      )
-    }
-
-    window.addEventListener('scroll', handleScroll, { passive: true })
-    handleScroll()
-
-    return () => {
-      window.removeEventListener('scroll', handleScroll)
-    }
-  }, [offset])
-
-  return isAtBottom
-}
diff --git a/spaces/whitead/paper-qa/README.md b/spaces/whitead/paper-qa/README.md
deleted file mode 100644
index a6f88b8efb0b87f5deae3d3b7ef6e107f8f941cc..0000000000000000000000000000000000000000
--- a/spaces/whitead/paper-qa/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: Paper Qa
-emoji: ❓
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: true
-license: mit
----
-
-# Paper QA
-
-This tool will enable asking questions of your uploaded text or PDF documents.
-It uses OpenAI's GPT models and thus you must enter your API key below. This
-tool is under active development and currently uses many tokens - up to 10,000
-for a single query. That is $0.10-0.20 per query, so please be careful!
-
-* [PaperQA](https://github.com/whitead/paper-qa) is the code used to build this tool.
-* [langchain](https://github.com/hwchase17/langchain) is the main library this tool utilizes.
diff --git a/spaces/wynb1314/bingAI/Dockerfile b/spaces/wynb1314/bingAI/Dockerfile
deleted file mode 100644
index 3698c7cb7938e025afc53b18a571ae2961fbdffe..0000000000000000000000000000000000000000
--- a/spaces/wynb1314/bingAI/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/xp3857/Image_Restoration_Colorization/Global/models/pix2pixHD_model_DA.py b/spaces/xp3857/Image_Restoration_Colorization/Global/models/pix2pixHD_model_DA.py
deleted file mode 100644
index 617589df30ef1d808115332f76a77acaaeba099c..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/Global/models/pix2pixHD_model_DA.py
+++ /dev/null
@@ -1,372 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import numpy as np
-import torch
-import os
-from torch.autograd import Variable
-from util.image_pool import ImagePool
-from .base_model import BaseModel
-from . import networks
-
-
-class Pix2PixHDModel(BaseModel):
-    def name(self):
-        return 'Pix2PixHDModel'
-
-    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
-        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True, True, True, True, True)
-
-        def loss_filter(g_gan, g_gan_feat, g_vgg, g_kl, d_real, d_fake, g_featd, featd_real, featd_fake):
-            return [l for (l, f) in zip((g_gan, g_gan_feat, g_vgg, g_kl, d_real, d_fake, g_featd, featd_real, featd_fake), flags) if f]
-
-        return loss_filter
-
-    def initialize(self, opt):
-        BaseModel.initialize(self, opt)
-        if opt.resize_or_crop != 'none' or not opt.isTrain:  # when training at full res this causes OOM
-            torch.backends.cudnn.benchmark = True
-        self.isTrain = opt.isTrain
-        self.use_features = opt.instance_feat or opt.label_feat  ## Clearly it is false
-        self.gen_features = self.use_features and not self.opt.load_features  ## it is also false
-        input_nc = opt.label_nc if opt.label_nc != 0 else opt.input_nc  ## Just is the origin input channel #
-
-        ##### define networks
-        # Generator network
-        netG_input_nc = input_nc
-        if not opt.no_instance:
-            netG_input_nc += 1
-        if self.use_features:
-            netG_input_nc += opt.feat_num
-        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, opt.k_size,
-                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers,
-                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids, opt=opt)
-
-        # Discriminator network
-        if self.isTrain:
-            use_sigmoid = opt.no_lsgan
-            netD_input_nc = opt.output_nc if opt.no_cgan else input_nc + opt.output_nc
-            if not opt.no_instance:
-                netD_input_nc += 1
-            self.netD = networks.define_D(netD_input_nc, opt.ndf, opt.n_layers_D, opt,opt.norm, use_sigmoid,
-                                          opt.num_D, not opt.no_ganFeat_loss, gpu_ids=self.gpu_ids)
-
-            self.feat_D=networks.define_D(64, opt.ndf, opt.n_layers_D, opt, opt.norm, use_sigmoid,
-                                          1, not opt.no_ganFeat_loss, gpu_ids=self.gpu_ids)
-
-        if self.opt.verbose:
-            print('---------- Networks initialized -------------')
-
-        # load networks
-        if not self.isTrain or opt.continue_train or opt.load_pretrain:
-            pretrained_path = '' if not self.isTrain else opt.load_pretrain
-            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
-
-            print("---------- G Networks reloaded -------------")
-            if self.isTrain:
-                self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)
-                self.load_network(self.feat_D, 'feat_D', opt.which_epoch, pretrained_path)
-                print("---------- D Networks reloaded -------------")
-
-
-                # set loss functions and optimizers
-        if self.isTrain:
-            if opt.pool_size > 0 and (len(self.gpu_ids)) > 1:  ## The pool_size is 0!
-                raise NotImplementedError("Fake Pool Not Implemented for MultiGPU")
-            self.fake_pool = ImagePool(opt.pool_size)
-            self.old_lr = opt.lr
-
-            # define loss functions
-            self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
-
-            self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)
-            self.criterionFeat = torch.nn.L1Loss()
-            if not opt.no_vgg_loss:
-                self.criterionVGG = networks.VGGLoss_torch(self.gpu_ids)
-
-            # Names so we can breakout loss
-            self.loss_names = self.loss_filter('G_GAN', 'G_GAN_Feat', 'G_VGG', 'G_KL', 'D_real', 'D_fake', 'G_featD', 'featD_real','featD_fake')
-
-            # initialize optimizers
-            # optimizer G
-            params = list(self.netG.parameters())
-            if self.gen_features:
-                params += list(self.netE.parameters())
-            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
-
-            # optimizer D
-            params = list(self.netD.parameters())
-            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
-
-            params = list(self.feat_D.parameters())
-            self.optimizer_featD = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
-
-            print("---------- Optimizers initialized -------------")
-
-            if opt.continue_train:
-                self.load_optimizer(self.optimizer_D, 'D', opt.which_epoch)
-                self.load_optimizer(self.optimizer_G, "G", opt.which_epoch)
-                self.load_optimizer(self.optimizer_featD,'featD',opt.which_epoch)
-                for param_groups in self.optimizer_D.param_groups:
-                    self.old_lr = param_groups['lr']
-
-                print("---------- Optimizers reloaded -------------")
-                print("---------- Current LR is %.8f -------------" % (self.old_lr))
-
-            ## We also want to re-load the parameters of optimizer.
-
-    def encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):
-        if self.opt.label_nc == 0:
-            input_label = label_map.data.cuda()
-        else:
-            # create one-hot vector for label map
-            size = label_map.size()
-            oneHot_size = (size[0], self.opt.label_nc, size[2], size[3])
-            input_label = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
-            input_label = input_label.scatter_(1, label_map.data.long().cuda(), 1.0)
-            if self.opt.data_type == 16:
-                input_label = input_label.half()
-
-        # get edges from instance map
-        if not self.opt.no_instance:
-            inst_map = inst_map.data.cuda()
-            edge_map = self.get_edges(inst_map)
-            input_label = torch.cat((input_label, edge_map), dim=1)
-        input_label = Variable(input_label, volatile=infer)
-
-        # real images for training
-        if real_image is not None:
-            real_image = Variable(real_image.data.cuda())
-
-        # instance map for feature encoding
-        if self.use_features:
-            # get precomputed feature maps
-            if self.opt.load_features:
-                feat_map = Variable(feat_map.data.cuda())
-            if self.opt.label_feat:
-                inst_map = label_map.cuda()
-
-        return input_label, inst_map, real_image, feat_map
-
-    def discriminate(self, input_label, test_image, use_pool=False):
-        if input_label is None:
-            input_concat = test_image.detach()
-        else:
-            input_concat = torch.cat((input_label, test_image.detach()), dim=1)
-        if use_pool:
-            fake_query = self.fake_pool.query(input_concat)
-            return self.netD.forward(fake_query)
-        else:
-            return self.netD.forward(input_concat)
-
-    def feat_discriminate(self,input):
-
-        return self.feat_D.forward(input.detach())
-
-
-    def forward(self, label, inst, image, feat, infer=False):
-        # Encode Inputs
-        input_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)
-
-        # Fake Generation
-        if self.use_features:
-            if not self.opt.load_features:
-                feat_map = self.netE.forward(real_image, inst_map)
-            input_concat = torch.cat((input_label, feat_map), dim=1)
-        else:
-            input_concat = input_label
-        hiddens = self.netG.forward(input_concat, 'enc')
-        noise = Variable(torch.randn(hiddens.size()).cuda(hiddens.data.get_device()))
-        # This is a reduced VAE implementation where we assume the outputs are multivariate Gaussian distribution with mean = hiddens and std_dev = all ones.
-        # We follow the the VAE of MUNIT (https://github.com/NVlabs/MUNIT/blob/master/networks.py)
-        fake_image = self.netG.forward(hiddens + noise, 'dec')
-
-        ####################
-        ##### GAN for the intermediate feature
-        real_old_feat =[]
-        syn_feat = []
-        for index,x in enumerate(inst):
-            if x==1:
-                real_old_feat.append(hiddens[index].unsqueeze(0))
-            else:
-                syn_feat.append(hiddens[index].unsqueeze(0))
-        L=min(len(real_old_feat),len(syn_feat))
-        real_old_feat=real_old_feat[:L]
-        syn_feat=syn_feat[:L]
-        real_old_feat=torch.cat(real_old_feat,0)
-        syn_feat=torch.cat(syn_feat,0)
-
-        pred_fake_feat=self.feat_discriminate(real_old_feat)
-        loss_featD_fake = self.criterionGAN(pred_fake_feat, False)
-        pred_real_feat=self.feat_discriminate(syn_feat)
-        loss_featD_real = self.criterionGAN(pred_real_feat, True)
-
-        pred_fake_feat_G=self.feat_D.forward(real_old_feat)
-        loss_G_featD=self.criterionGAN(pred_fake_feat_G,True)
-
-
-        #####################################
-        if self.opt.no_cgan:
-            # Fake Detection and Loss
-            pred_fake_pool = self.discriminate(None, fake_image, use_pool=True)
-            loss_D_fake = self.criterionGAN(pred_fake_pool, False)
-
-            # Real Detection and Loss
-            pred_real = self.discriminate(None, real_image)
-            loss_D_real = self.criterionGAN(pred_real, True)
-
-            # GAN loss (Fake Passability Loss)
-            pred_fake = self.netD.forward(fake_image)
-            loss_G_GAN = self.criterionGAN(pred_fake, True)
-        else:
-            # Fake Detection and Loss
-            pred_fake_pool = self.discriminate(input_label, fake_image, use_pool=True)
-            loss_D_fake = self.criterionGAN(pred_fake_pool, False)
-
-            # Real Detection and Loss
-            pred_real = self.discriminate(input_label, real_image)
-            loss_D_real = self.criterionGAN(pred_real, True)
-
-            # GAN loss (Fake Passability Loss)
-            pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1))
-            loss_G_GAN = self.criterionGAN(pred_fake, True)
-
-        loss_G_kl = torch.mean(torch.pow(hiddens, 2)) * self.opt.kl
-
-        # GAN feature matching loss
-        loss_G_GAN_Feat = 0
-        if not self.opt.no_ganFeat_loss:
-            feat_weights = 4.0 / (self.opt.n_layers_D + 1)
-            D_weights = 1.0 / self.opt.num_D
-            for i in range(self.opt.num_D):
-                for j in range(len(pred_fake[i]) - 1):
-                    loss_G_GAN_Feat += D_weights * feat_weights * \
-                                       self.criterionFeat(pred_fake[i][j],
-                                                          pred_real[i][j].detach()) * self.opt.lambda_feat
-
-        # VGG feature matching loss
-        loss_G_VGG = 0
-        if not self.opt.no_vgg_loss:
-            loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat
-
-        # Only return the fake_B image if necessary to save BW
-        return [self.loss_filter(loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_G_kl, loss_D_real, loss_D_fake,loss_G_featD, loss_featD_real, loss_featD_fake),
-                None if not infer else fake_image]
-
-    def inference(self, label, inst, image=None, feat=None):
-        # Encode Inputs
-        image = Variable(image) if image is not None else None
-        input_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)
-
-        # Fake Generation
-        if self.use_features:
-            if self.opt.use_encoded_image:
-                # encode the real image to get feature map
-                feat_map = self.netE.forward(real_image, inst_map)
-            else:
-                # sample clusters from precomputed features
-                feat_map = self.sample_features(inst_map)
-            input_concat = torch.cat((input_label, feat_map), dim=1)
-        else:
-            input_concat = input_label
-
-        if torch.__version__.startswith('0.4'):
-            with torch.no_grad():
-                fake_image = self.netG.forward(input_concat)
-        else:
-            fake_image = self.netG.forward(input_concat)
-        return fake_image
-
-    def sample_features(self, inst):
-        # read precomputed feature clusters
-        cluster_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, self.opt.cluster_path)
-        features_clustered = np.load(cluster_path, encoding='latin1').item()
-
-        # randomly sample from the feature clusters
-        inst_np = inst.cpu().numpy().astype(int)
-        feat_map = self.Tensor(inst.size()[0], self.opt.feat_num, inst.size()[2], inst.size()[3])
-        for i in np.unique(inst_np):
-            label = i if i < 1000 else i // 1000
-            if label in features_clustered:
-                feat = features_clustered[label]
-                cluster_idx = np.random.randint(0, feat.shape[0])
-
-                idx = (inst == int(i)).nonzero()
-                for k in range(self.opt.feat_num):
-                    feat_map[idx[:, 0], idx[:, 1] + k, idx[:, 2], idx[:, 3]] = feat[cluster_idx, k]
-        if self.opt.data_type == 16:
-            feat_map = feat_map.half()
-        return feat_map
-
-    def encode_features(self, image, inst):
-        image = Variable(image.cuda(), volatile=True)
-        feat_num = self.opt.feat_num
-        h, w = inst.size()[2], inst.size()[3]
-        block_num = 32
-        feat_map = self.netE.forward(image, inst.cuda())
-        inst_np = inst.cpu().numpy().astype(int)
-        feature = {}
-        for i in range(self.opt.label_nc):
-            feature[i] = np.zeros((0, feat_num + 1))
-        for i in np.unique(inst_np):
-            label = i if i < 1000 else i // 1000
-            idx = (inst == int(i)).nonzero()
-            num = idx.size()[0]
-            idx = idx[num // 2, :]
-            val = np.zeros((1, feat_num + 1))
-            for k in range(feat_num):
-                val[0, k] = feat_map[idx[0], idx[1] + k, idx[2], idx[3]].data[0]
-            val[0, feat_num] = float(num) / (h * w // block_num)
-            feature[label] = np.append(feature[label], val, axis=0)
-        return feature
-
-    def get_edges(self, t):
-        edge = torch.cuda.ByteTensor(t.size()).zero_()
-        edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
-        edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])
-        edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
-        edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
-        if self.opt.data_type == 16:
-            return edge.half()
-        else:
-            return edge.float()
-
-    def save(self, which_epoch):
-        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
-        self.save_network(self.netD, 'D', which_epoch, self.gpu_ids)
-        self.save_network(self.feat_D,'featD',which_epoch,self.gpu_ids)
-
-        self.save_optimizer(self.optimizer_G, "G", which_epoch)
-        self.save_optimizer(self.optimizer_D, "D", which_epoch)
-        self.save_optimizer(self.optimizer_featD,'featD',which_epoch)
-
-        if self.gen_features:
-            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)
-
-    def update_fixed_params(self):
-
-        params = list(self.netG.parameters())
-        if self.gen_features:
-            params += list(self.netE.parameters())
-        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
-        if self.opt.verbose:
-            print('------------ Now also finetuning global generator -----------')
-
-    def update_learning_rate(self):
-        lrd = self.opt.lr / self.opt.niter_decay
-        lr = self.old_lr - lrd
-        for param_group in self.optimizer_D.param_groups:
-            param_group['lr'] = lr
-        for param_group in self.optimizer_G.param_groups:
-            param_group['lr'] = lr
-        for param_group in self.optimizer_featD.param_groups:
-            param_group['lr'] = lr
-        if self.opt.verbose:
-            print('update learning rate: %f -> %f' % (self.old_lr, lr))
-        self.old_lr = lr
-
-
-class InferenceModel(Pix2PixHDModel):
-    def forward(self, inp):
-        label, inst = inp
-        return self.inference(label, inst)
diff --git a/spaces/yderre-aubay/midi-player-demo/src/components/ContextMenu.tsx b/spaces/yderre-aubay/midi-player-demo/src/components/ContextMenu.tsx
deleted file mode 100644
index 3f6476e78149e9b6b261a133aa265c5d7c8717b2..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/components/ContextMenu.tsx
+++ /dev/null
@@ -1,85 +0,0 @@
-import styled from "@emotion/styled"
-import * as Portal from "@radix-ui/react-portal"
-import { FC, ReactNode, useEffect } from "react"
-import { IPoint } from "../common/geometry"
-
-export const ContextMenuHotKey = styled.div`
-  font-size: 0.9em;
-  flex-grow: 1;
-  text-align: right;
-  color: ${({ theme }) => theme.secondaryTextColor};
-  margin-left: 2em;
-`
-
-const Wrapper = styled.div`
-  position: fixed;
-  left: 0;
-  top: 0;
-  right: 0;
-  bottom: 0;
-`
-
-const Content = styled.div`
-  position: absolute;
-  background: ${({ theme }) => theme.secondaryBackgroundColor};
-  border-radius: 0.5rem;
-  box-shadow: 0 1rem 3rem ${({ theme }) => theme.shadowColor};
-  border: 1px solid ${({ theme }) => theme.backgroundColor};
-  padding: 0.5rem 0;
-`
-
-const List = styled.ul`
-  list-style: none;
-  padding: 0;
-  margin: 0;
-`
-
-export interface ContextMenuProps {
-  isOpen: boolean
-  position: IPoint
-  handleClose: () => void
-  children?: ReactNode
-}
-
-const estimatedWidth = 200
-
-export const ContextMenu: FC<ContextMenuProps> = ({
-  isOpen,
-  handleClose,
-  position,
-  children,
-}) => {
-  // Menu cannot handle keydown while disabling focus, so we deal with global keydown event
-  useEffect(() => {
-    const onKeyDown = (e: KeyboardEvent) => {
-      if (isOpen && e.key === "Escape") {
-        handleClose()
-      }
-    }
-    document.addEventListener("keydown", onKeyDown)
-
-    return () => {
-      document.removeEventListener("keydown", onKeyDown)
-    }
-  }, [isOpen])
-
-  if (!isOpen) {
-    return <></>
-  }
-
-  // fix position to avoid placing menu outside of the screen
-  const fixedX = Math.min(position.x, window.innerWidth - estimatedWidth)
-
-  return (
-    <Portal.Root>
-      <Wrapper onClick={handleClose}>
-        <Content
-          style={{ left: fixedX, top: position.y }}
-          onClick={(e) => e.stopPropagation()}
-        >
-          <List>{children}</List>
-        </Content>
-      </Wrapper>
-    </Portal.Root>
-  )
-}
diff --git a/spaces/yerfor/SyntaSpeech/utils/commons/multiprocess_utils.py b/spaces/yerfor/SyntaSpeech/utils/commons/multiprocess_utils.py
deleted file mode 100644
index e2773543c702d2819559dfde4c5febab03899790..0000000000000000000000000000000000000000
--- a/spaces/yerfor/SyntaSpeech/utils/commons/multiprocess_utils.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import os
-import traceback
-from functools import partial
-from tqdm import tqdm
-
-
-def chunked_worker(worker_id, args_queue=None, results_queue=None, init_ctx_func=None):
-    ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None
-    while True:
-        args = args_queue.get()
-        if args == '<KILL>':
-            return
-        job_idx, map_func, arg = args
-        try:
-            map_func_ = partial(map_func, ctx=ctx) if ctx is not None else map_func
-            if isinstance(arg, dict):
-                res = map_func_(**arg)
-            elif isinstance(arg, (list, tuple)):
-                res = map_func_(*arg)
-            else:
-                res = map_func_(arg)
-            results_queue.put((job_idx, res))
-        except:
-            traceback.print_exc()
-            results_queue.put((job_idx, None))
-
-
-class MultiprocessManager:
-    def __init__(self, num_workers=None, init_ctx_func=None, multithread=False, queue_max=-1):
-        if multithread:
-            from multiprocessing.dummy import Queue, Process
-        else:
-            from multiprocessing import Queue, Process
-        if num_workers is None:
-            num_workers = int(os.getenv('N_PROC', os.cpu_count()))
-        self.num_workers = num_workers
-        self.results_queue = Queue(maxsize=-1)
-        self.jobs_pending = []
-        self.args_queue = Queue(maxsize=queue_max)
-        self.workers = []
-        self.total_jobs = 0
-        self.multithread = multithread
-        for i in range(num_workers):
-            if multithread:
-                p = Process(target=chunked_worker,
-                            args=(i, self.args_queue, self.results_queue, init_ctx_func))
-            else:
-                p = Process(target=chunked_worker,
-                            args=(i, self.args_queue, self.results_queue, init_ctx_func),
-                            daemon=True)
-            self.workers.append(p)
-            p.start()
-
-    def add_job(self, func, args):
-        if not self.args_queue.full():
-            self.args_queue.put((self.total_jobs, func, args))
-        else:
-            self.jobs_pending.append((self.total_jobs, func, args))
-        self.total_jobs += 1
-
-    def get_results(self):
-        self.n_finished = 0
-        while self.n_finished < self.total_jobs:
-            while len(self.jobs_pending) > 0 and not self.args_queue.full():
-                self.args_queue.put(self.jobs_pending[0])
-                self.jobs_pending = self.jobs_pending[1:]
-            job_id, res = self.results_queue.get()
-            yield job_id, res
-            self.n_finished += 1
-        for w in range(self.num_workers):
-            self.args_queue.put("<KILL>")
-        for w in self.workers:
-            w.join()
-
-    def close(self):
-        if not self.multithread:
-            for w in self.workers:
-                w.terminate()
-
-    def __len__(self):
-        return self.total_jobs
-
-
-def multiprocess_run_tqdm(map_func, args, num_workers=None, ordered=True, init_ctx_func=None,
-                          multithread=False, queue_max=-1, desc=None):
-    for i, res in tqdm(
-            multiprocess_run(map_func, args, num_workers, ordered, init_ctx_func, multithread,
-                             queue_max=queue_max),
-            total=len(args), desc=desc):
-        yield i, res
-
-
-def multiprocess_run(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, multithread=False,
-                     queue_max=-1):
-    """
-    Multiprocessing running chunked jobs.
-
-    Examples:
-    >>> for res in tqdm(multiprocess_run(job_func, args):
-    >>>     print(res)
-
-    :param map_func:
-    :param args:
-    :param num_workers:
-    :param ordered:
-    :param init_ctx_func:
-    :param q_max_size:
-    :param multithread:
-    :return:
-    """
-    if num_workers is None:
-        num_workers = int(os.getenv('N_PROC', os.cpu_count()))
-        # num_workers = 1
-    manager = MultiprocessManager(num_workers, init_ctx_func, multithread, queue_max=queue_max)
-    for arg in args:
-        manager.add_job(map_func, arg)
-    if ordered:
-        n_jobs = len(args)
-        results = ['<WAIT>' for _ in range(n_jobs)]
-        i_now = 0
-        for job_i, res in manager.get_results():
-            results[job_i] = res
-            while i_now < n_jobs and (not isinstance(results[i_now], str) or results[i_now] != '<WAIT>'):
-                yield i_now, results[i_now]
-                results[i_now] = None
-                i_now += 1
-    else:
-        for job_i, res in manager.get_results():
-            yield job_i, res
-    manager.close()
diff --git a/spaces/ygangang/VToonify/vtoonify/model/vtoonify.py b/spaces/ygangang/VToonify/vtoonify/model/vtoonify.py
deleted file mode 100644
index 6556a0a6c734be5f413f4683eb63c44f449c6af8..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/vtoonify.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import torch
-import numpy as np
-import math
-from torch import nn
-from model.stylegan.model import ConvLayer, EqualLinear, Generator, ResBlock
-from model.dualstylegan import AdaptiveInstanceNorm, AdaResBlock, DualStyleGAN
-import torch.nn.functional as F
-
-# IC-GAN: stylegan discriminator    
-class ConditionalDiscriminator(nn.Module):
-    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], use_condition=False, style_num=None):
-        super().__init__()
-
-        channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        convs = [ConvLayer(3, channels[size], 1)]
-
-        log_size = int(math.log(size, 2))
-
-        in_channel = channels[size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-
-            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-        self.use_condition = use_condition
-        
-        if self.use_condition:
-            self.condition_dim = 128
-            # map style degree to 64-dimensional vector
-            self.label_mapper = nn.Sequential(
-                nn.Linear(1, 64),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Linear(64, 64),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Linear(64, self.condition_dim//2),
-            )
-            # map style code index to 64-dimensional vector
-            self.style_mapper = nn.Embedding(style_num, self.condition_dim-self.condition_dim//2)
-        else:
-            self.condition_dim = 1
-            
-        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
-        self.final_linear = nn.Sequential(
-            EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
-            EqualLinear(channels[4], self.condition_dim),
-        )
-        
-    def forward(self, input, degree_label=None, style_ind=None):
-        out = self.convs(input)
-
-        batch, channel, height, width = out.shape
-        group = min(batch, self.stddev_group)
-        stddev = out.view(
-            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
-        )
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, height, width)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-        out = out.view(batch, -1)
-        
-        if self.use_condition:
-            h = self.final_linear(out)
-            condition = torch.cat((self.label_mapper(degree_label), self.style_mapper(style_ind)), dim=1)
-            out = (h * condition).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.condition_dim))
-        else:
-            out = self.final_linear(out)
-            
-        return out 
-    
-
-class VToonifyResBlock(nn.Module):
-    def __init__(self, fin):
-        super().__init__()
-
-        self.conv = nn.Conv2d(fin, fin, 3,  1, 1)
-        self.conv2 = nn.Conv2d(fin, fin, 3,  1, 1)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        
-    def forward(self, x):
-        out = self.lrelu(self.conv(x))
-        out = self.lrelu(self.conv2(out))      
-        out = (out + x) / math.sqrt(2)
-        return out    
-
-class Fusion(nn.Module):
-    def __init__(self, in_channels, skip_channels, out_channels):
-        super().__init__()
-
-        # create conv layers
-        self.conv = nn.Conv2d(in_channels + skip_channels, out_channels, 3, 1, 1, bias=True)
-        self.norm = AdaptiveInstanceNorm(in_channels + skip_channels, 128)
-        self.conv2 = nn.Conv2d(in_channels + skip_channels, 1, 3, 1, 1, bias=True)
-        #'''
-        self.linear = nn.Sequential(
-            nn.Linear(1, 64),
-            nn.LeakyReLU(negative_slope=0.2, inplace=True),
-            nn.Linear(64, 128),
-            nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        )
-
-    def forward(self, f_G, f_E, d_s=1):
-        # label of style degree
-        label = self.linear(torch.zeros(f_G.size(0),1).to(f_G.device) + d_s)
-        out = torch.cat([f_G, abs(f_G-f_E)], dim=1)
-        m_E = (F.relu(self.conv2(self.norm(out, label)))).tanh()
-        f_out = self.conv(torch.cat([f_G, f_E * m_E], dim=1))
-        return f_out, m_E
-    
-class VToonify(nn.Module):
-    def __init__(self,
-                 in_size=256,
-                 out_size=1024,
-                 img_channels=3,
-                 style_channels=512,
-                 num_mlps=8,
-                 channel_multiplier=2,
-                 num_res_layers=6,
-                 backbone = 'dualstylegan',
-                ):
-
-        super().__init__()
-
-        self.backbone = backbone
-        if self.backbone == 'dualstylegan':
-            # DualStyleGAN, with weights being fixed
-            self.generator = DualStyleGAN(out_size, style_channels, num_mlps, channel_multiplier)
-        else:
-            # StyleGANv2, with weights being fixed
-            self.generator = Generator(out_size, style_channels, num_mlps, channel_multiplier)
-
-        self.in_size = in_size
-        self.style_channels = style_channels
-        channels = self.generator.channels
-
-        # encoder
-        num_styles = int(np.log2(out_size)) * 2 - 2
-        encoder_res = [2**i for i in range(int(np.log2(in_size)), 4, -1)]
-        self.encoder = nn.ModuleList()
-        self.encoder.append(
-            nn.Sequential(
-                nn.Conv2d(img_channels+19, 32, 3, 1, 1, bias=True), 
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(32, channels[in_size], 3, 1, 1, bias=True),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True)))
-        
-        for res in encoder_res:
-            in_channels = channels[res]
-            if res > 32:
-                out_channels = channels[res // 2]
-                block = nn.Sequential(
-                    nn.Conv2d(in_channels, out_channels, 3, 2, 1, bias=True),
-                    nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                    nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=True),
-                    nn.LeakyReLU(negative_slope=0.2, inplace=True))
-                self.encoder.append(block)
-            else:
-                layers = []
-                for _ in range(num_res_layers):
-                    layers.append(VToonifyResBlock(in_channels))
-                self.encoder.append(nn.Sequential(*layers))
-                block = nn.Conv2d(in_channels, img_channels, 1, 1, 0, bias=True)
-                self.encoder.append(block)
-        
-        # trainable fusion module
-        self.fusion_out = nn.ModuleList()
-        self.fusion_skip = nn.ModuleList()
-        for res in encoder_res[::-1]:
-            num_channels = channels[res]
-            if self.backbone == 'dualstylegan':
-                self.fusion_out.append(
-                    Fusion(num_channels, num_channels, num_channels))
-            else:
-                self.fusion_out.append(
-                    nn.Conv2d(num_channels * 2, num_channels, 3, 1, 1, bias=True))
-
-            self.fusion_skip.append(
-                nn.Conv2d(num_channels + 3, 3, 3, 1, 1, bias=True))
-        
-        # Modified ModRes blocks in DualStyleGAN, with weights being fixed
-        if self.backbone == 'dualstylegan':
-            self.res = nn.ModuleList()
-            self.res.append(AdaResBlock(self.generator.channels[2 ** 2])) # for conv1, no use in this model
-            for i in range(3, 6):
-                out_channel = self.generator.channels[2 ** i]
-                self.res.append(AdaResBlock(out_channel, dilation=2**(5-i)))
-                self.res.append(AdaResBlock(out_channel, dilation=2**(5-i)))
-
-    
-    def forward(self, x, style, d_s=None, return_mask=False, return_feat=False):
-        # map style to W+ space
-        if style is not None and style.ndim < 3:
-            if self.backbone == 'dualstylegan':
-                resstyles = self.generator.style(style).unsqueeze(1).repeat(1, self.generator.n_latent, 1)
-            adastyles = style.unsqueeze(1).repeat(1, self.generator.n_latent, 1)
-        elif style is not None:
-            nB, nL, nD = style.shape
-            if self.backbone == 'dualstylegan':
-                resstyles = self.generator.style(style.reshape(nB*nL, nD)).reshape(nB, nL, nD)
-            adastyles = style
-        if self.backbone == 'dualstylegan':
-            adastyles = adastyles.clone()
-            for i in range(7, self.generator.n_latent):
-                adastyles[:, i] = self.generator.res[i](adastyles[:, i])
-
-        # obtain multi-scale content features
-        feat = x
-        encoder_features = []
-        # downsampling conv parts of E
-        for block in self.encoder[:-2]:
-            feat = block(feat)
-            encoder_features.append(feat)
-        encoder_features = encoder_features[::-1]
-        # Resblocks in E
-        for ii, block in enumerate(self.encoder[-2]):
-            feat = block(feat)
-            # adjust Resblocks with ModRes blocks
-            if self.backbone == 'dualstylegan':
-                feat = self.res[ii+1](feat, resstyles[:, ii+1], d_s)
-        # the last-layer feature of E (inputs of backbone)
-        out = feat
-        skip = self.encoder[-1](feat)
-        if return_feat:
-            return out, skip
-        
-        # 32x32 ---> higher res
-        _index = 1
-        m_Es = []
-        for conv1, conv2, to_rgb in zip(
-            self.stylegan().convs[6::2], self.stylegan().convs[7::2], self.stylegan().to_rgbs[3:]): 
-            
-            # pass the mid-layer features of E to the corresponding resolution layers of G
-            if 2 ** (5+((_index-1)//2)) <= self.in_size:
-                fusion_index = (_index - 1) // 2
-                f_E = encoder_features[fusion_index]
-
-                if self.backbone == 'dualstylegan':
-                    out, m_E = self.fusion_out[fusion_index](out, f_E, d_s)
-                    skip = self.fusion_skip[fusion_index](torch.cat([skip, f_E*m_E], dim=1))
-                    m_Es += [m_E]
-                else:
-                    out = self.fusion_out[fusion_index](torch.cat([out, f_E], dim=1))
-                    skip = self.fusion_skip[fusion_index](torch.cat([skip, f_E], dim=1))  
-            
-            # remove the noise input
-            batch, _, height, width = out.shape
-            noise = x.new_empty(batch, 1, height * 2, width * 2).normal_().detach() * 0.0
-            
-            out = conv1(out, adastyles[:, _index+6], noise=noise)
-            out = conv2(out, adastyles[:, _index+7], noise=noise)
-            skip = to_rgb(out, adastyles[:, _index+8], skip)
-            _index += 2
-
-        image = skip
-        if return_mask and self.backbone == 'dualstylegan':
-            return image, m_Es
-        return image
-    
-    def stylegan(self):
-        if self.backbone == 'dualstylegan':
-            return self.generator.generator
-        else:
-            return self.generator
-        
-    def zplus2wplus(self, zplus):
-        return self.stylegan().style(zplus.reshape(zplus.shape[0]*zplus.shape[1], zplus.shape[2])).reshape(zplus.shape)
\ No newline at end of file
diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/inference/landmark_smooth.py b/spaces/ygtxr1997/ReliableSwap_Demo/inference/landmark_smooth.py
deleted file mode 100644
index b5bda44989a353b1dc722e90a40efa1a254f70b2..0000000000000000000000000000000000000000
--- a/spaces/ygtxr1997/ReliableSwap_Demo/inference/landmark_smooth.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import cv2
-import numpy as np
-from scipy.signal import savgol_filter
-
-def kalman_filter(inputs: np.array,
-                  process_noise: float = 0.03,
-                  measure_noise: float = 0.01,
-                  ):
-    """ OpenCV - Kalman Filter
-    https://blog.csdn.net/angelfish91/article/details/61768575
-    https://blog.csdn.net/qq_23981335/article/details/82968422
-    """
-    assert inputs.ndim == 2, "inputs should be 2-dim np.array"
-
-    '''
-    它有3个输入参数，
-    dynam_params：状态空间的维数，这里为2；
-    measure_param：测量值的维数，这里也为2; 
-    control_params：控制向量的维数，默认为0。由于这里该模型中并没有控制变量，因此也为0。
-    '''
-    kalman = cv2.KalmanFilter(2,2)
-
-    kalman.measurementMatrix = np.array([[1,0],[0,1]],np.float32)
-    kalman.transitionMatrix = np.array([[1,0],[0,1]], np.float32)
-    kalman.processNoiseCov = np.array([[1,0],[0,1]], np.float32) * process_noise
-    kalman.measurementNoiseCov = np.array([[1,0],[0,1]], np.float32) * measure_noise
-    '''
-    kalman.measurementNoiseCov为测量系统的协方差矩阵，方差越小，预测结果越接近测量值，
-    kalman.processNoiseCov为模型系统的噪声，噪声越大，预测结果越不稳定，越容易接近模型系统预测值，且单步变化越大，
-    相反，若噪声小，则预测结果与上个计算结果相差不大。
-    '''
-
-    kalman.statePre = np.array([[inputs[0][0]],
-                                [inputs[0][1]]])
-
-    '''
-    Kalman Filtering
-    '''
-    outputs = np.zeros_like(inputs)
-    for i in range(len(inputs)):
-        mes = np.reshape(inputs[i,:],(2,1))
-
-        x = kalman.correct(mes)
-
-        y = kalman.predict()
-        outputs[i] = np.squeeze(y)
-        # print (kalman.statePost[0],kalman.statePost[1])
-        # print (kalman.statePre[0],kalman.statePre[1])
-        # print ('measurement:\t',mes[0],mes[1])
-        # print ('correct:\t',x[0],x[1])
-        # print ('predict:\t',y[0],y[1])
-        # print ('='*30)
-
-    return outputs
-
-
-def kalman_filter_landmark(landmarks: np.array,
-                           process_noise: float = 0.03,
-                           measure_noise: float = 0.01,
-                           ):
-    """ Kalman Filter for Landmarks
-    :param process_noise: large means unstable and close to model predictions
-    :param measure_noise: small means close to measurement
-    """
-    print('[Using Kalman Filter for Landmark Smoothing, process_noise=%f, measure_noise=%f]' %
-          (process_noise, measure_noise))
-
-    ''' 
-    landmarks: (#frames, key, xy)
-    '''
-    assert landmarks.ndim == 3, 'landmarks should be 3-dim np.array'
-    assert landmarks.dtype == 'float32', 'landmarks dtype should be float32'
-
-    for s1 in range(landmarks.shape[1]):
-        landmarks[:, s1] = kalman_filter(landmarks[:, s1],
-                                         process_noise,
-                                         measure_noise)
-    return landmarks
-
-
-def savgol_filter_landmark(landmarks: np.array,
-                           window_length: int = 25,
-                           poly_order: int = 2,
-                           ):
-    """ Savgol Filter for Landmarks
-    https://blog.csdn.net/kaever/article/details/105520941
-    """
-    print('[Using Savgol Filter for Landmark Smoothing, window_length=%d, poly_order=%d]' %
-          (window_length, poly_order))
-
-    ''' 
-    landmarks: (#frames, key, xy)
-    '''
-    assert landmarks.ndim == 3, 'landmarks should be 3-dim np.array'
-    assert landmarks.dtype == 'float32', 'landmarks dtype should be float32'
-    assert window_length % 2 == 1, 'window_length should be odd'
-
-    for s1 in range(landmarks.shape[1]):
-        for s2 in range(landmarks.shape[2]):
-            landmarks[:, s1, s2] = savgol_filter(landmarks[:, s1, s2],
-                                                 window_length,
-                                                 poly_order)
-    return landmarks
-
-if __name__ == '__main__':
-
-    pos = np.array([
-            [10,    50],
-            [12,    49],
-            [11,    52],
-            [13,    52.2],
-            [12.9,  50]], np.float32)
-
-    print(pos)
-    pos_filtered = kalman_filter(pos)
-    print(pos)
-    print(pos_filtered)
\ No newline at end of file
diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/infer_image.py b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/infer_image.py
deleted file mode 100644
index 953e9d3d97679fc631547b7cc2b36de64d67fb66..0000000000000000000000000000000000000000
--- a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/infer_image.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import os
-import cv2
-import numpy as np
-from PIL import Image
-import glob
-
-import torch
-import tqdm
-import shutil
-import argparse
-from third_party.GPEN.face_enhancement import FaceEnhancement
-
-make_abs_path = lambda fn: os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), fn))
-
-
-class GPENImageInfer(object):
-    def __init__(self, device):
-        super(GPENImageInfer, self).__init__()
-
-        model = {
-            "name": "GPEN-BFR-512",
-            "in_size": 512,
-            "out_size": 512,
-            "channel_multiplier": 2,
-            "narrow": 1,
-        }
-        faceenhancer = FaceEnhancement(
-            base_dir=make_abs_path('./'),
-            use_sr=True,
-            in_size=model["in_size"],
-            out_size=model["out_size"],
-            model=model["name"],
-            channel_multiplier=model["channel_multiplier"],
-            narrow=model["narrow"],
-            device=device,
-        )
-        self.faceenhancer = faceenhancer
-
-    def image_infer(self, in_img: np.ndarray):
-        """
-
-        :param in_img: np.ndarray, (H,W,BGR), in [0,255]
-        :return: out_img: np.ndarray, (H,W,BGR), in [0,255]
-        """
-        h, w, _ = in_img.shape
-        out_img, orig_faces, enhanced_faces = self.faceenhancer.process(in_img)
-        out_img = cv2.resize(out_img, (w, h))
-        return out_img
-
-    def ndarray_infer(self, in_ndarray: np.ndarray,
-                      save_folder: str = 'demo_images/out/',
-                      save_name: str = 'reen.png',
-                      ):
-        """
-
-        :param in_ndarray: np.ndarray, (N,H,W,BGR), in [0,255]
-        :param save_folder: not used
-        :param save_name: not used
-        :return: out_ndarray: np.ndarray, (N,H,W,BGR), in [0,255]
-        """
-        B, H, W, C = in_ndarray.shape
-
-        out_ndarray = np.zeros_like(in_ndarray, dtype=np.uint8)  # (N,H,W,BGR)
-        for b_idx in range(B):
-            single_img = in_ndarray[b_idx]
-            out_img = self.image_infer(single_img)  # (H,W,BGR), in [0,255]
-            out_ndarray[b_idx] = out_img
-        return out_ndarray
-
-    def batch_infer(self, in_batch: torch.Tensor,
-                          save_folder: str = 'demo_images/out/',
-                          save_name: str = 'reen.png',
-                          save_batch_idx: int = 0,
-                          ):
-        """
-
-        :param in_batch: (N,RGB,H,W), in [-1,1]
-        :return: out_batch: (N,RGB,H,W), in [-1,1]
-        """
-        B, C, H, W = in_batch.shape
-        device = in_batch.device
-
-        in_batch = ((in_batch + 1.) * 127.5).permute(0, 2, 3, 1)
-        in_batch = in_batch.cpu().numpy().astype(np.uint8)  # (N,H,W,RGB), in [0,255]
-        in_batch = in_batch[:, :, :, ::-1]  # (N,H,W,BGR)
-
-        out_batch = np.zeros_like(in_batch, dtype=np.uint8)  # (N,H,W,BGR)
-        for b_idx in range(B):
-            single_img = in_batch[b_idx]
-            out_img = self.image_infer(single_img)  # (H,W,BGR), in [0,255]
-            out_batch[b_idx] = out_img[:, :, ::-1]
-            if save_batch_idx is not None and b_idx == save_batch_idx:
-                cv2.imwrite(os.path.join(save_folder, save_name), out_img)
-        out_batch = torch.FloatTensor(out_batch).to(device)
-        out_batch = out_batch / 127.5 - 1.  # (N,H,W,RGB)
-        out_batch = out_batch.permute(0, 3, 1, 2)  # (N,RGB,H,W)
-        out_batch = out_batch.clamp(-1, 1)
-
-        return out_batch
-
-
-if __name__ == '__main__':
-    gpen = GPENImageInfer()
-
-    in_folder = 'examples/imgs/'
-    img_list = os.listdir(in_folder)
-
-    for img_name in img_list:
-        if 'gpen' in img_name:
-            continue
-
-        in_path = os.path.join(in_folder, img_name)
-        out_path = in_path.replace('.png', '_gpen.png')
-        out_path = in_path.replace('.jpg', '_gpen.jpg')
-
-        im = cv2.imread(in_path, cv2.IMREAD_COLOR)  # BGR
-        img = gpen.image_infer(im)
-        cv2.imwrite(out_path, img)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deta/modeling_deta.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deta/modeling_deta.py
deleted file mode 100644
index 9cd29e94088730476d363b7588363233cc50fd22..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deta/modeling_deta.py
+++ /dev/null
@@ -1,2762 +0,0 @@
-# coding=utf-8
-# Copyright 2022 SenseTime and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch DETA model."""
-
-
-import copy
-import math
-import warnings
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-from ...activations import ACT2FN
-from ...file_utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_scipy_available,
-    is_vision_available,
-    replace_return_docstrings,
-)
-from ...modeling_outputs import BaseModelOutput
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import meshgrid
-from ...utils import is_torchvision_available, logging, requires_backends
-from ..auto import AutoBackbone
-from .configuration_deta import DetaConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-if is_vision_available():
-    from transformers.image_transforms import center_to_corners_format
-
-if is_torchvision_available():
-    from torchvision.ops.boxes import batched_nms
-
-if is_scipy_available():
-    from scipy.optimize import linear_sum_assignment
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "DetaConfig"
-_CHECKPOINT_FOR_DOC = "jozhang97/deta-swin-large-o365"
-
-DETA_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "jozhang97/deta-swin-large-o365",
-    # See all DETA models at https://huggingface.co/models?filter=deta
-]
-
-
-@dataclass
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrDecoderOutput with DeformableDetr->Deta
-class DetaDecoderOutput(ModelOutput):
-    """
-    Base class for outputs of the DetaDecoder. This class adds two attributes to BaseModelOutputWithCrossAttentions,
-    namely:
-    - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
-    - a stacked tensor of intermediate reference points.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
-            Stacked intermediate hidden states (output of each layer of the decoder).
-        intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`):
-            Stacked intermediate reference points (reference points of each layer of the decoder).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    intermediate_hidden_states: torch.FloatTensor = None
-    intermediate_reference_points: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModelOutput with DeformableDetr->Deta,Deformable DETR->DETA
-class DetaModelOutput(ModelOutput):
-    """
-    Base class for outputs of the Deformable DETR encoder-decoder model.
-
-    Args:
-        init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
-            Initial reference points sent through the Transformer decoder.
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the decoder of the model.
-        intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
-            Stacked intermediate hidden states (output of each layer of the decoder).
-        intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
-            Stacked intermediate reference points (reference points of each layer of the decoder).
-        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, num_queries, hidden_size)`. Hidden-states of the decoder at the output of each layer
-            plus the initial embedding outputs.
-        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, num_queries,
-            num_queries)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted
-            average in the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_queries, num_heads, 4, 4)`.
-            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
-            weighted average in the cross-attention heads.
-        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the encoder of the model.
-        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
-            layer plus the initial embedding outputs.
-        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_queries, num_heads, 4, 4)`.
-            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
-            self-attention heads.
-        enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
-            Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are
-            picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
-            foreground and background).
-        enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
-            Logits of predicted bounding boxes coordinates in the first stage.
-    """
-
-    init_reference_points: torch.FloatTensor = None
-    last_hidden_state: torch.FloatTensor = None
-    intermediate_hidden_states: torch.FloatTensor = None
-    intermediate_reference_points: torch.FloatTensor = None
-    decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    enc_outputs_class: Optional[torch.FloatTensor] = None
-    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
-
-
-@dataclass
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrObjectDetectionOutput with DeformableDetr->Deta
-class DetaObjectDetectionOutput(ModelOutput):
-    """
-    Output type of [`DetaForObjectDetection`].
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
-            Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
-            bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
-            scale-invariant IoU loss.
-        loss_dict (`Dict`, *optional*):
-            A dictionary containing the individual losses. Useful for logging.
-        logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
-            Classification logits (including no-object) for all queries.
-        pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
-            Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
-            values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
-            possible padding). You can use [`~DetaProcessor.post_process_object_detection`] to retrieve the
-            unnormalized bounding boxes.
-        auxiliary_outputs (`list[Dict]`, *optional*):
-            Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
-            and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
-            `pred_boxes`) for each decoder layer.
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the decoder of the model.
-        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, num_queries, hidden_size)`. Hidden-states of the decoder at the output of each layer
-            plus the initial embedding outputs.
-        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, num_queries,
-            num_queries)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted
-            average in the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_queries, num_heads, 4, 4)`.
-            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
-            weighted average in the cross-attention heads.
-        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the encoder of the model.
-        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
-            layer plus the initial embedding outputs.
-        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_heads, 4,
-            4)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average
-            in the self-attention heads.
-        intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
-            Stacked intermediate hidden states (output of each layer of the decoder).
-        intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
-            Stacked intermediate reference points (reference points of each layer of the decoder).
-        init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
-            Initial reference points sent through the Transformer decoder.
-        enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
-            Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are
-            picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
-            foreground and background).
-        enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
-            Logits of predicted bounding boxes coordinates in the first stage.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    loss_dict: Optional[Dict] = None
-    logits: torch.FloatTensor = None
-    pred_boxes: torch.FloatTensor = None
-    auxiliary_outputs: Optional[List[Dict]] = None
-    init_reference_points: Optional[torch.FloatTensor] = None
-    last_hidden_state: Optional[torch.FloatTensor] = None
-    intermediate_hidden_states: Optional[torch.FloatTensor] = None
-    intermediate_reference_points: Optional[torch.FloatTensor] = None
-    decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    enc_outputs_class: Optional = None
-    enc_outputs_coord_logits: Optional = None
-
-
-def _get_clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def inverse_sigmoid(x, eps=1e-5):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->Deta
-class DetaFrozenBatchNorm2d(nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
-    torchvision.models.resnet[18,34,50,101] produce nans.
-    """
-
-    def __init__(self, n):
-        super().__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        num_batches_tracked_key = prefix + "num_batches_tracked"
-        if num_batches_tracked_key in state_dict:
-            del state_dict[num_batches_tracked_key]
-
-        super()._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def forward(self, x):
-        # move reshapes to the beginning
-        # to make it user-friendly
-        weight = self.weight.reshape(1, -1, 1, 1)
-        bias = self.bias.reshape(1, -1, 1, 1)
-        running_var = self.running_var.reshape(1, -1, 1, 1)
-        running_mean = self.running_mean.reshape(1, -1, 1, 1)
-        epsilon = 1e-5
-        scale = weight * (running_var + epsilon).rsqrt()
-        bias = bias - running_mean * scale
-        return x * scale + bias
-
-
-# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->Deta
-def replace_batch_norm(model):
-    r"""
-    Recursively replace all `torch.nn.BatchNorm2d` with `DetaFrozenBatchNorm2d`.
-
-    Args:
-        model (torch.nn.Module):
-            input model
-    """
-    for name, module in model.named_children():
-        if isinstance(module, nn.BatchNorm2d):
-            new_module = DetaFrozenBatchNorm2d(module.num_features)
-
-            new_module.weight.data.copy_(module.weight)
-            new_module.bias.data.copy_(module.bias)
-            new_module.running_mean.data.copy_(module.running_mean)
-            new_module.running_var.data.copy_(module.running_var)
-
-            model._modules[name] = new_module
-
-        if len(list(module.children())) > 0:
-            replace_batch_norm(module)
-
-
-class DetaBackboneWithPositionalEncodings(nn.Module):
-    """
-    Backbone model with positional embeddings.
-
-    nn.BatchNorm2d layers are replaced by DetaFrozenBatchNorm2d as defined above.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-
-        backbone = AutoBackbone.from_config(config.backbone_config)
-        with torch.no_grad():
-            replace_batch_norm(backbone)
-        self.model = backbone
-        self.intermediate_channel_sizes = self.model.channels
-
-        # TODO fix this
-        if config.backbone_config.model_type == "resnet":
-            for name, parameter in self.model.named_parameters():
-                if "stages.1" not in name and "stages.2" not in name and "stages.3" not in name:
-                    parameter.requires_grad_(False)
-
-        self.position_embedding = build_position_encoding(config)
-
-    def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
-        """
-        Outputs feature maps of latter stages C_3 through C_5 in ResNet if `config.num_feature_levels > 1`, otherwise
-        outputs feature maps of C_5.
-        """
-        # first, send pixel_values through the backbone to get list of feature maps
-        features = self.model(pixel_values).feature_maps
-
-        # next, create position embeddings
-        out = []
-        pos = []
-        for feature_map in features:
-            # downsample pixel_mask to match shape of corresponding feature_map
-            mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
-            position_embeddings = self.position_embedding(feature_map, mask).to(feature_map.dtype)
-            out.append((feature_map, mask))
-            pos.append(position_embeddings)
-
-        return out, pos
-
-
-# Copied from transformers.models.detr.modeling_detr._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, target_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`.
-    """
-    batch_size, source_len = mask.size()
-    target_len = target_len if target_len is not None else source_len
-
-    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, target_len, source_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrSinePositionEmbedding with DeformableDetr->Deta
-class DetaSinePositionEmbedding(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
-    need paper, generalized to work on images.
-    """
-
-    def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, pixel_values, pixel_mask):
-        if pixel_mask is None:
-            raise ValueError("No pixel mask provided")
-        y_embed = pixel_mask.cumsum(1, dtype=torch.float32)
-        x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
-        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
-        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding
-class DetaLearnedPositionEmbedding(nn.Module):
-    """
-    This module learns positional embeddings up to a fixed maximum size.
-    """
-
-    def __init__(self, embedding_dim=256):
-        super().__init__()
-        self.row_embeddings = nn.Embedding(50, embedding_dim)
-        self.column_embeddings = nn.Embedding(50, embedding_dim)
-
-    def forward(self, pixel_values, pixel_mask=None):
-        height, width = pixel_values.shape[-2:]
-        width_values = torch.arange(width, device=pixel_values.device)
-        height_values = torch.arange(height, device=pixel_values.device)
-        x_emb = self.column_embeddings(width_values)
-        y_emb = self.row_embeddings(height_values)
-        pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
-        pos = pos.permute(2, 0, 1)
-        pos = pos.unsqueeze(0)
-        pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
-        return pos
-
-
-# Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->Deta
-def build_position_encoding(config):
-    n_steps = config.d_model // 2
-    if config.position_embedding_type == "sine":
-        # TODO find a better way of exposing other arguments
-        position_embedding = DetaSinePositionEmbedding(n_steps, normalize=True)
-    elif config.position_embedding_type == "learned":
-        position_embedding = DetaLearnedPositionEmbedding(n_steps)
-    else:
-        raise ValueError(f"Not supported {config.position_embedding_type}")
-
-    return position_embedding
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.multi_scale_deformable_attention
-def multi_scale_deformable_attention(
-    value: Tensor, value_spatial_shapes: Tensor, sampling_locations: Tensor, attention_weights: Tensor
-) -> Tensor:
-    batch_size, _, num_heads, hidden_dim = value.shape
-    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
-    value_list = value.split([height.item() * width.item() for height, width in value_spatial_shapes], dim=1)
-    sampling_grids = 2 * sampling_locations - 1
-    sampling_value_list = []
-    for level_id, (height, width) in enumerate(value_spatial_shapes):
-        # batch_size, height*width, num_heads, hidden_dim
-        # -> batch_size, height*width, num_heads*hidden_dim
-        # -> batch_size, num_heads*hidden_dim, height*width
-        # -> batch_size*num_heads, hidden_dim, height, width
-        value_l_ = (
-            value_list[level_id].flatten(2).transpose(1, 2).reshape(batch_size * num_heads, hidden_dim, height, width)
-        )
-        # batch_size, num_queries, num_heads, num_points, 2
-        # -> batch_size, num_heads, num_queries, num_points, 2
-        # -> batch_size*num_heads, num_queries, num_points, 2
-        sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
-        # batch_size*num_heads, hidden_dim, num_queries, num_points
-        sampling_value_l_ = nn.functional.grid_sample(
-            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
-        )
-        sampling_value_list.append(sampling_value_l_)
-    # (batch_size, num_queries, num_heads, num_levels, num_points)
-    # -> (batch_size, num_heads, num_queries, num_levels, num_points)
-    # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
-    attention_weights = attention_weights.transpose(1, 2).reshape(
-        batch_size * num_heads, 1, num_queries, num_levels * num_points
-    )
-    output = (
-        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
-        .sum(-1)
-        .view(batch_size, num_heads * hidden_dim, num_queries)
-    )
-    return output.transpose(1, 2).contiguous()
-
-
-class DetaMultiscaleDeformableAttention(nn.Module):
-    """
-    Multiscale deformable attention as proposed in Deformable DETR.
-    """
-
-    def __init__(self, embed_dim: int, num_heads: int, n_levels: int, n_points: int):
-        super().__init__()
-        if embed_dim % num_heads != 0:
-            raise ValueError(
-                f"embed_dim (d_model) must be divisible by num_heads, but got {embed_dim} and {num_heads}"
-            )
-        dim_per_head = embed_dim // num_heads
-        # check if dim_per_head is power of 2
-        if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
-            warnings.warn(
-                "You'd better set embed_dim (d_model) in DetaMultiscaleDeformableAttention to make the"
-                " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
-                " implementation."
-            )
-
-        self.im2col_step = 64
-
-        self.d_model = embed_dim
-        self.n_levels = n_levels
-        self.n_heads = num_heads
-        self.n_points = n_points
-
-        self.sampling_offsets = nn.Linear(embed_dim, num_heads * n_levels * n_points * 2)
-        self.attention_weights = nn.Linear(embed_dim, num_heads * n_levels * n_points)
-        self.value_proj = nn.Linear(embed_dim, embed_dim)
-        self.output_proj = nn.Linear(embed_dim, embed_dim)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        nn.init.constant_(self.sampling_offsets.weight.data, 0.0)
-        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (
-            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
-            .view(self.n_heads, 1, 1, 2)
-            .repeat(1, self.n_levels, self.n_points, 1)
-        )
-        for i in range(self.n_points):
-            grid_init[:, :, i, :] *= i + 1
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        nn.init.constant_(self.attention_weights.weight.data, 0.0)
-        nn.init.constant_(self.attention_weights.bias.data, 0.0)
-        nn.init.xavier_uniform_(self.value_proj.weight.data)
-        nn.init.constant_(self.value_proj.bias.data, 0.0)
-        nn.init.xavier_uniform_(self.output_proj.weight.data)
-        nn.init.constant_(self.output_proj.bias.data, 0.0)
-
-    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
-        return tensor if position_embeddings is None else tensor + position_embeddings
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        position_embeddings: Optional[torch.Tensor] = None,
-        reference_points=None,
-        spatial_shapes=None,
-        level_start_index=None,
-        output_attentions: bool = False,
-    ):
-        # add position embeddings to the hidden states before projecting to queries and keys
-        if position_embeddings is not None:
-            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
-
-        batch_size, num_queries, _ = hidden_states.shape
-        batch_size, sequence_length, _ = encoder_hidden_states.shape
-        if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length:
-            raise ValueError(
-                "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
-            )
-
-        value = self.value_proj(encoder_hidden_states)
-        if attention_mask is not None:
-            # we invert the attention_mask
-            value = value.masked_fill(~attention_mask[..., None], float(0))
-        value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
-        sampling_offsets = self.sampling_offsets(hidden_states).view(
-            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
-        )
-        attention_weights = self.attention_weights(hidden_states).view(
-            batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
-        )
-        attention_weights = F.softmax(attention_weights, -1).view(
-            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
-        )
-        # batch_size, num_queries, n_heads, n_levels, n_points, 2
-        if reference_points.shape[-1] == 2:
-            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
-            sampling_locations = (
-                reference_points[:, :, None, :, None, :]
-                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
-            )
-        elif reference_points.shape[-1] == 4:
-            sampling_locations = (
-                reference_points[:, :, None, :, None, :2]
-                + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
-            )
-        else:
-            raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
-        # PyTorch implementation (for now)
-        output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights)
-        output = self.output_proj(output)
-
-        return output, attention_weights
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrMultiheadAttention with DeformableDetr->Deta,Deformable DETR->DETA
-class DetaMultiheadAttention(nn.Module):
-    """
-    Multi-headed attention from 'Attention Is All You Need' paper.
-
-    Here, we add position embeddings to the queries and keys (as explained in the Deformable DETR paper).
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        bias: bool = True,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        if self.head_dim * num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
-        return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
-        return tensor if position_embeddings is None else tensor + position_embeddings
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_embeddings: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        batch_size, target_len, embed_dim = hidden_states.size()
-        # add position embeddings to the hidden states before projecting to queries and keys
-        if position_embeddings is not None:
-            hidden_states_original = hidden_states
-            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
-
-        # get queries, keys and values
-        query_states = self.q_proj(hidden_states) * self.scaling
-        key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
-        value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)
-
-        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*proj_shape)
-
-        source_len = key_states.size(1)
-
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
-            raise ValueError(
-                f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        # expand attention_mask
-        if attention_mask is not None:
-            # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
-            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
-
-        if attention_mask is not None:
-            if attention_mask.size() != (batch_size, 1, target_len, source_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
-                    f" {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
-            attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
-            attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped
-
-
-class DetaEncoderLayer(nn.Module):
-    def __init__(self, config: DetaConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-        self.self_attn = DetaMultiscaleDeformableAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.encoder_attention_heads,
-            n_levels=config.num_feature_levels,
-            n_points=config.encoder_n_points,
-        )
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        position_embeddings: torch.Tensor = None,
-        reference_points=None,
-        spatial_shapes=None,
-        level_start_index=None,
-        output_attentions: bool = False,
-    ):
-        """
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Input to the layer.
-            attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
-                Attention mask.
-            position_embeddings (`torch.FloatTensor`, *optional*):
-                Position embeddings, to be added to `hidden_states`.
-            reference_points (`torch.FloatTensor`, *optional*):
-                Reference points.
-            spatial_shapes (`torch.LongTensor`, *optional*):
-                Spatial shapes of the backbone feature maps.
-            level_start_index (`torch.LongTensor`, *optional*):
-                Level start index.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
-        hidden_states, attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            encoder_hidden_states=hidden_states,
-            encoder_attention_mask=attention_mask,
-            position_embeddings=position_embeddings,
-            reference_points=reference_points,
-            spatial_shapes=spatial_shapes,
-            level_start_index=level_start_index,
-            output_attentions=output_attentions,
-        )
-
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        if self.training:
-            if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
-                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class DetaDecoderLayer(nn.Module):
-    def __init__(self, config: DetaConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-
-        # self-attention
-        self.self_attn = DetaMultiheadAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-        )
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        # cross-attention
-        self.encoder_attn = DetaMultiscaleDeformableAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.decoder_attention_heads,
-            n_levels=config.num_feature_levels,
-            n_points=config.decoder_n_points,
-        )
-        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        # feedforward neural networks
-        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
-        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Optional[torch.Tensor] = None,
-        reference_points=None,
-        spatial_shapes=None,
-        level_start_index=None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-    ):
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`):
-                Input to the layer of shape `(batch, seq_len, embed_dim)`.
-            position_embeddings (`torch.FloatTensor`, *optional*):
-                Position embeddings that are added to the queries and keys in the self-attention layer.
-            reference_points (`torch.FloatTensor`, *optional*):
-                Reference points.
-            spatial_shapes (`torch.LongTensor`, *optional*):
-                Spatial shapes.
-            level_start_index (`torch.LongTensor`, *optional*):
-                Level start index.
-            encoder_hidden_states (`torch.FloatTensor`):
-                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
-            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
-                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
-                values.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        # Self Attention
-        hidden_states, self_attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            position_embeddings=position_embeddings,
-            output_attentions=output_attentions,
-        )
-
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        second_residual = hidden_states
-
-        # Cross-Attention
-        cross_attn_weights = None
-        hidden_states, cross_attn_weights = self.encoder_attn(
-            hidden_states=hidden_states,
-            attention_mask=encoder_attention_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            position_embeddings=position_embeddings,
-            reference_points=reference_points,
-            spatial_shapes=spatial_shapes,
-            level_start_index=level_start_index,
-            output_attentions=output_attentions,
-        )
-
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = second_residual + hidden_states
-
-        hidden_states = self.encoder_attn_layer_norm(hidden_states)
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights, cross_attn_weights)
-
-        return outputs
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead
-class DetaClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, hidden_states: torch.Tensor):
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrPreTrainedModel with DeformableDetr->Deta
-class DetaPreTrainedModel(PreTrainedModel):
-    config_class = DetaConfig
-    base_model_prefix = "model"
-    main_input_name = "pixel_values"
-
-    def _init_weights(self, module):
-        std = self.config.init_std
-
-        if isinstance(module, DetaLearnedPositionEmbedding):
-            nn.init.uniform_(module.row_embeddings.weight)
-            nn.init.uniform_(module.column_embeddings.weight)
-        elif isinstance(module, DetaMultiscaleDeformableAttention):
-            module._reset_parameters()
-        elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        if hasattr(module, "reference_points") and not self.config.two_stage:
-            nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
-            nn.init.constant_(module.reference_points.bias.data, 0.0)
-        if hasattr(module, "level_embed"):
-            nn.init.normal_(module.level_embed)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, DetaDecoder):
-            module.gradient_checkpointing = value
-
-
-DETA_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`DetaConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-DETA_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it.
-
-            Pixel values can be obtained using [`AutoImageProcessor`]. See [`AutoImageProcessor.__call__`] for details.
-
-        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
-            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
-
-            - 1 for pixels that are real (i.e. **not masked**),
-            - 0 for pixels that are padding (i.e. **masked**).
-
-            [What are attention masks?](../glossary#attention-mask)
-
-        decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*):
-            Not used by default. Can be used to mask object queries.
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
-            can choose to directly pass a flattened representation of an image.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
-            Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
-            embedded representation.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrEncoder with DeformableDetr->Deta
-class DetaEncoder(DetaPreTrainedModel):
-    """
-    Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
-    [`DetaEncoderLayer`].
-
-    The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
-
-    Args:
-        config: DetaConfig
-    """
-
-    def __init__(self, config: DetaConfig):
-        super().__init__(config)
-
-        self.dropout = config.dropout
-        self.layers = nn.ModuleList([DetaEncoderLayer(config) for _ in range(config.encoder_layers)])
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @staticmethod
-    def get_reference_points(spatial_shapes, valid_ratios, device):
-        """
-        Get reference points for each feature map. Used in decoder.
-
-        Args:
-            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
-                Spatial shapes of each feature map.
-            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
-                Valid ratios of each feature map.
-            device (`torch.device`):
-                Device on which to create the tensors.
-        Returns:
-            `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
-        """
-        reference_points_list = []
-        for level, (height, width) in enumerate(spatial_shapes):
-            ref_y, ref_x = meshgrid(
-                torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device),
-                torch.linspace(0.5, width - 0.5, width, dtype=torch.float32, device=device),
-                indexing="ij",
-            )
-            # TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36
-            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height)
-            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width)
-            ref = torch.stack((ref_x, ref_y), -1)
-            reference_points_list.append(ref)
-        reference_points = torch.cat(reference_points_list, 1)
-        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
-        return reference_points
-
-    def forward(
-        self,
-        inputs_embeds=None,
-        attention_mask=None,
-        position_embeddings=None,
-        spatial_shapes=None,
-        level_start_index=None,
-        valid_ratios=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
-                - 1 for pixel features that are real (i.e. **not masked**),
-                - 0 for pixel features that are padding (i.e. **masked**).
-                [What are attention masks?](../glossary#attention-mask)
-            position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Position embeddings that are added to the queries and keys in each self-attention layer.
-            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
-                Spatial shapes of each feature map.
-            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
-                Starting index of each feature map.
-            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
-                Ratio of valid area in each feature level.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        hidden_states = inputs_embeds
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=inputs_embeds.device)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-        for i, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            layer_outputs = encoder_layer(
-                hidden_states,
-                attention_mask,
-                position_embeddings=position_embeddings,
-                reference_points=reference_points,
-                spatial_shapes=spatial_shapes,
-                level_start_index=level_start_index,
-                output_attentions=output_attentions,
-            )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrDecoder with DeformableDetr->Deta,Deformable DETR->DETA
-class DetaDecoder(DetaPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetaDecoderLayer`].
-
-    The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
-
-    Some tweaks for Deformable DETR:
-
-    - `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass.
-    - it also returns a stack of intermediate outputs and reference points from all decoding layers.
-
-    Args:
-        config: DetaConfig
-    """
-
-    def __init__(self, config: DetaConfig):
-        super().__init__(config)
-
-        self.dropout = config.dropout
-        self.layers = nn.ModuleList([DetaDecoderLayer(config) for _ in range(config.decoder_layers)])
-        self.gradient_checkpointing = False
-
-        # hack implementation for iterative bounding box refinement and two-stage Deformable DETR
-        self.bbox_embed = None
-        self.class_embed = None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        position_embeddings=None,
-        reference_points=None,
-        spatial_shapes=None,
-        level_start_index=None,
-        valid_ratios=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
-                The query embeddings that are passed into the decoder.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
-                of the decoder.
-            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
-                in `[0, 1]`:
-                - 1 for pixels that are real (i.e. **not masked**),
-                - 0 for pixels that are padding (i.e. **masked**).
-            position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
-                Position embeddings that are added to the queries and keys in each self-attention layer.
-            reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*):
-                Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area.
-            spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`):
-                Spatial shapes of the feature maps.
-            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*):
-                Indexes for the start of each feature level. In range `[0, sequence_length]`.
-            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*):
-                Ratio of valid area in each feature level.
-
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if inputs_embeds is not None:
-            hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
-        intermediate = ()
-        intermediate_reference_points = ()
-
-        for idx, decoder_layer in enumerate(self.layers):
-            if reference_points.shape[-1] == 4:
-                reference_points_input = (
-                    reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
-                )
-            else:
-                if reference_points.shape[-1] != 2:
-                    raise ValueError("Reference points' last dimension must be of size 2")
-                reference_points_input = reference_points[:, :, None] * valid_ratios[:, None]
-
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    position_embeddings=position_embeddings,
-                    encoder_hidden_states=encoder_hidden_states,
-                    reference_points=reference_points_input,
-                    spatial_shapes=spatial_shapes,
-                    level_start_index=level_start_index,
-                    encoder_attention_mask=encoder_attention_mask,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            # hack implementation for iterative bounding box refinement
-            if self.bbox_embed is not None:
-                tmp = self.bbox_embed[idx](hidden_states)
-                if reference_points.shape[-1] == 4:
-                    new_reference_points = tmp + inverse_sigmoid(reference_points)
-                    new_reference_points = new_reference_points.sigmoid()
-                else:
-                    if reference_points.shape[-1] != 2:
-                        raise ValueError(
-                            f"Reference points' last dimension must be of size 2, but is {reference_points.shape[-1]}"
-                        )
-                    new_reference_points = tmp
-                    new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)
-                    new_reference_points = new_reference_points.sigmoid()
-                reference_points = new_reference_points.detach()
-
-            intermediate += (hidden_states,)
-            intermediate_reference_points += (reference_points,)
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-                if encoder_hidden_states is not None:
-                    all_cross_attentions += (layer_outputs[2],)
-
-        # Keep batch_size as first dimension
-        intermediate = torch.stack(intermediate, dim=1)
-        intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    intermediate,
-                    intermediate_reference_points,
-                    all_hidden_states,
-                    all_self_attns,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return DetaDecoderOutput(
-            last_hidden_state=hidden_states,
-            intermediate_hidden_states=intermediate,
-            intermediate_reference_points=intermediate_reference_points,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The bare DETA Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without
-    any specific head on top.
-    """,
-    DETA_START_DOCSTRING,
-)
-class DetaModel(DetaPreTrainedModel):
-    def __init__(self, config: DetaConfig):
-        super().__init__(config)
-
-        if config.two_stage:
-            requires_backends(self, ["torchvision"])
-
-        # Create backbone with positional encoding
-        self.backbone = DetaBackboneWithPositionalEncodings(config)
-        intermediate_channel_sizes = self.backbone.intermediate_channel_sizes
-
-        # Create input projection layers
-        if config.num_feature_levels > 1:
-            num_backbone_outs = len(intermediate_channel_sizes)
-            input_proj_list = []
-            for _ in range(num_backbone_outs):
-                in_channels = intermediate_channel_sizes[_]
-                input_proj_list.append(
-                    nn.Sequential(
-                        nn.Conv2d(in_channels, config.d_model, kernel_size=1),
-                        nn.GroupNorm(32, config.d_model),
-                    )
-                )
-            for _ in range(config.num_feature_levels - num_backbone_outs):
-                input_proj_list.append(
-                    nn.Sequential(
-                        nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1),
-                        nn.GroupNorm(32, config.d_model),
-                    )
-                )
-                in_channels = config.d_model
-            self.input_proj = nn.ModuleList(input_proj_list)
-        else:
-            self.input_proj = nn.ModuleList(
-                [
-                    nn.Sequential(
-                        nn.Conv2d(intermediate_channel_sizes[-1], config.d_model, kernel_size=1),
-                        nn.GroupNorm(32, config.d_model),
-                    )
-                ]
-            )
-
-        if not config.two_stage:
-            self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model * 2)
-
-        self.encoder = DetaEncoder(config)
-        self.decoder = DetaDecoder(config)
-
-        self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))
-
-        if config.two_stage:
-            self.enc_output = nn.Linear(config.d_model, config.d_model)
-            self.enc_output_norm = nn.LayerNorm(config.d_model)
-            self.pos_trans = nn.Linear(config.d_model * 2, config.d_model * 2)
-            self.pos_trans_norm = nn.LayerNorm(config.d_model * 2)
-            self.pix_trans = nn.Linear(config.d_model, config.d_model)
-            self.pix_trans_norm = nn.LayerNorm(config.d_model)
-        else:
-            self.reference_points = nn.Linear(config.d_model, 2)
-
-        self.assign_first_stage = config.assign_first_stage
-        self.two_stage_num_proposals = config.two_stage_num_proposals
-
-        self.post_init()
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_encoder
-    def get_encoder(self):
-        return self.encoder
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_decoder
-    def get_decoder(self):
-        return self.decoder
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.freeze_backbone
-    def freeze_backbone(self):
-        for name, param in self.backbone.conv_encoder.model.named_parameters():
-            param.requires_grad_(False)
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.unfreeze_backbone
-    def unfreeze_backbone(self):
-        for name, param in self.backbone.conv_encoder.model.named_parameters():
-            param.requires_grad_(True)
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_valid_ratio
-    def get_valid_ratio(self, mask):
-        """Get the valid ratio of all feature maps."""
-
-        _, height, width = mask.shape
-        valid_height = torch.sum(mask[:, :, 0], 1)
-        valid_width = torch.sum(mask[:, 0, :], 1)
-        valid_ratio_heigth = valid_height.float() / height
-        valid_ratio_width = valid_width.float() / width
-        valid_ratio = torch.stack([valid_ratio_width, valid_ratio_heigth], -1)
-        return valid_ratio
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_proposal_pos_embed
-    def get_proposal_pos_embed(self, proposals):
-        """Get the position embedding of the proposals."""
-
-        num_pos_feats = self.config.d_model // 2
-        temperature = 10000
-        scale = 2 * math.pi
-
-        dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
-        dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
-        # batch_size, num_queries, 4
-        proposals = proposals.sigmoid() * scale
-        # batch_size, num_queries, 4, 128
-        pos = proposals[:, :, :, None] / dim_t
-        # batch_size, num_queries, 4, 64, 2 -> batch_size, num_queries, 512
-        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
-        return pos
-
-    def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes):
-        """Generate the encoder output proposals from encoded enc_output.
-
-        Args:
-            enc_output (Tensor[batch_size, sequence_length, hidden_size]): Output of the encoder.
-            padding_mask (Tensor[batch_size, sequence_length]): Padding mask for `enc_output`.
-            spatial_shapes (Tensor[num_feature_levels, 2]): Spatial shapes of the feature maps.
-
-        Returns:
-            `tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction.
-                - object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to
-                  directly predict a bounding box. (without the need of a decoder)
-                - output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse
-                  sigmoid.
-        """
-        batch_size = enc_output.shape[0]
-        proposals = []
-        _cur = 0
-        level_ids = []
-        for level, (height, width) in enumerate(spatial_shapes):
-            mask_flatten_ = padding_mask[:, _cur : (_cur + height * width)].view(batch_size, height, width, 1)
-            valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
-            valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
-
-            grid_y, grid_x = meshgrid(
-                torch.linspace(0, height - 1, height, dtype=torch.float32, device=enc_output.device),
-                torch.linspace(0, width - 1, width, dtype=torch.float32, device=enc_output.device),
-                indexing="ij",
-            )
-            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
-
-            scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2)
-            grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale
-            width_heigth = torch.ones_like(grid) * 0.05 * (2.0**level)
-            proposal = torch.cat((grid, width_heigth), -1).view(batch_size, -1, 4)
-            proposals.append(proposal)
-            _cur += height * width
-            level_ids.append(grid.new_ones(height * width, dtype=torch.long) * level)
-        output_proposals = torch.cat(proposals, 1)
-        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
-        output_proposals = torch.log(output_proposals / (1 - output_proposals))  # inverse sigmoid
-        output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf"))
-        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
-
-        # assign each pixel as an object query
-        object_query = enc_output
-        object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0))
-        object_query = object_query.masked_fill(~output_proposals_valid, float(0))
-        object_query = self.enc_output_norm(self.enc_output(object_query))
-        level_ids = torch.cat(level_ids)
-        return object_query, output_proposals, level_ids
-
-    @add_start_docstrings_to_model_forward(DETA_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=DetaModelOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        pixel_mask: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_outputs: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], DetaModelOutput]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, DetaModel
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("jozhang97/deta-swin-large-o365")
-        >>> model = DetaModel.from_pretrained("jozhang97/deta-swin-large-o365", two_stage=False)
-
-        >>> inputs = image_processor(images=image, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-
-        >>> last_hidden_states = outputs.last_hidden_state
-        >>> list(last_hidden_states.shape)
-        [1, 900, 256]
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        batch_size, num_channels, height, width = pixel_values.shape
-        device = pixel_values.device
-
-        if pixel_mask is None:
-            pixel_mask = torch.ones(((batch_size, height, width)), dtype=torch.long, device=device)
-
-        # Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper)
-        # First, sent pixel_values + pixel_mask through Backbone to obtain the features
-        # which is a list of tuples
-        features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)
-
-        # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
-        sources = []
-        masks = []
-        for level, (source, mask) in enumerate(features):
-            sources.append(self.input_proj[level](source))
-            masks.append(mask)
-            if mask is None:
-                raise ValueError("No attention mask was provided")
-
-        # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage
-        if self.config.num_feature_levels > len(sources):
-            _len_sources = len(sources)
-            for level in range(_len_sources, self.config.num_feature_levels):
-                if level == _len_sources:
-                    source = self.input_proj[level](features[-1][0])
-                else:
-                    source = self.input_proj[level](sources[-1])
-                mask = nn.functional.interpolate(pixel_mask[None].float(), size=source.shape[-2:]).to(torch.bool)[0]
-                pos_l = self.backbone.position_embedding(source, mask).to(source.dtype)
-                sources.append(source)
-                masks.append(mask)
-                position_embeddings_list.append(pos_l)
-
-        # Create queries
-        query_embeds = None
-        if not self.config.two_stage:
-            query_embeds = self.query_position_embeddings.weight
-
-        # Prepare encoder inputs (by flattening)
-        spatial_shapes = [(source.shape[2:]) for source in sources]
-        source_flatten = [source.flatten(2).transpose(1, 2) for source in sources]
-        mask_flatten = [mask.flatten(1) for mask in masks]
-
-        lvl_pos_embed_flatten = []
-        for level, pos_embed in enumerate(position_embeddings_list):
-            pos_embed = pos_embed.flatten(2).transpose(1, 2)
-            lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1)
-            lvl_pos_embed_flatten.append(lvl_pos_embed)
-
-        source_flatten = torch.cat(source_flatten, 1)
-        mask_flatten = torch.cat(mask_flatten, 1)
-        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
-        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=source_flatten.device)
-        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
-        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
-        valid_ratios = valid_ratios.float()
-
-        # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder
-        # Also provide spatial_shapes, level_start_index and valid_ratios
-        if encoder_outputs is None:
-            encoder_outputs = self.encoder(
-                inputs_embeds=source_flatten,
-                attention_mask=mask_flatten,
-                position_embeddings=lvl_pos_embed_flatten,
-                spatial_shapes=spatial_shapes,
-                level_start_index=level_start_index,
-                valid_ratios=valid_ratios,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        # Fifth, prepare decoder inputs
-        batch_size, _, num_channels = encoder_outputs[0].shape
-        enc_outputs_class = None
-        enc_outputs_coord_logits = None
-        if self.config.two_stage:
-            object_query_embedding, output_proposals, level_ids = self.gen_encoder_output_proposals(
-                encoder_outputs[0], ~mask_flatten, spatial_shapes
-            )
-
-            # hack implementation for two-stage DETA
-            # apply a detection head to each pixel (A.4 in paper)
-            # linear projection for bounding box binary classification (i.e. foreground and background)
-            enc_outputs_class = self.decoder.class_embed[-1](object_query_embedding)
-            # 3-layer FFN to predict bounding boxes coordinates (bbox regression branch)
-            delta_bbox = self.decoder.bbox_embed[-1](object_query_embedding)
-            enc_outputs_coord_logits = delta_bbox + output_proposals
-
-            # only keep top scoring `config.two_stage_num_proposals` proposals
-            topk = self.two_stage_num_proposals
-            proposal_logit = enc_outputs_class[..., 0]
-
-            if self.assign_first_stage:
-                proposal_boxes = center_to_corners_format(enc_outputs_coord_logits.sigmoid().float()).clamp(0, 1)
-                topk_proposals = []
-                for b in range(batch_size):
-                    prop_boxes_b = proposal_boxes[b]
-                    prop_logits_b = proposal_logit[b]
-
-                    # pre-nms per-level topk
-                    pre_nms_topk = 1000
-                    pre_nms_inds = []
-                    for lvl in range(len(spatial_shapes)):
-                        lvl_mask = level_ids == lvl
-                        pre_nms_inds.append(torch.topk(prop_logits_b.sigmoid() * lvl_mask, pre_nms_topk)[1])
-                    pre_nms_inds = torch.cat(pre_nms_inds)
-
-                    # nms on topk indices
-                    post_nms_inds = batched_nms(
-                        prop_boxes_b[pre_nms_inds], prop_logits_b[pre_nms_inds], level_ids[pre_nms_inds], 0.9
-                    )
-                    keep_inds = pre_nms_inds[post_nms_inds]
-
-                    if len(keep_inds) < self.two_stage_num_proposals:
-                        print(
-                            f"[WARNING] nms proposals ({len(keep_inds)}) < {self.two_stage_num_proposals}, running"
-                            " naive topk"
-                        )
-                        keep_inds = torch.topk(proposal_logit[b], topk)[1]
-
-                    # keep top Q/L indices for L levels
-                    q_per_l = topk // len(spatial_shapes)
-                    is_level_ordered = (
-                        level_ids[keep_inds][None]
-                        == torch.arange(len(spatial_shapes), device=level_ids.device)[:, None]
-                    )
-                    keep_inds_mask = is_level_ordered & (is_level_ordered.cumsum(1) <= q_per_l)  # LS
-                    keep_inds_mask = keep_inds_mask.any(0)  # S
-
-                    # pad to Q indices (might let ones filtered from pre-nms sneak by... unlikely because we pick high conf anyways)
-                    if keep_inds_mask.sum() < topk:
-                        num_to_add = topk - keep_inds_mask.sum()
-                        pad_inds = (~keep_inds_mask).nonzero()[:num_to_add]
-                        keep_inds_mask[pad_inds] = True
-
-                    keep_inds_topk = keep_inds[keep_inds_mask]
-                    topk_proposals.append(keep_inds_topk)
-                topk_proposals = torch.stack(topk_proposals)
-            else:
-                topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]
-
-            topk_coords_logits = torch.gather(
-                enc_outputs_coord_logits, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            )
-            topk_coords_logits = topk_coords_logits.detach()
-            reference_points = topk_coords_logits.sigmoid()
-            init_reference_points = reference_points
-            pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_logits)))
-            query_embed, target = torch.split(pos_trans_out, num_channels, dim=2)
-        else:
-            query_embed, target = torch.split(query_embeds, num_channels, dim=1)
-            query_embed = query_embed.unsqueeze(0).expand(batch_size, -1, -1)
-            target = target.unsqueeze(0).expand(batch_size, -1, -1)
-            reference_points = self.reference_points(query_embed).sigmoid()
-            init_reference_points = reference_points
-
-        decoder_outputs = self.decoder(
-            inputs_embeds=target,
-            position_embeddings=query_embed,
-            encoder_hidden_states=encoder_outputs[0],
-            encoder_attention_mask=mask_flatten,
-            reference_points=reference_points,
-            spatial_shapes=spatial_shapes,
-            level_start_index=level_start_index,
-            valid_ratios=valid_ratios,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if not return_dict:
-            enc_outputs = tuple(value for value in [enc_outputs_class, enc_outputs_coord_logits] if value is not None)
-            tuple_outputs = (init_reference_points,) + decoder_outputs + encoder_outputs + enc_outputs
-
-            return tuple_outputs
-
-        return DetaModelOutput(
-            init_reference_points=init_reference_points,
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
-            intermediate_reference_points=decoder_outputs.intermediate_reference_points,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-            enc_outputs_class=enc_outputs_class,
-            enc_outputs_coord_logits=enc_outputs_coord_logits,
-        )
-
-
-@add_start_docstrings(
-    """
-    DETA Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks
-    such as COCO detection.
-    """,
-    DETA_START_DOCSTRING,
-)
-class DetaForObjectDetection(DetaPreTrainedModel):
-    # When using clones, all layers > 0 will be clones, but layer 0 *is* required
-    _tied_weights_keys = [r"bbox_embed\.\d+"]
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrForObjectDetection.__init__ with DeformableDetr->Deta
-    def __init__(self, config: DetaConfig):
-        super().__init__(config)
-
-        # Deformable DETR encoder-decoder model
-        self.model = DetaModel(config)
-
-        # Detection heads on top
-        self.class_embed = nn.Linear(config.d_model, config.num_labels)
-        self.bbox_embed = DetaMLPPredictionHead(
-            input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
-        )
-
-        prior_prob = 0.01
-        bias_value = -math.log((1 - prior_prob) / prior_prob)
-        self.class_embed.bias.data = torch.ones(config.num_labels) * bias_value
-        nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)
-        nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)
-
-        # if two-stage, the last class_embed and bbox_embed is for region proposal generation
-        num_pred = (config.decoder_layers + 1) if config.two_stage else config.decoder_layers
-        if config.with_box_refine:
-            self.class_embed = _get_clones(self.class_embed, num_pred)
-            self.bbox_embed = _get_clones(self.bbox_embed, num_pred)
-            nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0)
-            # hack implementation for iterative bounding box refinement
-            self.model.decoder.bbox_embed = self.bbox_embed
-        else:
-            nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0)
-            self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)])
-            self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)])
-            self.model.decoder.bbox_embed = None
-        if config.two_stage:
-            # hack implementation for two-stage
-            self.model.decoder.class_embed = self.class_embed
-            for box_embed in self.bbox_embed:
-                nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @torch.jit.unused
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrForObjectDetection._set_aux_loss
-    def _set_aux_loss(self, outputs_class, outputs_coord):
-        # this is a workaround to make torchscript happy, as torchscript
-        # doesn't support dictionary with non-homogeneous values, such
-        # as a dict having both a Tensor and a list.
-        return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
-
-    @add_start_docstrings_to_model_forward(DETA_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=DetaObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        pixel_mask: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_outputs: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[List[dict]] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], DetaObjectDetectionOutput]:
-        r"""
-        labels (`List[Dict]` of len `(batch_size,)`, *optional*):
-            Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
-            following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
-            respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
-            in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, DetaForObjectDetection
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("jozhang97/deta-swin-large")
-        >>> model = DetaForObjectDetection.from_pretrained("jozhang97/deta-swin-large")
-
-        >>> inputs = image_processor(images=image, return_tensors="pt")
-        >>> outputs = model(**inputs)
-
-        >>> # convert outputs (bounding boxes and class logits) to COCO API
-        >>> target_sizes = torch.tensor([image.size[::-1]])
-        >>> results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[
-        ...     0
-        ... ]
-        >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        ...     box = [round(i, 2) for i in box.tolist()]
-        ...     print(
-        ...         f"Detected {model.config.id2label[label.item()]} with confidence "
-        ...         f"{round(score.item(), 3)} at location {box}"
-        ...     )
-        Detected cat with confidence 0.683 at location [345.85, 23.68, 639.86, 372.83]
-        Detected cat with confidence 0.683 at location [8.8, 52.49, 316.93, 473.45]
-        Detected remote with confidence 0.568 at location [40.02, 73.75, 175.96, 117.33]
-        Detected remote with confidence 0.546 at location [333.68, 77.13, 370.12, 187.51]
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # First, sent images through DETR base model to obtain encoder + decoder outputs
-        outputs = self.model(
-            pixel_values,
-            pixel_mask=pixel_mask,
-            decoder_attention_mask=decoder_attention_mask,
-            encoder_outputs=encoder_outputs,
-            inputs_embeds=inputs_embeds,
-            decoder_inputs_embeds=decoder_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2]
-        init_reference = outputs.init_reference_points if return_dict else outputs[0]
-        inter_references = outputs.intermediate_reference_points if return_dict else outputs[3]
-
-        # class logits + predicted bounding boxes
-        outputs_classes = []
-        outputs_coords = []
-
-        for level in range(hidden_states.shape[1]):
-            if level == 0:
-                reference = init_reference
-            else:
-                reference = inter_references[:, level - 1]
-            reference = inverse_sigmoid(reference)
-            outputs_class = self.class_embed[level](hidden_states[:, level])
-            delta_bbox = self.bbox_embed[level](hidden_states[:, level])
-            if reference.shape[-1] == 4:
-                outputs_coord_logits = delta_bbox + reference
-            elif reference.shape[-1] == 2:
-                delta_bbox[..., :2] += reference
-                outputs_coord_logits = delta_bbox
-            else:
-                raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}")
-            outputs_coord = outputs_coord_logits.sigmoid()
-            outputs_classes.append(outputs_class)
-            outputs_coords.append(outputs_coord)
-        # Keep batch_size as first dimension
-        outputs_class = torch.stack(outputs_classes, dim=1)
-        outputs_coord = torch.stack(outputs_coords, dim=1)
-
-        logits = outputs_class[:, -1]
-        pred_boxes = outputs_coord[:, -1]
-
-        loss, loss_dict, auxiliary_outputs = None, None, None
-        if labels is not None:
-            # First: create the matcher
-            matcher = DetaHungarianMatcher(
-                class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
-            )
-            # Second: create the criterion
-            losses = ["labels", "boxes", "cardinality"]
-            criterion = DetaLoss(
-                matcher=matcher,
-                num_classes=self.config.num_labels,
-                focal_alpha=self.config.focal_alpha,
-                losses=losses,
-                num_queries=self.config.num_queries,
-            )
-            criterion.to(logits.device)
-            # Third: compute the losses, based on outputs and labels
-            outputs_loss = {}
-            outputs_loss["logits"] = logits
-            outputs_loss["pred_boxes"] = pred_boxes
-            if self.config.auxiliary_loss:
-                intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4]
-                outputs_class = self.class_embed(intermediate)
-                outputs_coord = self.bbox_embed(intermediate).sigmoid()
-                auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
-                outputs_loss["auxiliary_outputs"] = auxiliary_outputs
-            if self.config.two_stage:
-                enc_outputs_coord = outputs.enc_outputs_coord_logits.sigmoid()
-                outputs["enc_outputs"] = {"pred_logits": outputs.enc_outputs_class, "pred_boxes": enc_outputs_coord}
-
-            loss_dict = criterion(outputs_loss, labels)
-            # Fourth: compute total loss, as a weighted sum of the various losses
-            weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient}
-            weight_dict["loss_giou"] = self.config.giou_loss_coefficient
-            if self.config.auxiliary_loss:
-                aux_weight_dict = {}
-                for i in range(self.config.decoder_layers - 1):
-                    aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
-                weight_dict.update(aux_weight_dict)
-            loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
-
-        if not return_dict:
-            if auxiliary_outputs is not None:
-                output = (logits, pred_boxes) + auxiliary_outputs + outputs
-            else:
-                output = (logits, pred_boxes) + outputs
-            tuple_outputs = ((loss, loss_dict) + output) if loss is not None else output
-
-            return tuple_outputs
-
-        dict_outputs = DetaObjectDetectionOutput(
-            loss=loss,
-            loss_dict=loss_dict,
-            logits=logits,
-            pred_boxes=pred_boxes,
-            auxiliary_outputs=auxiliary_outputs,
-            last_hidden_state=outputs.last_hidden_state,
-            decoder_hidden_states=outputs.decoder_hidden_states,
-            decoder_attentions=outputs.decoder_attentions,
-            cross_attentions=outputs.cross_attentions,
-            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
-            encoder_hidden_states=outputs.encoder_hidden_states,
-            encoder_attentions=outputs.encoder_attentions,
-            intermediate_hidden_states=outputs.intermediate_hidden_states,
-            intermediate_reference_points=outputs.intermediate_reference_points,
-            init_reference_points=outputs.init_reference_points,
-            enc_outputs_class=outputs.enc_outputs_class,
-            enc_outputs_coord_logits=outputs.enc_outputs_coord_logits,
-        )
-
-        return dict_outputs
-
-
-# Copied from transformers.models.detr.modeling_detr.dice_loss
-def dice_loss(inputs, targets, num_boxes):
-    """
-    Compute the DICE loss, similar to generalized IOU for masks
-
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs (0 for the negative class and 1 for the positive
-                 class).
-    """
-    inputs = inputs.sigmoid()
-    inputs = inputs.flatten(1)
-    numerator = 2 * (inputs * targets).sum(1)
-    denominator = inputs.sum(-1) + targets.sum(-1)
-    loss = 1 - (numerator + 1) / (denominator + 1)
-    return loss.sum() / num_boxes
-
-
-# Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss
-def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
-    """
-    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
-
-    Args:
-        inputs (`torch.FloatTensor` of arbitrary shape):
-            The predictions for each example.
-        targets (`torch.FloatTensor` with the same shape as `inputs`)
-            A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class
-            and 1 for the positive class).
-        alpha (`float`, *optional*, defaults to `0.25`):
-            Optional weighting factor in the range (0,1) to balance positive vs. negative examples.
-        gamma (`int`, *optional*, defaults to `2`):
-            Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.
-
-    Returns:
-        Loss tensor
-    """
-    prob = inputs.sigmoid()
-    ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-    # add modulating factor
-    p_t = prob * targets + (1 - prob) * (1 - targets)
-    loss = ce_loss * ((1 - p_t) ** gamma)
-
-    if alpha >= 0:
-        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-        loss = alpha_t * loss
-
-    return loss.mean(1).sum() / num_boxes
-
-
-class DetaLoss(nn.Module):
-    """
-    This class computes the losses for `DetaForObjectDetection`. The process happens in two steps: 1) we compute
-    hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched
-    ground-truth / prediction (supervised class and box).
-
-    Args:
-        matcher (`DetaHungarianMatcher`):
-            Module able to compute a matching between targets and proposals.
-        num_classes (`int`):
-            Number of object categories, omitting the special no-object category.
-        focal_alpha (`float`):
-            Alpha parameter in focal loss.
-        losses (`List[str]`):
-            List of all the losses to be applied. See `get_loss` for a list of all available losses.
-    """
-
-    def __init__(
-        self,
-        matcher,
-        num_classes,
-        focal_alpha,
-        losses,
-        num_queries,
-        assign_first_stage=False,
-        assign_second_stage=False,
-    ):
-        super().__init__()
-        self.matcher = matcher
-        self.num_classes = num_classes
-        self.focal_alpha = focal_alpha
-        self.losses = losses
-        self.assign_first_stage = assign_first_stage
-        self.assign_second_stage = assign_second_stage
-
-        if self.assign_first_stage:
-            self.stg1_assigner = DetaStage1Assigner()
-        if self.assign_second_stage:
-            self.stg2_assigner = DetaStage2Assigner(num_queries)
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_labels
-    def loss_labels(self, outputs, targets, indices, num_boxes):
-        """
-        Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor
-        of dim [nb_target_boxes]
-        """
-        if "logits" not in outputs:
-            raise KeyError("No logits were found in the outputs")
-        source_logits = outputs["logits"]
-
-        idx = self._get_source_permutation_idx(indices)
-        target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
-        target_classes = torch.full(
-            source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
-        )
-        target_classes[idx] = target_classes_o
-
-        target_classes_onehot = torch.zeros(
-            [source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1],
-            dtype=source_logits.dtype,
-            layout=source_logits.layout,
-            device=source_logits.device,
-        )
-        target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)
-
-        target_classes_onehot = target_classes_onehot[:, :, :-1]
-        loss_ce = (
-            sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2)
-            * source_logits.shape[1]
-        )
-        losses = {"loss_ce": loss_ce}
-
-        return losses
-
-    @torch.no_grad()
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_cardinality
-    def loss_cardinality(self, outputs, targets, indices, num_boxes):
-        """
-        Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
-
-        This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
-        """
-        logits = outputs["logits"]
-        device = logits.device
-        target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device)
-        # Count the number of predictions that are NOT "no-object" (which is the last class)
-        card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1)
-        card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float())
-        losses = {"cardinality_error": card_err}
-        return losses
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_boxes
-    def loss_boxes(self, outputs, targets, indices, num_boxes):
-        """
-        Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.
-
-        Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
-        are expected in format (center_x, center_y, w, h), normalized by the image size.
-        """
-        if "pred_boxes" not in outputs:
-            raise KeyError("No predicted boxes found in outputs")
-        idx = self._get_source_permutation_idx(indices)
-        source_boxes = outputs["pred_boxes"][idx]
-        target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0)
-
-        loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none")
-
-        losses = {}
-        losses["loss_bbox"] = loss_bbox.sum() / num_boxes
-
-        loss_giou = 1 - torch.diag(
-            generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes))
-        )
-        losses["loss_giou"] = loss_giou.sum() / num_boxes
-        return losses
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_source_permutation_idx
-    def _get_source_permutation_idx(self, indices):
-        # permute predictions following indices
-        batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)])
-        source_idx = torch.cat([source for (source, _) in indices])
-        return batch_idx, source_idx
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_target_permutation_idx
-    def _get_target_permutation_idx(self, indices):
-        # permute targets following indices
-        batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)])
-        target_idx = torch.cat([target for (_, target) in indices])
-        return batch_idx, target_idx
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.get_loss
-    def get_loss(self, loss, outputs, targets, indices, num_boxes):
-        loss_map = {
-            "labels": self.loss_labels,
-            "cardinality": self.loss_cardinality,
-            "boxes": self.loss_boxes,
-        }
-        if loss not in loss_map:
-            raise ValueError(f"Loss {loss} not supported")
-        return loss_map[loss](outputs, targets, indices, num_boxes)
-
-    def forward(self, outputs, targets):
-        """
-        This performs the loss computation.
-
-        Args:
-             outputs (`dict`, *optional*):
-                Dictionary of tensors, see the output specification of the model for the format.
-             targets (`List[dict]`, *optional*):
-                List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
-                losses applied, see each loss' doc.
-        """
-        outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"}
-
-        # Retrieve the matching between the outputs of the last layer and the targets
-        if self.assign_second_stage:
-            indices = self.stg2_assigner(outputs_without_aux, targets)
-        else:
-            indices = self.matcher(outputs_without_aux, targets)
-
-        # Compute the average number of target boxes accross all nodes, for normalization purposes
-        num_boxes = sum(len(t["class_labels"]) for t in targets)
-        num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
-        # (Niels): comment out function below, distributed training to be added
-        # if is_dist_avail_and_initialized():
-        #     torch.distributed.all_reduce(num_boxes)
-        # (Niels) in original implementation, num_boxes is divided by get_world_size()
-        num_boxes = torch.clamp(num_boxes, min=1).item()
-
-        # Compute all the requested losses
-        losses = {}
-        for loss in self.losses:
-            losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))
-
-        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
-        if "auxiliary_outputs" in outputs:
-            for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
-                if not self.assign_second_stage:
-                    indices = self.matcher(auxiliary_outputs, targets)
-                for loss in self.losses:
-                    l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes)
-                    l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
-                    losses.update(l_dict)
-
-        if "enc_outputs" in outputs:
-            enc_outputs = outputs["enc_outputs"]
-            bin_targets = copy.deepcopy(targets)
-            for bt in bin_targets:
-                bt["labels"] = torch.zeros_like(bt["labels"])
-            if self.assign_first_stage:
-                indices = self.stg1_assigner(enc_outputs, bin_targets)
-            else:
-                indices = self.matcher(enc_outputs, bin_targets)
-            for loss in self.losses:
-                kwargs = {}
-                if loss == "labels":
-                    # Logging is enabled only for the last layer
-                    kwargs["log"] = False
-                l_dict = self.get_loss(loss, enc_outputs, bin_targets, indices, num_boxes, **kwargs)
-                l_dict = {k + "_enc": v for k, v in l_dict.items()}
-                losses.update(l_dict)
-
-        return losses
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead
-class DetaMLPPredictionHead(nn.Module):
-    """
-    Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
-    height and width of a bounding box w.r.t. an image.
-
-    Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
-
-    """
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrHungarianMatcher with DeformableDetr->Deta
-class DetaHungarianMatcher(nn.Module):
-    """
-    This class computes an assignment between the targets and the predictions of the network.
-
-    For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
-    predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are
-    un-matched (and thus treated as non-objects).
-
-    Args:
-        class_cost:
-            The relative weight of the classification error in the matching cost.
-        bbox_cost:
-            The relative weight of the L1 error of the bounding box coordinates in the matching cost.
-        giou_cost:
-            The relative weight of the giou loss of the bounding box in the matching cost.
-    """
-
-    def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1):
-        super().__init__()
-        requires_backends(self, ["scipy"])
-
-        self.class_cost = class_cost
-        self.bbox_cost = bbox_cost
-        self.giou_cost = giou_cost
-        if class_cost == 0 and bbox_cost == 0 and giou_cost == 0:
-            raise ValueError("All costs of the Matcher can't be 0")
-
-    @torch.no_grad()
-    def forward(self, outputs, targets):
-        """
-        Args:
-            outputs (`dict`):
-                A dictionary that contains at least these entries:
-                * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
-                * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates.
-            targets (`List[dict]`):
-                A list of targets (len(targets) = batch_size), where each target is a dict containing:
-                * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of
-                  ground-truth
-                 objects in the target) containing the class labels
-                * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates.
-
-        Returns:
-            `List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where:
-            - index_i is the indices of the selected predictions (in order)
-            - index_j is the indices of the corresponding selected targets (in order)
-            For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
-        """
-        batch_size, num_queries = outputs["logits"].shape[:2]
-
-        # We flatten to compute the cost matrices in a batch
-        out_prob = outputs["logits"].flatten(0, 1).sigmoid()  # [batch_size * num_queries, num_classes]
-        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
-
-        # Also concat the target labels and boxes
-        target_ids = torch.cat([v["class_labels"] for v in targets])
-        target_bbox = torch.cat([v["boxes"] for v in targets])
-
-        # Compute the classification cost.
-        alpha = 0.25
-        gamma = 2.0
-        neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())
-        pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
-        class_cost = pos_cost_class[:, target_ids] - neg_cost_class[:, target_ids]
-
-        # Compute the L1 cost between boxes
-        bbox_cost = torch.cdist(out_bbox, target_bbox, p=1)
-
-        # Compute the giou cost between boxes
-        giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox))
-
-        # Final cost matrix
-        cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost
-        cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu()
-
-        sizes = [len(v["boxes"]) for v in targets]
-        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
-        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
-
-
-# Copied from transformers.models.detr.modeling_detr._upcast
-def _upcast(t: Tensor) -> Tensor:
-    # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
-    if t.is_floating_point():
-        return t if t.dtype in (torch.float32, torch.float64) else t.float()
-    else:
-        return t if t.dtype in (torch.int32, torch.int64) else t.int()
-
-
-# Copied from transformers.models.detr.modeling_detr.box_area
-def box_area(boxes: Tensor) -> Tensor:
-    """
-    Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
-
-    Args:
-        boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
-            Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
-            < x2` and `0 <= y1 < y2`.
-
-    Returns:
-        `torch.FloatTensor`: a tensor containing the area for each box.
-    """
-    boxes = _upcast(boxes)
-    return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-
-
-# Copied from transformers.models.detr.modeling_detr.box_iou
-def box_iou(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    width_height = (right_bottom - left_top).clamp(min=0)  # [N,M,2]
-    inter = width_height[:, :, 0] * width_height[:, :, 1]  # [N,M]
-
-    union = area1[:, None] + area2 - inter
-
-    iou = inter / union
-    return iou, union
-
-
-# Copied from transformers.models.detr.modeling_detr.generalized_box_iou
-def generalized_box_iou(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
-
-    Returns:
-        `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    if not (boxes1[:, 2:] >= boxes1[:, :2]).all():
-        raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}")
-    if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
-        raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}")
-    iou, union = box_iou(boxes1, boxes2)
-
-    top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2])
-    bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
-    width_height = (bottom_right - top_left).clamp(min=0)  # [N,M,2]
-    area = width_height[:, :, 0] * width_height[:, :, 1]
-
-    return iou - (area - union) / area
-
-
-# from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/layers/wrappers.py#L100
-def nonzero_tuple(x):
-    """
-    A 'as_tuple=True' version of torch.nonzero to support torchscript. because of
-    https://github.com/pytorch/pytorch/issues/38718
-    """
-    if torch.jit.is_scripting():
-        if x.dim() == 0:
-            return x.unsqueeze(0).nonzero().unbind(1)
-        return x.nonzero().unbind(1)
-    else:
-        return x.nonzero(as_tuple=True)
-
-
-# from https://github.com/facebookresearch/detectron2/blob/9921a2caa585d4fa66c4b534b6fab6e74d89b582/detectron2/modeling/matcher.py#L9
-class DetaMatcher(object):
-    """
-    This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will
-    have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements.
-
-    The matching is determined by the MxN match_quality_matrix, that characterizes how well each (ground-truth,
-    prediction)-pair match each other. For example, if the elements are boxes, this matrix may contain box
-    intersection-over-union overlap values.
-
-    The matcher returns (a) a vector of length N containing the index of the ground-truth element m in [0, M) that
-    matches to prediction n in [0, N). (b) a vector of length N containing the labels for each prediction.
-    """
-
-    def __init__(self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False):
-        """
-        Args:
-            thresholds (`list[float]`):
-                A list of thresholds used to stratify predictions into levels.
-            labels (`list[int`):
-                A list of values to label predictions belonging at each level. A label can be one of {-1, 0, 1}
-                signifying {ignore, negative class, positive class}, respectively.
-            allow_low_quality_matches (`bool`, *optional*, defaults to `False`):
-                If `True`, produce additional matches for predictions with maximum match quality lower than
-                high_threshold. See `set_low_quality_matches_` for more details.
-
-            For example,
-                thresholds = [0.3, 0.5] labels = [0, -1, 1] All predictions with iou < 0.3 will be marked with 0 and
-                thus will be considered as false positives while training. All predictions with 0.3 <= iou < 0.5 will
-                be marked with -1 and thus will be ignored. All predictions with 0.5 <= iou will be marked with 1 and
-                thus will be considered as true positives.
-        """
-        # Add -inf and +inf to first and last position in thresholds
-        thresholds = thresholds[:]
-        if thresholds[0] < 0:
-            raise ValueError("Thresholds should be positive")
-        thresholds.insert(0, -float("inf"))
-        thresholds.append(float("inf"))
-        # Currently torchscript does not support all + generator
-        if not all(low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])):
-            raise ValueError("Thresholds should be sorted.")
-        if not all(l in [-1, 0, 1] for l in labels):
-            raise ValueError("All labels should be either -1, 0 or 1")
-        if len(labels) != len(thresholds) - 1:
-            raise ValueError("Number of labels should be equal to number of thresholds - 1")
-        self.thresholds = thresholds
-        self.labels = labels
-        self.allow_low_quality_matches = allow_low_quality_matches
-
-    def __call__(self, match_quality_matrix):
-        """
-        Args:
-            match_quality_matrix (Tensor[float]): an MxN tensor, containing the
-                pairwise quality between M ground-truth elements and N predicted elements. All elements must be >= 0
-                (due to the us of `torch.nonzero` for selecting indices in `set_low_quality_matches_`).
-
-        Returns:
-            matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
-                ground-truth index in [0, M)
-            match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
-                whether a prediction is a true or false positive or ignored
-        """
-        assert match_quality_matrix.dim() == 2
-        if match_quality_matrix.numel() == 0:
-            default_matches = match_quality_matrix.new_full((match_quality_matrix.size(1),), 0, dtype=torch.int64)
-            # When no gt boxes exist, we define IOU = 0 and therefore set labels
-            # to `self.labels[0]`, which usually defaults to background class 0
-            # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds
-            default_match_labels = match_quality_matrix.new_full(
-                (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
-            )
-            return default_matches, default_match_labels
-
-        assert torch.all(match_quality_matrix >= 0)
-
-        # match_quality_matrix is M (gt) x N (predicted)
-        # Max over gt elements (dim 0) to find best gt candidate for each prediction
-        matched_vals, matches = match_quality_matrix.max(dim=0)
-
-        match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)
-
-        for l, low, high in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
-            low_high = (matched_vals >= low) & (matched_vals < high)
-            match_labels[low_high] = l
-
-        if self.allow_low_quality_matches:
-            self.set_low_quality_matches_(match_labels, match_quality_matrix)
-
-        return matches, match_labels
-
-    def set_low_quality_matches_(self, match_labels, match_quality_matrix):
-        """
-        Produce additional matches for predictions that have only low-quality matches. Specifically, for each
-        ground-truth G find the set of predictions that have maximum overlap with it (including ties); for each
-        prediction in that set, if it is unmatched, then match it to the ground-truth G.
-
-        This function implements the RPN assignment case (i) in Sec. 3.1.2 of :paper:`Faster R-CNN`.
-        """
-        # For each gt, find the prediction with which it has highest quality
-        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
-        # Find the highest quality match available, even if it is low, including ties.
-        # Note that the matches qualities must be positive due to the use of
-        # `torch.nonzero`.
-        _, pred_inds_with_highest_quality = nonzero_tuple(match_quality_matrix == highest_quality_foreach_gt[:, None])
-        # If an anchor was labeled positive only due to a low-quality match
-        # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B.
-        # This follows the implementation in Detectron, and is found to have no significant impact.
-        match_labels[pred_inds_with_highest_quality] = 1
-
-
-# from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/modeling/sampling.py#L9
-def subsample_labels(labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int):
-    """
-    Return `num_samples` (or fewer, if not enough found) random samples from `labels` which is a mixture of positives &
-    negatives. It will try to return as many positives as possible without exceeding `positive_fraction * num_samples`,
-    and then try to fill the remaining slots with negatives.
-
-    Args:
-        labels (Tensor): (N, ) label vector with values:
-            * -1: ignore
-            * bg_label: background ("negative") class
-            * otherwise: one or more foreground ("positive") classes
-        num_samples (int): The total number of labels with value >= 0 to return.
-            Values that are not sampled will be filled with -1 (ignore).
-        positive_fraction (float): The number of subsampled labels with values > 0
-            is `min(num_positives, int(positive_fraction * num_samples))`. The number of negatives sampled is
-            `min(num_negatives, num_samples - num_positives_sampled)`. In order words, if there are not enough
-            positives, the sample is filled with negatives. If there are also not enough negatives, then as many
-            elements are sampled as is possible.
-        bg_label (int): label index of background ("negative") class.
-
-    Returns:
-        pos_idx, neg_idx (Tensor):
-            1D vector of indices. The total length of both is `num_samples` or fewer.
-    """
-    positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0]
-    negative = nonzero_tuple(labels == bg_label)[0]
-
-    num_pos = int(num_samples * positive_fraction)
-    # protect against not enough positive examples
-    num_pos = min(positive.numel(), num_pos)
-    num_neg = num_samples - num_pos
-    # protect against not enough negative examples
-    num_neg = min(negative.numel(), num_neg)
-
-    # randomly select positive and negative examples
-    perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
-    perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]
-
-    pos_idx = positive[perm1]
-    neg_idx = negative[perm2]
-    return pos_idx, neg_idx
-
-
-def sample_topk_per_gt(pr_inds, gt_inds, iou, k):
-    if len(gt_inds) == 0:
-        return pr_inds, gt_inds
-    # find topk matches for each gt
-    gt_inds2, counts = gt_inds.unique(return_counts=True)
-    scores, pr_inds2 = iou[gt_inds2].topk(k, dim=1)
-    gt_inds2 = gt_inds2[:, None].repeat(1, k)
-
-    # filter to as many matches that gt has
-    pr_inds3 = torch.cat([pr[:c] for c, pr in zip(counts, pr_inds2)])
-    gt_inds3 = torch.cat([gt[:c] for c, gt in zip(counts, gt_inds2)])
-    return pr_inds3, gt_inds3
-
-
-# modified from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/modeling/roi_heads/roi_heads.py#L123
-class DetaStage2Assigner(nn.Module):
-    def __init__(self, num_queries, max_k=4):
-        super().__init__()
-        self.positive_fraction = 0.25
-        self.bg_label = 400  # number > 91 to filter out later
-        self.batch_size_per_image = num_queries
-        self.proposal_matcher = DetaMatcher(thresholds=[0.6], labels=[0, 1], allow_low_quality_matches=True)
-        self.k = max_k
-
-    def _sample_proposals(self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor):
-        """
-        Based on the matching between N proposals and M groundtruth, sample the proposals and set their classification
-        labels.
-
-        Args:
-            matched_idxs (Tensor): a vector of length N, each is the best-matched
-                gt index in [0, M) for each proposal.
-            matched_labels (Tensor): a vector of length N, the matcher's label
-                (one of cfg.MODEL.ROI_HEADS.IOU_LABELS) for each proposal.
-            gt_classes (Tensor): a vector of length M.
-
-        Returns:
-            Tensor: a vector of indices of sampled proposals. Each is in [0, N). Tensor: a vector of the same length,
-            the classification label for
-                each sampled proposal. Each sample is labeled as either a category in [0, num_classes) or the
-                background (num_classes).
-        """
-        has_gt = gt_classes.numel() > 0
-        # Get the corresponding GT for each proposal
-        if has_gt:
-            gt_classes = gt_classes[matched_idxs]
-            # Label unmatched proposals (0 label from matcher) as background (label=num_classes)
-            gt_classes[matched_labels == 0] = self.bg_label
-            # Label ignore proposals (-1 label)
-            gt_classes[matched_labels == -1] = -1
-        else:
-            gt_classes = torch.zeros_like(matched_idxs) + self.bg_label
-
-        sampled_fg_idxs, sampled_bg_idxs = subsample_labels(
-            gt_classes, self.batch_size_per_image, self.positive_fraction, self.bg_label
-        )
-
-        sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0)
-        return sampled_idxs, gt_classes[sampled_idxs]
-
-    def forward(self, outputs, targets, return_cost_matrix=False):
-        # COCO categories are from 1 to 90. They set num_classes=91 and apply sigmoid.
-
-        bs = len(targets)
-        indices = []
-        ious = []
-        for b in range(bs):
-            iou, _ = box_iou(
-                center_to_corners_format(targets[b]["boxes"]),
-                center_to_corners_format(outputs["init_reference"][b].detach()),
-            )
-            matched_idxs, matched_labels = self.proposal_matcher(
-                iou
-            )  # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.6, 0 ow]
-            (
-                sampled_idxs,
-                sampled_gt_classes,
-            ) = self._sample_proposals(  # list of sampled proposal_ids, sampled_id -> [0, num_classes)+[bg_label]
-                matched_idxs, matched_labels, targets[b]["labels"]
-            )
-            pos_pr_inds = sampled_idxs[sampled_gt_classes != self.bg_label]
-            pos_gt_inds = matched_idxs[pos_pr_inds]
-            pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou)
-            indices.append((pos_pr_inds, pos_gt_inds))
-            ious.append(iou)
-        if return_cost_matrix:
-            return indices, ious
-        return indices
-
-    def postprocess_indices(self, pr_inds, gt_inds, iou):
-        return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k)
-
-
-# modified from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/modeling/proposal_generator/rpn.py#L181
-class DetaStage1Assigner(nn.Module):
-    def __init__(self, t_low=0.3, t_high=0.7, max_k=4):
-        super().__init__()
-        self.positive_fraction = 0.5
-        self.batch_size_per_image = 256
-        self.k = max_k
-        self.t_low = t_low
-        self.t_high = t_high
-        self.anchor_matcher = DetaMatcher(
-            thresholds=[t_low, t_high], labels=[0, -1, 1], allow_low_quality_matches=True
-        )
-
-    def _subsample_labels(self, label):
-        """
-        Randomly sample a subset of positive and negative examples, and overwrite the label vector to the ignore value
-        (-1) for all elements that are not included in the sample.
-
-        Args:
-            labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned.
-        """
-        pos_idx, neg_idx = subsample_labels(label, self.batch_size_per_image, self.positive_fraction, 0)
-        # Fill with the ignore label (-1), then set positive and negative labels
-        label.fill_(-1)
-        label.scatter_(0, pos_idx, 1)
-        label.scatter_(0, neg_idx, 0)
-        return label
-
-    def forward(self, outputs, targets):
-        bs = len(targets)
-        indices = []
-        for b in range(bs):
-            anchors = outputs["anchors"][b]
-            if len(targets[b]["boxes"]) == 0:
-                indices.append(
-                    (
-                        torch.tensor([], dtype=torch.long, device=anchors.device),
-                        torch.tensor([], dtype=torch.long, device=anchors.device),
-                    )
-                )
-                continue
-            iou, _ = box_iou(
-                center_to_corners_format(targets[b]["boxes"]),
-                center_to_corners_format(anchors),
-            )
-            matched_idxs, matched_labels = self.anchor_matcher(
-                iou
-            )  # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.7, 0 if iou < 0.3, -1 ow]
-            matched_labels = self._subsample_labels(matched_labels)
-
-            all_pr_inds = torch.arange(len(anchors))
-            pos_pr_inds = all_pr_inds[matched_labels == 1]
-            pos_gt_inds = matched_idxs[pos_pr_inds]
-            pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou)
-            pos_pr_inds, pos_gt_inds = pos_pr_inds.to(anchors.device), pos_gt_inds.to(anchors.device)
-            indices.append((pos_pr_inds, pos_gt_inds))
-        return indices
-
-    def postprocess_indices(self, pr_inds, gt_inds, iou):
-        return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/modeling_groupvit.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/modeling_groupvit.py
deleted file mode 100644
index 59ff60ed765a510a83d1622fc73e53895a2d5495..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/modeling_groupvit.py
+++ /dev/null
@@ -1,1629 +0,0 @@
-# coding=utf-8
-# Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch GroupViT model."""
-
-
-import collections.abc
-import math
-from dataclasses import dataclass
-from typing import Any, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.utils.checkpoint
-from torch import nn
-
-from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_groupvit import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "nvidia/groupvit-gcc-yfcc"
-
-GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "nvidia/groupvit-gcc-yfcc",
-    # See all GroupViT models at https://huggingface.co/models?filter=groupvit
-]
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-# contrastive loss function, adapted from
-# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
-def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
-    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
-
-
-# Copied from transformers.models.clip.modeling_clip.clip_loss with clip->groupvit
-def groupvit_loss(similarity: torch.Tensor) -> torch.Tensor:
-    caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.t())
-    return (caption_loss + image_loss) / 2.0
-
-
-def hard_softmax(logits: torch.Tensor, dim: int):
-    y_soft = logits.softmax(dim)
-    # Straight through.
-    index = y_soft.max(dim, keepdim=True)[1]
-    y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
-    ret = y_hard - y_soft.detach() + y_soft
-
-    return ret
-
-
-def gumbel_softmax(logits: torch.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> torch.Tensor:
-    # more stable https://github.com/pytorch/pytorch/issues/41663
-    gumbel_dist = torch.distributions.gumbel.Gumbel(
-        torch.tensor(0.0, device=logits.device, dtype=logits.dtype),
-        torch.tensor(1.0, device=logits.device, dtype=logits.dtype),
-    )
-    gumbels = gumbel_dist.sample(logits.shape)
-
-    gumbels = (logits + gumbels) / tau  # ~Gumbel(logits,tau)
-    y_soft = gumbels.softmax(dim)
-
-    if hard:
-        # Straight through.
-        index = y_soft.max(dim, keepdim=True)[1]
-        y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
-        ret = y_hard - y_soft.detach() + y_soft
-    else:
-        # Reparametrization trick.
-        ret = y_soft
-    return ret
-
-
-def resize_attention_map(attentions, height, width, align_corners=False):
-    """
-    Args:
-        attentions (`torch.Tensor`): attention map of shape [batch_size, groups, feat_height*feat_width]
-        height (`int`): height of the output attention map
-        width (`int`): width of the output attention map
-        align_corners (`bool`, *optional*): the `align_corner` argument for `nn.functional.interpolate`.
-
-    Returns:
-        `torch.Tensor`: resized attention map of shape [batch_size, groups, height, width]
-    """
-
-    scale = (height * width // attentions.shape[2]) ** 0.5
-    if height > width:
-        feat_width = int(np.round(width / scale))
-        feat_height = attentions.shape[2] // feat_width
-    else:
-        feat_height = int(np.round(height / scale))
-        feat_width = attentions.shape[2] // feat_height
-
-    batch_size = attentions.shape[0]
-    groups = attentions.shape[1]  # number of group token
-    # [batch_size, groups, height*width, groups] -> [batch_size, groups, height, width]
-    attentions = attentions.reshape(batch_size, groups, feat_height, feat_width)
-    attentions = nn.functional.interpolate(
-        attentions, size=(height, width), mode="bilinear", align_corners=align_corners
-    )
-    return attentions
-
-
-def get_grouping_from_attentions(attentions, hw_shape):
-    """
-    Args:
-        attentions (`tuple(torch.FloatTensor)`: tuple of attention maps returned by `GroupViTVisionTransformer`
-        hw_shape (`tuple(int)`): height and width of the output attention map
-    Returns:
-        `torch.Tensor`: the attention map of shape [batch_size, groups, height, width]
-    """
-
-    attn_maps = []
-    with torch.no_grad():
-        prev_attn_masks = None
-        for attn_masks in attentions:
-            # [batch_size, num_groups, height x width] -> [batch_size, height x width, num_groups]
-            attn_masks = attn_masks.permute(0, 2, 1).contiguous()
-            if prev_attn_masks is None:
-                prev_attn_masks = attn_masks
-            else:
-                prev_attn_masks = prev_attn_masks @ attn_masks
-            # [batch_size, heightxwidth, num_groups] -> [batch_size, num_groups, heightxwidth] -> [batch_size, num_groups, height, width]
-            cur_attn_map = resize_attention_map(prev_attn_masks.permute(0, 2, 1).contiguous(), *hw_shape)
-            attn_maps.append(cur_attn_map)
-
-    # [batch_size, num_groups, height, width]
-    final_grouping = attn_maps[-1]
-
-    return final_grouping
-
-
-class GroupViTCrossAttentionLayer(nn.Module):
-    def __init__(self, config: GroupViTVisionConfig):
-        super().__init__()
-        self.attn = GroupViTAttention(config)
-        self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.mlp = GroupViTMLP(config)
-        self.norm_post = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, query, key):
-        x = query
-        x = x + self.attn(query, encoder_hidden_states=key)[0]
-        x = x + self.mlp(self.norm2(x))
-        x = self.norm_post(x)
-        return x
-
-
-class GroupViTAssignAttention(nn.Module):
-    def __init__(self, config: GroupViTVisionConfig):
-        super().__init__()
-        self.scale = config.hidden_size**-0.5
-
-        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
-        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
-        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
-        self.proj = nn.Linear(config.hidden_size, config.hidden_size)
-        self.assign_eps = config.assign_eps
-
-    def get_attn(self, attn, gumbel=True, hard=True):
-        if gumbel and self.training:
-            attn = gumbel_softmax(attn, dim=-2, hard=hard)
-        else:
-            if hard:
-                attn = hard_softmax(attn, dim=-2)
-            else:
-                attn = nn.functional.softmax(attn, dim=-2)
-
-        return attn
-
-    def forward(self, query, key):
-        value = key
-        # [batch_size, query_length, channels]
-        query = self.q_proj(query)
-
-        # [batch_size, key_length, channels]
-        key = self.k_proj(key)
-
-        # [batch_size, key_length, channels]
-        value = self.v_proj(value)
-
-        # [batch_size, query_length, key_length]
-        raw_attn = (query @ key.transpose(-2, -1)) * self.scale
-
-        attn = self.get_attn(raw_attn)
-        soft_attn = self.get_attn(raw_attn, gumbel=False, hard=False)
-
-        attn = attn / (attn.sum(dim=-1, keepdim=True) + self.assign_eps)
-
-        out = attn @ value
-
-        out = self.proj(out)
-
-        return out, soft_attn
-
-
-class GroupViTTokenAssign(nn.Module):
-    def __init__(self, config: GroupViTVisionConfig, num_group_token, num_output_group):
-        super().__init__()
-        self.num_output_group = num_output_group
-        # norm on group_tokens
-        self.norm_tokens = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        assign_mlp_ratio = (
-            config.assign_mlp_ratio
-            if isinstance(config.assign_mlp_ratio, collections.abc.Iterable)
-            else (config.assign_mlp_ratio, config.assign_mlp_ratio)
-        )
-        tokens_dim, channels_dim = [int(x * config.hidden_size) for x in assign_mlp_ratio]
-        self.mlp_inter = GroupViTMixerMLP(config, num_group_token, tokens_dim, num_output_group)
-        self.norm_post_tokens = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        # norm on x
-        self.norm_x = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.pre_assign_attn = GroupViTCrossAttentionLayer(config)
-
-        self.assign = GroupViTAssignAttention(config)
-        self.norm_new_x = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.mlp_channels = GroupViTMLP(config, config.hidden_size, channels_dim, config.hidden_size)
-
-    def project_group_token(self, group_tokens):
-        """
-        Args:
-            group_tokens (torch.Tensor): group tokens, [batch_size, num_group_tokens, channels]
-
-        Returns:
-            projected_group_tokens (torch.Tensor): [batch_size, num_output_groups, channels]
-        """
-        # [B, num_output_groups, C] <- [B, num_group_tokens, C]
-        projected_group_tokens = self.mlp_inter(group_tokens)
-        projected_group_tokens = self.norm_post_tokens(projected_group_tokens)
-        return projected_group_tokens
-
-    def forward(self, image_tokens, group_tokens):
-        """
-        Args:
-            image_tokens (`torch.Tensor`): image tokens, of shape [batch_size, input_length, channels]
-            group_tokens (`torch.Tensor`): group tokens, [batch_size, num_group_tokens, channels]
-        """
-
-        group_tokens = self.norm_tokens(group_tokens)
-        image_tokens = self.norm_x(image_tokens)
-        # [batch_size, num_output_groups, channels]
-        projected_group_tokens = self.project_group_token(group_tokens)
-        projected_group_tokens = self.pre_assign_attn(projected_group_tokens, image_tokens)
-        new_image_tokens, attention = self.assign(projected_group_tokens, image_tokens)
-        new_image_tokens += projected_group_tokens
-
-        new_image_tokens = new_image_tokens + self.mlp_channels(self.norm_new_x(new_image_tokens))
-
-        return new_image_tokens, attention
-
-
-@dataclass
-class GroupViTModelOutput(ModelOutput):
-    """
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
-            Contrastive loss for image-text similarity.
-        logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
-            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
-            similarity scores.
-        logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
-            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
-            similarity scores.
-        segmentation_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
-            Classification scores for each pixel.
-
-            <Tip warning={true}>
-
-            The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
-            to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
-            original image size as post-processing. You should always check your logits shape and resize as needed.
-
-            </Tip>
-
-        text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The text embeddings obtained by applying the projection layer to the pooled output of
-            [`GroupViTTextModel`].
-        image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The image embeddings obtained by applying the projection layer to the pooled output of
-            [`GroupViTVisionModel`].
-        text_model_output (`BaseModelOutputWithPooling`):
-            The output of the [`GroupViTTextModel`].
-        vision_model_output (`BaseModelOutputWithPooling`):
-            The output of the [`GroupViTVisionModel`].
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits_per_image: torch.FloatTensor = None
-    logits_per_text: torch.FloatTensor = None
-    segmentation_logits: torch.FloatTensor = None
-    text_embeds: torch.FloatTensor = None
-    image_embeds: torch.FloatTensor = None
-    text_model_output: BaseModelOutputWithPooling = None
-    vision_model_output: BaseModelOutputWithPooling = None
-
-    def to_tuple(self) -> Tuple[Any]:
-        return tuple(
-            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
-            for k in self.keys()
-        )
-
-
-class GroupViTPatchEmbeddings(nn.Module):
-    """
-    Image to Patch Embedding.
-    """
-
-    def __init__(
-        self,
-        image_size: int = 224,
-        patch_size: Union[int, Tuple[int, int]] = 16,
-        num_channels: int = 3,
-        embed_dim: int = 768,
-    ):
-        super().__init__()
-        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
-        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
-        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
-        self.image_size = image_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-
-        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
-        batch_size, num_channels, height, width = pixel_values.shape
-        if not interpolate_pos_encoding:
-            if height != self.image_size[0] or width != self.image_size[1]:
-                raise ValueError(
-                    f"Input image size ({height}*{width}) doesn't match model"
-                    f" ({self.image_size[0]}*{self.image_size[1]})."
-                )
-        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
-        return x
-
-
-class GroupViTVisionEmbeddings(nn.Module):
-    def __init__(self, config: GroupViTVisionConfig):
-        super().__init__()
-
-        self.patch_embeddings = GroupViTPatchEmbeddings(
-            image_size=config.image_size,
-            patch_size=config.patch_size,
-            num_channels=config.num_channels,
-            embed_dim=config.hidden_size,
-        )
-        num_patches = self.patch_embeddings.num_patches
-        self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches, config.hidden_size))
-        self.dropout = nn.Dropout(config.dropout)
-        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.config = config
-
-    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
-        """
-        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
-        resolution images.
-
-        Source:
-        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
-        """
-
-        npatch = embeddings.shape[1]
-        if npatch == self.position_embeddings.shape[1] and height == width:
-            return self.position_embeddings
-        patch_pos_embed = self.position_embeddings
-        num_original_pos_embed = patch_pos_embed.shape[1]
-        dim = embeddings.shape[-1]
-        feat_height = height // self.config.patch_size
-        feat_width = width // self.config.patch_size
-        # we add a small number to avoid floating point error in the interpolation
-        # see discussion at https://github.com/facebookresearch/dino/issues/8
-        feat_height, feat_width = feat_height + 0.1, feat_width + 0.1
-        original_height = original_width = math.sqrt(num_original_pos_embed)
-        reshaped_patch_pos_embed = patch_pos_embed.reshape(1, int(original_height), int(original_width), dim).permute(
-            0, 3, 1, 2
-        )
-        scale_factor = (feat_height / original_height, feat_width / original_width)
-        patch_pos_embed = nn.functional.interpolate(
-            reshaped_patch_pos_embed,
-            scale_factor=scale_factor,
-            mode="bicubic",
-            align_corners=False,
-        )
-        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
-        return patch_pos_embed
-
-    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
-        batch_size, num_channels, height, width = pixel_values.shape
-        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-
-        embeddings = self.layernorm(embeddings)
-
-        batch_size, seq_len, _ = embeddings.size()
-
-        # add positional encoding to each token
-        if interpolate_pos_encoding:
-            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
-        else:
-            embeddings = embeddings + self.position_embeddings
-
-        embeddings = self.dropout(embeddings)
-
-        return embeddings
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->GroupViT
-class GroupViTTextEmbeddings(nn.Module):
-    def __init__(self, config: GroupViTTextConfig):
-        super().__init__()
-        embed_dim = config.hidden_size
-
-        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
-        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer(
-            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
-        )
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-    ) -> torch.Tensor:
-        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, :seq_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.token_embedding(input_ids)
-
-        position_embeddings = self.position_embedding(position_ids)
-        embeddings = inputs_embeds + position_embeddings
-
-        return embeddings
-
-
-class GroupViTStage(nn.Module):
-    """This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
-
-    def __init__(
-        self,
-        config: GroupViTVisionConfig,
-        depth: int,
-        num_prev_group_token: int,
-        num_group_token: int,
-        num_output_group: int,
-    ):
-        super().__init__()
-        self.depth = depth
-        self.num_group_token = num_group_token
-        if num_group_token > 0:
-            self.group_token = nn.Parameter(torch.zeros(1, num_group_token, config.hidden_size))
-        else:
-            self.group_token = None
-        self.gradient_checkpointing = False
-        self.layers = nn.ModuleList([GroupViTEncoderLayer(config) for _ in range(depth)])
-
-        if num_group_token > 0:
-            self.downsample = GroupViTTokenAssign(
-                config=config,
-                num_group_token=num_group_token,
-                num_output_group=num_output_group,
-            )
-        else:
-            self.downsample = None
-
-        if num_prev_group_token > 0 and num_group_token > 0:
-            self.group_projector = nn.Sequential(
-                nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps),
-                GroupViTMixerMLP(config, num_prev_group_token, config.hidden_size // 2, num_group_token),
-            )
-        else:
-            self.group_projector = None
-
-    @property
-    def with_group_token(self):
-        return self.group_token is not None
-
-    def split_x(self, x):
-        if self.with_group_token:
-            return x[:, : -self.num_group_token], x[:, -self.num_group_token :]
-        else:
-            return x, None
-
-    def concat_x(self, x: torch.Tensor, group_token: Optional[torch.Tensor] = None) -> torch.Tensor:
-        if group_token is None:
-            return x
-        return torch.cat([x, group_token], dim=1)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        prev_group_token: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-                `(config.encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the grouping tensors of Grouping block.
-        """
-        if self.with_group_token:
-            group_token = self.group_token.expand(hidden_states.size(0), -1, -1)
-            if self.group_projector is not None:
-                group_token = group_token + self.group_projector(prev_group_token)
-        else:
-            group_token = None
-
-        x = hidden_states
-
-        cat_x = self.concat_x(x, group_token)
-        for layer in self.layers:
-            layer_out = layer(cat_x, attention_mask=None, causal_attention_mask=None)
-            cat_x = layer_out[0]
-
-        x, group_token = self.split_x(cat_x)
-
-        attention = None
-        if self.downsample is not None:
-            x, attention = self.downsample(x, group_token)
-
-        outputs = (x, group_token)
-        if output_attentions:
-            outputs = outputs + (attention,)
-
-        return outputs
-
-
-class GroupViTMLP(nn.Module):
-    def __init__(
-        self,
-        config: GroupViTVisionConfig,
-        hidden_size: Optional[int] = None,
-        intermediate_size: Optional[int] = None,
-        output_size: Optional[int] = None,
-    ):
-        super().__init__()
-        self.config = config
-        self.activation_fn = ACT2FN[config.hidden_act]
-        hidden_size = hidden_size if hidden_size is not None else config.hidden_size
-        intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
-        output_size = output_size if output_size is not None else hidden_size
-        self.fc1 = nn.Linear(hidden_size, intermediate_size)
-        self.fc2 = nn.Linear(intermediate_size, output_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-        return hidden_states
-
-
-class GroupViTMixerMLP(GroupViTMLP):
-    def forward(self, x):
-        x = super().forward(x.transpose(1, 2))
-        return x.transpose(1, 2)
-
-
-class GroupViTAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_heads
-        if self.head_dim * self.num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {self.num_heads})."
-            )
-        self.scale = self.head_dim**-0.5
-        self.dropout = config.attention_dropout
-
-        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        bsz, tgt_len, embed_dim = hidden_states.size()
-        is_cross_attention = encoder_hidden_states is not None
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scale
-        if is_cross_attention:
-            key_states = self._shape(self.k_proj(encoder_hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(encoder_hidden_states), -1, bsz)
-        else:
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        # apply the causal_attention_mask first
-        if causal_attention_mask is not None:
-            if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
-                    f" {causal_attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if output_attentions:
-            # this operation is a bit akward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->GroupViT
-class GroupViTEncoderLayer(nn.Module):
-    def __init__(self, config: GroupViTConfig):
-        super().__init__()
-        self.embed_dim = config.hidden_size
-        self.self_attn = GroupViTAttention(config)
-        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-        self.mlp = GroupViTMLP(config)
-        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        causal_attention_mask: torch.Tensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-                `(config.encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        hidden_states = self.layer_norm1(hidden_states)
-        hidden_states, attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            causal_attention_mask=causal_attention_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.layer_norm2(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class GroupViTPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = GroupViTConfig
-    base_model_prefix = "groupvit"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-
-        init_range = self.config.initializer_range
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=init_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-        factor = self.config.initializer_factor
-        if isinstance(module, GroupViTTextEmbeddings):
-            module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-            module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-        elif isinstance(module, GroupViTAttention):
-            factor = self.config.initializer_factor
-            in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            out_proj_std = (module.embed_dim**-0.5) * factor
-            nn.init.normal_(module.q_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.k_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.v_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.out_proj.weight, std=out_proj_std)
-        elif isinstance(module, GroupViTMLP):
-            factor = self.config.initializer_factor
-            in_proj_std = (
-                (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            )
-            fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
-            nn.init.normal_(module.fc1.weight, std=fc_std)
-            nn.init.normal_(module.fc2.weight, std=in_proj_std)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (GroupViTTextEncoder, GroupViTVisionEncoder)):
-            module.gradient_checkpointing = value
-
-
-GROUPVIT_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
-    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`GroupViTConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-GROUPVIT_TEXT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-GROUPVIT_VISION_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-GROUPVIT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
-            [`CLIPImageProcessor.__call__`] for details.
-        return_loss (`bool`, *optional*):
-            Whether or not to return the contrastive loss.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-class GroupViTVisionEncoder(nn.Module):
-    def __init__(self, config: GroupViTVisionConfig) -> None:
-        super().__init__()
-        self.config = config
-        self.stages = nn.ModuleList(
-            [
-                GroupViTStage(
-                    config=config,
-                    depth=config.depths[i],
-                    num_group_token=config.num_group_tokens[i],
-                    num_output_group=config.num_output_groups[i],
-                    num_prev_group_token=config.num_output_groups[i - 1] if i > 0 else 0,
-                )
-                for i in range(len(config.depths))
-            ]
-        )
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[tuple, BaseModelOutput]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        all_hidden_states = () if output_hidden_states else None
-        all_groupings = () if output_attentions else None
-
-        group_tokens = None
-
-        for i, stage in enumerate(self.stages):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_outputs = stage(hidden_states, group_tokens, output_attentions)
-
-            hidden_states = layer_outputs[0]
-            group_tokens = layer_outputs[1]
-
-            if output_attentions and layer_outputs[2] is not None:
-                all_groupings = all_groupings + (layer_outputs[2],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_groupings] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
-        )
-
-
-class GroupViTTextEncoder(nn.Module):
-    """
-    Transformer encoder consisting of `config.num_hidden_layers` self-attention layers. Each layer is a
-    [`GroupViTEncoderLayer`].
-
-    Args:
-        config: GroupViTTextConfig
-    """
-
-    def __init__(self, config: GroupViTTextConfig):
-        super().__init__()
-        self.config = config
-        self.layers = nn.ModuleList([GroupViTEncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        inputs_embeds,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Causal mask for the text model. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        hidden_states = inputs_embeds
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(encoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                )
-            else:
-                layer_outputs = encoder_layer(
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer with CLIPText->GroupViTText, CLIPEncoder->GroupViTTextEncoder, CLIP_TEXT->GROUPVIT_TEXT
-class GroupViTTextTransformer(nn.Module):
-    def __init__(self, config: GroupViTTextConfig):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-        self.embeddings = GroupViTTextEmbeddings(config)
-        self.encoder = GroupViTTextEncoder(config)
-        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
-
-        # For `pooled_output` computation
-        self.eos_token_id = config.eos_token_id
-
-    @add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is None:
-            raise ValueError("You have to specify input_ids")
-
-        input_shape = input_ids.size()
-        input_ids = input_ids.view(-1, input_shape[-1])
-
-        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
-
-        # CLIP's text model uses causal mask, prepare it here.
-        # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
-        causal_attention_mask = _make_causal_mask(input_shape, hidden_states.dtype, device=hidden_states.device)
-        # expand attention_mask
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
-
-        encoder_outputs = self.encoder(
-            inputs_embeds=hidden_states,
-            attention_mask=attention_mask,
-            causal_attention_mask=causal_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
-        last_hidden_state = self.final_layer_norm(last_hidden_state)
-
-        if self.eos_token_id == 2:
-            # The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
-            # A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added
-            # ------------------------------------------------------------
-            # text_embeds.shape = [batch_size, sequence_length, transformer.width]
-            # take features from the eot embedding (eot_token is the highest number in each sequence)
-            # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
-            pooled_output = last_hidden_state[
-                torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
-                input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
-            ]
-        else:
-            # The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)
-            pooled_output = last_hidden_state[
-                torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
-                # We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`)
-                (input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id)
-                .int()
-                .argmax(dim=-1),
-            ]
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-class GroupViTTextModel(GroupViTPreTrainedModel):
-    config_class = GroupViTTextConfig
-
-    def __init__(self, config: GroupViTTextConfig):
-        super().__init__(config)
-        self.text_model = GroupViTTextTransformer(config)
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.text_model.embeddings.token_embedding
-
-    def set_input_embeddings(self, value):
-        self.text_model.embeddings.token_embedding = value
-
-    @add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import CLIPTokenizer, GroupViTTextModel
-
-        >>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
-        >>> model = GroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
-        ```"""
-        return self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-
-class GroupViTVisionTransformer(nn.Module):
-    def __init__(self, config: GroupViTVisionConfig):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-
-        self.embeddings = GroupViTVisionEmbeddings(config)
-        self.encoder = GroupViTVisionEncoder(config)
-        self.layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
-
-    @add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
-    def forward(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if pixel_values is None:
-            raise ValueError("You have to specify pixel_values")
-
-        hidden_states = self.embeddings(pixel_values)
-
-        encoder_outputs = self.encoder(
-            hidden_states=hidden_states,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
-
-        # normalize the last hidden state
-        last_hidden_state = self.layernorm(last_hidden_state)
-        pooled_output = last_hidden_state.mean(dim=1)
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-class GroupViTVisionModel(GroupViTPreTrainedModel):
-    config_class = GroupViTVisionConfig
-    main_input_name = "pixel_values"
-
-    def __init__(self, config: GroupViTVisionConfig):
-        super().__init__(config)
-        self.vision_model = GroupViTVisionTransformer(config)
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> GroupViTPatchEmbeddings:
-        return self.vision_model.embeddings.patch_embeddings
-
-    @add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
-    def forward(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, GroupViTVisionModel
-
-        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
-        >>> model = GroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(images=image, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled CLS states
-        ```"""
-        return self.vision_model(
-            pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-
-@add_start_docstrings(GROUPVIT_START_DOCSTRING)
-class GroupViTModel(GroupViTPreTrainedModel):
-    config_class = GroupViTConfig
-
-    def __init__(self, config: GroupViTConfig):
-        super().__init__(config)
-
-        if not isinstance(config.text_config, GroupViTTextConfig):
-            raise ValueError(
-                "config.text_config is expected to be of type GroupViTTextConfig but is of type"
-                f" {type(config.text_config)}."
-            )
-
-        if not isinstance(config.vision_config, GroupViTVisionConfig):
-            raise ValueError(
-                "config.vision_config is expected to be of type GroupViTVisionConfig but is of type"
-                f" {type(config.vision_config)}."
-            )
-
-        text_config = config.text_config
-        vision_config = config.vision_config
-
-        self.projection_dim = config.projection_dim
-        self.projection_intermediate_dim = config.projection_intermediate_dim
-        self.text_embed_dim = text_config.hidden_size
-        self.vision_embed_dim = vision_config.hidden_size
-
-        self.text_model = GroupViTTextTransformer(text_config)
-        self.vision_model = GroupViTVisionTransformer(vision_config)
-
-        self.visual_projection = nn.Sequential(
-            nn.Linear(self.vision_embed_dim, self.projection_intermediate_dim, bias=True),
-            nn.BatchNorm1d(self.projection_intermediate_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.projection_intermediate_dim, self.projection_dim, bias=True),
-        )
-        self.text_projection = nn.Sequential(
-            nn.Linear(self.text_embed_dim, self.projection_intermediate_dim, bias=True),
-            nn.BatchNorm1d(self.projection_intermediate_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.projection_intermediate_dim, self.projection_dim, bias=True),
-        )
-        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
-    def get_text_features(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-        r"""
-        Returns:
-            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
-            applying the projection layer to the pooled output of [`GroupViTTextModel`].
-
-        Examples:
-
-        ```python
-        >>> from transformers import CLIPTokenizer, GroupViTModel
-
-        >>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
-        >>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-        >>> text_features = model.get_text_features(**inputs)
-        ```"""
-        # Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        text_outputs = self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = text_outputs[1]
-        text_features = self.text_projection(pooled_output)
-
-        return text_features
-
-    @add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
-    def get_image_features(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-        r"""
-        Returns:
-            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
-            applying the projection layer to the pooled output of [`GroupViTVisionModel`].
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, GroupViTModel
-
-        >>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
-        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(images=image, return_tensors="pt")
-
-        >>> image_features = model.get_image_features(**inputs)
-        ```"""
-        # Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = vision_outputs[1]  # pooled_output
-        image_features = self.visual_projection(pooled_output)
-
-        return image_features
-
-    @add_start_docstrings_to_model_forward(GROUPVIT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=GroupViTModelOutput, config_class=GroupViTConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        return_loss: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_segmentation: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, GroupViTModelOutput]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, GroupViTModel
-
-        >>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
-        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(
-        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
-        ... )
-
-        >>> outputs = model(**inputs)
-        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
-        >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
-        ```"""
-        # Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_segmentation = (
-            output_segmentation if output_segmentation is not None else self.config.output_segmentation
-        )
-        if output_segmentation:
-            output_attentions = True
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        text_outputs = self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        image_embeds = vision_outputs[1]
-        image_embeds = self.visual_projection(image_embeds)
-
-        text_embeds = text_outputs[1]
-        text_embeds = self.text_projection(text_embeds)
-
-        # normalized features
-        image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
-        text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
-
-        # cosine similarity as logits
-        logit_scale = self.logit_scale.exp()
-        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
-        logits_per_image = logits_per_text.t()
-
-        seg_logits = None
-        if output_segmentation:
-            # grouped features
-            # [batch_size_image, num_group, hidden_size]
-            image_group_embeds = vision_outputs[0]
-            # [batch_size_image*num_group, hidden_size]
-            image_group_embeds = self.visual_projection(image_group_embeds.reshape(-1, image_group_embeds.shape[-1]))
-            if output_hidden_states:
-                attentions = vision_outputs[3]
-            else:
-                attentions = vision_outputs[2]
-            # [batch_size_image, num_group, height, width]
-            grouping = get_grouping_from_attentions(attentions, pixel_values.shape[2:])
-
-            # normalized features
-            image_group_embeds = image_group_embeds / image_group_embeds.norm(dim=-1, keepdim=True)
-            # [batch_size_image x num_group, batch_size_text]
-            logits_per_image_group = torch.matmul(image_group_embeds, text_embeds.t()) * logit_scale
-            # [batch_size_image, batch_size_text, num_group]
-            logits_per_image_group = logits_per_image_group.reshape(
-                image_embeds.shape[0], -1, text_embeds.shape[0]
-            ).permute(0, 2, 1)
-
-            # [batch_size_image, batch_size_text, height x width]
-            flatten_grouping = grouping.reshape(grouping.shape[0], grouping.shape[1], -1)
-
-            # [batch_size_image, batch_size_text, height, width]
-            seg_logits = torch.matmul(logits_per_image_group, flatten_grouping) * logit_scale
-            seg_logits = seg_logits.reshape(
-                seg_logits.shape[0], seg_logits.shape[1], grouping.shape[2], grouping.shape[3]
-            )
-
-        loss = None
-        if return_loss:
-            loss = groupvit_loss(logits_per_text)
-
-        if not return_dict:
-            if seg_logits is not None:
-                output = (
-                    logits_per_image,
-                    logits_per_text,
-                    seg_logits,
-                    text_embeds,
-                    image_embeds,
-                    text_outputs,
-                    vision_outputs,
-                )
-            else:
-                output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
-            return ((loss,) + output) if loss is not None else output
-
-        return GroupViTModelOutput(
-            loss=loss,
-            logits_per_image=logits_per_image,
-            logits_per_text=logits_per_text,
-            segmentation_logits=seg_logits,
-            text_embeds=text_embeds,
-            image_embeds=image_embeds,
-            text_model_output=text_outputs,
-            vision_model_output=vision_outputs,
-        )
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/config/__init__.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/config/__init__.py
deleted file mode 100644
index 4e648e632d55c70f160d49630378d202fbde4e45..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/config/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .compat import downgrade_config, upgrade_config
-from .config import CfgNode, get_cfg, global_cfg, set_global_cfg, configurable
-from .instantiate import instantiate
-from .lazy import LazyCall, LazyConfig
-
-__all__ = [
-    "CfgNode",
-    "get_cfg",
-    "global_cfg",
-    "set_global_cfg",
-    "downgrade_config",
-    "upgrade_config",
-    "configurable",
-    "instantiate",
-    "LazyCall",
-    "LazyConfig",
-]
-
-
-from detectron2.utils.env import fixup_module_metadata
-
-fixup_module_metadata(__name__, globals(), __all__)
-del fixup_module_metadata
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/dev/packaging/gen_install_table.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/dev/packaging/gen_install_table.py
deleted file mode 100644
index b4c852dc53de613707b9668f748184c2b63b9dea..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/dev/packaging/gen_install_table.py
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-# -*- coding: utf-8 -*-
-
-import argparse
-
-template = """<details><summary> install </summary><pre><code>\
-python -m pip install detectron2{d2_version} -f \\
-  https://dl.fbaipublicfiles.com/detectron2/wheels/{cuda}/torch{torch}/index.html
-</code></pre> </details>"""
-CUDA_SUFFIX = {
-    "11.3": "cu113",
-    "11.1": "cu111",
-    "11.0": "cu110",
-    "10.2": "cu102",
-    "10.1": "cu101",
-    "10.0": "cu100",
-    "9.2": "cu92",
-    "cpu": "cpu",
-}
-
-
-def gen_header(torch_versions):
-    return '<table class="docutils"><tbody><th width="80"> CUDA </th>' + "".join(
-        [
-            '<th valign="bottom" align="left" width="100">torch {}</th>'.format(t)
-            for t in torch_versions
-        ]
-    )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--d2-version", help="detectron2 version number, default to empty")
-    args = parser.parse_args()
-    d2_version = f"=={args.d2_version}" if args.d2_version else ""
-
-    all_versions = (
-        [("1.8", k) for k in ["11.1", "10.2", "10.1", "cpu"]]
-        + [("1.9", k) for k in ["11.1", "10.2", "cpu"]]
-        + [("1.10", k) for k in ["11.3", "11.1", "10.2", "cpu"]]
-    )
-
-    torch_versions = sorted(
-        {k[0] for k in all_versions}, key=lambda x: int(x.split(".")[1]), reverse=True
-    )
-    cuda_versions = sorted(
-        {k[1] for k in all_versions}, key=lambda x: float(x) if x != "cpu" else 0, reverse=True
-    )
-
-    table = gen_header(torch_versions)
-    for cu in cuda_versions:
-        table += f""" <tr><td align="left">{cu}</td>"""
-        cu_suffix = CUDA_SUFFIX[cu]
-        for torch in torch_versions:
-            if (torch, cu) in all_versions:
-                cell = template.format(d2_version=d2_version, cuda=cu_suffix, torch=torch)
-            else:
-                cell = ""
-            table += f"""<td align="left">{cell} </td> """
-        table += "</tr>"
-    table += "</tbody></table>"
-    print(table)
diff --git a/spaces/ynhe/AskAnything/models/swin_transformer.py b/spaces/ynhe/AskAnything/models/swin_transformer.py
deleted file mode 100644
index c1affc9a8695474e831ad060343c1988d750dc5f..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/swin_transformer.py
+++ /dev/null
@@ -1,654 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu
-# --------------------------------------------------------
-
-import numpy as np
-from scipy import interpolate
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            # calculate attention mask for SW-MSA
-            H, W = self.input_resolution
-            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-            h_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            w_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            cnt = 0
-            for h in h_slices:
-                for w in w_slices:
-                    img_mask[:, h, w, :] = cnt
-                    cnt += 1
-
-            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def forward(self, x):
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops
-
-
-class SwinTransformer(nn.Module):
-    r""" Swin Transformer
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 224
-        patch_size (int | tuple(int)): Patch size. Default: 4
-        in_chans (int): Number of input image channels. Default: 3
-        num_classes (int): Number of classes for classification head. Default: 1000
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
-                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, **kwargs):
-        super().__init__()
-
-        self.num_classes = num_classes
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
-                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
-                                                 patches_resolution[1] // (2 ** i_layer)),
-                               depth=depths[i_layer],
-                               num_heads=num_heads[i_layer],
-                               window_size=window_size,
-                               mlp_ratio=self.mlp_ratio,
-                               qkv_bias=qkv_bias, qk_scale=qk_scale,
-                               drop=drop_rate, attn_drop=attn_drop_rate,
-                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                               norm_layer=norm_layer,
-                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                               use_checkpoint=use_checkpoint)
-            self.layers.append(layer)
-
-        self.norm = norm_layer(self.num_features)
-        self.avgpool = nn.AdaptiveAvgPool1d(1)
-        # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def forward(self, x, idx_to_group_img=None, image_atts=None, **kwargs):
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x)
-
-        x = self.norm(x)  # B L C
-
-        x_cls = self.avgpool(x.transpose(1, 2))  # B C 1
-
-        if idx_to_group_img is None:
-            return torch.cat([x_cls.transpose(1, 2), x], dim=1)
-        else:
-            x_bs = torch.gather(x, dim=0, index=idx_to_group_img.view(-1, 1, 1).expand(-1, x.shape[1], x.shape[2]))
-            weights = image_atts[:, 1:].unsqueeze(2)  # B L 1
-            x_bs_cls = torch.sum((weights * x_bs).transpose(1, 2), dim=-1, keepdim=True)   # B C 1
-            x_bs_cls = x_bs_cls / torch.sum(weights.transpose(1, 2), dim=-1, keepdim=True)  # avgpool
-
-            return torch.cat([x_bs_cls.transpose(1, 2), x_bs], dim=1), \
-                   torch.cat([x_cls.transpose(1, 2), x], dim=1)
-
-    def flops(self):
-        flops = 0
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
-        flops += self.num_features * self.num_classes
-        return flops
-
-
-def interpolate_relative_pos_embed(rel_pos_bias, dst_num_pos, param_name=''):
-    # from: https://github.com/microsoft/unilm/blob/8a0a1c1f4e7326938ea7580a00d56d7f17d65612/beit/run_class_finetuning.py#L348
-
-    # rel_pos_bias: relative_position_bias_table
-    src_num_pos, num_attn_heads = rel_pos_bias.size()
-
-    num_extra_tokens = 0
-    src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
-    dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)
-    if src_size != dst_size:
-        print("Position interpolate %s from %dx%d to %dx%d" % (param_name, src_size, src_size, dst_size, dst_size))
-
-        # extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
-        # rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
-
-        def geometric_progression(a, r, n):
-            return a * (1.0 - r ** n) / (1.0 - r)
-
-        left, right = 1.01, 1.5
-        while right - left > 1e-6:
-            q = (left + right) / 2.0
-            gp = geometric_progression(1, q, src_size // 2)
-            if gp > dst_size // 2:
-                right = q
-            else:
-                left = q
-
-        # if q > 1.090307:
-        #     q = 1.090307
-
-        dis = []
-        cur = 1
-        for i in range(src_size // 2):
-            dis.append(cur)
-            cur += q ** (i + 1)
-
-        r_ids = [-_ for _ in reversed(dis)]
-
-        x = r_ids + [0] + dis
-        y = r_ids + [0] + dis
-
-        t = dst_size // 2.0
-        dx = np.arange(-t, t + 0.1, 1.0)
-        dy = np.arange(-t, t + 0.1, 1.0)
-
-        # print("Original positions = %s" % str(x))
-        # print("Target positions = %s" % str(dx))
-
-        all_rel_pos_bias = []
-
-        for i in range(num_attn_heads):
-            z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()
-            f = interpolate.interp2d(x, y, z, kind='cubic')
-            all_rel_pos_bias.append(
-                torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))
-
-        rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
-
-    return rel_pos_bias
\ No newline at end of file
diff --git a/spaces/youngtsai/Mandarin-TTS/bert/prosody_tool.py b/spaces/youngtsai/Mandarin-TTS/bert/prosody_tool.py
deleted file mode 100644
index 690a1bab4701b7912840c09447d8e0de8ebecb5a..0000000000000000000000000000000000000000
--- a/spaces/youngtsai/Mandarin-TTS/bert/prosody_tool.py
+++ /dev/null
@@ -1,426 +0,0 @@
-def is_chinese(uchar):
-    if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
-        return True
-    else:
-        return False
-
-
-pinyin_dict = {
-    "a": ("^", "a"),
-    "ai": ("^", "ai"),
-    "an": ("^", "an"),
-    "ang": ("^", "ang"),
-    "ao": ("^", "ao"),
-    "ba": ("b", "a"),
-    "bai": ("b", "ai"),
-    "ban": ("b", "an"),
-    "bang": ("b", "ang"),
-    "bao": ("b", "ao"),
-    "be": ("b", "e"),
-    "bei": ("b", "ei"),
-    "ben": ("b", "en"),
-    "beng": ("b", "eng"),
-    "bi": ("b", "i"),
-    "bian": ("b", "ian"),
-    "biao": ("b", "iao"),
-    "bie": ("b", "ie"),
-    "bin": ("b", "in"),
-    "bing": ("b", "ing"),
-    "bo": ("b", "o"),
-    "bu": ("b", "u"),
-    "ca": ("c", "a"),
-    "cai": ("c", "ai"),
-    "can": ("c", "an"),
-    "cang": ("c", "ang"),
-    "cao": ("c", "ao"),
-    "ce": ("c", "e"),
-    "cen": ("c", "en"),
-    "ceng": ("c", "eng"),
-    "cha": ("ch", "a"),
-    "chai": ("ch", "ai"),
-    "chan": ("ch", "an"),
-    "chang": ("ch", "ang"),
-    "chao": ("ch", "ao"),
-    "che": ("ch", "e"),
-    "chen": ("ch", "en"),
-    "cheng": ("ch", "eng"),
-    "chi": ("ch", "iii"),
-    "chong": ("ch", "ong"),
-    "chou": ("ch", "ou"),
-    "chu": ("ch", "u"),
-    "chua": ("ch", "ua"),
-    "chuai": ("ch", "uai"),
-    "chuan": ("ch", "uan"),
-    "chuang": ("ch", "uang"),
-    "chui": ("ch", "uei"),
-    "chun": ("ch", "uen"),
-    "chuo": ("ch", "uo"),
-    "ci": ("c", "ii"),
-    "cong": ("c", "ong"),
-    "cou": ("c", "ou"),
-    "cu": ("c", "u"),
-    "cuan": ("c", "uan"),
-    "cui": ("c", "uei"),
-    "cun": ("c", "uen"),
-    "cuo": ("c", "uo"),
-    "da": ("d", "a"),
-    "dai": ("d", "ai"),
-    "dan": ("d", "an"),
-    "dang": ("d", "ang"),
-    "dao": ("d", "ao"),
-    "de": ("d", "e"),
-    "dei": ("d", "ei"),
-    "den": ("d", "en"),
-    "deng": ("d", "eng"),
-    "di": ("d", "i"),
-    "dia": ("d", "ia"),
-    "dian": ("d", "ian"),
-    "diao": ("d", "iao"),
-    "die": ("d", "ie"),
-    "ding": ("d", "ing"),
-    "diu": ("d", "iou"),
-    "dong": ("d", "ong"),
-    "dou": ("d", "ou"),
-    "du": ("d", "u"),
-    "duan": ("d", "uan"),
-    "dui": ("d", "uei"),
-    "dun": ("d", "uen"),
-    "duo": ("d", "uo"),
-    "e": ("^", "e"),
-    "ei": ("^", "ei"),
-    "en": ("^", "en"),
-    "ng": ("^", "en"),
-    "eng": ("^", "eng"),
-    "er": ("^", "er"),
-    "fa": ("f", "a"),
-    "fan": ("f", "an"),
-    "fang": ("f", "ang"),
-    "fei": ("f", "ei"),
-    "fen": ("f", "en"),
-    "feng": ("f", "eng"),
-    "fo": ("f", "o"),
-    "fou": ("f", "ou"),
-    "fu": ("f", "u"),
-    "ga": ("g", "a"),
-    "gai": ("g", "ai"),
-    "gan": ("g", "an"),
-    "gang": ("g", "ang"),
-    "gao": ("g", "ao"),
-    "ge": ("g", "e"),
-    "gei": ("g", "ei"),
-    "gen": ("g", "en"),
-    "geng": ("g", "eng"),
-    "gong": ("g", "ong"),
-    "gou": ("g", "ou"),
-    "gu": ("g", "u"),
-    "gua": ("g", "ua"),
-    "guai": ("g", "uai"),
-    "guan": ("g", "uan"),
-    "guang": ("g", "uang"),
-    "gui": ("g", "uei"),
-    "gun": ("g", "uen"),
-    "guo": ("g", "uo"),
-    "ha": ("h", "a"),
-    "hai": ("h", "ai"),
-    "han": ("h", "an"),
-    "hang": ("h", "ang"),
-    "hao": ("h", "ao"),
-    "he": ("h", "e"),
-    "hei": ("h", "ei"),
-    "hen": ("h", "en"),
-    "heng": ("h", "eng"),
-    "hong": ("h", "ong"),
-    "hou": ("h", "ou"),
-    "hu": ("h", "u"),
-    "hua": ("h", "ua"),
-    "huai": ("h", "uai"),
-    "huan": ("h", "uan"),
-    "huang": ("h", "uang"),
-    "hui": ("h", "uei"),
-    "hun": ("h", "uen"),
-    "huo": ("h", "uo"),
-    "ji": ("j", "i"),
-    "jia": ("j", "ia"),
-    "jian": ("j", "ian"),
-    "jiang": ("j", "iang"),
-    "jiao": ("j", "iao"),
-    "jie": ("j", "ie"),
-    "jin": ("j", "in"),
-    "jing": ("j", "ing"),
-    "jiong": ("j", "iong"),
-    "jiu": ("j", "iou"),
-    "ju": ("j", "v"),
-    "juan": ("j", "van"),
-    "jue": ("j", "ve"),
-    "jun": ("j", "vn"),
-    "ka": ("k", "a"),
-    "kai": ("k", "ai"),
-    "kan": ("k", "an"),
-    "kang": ("k", "ang"),
-    "kao": ("k", "ao"),
-    "ke": ("k", "e"),
-    "kei": ("k", "ei"),
-    "ken": ("k", "en"),
-    "keng": ("k", "eng"),
-    "kong": ("k", "ong"),
-    "kou": ("k", "ou"),
-    "ku": ("k", "u"),
-    "kua": ("k", "ua"),
-    "kuai": ("k", "uai"),
-    "kuan": ("k", "uan"),
-    "kuang": ("k", "uang"),
-    "kui": ("k", "uei"),
-    "kun": ("k", "uen"),
-    "kuo": ("k", "uo"),
-    "la": ("l", "a"),
-    "lai": ("l", "ai"),
-    "lan": ("l", "an"),
-    "lang": ("l", "ang"),
-    "lao": ("l", "ao"),
-    "le": ("l", "e"),
-    "lei": ("l", "ei"),
-    "leng": ("l", "eng"),
-    "li": ("l", "i"),
-    "lia": ("l", "ia"),
-    "lian": ("l", "ian"),
-    "liang": ("l", "iang"),
-    "liao": ("l", "iao"),
-    "lie": ("l", "ie"),
-    "lin": ("l", "in"),
-    "ling": ("l", "ing"),
-    "liu": ("l", "iou"),
-    "lo": ("l", "o"),
-    "long": ("l", "ong"),
-    "lou": ("l", "ou"),
-    "lu": ("l", "u"),
-    "lv": ("l", "v"),
-    "luan": ("l", "uan"),
-    "lve": ("l", "ve"),
-    "lue": ("l", "ve"),
-    "lun": ("l", "uen"),
-    "luo": ("l", "uo"),
-    "ma": ("m", "a"),
-    "mai": ("m", "ai"),
-    "man": ("m", "an"),
-    "mang": ("m", "ang"),
-    "mao": ("m", "ao"),
-    "me": ("m", "e"),
-    "mei": ("m", "ei"),
-    "men": ("m", "en"),
-    "meng": ("m", "eng"),
-    "mi": ("m", "i"),
-    "mian": ("m", "ian"),
-    "miao": ("m", "iao"),
-    "mie": ("m", "ie"),
-    "min": ("m", "in"),
-    "ming": ("m", "ing"),
-    "miu": ("m", "iou"),
-    "mo": ("m", "o"),
-    "mou": ("m", "ou"),
-    "mu": ("m", "u"),
-    "na": ("n", "a"),
-    "nai": ("n", "ai"),
-    "nan": ("n", "an"),
-    "nang": ("n", "ang"),
-    "nao": ("n", "ao"),
-    "ne": ("n", "e"),
-    "nei": ("n", "ei"),
-    "nen": ("n", "en"),
-    "neng": ("n", "eng"),
-    "ni": ("n", "i"),
-    "nia": ("n", "ia"),
-    "nian": ("n", "ian"),
-    "niang": ("n", "iang"),
-    "niao": ("n", "iao"),
-    "nie": ("n", "ie"),
-    "nin": ("n", "in"),
-    "ning": ("n", "ing"),
-    "niu": ("n", "iou"),
-    "nong": ("n", "ong"),
-    "nou": ("n", "ou"),
-    "nu": ("n", "u"),
-    "nv": ("n", "v"),
-    "nuan": ("n", "uan"),
-    "nve": ("n", "ve"),
-    "nue": ("n", "ve"),
-    "nuo": ("n", "uo"),
-    "o": ("^", "o"),
-    "ou": ("^", "ou"),
-    "pa": ("p", "a"),
-    "pai": ("p", "ai"),
-    "pan": ("p", "an"),
-    "pang": ("p", "ang"),
-    "pao": ("p", "ao"),
-    "pe": ("p", "e"),
-    "pei": ("p", "ei"),
-    "pen": ("p", "en"),
-    "peng": ("p", "eng"),
-    "pi": ("p", "i"),
-    "pian": ("p", "ian"),
-    "piao": ("p", "iao"),
-    "pie": ("p", "ie"),
-    "pin": ("p", "in"),
-    "ping": ("p", "ing"),
-    "po": ("p", "o"),
-    "pou": ("p", "ou"),
-    "pu": ("p", "u"),
-    "qi": ("q", "i"),
-    "qia": ("q", "ia"),
-    "qian": ("q", "ian"),
-    "qiang": ("q", "iang"),
-    "qiao": ("q", "iao"),
-    "qie": ("q", "ie"),
-    "qin": ("q", "in"),
-    "qing": ("q", "ing"),
-    "qiong": ("q", "iong"),
-    "qiu": ("q", "iou"),
-    "qu": ("q", "v"),
-    "quan": ("q", "van"),
-    "que": ("q", "ve"),
-    "qun": ("q", "vn"),
-    "ran": ("r", "an"),
-    "rang": ("r", "ang"),
-    "rao": ("r", "ao"),
-    "re": ("r", "e"),
-    "ren": ("r", "en"),
-    "reng": ("r", "eng"),
-    "ri": ("r", "iii"),
-    "rong": ("r", "ong"),
-    "rou": ("r", "ou"),
-    "ru": ("r", "u"),
-    "rua": ("r", "ua"),
-    "ruan": ("r", "uan"),
-    "rui": ("r", "uei"),
-    "run": ("r", "uen"),
-    "ruo": ("r", "uo"),
-    "sa": ("s", "a"),
-    "sai": ("s", "ai"),
-    "san": ("s", "an"),
-    "sang": ("s", "ang"),
-    "sao": ("s", "ao"),
-    "se": ("s", "e"),
-    "sen": ("s", "en"),
-    "seng": ("s", "eng"),
-    "sha": ("sh", "a"),
-    "shai": ("sh", "ai"),
-    "shan": ("sh", "an"),
-    "shang": ("sh", "ang"),
-    "shao": ("sh", "ao"),
-    "she": ("sh", "e"),
-    "shei": ("sh", "ei"),
-    "shen": ("sh", "en"),
-    "sheng": ("sh", "eng"),
-    "shi": ("sh", "iii"),
-    "shou": ("sh", "ou"),
-    "shu": ("sh", "u"),
-    "shua": ("sh", "ua"),
-    "shuai": ("sh", "uai"),
-    "shuan": ("sh", "uan"),
-    "shuang": ("sh", "uang"),
-    "shui": ("sh", "uei"),
-    "shun": ("sh", "uen"),
-    "shuo": ("sh", "uo"),
-    "si": ("s", "ii"),
-    "song": ("s", "ong"),
-    "sou": ("s", "ou"),
-    "su": ("s", "u"),
-    "suan": ("s", "uan"),
-    "sui": ("s", "uei"),
-    "sun": ("s", "uen"),
-    "suo": ("s", "uo"),
-    "ta": ("t", "a"),
-    "tai": ("t", "ai"),
-    "tan": ("t", "an"),
-    "tang": ("t", "ang"),
-    "tao": ("t", "ao"),
-    "te": ("t", "e"),
-    "tei": ("t", "ei"),
-    "teng": ("t", "eng"),
-    "ti": ("t", "i"),
-    "tian": ("t", "ian"),
-    "tiao": ("t", "iao"),
-    "tie": ("t", "ie"),
-    "ting": ("t", "ing"),
-    "tong": ("t", "ong"),
-    "tou": ("t", "ou"),
-    "tu": ("t", "u"),
-    "tuan": ("t", "uan"),
-    "tui": ("t", "uei"),
-    "tun": ("t", "uen"),
-    "tuo": ("t", "uo"),
-    "wa": ("^", "ua"),
-    "wai": ("^", "uai"),
-    "wan": ("^", "uan"),
-    "wang": ("^", "uang"),
-    "wei": ("^", "uei"),
-    "wen": ("^", "uen"),
-    "weng": ("^", "ueng"),
-    "wo": ("^", "uo"),
-    "wu": ("^", "u"),
-    "xi": ("x", "i"),
-    "xia": ("x", "ia"),
-    "xian": ("x", "ian"),
-    "xiang": ("x", "iang"),
-    "xiao": ("x", "iao"),
-    "xie": ("x", "ie"),
-    "xin": ("x", "in"),
-    "xing": ("x", "ing"),
-    "xiong": ("x", "iong"),
-    "xiu": ("x", "iou"),
-    "xu": ("x", "v"),
-    "xuan": ("x", "van"),
-    "xue": ("x", "ve"),
-    "xun": ("x", "vn"),
-    "ya": ("^", "ia"),
-    "yan": ("^", "ian"),
-    "yang": ("^", "iang"),
-    "yao": ("^", "iao"),
-    "ye": ("^", "ie"),
-    "yi": ("^", "i"),
-    "yin": ("^", "in"),
-    "ying": ("^", "ing"),
-    "yo": ("^", "iou"),
-    "yong": ("^", "iong"),
-    "you": ("^", "iou"),
-    "yu": ("^", "v"),
-    "yuan": ("^", "van"),
-    "yue": ("^", "ve"),
-    "yun": ("^", "vn"),
-    "za": ("z", "a"),
-    "zai": ("z", "ai"),
-    "zan": ("z", "an"),
-    "zang": ("z", "ang"),
-    "zao": ("z", "ao"),
-    "ze": ("z", "e"),
-    "zei": ("z", "ei"),
-    "zen": ("z", "en"),
-    "zeng": ("z", "eng"),
-    "zha": ("zh", "a"),
-    "zhai": ("zh", "ai"),
-    "zhan": ("zh", "an"),
-    "zhang": ("zh", "ang"),
-    "zhao": ("zh", "ao"),
-    "zhe": ("zh", "e"),
-    "zhei": ("zh", "ei"),
-    "zhen": ("zh", "en"),
-    "zheng": ("zh", "eng"),
-    "zhi": ("zh", "iii"),
-    "zhong": ("zh", "ong"),
-    "zhou": ("zh", "ou"),
-    "zhu": ("zh", "u"),
-    "zhua": ("zh", "ua"),
-    "zhuai": ("zh", "uai"),
-    "zhuan": ("zh", "uan"),
-    "zhuang": ("zh", "uang"),
-    "zhui": ("zh", "uei"),
-    "zhun": ("zh", "uen"),
-    "zhuo": ("zh", "uo"),
-    "zi": ("z", "ii"),
-    "zong": ("z", "ong"),
-    "zou": ("z", "ou"),
-    "zu": ("z", "u"),
-    "zuan": ("z", "uan"),
-    "zui": ("z", "uei"),
-    "zun": ("z", "uen"),
-    "zuo": ("z", "uo"),
-}
diff --git a/spaces/ysharma/Low-rank-Adaptation/run_lora_db.sh b/spaces/ysharma/Low-rank-Adaptation/run_lora_db.sh
deleted file mode 100644
index 1a4605b5ed2805a91bd3f2ca63c0f4b2ed0bda90..0000000000000000000000000000000000000000
--- a/spaces/ysharma/Low-rank-Adaptation/run_lora_db.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#https://github.com/huggingface/diffusers/tree/main/examples/dreambooth
-export MODEL_NAME="stabilityai/stable-diffusion-2-1-base"
-export INSTANCE_DIR="./data_example"
-export OUTPUT_DIR="./output_example"
-
-accelerate launch train_lora_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="style of sks" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=1e-4 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=30000
\ No newline at end of file
diff --git a/spaces/ysharma/Voice-to-Youtube/app.py b/spaces/ysharma/Voice-to-Youtube/app.py
deleted file mode 100644
index 0a82eb9ea8272c8c6ae395041585f8fec590f155..0000000000000000000000000000000000000000
--- a/spaces/ysharma/Voice-to-Youtube/app.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import gradio as gr
-import urllib.request
-import re
-import whisper
-import os
-import requests
-
-# Whisper: Speech-to-text
-model = whisper.load_model("base")
-model_med = whisper.load_model("medium")
-
-# Whisper - speech-to-text
-def whisper_stt(audio):
-  print("Inside Whisper TTS")
-  # load audio and pad/trim it to fit 30 seconds
-  audio = whisper.load_audio(audio)
-  audio = whisper.pad_or_trim(audio)
-  
-  # make log-Mel spectrogram and move to the same device as the model
-  mel = whisper.log_mel_spectrogram(audio).to(model.device)
-  
-  # detect the spoken language
-  _, probs = model.detect_language(mel)
-  lang = max(probs, key=probs.get)
-  print(f"Detected language: {max(probs, key=probs.get)}")
-  
-  # decode the audio
-  options_transc = whisper.DecodingOptions(fp16 = False, language=lang, task='transcribe') #lang
-  options_transl = whisper.DecodingOptions(fp16 = False, language='en', task='translate') #lang
-  result_transc = whisper.decode(model_med, mel, options_transc)
-  result_transl = whisper.decode(model_med, mel, options_transl)
-  
-  # print the recognized text
-  print(f"transcript is : {result_transc.text}")
-  print(f"translation is : {result_transl.text}")
-
-  return result_transc.text, result_transl.text, lang
-
-def engine(audio):
-  # Get voice query to text
-  transcribe, translation, lang = whisper_stt(audio)
-  # Get Video result
-  video_url, video_id = get_youtube_video(translation)
-  # Display video
-  html_out1, html_out2 = display_vid(video_id) 
-  return html_out1, html_out2, translation 
-
-
-def get_youtube_video(query):
-  search_term = '+'.join(query.split())
-  html = urllib.request.urlopen("https://www.youtube.com/results?sp=mAEB&search_query="+search_term)
-  # samples : "https://www.youtube.com/results?sp=mAEB&search_query=openai+whisper+model"
-  # samples : "https://www.youtube.com/results?sp=mAEB&search_query=gradio+for+ml+model+frontend"
-  video_ids = re.findall(r"watch\?v=(\S{11})", html.read().decode())
-  # samples : https://www.youtube.com/watch?v=HbY51mVKrcE
-  video_url = "https://www.youtube.com/watch?v="+video_ids[0]
-  return video_url, video_ids
-
-
-def display_vid(video_ids): 
-  print("******** display_vid ********")
-  
-  # created embedding  
-  html_out1 = '<iframe width="730" height="400" src="https://www.youtube.com/embed/'+ video_ids[0] + '" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'
-  html_out2 = '<iframe width="730" height="400" src="https://www.youtube.com/embed/'+ video_ids[1] + '" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'
-  print(f"html output 1 is : {html_out1}")
-  print(f"html output 2 is : {html_out2}")
-  
-  return html_out1, html_out2
-
-# not using currently
-def set_example_question(sample_question):
-    print(f"******* Inside Sample Questions ********")
-    print(f"Sample Question coming from Radio box is : {sample_question}")
-    print("What is the Return value : {gr.Radio.update(value=sample_question)}")
-    return gr.Radio.update(value=sample_question) #input_ques.update(example)
-
-demo = gr.Blocks()
-
-with demo:
-  gr.Markdown("<h1><center>Voice to Youtube Search</center></h1>")
-  gr.Markdown(
-        """<center>Whisper powered voice search app to search and display Youtube Videos on Spaces.</center><br><center>Best Part you don't have to step away from Spaces !</center>
-        """
-    )
-  with gr.Column():
-    with gr.Row():
-      in_audio = gr.Audio(source="microphone",  type="filepath", label='Record your voice search query here in English, Spanish or French for best results-')
-      b1 = gr.Button("Whisper powered Youtube search")
-      out_textbox = gr.Textbox(label="Whisper Specch to Text transcript")
-      
-    with gr.Row():
-      output_vid1 = gr.HTML(label="First Video Result from Youtube", show_label=True)
-      output_vid2 = gr.HTML(label="Second Video Result from Youtube", show_label=True)
-      b1.click(engine, inputs=[in_audio], outputs=[output_vid1, output_vid2, out_textbox])
-      
-  with gr.Row(): 
-    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=ysharma_Voice-to-Youtube)")
-
-demo.launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git "a/spaces/yunfei0710/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py" "b/spaces/yunfei0710/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
deleted file mode 100644
index f1a89a7ec94f30a00b58ad85661a876efc0ac4d2..0000000000000000000000000000000000000000
--- "a/spaces/yunfei0710/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
+++ /dev/null
@@ -1,114 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import tiktoken
-    print('begin analysis on:', file_name)
-
-    ############################## <第 0 步，切割PDF> ##################################
-    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-    # 的长度必须小于 2500 个 Token
-    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-    file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-    page_one = str(page_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
-    
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-    
-    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-    final_results = []
-    final_results.append(paper_meta)
-
-    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-    iteration_results = []
-    last_iteration_result = paper_meta  # 初始值是摘要
-    MAX_WORD_TOTAL = 4096
-    n_fragment = len(paper_fragments)
-    if n_fragment >= 20: print('文章极长，不能达到预期效果')
-    for i in range(n_fragment):
-        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
-        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                           llm_kwargs, chatbot, 
-                                                                           history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                           sys_prompt="Extract the main idea of this section."  # 提示
-                                                                        ) 
-        iteration_results.append(gpt_say)
-        last_iteration_result = gpt_say
-
-    ############################## <第 3 步，整理history> ##################################
-    final_results.extend(iteration_results)
-    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
-    # 接下来两句话只显示在界面上，不起实际作用
-    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
-    chatbot.append([i_say_show_user, gpt_say])
-
-    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
-    from .crazy_utils import input_clipping
-    _, final_results = input_clipping("", final_results, max_token_limit=3200)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-
-@CatchException
-def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            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```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        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}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    txt = file_manifest[0]
-    # 开始正式执行任务
-    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/zengwj/GPT2-chitchat-training-huggingface/install.sh b/spaces/zengwj/GPT2-chitchat-training-huggingface/install.sh
deleted file mode 100644
index 46d8df8e3b264ecb823250abc17d68f97007d251..0000000000000000000000000000000000000000
--- a/spaces/zengwj/GPT2-chitchat-training-huggingface/install.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/bin/sh
-cd /workdir
-[ -f train_path.txt ] || exit 1  #没放语料退出...
-export HOME=/workdir
-export PATH="$PATH:/workdir/.local/bin"
-git clone https://github.com/yangjianxin1/GPT2-chitchat
-cd GPT2-chitchat
-pip3 install torch==1.7.0 --extra-index-url https://download.pytorch.org/whl/cpu #如需更改可参考pytorch.org
-mv ../req.txt .
-mv ../train.py .  #在每4步后加了10s停顿
-mv ../train_path.txt .
-pip3 install -r req.txt #修复版 原项目里的几个包改名了
-wget `cat train_path.txt` -O train_100w.pkl
-[ -f train_100w.pkl ] || exit 1 #下载失败 退出
-mv train_100w.pkl data
-#python3 preprocess.py
-wget 'https://github.com/bitepeng/easyhttpd/blob/main/bin/linux/easyhttpd?raw=true'  #EasyHttpd
-mv easyhttpd* easyhttpd
-chmod +x easyhttpd
-./easyhttpd -p :7860 -r ./ &
-python3 train.py --epochs 30 --no_cuda --batch_size 2 --train_path data/train_100w.pkl
-cat #如果进程全部退出会悲剧... 想要重新跑就Reboot/git push 不用了就等它自己休眠吧
diff --git a/spaces/zhang-wei-jian/docker/node_modules/koa-convert/README.md b/spaces/zhang-wei-jian/docker/node_modules/koa-convert/README.md
deleted file mode 100644
index d4474c38700efb14281a9c9a6d75cd35b841a495..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/koa-convert/README.md
+++ /dev/null
@@ -1,166 +0,0 @@
-# koa-convert
-
-[![Build Status][travis-img]][travis-url]
-[![NPM version][npm-badge]][npm-url]
-[![License][license-badge]][license-url]
-![Code Size][code-size-badge]
-
-<!-- [![Coverage Status][coverage-img]][coverage-url] -->
-
-<!-- ***************** -->
-
-[travis-img]: https://travis-ci.org/koajs/convert.svg?branch=master
-[travis-url]: https://travis-ci.org/koajs/convert
-
-<!-- 
-  WAIT FOR ACCESS 
-  [coverage-img]: https://coveralls.io/repos/github/koajs/convert/badge.svg?branch=master
-  [coverage-url]: https://coveralls.io/github/koajs/convert?branch=master
--->
-
-[npm-badge]: https://img.shields.io/npm/v/koa-better-request-id.svg?style=flat
-[npm-url]: https://www.npmjs.com/package/koa-better-request-id
-[license-badge]: https://img.shields.io/badge/license-MIT-green.svg?style=flat-square
-[license-url]: https://github.com/koajs/koa-convert/blob/master/LICENSE
-[code-size-badge]: https://img.shields.io/github/languages/code-size/koajs/koa-convert
-
-<!-- ***************** -->
-
-Convert Koa legacy (0.x & 1.x) generator middleware to modern promise middleware (2.x).
-
-It could also convert modern promise middleware back to legacy generator middleware (useful to help modern middleware support Koa v0.x or v1.x).
-
-## Note
-
-Router middleware is special case here. Because it reimplements middleware composition internally, we cannot not simply convert it.
-
-You may use following packages for [routing](https://github.com/koajs/koa/wiki#routing-and-mounting), which are koa 2.x ready now:
-
-* [koa-route@3.0.0](https://github.com/koajs/route/tree/next)
-* [koa-simple-router](https://github.com/gyson/koa-simple-router)
-* [koa-router@next](https://github.com/alexmingoia/koa-router/tree/master)
-* [koa-66](https://github.com/menems/koa-66)
-
-## Installation
-
-```bash
-# npm ..
-$ npm i koa-convert
-# yarn ..
-$ yarn add koa-convert
-```
-
-## Usage
-
-```js
-const Koa = require('koa') // koa v2.x
-const convert = require('koa-convert')
-const app = new Koa()
-
-app.use(modernMiddleware)
-
-app.use(convert(legacyMiddleware))
-
-app.use(convert.compose(legacyMiddleware, modernMiddleware))
-
-function * legacyMiddleware (next) {
-  // before
-  yield next
-  // after
-}
-
-function modernMiddleware (ctx, next) {
-  // before
-  return next().then(() => {
-    // after
-  })
-}
-```
-
-## Distinguish legacy and modern middleware
-
-In koa 0.x and 1.x (without experimental flag), `app.use` has an assertion that all (legacy) middleware must be generator function and it's tested with `fn.constructor.name == 'GeneratorFunction'` at [here](https://github.com/koajs/koa/blob/7fe29d92f1e826d9ce36029e1b9263b94cba8a7c/lib/application.js#L105).
-
-Therefore, we can distinguish legacy and modern middleware with `fn.constructor.name == 'GeneratorFunction'`.
-
-## Migration
-
-`app.use(legacyMiddleware)` is everywhere in 0.x and 1.x and it would be painful to manually change all of them to `app.use(convert(legacyMiddleware))`.
-
-You can use following snippet to make migration easier.
-
-```js
-const _use = app.use
-app.use = x => _use.call(app, convert(x))
-```
-
-The above snippet will override `app.use` method and implicitly convert all legacy generator middleware to modern promise middleware.
-
-Therefore, you can have both `app.use(modernMiddleware)` and `app.use(legacyMiddleware)` and your 0.x or 1.x should work without modification.
-
-Complete example:
-
-```js
-const Koa = require('koa') // v2.x
-const convert = require('koa-convert')
-const app = new Koa()
-
-// ---------- override app.use method ----------
-
-const _use = app.use
-app.use = x => _use.call(app, convert(x))
-
-// ---------- end ----------
-
-app.use(modernMiddleware)
-
-// this will be converted to modern promise middleware implicitly
-app.use(legacyMiddleware)
-
-function * legacyMiddleware (next) {
-  // before
-  yield next
-  // after
-}
-
-function modernMiddleware (ctx, next) {
-  // before
-  return next().then(() => {
-    // after
-  })
-}
-```
-
-## API
-
-#### `convert()`
-
-Convert legacy generator middleware to modern promise middleware.
-
-```js
-modernMiddleware = convert(legacyMiddleware)
-```
-
-#### `convert.compose()`
-
-Convert and compose multiple middleware (could mix legacy and modern ones) and return modern promise middleware.
-
-```js
-composedModernMiddleware = convert.compose(legacyMiddleware, modernMiddleware)
-// or
-composedModernMiddleware = convert.compose([legacyMiddleware, modernMiddleware])
-```
-
-#### `convert.back()`
-
-Convert modern promise middleware back to legacy generator middleware.
-
-This is useful to help modern promise middleware support koa 0.x or 1.x.
-
-```js
-legacyMiddleware = convert.back(modernMiddleware)
-```
-
-## License
-
-[MIT](LICENSE)
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/pages/api/healthz.ts b/spaces/zhaoys/wfms-kuiwenc/src/pages/api/healthz.ts
deleted file mode 100644
index f6ae44ff0fd66ccd3f7feaa550025fbf2a83bf77..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/pages/api/healthz.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  res.status(200).end('ok')
-}