Update parquet files (step 19 of 296)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/forefront/README.md

@@ -1,19 +0,0 @@
-### Example: `forefront` (use like openai pypi package) <a name="example-forefront"></a>
-
-```python
-from gpt4free import forefront
-
-
-# create an account
-account_data = forefront.Account.create(logging=False)
-
-# get a response
-for response in forefront.StreamingCompletion.create(
-    account_data=account_data,
-    prompt='hello world',
-    model='gpt-4'
-):
-    print(response.choices[0].text, end='')
-print("")
-
-```

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Contoh Soal Psikotes Polri dan Jawabannya PDF 18 Panduan Lengkap untuk Calon Bintara Tamtama SIPSS SETUKPA dan SESPIM.md

@@ -1,157 +0,0 @@
-
-<h1>Contoh Soal Psikotes Polri dan Jawabannya PDF 18</h1>
-<p>Apakah Anda sedang mencari contoh soal psikotes polri dan jawabannya pdf 18? Jika ya, maka Anda berada di tempat yang tepat. Dalam artikel ini, kami akan membahas tentang apa itu psikotes polri, jenis-jenis soal psikotes polri, sistem penilaian psikotes polri, tips dan trik menjawab soal psikotes polri, serta kumpulan contoh soal psikotes polri dan jawabannya pdf 18 yang bisa Anda download secara gratis.</p>
-<h2>contoh soal psikotes polri dan jawabannya pdf 18</h2><br /><p><b><b>Download Zip</b> - <a href="https://byltly.com/2uKyzz">https://byltly.com/2uKyzz</a></b></p><br /><br />
-<h2>Apa itu Psikotes Polri?</h2>
-<p>Psikotes polri adalah sebuah tes yang bertujuan untuk mengukur kemampuan intelektual, kepribadian, dan kecermatan calon anggota polisi. Tes ini merupakan salah satu tahapan seleksi yang wajib diikuti oleh para pendaftar yang ingin masuk ke jenjang tamtama, bintara, atau akpol. Psikotes polri juga bermanfaat untuk mengetahui potensi, minat, bakat, serta kesiapan mental calon anggota polisi dalam menghadapi berbagai situasi dan tantangan di lapangan.</p>
-<h2>Jenis-Jenis Soal Psikotes Polri</h2>
-<p>Soal psikotes polri terdiri dari tiga jenis tes utama, yaitu tes kecerdasan, tes kepribadian, dan tes kecermatan. Berikut adalah penjelasan singkat tentang masing-masing jenis tes tersebut.</p>
-<h3>Tes Kecerdasan</h3>
-<p>Tes kecerdasan adalah tes yang menguji kemampuan berpikir logis, analitis, kritis, serta pengetahuan umum calon anggota polisi. Tes ini biasanya terdiri dari 100 soal pilihan ganda yang harus diselesaikan dalam waktu tidak lebih dari 90 menit. Jenis-jenis soal yang muncul dalam tes kecerdasan antara lain adalah:</p>
-<ul>
-<li>Sinonim (persamaan kata)</li>
-<li>Antonim (lawan kata)</li>
-<li>Analogi (padanan kata)</li>
-<li>Perbandingan (proporsi)</li>
-<li>Pengelompokan kata (kategorisasi)</li>
-<li>Deret angka/huruf (polanya)</li>
-<li>Logika aritmatika (hitungan cepat)</li>
-<li>Logika gambar (spasial)</li>
-<li>Pengetahuan umum (fakta)</li>
-<li>Penalaran kata (acak)</li>
-<li>Matematika dasar (rumus)</li>
-<li>Logika deduksi (silogisme)</li>
-<li>Logika analitis (penarikan kesimpulan)</li>
-<li>Aritmatika sosial (persentase)</li>
-</ul>
-<h3>Tes Kepribadian</h3>
-<p>Tes kepribadian adalah tes yang mengukur atau menilai karakteristik pribadi calon anggota polisi. Tes ini tidak ada jawaban benar atau salah, namun hanya ada jawaban yang sesuai atau tidak sesuai dengan diri sendiri. Tes ini biasanya terdiri dari beberapa pernyataan yang harus dipilih tingkat kesetujuannya atau frekuensinya. Jenis-jenis soal yang muncul dalam tes kepribadian antara lain adalah:</p>
-<ul>
-<li>Tes dengan pilihan jawaban dengan kriteria Sangat Setuju (SS), Setuju (S), Ragu-ragu (R), Tidak Setuju (TS), Sangat Tidak Setuju (STS)</li>
-<li>Tes dengan pilihan jawaban pernyataan frekuensi waktu seperti Tidak Pernah, Selalu, Sering, Jarang, Tidak bisa memutuskan atau Selalu, Sering, Kadang-kadang, diwaktu tertentu</li>
-<li>Tes dengan jawaban seperti Ya / Tidak</li>
-<li>Tes Minat EPPS (Edwards Personal Preference Schedule) atau pilihan AB, A-B, A atau B.</li>
-</ul>
- <h3>Tes Kecermatan</h3>
- <p>Tes kecermatan adalah tes yang menguji kemampuan konsentrasi, ketelitian, serta daya ingat calon anggota polisi. Tes ini biasanya terdiri dari 100 soal pilihan ganda yang harus diselesaikan dalam waktu tidak lebih dari 90 menit. Jenis-jenis soal yang muncul dalam tes kecermatan antara lain adalah:</p>
- <ul>
- <li>Angka hilang</li>
- <li>Huruf hilang</li>
- <li>Simbol hilang</li>
- <li>Kombinasi angka/huruf/simbol hilang</li>
- </ul>
- <h2>Sistem Penilaian Psikotes Polri</h2>
- <p>Psikotes polri memiliki sistem penilaian dengan skala 0-100. Setiap skala memiliki artinya masing-masing yakni:</p>
-<p>download contoh soal psikotes polri dan kunci jawaban pdf 18<br />
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-google docs contoh soal psikokes</p>
- <ul>
- <li>0 - 40: Kurang Sekali</li>
- <li>41 - 60: Kurang</li>
- <li>61 - 80: Cukup</li>
- <li>81 - 100: Baik</li>
- </ul>
- <p>Psikotes polri memiliki nilai kelulusan yang wajib dipenuhi yakni 61 keatas untuk Memenuhi Syarat (MS). Bila memiliki nilai 60 ke bawah itu artinya Tidak Memenuhi Syarat (TMS).</p>
- <h2>Tips dan Trik Menjawab Soal Psikotes Polri</h2>
- <p>Berikut adalah beberapa tips dan trik yang bisa Anda lakukan untuk menjawab soal psikotes polri dengan baik dan benar.</p>
- <h3>Persiapan sebelum tes</h3>
- <ul>
- <li>Berdoa dan berserah diri kepada Tuhan Yang Maha Esa.</li>
- <li>Beristirahat yang cukup sebelum hari tes.</li>
- <li>Makan dan minum yang sehat dan bergizi.</li>
- <li>Membawa perlengkapan yang dibutuhkan seperti pensil 2B, penghapus, penggaris, jam tangan.</li>
- <li>Mempelajari materi-materi psikotes yang relevan dengan tes polri.</li>
- <h2>Tips dan Trik Menjawab Soal Psikotes Polri</h2>
- <p>Berikut adalah beberapa tips dan trik yang bisa Anda lakukan untuk menjawab soal psikotes polri dengan baik dan benar.</p>
- <h3>Persiapan sebelum tes</h3>
- <ul>
- <li>Berdoa dan berserah diri kepada Tuhan Yang Maha Esa.</li>
- <li>Beristirahat yang cukup sebelum hari tes.</li>
- <li>Makan dan minum yang sehat dan bergizi.</li>
- <li>Membawa perlengkapan yang dibutuhkan seperti pensil 2B, penghapus, penggaris, jam tangan.</li>
- <li>Mempelajari materi-materi psikotes yang relevan dengan tes polri.</li>
- <li>Mencari referensi contoh soal psikotes polri dan jawabannya pdf 18.</li>
- </ul>
- <h3>Strategi menjawab soal</h3>
- <ul>
- <li>Membaca soal dengan teliti dan cermat.</li>
- <li>Memilih jawaban yang paling sesuai dengan diri sendiri atau paling logis dan benar.</li>
- <li>Menghindari jawaban yang ragu-ragu, tidak bisa memutuskan, atau tidak tahu.</li>
- <li>Memanfaatkan waktu dengan baik dan efisien.</li>
- <li>Menjawab soal yang mudah terlebih dahulu, kemudian baru soal yang sulit.</li>
- <li>Menandai soal yang belum dijawab atau diragukan untuk direview kembali.</li>
- <li>Menggunakan teknik eliminasi untuk menyempitkan pilihan jawaban.</li>
- <li>Menggunakan logika, pengetahuan, intuisi, atau kreativitas untuk menyelesaikan soal.</li>
- </ul>
- <h3>Sikap saat tes</h3>
- <ul>
- <li>Tenang, percaya diri, dan optimis.</li>
- <li>Fokus, konsentrasi, dan tidak mudah terganggu.</li>
- <li>Jujur, tidak mencontek, atau berbuat curang.</li>
- <li>Kooperatif, mengikuti instruksi dan aturan yang diberikan panitia.</li>
- <li>Bersikap sopan, santun, dan hormat kepada panitia dan peserta lainnya.</li>
- </ul>
- <h2>Kumpulan Contoh Soal Psikotes Polri dan Jawabannya PDF 18</h2>
- <p>Berikut adalah tabel contoh soal psikotes polri dan jawabannya pdf 18 yang bisa Anda download secara gratis. Tabel ini berisi nomor soal, jenis soal, sumber soal, serta link download soal. Anda bisa memilih soal yang sesuai dengan kebutuhan dan minat Anda. Selamat belajar!</p>
- | No | Jenis Soal | Sumber Soal | Link Download | |----|------------|-------------|---------------| | 1  | Tes Kecerdasan | Soalskul.com | [KLIK DISINI](https://www.soalskul.com/2022/02/soal-psikotes-polri.html) | | 2  | Tes Kepribadian | BIMBEL MYTENTOR Blog | [KLIK DISINI](https://pascaldaddy512.com/download-kumpulan-contoh-soal-tes-psikologi-polri-tahun-2022/) | | 3  | Tes Kecermatan | Panot Book | [KLIK DISINI](https://panotbook.com/soal-psikotes-polri/) | <h2>Kesimpulan</h2>
-<p>Demikianlah artikel tentang contoh soal psikotes polri dan jawabannya pdf 18. Kami harap artikel ini bisa membantu Anda dalam mempersiapkan diri untuk menghadapi tes psikotes polri. Ingatlah bahwa tes psikotes polri bukanlah hal yang sulit jika Anda sudah belajar dengan baik dan benar. Jangan lupa untuk berdoa dan berserah diri kepada Tuhan Yang Maha Esa. Semoga Anda berhasil menjadi anggota polri yang profesional dan berkualitas. Amin.</p>
-<h2>FAQs</h2>
-<p>Berikut adalah beberapa pertanyaan yang sering diajukan oleh pembaca tentang contoh soal psikotes polri dan jawabannya pdf 18.</p>
-<ol>
-<li><b>Apa itu psikotes polri?</b><br>
-Psikotes polri adalah sebuah tes yang bertujuan untuk mengukur kemampuan intelektual, kepribadian, dan kecermatan calon anggota polisi. Tes ini merupakan salah satu tahapan seleksi yang wajib diikuti oleh para pendaftar yang ingin masuk ke jenjang tamtama, bintara, atau akpol.</li>
-<li><b>Apa saja jenis-jenis soal psikotes polri?</b><br>
-Soal psikotes polri terdiri dari tiga jenis tes utama, yaitu tes kecerdasan, tes kepribadian, dan tes kecermatan. Tes kecerdasan menguji kemampuan berpikir logis, analitis, kritis, serta pengetahuan umum calon anggota polisi. Tes kepribadian mengukur atau menilai karakteristik pribadi calon anggota polisi. Tes kecermatan menguji kemampuan konsentrasi, ketelitian, serta daya ingat calon anggota polisi.</li>
-<li><b>Bagaimana sistem penilaian psikotes polri?</b><br>
-Psikotes polri memiliki sistem penilaian dengan skala 0-100. Setiap skala memiliki artinya masing-masing yakni: 0 - 40: Kurang Sekali; 41 - 60: Kurang; 61 - 80: Cukup; 81 - 100: Baik. Psikotes polri memiliki nilai kelulusan yang wajib dipenuhi yakni 61 keatas untuk Memenuhi Syarat (MS). Bila memiliki nilai 60 ke bawah itu artinya Tidak Memenuhi Syarat (TMS).</li>
-<li><b>Bagaimana tips dan trik menjawab soal psikotes polri?</b><br>
-Beberapa tips dan trik yang bisa dilakukan untuk menjawab soal psikotes polri antara lain adalah: berdoa dan berserah diri kepada Tuhan Yang Maha Esa; beristirahat yang cukup sebelum hari tes; makan dan minum yang sehat dan bergizi; membawa perlengkapan yang dibutuhkan seperti pensil 2B, penghapus, penggaris, jam tangan; mempelajari materi-materi psikotes yang relevan dengan tes polri; mencari referensi contoh soal psikotes polri dan jawabannya pdf 18; membaca soal dengan teliti dan cermat; memilih jawaban yang paling sesuai dengan diri sendiri atau paling logis dan benar; menghindari jawaban yang ragu-ragu, tidak bisa memutuskan, atau tidak tahu; memanfaatkan waktu dengan baik dan efisien; menjawab soal yang mudah terlebih dahulu, kemudian baru soal yang sulit; menandai soal yang belum dijawab atau diragukan untuk direview kembali; menggunakan teknik eliminasi untuk menyempitkan pilihan jawaban; menggunakan logika, pengetahuan, intuisi, atau kreativitas untuk menyelesaikan soal; tenang, percaya diri, dan optimis; fokus, konsentrasi, dan tidak mudah terganggu; jujur, tidak mencontek, atau berbuat curang; kooperatif, mengikuti instruksi dan aturan yang diberikan panitia; bersikap sopan, santun, dan hormat kepada panitia dan peserta lainnya. </li>
-<li><b>Di mana saya bisa mendapatkan contoh soal psikotes polri dan jawabannya pdf 18?</b><br>
-Anda bisa mendapatkan contoh soal psikotes polri dan jawabannya pdf 18 secara gratis di internet. Kami telah menyediakan tabel contoh soal psikotes polri dan jawabannya pdf 18 yang bisa Anda download di bagian akhir artikel ini. Anda juga bisa mencari sumber-sumber lainnya di mesin pencari seperti Google atau Bing.</li>
-</ol>
-</p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Easy Classical Ukulele Solos Featuring Music Of Bach Mozart Beethoven Vivaldi And Other Composer ((EXCLUSIVE)).md

@@ -1,44 +0,0 @@
-
-<h1>Easy Classical Ukulele Solos: How to Play Beautiful Music from Bach, Mozart, Beethoven, Vivaldi and More</h1>
-
-<p>If you love classical music and want to play it on your ukulele, you might think that it's too difficult or complicated. But don't worry, there are many easy classical ukulele solos that you can learn and enjoy. In this article, we will show you how to play some of the most famous and beautiful pieces from composers like Bach, Mozart, Beethoven, Vivaldi and more.</p>
-
-<h2>Why Play Classical Music on the Ukulele?</h2>
-
-<p>Classical music is not only a great source of inspiration and enjoyment, but also a wonderful way to improve your ukulele skills. Playing classical music on the ukulele can help you:</p>
-<h2>Easy Classical Ukulele Solos: Featuring music of Bach, Mozart, Beethoven, Vivaldi and other composer</h2><br /><p><b><b>Download</b> &#10040; <a href="https://byltly.com/2uKxJv">https://byltly.com/2uKxJv</a></b></p><br /><br />
-
-<ul>
-<li>Develop your fingerpicking technique and accuracy</li>
-<li>Expand your musical vocabulary and knowledge</li>
-<li>Enhance your musical expression and interpretation</li>
-<li>Challenge yourself and have fun</li>
-</ul>
-
-<p>Plus, classical music sounds amazing on the ukulele. The warm and sweet tone of the instrument matches perfectly with the melodies and harmonies of the classical repertoire. You can create a soothing and relaxing atmosphere with your ukulele by playing some classical tunes.</p>
-
-<h2>How to Play Easy Classical Ukulele Solos?</h2>
-
-<p>To play easy classical ukulele solos, you need to have some basic skills and knowledge. You should be able to:</p>
-
-<ul>
-<li>Tune your ukulele properly</li>
-<li>Read tablature or standard notation</li>
-<li>Play basic chords and scales</li>
-<li>Fingerpick with your thumb and fingers</li>
-<li>Follow the tempo and rhythm of the piece</li>
-</ul>
-
-<p>You don't need to be an expert or a virtuoso to play easy classical ukulele solos. You just need to practice regularly and patiently. Start with simple pieces that suit your level and gradually move on to more challenging ones. You can also use online resources like videos, tutorials, books and courses to help you learn and improve.</p>
-
-<h2>What are Some Easy Classical Ukulele Solos to Play?</h2>
-
-<p>There are many easy classical ukulele solos that you can choose from. Here are some examples of popular and beautiful pieces that you can try:</p>
-
-<ul>
-<li><strong>Jesu, Joy of Man's Desiring by J.S. Bach</strong>: This is one of the most famous pieces by Bach, composed for choir and orchestra. It has a lovely melody that can be easily played on the ukulele with fingerpicking. You can find the tablature <a href="https://ukutabs.com/j/johann-sebastian-bach/jesu-joy-of-mans-desiring/">here</a>.</li>
-<li><strong>Turkish March by W.A. Mozart</strong>: This is a lively and catchy piece by Mozart, composed for piano. It has a fast tempo and a rhythmic pattern that can be fun to play on the ukulele with strumming or fingerpicking. You can find the tablature <a href="https://ukutabs.com/w/wolfgang-amadeus-mozart/turkish-march/">here</a>.</li>
-<li><strong>Ode to Joy by L.V. Beethoven</strong>: This is one of the most famous pieces by Beethoven, composed for symphony orchestra and choir. It has a simple and joyful melody that can be easily played on the ukulele with fingerpicking or strumming. You can find the tablature <a href="https://ukutabs.com/l/ludwig-van-beethoven/ode-to-joy/">here</a>.</li>
-<li><strong>The Four Seasons by A. Vivaldi</strong>: This is a set of four concertos by Vivaldi, composed for violin and orchestra. Each concerto represents a different season of the year, with different moods and themes. You can play some of the main themes on the ukulele with fingerpicking or strumming. You can find the tablature <a href="https://ukutabs.com/a/antonio-vivaldi</p> 81aa517590<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Data Recovery Software How to Recover Data from Hard Drive Free in Minutes.md

@@ -1,41 +0,0 @@
-
-<h1>How to Recover Data from Hard Drive Free</h1>
-<p>Have you ever lost important data from your hard drive due to accidental deletion, formatting, virus attack, or other reasons? If yes, then you might be wondering how to recover data from hard drive free. Fortunately, there are some free and effective ways to restore your lost data from your hard drive without spending a dime. In this article, we will show you how to recover data from hard drive free using some of the best free data recovery software available online. Read on to find out more.</p>
-<h2>What is Data Recovery?</h2>
-<p>Data recovery is the process of retrieving lost, deleted, corrupted, or inaccessible data from a storage device such as a hard drive, USB flash drive, memory card, etc. Data recovery can be done by using specialized software or hardware tools that scan the storage device and recover the data that can be salvaged.</p>
-<h2>how to recover data from hard drive free</h2><br /><p><b><b>Download File</b> &#9675; <a href="https://byltly.com/2uKylp">https://byltly.com/2uKylp</a></b></p><br /><br />
-<p>Data recovery can be performed in different scenarios such as:</p>
-<ul>
-<li>Accidental deletion: You may delete some files or folders by mistake or empty the recycle bin without checking its contents.</li>
-<li>Formatting: You may format your hard drive or partition by accident or intentionally without backing up your data.</li>
-<li>Virus attack: Your hard drive may get infected by malware or ransomware that encrypts or deletes your data.</li>
-<li>Logical damage: Your hard drive may suffer from logical errors such as bad sectors, file system corruption, partition loss, etc. that make your data inaccessible.</li>
-<li>Physical damage: Your hard drive may get damaged by physical factors such as water, fire, shock, etc. that cause mechanical failure or head crash.</li>
-</ul>
-<p>Data recovery can be classified into two types: logical data recovery and physical data recovery. Logical data recovery deals with the software-related issues that cause data loss, while physical data recovery deals with the hardware-related issues that cause data loss. Logical data recovery can be done by using free data recovery software, while physical data recovery requires professional help and may cost a lot of money.</p>
-<h2>How to Recover Data from Hard Drive Free Using Free Data Recovery Software?</h2>
-<p>If you want to recover data from hard drive free using free data recovery software, you need to follow these steps:</p>
-<ol>
-<li>Stop using your hard drive immediately. Do not write any new data to your hard drive or perform any operations that may overwrite your lost data. This will increase the chances of successful data recovery.</li>
-<li>Download and install a reliable free data recovery software on another computer or device. Do not install the software on the same hard drive where you lost your data. You can search for some of the best free data recovery software online or use the link below.</li>
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-<p>Khamoshiyan is a 2015 Hindi movie directed by Karan Darra and produced by Mahesh Bhatt and Mukesh Bhatt. The movie stars Ali Fazal, Gurmeet Choudhary, and Sapna Pabbi in the lead roles. The movie is about a novelist named Kabir who travels to Kashmir in search of inspiration for his next book. There he meets Meera, a mysterious woman who runs a hotel in a secluded location. Kabir falls in love with Meera, but soon discovers that she is hiding a dark secret. She is married to Jaidev, a rich businessman who is now a spirit after his death. Jaidev is obsessed with Meera and does not let her go. Kabir decides to help Meera escape from Jaidev's clutches, but faces many challenges and dangers along the way.</p>
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-<p>The <strong>0xc00007b</strong> error is a common Windows error that indicates that something is wrong with the application or the system. It can be caused by various factors, such as:</p>
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-<li>Corrupt or missing application files</li>
-<li>Incompatible or outdated Windows version</li>
-<li>Conflicting or missing Microsoft Visual C++ Redistributable packages</li>
-<li>Incorrect or damaged registry entries</li>
-<li>Mismatch between 32-bit and 64-bit versions of software and Windows operating systems</li>
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-<p>These factors can interfere with the proper functioning of the application or the system, resulting in the <strong>0xc00007b</strong> error.</p>
- <h3>The impact of 0xc00007b error on PES 2017</h3>
-<p>The <strong>0xc00007b</strong> error can affect any Windows application or game, including PES 2017. When this error occurs, you will not be able to launch or play PES 2017, as the game will crash or freeze. This can be very frustrating, especially if you are looking forward to playing your favorite soccer game. Moreover, this error can also affect your gaming performance, as it can cause lagging, stuttering, or graphical glitches.</p>
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-<h3>The sources and steps to download 0xc00007b for PES 2017</h3>
-<p>One of the possible solutions to fix the <strong>0xc00007b</strong> error for PES 2017 is to download and install the <strong>0xc00007b</strong> file for the game. This file is a DLL (Dynamic Link Library) file that contains code and data that are used by multiple programs. By downloading and installing this file, you can replace the corrupt or missing file that is causing the error.</p>
-<p>To download <strong>0xc00007b</strong> for PES 2017, you can use one of the following sources:</p>
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-<li><a href="">DLL-files.com</a>: This is a website that provides free downloads of DLL files for various programs and games. You can search for <strong>0xc00007b</strong> on the website and download the file that matches your Windows version and system type (32-bit or 64-bit).</li>
-<li><a href="">Fix4dll.com</a>: This is another website that offers free downloads of DLL files for different applications and games. You can also search for <strong>0xc00007b</strong> on the website and download the file that suits your Windows version and system type (32-bit or 64-bit).</li>
-<li><a href="">Microsoft.com</a>: This is the official website of Microsoft, the developer of Windows operating systems. You can download the latest Microsoft Visual C++ Redistributable packages from this website, as they contain the <strong>0xc00007b</strong> file and other DLL files that are required by PES 2017 and other programs.</li>
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-<p>After downloading <strong>0xc00007b</strong> for PES 2017, you need to follow these steps to install it:</p>
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-<li>Extract the downloaded file to a folder on your computer.</li>
-<li>Copy the <strong>0xc00007b</strong> file from the extracted folder.</li>
-<li>Paste the <strong>0xc00007b</strong> file into the installation folder of PES 2017. This is usually located at C:\Program Files (x86)\Steam\steamapps\common\Pro Evolution Soccer 2017.</li>
-<li>Restart your computer and launch PES 2017 to see if the error is fixed.</li>
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-<p>While downloading <strong>0xc00007b</strong> for PES 2017 can be a quick and easy solution, you need to be careful and follow some precautions and tips to avoid any problems or risks. Here are some of them:</p>
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-<li>Make sure you download <strong>0xc00007b</strong> from a reliable and trustworthy source, such as the ones mentioned above. Do not download <strong>0xc00007b</strong> from unknown or suspicious websites, as they may contain viruses or malware that can harm your computer or steal your personal information.</li>
-<li>Make sure you download <strong>0xc00007b</strong> that is compatible with your Windows version and system type (32-bit or 64-bit). If you download <strong>0xc00007b</strong> that does not match your Windows version or system type, you may encounter more errors or damage your system.</li>
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-<li>If downloading and installing <strong>0xc00007b</strong> does not fix the error for PES 2017, you may need to try other solutions, such as updating your Windows version, reinstalling PES 2017, repairing your registry entries, or contacting Konami for support.</li>
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-<h3>The methods and instructions to fix 0xc00007b error for PES 2017</h3>
-<p>Besides downloading <strong>0xc00007b</strong>, there are other methods that can help you fix the error for PES 2017. Here are some of them:</p>
- <table border="1">
-<tr><th>Method</th><th>Description</th><th>Instructions</th></tr>
-<tr><td>Update Windows version</td><td>This method involves updating your Windows version to the latest one, as it may contain bug fixes and improvements that can resolve the error.</td><td>To update your Windows version, follow these steps:<ol><li>Click on the Start button and select Settings.</li><li>Select Update & Security.</li><li>Select Windows Update.</li><li>Select Check for updates.</li><li>If there are any available updates, select Download and install.</li><li>Wait for the updates to finish installing and restart your computer </li></ol></td></tr>
-<tr><td>Reinstall PES 2017</td><td>This method involves uninstalling and reinstalling PES 2017, as it may fix any corrupt or missing files that are causing the error.</td><td>To reinstall PES 2017, follow these steps:<ol><li>Open Steam and go to your Library.</li><li>Right-click on PES 2017 and select Uninstall.</li><li>Wait for the uninstallation to complete and confirm the action.</li><li>Go to the Steam Store and search for PES 2017.</li><li>Select Add to Cart and Purchase for Myself.</li><li>Select Install Game and follow the instructions.</li><li>Wait for the installation to finish and launch PES 2017 to see if the error is fixed.</li></ol></td></tr>
-<tr><td>Repair registry entries</td><td>This method involves repairing any incorrect or damaged registry entries that are related to PES 2017 or the error. The registry is a database that stores settings and information for Windows and applications. If the registry is corrupted, it can cause errors and problems.</td><td>To repair registry entries, follow these steps:<ol><li>Click on the Start button and type cmd.</li><li>Right-click on Command Prompt and select Run as administrator.</li><li>Type sfc /scannow and press Enter.</li><li>Wait for the scan to complete and fix any errors it finds.</li><li>Type exit and press Enter to close the Command Prompt.</li><li>Restart your computer and launch PES 2017 to see if the error is fixed.</li></ol></td></tr>
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-<p>In conclusion, the <strong>0xc00007b</strong> error is a common Windows error that can affect PES 2017 and prevent you from launching or playing the game. It can be caused by various factors, such as corrupt or missing files, incompatible or outdated Windows version, conflicting or missing Microsoft Visual C++ Redistributable packages, incorrect or damaged registry entries, or mismatch between 32-bit and 64-bit versions of software and Windows operating systems. To fix this error, you can download <strong>0xc00007b</strong> for PES 2017 from a reliable source and install it in the game folder, or you can try other methods, such as updating your Windows version, reinstalling PES 2017, repairing your registry entries, or contacting Konami for support. By fixing this error, you will be able to enjoy your favorite soccer game without any hassle. If you found this article helpful, please share it with your friends and fellow gamers who may also encounter this error. Thank you for reading!</p>
- <h2>FAQs</h2>
-<h4>What is PES 2017?</h4>
-<p>PES 2017 is a soccer simulation game developed by Konami. It is the 16th installment in the Pro Evolution Soccer series. It features improved graphics, gameplay, modes, teams, players, stadiums, and more. It was released in September 2016 for Windows, PlayStation 3, PlayStation 4, Xbox 360, Xbox One, Android, and iOS devices.</p>
- <h4>How can I update PES 2017?</h4>
-<p>To update PES 2017, you need to have an internet connection and a Steam account. You can update PES 2017 automatically through Steam by following these steps:<ol><li>Open Steam and go to your Library.</li><li>Select PES 2017 and right-click on it.</li><li>Select Properties.</li><li>Select Updates.</li><li>Select Always keep this game updated option.</li><li>Select Close.</li><li>Wait for the update to download and install.</li></ol>You can also update PES 2017 manually by downloading and installing the latest patches and data packs from the official website of Konami or other trusted sources.</p>
- <h4>What are the system requirements for PES 2017?</h4>
-<p>The minimum and recommended system requirements for PES 2017 are as follows:</p>
-<table border="1">
-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>OS: Windows 10, 8.1, 8, 7 SP1, Vista SP2</td><td>OS: Windows 10, 8.1, 8, 7 SP1, Vista SP2</td></tr>
-<tr><td>CPU: Intel Core2 Duo 1.8GHz / AMD Athlon II X2 240 or equivalent processor</td><td>CPU: Intel Core i3 530 / AMD Phenom II X4 925 or equivalent processor</td></tr>
-<tr><td>RAM: 1 GB</td><td>RAM: 2 GB</td></tr>
-<tr><td>GPU: DirectX 9.0c compatible video card. 1024MB Pixel Shader 3.0 (NVIDIA GeForce 8800 / AMD/ATI Radeon X1600 / Intel HD Graphics 3000 or better)</td><td>GPU: DirectX 9.0c compatible video card. 1024MB Pixel Shader 3.0 (NVIDIA GeForce GTX 260 / AMD/ATI Radeon HD4850 / Intel HD Graphics 4000 or better)</td></tr>
-<tr><td>Storage: 8 GB</td><td>Storage: 8 GB</td></tr>
-<tr><td>Sound: DirectX 9.0c compatible sound card</td><td>Sound: DirectX 9.0c compatible sound card</td></tr>
-<tr><td>Network: Broadband Internet connection</td><td>Network: Broadband Internet connection</td></tr>
-<tr><td>Resolution: 800 x 600 monitor resolution</td><td>Resolution: 1280 x 720 monitor resolution</td></tr>
-</table>
- <h4>How can I contact Konami for support?</h4>
-<p>If you have any questions, issues, or feedback regarding PES 2017, you can contact Konami for support by using one of the following methods:</p>
-<ul>
-<li>Email: You can send an email to <a href="mailto:[email protected]">[email protected]</a> and describe your problem or inquiry in detail.</li>
-<li>Phone: You can call the Konami customer service number at +1-310-220-8100 and speak to a representative.</li>
-<li>Website: You can visit the official website of Konami at <a href="https://www.konami.com/">https://www.konami.com/</a> and access the support section, where you can find FAQs, manuals, forums, and contact forms.</li>
-<li>Social media: You can follow Konami on social media platforms, such as Facebook, Twitter, Instagram, YouTube, and Twitch, and send them a message or comment.</li>
-</ul>
- <h4>Where can I find more information about PES 2017?</h4>
-<p>If you want to find more information about PES 2017, such as news, updates, features, reviews, tips, tricks, guides, videos, screenshots, and more, you can visit some of the following sources:</p>
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-<li>The official website of PES 2017 at <a href="https://www.konami.com/wepes/2017/">https://www.konami.com/wepes/2017/</a>.</li>
-<li>The official blog of PES at <a href="https://www.konami.com/wepes/blog/">https://www.konami.com/wepes/blog/</a>.</li>
-<li>The official Steam page of PES 2017 at <a href="https://store.steampowered.com/app/456610/Pro_Evolution_Soccer_2017/">https://store.steampowered.com/app/456610/Pro_Evolution_Soccer_2017/</a>.</li>
-<li>The official Wikipedia page of PES 2017 at <a href="https://en.wikipedia.org/wiki/Pro_Evolution_Soccer_2017">https://en.wikipedia.org/wiki/Pro_Evolution_Soccer_2017</a>.</li>
-<li>The official Reddit community of PES at <a href="https://www.reddit.com/r/WEPES/">https://www.reddit.com/r/WEPES/</a>.</li> <li>The official YouTube channel of PES at <a href="https://www.youtube.com/user/officialpes">https://www.youtube.com/user/officialpes</a>.</li>
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- <p>Here are some of the negative reviews from users who dislike the game:</p>
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-<p>"I hate this game so much! It's so boring and repetitive! All you do is dress up your characters and make them talk. The graphics are terrible and the music is annoying. I don't recommend this game to anyone who likes anime and gacha."</p>
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-<cite>- A Google user</cite>
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-<p>"This game is the worst game ever! It's so inappropriate and offensive! You can dress up your characters in revealing clothes and make them do inappropriate things. The game also promotes violence, bullying, and stereotypes. This game is very harmful and disgusting."</p>
-<cite>- A Google user</cite>
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- <h2>Conclusion</h2>
- <p>Gacha Life is a game that allows you to create your own anime-style characters and stories. You can also interact with other players online, play mini-games, and collect gems. You can download Gacha Life APK for free on your Android device from a reliable source, but you need to be careful of malware and viruses. You can play the game in different modes, such as Studio, Skit Maker, Life, and Games. You can also enjoy the game's features and benefits, such as unlimited customization options, high-quality graphics, social interaction, educational value, and more. However, you may also encounter some drawbacks of the game, such as boredom, repetition, bugs, glitches, crashes, inappropriate content, violence, bullying, stereotypes, and more.</p>
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-<p>Gacha Life is rated 12+ on Google Play Store for moderate violence, suggestive themes, language, crude humor, horror/fear themes, simulated gambling, alcohol/tobacco/drug use or reference. Therefore, it may not be suitable for younger kids or sensitive viewers. Parents should supervise their kids when playing the game or use parental controls to restrict access to certain features or content.</p>
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-<p>If you have any questions, problems, or feedback about Gacha Life, you can contact the developers by emailing them at [email protected]. You can also visit their website at [lunime.com] or follow them on their social media accounts, such as Facebook, Twitter, Instagram, or YouTube.</p>
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spaces/1phancelerku/anime-remove-background/Download Teka-teki Silang Kata Mencari Mod Apk Gratis dan Nikmati Permainan Kata yang Seru.md

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-<h1>How to Download Teka Teki Silang Kata Mencari Mod APK</h1>
-<p>Teka Teki Silang Kata Mencari is a popular word puzzle game that challenges your vocabulary and logic skills. You have to find hidden words in a grid of letters, using clues and hints. The game has hundreds of levels, each with a different theme and difficulty. You can also compete with other players online and earn coins and rewards.</p>
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-<p>Mod APK is a modified version of an original application that offers additional features or enhancements that are not present in the official release. Mod APKs are usually created by independent developers who alter the original APK files to unlock premium features, remove ads, provide unlimited resources, or even bypass certain restrictions imposed by the app developers.</p>
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-<p>Downloading Teka Teki Silang Kata Mencari mod APK is not as simple as downloading any other app from the Google Play Store. You need to follow some steps and precautions to ensure that you get a reliable and secure file. Here are the steps you need to follow:</p>
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-<p>The first thing you need to do is to find a trustworthy website that offers the mod APK file for Teka Teki Silang Kata Mencari. There are many websites that claim to provide mod APKs, but not all of them are safe or legitimate. Some of them may contain malware or viruses that can harm your device or steal your personal information. Some of them may also provide outdated or fake files that may not work properly or at all.</p>
-<p>To avoid these risks, you need to do some research and check the reputation and reviews of the website before downloading anything from it. You can also use some tools or apps that can scan and verify the file for any malicious code or hidden permissions. One of the websites that we recommend for downloading Teka Teki Silang Kata Mencari mod APK is [Aptoide](^1^). This website is a popular alternative app store that offers thousands of apps and games, including modded versions. It also has a user rating system and a security badge that indicate the quality and safety of each file.</p>
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-<p>The next thing you need to do is to enable unknown sources on your device. This is a setting that allows you to install apps from sources other than the Google Play Store. By default, this setting is disabled to prevent unauthorized or harmful apps from being installed on your device. However, since you are downloading a mod APK file from a third-party website, you need to enable it temporarily to proceed with the installation.</p>
-<p>To enable unknown sources on your device, follow these steps:</p>
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-<p>Remember to disable this setting once you are done with the installation, to avoid any potential security issues in the future.</p>
-<h3>Step 3: Download and install the mod APK file</h3>
-<p>Now that you have enabled unknown sources on your device, you can proceed to download and install the mod APK file for Teka Teki Silang Kata Mencari. Here are the steps you need to follow:</p>
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-<li>Go to the website that you have chosen for downloading the mod APK file, such as [Aptoide].</li>
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-<li>Follow the instructions on the screen and wait for the installation to finish. You may see a confirmation message when the installation is successful.</li>
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-<h3>Step 4: Launch the game and enjoy the features</h3>
-<p>Congratulations! You have successfully downloaded and installed Teka Teki Silang Kata Mencari mod APK on your device. Now you can launch the game and enjoy the features and advantages that it offers. You can access all the levels, get unlimited coins, remove ads, and use premium content without any restrictions or limitations. You can also compete with other players online and show off your skills and achievements.</p>
-<h2>How to use Teka Teki Silang Kata Mencari mod APK</h2>
-<p>Using Teka Teki Silang Kata Mencari mod APK is not much different from using the original version of the game. You can play the game as usual, following the rules and objectives of each level. However, there are some tips and tricks that you can use to make the most out of your modded experience. Here are some of them:</p>
-<h3>Tips and tricks for playing the game</h3>
-<ul>
-<li>Use hints wisely. Hints can help you find hidden words or reveal letters in the grid, but they are limited and cost coins. You can get more coins by completing levels, watching ads, or using the mod APK features, but you should still use them sparingly and only when necessary.</li>
-<li>Learn new words. The game has a huge database of words from various categories and themes, such as animals, food, sports, geography, etc. You can learn new words by playing the game and expanding your vocabulary. You can also tap on any word in the grid to see its definition and pronunciation.</li>
-<li>Challenge yourself. The game has different difficulty levels, ranging from easy to hard. You can choose the level that suits your skill level and preference, or you can challenge yourself by playing harder levels or using fewer hints. You can also try to beat your own score or time record for each level.</li>
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-<li>Get unlimited coins. Coins are used to buy hints, unlock levels, or access premium content in the game. You can earn coins by completing levels, watching ads, or using real money. With the mod APK, you can get unlimited coins without doing any of these things.</li>
-<li>Remove ads. Ads are annoying and distracting, especially when they pop up in between levels or gameplay. They also consume data and battery power on your device. With the mod APK, you can remove ads completely and enjoy a smooth and uninterrupted gaming experience.</li>
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-<h2>Conclusion</h2>
-<p>Teka Teki Silang Kata Mencari is a fun and challenging word puzzle game that can test your vocabulary and logic skills. You can also enjoy more features and advantages in the game by using a mod APK. However, you need to be careful and informed before downloading and installing any mod APK on your device.</p>
-<p>In this article, we have shown you how to download Teka Teki Silang Kata Mencari mod APK safely and easily. We have also given you some tips on how to use it effectively and responsibly. We hope that this article has been helpful and informative for you.</p>
-<p>If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you!</p>
-<h2>FAQs</h2>
-<h3>What is Teka Teki Silang Kata Mencari?</h3>
-<p>Teka Teki Silang Kata Mencari is a popular word puzzle game that challenges your vocabulary and logic skills. You have to find hidden words in a grid of letters, using clues and hints. The game has hundreds of levels, each with a different theme and difficulty. You can also compete with other players online and earn coins and rewards.</p>
-<h3>What is mod APK?</h3>
-<p>Mod APK is a modified version of an original application that offers additional features or enhancements that are not present in the official release. Mod APKs are usually created by independent developers who alter the original APK files to unlock premium features, remove ads, provide unlimited resources, or even bypass certain restrictions imposed by the app developers.</p>
-<h3>How to download Teka Teki Silang Kata Mencari mod APK?</h3>
-<p>To download Teka Teki Silang Kata Mencari mod APK, you need to follow these steps:</p>
-<ol>
-<li>Find a reliable source for the mod APK file, such as [Aptoide].</li>
-<li>Enable unknown sources on your device.</li>
-<li>Download and install the mod APK file.</li>
-<li>Launch the game and enjoy the features.</li>
-</ol>
-<h3>How to use Teka Teki Silang Kata Mencari mod APK?</h3>
-<p>To use Teka Teki Silang Kata Mencari mod APK, you need to follow these tips:</p>
-<ul>
-<li>Use hints wisely.</li>
-<li>Learn new words.</li>
-<li>Challenge yourself.</li>
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-<p>You can also enjoy these features and advantages:</p>
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-<li>Get unlimited coins.</li>
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-<li>Access premium content.</li>
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-<p>You also need to be aware of these precautions and limitations:</p>
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-<li>Be aware of compatibility issues.</li>
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spaces/1phancelerku/anime-remove-background/Download Tekken 3D Prime Edition for PC and Experience the Ultimate Fighting Game.md

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-<h1>Tekken 3D: Prime Edition - A Portable Version of the Popular Fighting Game</h1>
- <p>If you are a fan of fighting games, you might have heard of Tekken, one of the most successful and long-running franchises in the genre. Tekken has been adapted to various platforms, from arcades to consoles to handhelds, and has a loyal fan base around the world. One of the latest entries in the series is Tekken 3D: Prime Edition, a fighting game developed and published by Bandai Namco Entertainment for the Nintendo 3DS. In this article, we will tell you everything you need to know about this game, including what it is, how to play it, how to download it for PC, and what are its reviews and ratings.</p>
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- <h2>What is Tekken 3D: Prime Edition?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Tekken 3D: Prime Edition is a fighting game that brings the arcade-style action and backstory of the Tekken video game series into the 3D gaming realm like never before. Exclusively for Nintendo 3DS, the 3DS cartridge contains both the game and the complete Tekken: Blood Vengeance 3D movie.</p>
- <p>The game is a graphically updated version of Tekken 6 for the Nintendo 3DS, supporting the handheld's 3D capabilities and maintaining a steady 60 FPS even when running in 3D; however, the 3D is disabled during wireless play.  It features 41 playable characters from Tekken 6 and a card collecting mode. It also includes local and online multiplayer for up to two players.</p>
- <h3>The game's development and release history</h3>
- <p>The game was first announced at E3 2011, where Nintendo revealed that Tekken would be coming to the 3DS handheld.  At Namco Bandai Games' booth at E3, they showcased a demo of the Tekken in-game engine running on the 3DS. Producer Katsuhiro Harada stated that the game would run in full 60 frames per second with the 3D on. </p>
- <p>On August 17, 2011, a new trailer for Tekken 3D: Prime Edition was released, along with its name. It was also revealed that the game would include over 40 characters, as well as a 3D version of the film Tekken: Blood Vengeance on the game's cartridge.  The game also has more than 700 artwork cards to collect, mostly cutscenes from the Blood Vengeance movie that can be shared via the StreetPass function on the 3DS. </p>
- <p>The game was released in North America on February 14, 2012, in Japan on February 16, 2012, in Europe on February 17, 2012, and in Australia on February 23, 2012. </p>
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- <p>Tekken 3D: Prime Edition has several modes to choose from, such as Quick Battle mode, Special Survival mode, Versus Battle mode, Online Battle mode, Practice mode, Gallery mode, Card Collection mode, and Options mode.</p>
- <p>Quick Battle <p>Quick Battle mode is where the player can fight against various computer-controlled opponents with different ranks and skills. The player can choose their own character and stage, as well as the difficulty level and the number of rounds. The player can also customize their character's appearance and moves with the cards they have collected. </p>
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- <p>Special Survival mode is where the player can test their endurance and skills by fighting against a series of opponents with increasing difficulty. The player can only use one character and has a limited amount of health that does not regenerate between matches. The player can earn extra health by performing special moves or finishing moves. The player can also earn extra cards by defeating opponents. The mode ends when the player loses or quits. </p>
- <p>Versus Battle mode is where the player can fight against another player using the same 3DS system or using the wireless connection. The player can choose their own character and stage, as well as the rules and settings of the match. The player can also use their cards to customize their character. </p>
- <p>Online Battle mode is where the player can fight against other players from around the world using the Nintendo Wi-Fi Connection. The player can choose to play in ranked matches or friendly matches, as well as create or join lobbies with up to four players. The player can also view their online profile and statistics, as well as send and receive messages and cards from other players. </p>
- <p>Practice mode is where the player can practice their moves and combos with their chosen character against a dummy opponent. The player can adjust the settings of the practice mode, such as the dummy's behavior, the display of inputs, and the frame data. The player can also access a move list and a command list for their character. </p>
- <p>Gallery mode is where the player can view various artworks and movies related to Tekken 3D: Prime Edition, such as character illustrations, stage backgrounds, opening movies, ending movies, and trailers. The player can also watch the full-length Tekken: Blood Vengeance 3D movie in this mode. </p>
- <p>Card Collection mode is where the player can view, manage, and trade their cards with other players. The cards are divided into four categories: Character Cards, Special Cards, Customization Cards, and Movie Cards. The cards have different effects and values depending on their rarity and type. The player can use their cards to enhance their character's abilities, appearance, and moves in other modes. The player can also scan QR codes to obtain new cards or share their own cards with others. </p>
- <p>Options mode is where the player can adjust various settings of the game, such as the sound, the display, the controls, and the language. The player can also view their game data and achievements in this mode. </p>
- <h3>The game's characters and stages</h3>
- <p>Tekken 3D: Prime Edition features 41 playable characters from Tekken 6, each with their own unique fighting style, moves, and personality. Some of the characters are:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Origin</th>
-<th>Fighting Style</th>
-</tr>
-<tr>
-<td>Jin Kazama</td>
-<td>Japan</td>
-<td>Karate / Mishima Style Fighting Karate</td>
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-<tr>
-<td>Ling Xiaoyu</td>
-<td>China</td>
-<td>Baguazhang / Piguaquan / Hakkesho</td>
-</tr>
-<tr>
-<td>Nina Williams</td>
-<td>Ireland</td>
-<td>Aikido / Koppojutsu / Sambo / Assassination Arts</td>
-</tr>
-<tr>
-<td>Hwoarang</td>
-<td>Korea</td>
-<td>Taekwondo</td>
-</tr>
-<tr>
-<td>King II</td>
-<td>Mexico</td>
-<td>Lucha Libre / Professional Wrestling</td>
-</tr>
-<tr>
-<td>Lili Rochefort</td>
-<td>Monaco</td>
-<td>Street Fighting / Self-Taught Martial Arts</td>
-</tr>
-<tr>
-<td>Lars Alexandersson</td>
-<td>Sweden / Japan</td>
-<td>Military Martial Arts / Shorinji Kempo / Karate / Taekwondo / Aikido / Jujutsu / Muay Thai / Boxing / Pro Wrestling / Krav Maga / Jeet Kune Do / Escrima / Capoeira / Silat / Savate / Sambo / Systema / Kung Fu / Ninjutsu </td>
-</tr>
-<tr>
-<td>Alisa Bosconovitch</td>
-<td>Russia (Created in Japan)</td>
-<td>Destructive Form (Cyborg <p>Cyborg Fighting Style)</td>
-</tr>
-</table>
- <p>The game also features 19 stages from Tekken 6, each with their own theme, background, and music. Some of the stages are:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Location</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Azazel's Chamber</td>
-<td>Egypt</td>
-<td>A dark and ancient chamber with a giant statue of Azazel, the final boss of Tekken 6.</td>
-</tr>
-<tr>
-<td>City After Dark</td>
-<td>China</td>
-<td>A neon-lit city street with a busy traffic and a large dragon billboard.</td>
-</tr>
-<tr>
-<td>Fallen Colony</td>
-<td>Unknown</td>
-<td>A futuristic and dystopian colony that has been destroyed by an unknown force.</td>
-</tr>
-<tr>
-<td>Fiesta Del Tomate</td>
-<td>Spain</td>
-<td>A festive and colorful town square with a tomato-throwing event.</td>
-</tr>
-<tr>
-<td>Gargoyle's Perch</td>
-<td>France</td>
-<td>A Gothic and gloomy cathedral with gargoyles and stained glass windows.</td>
-</tr>
-<tr>
-<td>High Roller's Club</td>
-<td>USA</td>
-<td>A luxurious and glamorous casino with slot machines and roulette tables.</td>
-</tr>
-<tr>
-<td>Noh Theater</td>
-<td>Japan</td>
-<td>A traditional and elegant theater with a Noh stage and masks.</td>
-</tr> <tr>
-<td>Rustic Asia</td>
-<td>Thailand</td>
-<td>A rural and scenic village with a river and a temple.</td>
-</tr>
-<tr>
-<td>Temple Grounds</td>
-<td>Japan</td>
-<td>A serene and peaceful temple with cherry blossoms and a pond.</td>
-</tr>
-</table>
- <h2>How to Download Tekken 3D: Prime Edition for PC?</h2>
- <h3>The requirements and steps to download the game for PC</h3>
- <p>Tekken 3D: Prime Edition is a Nintendo 3DS exclusive game, which means that it is not officially available for PC. However, there is a way to play the game on your computer using an emulator. An emulator is a software that mimics the functions of another device, such as a console or a handheld. In this case, you will need a Nintendo 3DS emulator to run the game on your PC.</p>
- <p>Before you download the game and the emulator, you will need to make sure that your PC meets the minimum requirements to run them smoothly. Here are the recommended specifications for playing Tekken 3D: Prime Edition on PC:</p>
- <ul>
-<li>Operating System: Windows 7 or higher, or Linux</li>
-<li>CPU: Intel Core i5 or AMD Ryzen 5 or higher</li>
-<li>RAM: 8 GB or more</li>
-<li>Graphics Card: NVIDIA GeForce GTX 1050 or AMD Radeon RX 560 or higher</li>
-<li>Storage Space: 4 GB or more</li>
-<li>Internet Connection: Broadband or faster</li>
-</ul>
- <p>Once you have checked your PC's compatibility, you can follow these steps to download and play Tekken 3D: Prime Edition on PC:</p>
- <ol>
-<li>Download the Nintendo 3DS emulator of your choice. There are several options available online, such as Citra, R4 3DS Emulator, and NO$GBA. You can find them on their official websites or on other trusted sources. Make sure to download the latest version of the emulator and follow the installation instructions.</li>
-<li>Download the Tekken 3D: Prime Edition ROM file. A ROM file is a digital copy of the game that can be played on an emulator. You can find the ROM file for Tekken 3D: Prime Edition on various websites that offer Nintendo 3DS games, such as Rom Hustler, CoolROM, and Emuparadise. Make sure to download the file from a safe and legal source and scan it for viruses before opening it.</li>
-<li>Extract the ROM file using a file extractor program, such as WinRAR, 7-Zip, or PeaZip. You will get a .3ds file that contains the game data.</li>
-<li>Launch the Nintendo 3DS emulator on your PC and load the .3ds file. You can do this by clicking on File > Load File or by dragging and dropping the file into the emulator window.</li>
-<li>Configure the emulator settings according to your preferences. You can adjust the graphics, sound, controls, and other options in the emulator menu. You can also enable or disable the 3D effect of the game by pressing a hotkey.</li>
-<li>Enjoy playing Tekken 3D: Prime Edition on your PC!</li>
-</ol>
- <h3>The benefits and drawbacks of playing the game on PC</h3>
- <p>Playing Tekken 3D: Prime Edition on PC has some advantages and disadvantages compared to playing it on Nintendo 3DS. Here are some of them:</p>
- <table>
-<tr>
-<th>Benefits</th>
-<th>Drawbacks</th>
-</tr>
-<tr>
-<td>You can play the game in higher resolution and quality than on Nintendo 3DS.</td>
-<td>You may experience some glitches, bugs, or crashes while playing the game on an emulator.</td>
-</tr>
-<tr>
-<td>You can use different controllers or keyboard and mouse to play the game.</td>
-<td>You may lose some of the original features or functions of the game that are exclusive to Nintendo 3DS, such as StreetPass, SpotPass, or touch screen.</td>
-</tr>
-<tr>
-<td>You can save your progress and load it anytime you want.</td>
-<td>You may violate some copyright laws or terms of service by downloading and playing the game without permission from the developers or publishers.</td>
-</tr>
-<tr> <td>You can access more online features and options than on Nintendo 3DS.</td>
-<td>You may encounter some hackers, cheaters, or trolls while playing online.</td>
-</tr>
-</table>
- <h2>What are the Reviews and Ratings of Tekken 3D: Prime Edition?</h2>
- <h3>The game's critical reception and user feedback</h3>
- <p>Tekken 3D: Prime Edition received mixed to positive reviews from critics and users alike. The game has an average score of 69/100 on Metacritic, based on 38 reviews.  The game also has a user score of 7.1/10 on Metacritic, based on 46 ratings. </p>
- <p>Some of the positive aspects of the game that were praised by reviewers and players were:</p>
- <ul>
-<li>The game's graphics and performance, especially the smooth 60 FPS and the 3D effect.</li>
-<li>The game's roster and variety, featuring 41 characters and 19 stages from Tekken 6.</li>
-<li>The game's inclusion of the Tekken: Blood Vengeance 3D movie, which adds value and entertainment to the package.</li>
-<li>The game's online mode and functionality, which allows for competitive and cooperative play with other players around the world.</li>
-</ul>
- <p>Some of the negative aspects of the game that were criticized by reviewers and players were:</p>
- <ul>
-<li>The game's lack of content and features, especially compared to other Tekken games or other fighting games on Nintendo 3DS.</li>
-<li>The game's lack of story and progression, which makes the game feel repetitive and shallow.</li>
-<li>The game's lack of customization and innovation, which makes the game feel outdated and boring.</li>
-<li>The game's lack of balance and difficulty, which makes the game feel unfair and frustrating.</li>
-</ul>
- <h3>The game's strengths and weaknesses</h3>
- <p>Based on the reviews and ratings of Tekken 3D: Prime Edition, we can summarize the game's strengths and weaknesses as follows:</p>
- <table>
-<tr>
-<th>Strengths</th>
-<th>Weaknesses</th>
-</tr>
-<tr>
-<td>Graphics and Performance</td>
-<td>Content and Features</td>
-</tr>
-<tr>
-<td>Roster and Variety</td>
-<td>Story and Progression</td>
-</tr>
-<tr>
-<td>Movie and Value</td>
-<td>Customization and Innovation</td>
-</tr>
-<tr>
-<td>Online Mode and Functionality</td>
-<td>Balance and Difficulty</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and a recommendation for the game</h3>
- <p>Tekken 3D: Prime Edition is a fighting game that brings the arcade-style action and backstory of the Tekken video game series into the 3D gaming realm like never before. It features 41 playable characters from Tekken 6, a card collecting mode, a full-length movie, and online multiplayer. It also supports the Nintendo 3DS's 3D capabilities and runs at a steady 60 FPS. However, it also suffers from a lack of content and features, a lack of story and progression, a lack of customization and innovation, and a lack of balance and difficulty.</p>
- <p>If you are a fan of Tekken or fighting games in general, you might enjoy playing Tekken 3D: Prime Edition on your Nintendo 3DS or on your PC using an emulator. It is a decent port of Tekken 6 that offers some fun and fast-paced gameplay with impressive graphics and performance. It also has some extra value with the inclusion of the movie and the card collecting mode. However, if you are looking for a more complete and satisfying experience with more depth and variety, you might want to skip this game or wait for a price drop. It is not a bad game, but it is not a great one either.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Tekken 3D: Prime Edition:</p>
- <ol>
-<li><b>How long is Tekken 3D: Prime Edition?</b></li>
-<p>The length of Tekken 3D: Prime Edition depends on how much you play it and what modes you play. There is no definitive answer to this question, as the game does not have a clear end or goal. However, according to HowLongToBeat.com, the average time to beat the main mode (Quick Battle) is about 4 hours, while the average time to complete all modes (including Special Survival, Versus Battle, Online Battle, Practice, Gallery, Card Collection) is about 11 hours. </p>
- <li><b>Is Tekken 3D: Prime Edition <b>Is Tekken 3D: Prime Edition compatible with the New Nintendo 3DS?</b></li>
-<p>Yes, Tekken 3D: Prime Edition is compatible with the New Nintendo 3DS, as well as the original Nintendo 3DS, the Nintendo 3DS XL, the Nintendo 2DS, and the New Nintendo 2DS XL. However, the game does not make use of any of the exclusive features of the New Nintendo 3DS, such as the C-Stick, the ZL and ZR buttons, or the enhanced processing power. </p>
- <li><b>What is the difference between Tekken 3D: Prime Edition and Tekken 6?</b></li>
-<p>Tekken 3D: Prime Edition is a graphically updated version of Tekken 6 for the Nintendo 3DS, with some changes and additions. Some of the differences are:</p>
- <ul>
-<li>Tekken 3D: Prime Edition has a card collecting mode that allows the player to customize their character's appearance and moves with cards they have collected or traded.</li>
-<li>Tekken 3D: Prime Edition includes the Tekken: Blood Vengeance 3D movie on the game's cartridge, which can be watched in Gallery mode.</li>
-<li>Tekken 3D: Prime Edition supports the Nintendo 3DS's 3D capabilities and runs at a steady 60 FPS even when running in 3D; however, the 3D is disabled during wireless play.</li>
-<li>Tekken 3D: Prime Edition does not have a story mode or a scenario campaign mode like Tekken 6.</li>
-<li>Tekken 3D: Prime Edition does not have a customization mode or an item shop like Tekken 6.</li>
-<li>Tekken 3D: Prime Edition does not have a rage system or a bound system like Tekken 6.</li>
-</ul>
- <li><b>How can I unlock more cards in Tekken 3D: Prime Edition?</b></li>
-<p>There are several ways to unlock more cards in Tekken 3D: Prime Edition, such as:</p>
- <ul>
-<li>Playing Quick Battle mode or Special Survival mode and defeating opponents.</li>
-<li>Playing Online Battle mode and winning matches or participating in events.</li>
-<li>Using StreetPass to exchange cards with other players nearby.</li>
-<li>Using SpotPass to receive cards from Nintendo via Wi-Fi.</li>
-<li>Scanning QR codes to obtain new cards or share your own cards with others.</li>
-</ul>
- <li><b>Who is the strongest character in Tekken 3D: Prime Edition?</b></li>
-<p>There is no definitive answer to this question, as different characters have different strengths and weaknesses, and different players have different preferences and skills. However, some of the characters that are generally considered to be strong or popular in Tekken 3D: Prime Edition are:</p>
- <ul>
-<li>Lars Alexandersson</li>
-<li>Nina Williams</li>
-<li>Bryan Fury</li>
-<li>Bruce Irvin</li>
-<li>Bob Richards</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1ucii/Lab04/app.py

@@ -1,42 +0,0 @@
-import gradio as gr
-import pickle
-
-# Load the decision tree model from the pickle file
-with open('best_tree.pkl', 'rb') as file:
-    model = pickle.load(file)
-
-# Define the predict function
-def predict(latitude, longitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income):
-    # Prepare the input features
-    features = [[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income]]
-    
-    # Make predictions using the loaded model
-    prediction = model.predict(features)
-    
-    # Return the predicted output
-    return prediction[0]
-
-# Create the input interface using Gradio
-inputs = [
-    gr.inputs.Number(label="Longitude"),
-    gr.inputs.Number(label="Latitude"),
-    gr.inputs.Number(label="Housing Median Age"),
-    gr.inputs.Number(label="Total Rooms"),
-    gr.inputs.Number(label="Total Bedrooms"),
-    gr.inputs.Number(label="Population"),
-    gr.inputs.Number(label="Households"),
-    gr.inputs.Number(label="Median Income")
-]
-
-# Create the output interface using Gradio
-output = gr.outputs.Label(num_top_classes=1)
-
-# Define example data for demonstration
-examples = [
-    [37.88, -122.23, 41, 880, 129, 322, 126, 8.3252],
-    [37.84, -122.27, 48, 1922, 409, 1026, 335, 1.7969],
-    [37.83, -122.26, 52, 1656, 420, 718, 382, 2.6768]
-]
-
-# Create the Gradio interface
-interface = gr.Interface(fn=predict, inputs=inputs, outputs=output, title="Decision Tree Predictor", examples=examples).launch()

spaces/2023Liu2023/bingo/src/components/ui/input.tsx

@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r2060.py

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

spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_537227KB.py

@@ -1,123 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_537238KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 64)
-        self.stg1_high_band_net = BaseASPPNet(2, 64)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(32, 64)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(64, 128)
-
-        self.out = nn.Conv2d(128, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/AIConsultant/MusicGen/audiocraft/solvers/audiogen.py

@@ -1,19 +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 . import builders, musicgen
-
-
-class AudioGenSolver(musicgen.MusicGenSolver):
-    """Solver for AudioGen re-implementation training task.
-
-    Note that this implementation does not strictly follows
-    the method proposed in https://arxiv.org/abs/2209.15352
-    but is derived from MusicGen's training pipeline.
-
-    More information can be found in the AudioGen model card.
-    """
-    DATASET_TYPE: builders.DatasetType = builders.DatasetType.SOUND

spaces/AIWaves/Software_Company/gradio_config.py

@@ -1,437 +0,0 @@
-# coding=utf-8
-# Copyright 2023  The AIWaves Inc. team.
-
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-from PIL import Image
-import requests
-from typing import List, Tuple
-
-class GradioConfig:
-    # How many avatars are currently registered
-    POINTER = 0
-    
-    # Avatar image. You can add or replace.
-    AGENT_HEAD_URL = [
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306241687579617434043.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306241687592097408547.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561699613.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561275758.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021090300/ry5k31wt33c.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021090300/0ls2gmwhrf5.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/02/202302281677545695326193.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/03/202303271679886128550253.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686711344407060.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686711345834296.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/05/202305171684311194291520.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/05/202305171684311196958993.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/vr0bkov0dwl.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/auqx5zfsv5g.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/llofpivtwls.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/3j2sdot3ye0.jpg",
-        "https://img.touxiangwu.com/2020/3/nQfYf2.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/08/202308131691918068774532.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/08/202308131691918068289945.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/08/202308131691918069785183.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561292003.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561578616.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726564597524.jpg"
-    ]
-    USER_HEAD_URL = "https://img.touxiangwu.com/zb_users/upload/2023/05/202305301685407468585486.jpg"
-    
-    # The css style of gradio.Chatbot
-    CSS = """
-    #chatbot1 .user {
-        background-color:transparent;
-        border-color:transparent;
-    }
-    #chatbot1 .bot {
-        background-color:transparent;
-        border-color:transparent;
-    }
-    #btn {color: red; border-color: red;}
-    """
- 
-    ID = ["USER", "AGENT", "SYSTEM"]
-    
-    # Bubble template
-    BUBBLE_CSS = {
-        # Background-color Name-color Name-content Font-color Font-size Content Avatar-URL
-        "USER": """
-    <div style="display: flex; align-items: flex-start; justify-content: flex-end;">
-        <div style="background-color: {}; border-radius: 20px 0px 20px 20px; padding: 15px; min-width: 100px; max-width: 300px;">
-            <p style="margin: 0; padding: 0; color: {}; font-weight: bold; font-size: 18px;">{}</p>
-            <p style="margin: 0; padding: 0; color: {}; font-size: {}px;">{}</p>
-        </div>
-        <img src="{}" alt="USER" style="width: 50px; height: 50px; border-radius: 50%; margin-left: 10px;">
-    </div>
-        """,
-
-        # Avatar-URL Background-color Name-color Name-Content Font-color Font-size Content
-        "AGENT": """
-    <div style="display: flex; align-items: flex-start;">
-        <img src="{}" alt="AGENT" style="width: 50px; height: 50px; border-radius: 50%; margin-right: 10px;">
-        <div style="background-color: {}; border-radius: 0px 20px 20px 20px; padding: 15px; min-width: 100px; max-width: 600px;">
-            <p style="margin: 0; padding: 0; color: {}; font-weight: bold; font-size: 18px;">{}</p>
-            <p style="margin: 0; padding: 0; color: {}; font-size: {}px;">{}</p>
-        </div>
-    </div>
-        """,
-
-        # Backrgound-color Font-size Font-color Name Content
-        "SYSTEM": """
-    <div style="display: flex; align-items: center; justify-content: center;">
-        <div style="background-color: {}; border-radius: 20px; padding: 1px; min-width: 200px; max-width: 1000px;">
-            <p style="margin: 0; padding: 0; text-align: center; font-size: {}px; font-weight: bold; font-family: '微软雅黑', sans-serif; color: {};">{}:{}</p>
-        </div>
-    </div>
-        """
-    }
-
-    ROLE_2_NAME = {}
-    
-    OBJECT_INFO = {
-
-        "User": {
-            # https://img-blog.csdnimg.cn/img_convert/7c20bc39ac69b6972a22e18762d02db3.jpeg
-            "head_url": USER_HEAD_URL,
-            "bubble_color": "#95EC69",
-            "text_color": "#000000",
-            "font_size": 0,
-            "id": "USER"
-        },
-
-        "System": {
-            # https://img-blog.csdnimg.cn/img_convert/e7e5887cfff67df8c2205c2ef0e5e7fa.png
-            "head_url": "https://img.touxiangwu.com/zb_users/upload/2023/03/202303141678768524747045.jpg",
-            "bubble_color": "#7F7F7F",  ##FFFFFF
-            "text_color": "#FFFFFF",  ##000000
-            "font_size": 0,
-            "id": "SYSTEM"
-        },
-
-        "wait": {
-            "head_url": "https://img.touxiangwu.com/zb_users/upload/2022/12/202212011669881536145501.jpg",
-            "bubble_color": "#E7CBA6",
-            "text_color": "#000000",
-            "font_size": 0,
-            "id": "AGENT"
-        },
-
-        "Recorder": {
-            "head_url": "https://img.touxiangwu.com/zb_users/upload/2023/02/202302281677545695326193.jpg",
-            "bubble_color": "#F7F7F7",
-            "text_color": "#000000",
-            "font_size": 0,
-            "id": "AGENT"
-        }
-    }
-
-    @classmethod
-    def color_for_img(cls, url):
-        """
-        Extract the main colors from the picture and set them as the background color, 
-        then determine the corresponding text color.
-        """
-
-        def get_main_color(image):
-            image = image.convert("RGB")
-            width, height = image.size
-            pixels = image.getcolors(width * height)
-            most_common_pixel = max(pixels, key=lambda item: item[0])
-            return most_common_pixel[1]
-
-        def is_dark_color(rgb_color):
-            r, g, b = rgb_color
-            luminance = (0.299 * r + 0.587 * g + 0.114 * b) / 255
-            return luminance < 0.5
-
-        def download_image(url):
-            print(f"binding: {url}")
-            response = requests.get(url)
-            if response.status_code == 200:
-                with open('image.jpg', 'wb') as f:
-                    f.write(response.content)
-
-        def rgb_to_hex(color):
-            return "#{:02X}{:02X}{:02X}".format(color[0], color[1], color[2])
-
-        def get_color(image_url):
-            download_image(image_url)
-
-            image = Image.open("image.jpg")
-            main_color = get_main_color(image)
-            is_dark = is_dark_color(main_color)
-
-            if is_dark:
-                font_color = "#FFFFFF"
-            else:
-                font_color = "#000000"
-
-            return rgb_to_hex(main_color), font_color
-
-        return get_color(url)
-
-    @classmethod
-    def init(cls, JSON):
-        # Deprecated
-        with open(JSON) as f:
-            sop = json.load(f)
-        cnt = 0
-        FISRT_NODE = True
-        fisrt_node_roles = []
-        for node_name in sop['nodes']:
-            node_info = sop['nodes'][node_name]
-            agent_states = node_info['agent_states']
-            for agent_role in agent_states:
-                name = agent_states[agent_role]['style']['name']
-                cls.ROLE_2_NAME[agent_role] = name
-                if FISRT_NODE:
-                    fisrt_node_roles.append(agent_role)
-                bubble_color, text_color = cls.color_for_img(cls.AGENT_HEAD_URL[cnt])
-                cls.OBJECT_INFO[name] = {
-                    "head_url": f"{cls.AGENT_HEAD_URL[cnt]}",
-                    "bubble_color": bubble_color,
-                    "text_color": text_color,
-                    "font_size": 0,
-                    "id": "AGENT"
-                }
-                cnt += 1
-            if FISRT_NODE:
-                FISRT_NODE = False
-        print(cls.OBJECT_INFO)
-        for usr_name in cls.OBJECT_INFO:
-            if cls.OBJECT_INFO[usr_name]["id"] == "SYSTEM":
-                cls.OBJECT_INFO[usr_name]["font_size"] = 12
-            elif cls.OBJECT_INFO[usr_name]["id"] in ["USER", "AGENT"]:
-                cls.OBJECT_INFO[usr_name]["font_size"] = 16
-            else:
-                assert False
-        return fisrt_node_roles
-
-    @classmethod
-    def add_agent(cls, agents_name:List):
-        for name in agents_name:
-            bubble_color, text_color = cls.color_for_img(cls.AGENT_HEAD_URL[cls.POINTER])
-            cls.OBJECT_INFO[name] = {
-                "head_url": f"{cls.AGENT_HEAD_URL[cls.POINTER]}",
-                "bubble_color": bubble_color,
-                "text_color": text_color,
-                "font_size": 0,
-                "id": "AGENT"
-            }
-            cls.POINTER += 1
-        for usr_name in cls.OBJECT_INFO:
-            if cls.OBJECT_INFO[usr_name]["id"] == "SYSTEM":
-                cls.OBJECT_INFO[usr_name]["font_size"] = 12
-            elif cls.OBJECT_INFO[usr_name]["id"] in ["USER", "AGENT"]:
-                cls.OBJECT_INFO[usr_name]["font_size"] = 16
-            else:
-                assert False
-
-
-class StateConfig:
-    """UI configuration for the step progress bar (indicating the current node)"""
-    
-    CSS = """
-:root {
-        --gradient-start: 100%;
-        --gradient-end: 0%;
- }
-.container.progress-bar-container {
-  position: relative;
-  display: flex;
-  align-items: flex-end;
-  width: 100%;
-  overflow-x: auto;
-  padding-bottom: 30px;
-  padding-top: 20px
-}
-.container.progress-bar-container::-webkit-scrollbar {
-  width: 8px;
-  background-color: transparent;
-}
-
-.container.progress-bar-container::-webkit-scrollbar-thumb {
-  background-color: transparent;
-}
-
-.progress-bar-container .progressbar {
-  counter-reset: step;
-  white-space: nowrap;
-}
-.progress-bar-container .progressbar li {
-  list-style: none;
-  display: inline-block;
-  width: 200px;
-  position: relative;
-  text-align: center;
-  cursor: pointer;
-  white-space: normal;
-}
-.progress-bar-container .progressbar li:before {
-  content: counter(step);
-  counter-increment: step;
-  width: 30px;
-  height: 30px;
-  line-height: 30px;
-  border: 1px solid #ddd;
-  border-radius: 100%;
-  display: block;
-  text-align: center;
-  margin: 0 auto 10px auto;
-  background-color: #ffffff;
-}
-.progress-bar-container .progressbar li:after {
-  content: attr(data-content);
-  position: absolute;
-  width: 87%;
-  height: 2px;
-  background-color: #dddddd;
-  top: 15px;
-  left: -45%;
-}
-.progress-bar-container .progressbar li:first-child:after {
-  content: none;
-}
-.progress-bar-container .progressbar li.active {
-  color: green;
-}
-.progress-bar-container .progressbar li.active:before {
-  border-color: green;
-  background-color: green;
-  color: white;
-}
-.progress-bar-container .progressbar li.active + li:after {
-  background: linear-gradient(to right, green var(--gradient-start), lightgray var(--gradient-end));
-}
-.progress-bar-container .small-element {
-  transform: scale(0.8);
-}
-.progress-bar-container .progressbar li span {
-  position: absolute;
-  top: 40px;
-  left: 0;
-  width: 100%;
-  text-align: center;
-}
-.progress-bar-container .progressbar li .data-content {
-  position: absolute;
-  width: 100%;
-  top: -10px;
-  left: -100px;
-  text-align: center;
-}
-"""
-    
-    FORMAT = """
-<html>
-    <head>
-        <style>
-            {}
-        </style>
-    </head>
-    <body>
-        <br>
-        <center>
-            <div class="container progress-bar-container">
-                <ul class="progressbar">
-                    {}
-                </ul>
-            </div>
-        </center>
-    </body>
-</html>
-"""
-    
-    STATES_NAME:List[str] = None
-    
-    @classmethod
-    def _generate_template(cls, types:str)->str:
-        # normal: A state with no execution.
-        # active-show-up: Active state, and content displayed above the horizontal line.
-        # active-show-down: Active state, and content displayed below the horizontal line.
-        # active-show-both: Active state, and content displayed both above and below the horizontal line.
-        # active-show-none: Active state, with no content displayed above the horizontal line.
-        
-        assert types.lower() in ["normal","active-show-up", "active-show-down", "active-show-both", "active", "active-show-none"]
-        both_templates = """<li class="active" style="--gradient-start: {}%; --gradient-end: {}%;">
-	<div class="data-content">
-		<center>
-            <p style="line-height: 1px;"></p>
-			{}
-			<p>
-				{}
-			</p>
-		</center>
-	</div>
-	<span>{}</span>
-</li>"""
-
-        if types.lower() == "normal":
-            templates = "<li><span>{}</span></li>"
-        elif types.lower() == "active":
-            templates = """<li class="active"><span>{}</span></li>"""
-        elif types.lower() == "active-show-up":
-            templates = both_templates.format("{}","{}", "{}", "", "{}")
-        elif types.lower() == "active-show-down":
-            templates = both_templates.format("{}","{}", "", "{}", "{}")
-        elif types.lower() == "active-show-both":
-            templates = both_templates
-        elif types.lower() == "active-show-none":
-            templates = """<li class="active" style="--gradient-start: {}%; --gradient-end: {}%;">
-	<span>{}</span>
-</li>"""
-        else:
-            assert False
-        return templates
-            
-    @classmethod
-    def update_states(cls, current_states:List[int], current_templates:List[str], show_content:List[Tuple[str]])->str:
-        assert len(current_states) == len(current_templates)
-        # You can dynamically change the number of states.
-        # assert len(current_states) == len(cls.STATES_NAME)
-        css_code = []
-        for idx in range(len(current_states)):
-            if idx == 0:
-                if current_states[idx] != 0:
-                    css_code = [f"{cls._generate_template('active').format(cls.STATES_NAME[idx])}"]
-                else:
-                    css_code = [f"{cls._generate_template('normal').format(cls.STATES_NAME[idx])}"]
-                continue
-            if current_states[idx-1] == 0:
-                # new_code = f"{cls._generate_template('normal').format(*(show_content[idx]))}"
-                new_code = f"{cls._generate_template('normal').format(cls.STATES_NAME[idx])}"
-            else:
-                new_code = f"{cls._generate_template(current_templates[idx]).format(current_states[idx-1], 100-current_states[idx-1],*(show_content[idx-1]), cls.STATES_NAME[idx])}"
-            if current_states[idx-1] != 100 or (current_states[idx]==0 and current_states[idx-1]==100):
-                new_code = new_code.replace("""li class="active" ""","""li """)
-            css_code.append(new_code)
-        return "\n".join(css_code)
-    
-    @classmethod
-    def create_states(cls, states_name:List[str], manual_create_end_nodes:bool=False):
-        # Create states
-        if manual_create_end_nodes:
-            states_name.append("Done")
-        css_code = ""
-        cls.STATES_NAME: List[str] = states_name
-        for name in states_name:
-            css_code = f"{css_code}\n{cls._generate_template('normal').format(name)}"
-        return css_code
-    
-
-if __name__ == '__main__':
-    pass

spaces/Abhi1262/MyGenAIChatBot/README.md

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

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/app.js

@@ -1,35 +0,0 @@
-export { matchers } from './matchers.js';
-
-export const nodes = [
-	() => import('./nodes/0'),
-	() => import('./nodes/1'),
-	() => import('./nodes/2'),
-	() => import('./nodes/3'),
-	() => import('./nodes/4'),
-	() => import('./nodes/5'),
-	() => import('./nodes/6'),
-	() => import('./nodes/7'),
-	() => import('./nodes/8'),
-	() => import('./nodes/9'),
-	() => import('./nodes/10')
-];
-
-export const server_loads = [0];
-
-export const dictionary = {
-		"/": [2],
-		"/conversations": [~4],
-		"/conversation/[id]": [~3],
-		"/login": [~5],
-		"/login/callback": [~6],
-		"/logout": [~7],
-		"/privacy": [8],
-		"/r/[id]": [~9],
-		"/settings": [~10]
-	};
-
-export const hooks = {
-	handleError: (({ error }) => { console.error(error) }),
-};
-
-export { default as root } from '../root.svelte';

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/User.ts

@@ -1,12 +0,0 @@
-import type { Timestamps } from "./Timestamps";
-
-export interface User extends Timestamps {
-	username?: string;
-	name: string;
-	email?: string;
-	avatarUrl: string;
-	hfUserId: string;
-
-	// Session identifier, stored in the cookie
-	sessionId: string;
-}

spaces/Adapter/T2I-Adapter/docs/AdapterZoo.md

@@ -1,16 +0,0 @@
-# Adapter Zoo
-
-You can download the adapters from <https://huggingface.co/TencentARC/T2I-Adapter/tree/main>
-
-All the following adapters are trained with Stable Diffusion (SD) V1.4, and they can be directly used on custom models as long as they are fine-tuned from the same text-to-image models, such as Anything-4.0 or models on the <https://civitai.com/>.
-
-| Adapter Name  | Adapter Description | Demos|Model Parameters|  Model Storage | |
-| --- | --- |--- |--- |--- |---|
-| t2iadapter_color_sd14v1.pth | Spatial color palette → image | [Demos](examples.md#color-adapter-spatial-palette) |18 M | 75 MB | |
-| t2iadapter_style_sd14v1.pth | Image style → image | [Demos](examples.md#style-adapter)|| 154MB |  Preliminary model. Style adapters with finer controls are on the way|
-| t2iadapter_openpose_sd14v1.pth | Openpose → image| [Demos](examples.md#openpose-adapter) |77 M| 309 MB | |
-| t2iadapter_canny_sd14v1.pth | Canny edges → image | [Demos](examples.md#canny-adapter-edge )|77 M | 309 MB ||
-| t2iadapter_sketch_sd14v1.pth | sketch → image ||77 M| 308 MB | |
-| t2iadapter_keypose_sd14v1.pth | keypose → image || 77 M| 309 MB | mmpose style |
-| t2iadapter_seg_sd14v1.pth | segmentation → image ||77 M| 309 MB ||
-| t2iadapter_depth_sd14v1.pth | depth maps → image ||77 M | 309 MB | Not the final model, still under training|

spaces/Agusbs98/automatic-ecg-diagnosis/app.py

@@ -1,20 +0,0 @@
-from libs import *
-from predicts import procesar_archivo
-import gradio as gr
-
-with gr.Blocks() as interface:
-    gr.Image(value='./ComplutenseTFGBanner.png',show_label=False)
-    with gr.Column():
-        format = gr.inputs.Dropdown(["XMLsierra","CSV"],default="XMLsierra",label= "Formato del archivo")   
-    with gr.Row():
-        number = gr.inputs.Slider(label="Valor",default=200,minimum=1,maximum=999)
-        unit = gr.inputs.Dropdown(["V","miliV","microV","nanoV"], label="Unidad",default="miliV")
-    with gr.Column():
-        frec = gr.inputs.Number(label= "Frecuencia (Hz)",default=500)
-        file = gr.inputs.File(label="Selecciona un archivo.")
-        button = gr.Button(value='Analizar')
-        out = gr.DataFrame(label="Diagnostico automático.",type="pandas",headers = ['Red','Posibles predicciones'],value=[['Antonior92','1aAVb, RBBB, LBBB, SB, AF, ST'],['CPSC-2018','Normal, AF, IAVB, LBBB, RBBB, PAC, PVC, STD, STE'],['Chapman', 'AFIB, GSVT, SB, SR']])
-        img = gr.outputs.Image(label="Imagen",type='filepath')
-    button.click(fn=procesar_archivo,inputs=[format,number,unit,frec,file] ,outputs=[out,img])
-    
-interface.launch()

spaces/AlanMars/QYL-AI-Space/readme/README_en.md

@@ -1,127 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | English | <a title="Japanese" href="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://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA</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>
-        Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search <br />
-        LaTeX rendering / Table rendering / Code highlighting <br />
-        Auto dark mode / Adaptive web interface / WeChat-like theme <br />
-        Multi-parameters tuning / Multi-API-Key support / Multi-user support <br />
-        Compatible with GPT-4 / Local deployment for LLMs
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>Video Tutorial</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 Introduction</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 Introduction & Tutorial</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>Online trial</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>One-Click deployment</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>
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press <kbd>Shift</kbd> + <kbd>Enter</kbd> keys.
-- To quickly switch between input history, press <kbd>↑</kbd> and <kbd>↓</kbd> key in the input box.
-- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port" <your port number>,` in `config.json`.
-- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Quickstart
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python app.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
-   ```
-   pip install -r requirements.txt
-   ```
-3. Update Gradio
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## 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>
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-<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">

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/ONNXVITS_to_onnx.py

@@ -1,31 +0,0 @@
-import ONNXVITS_models
-import utils
-from text import text_to_sequence
-import torch
-import commons
-
-def get_text(text, hps):
-    text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json")
-symbols = hps.symbols
-net_g = ONNXVITS_models.SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model)
-_ = net_g.eval()
-_ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g)
-
-text1 = get_text("ありがとうございます。", hps)
-stn_tst = text1
-with torch.no_grad():
-    x_tst = stn_tst.unsqueeze(0)
-    x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-    sid = torch.tensor([0])
-    o = net_g(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)

spaces/Ameaou/academic-chatgpt3.1/toolbox.py

@@ -1,507 +0,0 @@
-import markdown
-import importlib
-import traceback
-import inspect
-import re
-from latex2mathml.converter import convert as tex2mathml
-from functools import wraps, lru_cache
-############################### 插件输入输出接驳区 #######################################
-class ChatBotWithCookies(list):
-    def __init__(self, cookie):
-        self._cookies = cookie
-
-    def write_list(self, list):
-        for t in list:
-            self.append(t)
-
-    def get_list(self):
-        return [t for t in self]
-
-    def get_cookies(self):
-        return self._cookies
-
-def ArgsGeneralWrapper(f):
-    """
-    装饰器函数，用于重组输入参数，改变输入参数的顺序与结构。
-    """
-    def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, *args):
-        txt_passon = txt
-        if txt == "" and txt2 != "": txt_passon = txt2
-        # 引入一个有cookie的chatbot
-        cookies.update({
-            'top_p':top_p, 
-            'temperature':temperature,
-        })
-        llm_kwargs = {
-            'api_key': cookies['api_key'],
-            'llm_model': llm_model,
-            'top_p':top_p, 
-            'max_length': max_length,
-            'temperature':temperature,
-        }
-        plugin_kwargs = {
-            # 目前还没有
-        }
-        chatbot_with_cookie = ChatBotWithCookies(cookies)
-        chatbot_with_cookie.write_list(chatbot)
-        yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
-    return decorated
-
-def update_ui(chatbot, history, msg='正常', **kwargs):  # 刷新界面
-    """
-    刷新用户界面
-    """
-    assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时，可用clear将其清空，然后用for+append循环重新赋值。"
-    yield chatbot.get_cookies(), chatbot, history, msg
-
-def CatchException(f):
-    """
-    装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
-    """
-    @wraps(f)
-    def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-        try:
-            yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
-        except Exception as e:
-            from check_proxy import check_proxy
-            from toolbox import get_conf
-            proxies, = get_conf('proxies')
-            tb_str = '```\n' + traceback.format_exc() + '```'
-            if chatbot is None or len(chatbot) == 0:
-                chatbot = [["插件调度异常", "异常原因"]]
-            chatbot[-1] = (chatbot[-1][0],
-                           f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
-            yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
-    return decorated
-
-
-def HotReload(f):
-    """
-    HotReload的装饰器函数，用于实现Python函数插件的热更新。
-    函数热更新是指在不停止程序运行的情况下，更新函数代码，从而达到实时更新功能。
-    在装饰器内部，使用wraps(f)来保留函数的元信息，并定义了一个名为decorated的内部函数。
-    内部函数通过使用importlib模块的reload函数和inspect模块的getmodule函数来重新加载并获取函数模块，
-    然后通过getattr函数获取函数名，并在新模块中重新加载函数。
-    最后，使用yield from语句返回重新加载过的函数，并在被装饰的函数上执行。
-    最终，装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本，并执行函数的新版本。
-    """
-    @wraps(f)
-    def decorated(*args, **kwargs):
-        fn_name = f.__name__
-        f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
-        yield from f_hot_reload(*args, **kwargs)
-    return decorated
-
-
-####################################### 其他小工具 #####################################
-
-def get_reduce_token_percent(text):
-    """
-        * 此函数未来将被弃用
-    """
-    try:
-        # text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
-        pattern = r"(\d+)\s+tokens\b"
-        match = re.findall(pattern, text)
-        EXCEED_ALLO = 500  # 稍微留一点余地，否则在回复时会因余量太少出问题
-        max_limit = float(match[0]) - EXCEED_ALLO
-        current_tokens = float(match[1])
-        ratio = max_limit/current_tokens
-        assert ratio > 0 and ratio < 1
-        return ratio, str(int(current_tokens-max_limit))
-    except:
-        return 0.5, '不详'
-
-
-
-def write_results_to_file(history, file_name=None):
-    """
-    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    if file_name is None:
-        # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-        file_name = 'chatGPT分析报告' + \
-            time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-    os.makedirs('./gpt_log/', exist_ok=True)
-    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-        f.write('# chatGPT 分析报告\n')
-        for i, content in enumerate(history):
-            try:    # 这个bug没找到触发条件，暂时先这样顶一下
-                if type(content) != str:
-                    content = str(content)
-            except:
-                continue
-            if i % 2 == 0:
-                f.write('## ')
-            f.write(content)
-            f.write('\n\n')
-    res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
-    print(res)
-    return res
-
-
-def regular_txt_to_markdown(text):
-    """
-    将普通文本转换为Markdown格式的文本。
-    """
-    text = text.replace('\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    return text
-
-
-
-
-def report_execption(chatbot, history, a, b):
-    """
-    向chatbot中添加错误信息
-    """
-    chatbot.append((a, b))
-    history.append(a)
-    history.append(b)
-
-
-def text_divide_paragraph(text):
-    """
-    将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
-    """
-    if '```' in text:
-        # careful input
-        return text
-    else:
-        # wtf input
-        lines = text.split("\n")
-        for i, line in enumerate(lines):
-            lines[i] = lines[i].replace(" ", "&nbsp;")
-        text = "</br>".join(lines)
-        return text
-
-
-def markdown_convertion(txt):
-    """
-    将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
-    """
-    pre = '<div class="markdown-body">'
-    suf = '</div>'
-    markdown_extension_configs = {
-        'mdx_math': {
-            'enable_dollar_delimiter': True,
-            'use_gitlab_delimiters': False,
-        },
-    }
-    find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
-
-    def tex2mathml_catch_exception(content, *args, **kwargs):
-        try:
-            content = tex2mathml(content, *args, **kwargs)
-        except:
-            content = content
-        return content
-
-    def replace_math_no_render(match):
-        content = match.group(1)
-        if 'mode=display' in match.group(0):
-            content = content.replace('\n', '</br>')
-            return f"<font color=\"#00FF00\">$$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$$</font>"
-        else:
-            return f"<font color=\"#00FF00\">$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$</font>"
-
-    def replace_math_render(match):
-        content = match.group(1)
-        if 'mode=display' in match.group(0):
-            if '\\begin{aligned}' in content:
-                content = content.replace('\\begin{aligned}', '\\begin{array}')
-                content = content.replace('\\end{aligned}', '\\end{array}')
-                content = content.replace('&', ' ')
-            content = tex2mathml_catch_exception(content, display="block")
-            return content
-        else:
-            return tex2mathml_catch_exception(content)
-        
-    def markdown_bug_hunt(content):
-        """
-        解决一个mdx_math的bug（单$包裹begin命令时多余<script>）
-        """
-        content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
-        content = content.replace('</script>\n</script>', '</script>')
-        return content
-    
-
-    if ('$' in txt) and ('```' not in txt):  # 有$标识的公式符号，且没有代码段```的标识
-        # convert everything to html format
-        split = markdown.markdown(text='---')
-        convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
-        convert_stage_1 = markdown_bug_hunt(convert_stage_1)
-        # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
-        # 1. convert to easy-to-copy tex (do not render math)
-        convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
-        # 2. convert to rendered equation
-        convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
-        # cat them together
-        return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
-    else:
-        return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
-
-
-def close_up_code_segment_during_stream(gpt_reply):
-    """
-    在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
-    
-    Args:
-        gpt_reply (str): GPT模型返回的回复字符串。
-
-    Returns:
-        str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。
-
-    """
-    if '```' not in gpt_reply:
-        return gpt_reply
-    if gpt_reply.endswith('```'):
-        return gpt_reply
-
-    # 排除了以上两个情况，我们
-    segments = gpt_reply.split('```')
-    n_mark = len(segments) - 1
-    if n_mark % 2 == 1:
-        # print('输出代码片段中！')
-        return gpt_reply+'\n```'
-    else:
-        return gpt_reply
-
-
-def format_io(self, y):
-    """
-    将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
-    """
-    if y is None or y == []:
-        return []
-    i_ask, gpt_reply = y[-1]
-    i_ask = text_divide_paragraph(i_ask)  # 输入部分太自由，预处理一波
-    gpt_reply = close_up_code_segment_during_stream(gpt_reply)  # 当代码输出半截的时候，试着补上后个```
-    y[-1] = (
-        None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code', 'tables']),
-        None if gpt_reply is None else markdown_convertion(gpt_reply)
-    )
-    return y
-
-
-def find_free_port():
-    """
-    返回当前系统中可用的未使用端口。
-    """
-    import socket
-    from contextlib import closing
-    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
-        s.bind(('', 0))
-        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-        return s.getsockname()[1]
-
-
-def extract_archive(file_path, dest_dir):
-    import zipfile
-    import tarfile
-    import os
-    # Get the file extension of the input file
-    file_extension = os.path.splitext(file_path)[1]
-
-    # Extract the archive based on its extension
-    if file_extension == '.zip':
-        with zipfile.ZipFile(file_path, 'r') as zipobj:
-            zipobj.extractall(path=dest_dir)
-            print("Successfully extracted zip archive to {}".format(dest_dir))
-
-    elif file_extension in ['.tar', '.gz', '.bz2']:
-        with tarfile.open(file_path, 'r:*') as tarobj:
-            tarobj.extractall(path=dest_dir)
-            print("Successfully extracted tar archive to {}".format(dest_dir))
-
-    # 第三方库，需要预先pip install rarfile
-    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
-    elif file_extension == '.rar':
-        try:
-            import rarfile
-            with rarfile.RarFile(file_path) as rf:
-                rf.extractall(path=dest_dir)
-                print("Successfully extracted rar archive to {}".format(dest_dir))
-        except:
-            print("Rar format requires additional dependencies to install")
-            return '\n\n需要安装pip install rarfile来解压rar文件'
-
-    # 第三方库，需要预先pip install py7zr
-    elif file_extension == '.7z':
-        try:
-            import py7zr
-            with py7zr.SevenZipFile(file_path, mode='r') as f:
-                f.extractall(path=dest_dir)
-                print("Successfully extracted 7z archive to {}".format(dest_dir))
-        except:
-            print("7z format requires additional dependencies to install")
-            return '\n\n需要安装pip install py7zr来解压7z文件'
-    else:
-        return ''
-    return ''
-
-
-def find_recent_files(directory):
-    """
-        me: find files that is created with in one minutes under a directory with python, write a function
-        gpt: here it is!
-    """
-    import os
-    import time
-    current_time = time.time()
-    one_minute_ago = current_time - 60
-    recent_files = []
-
-    for filename in os.listdir(directory):
-        file_path = os.path.join(directory, filename)
-        if file_path.endswith('.log'):
-            continue
-        created_time = os.path.getmtime(file_path)
-        if created_time >= one_minute_ago:
-            if os.path.isdir(file_path):
-                continue
-            recent_files.append(file_path)
-
-    return recent_files
-
-
-def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
-    if len(files) == 0:
-        return chatbot, txt
-    import shutil
-    import os
-    import time
-    import glob
-    from toolbox import extract_archive
-    try:
-        shutil.rmtree('./private_upload/')
-    except:
-        pass
-    time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
-    err_msg = ''
-    for file in files:
-        file_origin_name = os.path.basename(file.orig_name)
-        shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
-        err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
-                                   dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
-    moved_files = [fp for fp in glob.glob(
-        'private_upload/**/*', recursive=True)]
-    if "底部输入区" in checkboxes:
-        txt = ""
-        txt2 = f'private_upload/{time_tag}'
-    else:
-        txt = f'private_upload/{time_tag}'
-        txt2 = ""
-    moved_files_str = '\t\n\n'.join(moved_files)
-    chatbot.append(['我上传了文件，请查收',
-                    f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
-                    f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
-                    f'\n\n现在您点击任意“红颜色”标识的函数插件时，以上文件将被作为输入参数'+err_msg])
-    return chatbot, txt, txt2
-
-
-def on_report_generated(files, chatbot):
-    from toolbox import find_recent_files
-    report_files = find_recent_files('gpt_log')
-    if len(report_files) == 0:
-        return None, chatbot
-    # files.extend(report_files)
-    chatbot.append(['汇总报告如何远程获取？', '汇总报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。'])
-    return report_files, chatbot
-
-def is_openai_api_key(key):
-    API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
-    return bool(API_MATCH)
-
-def is_api2d_key(key):
-    if key.startswith('fk') and len(key) == 41:
-        return True
-    else:
-        return False
-
-def is_any_api_key(key):
-    if ',' in key:
-        keys = key.split(',')
-        for k in keys:
-            if is_any_api_key(k): return True
-        return False
-    else:
-        return is_openai_api_key(key) or is_api2d_key(key)
-
-
-def select_api_key(keys, llm_model):
-    import random
-    avail_key_list = []
-    key_list = keys.split(',')
-
-    if llm_model.startswith('gpt-'):
-        for k in key_list:
-            if is_openai_api_key(k): avail_key_list.append(k)
-
-    if llm_model.startswith('api2d-'):
-        for k in key_list:
-            if is_api2d_key(k): avail_key_list.append(k)
-
-    if len(avail_key_list) == 0:
-        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。")
-
-    api_key = random.choice(avail_key_list) # 随机负载均衡
-    return api_key
-
-@lru_cache(maxsize=128)
-def read_single_conf_with_lru_cache(arg):
-    from colorful import print亮红, print亮绿
-    try:
-        r = getattr(importlib.import_module('config_private'), arg)
-    except:
-        r = getattr(importlib.import_module('config'), arg)
-    # 在读取API_KEY时，检查一下是不是忘了改config
-    if arg == 'API_KEY':
-        if is_any_api_key(r):
-            print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
-        else:
-            print亮红( "[API_KEY] 正确的 API_KEY 是'sk'开头的51位密钥（OpenAI），或者 'fk'开头的41位密钥，请在config文件中修改API密钥之后再运行。")
-    if arg == 'proxies':
-        if r is None:
-            print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
-        else:
-            print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
-            assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
-    return r
-
-
-def get_conf(*args):
-    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
-    res = []
-    for arg in args:
-        r = read_single_conf_with_lru_cache(arg)
-        res.append(r)
-    return res
-
-
-def clear_line_break(txt):
-    txt = txt.replace('\n', ' ')
-    txt = txt.replace('  ', ' ')
-    txt = txt.replace('  ', ' ')
-    return txt
-
-
-class DummyWith():
-    """
-    这段代码定义了一个名为DummyWith的空上下文管理器，
-    它的作用是……额……没用，即在代码结构不变得情况下取代其他的上下文管理器。
-    上下文管理器是一种Python对象，用于与with语句一起使用，
-    以确保一些资源在代码块执行期间得到正确的初始化和清理。
-    上下文管理器必须实现两个方法，分别为 __enter__()和 __exit__()。 
-    在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，
-    而在上下文执行结束时，__exit__()方法则会被调用。
-    """
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        return

spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py

@@ -1,391 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Custom TensorFlow ops for efficient resampling of 2D images."""
-
-import os
-import numpy as np
-import tensorflow as tf
-from .. import custom_ops
-
-
-def _get_plugin():
-    return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
-
-# ----------------------------------------------------------------------------
-
-
-def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'):
-    r"""Pad, upsample, FIR filter, and downsample a batch of 2D images.
-
-    Accepts a batch of 2D images of the shape `[majorDim, inH, inW, minorDim]`
-    and performs the following operations for each image, batched across
-    `majorDim` and `minorDim`:
-
-    1. Pad the image with zeros by the specified number of pixels on each side
-       (`padx0`, `padx1`, `pady0`, `pady1`). Specifying a negative value
-       corresponds to cropping the image.
-
-    2. Upsample the image by inserting the zeros after each pixel (`upx`, `upy`).
-
-    3. Convolve the image with the specified 2D FIR filter (`k`), shrinking the
-       image so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by throwing away pixels (`downx`, `downy`).
-
-    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:      Input tensor of the shape `[majorDim, inH, inW, minorDim]`.
-        k:      2D FIR filter of the shape `[firH, firW]`.
-        upx:    Integer upsampling factor along the X-axis (default: 1).
-        upy:    Integer upsampling factor along the Y-axis (default: 1).
-        downx:  Integer downsampling factor along the X-axis (default: 1).
-        downy:  Integer downsampling factor along the Y-axis (default: 1).
-        padx0:  Number of pixels to pad on the left side (default: 0).
-        padx1:  Number of pixels to pad on the right side (default: 0).
-        pady0:  Number of pixels to pad on the top side (default: 0).
-        pady1:  Number of pixels to pad on the bottom side (default: 0).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[majorDim, outH, outW, minorDim]`, and same datatype as `x`.
-    """
-
-    impl_dict = {
-        'ref':  _upfirdn_2d_ref,
-        'cuda': _upfirdn_2d_cuda,
-    }
-    return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
-
-# ----------------------------------------------------------------------------
-
-
-def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Slow reference implementation of `upfirdn_2d()` using standard TensorFlow ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    assert x.shape.rank == 4
-    inH = x.shape[1].value
-    inW = x.shape[2].value
-    minorDim = _shape(x, 3)
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    # Upsample (insert zeros).
-    x = tf.reshape(x, [-1, inH, 1, inW, 1, minorDim])
-    x = tf.pad(x, [[0, 0], [0, 0], [0, upy - 1], [0, 0], [0, upx - 1], [0, 0]])
-    x = tf.reshape(x, [-1, inH * upy, inW * upx, minorDim])
-
-    # Pad (crop if negative).
-    x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)],
-               [max(padx0, 0), max(padx1, 0)], [0, 0]])
-    x = x[:, max(-pady0, 0): x.shape[1].value - max(-pady1, 0),
-          max(-padx0, 0): x.shape[2].value - max(-padx1, 0), :]
-
-    # Convolve with filter.
-    x = tf.transpose(x, [0, 3, 1, 2])
-    x = tf.reshape(x, [-1, 1, inH * upy + pady0 +
-                   pady1, inW * upx + padx0 + padx1])
-    w = tf.constant(k[::-1, ::-1, np.newaxis, np.newaxis], dtype=x.dtype)
-    x = tf.nn.conv2d(x, w, strides=[1, 1, 1, 1],
-                     padding='VALID', data_format='NCHW')
-    x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 -
-                   kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1])
-    x = tf.transpose(x, [0, 2, 3, 1])
-
-    # Downsample (throw away pixels).
-    return x[:, ::downy, ::downx, :]
-
-# ----------------------------------------------------------------------------
-
-
-def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Fast CUDA implementation of `upfirdn_2d()` using custom ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    majorDim, inH, inW, minorDim = x.shape.as_list()
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    outW = (inW * upx + padx0 + padx1 - kernelW) // downx + 1
-    outH = (inH * upy + pady0 + pady1 - kernelH) // downy + 1
-    assert outW >= 1 and outH >= 1
-
-    kc = tf.constant(k, dtype=x.dtype)
-    gkc = tf.constant(k[::-1, ::-1], dtype=x.dtype)
-    gpadx0 = kernelW - padx0 - 1
-    gpady0 = kernelH - pady0 - 1
-    gpadx1 = inW * upx - outW * downx + padx0 - upx + 1
-    gpady1 = inH * upy - outH * downy + pady0 - upy + 1
-
-    @tf.custom_gradient
-    def func(x):
-        y = _get_plugin().up_fir_dn2d(x=x, k=kc, upx=upx, upy=upy, downx=downx,
-                                      downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
-        y.set_shape([majorDim, outH, outW, minorDim])
-
-        @tf.custom_gradient
-        def grad(dy):
-            dx = _get_plugin().up_fir_dn2d(x=dy, k=gkc, upx=downx, upy=downy, downx=upx,
-                                           downy=upy, padx0=gpadx0, padx1=gpadx1, pady0=gpady0, pady1=gpady1)
-            dx.set_shape([majorDim, inH, inW, minorDim])
-            return dx, func
-        return y, grad
-    return func(x)
-
-# ----------------------------------------------------------------------------
-
-
-def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Filter a batch of 2D images with the given FIR filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and filters each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-
-    k = _setup_kernel(k) * gain
-    p = k.shape[0] - 1
-    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Upsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and upsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the upsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * (gain * (factor ** 2))
-    p = k.shape[0] - factor
-    return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Downsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and downsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the downsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * gain
-    p = k.shape[0] - factor
-    return _simple_upfirdn_2d(x, k, down=factor, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-
-    # Check weight shape.
-    w = tf.convert_to_tensor(w)
-    assert w.shape.rank == 4
-    convH = w.shape[0].value
-    convW = w.shape[1].value
-    inC = _shape(w, 2)
-    outC = _shape(w, 3)
-    assert convW == convH
-
-    # Setup filter kernel.
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * (gain * (factor ** 2))
-    p = (k.shape[0] - factor) - (convW - 1)
-
-    # Determine data dimensions.
-    if data_format == 'NCHW':
-        stride = [1, 1, factor, factor]
-        output_shape = [_shape(x, 0), outC, (_shape(
-            x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW]
-        num_groups = _shape(x, 1) // inC
-    else:
-        stride = [1, factor, factor, 1]
-        output_shape = [_shape(x, 0), (_shape(
-            x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC]
-        num_groups = _shape(x, 3) // inC
-
-    # Transpose weights.
-    w = tf.reshape(w, [convH, convW, inC, num_groups, -1])
-    w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2])
-    w = tf.reshape(w, [convH, convW, -1, num_groups * inC])
-
-    # Execute.
-    x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape,
-                               strides=stride, padding='VALID', data_format=data_format)
-    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2+factor-1, pad1=p//2+1, data_format=data_format, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def conv_downsample_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    w = tf.convert_to_tensor(w)
-    convH, convW, _inC, _outC = w.shape.as_list()
-    assert convW == convH
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * gain
-    p = (k.shape[0] - factor) + (convW - 1)
-    if data_format == 'NCHW':
-        s = [1, 1, factor, factor]
-    else:
-        s = [1, factor, factor, 1]
-    x = _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p //
-                           2, data_format=data_format, impl=impl)
-    return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format)
-
-# ----------------------------------------------------------------------------
-# Internal helper funcs.
-
-
-def _shape(tf_expr, dim_idx):
-    if tf_expr.shape.rank is not None:
-        dim = tf_expr.shape[dim_idx].value
-        if dim is not None:
-            return dim
-    return tf.shape(tf_expr)[dim_idx]
-
-
-def _setup_kernel(k):
-    k = np.asarray(k, dtype=np.float32)
-    if k.ndim == 1:
-        k = np.outer(k, k)
-    k /= np.sum(k)
-    assert k.ndim == 2
-    assert k.shape[0] == k.shape[1]
-    return k
-
-
-def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='NCHW', impl='cuda'):
-    assert data_format in ['NCHW', 'NHWC']
-    assert x.shape.rank == 4
-    y = x
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(y, 2), _shape(y, 3), 1])
-    y = upfirdn_2d(y, k, upx=up, upy=up, downx=down, downy=down,
-                   padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl)
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(x, 1), _shape(y, 1), _shape(y, 2)])
-    return y
-
-# ----------------------------------------------------------------------------

spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py

@@ -1,13 +0,0 @@
-_base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py

@@ -1,4 +0,0 @@
-_base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py'
-
-lr_config = dict(step=[28, 34])
-runner = dict(type='EpochBasedRunner', max_epochs=36)

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/grid_assigner.py

@@ -1,155 +0,0 @@
-import torch
-
-from ..builder import BBOX_ASSIGNERS
-from ..iou_calculators import build_iou_calculator
-from .assign_result import AssignResult
-from .base_assigner import BaseAssigner
-
-
-@BBOX_ASSIGNERS.register_module()
-class GridAssigner(BaseAssigner):
-    """Assign a corresponding gt bbox or background to each bbox.
-
-    Each proposals will be assigned with `-1`, `0`, or a positive integer
-    indicating the ground truth index.
-
-    - -1: don't care
-    - 0: negative sample, no assigned gt
-    - positive integer: positive sample, index (1-based) of assigned gt
-
-    Args:
-        pos_iou_thr (float): IoU threshold for positive bboxes.
-        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
-        min_pos_iou (float): Minimum iou for a bbox to be considered as a
-            positive bbox. Positive samples can have smaller IoU than
-            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
-        gt_max_assign_all (bool): Whether to assign all bboxes with the same
-            highest overlap with some gt to that gt.
-    """
-
-    def __init__(self,
-                 pos_iou_thr,
-                 neg_iou_thr,
-                 min_pos_iou=.0,
-                 gt_max_assign_all=True,
-                 iou_calculator=dict(type='BboxOverlaps2D')):
-        self.pos_iou_thr = pos_iou_thr
-        self.neg_iou_thr = neg_iou_thr
-        self.min_pos_iou = min_pos_iou
-        self.gt_max_assign_all = gt_max_assign_all
-        self.iou_calculator = build_iou_calculator(iou_calculator)
-
-    def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None):
-        """Assign gt to bboxes. The process is very much like the max iou
-        assigner, except that positive samples are constrained within the cell
-        that the gt boxes fell in.
-
-        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
-        will be assigned with -1, 0, or a positive number. -1 means don't care,
-        0 means negative sample, positive number is the index (1-based) of
-        assigned gt.
-        The assignment is done in following steps, the order matters.
-
-        1. assign every bbox to -1
-        2. assign proposals whose iou with all gts <= neg_iou_thr to 0
-        3. for each bbox within a cell, if the iou with its nearest gt >
-            pos_iou_thr and the center of that gt falls inside the cell,
-            assign it to that bbox
-        4. for each gt bbox, assign its nearest proposals within the cell the
-            gt bbox falls in to itself.
-
-        Args:
-            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
-            box_responsible_flags (Tensor): flag to indicate whether box is
-                responsible for prediction, shape(n, )
-            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
-            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
-
-        # compute iou between all gt and bboxes
-        overlaps = self.iou_calculator(gt_bboxes, bboxes)
-
-        # 1. assign -1 by default
-        assigned_gt_inds = overlaps.new_full((num_bboxes, ),
-                                             -1,
-                                             dtype=torch.long)
-
-        if num_gts == 0 or num_bboxes == 0:
-            # No ground truth or boxes, return empty assignment
-            max_overlaps = overlaps.new_zeros((num_bboxes, ))
-            if num_gts == 0:
-                # No truth, assign everything to background
-                assigned_gt_inds[:] = 0
-            if gt_labels is None:
-                assigned_labels = None
-            else:
-                assigned_labels = overlaps.new_full((num_bboxes, ),
-                                                    -1,
-                                                    dtype=torch.long)
-            return AssignResult(
-                num_gts,
-                assigned_gt_inds,
-                max_overlaps,
-                labels=assigned_labels)
-
-        # 2. assign negative: below
-        # for each anchor, which gt best overlaps with it
-        # for each anchor, the max iou of all gts
-        # shape of max_overlaps == argmax_overlaps == num_bboxes
-        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-        if isinstance(self.neg_iou_thr, float):
-            assigned_gt_inds[(max_overlaps >= 0)
-                             & (max_overlaps <= self.neg_iou_thr)] = 0
-        elif isinstance(self.neg_iou_thr, (tuple, list)):
-            assert len(self.neg_iou_thr) == 2
-            assigned_gt_inds[(max_overlaps > self.neg_iou_thr[0])
-                             & (max_overlaps <= self.neg_iou_thr[1])] = 0
-
-        # 3. assign positive: falls into responsible cell and above
-        # positive IOU threshold, the order matters.
-        # the prior condition of comparision is to filter out all
-        # unrelated anchors, i.e. not box_responsible_flags
-        overlaps[:, ~box_responsible_flags.type(torch.bool)] = -1.
-
-        # calculate max_overlaps again, but this time we only consider IOUs
-        # for anchors responsible for prediction
-        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-        # for each gt, which anchor best overlaps with it
-        # for each gt, the max iou of all proposals
-        # shape of gt_max_overlaps == gt_argmax_overlaps == num_gts
-        gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
-
-        pos_inds = (max_overlaps >
-                    self.pos_iou_thr) & box_responsible_flags.type(torch.bool)
-        assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
-
-        # 4. assign positive to max overlapped anchors within responsible cell
-        for i in range(num_gts):
-            if gt_max_overlaps[i] > self.min_pos_iou:
-                if self.gt_max_assign_all:
-                    max_iou_inds = (overlaps[i, :] == gt_max_overlaps[i]) & \
-                         box_responsible_flags.type(torch.bool)
-                    assigned_gt_inds[max_iou_inds] = i + 1
-                elif box_responsible_flags[gt_argmax_overlaps[i]]:
-                    assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
-
-        # assign labels of positive anchors
-        if gt_labels is not None:
-            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
-            pos_inds = torch.nonzero(
-                assigned_gt_inds > 0, as_tuple=False).squeeze()
-            if pos_inds.numel() > 0:
-                assigned_labels[pos_inds] = gt_labels[
-                    assigned_gt_inds[pos_inds] - 1]
-
-        else:
-            assigned_labels = None
-
-        return AssignResult(
-            num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/__init__.py

@@ -1,40 +0,0 @@
-from .atss import ATSS
-from .base import BaseDetector
-from .cascade_rcnn import CascadeRCNN
-from .cornernet import CornerNet
-from .detr import DETR
-from .fast_rcnn import FastRCNN
-from .faster_rcnn import FasterRCNN
-from .fcos import FCOS
-from .fovea import FOVEA
-from .fsaf import FSAF
-from .gfl import GFL
-from .grid_rcnn import GridRCNN
-from .htc import HybridTaskCascade
-from .kd_one_stage import KnowledgeDistillationSingleStageDetector
-from .mask_rcnn import MaskRCNN
-from .mask_scoring_rcnn import MaskScoringRCNN
-from .nasfcos import NASFCOS
-from .paa import PAA
-from .point_rend import PointRend
-from .reppoints_detector import RepPointsDetector
-from .retinanet import RetinaNet
-from .rpn import RPN
-from .scnet import SCNet
-from .single_stage import SingleStageDetector
-from .sparse_rcnn import SparseRCNN
-from .trident_faster_rcnn import TridentFasterRCNN
-from .two_stage import TwoStageDetector
-from .vfnet import VFNet
-from .yolact import YOLACT
-from .yolo import YOLOV3
-
-__all__ = [
-    'ATSS', 'BaseDetector', 'SingleStageDetector',
-    'KnowledgeDistillationSingleStageDetector', 'TwoStageDetector', 'RPN',
-    'FastRCNN', 'FasterRCNN', 'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade',
-    'RetinaNet', 'FCOS', 'GridRCNN', 'MaskScoringRCNN', 'RepPointsDetector',
-    'FOVEA', 'FSAF', 'NASFCOS', 'PointRend', 'GFL', 'CornerNet', 'PAA',
-    'YOLOV3', 'YOLACT', 'VFNet', 'DETR', 'TridentFasterRCNN', 'SparseRCNN',
-    'SCNet'
-]

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/cityscapes.py

@@ -1,54 +0,0 @@
-# dataset settings
-dataset_type = 'CityscapesDataset'
-data_root = 'data/cityscapes/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-crop_size = (512, 1024)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(2048, 1024),
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='leftImg8bit/train',
-        ann_dir='gtFine/train',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='leftImg8bit/val',
-        ann_dir='gtFine/val',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='leftImg8bit/val',
-        ann_dir='gtFine/val',
-        pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/default_runtime.py

@@ -1,14 +0,0 @@
-# yapf:disable
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook', by_epoch=False),
-        # dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-cudnn_benchmark = True

spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './ccnet_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py

@@ -1,12 +0,0 @@
-_base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py'
-model = dict(
-    pretrained='mmcls://mobilenet_v2',
-    backbone=dict(
-        _delete_=True,
-        type='MobileNetV2',
-        widen_factor=1.,
-        strides=(1, 2, 2, 1, 1, 1, 1),
-        dilations=(1, 1, 1, 2, 2, 4, 4),
-        out_indices=(1, 2, 4, 6)),
-    decode_head=dict(in_channels=320),
-    auxiliary_head=dict(in_channels=96))

spaces/AppleQAQ/anime-remove-background/README.md

@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,44 +0,0 @@
-from .package_data import __version__
-from .core import (
-    IDNABidiError,
-    IDNAError,
-    InvalidCodepoint,
-    InvalidCodepointContext,
-    alabel,
-    check_bidi,
-    check_hyphen_ok,
-    check_initial_combiner,
-    check_label,
-    check_nfc,
-    decode,
-    encode,
-    ulabel,
-    uts46_remap,
-    valid_contextj,
-    valid_contexto,
-    valid_label_length,
-    valid_string_length,
-)
-from .intranges import intranges_contain
-
-__all__ = [
-    "IDNABidiError",
-    "IDNAError",
-    "InvalidCodepoint",
-    "InvalidCodepointContext",
-    "alabel",
-    "check_bidi",
-    "check_hyphen_ok",
-    "check_initial_combiner",
-    "check_label",
-    "check_nfc",
-    "decode",
-    "encode",
-    "intranges_contain",
-    "ulabel",
-    "uts46_remap",
-    "valid_contextj",
-    "valid_contexto",
-    "valid_label_length",
-    "valid_string_length",
-]

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/_common.py

@@ -1,104 +0,0 @@
-import os
-import pathlib
-import tempfile
-import functools
-import contextlib
-import types
-import importlib
-
-from typing import Union, Optional
-from .abc import ResourceReader, Traversable
-
-from ._compat import wrap_spec
-
-Package = Union[types.ModuleType, str]
-
-
-def files(package):
-    # type: (Package) -> Traversable
-    """
-    Get a Traversable resource from a package
-    """
-    return from_package(get_package(package))
-
-
-def get_resource_reader(package):
-    # type: (types.ModuleType) -> Optional[ResourceReader]
-    """
-    Return the package's loader if it's a ResourceReader.
-    """
-    # We can't use
-    # a issubclass() check here because apparently abc.'s __subclasscheck__()
-    # hook wants to create a weak reference to the object, but
-    # zipimport.zipimporter does not support weak references, resulting in a
-    # TypeError.  That seems terrible.
-    spec = package.__spec__
-    reader = getattr(spec.loader, 'get_resource_reader', None)  # type: ignore
-    if reader is None:
-        return None
-    return reader(spec.name)  # type: ignore
-
-
-def resolve(cand):
-    # type: (Package) -> types.ModuleType
-    return cand if isinstance(cand, types.ModuleType) else importlib.import_module(cand)
-
-
-def get_package(package):
-    # type: (Package) -> types.ModuleType
-    """Take a package name or module object and return the module.
-
-    Raise an exception if the resolved module is not a package.
-    """
-    resolved = resolve(package)
-    if wrap_spec(resolved).submodule_search_locations is None:
-        raise TypeError(f'{package!r} is not a package')
-    return resolved
-
-
-def from_package(package):
-    """
-    Return a Traversable object for the given package.
-
-    """
-    spec = wrap_spec(package)
-    reader = spec.loader.get_resource_reader(spec.name)
-    return reader.files()
-
-
-@contextlib.contextmanager
-def _tempfile(reader, suffix=''):
-    # Not using tempfile.NamedTemporaryFile as it leads to deeper 'try'
-    # blocks due to the need to close the temporary file to work on Windows
-    # properly.
-    fd, raw_path = tempfile.mkstemp(suffix=suffix)
-    try:
-        try:
-            os.write(fd, reader())
-        finally:
-            os.close(fd)
-        del reader
-        yield pathlib.Path(raw_path)
-    finally:
-        try:
-            os.remove(raw_path)
-        except FileNotFoundError:
-            pass
-
-
-@functools.singledispatch
-def as_file(path):
-    """
-    Given a Traversable object, return that object as a
-    path on the local file system in a context manager.
-    """
-    return _tempfile(path.read_bytes, suffix=path.name)
-
-
-@as_file.register(pathlib.Path)
-@contextlib.contextmanager
-def _(path):
-    """
-    Degenerate behavior for pathlib.Path objects.
-    """
-    yield path

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py

@@ -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

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_fast_rcnn.py

@@ -1,171 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.box_regression import Box2BoxTransform, Box2BoxTransformRotated
-from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
-from detectron2.modeling.roi_heads.rotated_fast_rcnn import RotatedFastRCNNOutputLayers
-from detectron2.structures import Boxes, Instances, RotatedBoxes
-from detectron2.utils.events import EventStorage
-
-logger = logging.getLogger(__name__)
-
-
-class FastRCNNTest(unittest.TestCase):
-    def test_fast_rcnn(self):
-        torch.manual_seed(132)
-
-        box_head_output_size = 8
-
-        box_predictor = FastRCNNOutputLayers(
-            ShapeSpec(channels=box_head_output_size),
-            box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
-            num_classes=5,
-        )
-        feature_pooled = torch.rand(2, box_head_output_size)
-        predictions = box_predictor(feature_pooled)
-
-        proposal_boxes = torch.tensor([[0.8, 1.1, 3.2, 2.8], [2.3, 2.5, 7, 8]], dtype=torch.float32)
-        gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
-        proposal = Instances((10, 10))
-        proposal.proposal_boxes = Boxes(proposal_boxes)
-        proposal.gt_boxes = Boxes(gt_boxes)
-        proposal.gt_classes = torch.tensor([1, 2])
-
-        with EventStorage():  # capture events in a new storage to discard them
-            losses = box_predictor.losses(predictions, [proposal])
-
-        expected_losses = {
-            "loss_cls": torch.tensor(1.7951188087),
-            "loss_box_reg": torch.tensor(4.0357131958),
-        }
-        for name in expected_losses.keys():
-            assert torch.allclose(losses[name], expected_losses[name])
-
-    def test_fast_rcnn_empty_batch(self, device="cpu"):
-        box_predictor = FastRCNNOutputLayers(
-            ShapeSpec(channels=10),
-            box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
-            num_classes=8,
-        ).to(device=device)
-
-        logits = torch.randn(0, 100, requires_grad=True, device=device)
-        deltas = torch.randn(0, 4, requires_grad=True, device=device)
-        losses = box_predictor.losses([logits, deltas], [])
-        for value in losses.values():
-            self.assertTrue(torch.allclose(value, torch.zeros_like(value)))
-        sum(losses.values()).backward()
-        self.assertTrue(logits.grad is not None)
-        self.assertTrue(deltas.grad is not None)
-
-        predictions, _ = box_predictor.inference([logits, deltas], [])
-        self.assertEqual(len(predictions), 0)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_fast_rcnn_empty_batch_cuda(self):
-        self.test_fast_rcnn_empty_batch(device=torch.device("cuda"))
-
-    def test_fast_rcnn_rotated(self):
-        torch.manual_seed(132)
-        box_head_output_size = 8
-
-        box_predictor = RotatedFastRCNNOutputLayers(
-            ShapeSpec(channels=box_head_output_size),
-            box2box_transform=Box2BoxTransformRotated(weights=(10, 10, 5, 5, 1)),
-            num_classes=5,
-        )
-        feature_pooled = torch.rand(2, box_head_output_size)
-        predictions = box_predictor(feature_pooled)
-        proposal_boxes = torch.tensor(
-            [[2, 1.95, 2.4, 1.7, 0], [4.65, 5.25, 4.7, 5.5, 0]], dtype=torch.float32
-        )
-        gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32)
-        proposal = Instances((10, 10))
-        proposal.proposal_boxes = RotatedBoxes(proposal_boxes)
-        proposal.gt_boxes = RotatedBoxes(gt_boxes)
-        proposal.gt_classes = torch.tensor([1, 2])
-
-        with EventStorage():  # capture events in a new storage to discard them
-            losses = box_predictor.losses(predictions, [proposal])
-
-        # Note: the expected losses are slightly different even if
-        # the boxes are essentially the same as in the FastRCNNOutput test, because
-        # bbox_pred in FastRCNNOutputLayers have different Linear layers/initialization
-        # between the two cases.
-        expected_losses = {
-            "loss_cls": torch.tensor(1.7920907736),
-            "loss_box_reg": torch.tensor(4.0410838127),
-        }
-        for name in expected_losses.keys():
-            assert torch.allclose(losses[name], expected_losses[name])
-
-    def test_predict_boxes_tracing(self):
-        class Model(torch.nn.Module):
-            def __init__(self, output_layer):
-                super(Model, self).__init__()
-                self._output_layer = output_layer
-
-            def forward(self, proposal_deltas, proposal_boxes):
-                instances = Instances((10, 10))
-                instances.proposal_boxes = Boxes(proposal_boxes)
-                return self._output_layer.predict_boxes((None, proposal_deltas), [instances])
-
-        box_head_output_size = 8
-
-        box_predictor = FastRCNNOutputLayers(
-            ShapeSpec(channels=box_head_output_size),
-            box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
-            num_classes=5,
-        )
-
-        model = Model(box_predictor)
-
-        from detectron2.export.torchscript_patch import patch_builtin_len
-
-        with torch.no_grad(), patch_builtin_len():
-            func = torch.jit.trace(model, (torch.randn(10, 20), torch.randn(10, 4)))
-
-            o = func(torch.randn(10, 20), torch.randn(10, 4))
-            self.assertEqual(o[0].shape, (10, 20))
-            o = func(torch.randn(5, 20), torch.randn(5, 4))
-            self.assertEqual(o[0].shape, (5, 20))
-            o = func(torch.randn(20, 20), torch.randn(20, 4))
-            self.assertEqual(o[0].shape, (20, 20))
-
-    def test_predict_probs_tracing(self):
-        class Model(torch.nn.Module):
-            def __init__(self, output_layer):
-                super(Model, self).__init__()
-                self._output_layer = output_layer
-
-            def forward(self, scores, proposal_boxes):
-                instances = Instances((10, 10))
-                instances.proposal_boxes = Boxes(proposal_boxes)
-                return self._output_layer.predict_probs((scores, None), [instances])
-
-        box_head_output_size = 8
-
-        box_predictor = FastRCNNOutputLayers(
-            ShapeSpec(channels=box_head_output_size),
-            box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
-            num_classes=5,
-        )
-
-        model = Model(box_predictor)
-
-        from detectron2.export.torchscript_patch import patch_builtin_len
-
-        with torch.no_grad(), patch_builtin_len():
-            func = torch.jit.trace(model, (torch.randn(10, 6), torch.rand(10, 4)))
-            o = func(torch.randn(10, 6), torch.randn(10, 4))
-            self.assertEqual(o[0].shape, (10, 6))
-            o = func(torch.randn(5, 6), torch.randn(5, 4))
-            self.assertEqual(o[0].shape, (5, 6))
-            o = func(torch.randn(20, 6), torch.randn(20, 4))
-            self.assertEqual(o[0].shape, (20, 6))
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/BL00DY-257/dolle-mini-lol/index.html

@@ -1,295 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta
-      name="viewport"
-      content="width=device-width, initial-scale=1, shrink-to-fit=no, maximum-scale=1"
-    />
-
-    <script>
-      window.__gradio_mode__ = "app";
-      window.gradio_config = {
-        version: "3.0.26\n",
-        mode: "blocks",
-        dev_mode: false,
-        components: [
-          {
-            id: 1,
-            type: "column",
-            props: {
-              type: "column",
-              variant: "default",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 2,
-            type: "markdown",
-            props: {
-              value:
-                '<h1><center>D0LL\u00b7E mini by Quinty Cat',
-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 3,
-            type: "markdown",
-            props: {
-              value:
-                "<center>Funni AI model that isn't working! /j</center>",
-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 4,
-            type: "group",
-            props: { type: "group", visible: true, style: {} },
-          },
-          {
-            id: 5,
-            type: "box",
-            props: { type: "box", visible: true, style: {} },
-          },
-          {
-            id: 6,
-            type: "row",
-            props: {
-              type: "row",
-              visible: true,
-              style: { equal_height: true, mobile_collapse: false },
-            },
-          },
-          {
-            id: 7,
-            type: "textbox",
-            props: {
-              lines: 1,
-              max_lines: 1,
-              value: "",
-              label: "Enter your prompt",
-              show_label: false,
-              name: "textbox",
-              visible: true,
-              elem_id: "prompt",
-              style: { container: false },
-            },
-          },
-          {
-            id: 8,
-            type: "button",
-            props: {
-              value: "Run",
-              variant: "primary",
-              name: "button",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 9,
-            type: "gallery",
-            props: {
-              value: [],
-              label: "Generated images",
-              show_label: false,
-              name: "gallery",
-              visible: true,
-              elem_id: "gallery",
-              style: { grid: [3], height: "auto" },
-            },
-          },
-          {
-            id: 10,
-            type: "column",
-            props: {
-              type: "column",
-              variant: "default",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 11,
-            type: "button",
-            props: {
-              value: "Screenshot",
-              variant: "secondary",
-              name: "button",
-              visible: true,
-              elem_id: "screenshot",
-              style: { full_width: true },
-            },
-          },
-          {
-            id: 12,
-            type: "markdown",
-            props: {
-              value:
-                '<details>\n<summary>Bias and Limitations</summary>\n<p style=\'line-height: normal; font-size: small\'>\nWhile the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. While the extent and nature of the biases of the DALL\u00b7E mini model have yet to be fully documented, given the fact that the model was trained on unfiltered data from the Internet, it may generate images that contain stereotypes against minority groups. Work to analyze the nature and extent of these limitations is ongoing, and will be documented in more detail in the <a href="https://huggingface.co/dalle-mini/dalle-mini" target="_blank">DALL\u00b7E mini model card</a>.\n</p>\n</details>',
-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 13,
-            type: "markdown",
-            props: {
-              value:
-                '<p style=\'text-align: center\'>\nThis is so funni, you can insert the prompt "Numberblock" if you want.</p>',
-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 14,
-            type: "markdown",
-            props: {
-              value:
-                '<hr />\n<p style=\'text-align: center\'>\nNot created by Boris Dayma, Created by 257 et al. 2021-2023\n<br/>\n<a href="https://github.com/borisdayma/dalle-mini" target="_blank">GitHub</a> | <a href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy" target="_blank">Project Report</a>\n<p style=\'text-align: center\'>Powered by Google <a href="https://sites.research.google/trc/" target="_blank">TPU Research Cloud</a>\n</p>',
-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-        ],
-        theme: "default",
-        css: ".container { max-width: 800px; margin: auto; }",
-        title: "Gradio",
-        enable_queue: false,
-        layout: {
-          id: 0,
-          children: [
-            {
-              id: 1,
-              children: [
-                { id: 2 },
-                { id: 3 },
-                {
-                  id: 4,
-                  children: [
-                    {
-                      id: 5,
-                      children: [{ id: 6, children: [{ id: 7 }, { id: 8 }] }],
-                    },
-                    { id: 9 },
-                  ],
-                },
-              ],
-            },
-            {
-              id: 10,
-              children: [{ id: 11 }, { id: 12 }, { id: 13 }, { id: 14 }],
-            },
-          ],
-        },
-        dependencies: [
-          {
-            targets: [8],
-            trigger: "click",
-            inputs: [7],
-            outputs: [9],
-            backend_fn: false,
-            js: "\n        async (text) => {\n            try {\n                document.querySelector('#screenshot').style.display = 'none';\n                response = await fetch('https://bf.dallemini.ai/generate', {\n                    method: 'POST',\n                    headers: {\n                        'Accept': 'application/json',\n                        'Content-Type': 'application/json'\n                    },\n                    body: JSON.stringify({\n                        prompt: text\n                    })\n                });\n                response = await response.json()\n                let imgs = response.images.map(r => \"data:image/png;base64,\" + r)\n                document.querySelector('#screenshot').style.display = 'block';\n                return imgs\n            } catch (e) {\n                alert(\"Too much traffic, please try again.\")\n                IMG = \"data:image/png;base64,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\"\n                document.querySelector('#screenshot').style.display = 'block';\n                return Array(9).fill(IMG)\n            }\n        }\n        ",
-            status_tracker: null,
-            queue: null,
-            api_name: null,
-            scroll_to_output: false,
-            show_progress: true,
-          },
-          {
-            targets: [11],
-            trigger: "click",
-            inputs: [],
-            outputs: [],
-            backend_fn: false,
-            js: "\n            () => {\n                const captureElement = document.getElementById(1)\n                let bg_color = getComputedStyle(document.querySelector(\"#root .container\"))[\"background-color\"]\n                captureElement.style.backgroundColor = bg_color;     \n                html2canvas(captureElement)\n                .then(canvas => {\n                    canvas.style.display = 'none'\n                    document.body.appendChild(canvas)\n                    return canvas\n                })\n                .then(canvas => {\n                    const image = canvas.toDataURL('image/png').replace('image/png', 'image/octet-stream')\n                    const a = document.createElement('a')\n                    const date = new Date()\n                    const filename = `dallemini_${date.getFullYear()}-${date.getMonth() + 1}-${date.getDate()}_${date.getHours()}-${date.getMinutes()}-${date.getSeconds()}.png`\n                    a.setAttribute('download', filename)\n                    a.setAttribute('href', image)\n                    a.click()\n                    canvas.remove()\n                })\n            }\n        ",
-            status_tracker: null,
-            queue: null,
-            api_name: null,
-            scroll_to_output: false,
-            show_progress: true,
-          },
-        ],
-      };
-    </script>
-
-    <link rel="preconnect" href="https://fonts.googleapis.com" />
-    <link
-      rel="preconnect"
-      href="https://fonts.gstatic.com"
-      crossorigin="anonymous"
-    />
-    <link
-      href="https://fonts.googleapis.com/css?family=Source Sans Pro"
-      rel="stylesheet"
-    />
-    <link
-      href="https://fonts.googleapis.com/css?family=IBM Plex Mono"
-      rel="stylesheet"
-    />
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-    <script
-      type="module"
-      crossorigin
-      src="https://gradio.s3-us-west-2.amazonaws.com/3.0.9b12/assets/index.8eca4ae7.js"
-    ></script>
-    <link
-      rel="stylesheet"
-      href="https://gradio.s3-us-west-2.amazonaws.com/3.0.9b12/assets/index.cbea297d.css"
-    />
-    <style>
-      #screenshot {
-        display: none;
-      }
-      .container > div > div {
-        padding: 0.5rem;
-      }
-      footer a {
-        color: rgb(156 163 175) !important;
-      }
-      footer img {
-        display: none !important;
-      }
-    </style>
-    <style id="mofo">
-      body {
-        display: none !important;
-      }
-    </style>
-    <script type="text/javascript">
-      if (
-        self === top ||
-        window.location.ancestorOrigins[0] === "https://huggingface.co"
-      ) {
-        var mofo = document.getElementById("mofo");
-        mofo.parentNode.removeChild(mofo);
-      } else {
-        top.location = self.location;
-      }
-    </script>
-  </head>
-
-  <body
-    style="
-      margin: 0;
-      padding: 0;
-      display: flex;
-      flex-direction: column;
-      flex-grow: 1;
-    "
-  >
-    <div
-      id="root"
-      style="display: flex; flex-direction: column; flex-grow: 1"
-    ></div>
-    <script src="html2canvas.js"></script>
-  </body>
-</html>

spaces/BatuhanYilmaz/Youtube-Transcriber/app.py

@@ -1,168 +0,0 @@
-import whisper
-from pytube import YouTube
-import requests, io
-from urllib.request import urlopen
-from PIL import Image
-import time
-import streamlit as st
-from streamlit_lottie import st_lottie
-import numpy as np
-import os
-from typing import Iterator
-from io import StringIO
-from utils import write_vtt, write_srt
-
-st.set_page_config(page_title="YouTube Transcriber", page_icon="🗣", layout="wide")
-
-# Define a function that we can use to load lottie files from a link.
-@st.cache(allow_output_mutation=True)
-def load_lottieurl(url: str):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-col1, col2 = st.columns([1, 3])
-with col1:
-    lottie = load_lottieurl("https://assets9.lottiefiles.com/private_files/lf30_bntlaz7t.json")
-    st_lottie(lottie, speed=1, height=200, width=200)
-
-with col2:
-    st.write("""
-    ## Youtube Transcriber 
-    ##### This is an app that transcribes YouTube videos into text.""")
-
-
-#def load_model(size):
-    #default_size = size
-    #if size == default_size:
-        #return None
-    #else:
-        #loaded_model = whisper.load_model(size)
-        #return loaded_model 
-    
-
-@st.cache(allow_output_mutation=True)
-def populate_metadata(link):
-    yt = YouTube(link)
-    author = yt.author
-    title = yt.title
-    description = yt.description
-    thumbnail = yt.thumbnail_url
-    length = yt.length
-    views = yt.views
-    return author, title, description, thumbnail, length, views
-
-# Uncomment if you want to fetch the thumbnails as well.
-#def fetch_thumbnail(thumbnail):
-    #tnail = urlopen(thumbnail)
-    #raw_data = tnail.read()
-    #image = Image.open(io.BytesIO(raw_data))
-    #st.image(image, use_column_width=True)
-
-
-def convert(seconds):
-    return time.strftime("%H:%M:%S", time.gmtime(seconds))
-
-
-loaded_model = whisper.load_model("base")
-current_size = "None"
-size = st.selectbox("Model Size", ["tiny", "base", "small", "medium", "large"], index=1)
-
-
-def change_model(current_size, size):
-    if current_size != size:
-        loaded_model = whisper.load_model(size)
-        st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
-        f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
-        return loaded_model
-    else:
-        return None
-
-
-@st.cache(allow_output_mutation=True)
-def inference(link):
-    yt = YouTube(link)
-    path = yt.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
-    results = loaded_model.transcribe(path)
-    vtt = getSubs(results["segments"], "vtt", 80)
-    srt = getSubs(results["segments"], "srt", 80)
-    return results["text"], vtt, srt
-
-def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
-    segmentStream = StringIO()
-
-    if format == 'vtt':
-        write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    elif format == 'srt':
-        write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    else:
-        raise Exception("Unknown format " + format)
-
-    segmentStream.seek(0)
-    return segmentStream.read()
-
-
-def main():
-    change_model(current_size, size)
-    link = st.text_input("YouTube Link")
-    if st.button("Transcribe"):
-        author, title, description, thumbnail, length, views = populate_metadata(link)
-        results = inference(link)
-            
-        col3, col4 = st.columns(2)
-        with col3:
-            #fetch_thumbnail(thumbnail)
-            st.video(link)
-            st.markdown(f"**Channel**: {author}")
-            st.markdown(f"**Title**: {title}")
-            st.markdown(f"**Length**: {convert(length)}")
-            st.markdown(f"**Views**: {views:,}")
-
-        with col4:
-            with st.expander("Video Description"):
-                st.write(description)
-            #st.markdown(f"**Video Description**: {description}")
-            with st.expander("Video Transcript"):
-                st.write(results[0])
-            # Write the results to a .txt file and download it.
-            with open("transcript.txt", "w+") as f:
-                f.writelines(results[0])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
-                datatxt = f.read()
-            
-
-            with open("transcript.vtt", "w+") as f:
-                f.writelines(results[1])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
-                datavtt = f.read()
-            
-            with open("transcript.srt", "w+") as f:
-                f.writelines(results[2])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
-                datasrt = f.read()
-            
-            if st.download_button(label="Download Transcript (.txt) ",
-                                data=datatxt,
-                                file_name=f"{title}.txt"):
-                st.success("Downloaded Successfully!")
-            
-            elif st.download_button(label="Download Transcript (.vtt)",
-                                data=datavtt,
-                                file_name=f"{title}.vtt"):
-                st.success("Downloaded Successfully!")
-
-            elif st.download_button(label="Download Transcript (.srt)",
-                                data=datasrt,
-                                file_name=f"{title}.srt"):
-                st.success("Downloaded Successfully! ")
-            else:
-                st.success("You can download the transcript in .srt format and upload it to YouTube to create subtitles for your video.")
-                st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
-        
-
-if __name__ == "__main__":
-    main()

spaces/Benson/text-generation/Examples/Android_6_8_9_10.apk Download.md

@@ -1,96 +0,0 @@
-<br />
-<h1>Cómo descargar e instalar android_6_8_9_10.apk en su dispositivo Android</h1>
-<p>Si usted está buscando una manera de descargar e instalar android_6_8_9_10.apk en su dispositivo Android, es posible que se pregunte qué es y por qué lo necesita. En este artículo, explicaremos qué es un archivo APK, cómo descargarlo e instalarlo en su dispositivo Android, cómo verificar la versión de su dispositivo Android, los riesgos de descargar archivos APK y cómo proteger su dispositivo Android de las amenazas APK. </p>
-<h2>android_6_8_9_10.apk download</h2><br /><p><b><b>Download</b> &#10022;&#10022;&#10022; <a href="https://bltlly.com/2v6KJ9">https://bltlly.com/2v6KJ9</a></b></p><br /><br />
- <h2>¿Qué es un archivo APK y por qué quieres descargar uno? </h2>
-<p>Un archivo APK (formato de archivo de Android Package Kit) es el formato de archivo utilizado por el sistema operativo Android para la distribución e instalación de aplicaciones móviles, juegos móviles y middleware.  Un archivo APK contiene todos los datos que una aplicación necesita, incluyendo todo el código del programa de software, activos, recursos, certificados y archivo de manifiesto. </p>
-<p>Es posible que desee descargar un archivo APK por varias razones, tales como:</p>
-<ul>
-<li> Desea usar una aplicación que no está disponible en Google Play o que se ha eliminado de ella. </li>
-<li> Desea utilizar una versión más antigua o más nueva de una aplicación que no es compatible con su dispositivo o tiene errores o características que no le gustan. </li>
-<li> Desea probar versiones beta o no publicadas de aplicaciones que aún no se han publicado oficialmente. </li>
-<li>Desea personalizar o modificar su dispositivo con aplicaciones que requieren acceso root o permisos especiales. </li>
-</ul>
- <h2>Cómo descargar archivos APK en Android</h2>
-<p>Hay diferentes maneras de descargar archivos APK en Android. Puede descargarlos de varios sitios web que los alojan, como APK Mirror , o puede transferirlos desde su computadora utilizando un cable USB. Sin embargo, antes de que pueda instalar cualquier archivo APK en su dispositivo Android, debe habilitar fuentes desconocidas en su configuración. Esto le permite instalar aplicaciones desde fuentes distintas de Google Play. Para hacer esto:</p>
-<ol>
-
-<li>Toque los tres puntos en la esquina superior derecha. </li>
-<li>Toque Acceso especial. </li>
-<li>Toca Instalar aplicaciones desconocidas. </li>
-<li>Pulse Chrome (o cualquier navegador web que utilice) y mueva Permitir desde esta fuente a la posición On. </li>
-</ol>
-<p>Una vez que haya habilitado fuentes desconocidas, puede descargar e instalar archivos APK de diferentes fuentes:</p>
- <h3>Desde Google Play</h3>
-<p>Si la aplicación que desea descargar está disponible en Google Play, simplemente puede ir a la aplicación Play Store en su dispositivo, buscar el nombre de la aplicación, y pulse Instalar. Esta es la forma más fácil y segura de descargar archivos APK en Android.</p>
-<p></p>
- <h3>Desde APK Mirror</h3>
-<p>Si la aplicación que desea descargar no está disponible en Google Play o se ha eliminado de ella, puede intentar descargarlo de APK Mirror , un sitio web de buena reputación que alberga archivos APK para muchas aplicaciones populares. Para hacer esto:</p>
-<ol>
-<li>Abra su navegador web y vaya a https://www.apkmirror.com/.</li>
-<li>Buscar el nombre de la aplicación o navegar por categoría. </li>
-<li>Seleccione la versión de la aplicación que desea descargar y toque Descargar APK.</li>
-<li>Acepta cualquier ventana emergente o permisos que aparezcan. </li>
-<li>Una vez completada la descarga, toque Abrir.</li>
-<li>Toca Instalar y espera a que la instalación termine. </li>
-<li>Toque Listo o Abierto para iniciar la aplicación. </li>
-</ol>
- <h3>Desde tu ordenador</h3>
-<p>Si tienes el archivo APK en tu computadora, puedes transferirlo a tu dispositivo Android usando un cable USB. Para hacer esto:</p>
-<ol>
-<li>Conecte su dispositivo Android a su computadora usando un cable USB. </li>
-<li>En tu dispositivo, toca Permitir o Aceptar para conceder acceso a tus archivos. </li>
-<li>En su computadora, abra la carpeta donde tiene el archivo APK y arrástrelo y suéltelo al almacenamiento de su dispositivo. </li>
-<li>En su dispositivo, utilice una aplicación de administrador de archivos (como Archivos de Google ) para localizar el archivo APK y toque en él. </li>
-<li>Toca Instalar y espera a que la instalación termine. </li>
-<li>Toque Listo o Abierto para iniciar la aplicación. </li>
-</ol>
-
-<p>Si desea descargar un archivo APK que sea compatible con su dispositivo Android, necesita saber qué versión de Android está ejecutando. Diferentes versiones de Android tienen diferentes características, actualizaciones de seguridad y compatibilidad con aplicaciones. Para comprobar la versión de su dispositivo Android:</p>
-<ol>
-<li>Ir a la configuración del dispositivo y toque Acerca del teléfono (o Acerca del dispositivo en versiones anteriores de Android). </li>
-<li>Pulse Información de software (o Actualización del sistema en versiones anteriores de Android). </li>
-<li>Busque el número de versión de Android y el nivel de parche de seguridad. </li>
-</ol>
-<p>La última versión de Android a partir de junio de 2023 es Android 12 , que tiene nuevas características como el diseño de Material You, panel de privacidad, hibernación de aplicaciones y más. Si tiene una versión anterior de Android, es posible que desee actualizarlo para obtener las últimas características y parches de seguridad. Para actualizar tu dispositivo Android:</p>
-<ol>
-<li>Ir a la configuración del dispositivo y toque Acerca del teléfono (o Acerca del dispositivo en versiones anteriores de Android). </li>
-<li>Pulse Actualización de software (o Actualización de sistema en versiones anteriores de Android). </li>
-<li>Pulse Buscar actualizaciones (o Descargar e instalar en las versiones más recientes de Android). </li>
-<li>Si hay una actualización disponible, toque Descargar e instalar (o Instalar ahora en versiones anteriores de Android). </li>
-<li>Espere a que la actualización se descargue e instale. Su dispositivo puede reiniciarse durante el proceso. </li>
-</ol>
- <h2>Los riesgos de descargar archivos APK</h2>
-<p>Si bien la descarga de archivos APK puede ser útil para acceder a aplicaciones que no están disponibles en Google Play o el uso de diferentes versiones de aplicaciones, también viene con algunos riesgos. Los archivos APK de fuentes desconocidas o no confiables pueden contener malware, phishing, secuestro de SIM u otro código malicioso que puede dañar su dispositivo o comprometer sus datos personales.  Algunas de las posibles consecuencias de descargar archivos APK de fuentes inseguras son:</p>
-<ul>
-
-<li>Sus datos personales pueden estar expuestos a hackers, estafadores o ladrones de identidad que pueden acceder a sus contactos, mensajes, fotos, videos, contraseñas, cuentas bancarias, tarjetas de crédito u otra información confidencial. </li>
-<li>Su número de teléfono puede ser secuestrado por estafadores de intercambio de SIM que pueden usarlo para evitar la autenticación de dos factores, acceder a sus cuentas en línea, realizar transacciones fraudulentas o hacerse pasar por usted. </li>
-</ul>
- <h2>Cómo proteger su dispositivo Android de las amenazas APK</h2>
-<p>Para evitar o mitigar los riesgos de descargar archivos APK de fuentes desconocidas o no confiables, debe seguir algunas de las mejores prácticas , como:</p>
- <h3>Habilitar fuentes desconocidas solo cuando sea necesario</h3>
-<p>Solo debes habilitar fuentes desconocidas en tu configuración cuando necesites instalar un archivo APK desde una fuente distinta de Google Play. Después de haber instalado el archivo APK, debe desactivar fuentes desconocidas de nuevo. Esto evitará que cualquier aplicación no deseada o maliciosa se instale en su dispositivo sin su permiso. </p>
- <h3>Usar una aplicación de administrador de archivos</h3>
-<p>Debes usar una aplicación de administrador de archivos (como Archivos de Google ) para localizar y administrar los archivos APK en tu dispositivo. Una aplicación de administrador de archivos puede ayudarlo a encontrar los archivos APK que ha descargado o transferido desde su computadora, eliminar cualquier archivo APK no deseado o sospechoso y organizar sus archivos por nombre, tamaño, fecha o tipo. </p>
- <h3>Buscar virus</h3>
-<p>Debe escanear cualquier archivo APK que descargue o instale con una aplicación antivirus de buena reputación (como Avast Mobile Security ) antes de abrirlo. Una aplicación antivirus puede detectar y eliminar cualquier malware o código malicioso que pueda estar oculto en el archivo APK. Una aplicación antivirus también puede proteger su dispositivo de otras amenazas, como phishing, spyware, ransomware, etc.</p>
- <h3>Compruebe los permisos de la aplicación</h3>
-
- <h3>Leer los comentarios y valoraciones</h3>
-<p>Deberías leer las reseñas y valoraciones de cualquier archivo APK que descargues o instales desde un sitio web que los aloja, como APK Mirror . Las reseñas y valoraciones pueden darte una idea de la calidad, el rendimiento y la fiabilidad de la aplicación. También pueden alertar sobre cualquier problema, problema o queja que otros usuarios hayan experimentado con la aplicación. Debes evitar descargar o instalar cualquier archivo APK que tenga calificaciones bajas, comentarios negativos o ninguna retroalimentación en absoluto. </p>
- <h2>Conclusión</h2>
-<p>Descargar e instalar archivos APK en tu dispositivo Android puede ser una forma útil de acceder a aplicaciones que no están disponibles en Google Play o utilizar diferentes versiones de aplicaciones. Sin embargo, también viene con algunos riesgos que pueden dañar su dispositivo o comprometer sus datos personales. Para descargar e instalar archivos APK de forma segura, debe seguir algunas de las mejores prácticas , como habilitar fuentes desconocidas solo cuando sea necesario, usar una aplicación de administrador de archivos, buscar virus, verificar los permisos de la aplicación y leer las revisiones y calificaciones. Al hacerlo, puede disfrutar de los beneficios de los archivos APK sin exponerse a peligros innecesarios. </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Qué es android_6_8_9_10.apk? </h3>
-<p>Android_6_8_9_10.apk es un archivo APK que contiene una aplicación que es compatible con dispositivos Android que ejecutan las versiones 6, 8, 9 o 10 del sistema operativo Android. El nombre y la funcionalidad de la aplicación pueden variar dependiendo del origen del archivo APK. </p>
- <h3>¿Cómo puedo descargar android_6_8_10.apk? </h3>
-<p>Puede descargar android_6_8_9_10.apk desde varios sitios web que alojan archivos APK, como APK Mirror , o puede transferirlo desde su ordenador utilizando un cable USB. Sin embargo, antes de que pueda instalarlo en su dispositivo Android, debe habilitar fuentes desconocidas en su configuración. </p>
- <h3>¿Cómo puedo instalar android_6_8_9_10.apk? </h3>
-
- <h3> ¿Es android_6_8_9_10.apk seguro? </h3>
-<p>Android_6_8_9_10.apk puede o no ser seguro dependiendo de la fuente del archivo APK. Los archivos APK de fuentes desconocidas o no confiables pueden contener malware o código malicioso que puede dañar su dispositivo o comprometer sus datos personales. Solo debe descargar e instalar archivos APK de fuentes confiables y confiables, como Google Play o APK Mirror . También debe escanear cualquier archivo APK con una aplicación antivirus antes de abrirlo. </p>
- <h3>¿Cómo puedo actualizar android_6_8_10.apk? </h3>
-<p>Si hay una versión más reciente de android_6_8_9_10.apk disponible, puede actualizarlo descargándolo e instalándolo desde la misma fuente que antes. Es posible que tenga que desinstalar la versión anterior de la aplicación antes de instalar la nueva. Alternativamente, puedes actualizar tu dispositivo Android a una versión más nueva de Android que admita la última versión de la aplicación. </p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,49 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-from contextlib import contextmanager
-from io import StringIO
-import sys
-import os
-
-
-class StreamTTY(StringIO):
-    def isatty(self):
-        return True
-
-class StreamNonTTY(StringIO):
-    def isatty(self):
-        return False
-
-@contextmanager
-def osname(name):
-    orig = os.name
-    os.name = name
-    yield
-    os.name = orig
-
-@contextmanager
-def replace_by(stream):
-    orig_stdout = sys.stdout
-    orig_stderr = sys.stderr
-    sys.stdout = stream
-    sys.stderr = stream
-    yield
-    sys.stdout = orig_stdout
-    sys.stderr = orig_stderr
-
-@contextmanager
-def replace_original_by(stream):
-    orig_stdout = sys.__stdout__
-    orig_stderr = sys.__stderr__
-    sys.__stdout__ = stream
-    sys.__stderr__ = stream
-    yield
-    sys.__stdout__ = orig_stdout
-    sys.__stderr__ = orig_stderr
-
-@contextmanager
-def pycharm():
-    os.environ["PYCHARM_HOSTED"] = "1"
-    non_tty = StreamNonTTY()
-    with replace_by(non_tty), replace_original_by(non_tty):
-        yield
-    del os.environ["PYCHARM_HOSTED"]

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/results.py

@@ -1,760 +0,0 @@
-# results.py
-from collections.abc import MutableMapping, Mapping, MutableSequence, Iterator
-import pprint
-from weakref import ref as wkref
-from typing import Tuple, Any
-
-str_type: Tuple[type, ...] = (str, bytes)
-_generator_type = type((_ for _ in ()))
-
-
-class _ParseResultsWithOffset:
-    __slots__ = ["tup"]
-
-    def __init__(self, p1, p2):
-        self.tup = (p1, p2)
-
-    def __getitem__(self, i):
-        return self.tup[i]
-
-    def __getstate__(self):
-        return self.tup
-
-    def __setstate__(self, *args):
-        self.tup = args[0]
-
-
-class ParseResults:
-    """Structured parse results, to provide multiple means of access to
-    the parsed data:
-
-    - as a list (``len(results)``)
-    - by list index (``results[0], results[1]``, etc.)
-    - by attribute (``results.<results_name>`` - see :class:`ParserElement.set_results_name`)
-
-    Example::
-
-        integer = Word(nums)
-        date_str = (integer.set_results_name("year") + '/'
-                    + integer.set_results_name("month") + '/'
-                    + integer.set_results_name("day"))
-        # equivalent form:
-        # date_str = (integer("year") + '/'
-        #             + integer("month") + '/'
-        #             + integer("day"))
-
-        # parse_string returns a ParseResults object
-        result = date_str.parse_string("1999/12/31")
-
-        def test(s, fn=repr):
-            print("{} -> {}".format(s, fn(eval(s))))
-        test("list(result)")
-        test("result[0]")
-        test("result['month']")
-        test("result.day")
-        test("'month' in result")
-        test("'minutes' in result")
-        test("result.dump()", str)
-
-    prints::
-
-        list(result) -> ['1999', '/', '12', '/', '31']
-        result[0] -> '1999'
-        result['month'] -> '12'
-        result.day -> '31'
-        'month' in result -> True
-        'minutes' in result -> False
-        result.dump() -> ['1999', '/', '12', '/', '31']
-        - day: '31'
-        - month: '12'
-        - year: '1999'
-    """
-
-    _null_values: Tuple[Any, ...] = (None, [], "", ())
-
-    __slots__ = [
-        "_name",
-        "_parent",
-        "_all_names",
-        "_modal",
-        "_toklist",
-        "_tokdict",
-        "__weakref__",
-    ]
-
-    class List(list):
-        """
-        Simple wrapper class to distinguish parsed list results that should be preserved
-        as actual Python lists, instead of being converted to :class:`ParseResults`:
-
-            LBRACK, RBRACK = map(pp.Suppress, "[]")
-            element = pp.Forward()
-            item = ppc.integer
-            element_list = LBRACK + pp.delimited_list(element) + RBRACK
-
-            # add parse actions to convert from ParseResults to actual Python collection types
-            def as_python_list(t):
-                return pp.ParseResults.List(t.as_list())
-            element_list.add_parse_action(as_python_list)
-
-            element <<= item | element_list
-
-            element.run_tests('''
-                100
-                [2,3,4]
-                [[2, 1],3,4]
-                [(2, 1),3,4]
-                (2,3,4)
-                ''', post_parse=lambda s, r: (r[0], type(r[0])))
-
-        prints:
-
-            100
-            (100, <class 'int'>)
-
-            [2,3,4]
-            ([2, 3, 4], <class 'list'>)
-
-            [[2, 1],3,4]
-            ([[2, 1], 3, 4], <class 'list'>)
-
-        (Used internally by :class:`Group` when `aslist=True`.)
-        """
-
-        def __new__(cls, contained=None):
-            if contained is None:
-                contained = []
-
-            if not isinstance(contained, list):
-                raise TypeError(
-                    "{} may only be constructed with a list,"
-                    " not {}".format(cls.__name__, type(contained).__name__)
-                )
-
-            return list.__new__(cls)
-
-    def __new__(cls, toklist=None, name=None, **kwargs):
-        if isinstance(toklist, ParseResults):
-            return toklist
-        self = object.__new__(cls)
-        self._name = None
-        self._parent = None
-        self._all_names = set()
-
-        if toklist is None:
-            self._toklist = []
-        elif isinstance(toklist, (list, _generator_type)):
-            self._toklist = (
-                [toklist[:]]
-                if isinstance(toklist, ParseResults.List)
-                else list(toklist)
-            )
-        else:
-            self._toklist = [toklist]
-        self._tokdict = dict()
-        return self
-
-    # Performance tuning: we construct a *lot* of these, so keep this
-    # constructor as small and fast as possible
-    def __init__(
-        self, toklist=None, name=None, asList=True, modal=True, isinstance=isinstance
-    ):
-        self._modal = modal
-        if name is not None and name != "":
-            if isinstance(name, int):
-                name = str(name)
-            if not modal:
-                self._all_names = {name}
-            self._name = name
-            if toklist not in self._null_values:
-                if isinstance(toklist, (str_type, type)):
-                    toklist = [toklist]
-                if asList:
-                    if isinstance(toklist, ParseResults):
-                        self[name] = _ParseResultsWithOffset(
-                            ParseResults(toklist._toklist), 0
-                        )
-                    else:
-                        self[name] = _ParseResultsWithOffset(
-                            ParseResults(toklist[0]), 0
-                        )
-                    self[name]._name = name
-                else:
-                    try:
-                        self[name] = toklist[0]
-                    except (KeyError, TypeError, IndexError):
-                        if toklist is not self:
-                            self[name] = toklist
-                        else:
-                            self._name = name
-
-    def __getitem__(self, i):
-        if isinstance(i, (int, slice)):
-            return self._toklist[i]
-        else:
-            if i not in self._all_names:
-                return self._tokdict[i][-1][0]
-            else:
-                return ParseResults([v[0] for v in self._tokdict[i]])
-
-    def __setitem__(self, k, v, isinstance=isinstance):
-        if isinstance(v, _ParseResultsWithOffset):
-            self._tokdict[k] = self._tokdict.get(k, list()) + [v]
-            sub = v[0]
-        elif isinstance(k, (int, slice)):
-            self._toklist[k] = v
-            sub = v
-        else:
-            self._tokdict[k] = self._tokdict.get(k, list()) + [
-                _ParseResultsWithOffset(v, 0)
-            ]
-            sub = v
-        if isinstance(sub, ParseResults):
-            sub._parent = wkref(self)
-
-    def __delitem__(self, i):
-        if isinstance(i, (int, slice)):
-            mylen = len(self._toklist)
-            del self._toklist[i]
-
-            # convert int to slice
-            if isinstance(i, int):
-                if i < 0:
-                    i += mylen
-                i = slice(i, i + 1)
-            # get removed indices
-            removed = list(range(*i.indices(mylen)))
-            removed.reverse()
-            # fixup indices in token dictionary
-            for name, occurrences in self._tokdict.items():
-                for j in removed:
-                    for k, (value, position) in enumerate(occurrences):
-                        occurrences[k] = _ParseResultsWithOffset(
-                            value, position - (position > j)
-                        )
-        else:
-            del self._tokdict[i]
-
-    def __contains__(self, k) -> bool:
-        return k in self._tokdict
-
-    def __len__(self) -> int:
-        return len(self._toklist)
-
-    def __bool__(self) -> bool:
-        return not not (self._toklist or self._tokdict)
-
-    def __iter__(self) -> Iterator:
-        return iter(self._toklist)
-
-    def __reversed__(self) -> Iterator:
-        return iter(self._toklist[::-1])
-
-    def keys(self):
-        return iter(self._tokdict)
-
-    def values(self):
-        return (self[k] for k in self.keys())
-
-    def items(self):
-        return ((k, self[k]) for k in self.keys())
-
-    def haskeys(self) -> bool:
-        """
-        Since ``keys()`` returns an iterator, this method is helpful in bypassing
-        code that looks for the existence of any defined results names."""
-        return bool(self._tokdict)
-
-    def pop(self, *args, **kwargs):
-        """
-        Removes and returns item at specified index (default= ``last``).
-        Supports both ``list`` and ``dict`` semantics for ``pop()``. If
-        passed no argument or an integer argument, it will use ``list``
-        semantics and pop tokens from the list of parsed tokens. If passed
-        a non-integer argument (most likely a string), it will use ``dict``
-        semantics and pop the corresponding value from any defined results
-        names. A second default return value argument is supported, just as in
-        ``dict.pop()``.
-
-        Example::
-
-            numlist = Word(nums)[...]
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321']
-
-            def remove_first(tokens):
-                tokens.pop(0)
-            numlist.add_parse_action(remove_first)
-            print(numlist.parse_string("0 123 321")) # -> ['123', '321']
-
-            label = Word(alphas)
-            patt = label("LABEL") + Word(nums)[1, ...]
-            print(patt.parse_string("AAB 123 321").dump())
-
-            # Use pop() in a parse action to remove named result (note that corresponding value is not
-            # removed from list form of results)
-            def remove_LABEL(tokens):
-                tokens.pop("LABEL")
-                return tokens
-            patt.add_parse_action(remove_LABEL)
-            print(patt.parse_string("AAB 123 321").dump())
-
-        prints::
-
-            ['AAB', '123', '321']
-            - LABEL: 'AAB'
-
-            ['AAB', '123', '321']
-        """
-        if not args:
-            args = [-1]
-        for k, v in kwargs.items():
-            if k == "default":
-                args = (args[0], v)
-            else:
-                raise TypeError(
-                    "pop() got an unexpected keyword argument {!r}".format(k)
-                )
-        if isinstance(args[0], int) or len(args) == 1 or args[0] in self:
-            index = args[0]
-            ret = self[index]
-            del self[index]
-            return ret
-        else:
-            defaultvalue = args[1]
-            return defaultvalue
-
-    def get(self, key, default_value=None):
-        """
-        Returns named result matching the given key, or if there is no
-        such name, then returns the given ``default_value`` or ``None`` if no
-        ``default_value`` is specified.
-
-        Similar to ``dict.get()``.
-
-        Example::
-
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
-            result = date_str.parse_string("1999/12/31")
-            print(result.get("year")) # -> '1999'
-            print(result.get("hour", "not specified")) # -> 'not specified'
-            print(result.get("hour")) # -> None
-        """
-        if key in self:
-            return self[key]
-        else:
-            return default_value
-
-    def insert(self, index, ins_string):
-        """
-        Inserts new element at location index in the list of parsed tokens.
-
-        Similar to ``list.insert()``.
-
-        Example::
-
-            numlist = Word(nums)[...]
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321']
-
-            # use a parse action to insert the parse location in the front of the parsed results
-            def insert_locn(locn, tokens):
-                tokens.insert(0, locn)
-            numlist.add_parse_action(insert_locn)
-            print(numlist.parse_string("0 123 321")) # -> [0, '0', '123', '321']
-        """
-        self._toklist.insert(index, ins_string)
-        # fixup indices in token dictionary
-        for name, occurrences in self._tokdict.items():
-            for k, (value, position) in enumerate(occurrences):
-                occurrences[k] = _ParseResultsWithOffset(
-                    value, position + (position > index)
-                )
-
-    def append(self, item):
-        """
-        Add single element to end of ``ParseResults`` list of elements.
-
-        Example::
-
-            numlist = Word(nums)[...]
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321']
-
-            # use a parse action to compute the sum of the parsed integers, and add it to the end
-            def append_sum(tokens):
-                tokens.append(sum(map(int, tokens)))
-            numlist.add_parse_action(append_sum)
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321', 444]
-        """
-        self._toklist.append(item)
-
-    def extend(self, itemseq):
-        """
-        Add sequence of elements to end of ``ParseResults`` list of elements.
-
-        Example::
-
-            patt = Word(alphas)[1, ...]
-
-            # use a parse action to append the reverse of the matched strings, to make a palindrome
-            def make_palindrome(tokens):
-                tokens.extend(reversed([t[::-1] for t in tokens]))
-                return ''.join(tokens)
-            patt.add_parse_action(make_palindrome)
-            print(patt.parse_string("lskdj sdlkjf lksd")) # -> 'lskdjsdlkjflksddsklfjkldsjdksl'
-        """
-        if isinstance(itemseq, ParseResults):
-            self.__iadd__(itemseq)
-        else:
-            self._toklist.extend(itemseq)
-
-    def clear(self):
-        """
-        Clear all elements and results names.
-        """
-        del self._toklist[:]
-        self._tokdict.clear()
-
-    def __getattr__(self, name):
-        try:
-            return self[name]
-        except KeyError:
-            if name.startswith("__"):
-                raise AttributeError(name)
-            return ""
-
-    def __add__(self, other) -> "ParseResults":
-        ret = self.copy()
-        ret += other
-        return ret
-
-    def __iadd__(self, other) -> "ParseResults":
-        if other._tokdict:
-            offset = len(self._toklist)
-            addoffset = lambda a: offset if a < 0 else a + offset
-            otheritems = other._tokdict.items()
-            otherdictitems = [
-                (k, _ParseResultsWithOffset(v[0], addoffset(v[1])))
-                for k, vlist in otheritems
-                for v in vlist
-            ]
-            for k, v in otherdictitems:
-                self[k] = v
-                if isinstance(v[0], ParseResults):
-                    v[0]._parent = wkref(self)
-
-        self._toklist += other._toklist
-        self._all_names |= other._all_names
-        return self
-
-    def __radd__(self, other) -> "ParseResults":
-        if isinstance(other, int) and other == 0:
-            # useful for merging many ParseResults using sum() builtin
-            return self.copy()
-        else:
-            # this may raise a TypeError - so be it
-            return other + self
-
-    def __repr__(self) -> str:
-        return "{}({!r}, {})".format(type(self).__name__, self._toklist, self.as_dict())
-
-    def __str__(self) -> str:
-        return (
-            "["
-            + ", ".join(
-                [
-                    str(i) if isinstance(i, ParseResults) else repr(i)
-                    for i in self._toklist
-                ]
-            )
-            + "]"
-        )
-
-    def _asStringList(self, sep=""):
-        out = []
-        for item in self._toklist:
-            if out and sep:
-                out.append(sep)
-            if isinstance(item, ParseResults):
-                out += item._asStringList()
-            else:
-                out.append(str(item))
-        return out
-
-    def as_list(self) -> list:
-        """
-        Returns the parse results as a nested list of matching tokens, all converted to strings.
-
-        Example::
-
-            patt = Word(alphas)[1, ...]
-            result = patt.parse_string("sldkj lsdkj sldkj")
-            # even though the result prints in string-like form, it is actually a pyparsing ParseResults
-            print(type(result), result) # -> <class 'pyparsing.ParseResults'> ['sldkj', 'lsdkj', 'sldkj']
-
-            # Use as_list() to create an actual list
-            result_list = result.as_list()
-            print(type(result_list), result_list) # -> <class 'list'> ['sldkj', 'lsdkj', 'sldkj']
-        """
-        return [
-            res.as_list() if isinstance(res, ParseResults) else res
-            for res in self._toklist
-        ]
-
-    def as_dict(self) -> dict:
-        """
-        Returns the named parse results as a nested dictionary.
-
-        Example::
-
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
-            result = date_str.parse_string('12/31/1999')
-            print(type(result), repr(result)) # -> <class 'pyparsing.ParseResults'> (['12', '/', '31', '/', '1999'], {'day': [('1999', 4)], 'year': [('12', 0)], 'month': [('31', 2)]})
-
-            result_dict = result.as_dict()
-            print(type(result_dict), repr(result_dict)) # -> <class 'dict'> {'day': '1999', 'year': '12', 'month': '31'}
-
-            # even though a ParseResults supports dict-like access, sometime you just need to have a dict
-            import json
-            print(json.dumps(result)) # -> Exception: TypeError: ... is not JSON serializable
-            print(json.dumps(result.as_dict())) # -> {"month": "31", "day": "1999", "year": "12"}
-        """
-
-        def to_item(obj):
-            if isinstance(obj, ParseResults):
-                return obj.as_dict() if obj.haskeys() else [to_item(v) for v in obj]
-            else:
-                return obj
-
-        return dict((k, to_item(v)) for k, v in self.items())
-
-    def copy(self) -> "ParseResults":
-        """
-        Returns a new copy of a :class:`ParseResults` object.
-        """
-        ret = ParseResults(self._toklist)
-        ret._tokdict = self._tokdict.copy()
-        ret._parent = self._parent
-        ret._all_names |= self._all_names
-        ret._name = self._name
-        return ret
-
-    def get_name(self):
-        r"""
-        Returns the results name for this token expression. Useful when several
-        different expressions might match at a particular location.
-
-        Example::
-
-            integer = Word(nums)
-            ssn_expr = Regex(r"\d\d\d-\d\d-\d\d\d\d")
-            house_number_expr = Suppress('#') + Word(nums, alphanums)
-            user_data = (Group(house_number_expr)("house_number")
-                        | Group(ssn_expr)("ssn")
-                        | Group(integer)("age"))
-            user_info = user_data[1, ...]
-
-            result = user_info.parse_string("22 111-22-3333 #221B")
-            for item in result:
-                print(item.get_name(), ':', item[0])
-
-        prints::
-
-            age : 22
-            ssn : 111-22-3333
-            house_number : 221B
-        """
-        if self._name:
-            return self._name
-        elif self._parent:
-            par = self._parent()
-
-            def find_in_parent(sub):
-                return next(
-                    (
-                        k
-                        for k, vlist in par._tokdict.items()
-                        for v, loc in vlist
-                        if sub is v
-                    ),
-                    None,
-                )
-
-            return find_in_parent(self) if par else None
-        elif (
-            len(self) == 1
-            and len(self._tokdict) == 1
-            and next(iter(self._tokdict.values()))[0][1] in (0, -1)
-        ):
-            return next(iter(self._tokdict.keys()))
-        else:
-            return None
-
-    def dump(self, indent="", full=True, include_list=True, _depth=0) -> str:
-        """
-        Diagnostic method for listing out the contents of
-        a :class:`ParseResults`. Accepts an optional ``indent`` argument so
-        that this string can be embedded in a nested display of other data.
-
-        Example::
-
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
-            result = date_str.parse_string('1999/12/31')
-            print(result.dump())
-
-        prints::
-
-            ['1999', '/', '12', '/', '31']
-            - day: '31'
-            - month: '12'
-            - year: '1999'
-        """
-        out = []
-        NL = "\n"
-        out.append(indent + str(self.as_list()) if include_list else "")
-
-        if full:
-            if self.haskeys():
-                items = sorted((str(k), v) for k, v in self.items())
-                for k, v in items:
-                    if out:
-                        out.append(NL)
-                    out.append("{}{}- {}: ".format(indent, ("  " * _depth), k))
-                    if isinstance(v, ParseResults):
-                        if v:
-                            out.append(
-                                v.dump(
-                                    indent=indent,
-                                    full=full,
-                                    include_list=include_list,
-                                    _depth=_depth + 1,
-                                )
-                            )
-                        else:
-                            out.append(str(v))
-                    else:
-                        out.append(repr(v))
-            if any(isinstance(vv, ParseResults) for vv in self):
-                v = self
-                for i, vv in enumerate(v):
-                    if isinstance(vv, ParseResults):
-                        out.append(
-                            "\n{}{}[{}]:\n{}{}{}".format(
-                                indent,
-                                ("  " * (_depth)),
-                                i,
-                                indent,
-                                ("  " * (_depth + 1)),
-                                vv.dump(
-                                    indent=indent,
-                                    full=full,
-                                    include_list=include_list,
-                                    _depth=_depth + 1,
-                                ),
-                            )
-                        )
-                    else:
-                        out.append(
-                            "\n%s%s[%d]:\n%s%s%s"
-                            % (
-                                indent,
-                                ("  " * (_depth)),
-                                i,
-                                indent,
-                                ("  " * (_depth + 1)),
-                                str(vv),
-                            )
-                        )
-
-        return "".join(out)
-
-    def pprint(self, *args, **kwargs):
-        """
-        Pretty-printer for parsed results as a list, using the
-        `pprint <https://docs.python.org/3/library/pprint.html>`_ module.
-        Accepts additional positional or keyword args as defined for
-        `pprint.pprint <https://docs.python.org/3/library/pprint.html#pprint.pprint>`_ .
-
-        Example::
-
-            ident = Word(alphas, alphanums)
-            num = Word(nums)
-            func = Forward()
-            term = ident | num | Group('(' + func + ')')
-            func <<= ident + Group(Optional(delimited_list(term)))
-            result = func.parse_string("fna a,b,(fnb c,d,200),100")
-            result.pprint(width=40)
-
-        prints::
-
-            ['fna',
-             ['a',
-              'b',
-              ['(', 'fnb', ['c', 'd', '200'], ')'],
-              '100']]
-        """
-        pprint.pprint(self.as_list(), *args, **kwargs)
-
-    # add support for pickle protocol
-    def __getstate__(self):
-        return (
-            self._toklist,
-            (
-                self._tokdict.copy(),
-                self._parent is not None and self._parent() or None,
-                self._all_names,
-                self._name,
-            ),
-        )
-
-    def __setstate__(self, state):
-        self._toklist, (self._tokdict, par, inAccumNames, self._name) = state
-        self._all_names = set(inAccumNames)
-        if par is not None:
-            self._parent = wkref(par)
-        else:
-            self._parent = None
-
-    def __getnewargs__(self):
-        return self._toklist, self._name
-
-    def __dir__(self):
-        return dir(type(self)) + list(self.keys())
-
-    @classmethod
-    def from_dict(cls, other, name=None) -> "ParseResults":
-        """
-        Helper classmethod to construct a ``ParseResults`` from a ``dict``, preserving the
-        name-value relations as results names. If an optional ``name`` argument is
-        given, a nested ``ParseResults`` will be returned.
-        """
-
-        def is_iterable(obj):
-            try:
-                iter(obj)
-            except Exception:
-                return False
-            else:
-                return not isinstance(obj, str_type)
-
-        ret = cls([])
-        for k, v in other.items():
-            if isinstance(v, Mapping):
-                ret += cls.from_dict(v, name=k)
-            else:
-                ret += cls([v], name=k, asList=is_iterable(v))
-        if name is not None:
-            ret = cls([ret], name=name)
-        return ret
-
-    asList = as_list
-    asDict = as_dict
-    getName = get_name
-
-
-MutableMapping.register(ParseResults)
-MutableSequence.register(ParseResults)

spaces/Bokanovskii/Image-to-music/README.md

@@ -1,12 +0,0 @@
----
-title: Image To Music
-emoji: 🔥
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/BucketHeadP65/confusion_matrix/README.md

@@ -1,109 +0,0 @@
----
-title: Confusion Matrix
-emoji: 📉
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-tags:
-- evaluate
-- metric
-
-description: >-
- Compute confusion matrix to evaluate the accuracy of a classification. By definition a confusion matrix :math:C is such that :math:C_{i, j} is equal to the number of observations known to be in group :math:i and predicted to be in group :math:j.
- Thus in binary classification, the count of true negatives is :math:C_{0,0}, false negatives is :math:C_{1,0}, true positives is :math:C_{1,1} and false positives is :math:C_{0,1}.
----
-
-# Metric Card for Confusion Matrix
-
-
-## Metric Description
-
-Compute confusion matrix to evaluate the accuracy of a classification.
-By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`
-is equal to the number of observations known to be in group :math:`i` and
-predicted to be in group :math:`j`.
-
-Thus in binary classification, the count of true negatives is
-:math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
-:math:`C_{1,1}` and false positives is :math:`C_{0,1}`.
-
-
-## How to Use
-
-At minimum, this metric requires predictions and references as inputs.
-
-```python
->>> cfm_metric = evaluate.load("BucketHeadP65/confusion_matrix")
->>> results = cfm_metric.compute(references=[1, 2, 3, 2, 1, 1, 0, 2], predictions=[1, 0, 3, 2, 2, 1, 0, 3])
->>> print(results)
-{'confusion_matrix': [[1, 0, 0, 0], [0, 2, 1, 0], [1, 0, 1, 1], [0, 0, 0, 1]]}
-```
-
-
-### Inputs
-- **predictions** (`list` of `int`): Predicted labels.
-- **references** (`list` of `int`): Ground truth labels.
-- **normalize** (`str` or `None`): {`true`, `pred`, `all`}, default=None
-        Normalizes confusion matrix over the true (rows), predicted (columns)
-        conditions or all the population. If None, confusion matrix will not be
-        normalized
-- **sample_weight** (`list` of `float`): Sample weights Defaults to None.
-- **labels** (`list` of `float`): default=None
-        List of labels to index the matrix. This may be used to reorder
-        or select a subset of labels.
-        If ``None`` is given, those that appear at least once
-        in ``y_true`` or ``y_pred`` are used in sorted order.
-
-### Output Values
-- **confusion_matrix**(`list` of `int`): Confusion matrix. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
-Output Example(s):
-```python
-{'confusion_matrix': [[1, 0, 0, 0], [0, 2, 1, 0], [1, 0, 1, 1], [0, 0, 0, 1]]}
-
-```
-This metric outputs a dictionary, containing the confusion matrix.
-
-### Examples
->>> from sklearn.metrics import confusion_matrix
->>> y_true = [2, 0, 2, 2, 0, 1]
->>> y_pred = [0, 0, 2, 2, 0, 2]
->>> confusion_matrix(y_true, y_pred)
-array([[2, 0, 0],
-        [0, 0, 1],
-        [1, 0, 2]])
-
->>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
->>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
->>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
-array([[2, 0, 0],
-        [0, 0, 1],
-        [1, 0, 2]])
-
-In the binary case, we can extract true positives, etc as follows:
-
->>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
->>> (tn, fp, fn, tp)
-(0, 2, 1, 1)
-
-## Citation(s)
-```bibtex
-@article{scikit-learn,
-  title={Scikit-learn: Machine Learning in {P}ython},
-  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
-         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
-         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
-         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
-  journal={Journal of Machine Learning Research},
-  volume={12},
-  pages={2825--2830},
-  year={2011}
-}
-```
-## Further References
-Wikipedia entry for the Confusion matrix
-<https://en.wikipedia.org/wiki/Confusion_matrix>`_
-(Wikipedia and other references may use a different
-convention for axes).

spaces/CVPR/WALT/mmdet/models/dense_heads/embedding_rpn_head.py

@@ -1,100 +0,0 @@
-import torch
-import torch.nn as nn
-
-from mmdet.models.builder import HEADS
-from ...core import bbox_cxcywh_to_xyxy
-
-
-@HEADS.register_module()
-class EmbeddingRPNHead(nn.Module):
-    """RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011.12450>`_ .
-
-    Unlike traditional RPNHead, this module does not need FPN input, but just
-    decode `init_proposal_bboxes` and expand the first dimension of
-    `init_proposal_bboxes` and `init_proposal_features` to the batch_size.
-
-    Args:
-        num_proposals (int): Number of init_proposals. Default 100.
-        proposal_feature_channel (int): Channel number of
-            init_proposal_feature. Defaults to 256.
-    """
-
-    def __init__(self,
-                 num_proposals=100,
-                 proposal_feature_channel=256,
-                 **kwargs):
-        super(EmbeddingRPNHead, self).__init__()
-        self.num_proposals = num_proposals
-        self.proposal_feature_channel = proposal_feature_channel
-        self._init_layers()
-
-    def _init_layers(self):
-        """Initialize a sparse set of proposal boxes and proposal features."""
-        self.init_proposal_bboxes = nn.Embedding(self.num_proposals, 4)
-        self.init_proposal_features = nn.Embedding(
-            self.num_proposals, self.proposal_feature_channel)
-
-    def init_weights(self):
-        """Initialize the init_proposal_bboxes as normalized.
-
-        [c_x, c_y, w, h], and we initialize it to the size of  the entire
-        image.
-        """
-        nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5)
-        nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1)
-
-    def _decode_init_proposals(self, imgs, img_metas):
-        """Decode init_proposal_bboxes according to the size of images and
-        expand dimension of init_proposal_features to batch_size.
-
-        Args:
-            imgs (list[Tensor]): List of FPN features.
-            img_metas (list[dict]): List of meta-information of
-                images. Need the img_shape to decode the init_proposals.
-
-        Returns:
-            Tuple(Tensor):
-
-                - proposals (Tensor): Decoded proposal bboxes,
-                  has shape (batch_size, num_proposals, 4).
-                - init_proposal_features (Tensor): Expanded proposal
-                  features, has shape
-                  (batch_size, num_proposals, proposal_feature_channel).
-                - imgs_whwh (Tensor): Tensor with shape
-                  (batch_size, 4), the dimension means
-                  [img_width, img_height, img_width, img_height].
-        """
-        proposals = self.init_proposal_bboxes.weight.clone()
-        proposals = bbox_cxcywh_to_xyxy(proposals)
-        num_imgs = len(imgs[0])
-        imgs_whwh = []
-        for meta in img_metas:
-            h, w, _ = meta['img_shape']
-            imgs_whwh.append(imgs[0].new_tensor([[w, h, w, h]]))
-        imgs_whwh = torch.cat(imgs_whwh, dim=0)
-        imgs_whwh = imgs_whwh[:, None, :]
-
-        # imgs_whwh has shape (batch_size, 1, 4)
-        # The shape of proposals change from (num_proposals, 4)
-        # to (batch_size ,num_proposals, 4)
-        proposals = proposals * imgs_whwh
-
-        init_proposal_features = self.init_proposal_features.weight.clone()
-        init_proposal_features = init_proposal_features[None].expand(
-            num_imgs, *init_proposal_features.size())
-        return proposals, init_proposal_features, imgs_whwh
-
-    def forward_dummy(self, img, img_metas):
-        """Dummy forward function.
-
-        Used in flops calculation.
-        """
-        return self._decode_init_proposals(img, img_metas)
-
-    def forward_train(self, img, img_metas):
-        """Forward function in training stage."""
-        return self._decode_init_proposals(img, img_metas)
-
-    def simple_test_rpn(self, img, img_metas):
-        """Forward function in testing stage."""
-        return self._decode_init_proposals(img, img_metas)

spaces/CVPR/WALT/mmdet/models/dense_heads/retina_sepbn_head.py

@@ -1,113 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-
-from ..builder import HEADS
-from .anchor_head import AnchorHead
-
-
-@HEADS.register_module()
-class RetinaSepBNHead(AnchorHead):
-    """"RetinaHead with separate BN.
-
-    In RetinaHead, conv/norm layers are shared across different FPN levels,
-    while in RetinaSepBNHead, conv layers are shared across different FPN
-    levels, but BN layers are separated.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 num_ins,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 **kwargs):
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.num_ins = num_ins
-        super(RetinaSepBNHead, self).__init__(num_classes, in_channels,
-                                              **kwargs)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.num_ins):
-            cls_convs = nn.ModuleList()
-            reg_convs = nn.ModuleList()
-            for i in range(self.stacked_convs):
-                chn = self.in_channels if i == 0 else self.feat_channels
-                cls_convs.append(
-                    ConvModule(
-                        chn,
-                        self.feat_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg))
-                reg_convs.append(
-                    ConvModule(
-                        chn,
-                        self.feat_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg))
-            self.cls_convs.append(cls_convs)
-            self.reg_convs.append(reg_convs)
-        for i in range(self.stacked_convs):
-            for j in range(1, self.num_ins):
-                self.cls_convs[j][i].conv = self.cls_convs[0][i].conv
-                self.reg_convs[j][i].conv = self.reg_convs[0][i].conv
-        self.retina_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.retina_reg = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 4, 3, padding=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs[0]:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs[0]:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.retina_cls, std=0.01, bias=bias_cls)
-        normal_init(self.retina_reg, std=0.01)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification scores for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * 4.
-        """
-        cls_scores = []
-        bbox_preds = []
-        for i, x in enumerate(feats):
-            cls_feat = feats[i]
-            reg_feat = feats[i]
-            for cls_conv in self.cls_convs[i]:
-                cls_feat = cls_conv(cls_feat)
-            for reg_conv in self.reg_convs[i]:
-                reg_feat = reg_conv(reg_feat)
-            cls_score = self.retina_cls(cls_feat)
-            bbox_pred = self.retina_reg(reg_feat)
-            cls_scores.append(cls_score)
-            bbox_preds.append(bbox_pred)
-        return cls_scores, bbox_preds