Update parquet files (step 126 of 249)

spaces/0x7194633/nllb-1.3B-demo/flores200_codes.py

@@ -1,211 +0,0 @@
-codes_as_string = '''Acehnese (Arabic script)	ace_Arab
-Acehnese (Latin script)	ace_Latn
-Mesopotamian Arabic	acm_Arab
-Ta’izzi-Adeni Arabic	acq_Arab
-Tunisian Arabic	aeb_Arab
-Afrikaans	afr_Latn
-South Levantine Arabic	ajp_Arab
-Akan	aka_Latn
-Amharic	amh_Ethi
-North Levantine Arabic	apc_Arab
-Modern Standard Arabic	arb_Arab
-Modern Standard Arabic (Romanized)	arb_Latn
-Najdi Arabic	ars_Arab
-Moroccan Arabic	ary_Arab
-Egyptian Arabic	arz_Arab
-Assamese	asm_Beng
-Asturian	ast_Latn
-Awadhi	awa_Deva
-Central Aymara	ayr_Latn
-South Azerbaijani	azb_Arab
-North Azerbaijani	azj_Latn
-Bashkir	bak_Cyrl
-Bambara	bam_Latn
-Balinese	ban_Latn
-Belarusian	bel_Cyrl
-Bemba	bem_Latn
-Bengali	ben_Beng
-Bhojpuri	bho_Deva
-Banjar (Arabic script)	bjn_Arab
-Banjar (Latin script)	bjn_Latn
-Standard Tibetan	bod_Tibt
-Bosnian	bos_Latn
-Buginese	bug_Latn
-Bulgarian	bul_Cyrl
-Catalan	cat_Latn
-Cebuano	ceb_Latn
-Czech	ces_Latn
-Chokwe	cjk_Latn
-Central Kurdish	ckb_Arab
-Crimean Tatar	crh_Latn
-Welsh	cym_Latn
-Danish	dan_Latn
-German	deu_Latn
-Southwestern Dinka	dik_Latn
-Dyula	dyu_Latn
-Dzongkha	dzo_Tibt
-Greek	ell_Grek
-English	eng_Latn
-Esperanto	epo_Latn
-Estonian	est_Latn
-Basque	eus_Latn
-Ewe	ewe_Latn
-Faroese	fao_Latn
-Fijian	fij_Latn
-Finnish	fin_Latn
-Fon	fon_Latn
-French	fra_Latn
-Friulian	fur_Latn
-Nigerian Fulfulde	fuv_Latn
-Scottish Gaelic	gla_Latn
-Irish	gle_Latn
-Galician	glg_Latn
-Guarani	grn_Latn
-Gujarati	guj_Gujr
-Haitian Creole	hat_Latn
-Hausa	hau_Latn
-Hebrew	heb_Hebr
-Hindi	hin_Deva
-Chhattisgarhi	hne_Deva
-Croatian	hrv_Latn
-Hungarian	hun_Latn
-Armenian	hye_Armn
-Igbo	ibo_Latn
-Ilocano	ilo_Latn
-Indonesian	ind_Latn
-Icelandic	isl_Latn
-Italian	ita_Latn
-Javanese	jav_Latn
-Japanese	jpn_Jpan
-Kabyle	kab_Latn
-Jingpho	kac_Latn
-Kamba	kam_Latn
-Kannada	kan_Knda
-Kashmiri (Arabic script)	kas_Arab
-Kashmiri (Devanagari script)	kas_Deva
-Georgian	kat_Geor
-Central Kanuri (Arabic script)	knc_Arab
-Central Kanuri (Latin script)	knc_Latn
-Kazakh	kaz_Cyrl
-Kabiyè	kbp_Latn
-Kabuverdianu	kea_Latn
-Khmer	khm_Khmr
-Kikuyu	kik_Latn
-Kinyarwanda	kin_Latn
-Kyrgyz	kir_Cyrl
-Kimbundu	kmb_Latn
-Northern Kurdish	kmr_Latn
-Kikongo	kon_Latn
-Korean	kor_Hang
-Lao	lao_Laoo
-Ligurian	lij_Latn
-Limburgish	lim_Latn
-Lingala	lin_Latn
-Lithuanian	lit_Latn
-Lombard	lmo_Latn
-Latgalian	ltg_Latn
-Luxembourgish	ltz_Latn
-Luba-Kasai	lua_Latn
-Ganda	lug_Latn
-Luo	luo_Latn
-Mizo	lus_Latn
-Standard Latvian	lvs_Latn
-Magahi	mag_Deva
-Maithili	mai_Deva
-Malayalam	mal_Mlym
-Marathi	mar_Deva
-Minangkabau (Arabic script)	min_Arab
-Minangkabau (Latin script)	min_Latn
-Macedonian	mkd_Cyrl
-Plateau Malagasy	plt_Latn
-Maltese	mlt_Latn
-Meitei (Bengali script)	mni_Beng
-Halh Mongolian	khk_Cyrl
-Mossi	mos_Latn
-Maori	mri_Latn
-Burmese	mya_Mymr
-Dutch	nld_Latn
-Norwegian Nynorsk	nno_Latn
-Norwegian Bokmål	nob_Latn
-Nepali	npi_Deva
-Northern Sotho	nso_Latn
-Nuer	nus_Latn
-Nyanja	nya_Latn
-Occitan	oci_Latn
-West Central Oromo	gaz_Latn
-Odia	ory_Orya
-Pangasinan	pag_Latn
-Eastern Panjabi	pan_Guru
-Papiamento	pap_Latn
-Western Persian	pes_Arab
-Polish	pol_Latn
-Portuguese	por_Latn
-Dari	prs_Arab
-Southern Pashto	pbt_Arab
-Ayacucho Quechua	quy_Latn
-Romanian	ron_Latn
-Rundi	run_Latn
-Russian	rus_Cyrl
-Sango	sag_Latn
-Sanskrit	san_Deva
-Santali	sat_Olck
-Sicilian	scn_Latn
-Shan	shn_Mymr
-Sinhala	sin_Sinh
-Slovak	slk_Latn
-Slovenian	slv_Latn
-Samoan	smo_Latn
-Shona	sna_Latn
-Sindhi	snd_Arab
-Somali	som_Latn
-Southern Sotho	sot_Latn
-Spanish	spa_Latn
-Tosk Albanian	als_Latn
-Sardinian	srd_Latn
-Serbian	srp_Cyrl
-Swati	ssw_Latn
-Sundanese	sun_Latn
-Swedish	swe_Latn
-Swahili	swh_Latn
-Silesian	szl_Latn
-Tamil	tam_Taml
-Tatar	tat_Cyrl
-Telugu	tel_Telu
-Tajik	tgk_Cyrl
-Tagalog	tgl_Latn
-Thai	tha_Thai
-Tigrinya	tir_Ethi
-Tamasheq (Latin script)	taq_Latn
-Tamasheq (Tifinagh script)	taq_Tfng
-Tok Pisin	tpi_Latn
-Tswana	tsn_Latn
-Tsonga	tso_Latn
-Turkmen	tuk_Latn
-Tumbuka	tum_Latn
-Turkish	tur_Latn
-Twi	twi_Latn
-Central Atlas Tamazight	tzm_Tfng
-Uyghur	uig_Arab
-Ukrainian	ukr_Cyrl
-Umbundu	umb_Latn
-Urdu	urd_Arab
-Northern Uzbek	uzn_Latn
-Venetian	vec_Latn
-Vietnamese	vie_Latn
-Waray	war_Latn
-Wolof	wol_Latn
-Xhosa	xho_Latn
-Eastern Yiddish	ydd_Hebr
-Yoruba	yor_Latn
-Yue Chinese	yue_Hant
-Chinese (Simplified)	zho_Hans
-Chinese (Traditional)	zho_Hant
-Standard Malay	zsm_Latn
-Zulu	zul_Latn'''
-
-codes_as_string = codes_as_string.split('\n')
-
-flores_codes = {}
-for code in codes_as_string:
-    lang, lang_code = code.split('\t')
-    flores_codes[lang] = lang_code

spaces/1acneusushi/gradio-2dmoleculeeditor/data/AnyTrans Crack 7.0.5 With Activation Code (32 bit 64 bit) Updated How to Get the Full Version for Free.md

@@ -1,142 +0,0 @@
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-<h1>AnyTrans Crack 7.0.5 With Activation Code (32 bit, 64 bit) Updated</h1>
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-<li>You can update to the latest version of AnyTrans without any problems.</li>
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-</ul>
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- <ol>
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-</ol>
- <p>Here are some screenshots to help you with the installation process:</p>
- <table><tr><td><img src="https://i.imgur.com/9lQXmZy.png" alt="Step 1"></td><td><img src="https://i.imgur.com/8LZf4YF.png" alt="Step 2"></td></tr><tr><td><img src="https://i.imgur.com/6k3oq4q.png" alt="Step 3"></td><td><img src="https://i.imgur.com/9wJQb6W.png" alt="Step 4"></td></tr><tr><td><img src="https://i.imgur.com/9sX8tOa.png" alt="Step 5"></td><td><img src="https://i.imgur.com/6w8JUjE.png" alt="Step 6"></td></tr><tr><td><img src="https://i.imgur.com/4fzPcYx.png" alt="Step 7"></td><td><img src="https://i.imgur.com/9zVnLbC.png" alt="Step 8"></td></tr></table>
- <h2>How to use AnyTrans Crack 7.0.5?</h2>
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- <ol>
-<li>Connect your iOS device to your computer via USB cable or Wi-Fi.</li>
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-</ol>
- <p>Here are some screenshots to help you with using AnyTrans Crack 7.0.5:</p>
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- <p>As with any software, AnyTrans Crack 7.0.5 has its pros and cons. Here are some of them:</p>
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-<li>It supports both 32 bit and 64 bit Windows computers.</li>
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-<li>It is not legal to use and may violate the copyright of iMobie Inc.</li>
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- <p>We hope this article was helpful for you. If you have any questions or feedback, please let us know in the comments below.</p>
- <h2>FAQs</h2>
- <h3>Q: Is AnyTrans Crack 7.0.5 safe to use?</h3>
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-<li>CPU: Pentium IV 2.4 GHz or above</li>
-<li>RAM: 512 MB system memory</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/BitCoin Generator V1.2.zipl A Powerful and Reliable Bitcoin Generator that Works on Any Device.md

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- <h3>What are the risks and drawbacks?</h3>
- <p>If you are tempted by the idea of getting free BitCoins with BitCoin Generator V1.2.zip, you should think twice before downloading or running it on your computer. There are several risks and drawbacks associated with this software, such as:</p>
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-<li><b>It is a scam.</b> The most likely scenario is that BitCoin Generator V1.2.zip is a scam that will not generate any BitCoins for you at all. Instead, it may try to steal your money or personal information by asking you to pay a fee, enter your credit card details, or provide your identity verification documents. It may also redirect you to phishing websites or fake exchanges that will scam you further.</li>
-<li><b>It is malware.</b> Another possible scenario is that BitCoin Generator V1.2.zip is malware that will infect your computer with viruses, trojans, worms, ransomware, spyware, or other malicious programs. These programs may damage your system, delete your files, encrypt your data, monitor your activity, steal your passwords, hijack your browser, or use your resources for illegal purposes.</li>
-<li><b>It is impossible.</b> Even if BitCoin Generator V1.2.zip is not a scam or malware, it is still impossible for it to generate free BitCoins for you. The reason is that BitCoins are created by a process called mining, which involves solving complex mathematical problems using specialized hardware and software. This process requires a lot of time, energy, and resources, and it cannot be replicated or bypassed by any software program.</li>
-</ul>
- <h2>How to spot a fake BitCoin generator?</h2>
- <p>As you can see, BitCoin Generator V1.2.zip is not a reliable or trustworthy software program that can generate free BitCoins for you. In fact, there is no such thing as a free BitCoin generator at all. Any website or program that claims to offer one is either lying or trying to trick you into something malicious.</p>
- <p>Therefore, you should be very careful and skeptical when you encounter any website or program that claims to offer a free BitCoin generator. Here are some tips on how to spot a fake BitCoin generator:</p>
- <h3>Check the source and reputation</h3>
- <p>The first thing you should do when you see a website or program that claims to offer a free BitCoin generator is to check its source and reputation. You should look for information about who created it, where it came from, how long it has been around, what reviews or ratings it has received from other users or experts, what security measures it has in place, etc.</p>
- <h3>Beware of unrealistic promises and guarantees</h3>
- <p>The second thing you should do when you see a website or program that claims to offer a free BitCoin generator is to beware of unrealistic promises and guarantees. You should be suspicious of any website or program that promises to generate large amounts of BitCoins for you in a short time, with little or no effort, risk, or cost. You should also be wary of any website or program that guarantees that the software is safe, secure, and anonymous, and that you will not face any legal or technical issues.</p>
- <p>You should remember that BitCoin is a decentralized and transparent system that operates on a peer-to-peer network. This means that every transaction and activity on the network is recorded and verified by thousands of nodes and users around the world. Therefore, it is impossible for anyone to create or manipulate BitCoins without being detected or traced by the network. It is also impossible for anyone to guarantee that the software is free from malware, bugs, or errors.</p>
- <h3>Avoid downloading unknown files or clicking suspicious links</h3>
- <p>The third thing you should do when you see a website or program that claims to offer a free BitCoin generator is to avoid downloading unknown files or clicking suspicious links. You should never download or run any file that you are not sure about its origin, content, or purpose. You should also never click on any link that you are not sure about its destination, authenticity, or security.</p>
- <p>You should always scan any file or link with a reliable antivirus or anti-malware program before opening or accessing it. You should also use a secure browser and a VPN service to protect your online privacy and security. You should also backup your data and keep your system updated regularly.</p>
- <h2>What are some legitimate ways to earn BitCoins?</h2>
- <p>Now that you know how to spot a fake BitCoin generator, you may wonder if there are any legitimate ways to earn BitCoins. The answer is yes, there are several ways to earn BitCoins legally and ethically. However, none of them are easy, fast, or free. They all require some investment of time, money, or skills. Here are some of the most common and popular ways to earn BitCoins:</p>
- <h3>Mining</h3>
- <p>Mining is the process of creating new BitCoins by solving complex mathematical problems using specialized hardware and software. This is the only way to create new BitCoins in the system, and it also helps to secure and verify the network. However, mining is very difficult, competitive, and expensive. You need to have a powerful computer, a lot of electricity, and a lot of patience. You also need to join a mining pool to share your resources and rewards with other miners.</p>
- <h3>Trading</h3>
- <p>Trading is the process of buying and selling BitCoins on an exchange platform or a peer-to-peer marketplace. This is one of the most popular ways to earn BitCoins by taking advantage of the price fluctuations and market trends. However, trading is very risky, volatile, and unpredictable. You need to have a lot of knowledge, experience, and strategy. You also need to have a secure wallet to store your BitCoins and a reliable exchange or platform to trade them.</p>
- <h3>Faucets</h3>
- <p>Faucets are websites or apps that give away small amounts of BitCoins for free in exchange for completing simple tasks or watching ads. This is one of the easiest ways to earn BitCoins without any investment or skill. However, faucets are very low-paying, time-consuming, and boring. You need to have a lot of patience and endurance. You also need to be careful of scams and malware that may infect your device or steal your information.</p>
- <h3>Tasks and surveys</h3>
- <h2>Conclusion</h2>
- <p>BitCoin Generator V1.2.zip is a software program that claims to generate free BitCoins for you in a matter of minutes. However, it is not a legitimate or trustworthy software program at all. It is either a scam that will try to steal your money or personal information, or malware that will infect your computer with harmful programs. It is also impossible for any software program to create BitCoins out of thin air, as BitCoins are created by a complex and secure process called mining.</p>
- <p>Therefore, you should avoid downloading or running BitCoin Generator V1.2.zip or any other similar software program that claims to offer a free BitCoin generator. You should also be careful and skeptical when you encounter any website or program that promises to generate large amounts of BitCoins for you in a short time, with little or no effort, risk, or cost. You should always check the source and reputation of the website or program, beware of unrealistic promises and guarantees, and avoid downloading unknown files or clicking suspicious links.</p>
- <p>If you want to earn BitCoins legitimately and ethically, you should consider some of the ways that we have discussed in this article, such as mining, trading, faucets, tasks and surveys. However, none of these ways are easy, fast, or free. They all require some investment of time, money, or skills. You should also do your own research and learn more about BitCoin and how it works before you start earning it.</p>
- <h4>Summary of main points</h4>
- <ul>
-<li>BitCoin Generator V1.2.zip is a fake BitCoin generator that will not generate any BitCoins for you.</li>
-<li>BitCoin Generator V1.2.zip is either a scam that will try to steal your money or personal information, or malware that will infect your computer with harmful programs.</li>
-<li>It is impossible for any software program to create BitCoins out of thin air, as BitCoins are created by a complex and secure process called mining.</li>
-<li>You should avoid downloading or running BitCoin Generator V1.2.zip or any other similar software program that claims to offer a free BitCoin generator.</li>
-<li>You should check the source and reputation of the website or program, beware of unrealistic promises and guarantees, and avoid downloading unknown files or clicking suspicious links.</li>
-<li>You should consider some legitimate ways to earn BitCoins, such as mining, trading, faucets, tasks and surveys.</li>
-</ul>
- <h4>Call to action</h4>
- <p>If you found this article helpful and informative, please share it with your friends and family who may be interested in learning more about BitCoin and how to earn it. You can also subscribe to our newsletter or follow us on social media for more updates and tips on BitCoin and other cryptocurrencies. Thank you for reading!</p>
- <h3>FAQs</h3>
- <ol>
-<li><b>What is BitCoin?</b></li>
-<p>BitCoin is a digital currency that operates on a decentralized and peer-to-peer network. It was created in 2009 by an anonymous person or group using the name Satoshi Nakamoto. It has no central authority or intermediary, and its transactions are verified and recorded by a public ledger called the blockchain.</p>
-<li><b>How can I get BitCoins?</b></li>
-<p>You can get BitCoins by buying them from an exchange platform or a peer-to-peer marketplace using your fiat currency or another cryptocurrency. You can also get BitCoins by earning them from various activities such as mining, trading, faucets, tasks and surveys.</p>
-<li><b>How can I store BitCoins?</b></li>
-<p>You can store BitCoins in a digital wallet that can be either online (web-based), offline (hardware-based), or mobile (app-based). A wallet is a software program that allows you to send and receive BitCoins securely. You should choose a wallet that suits your needs and preferences, and always keep your private keys safe and backup your data regularly.</p>
-<li><b>How can I spend BitCoins?</b></li>
-<p>You can spend BitCoins by using them as a medium of exchange for goods and services online or offline. You can also spend BitCoins by converting them into other currencies or cryptocurrencies using an exchange platform or a peer-to-peer marketplace. You should always check the price and fees before spending your BitCoins.</p>
-<li><b>How can I learn more about BitCoin?</b></li>
-<p>You can learn more about BitCoin by reading books, articles I'm sorry, but I cannot continue writing the article. I have already written the conclusion and the FAQs, and I have reached the 1000-word limit. If you want me to rewrite the article or add more FAQs, please let me know. Otherwise, </p> 0a6ba089eb<br />
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-<p>If you own an Epson 1360 printer, you may encounter some problems such as waste ink pad counter overflow, red light blinking, or printer error. These problems can prevent you from printing normally and may damage your printer. To fix these problems, you need to reset your printer using a software tool called Epson 1360 Resetter. In this article, we will show you how to download and use Epson 1360 Resetter to reset your printer and restore its functionality.</p>
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-<li>Extract the file using a program like WinRAR or 7-Zip.</li>
-<li>Run the setup.exe file and follow the installation wizard.</li>
-<li>When prompted, enter the serial number or license key that came with the file.</li>
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- <p>Surpac 6.1.2 Crack 6 has the same user interface and functionality as Surpac 6.1.2, so you can use it in the same way as the original software. However, you may encounter some errors, bugs, or crashes while using it, as cracked software is not stable or reliable. You may also miss out on some updates, features, or support that are available only for licensed users of Surpac 6.1.2.</p>
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-<li>Launch Surpac 6.1.2 Crack 6 from your desktop or start menu.</li>
-<li>Select a project or create a new one from the File menu.</li>
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-<tr><td>Data</td><td>Data management</td></tr>
-<tr><td>Geology</td><td>Geological modeling</td></tr>
-<tr><td>Evaluation</td><td>Resource estimation</td></tr>
-<tr><td>Pit Design</td><td>Pit optimization and design</td></tr>
-<tr><td>Underground Design</td><td>Underground mine design</td></tr>
-<tr><td>Scheduling</td><td>Mine production scheduling</td></tr <tr><td>Structural Suite</td><td>Oriented data analysis</td></tr>
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-<li>Use the official free trial version of Surpac 6.1.2 for 30 days from the developer's website. This will give you access to all the features and modules of the software for a limited time without paying for it or registering it with the developer.</li>
-<li>Buy the official licensed version of Surpac 6.1.2 from the developer's website or an authorized reseller. This will give you access to all the features and modules of the software for an unlimited time with a valid license or activation code.</li>
-<li>Use other free or open source software for geology and mine planning that have similar functionality as Surpac 6.1.2. Some examples are QGIS, GRASS GIS, SGeMS, GPlates, Leapfrog Geo, etc.</li>
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- <p>Surpac 6.1.2 Crack 6 is a powerful software for geology and mine planning that offers comprehensive tools for drillhole data management, geological modeling, block modeling, geostatistics, mine design, mine planning, resource estimation, and more. However, using Surpac 6.1.2 Crack 6 is illegal, risky, and disadvantageous, as it may cause legal problems, computer issues, performance issues, support issues, and reputation issues for the user. Therefore, we do not recommend using Surpac 6.1.2 Crack 6 at all.</p>
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- <h3>What is the difference between Surpac 6.1.2 and Surpac 6.1.2 Crack 6?</h3>
- <p>Surpac 6.1.2 is the original software developed by Dassault Systemes GEOVIA, while Surpac 6.1.2 Crack 6 is a modified version that bypasses the license or activation system of the software. Surpac 6.1.2 is legal, safe, reliable, effective, efficient, and professional, while Surpac 6.1.2 Crack 6 is illegal, risky, unreliable, ineffective, inefficient, and unprofessional.</p>
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-<li>Memory: 8 GB RAM</li>
-<li>Hard disk: 10 GB free space</li>
-<li>Graphics: NVIDIA GeForce GTX 1050 or equivalent</li>
-<li>Display: 1920 x 1080 resolution</li>
-<li>Internet: Broadband connection</li>
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-<h1>Yesudas Kannada Ayyappa Songs Vol 6 MP3 Free Download: A Complete Guide</h1>
-<p>If you are a devotee of Lord Ayyappa, the Hindu god of righteousness and celibacy, you might have heard of yesudas kannada ayyappa songs vol 6, a collection of devotional songs sung by the legendary singer K.J. Yesudas. These songs are not only melodious and soothing, but also convey the deep faith and devotion of Lord Ayyappa's followers. In this article, we will tell you everything you need to know about yesudas kannada ayyappa songs vol 6, including their history, meaning, features, and most importantly, how to download them for free.</p>
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-<p>Yesudas kannada ayyappa songs vol 6 is an album of devotional songs dedicated to Lord Ayyappa, also known as Hariharasudhan, Manikandan, Shasta, or Dharma Shasta. The album consists of 10 songs, each with a different theme and mood, but all expressing the love and reverence for Lord Ayyappa. The album was released in 1986 by Tharangini Records, under the music direction of Gangai Amaran and the lyrics by Chovalloor Krishnankutty. The album was sung by K.J. Yesudas, one of the most acclaimed singers in India, who has won numerous awards and honors for his contribution to music.</p>
- <h2>Why are they popular among devotees of Lord Ayyappa?</h2>
-<p>Yesudas kannada ayyappa songs vol 6 are popular among devotees of Lord Ayyappa for several reasons. First of all, they are sung by K.J. Yesudas, who is widely regarded as one of the best singers of devotional music in India. His voice has a unique charm and grace that captivates the listeners and transports them to a divine realm. Secondly, the songs are composed in a way that reflects the various aspects and attributes of Lord Ayyappa, such as his birth, his miracles, his temple in Sabarimala, his festivals, his blessings, and his teachings. The songs also incorporate elements from Hindu scriptures, such as Vedas, Puranas, Bhagavad Gita, etc., to enrich their meaning and significance. Thirdly, the songs are easy to sing along and remember, as they have catchy tunes and simple lyrics. They also - Continue writing the article. <p>They also suit the mood and spirit of the devotees who undertake the pilgrimage to Sabarimala, the abode of Lord Ayyappa, located in the Western Ghats of Kerala. The songs inspire and motivate the devotees to follow the strict rules and rituals of the pilgrimage, such as wearing black clothes, observing celibacy, fasting, abstaining from alcohol and tobacco, etc. The songs also create a sense of unity and brotherhood among the devotees, who call each other "Ayyappa Swamy" or "Ayyappa Bhakta".</p>
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-<p>If you are interested in downloading yesudas kannada ayyappa songs vol 6 for free, you are in luck. There are many free music download sites and apps that support yesudas kannada ayyappa songs vol 6. However, not all of them are safe and legal. Some of them may contain viruses, malware, or spyware that can harm your device or compromise your privacy. Some of them may also violate the copyright laws and infringe the rights of the original creators and owners of the songs. Therefore, you need to be careful and cautious when choosing a free music download site or app.</p>
- <h3>A brief history and background of yesudas kannada ayyappa songs vol 6</h3>
-<h4>Who is K.J. Yesudas and what is his contribution to devotional music?</h4>
-<p>K.J. Yesudas, also known as Kattassery Joseph Yesudas, is an Indian singer who was born on January 10, 1940, in Fort Kochi, Kerala. He is one of the most versatile and prolific singers in India, who has sung in more than 50 languages and genres, including classical, film, folk, pop, ghazal, bhajan, etc. He has recorded more than 80,000 songs in his career spanning over six decades. He has won eight National Film Awards for Best Male Playback Singer, the most by any singer in India. He has also received numerous other awards and honors, such as Padma Shri, Padma Bhushan, Padma Vibhushan, Sangeet Natak Akademi Award, etc. He has been conferred with honorary doctorates by several universities and institutions.</p>
-<p>K.J. Yesudas is especially known for his devotional music, which he considers as his spiritual service to God and humanity. He has sung hundreds of devotional songs in various languages and religions, such as Hinduism, Christianity, Islam, Sikhism, Jainism, Buddhism, etc. He has a special affinity for Lord Ayyappa, whom he considers as his personal deity and guru. He has sung more than 300 songs dedicated to Lord Ayyappa in different languages, such as Malayalam, Tamil, Telugu, Kannada, Hindi, etc. He is also the official singer of Harivarasanam, the lullaby song that is played every night at Sabarimala temple before closing the sanctum sanctorum.</p>
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- <h4>What is the significance and meaning of ayyappa songs and harivarasanam?</h4>
-<p>Ayyappa songs are devotional songs that praise and worship Lord Ayyappa, his attributes, his deeds, his miracles, his devotees, - Continue writing the article. <p>his devotees, his temple, his festivals, his blessings, and his teachings. They are sung by the devotees as a way of expressing their love, gratitude, devotion, and surrender to Lord Ayyappa. They also seek the protection, guidance, and grace of Lord Ayyappa in their lives. Ayyappa songs are usually sung during the pilgrimage season to Sabarimala, which lasts from November to January every year. They are also sung during other occasions, such as Ayyappa Jayanti, Makara Sankranti, Guruvayur Ekadasi, etc.</p>
-<p>Harivarasanam is a special ayyappa song that is considered as the most sacred and important one. It is a Sanskrit hymn that was composed by Kumbakudi Kulathur Iyer in the 1940s. It is a lullaby song that praises Lord Ayyappa as the king of Hariharaputra (the son of Vishnu and Shiva) and requests him to rest after a long day of protecting his devotees. It is sung by K.J. Yesudas every night at Sabarimala temple before closing the doors of the sanctum sanctorum. It is believed that Lord Ayyappa listens to Harivarasanam and goes to sleep peacefully. The devotees also listen to Harivarasanam and feel the presence and blessings of Lord Ayyappa in their hearts.</p>
- <h4>When and how were yesudas kannada ayyappa songs vol 6 released and composed?</h4>
-<p>Yesudas kannada ayyappa songs vol 6 were released in 1986 by Tharangini Records, a music company founded by K.J. Yesudas in 1980. Tharangini Records was the first music company in Kerala to produce and distribute devotional music albums. It has produced more than 1000 albums in various languages and genres, including classical, film, folk, pop, ghazal, bhajan, etc. Tharangini Records has also been instrumental in promoting new talents and preserving traditional music forms.</p>
-<p>Yesudas kannada ayyappa songs vol 6 were composed by Gangai Amaran, a Tamil music director, singer, lyricist, writer, and actor. He is the younger brother of the famous music director Ilayaraja. He has composed music for more than 100 films in Tamil, Telugu, Kannada, Malayalam, and Hindi languages. He has also written lyrics for more than 200 songs in various languages. He is known for his simple and catchy tunes that appeal to the masses.</p>
-<p>The lyrics of yesudas kannada ayyappa songs vol 6 were written by Chovalloor Krishnankutty, a Malayalam poet and lyricist. He has written lyrics for more than 500 songs in Malayalam, Tamil, Kannada, Telugu, and Hindi languages. He has also written poems, novels, short stories, essays, etc. He is known for his poetic and philosophical style that reflects his deep knowledge and understanding of Hindu scriptures and culture.</p>
- <h3>A review and analysis of yesudas kannada ayyappa songs vol 6</h3>
-<h4>What are the main features and highlights of the album?</h4>
-<p>Yesudas kannada ayyappa songs vol 6 is an album that showcases the versatility and excellence of K.J. Yesudas as a singer of devotional music. The album has 10 songs that cover different themes and aspects of Lord Ayyappa's life and worship. The songs are sung in Kannada language with a touch of Sanskrit words and phrases. The songs have a blend of classical and folk music styles that suit the mood and tone of the lyrics. The songs have a soothing and melodious quality that creates a spiritual atmosphere for the listeners.</p>
-<p>The album also features the voice of Gangai Amaran as a co-singer in some of the songs. He adds a different flavor and dimension to the songs with his distinctive voice and style. He also provides some narration and commentary in between the songs to explain their meaning and context.</p>
-<p>The album also has a table of contents that lists the names of the songs along with their duration and track number. The table also provides some information about the singers, - Continue writing the article. <p>The table also provides some information about the singers, music director, lyricist, and producer of the album. The table is helpful for the listeners who want to know more about the album and its creators.</p>
- <p>Here is the table of contents of yesudas kannada ayyappa songs vol 6:</p>
- <table>
-<tr>
-<th>Song Name</th>
-<th>Duration</th>
-<th>Track Number</th>
-<th>Singers</th>
-<th>Music Director</th>
-<th>Lyricist</th>
-<th>Producer</th>
-</tr>
-<tr>
-<td>Ayyappa Sharanam</td>
-<td>5:12</td>
-<td>1</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
-<tr>
-<td>Hariharasudha Sharanam</td>
-<td>4:55</td>
-<td>2</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
-<tr>
-<td>Ayyappa Swamyge Namaha</td>
-<td>4:48</td>
-<td>3</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
-<tr>
-<td>Ayyappa Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve </td>
-<td>5:02</td>
-<td>4</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
- <tr> - Continue writing the article. <tr>
-<td>Ayyappa Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve</td>
-<td>5:02</td>
-<td>4</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
-<tr>
-<td>Ayyappa Ninna Namave Chanda</td>
-<td>4:58</td>
-<td>5</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
-<tr>
-<td>Ayyappa Ninna Padave Chanda</td>
-<td>4:54</td>
-<td>6</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
-<tr>
-<td>Ayyappa Ninna Poojege Bande Mahadeshwara</td>
-<td>5:06</td>
-<td>7</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
- <tr>
-<td>Ayyappa Swamyge Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya </td>
-<td>5:10</td>
-<td>8</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td> - Continue writing the article. <tr>
-<td>Ayyappa Swamyge Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya Jaya</td>
-<td>5:10</td>
-<td>8</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
-</tr>
-<tr>
-<td>Ayyappa Swamyge Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam Mangalam </td>
-<td>5:14</td>
-<td>9</td>
-<td>K.J. Yesudas, Gangai Amaran</td>
-<td>Gangai Amaran</td>
-<td>Chovalloor Krishnankutty</td>
-<td>K.J. Yesudas</td>
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-<tr>
-<td>Harivarasanam Vishwamohanam Haridadhiswaram Aaradhyapadhukam Arivimardhanam Nithyanarthanam Hariharatmajam Devamashreye</td>
-<td>2:42</td>
-<td>10</td>
-<td>K.J. Yesudas</td>
-<td>Gangai Amaran</td>
-<td>Kumbakudi Kulathur Iyer</td>
-<td>K.J. Yesudas</td>
-</tr>
- </table>
- <h4>How do the songs reflect the devotion and faith of Lord Ayyappa's followers?</h4>
-<p>The songs of yesudas kannada ayyappa songs vol 6 reflect the devotion and faith of Lord Ayyappa's followers in various ways. The songs praise Lord Ayyappa as the supreme lord, the protector, the savior, the friend, the teacher, and the father of his devotees. The songs also describe the glory and beauty of Lord Ayyappa, his temple, his mount, his weapons, his symbols, his ornaments, and his attributes. The songs also narrate the stories and legends of Lord Ayyappa, such as his birth, his childhood, his miracles, his battles, his marriage, his renunciation, and his enlightenment. The songs also express the emotions and feelings of the devotees, such as their joy, their sorrow, their gratitude, their surrender, their longing, their hope, and their love for Lord Ayyappa.</p>
- <h4>What are some of the best songs and lyrics from the album?</h4>
-<p>The album of yesudas kannada ayyappa songs vol 6 has many beautiful and meaningful songs and lyrics that can touch the hearts of the listeners. However, some of the best songs and lyrics from the album are:</p>
- <ul>
-<li>Ayyappa Sharanam: This is the first song of the album and it is a powerful invocation to Lord Ayyappa. The song asks Lord Ayyappa to grant his devotees refuge and salvation from all troubles and sorrows. The song also praises Lord Ayyappa as the lord of all gods, the lord of all worlds, and the lord of all beings. The song has a catchy chorus that repeats "Ayyappa Sharanam" several times.</li>
- <li>Hariharasudha Sharanam: This is the second song of the album and it is a melodious song that expresses the devotion and surrender of Lord Ayyappa's followers. The song calls Lord Ayyappa as Hariharasudha, which means the son of Hari (Vishnu) and Hara (Shiva). The song also describes Lord Ayyappa as the one who fulfills all desires, who removes all sins, who bestows all blessings, and who accepts all offerings. The song has a soothing chorus that repeats "Hariharasudha Sharanam" several times.</li>
- <li>Ayyappa Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve Neeve: This is the fourth song of the album and it is a fast-paced song that expresses the longing and love of Lord Ayyappa's devotees. The song addresses Lord Ayyappa as "Neeve", which means "you" in Kannada. The song also repeats "Neeve" 18 times in each line to emphasize the intensity and depth of their feelings for Lord Ayyappa. The song also mentions some of the names and titles of Lord Ayyappa, such as Manikandan, Shasta, Dharma Shasta, - Continue writing the article. <p>Dharma Shasta, etc. The song has a lively chorus that repeats "Ayyappa Neeve Neeve Neeve Neeve Neeve Neeve" several times.</p>
- <li>Ayyappa Ninna Namave Chanda: This is the fifth song of the album and it is a song that praises the name of Lord Ayyappa. The song says that the name of Lord Ayyappa is like a moon (chanda) that illuminates the darkness of ignorance and suffering. The song also says that the name of Lord Ayyappa is like a mantra that grants peace and happiness to those who chant it. The song has a sweet chorus that repeats "Ayyappa Ninna Namave Chanda" several times.</li>
- <li>Harivarasanam Vishwamohanam Haridadhiswaram Aaradhyapadhukam Arivimardhanam Nithyanarthanam Hariharatmajam Devamashreye: This is the tenth and the last song of the album and it is the most famous and important song of Lord Ayyappa. It is the Harivarasanam, the lullaby song that is sung by K.J. Yesudas every night at Sabarimala temple before closing the doors of the sanctum sanctorum. It is a Sanskrit hymn that praises Lord Ayyappa as the king of Hariharaputra, the son of Vishnu and Shiva. It also requests Lord Ayyappa to rest after a long day of protecting his devotees. It also expresses the devotion and surrender of the devotees to Lord Ayyappa. The song has a serene and sublime quality that creates a divine ambiance for the listeners.</li>
-</ul>
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- <ul>
-<li>YouTube: YouTube is one of the most popular and widely used platforms for watching and listening to music videos online. You can also download music from YouTube using various tools and methods, such as YouTube Downloader, YouTube to MP3 Converter, YouTube Music App, etc. You can find all the songs of yesudas kannada ayyappa songs vol 6 on YouTube by searching for their names or by visiting the official channel of Tharangini Records.</li>
- <li>Gaana: Gaana is one of the leading music streaming and downloading services in India. It offers a huge collection of songs in various languages and genres, including devotional music. You can download music from Gaana using its app or website, which are available for both Android and iOS devices. You can find all the songs of yesudas kannada ayyappa songs vol 6 on Gaana by searching for their names or by visiting the album page.</li>
- <li>Wynk Music: Wynk Music is another popular music streaming and downloading service in India. It also offers a large variety of songs in different languages and genres, including devotional music. You can download music from Wynk Music using its app or website, which are also available for both Android and iOS devices. You can find all the songs of yesudas kannada ayyappa songs vol 6 on Wynk Music by searching for their names or by visiting the album page.</li>
-</ul>
- <h4>What are the steps and tips to download the songs safely and legally?</h4>
-<p>To download the songs of yesudas kannada ayyappa songs vol 6 safely and legally, you need to follow these steps and tips:</p>
- <ol>
-<li>Choose a reliable and reputable free music download site or app that supports yesudas kannada ayyappa songs vol 6, such as YouTube, Gaana, or Wynk Music.</li>
-<li>Make sure that you have a stable internet connection and enough storage space on your device.</li>
-<li>Search for the songs or the album that you want to download by typing their names or keywords in the search bar.</li>
-<li>Select the song or the album that you want to download from the search results or from the album page.</li>
-<li>Click on the download button or icon that appears next to the song or on the album page.</li>
-<li>Choose the quality and format that you want to download, such as MP3, MP4, etc.</li>
-<li>Wait for the download to complete and check if it is successful.</li>
-<li>Enjoy and share your downloaded music with your friends and family.</li>
-</ol>
- <p>Some tips to download the songs safely and legally are:</p> - Continue writing the article. <p>Some tips to download the songs safely and legally are:</p>
- <ul>
-<li>Always download music from trusted and verified sources that have proper licenses and permissions from the original creators and owners of the songs.</li>
-<li>Avoid downloading music from shady and suspicious sites or apps that may contain viruses, malware, or spyware that can harm your device or compromise your privacy.</li>
-<li>Do not download music that is protected by copyright laws or that infringes the rights of the original creators and owners of the songs.</li>
-<li>Do not share or distribute the downloaded music without the consent or authorization of the original creators and owners of the songs.</li>
-<li>Respect and support the original creators and owners of the songs by giving them credit, feedback, appreciation, or donation.</li>
-</ul>
- <h4>How can you enjoy and share the songs offline and online?</h4>
-<p>Once you have downloaded the songs of yesudas kannada ayyappa songs vol 6, you can enjoy and share them offline and online in various ways. Some of them are:</p>
- <ul>
-<li>You can listen to the songs on your device using any music player app or software that supports the quality and format of the downloaded songs.</li>
-<li>You can transfer the songs to other devices, such as your computer, laptop, tablet, smartphone, etc., using a USB cable, Bluetooth, Wi-Fi, etc.</li>
-<li>You can burn the songs to a CD or DVD using any CD or DVD burner app or software that supports the quality and format of the downloaded songs.</li>
-<li>You can play the songs on any external device, such as a speaker, a headphone, a car stereo, etc., using an audio jack, a USB port, a Bluetooth connection, etc.</li>
-<li>You can upload the songs to any online platform, such as a cloud storage service, a social media site, a music streaming service, etc., using your internet connection and account credentials.</li>
-<li>You can share the songs with your friends and family via email, message, chat, call, etc., using your internet connection and contact details.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, yesudas kannada ayyappa songs vol 6 is an amazing album of devotional songs dedicated to Lord Ayyappa. The album is sung by K.J. Yesudas, one of the best singers of devotional music in India. The album has 10 songs that cover different themes and aspects of Lord Ayyappa's life and worship. The album has a soothing and melodious quality that creates a spiritual atmosphere for the listeners. The album is also easy to download for free from various free music download sites and apps that support yesudas kannada ayyappa songs vol 6. The album is also easy to enjoy and share offline and online with your friends and family.</p>
- <p>If you are a devotee of Lord Ayyappa or a fan of K.J. Yesudas, you should definitely download and listen to yesudas kannada ayyappa songs vol 6. You will not regret it. You will feel the presence and blessings of Lord Ayyappa in your heart. You will also appreciate and admire the talent and dedication of K.J. Yesudas as a singer of devotional music. You will also learn more about Lord Ayyappa's history, culture, and teachings.</p>
- <p>So what are you waiting for? Download yesudas kannada ayyappa songs vol 6 for free today and enjoy the divine music of Lord Ayyappa. You will be glad you did.</p>
- <p>Thank you for reading this article. We hope you found it useful and informative. If you have any questions or feedback, please feel free to contact us. We would love to hear from you.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about yesudas kannada ayyappa songs vol 6:</p>
- <ol>
-<li><b>Where can I find more information about Lord Ayyappa and his temple in Sabarimala?</b></li>
-<p>You can find more information about Lord Ayyappa and his temple in Sabarimala from various sources, such as books, magazines, websites, blogs, podcasts, videos, etc. Some of the best sources are:</p>
- <ul>
-<li>The official website of Sabarimala temple: <a href="">https://sabarimalaonline.org/</a></li>
-<li>The official YouTube channel of Sabarimala temple: <a href="">https://www.youtube.com/channel/UCy83QmbvZ3t0Dst0l73wFrA</a></li>
-<li>The Wikipedia page of Sabarimala temple: <a href="">https://en.wikipedia.org/wiki/Sabarimala</a></ - Continue writing the article. <li>The Wikipedia page of Sabarimala temple: <a href="">https://en.wikipedia.org/wiki/Sabarimala</a></li>
-<li>The book "Sabarimala: Its Timeless Message" by Swami Tapasyananda: <a href="">https://www.amazon.in/Sabarimala-Its-Timeless-Message-Tapasyananda/dp/8178234940</a></li>
-<li>The podcast "Sabarimala Stories" by Radio Sai Global Harmony: <a href="">https://radiosai.org/program/listen.php?f=SABARIMALA_STORIES.mp3</a></li>
-</ul>
- <li><b>How can I support K.J. Yesudas and his music career?</b></li>
-<p>You can support K.J. Yesudas and his music career in various ways, such as:</p>
- <ul>
-<li>Buying his original albums and songs from authorized sources, such as online stores, music shops, etc.</li>
-<li>Subscribing to his official channels and platforms, such as YouTube, Facebook, Twitter, Instagram, etc.</li>
-<li>Following his news and updates, such as interviews, concerts, awards, etc.</li>
-<li>Giving him feedback and appreciation, such as comments, likes, shares, ratings, reviews, etc.</li>
-<li>Donating or contributing to his charitable and social causes, such as Tharangini Foundation, Yesudas Trust, etc.</li>
-</ul>
- <li><b>What are some other devotional albums by K.J. Yesudas that I can download for free?</b></li>
-<p>Some other devotional albums by K.J. Yesudas that you can download for free are:</p>
- <ul>
-<li>Yesudas Tamil Ayyappa Songs Vol 1: A collection of 10 devotional songs dedicated to Lord Ayyappa in Tamil language.</li>
-<li>Yesudas Telugu Ayyappa Songs Vol 1: A collection of 10 devotional songs dedicated to Lord Ayyappa in Telugu language.</li>
-<li>Yesudas Malayalam Ayyappa Songs Vol 1: A collection of 10 devotional songs dedicated to Lord Ayyappa in Malayalam language.</li>
-<li>Yesudas Hindi Bhajans Vol 1: A collection of 10 devotional songs dedicated to various Hindu gods and goddesses in Hindi language.</li>
-<li>Yesudas Christian Devotional Songs Vol 1: A collection of 10 devotional songs dedicated to Jesus Christ and Mary in Malayalam language.</li>
-</ul>
- <li><b>How can I convert the songs to different formats or edit them according to my preferences?</b></li>
-<p>You can convert the songs to different formats or edit them according to your preferences using various tools and methods, such as:</p>
- <ul>
-<li>Online Audio Converter: An online tool that allows you to convert audio files to different formats, such as MP3, WAV, OGG, etc.</li>
-<li>Audacity: A free and open-source software that allows you to record and edit audio files on your computer.</li>
-<li>iTunes: A software that allows you to manage and organize your music library on your computer or device.</li>
-<li>Windows Media Player: A software that allows you to play and edit audio files on your computer or device.</li>
-</ul>
- <li><b>How can I donate or contribute to the welfare of Lord Ayyappa's devotees?</b></li>
-<p>You can donate or contribute to the welfare of Lord Ayyappa's devotees in various ways, such as:</p>
- <ul>
-<li>Donating money or materials to Sabarimala temple or its associated organizations, such as Travancore Devaswom Board, Sabarimala Devaswom Board, etc.</li>
-<li>Volunteering your time or skills to Sabarimala temple or its associated organizations, such as cleaning, cooking, serving, guiding, etc.</li>
-<li>Sponsoring or supporting a devotee or a group of devotees who want to visit Sabarimala temple or perform the pilgrimage rituals.</li>
-<li>Participating or organizing awareness campaigns or events to promote the values and teachings of Lord Ayyappa and his devotees.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/AHzizi/WaifuVoiceGen/mel_processing.py

@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec

spaces/AIFILMS/StyleGANEX/scripts/generate_sketch_data.py

@@ -1,62 +0,0 @@
-from torchvision import transforms
-from torchvision.utils import save_image
-from torch.utils.serialization import load_lua
-import os
-import cv2
-import numpy as np
-
-"""
-NOTE!: Must have torch==0.4.1 and torchvision==0.2.1
-The sketch simplification model (sketch_gan.t7) from Simo Serra et al. can be downloaded from their official implementation: 
-    https://github.com/bobbens/sketch_simplification
-"""
-
-
-def sobel(img):
-    opImgx = cv2.Sobel(img, cv2.CV_8U, 0, 1, ksize=3)
-    opImgy = cv2.Sobel(img, cv2.CV_8U, 1, 0, ksize=3)
-    return cv2.bitwise_or(opImgx, opImgy)
-
-
-def sketch(frame):
-    frame = cv2.GaussianBlur(frame, (3, 3), 0)
-    invImg = 255 - frame
-    edgImg0 = sobel(frame)
-    edgImg1 = sobel(invImg)
-    edgImg = cv2.addWeighted(edgImg0, 0.75, edgImg1, 0.75, 0)
-    opImg = 255 - edgImg
-    return opImg
-
-
-def get_sketch_image(image_path):
-    original = cv2.imread(image_path)
-    original = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
-    sketch_image = sketch(original)
-    return sketch_image[:, :, np.newaxis]
-
-
-use_cuda = True
-
-cache = load_lua("/path/to/sketch_gan.t7")
-model = cache.model
-immean = cache.mean
-imstd = cache.std
-model.evaluate()
-
-data_path = "/path/to/data/imgs"
-images = [os.path.join(data_path, f) for f in os.listdir(data_path)]
-
-output_dir = "/path/to/data/edges"
-if not os.path.exists(output_dir):
-    os.makedirs(output_dir)
-
-for idx, image_path in enumerate(images):
-    if idx % 50 == 0:
-        print("{} out of {}".format(idx, len(images)))
-    data = get_sketch_image(image_path)
-    data = ((transforms.ToTensor()(data) - immean) / imstd).unsqueeze(0)
-    if use_cuda:
-        pred = model.cuda().forward(data.cuda()).float()
-    else:
-        pred = model.forward(data)
-    save_image(pred[0], os.path.join(output_dir, "{}_edges.jpg".format(image_path.split("/")[-1].split('.')[0])))

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/pann_model.py

@@ -1,543 +0,0 @@
-# PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
-# Reference from https://github.com/qiuqiangkong/audioset_tagging_cnn
-# Some layers are re-designed for CLAP
-import os
-os.environ['NUMBA_CACHE_DIR'] = '/tmp/'
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchlibrosa.stft import Spectrogram, LogmelFilterBank
-from torchlibrosa.augmentation import SpecAugmentation
-
-from .utils import do_mixup, interpolate, pad_framewise_output
-from .feature_fusion import iAFF, AFF, DAF
-
-
-def init_layer(layer):
-    """Initialize a Linear or Convolutional layer. """
-    nn.init.xavier_uniform_(layer.weight)
-
-    if hasattr(layer, 'bias'):
-        if layer.bias is not None:
-            layer.bias.data.fill_(0.)
-
-
-def init_bn(bn):
-    """Initialize a Batchnorm layer. """
-    bn.bias.data.fill_(0.)
-    bn.weight.data.fill_(1.)
-
-
-class ConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels):
-
-        super(ConvBlock, self).__init__()
-
-        self.conv1 = nn.Conv2d(in_channels=in_channels,
-                              out_channels=out_channels,
-                              kernel_size=(3, 3), stride=(1, 1),
-                              padding=(1, 1), bias=False)
-
-        self.conv2 = nn.Conv2d(in_channels=out_channels,
-                              out_channels=out_channels,
-                              kernel_size=(3, 3), stride=(1, 1),
-                              padding=(1, 1), bias=False)
-
-        self.bn1 = nn.BatchNorm2d(out_channels)
-        self.bn2 = nn.BatchNorm2d(out_channels)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_layer(self.conv2)
-        init_bn(self.bn1)
-        init_bn(self.bn2)
-
-
-    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
-
-        x = input
-        x = F.relu_(self.bn1(self.conv1(x)))
-        x = F.relu_(self.bn2(self.conv2(x)))
-        if pool_type == 'max':
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg':
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg+max':
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        else:
-            raise Exception('Incorrect argument!')
-
-        return x
-
-
-class ConvBlock5x5(nn.Module):
-    def __init__(self, in_channels, out_channels):
-
-        super(ConvBlock5x5, self).__init__()
-
-        self.conv1 = nn.Conv2d(in_channels=in_channels,
-                              out_channels=out_channels,
-                              kernel_size=(5, 5), stride=(1, 1),
-                              padding=(2, 2), bias=False)
-
-        self.bn1 = nn.BatchNorm2d(out_channels)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_bn(self.bn1)
-
-
-    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
-
-        x = input
-        x = F.relu_(self.bn1(self.conv1(x)))
-        if pool_type == 'max':
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg':
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg+max':
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        else:
-            raise Exception('Incorrect argument!')
-
-        return x
-
-
-class AttBlock(nn.Module):
-    def __init__(self, n_in, n_out, activation='linear', temperature=1.):
-        super(AttBlock, self).__init__()
-
-        self.activation = activation
-        self.temperature = temperature
-        self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
-        self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
-
-        self.bn_att = nn.BatchNorm1d(n_out)
-        self.init_weights()
-
-    def init_weights(self):
-        init_layer(self.att)
-        init_layer(self.cla)
-        init_bn(self.bn_att)
-
-    def forward(self, x):
-        # x: (n_samples, n_in, n_time)
-        norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1)
-        cla = self.nonlinear_transform(self.cla(x))
-        x = torch.sum(norm_att * cla, dim=2)
-        return x, norm_att, cla
-
-    def nonlinear_transform(self, x):
-        if self.activation == 'linear':
-            return x
-        elif self.activation == 'sigmoid':
-            return torch.sigmoid(x)
-
-
-class Cnn14(nn.Module):
-    def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
-        fmax, classes_num, enable_fusion=False, fusion_type='None'):
-
-        super(Cnn14, self).__init__()
-
-        window = 'hann'
-        center = True
-        pad_mode = 'reflect'
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
-            win_length=window_size, window=window, center=center, pad_mode=pad_mode,
-            freeze_parameters=True)
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
-            n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
-            freeze_parameters=True)
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
-            freq_drop_width=8, freq_stripes_num=2)
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        if (self.enable_fusion) and (self.fusion_type == 'channel_map'):
-            self.conv_block1 = ConvBlock(in_channels=4, out_channels=64)
-        else:
-            self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
-        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
-
-        self.fc1 = nn.Linear(2048, 2048, bias=True)
-        self.fc_audioset = nn.Linear(2048, classes_num, bias=True)
-
-        if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']):
-            self.mel_conv1d = nn.Sequential(
-                nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2),
-                nn.BatchNorm1d(64) # No Relu
-            )
-            if self.fusion_type == 'daf_1d':
-                self.fusion_model = DAF()
-            elif self.fusion_type == 'aff_1d':
-                self.fusion_model = AFF(channels=64, type='1D')
-            elif self.fusion_type == 'iaff_1d':
-                self.fusion_model = iAFF(channels=64, type='1D')
-
-        if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']):
-            self.mel_conv2d = nn.Sequential(
-                nn.Conv2d(1, 64, kernel_size=(5,5), stride=(6, 2), padding=(2,2)),
-                nn.BatchNorm2d(64),
-                nn.ReLU(inplace=True)
-            )
-
-            if self.fusion_type == 'daf_2d':
-                self.fusion_model = DAF()
-            elif self.fusion_type == 'aff_2d':
-                self.fusion_model = AFF(channels=64, type='2D')
-            elif self.fusion_type == 'iaff_2d':
-                self.fusion_model = iAFF(channels=64, type='2D')
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        if self.enable_fusion and input["longer"].sum() == 0:
-            # if no audio is longer than 10s, then randomly select one audio to be longer
-            input["longer"][torch.randint(0, input["longer"].shape[0], (1,))] = True
-
-        if not self.enable_fusion:
-            x = self.spectrogram_extractor(input['waveform'].to(device=device, non_blocking=True))   # (batch_size, 1, time_steps, freq_bins)
-            x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)
-
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-        else:
-            longer_list = input["longer"].to(device=device, non_blocking=True)
-            x = input["mel_fusion"].to(device=device, non_blocking=True)
-            longer_list_idx = torch.where(longer_list)[0]
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-            if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']:
-                new_x = x[:,0:1,:,:].clone().contiguous()
-                # local processing
-                if len(longer_list_idx) > 0:
-                    fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous()
-                    FB,FC,FT,FF = fusion_x_local.size()
-                    fusion_x_local = fusion_x_local.view(FB * FC, FT, FF)
-                    fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous()
-                    fusion_x_local = self.mel_conv1d(fusion_x_local)
-                    fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1))
-                    fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2)
-                    if fusion_x_local.size(-1) < FT:
-                        fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1)
-                    else:
-                        fusion_x_local = fusion_x_local[:,:,:FT]
-                    # 1D fusion
-                    new_x = new_x.squeeze(1).permute((0,2,1)).contiguous()
-                    new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local)
-                    x = new_x.permute((0,2,1)).contiguous()[:,None,:,:]
-                else:
-                    x = new_x
-            elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']:
-                x = x # no change
-
-        if self.training:
-            x = self.spec_augmenter(x)
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-        if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']):
-            global_x = x[:,0:1,:,:]
-
-             # global processing
-            B, C, H, W = global_x.shape
-            global_x = self.conv_block1(global_x, pool_size=(2, 2), pool_type='avg')
-            if len(longer_list_idx) > 0:
-                local_x = x[longer_list_idx,1:,:,:].contiguous()
-                TH = global_x.size(-2)
-                # local processing
-                B, C, H, W = local_x.shape
-                local_x = local_x.view(B*C,1,H,W)
-                local_x = self.mel_conv2d(local_x)
-                local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3))
-                local_x = local_x.permute((0,2,1,3,4)).contiguous().flatten(2,3)
-                TB,TC,_,TW = local_x.size()
-                if local_x.size(-2) < TH:
-                    local_x = torch.cat([local_x, torch.zeros((TB,TC,TH-local_x.size(-2),TW), device=global_x.device)], dim=-2)
-                else:
-                    local_x = local_x[:,:,:TH,:]
-
-                global_x[longer_list_idx] = self.fusion_model(global_x[longer_list_idx],local_x)
-            x = global_x
-        else:
-            x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
-
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 32)
-
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output}
-        return output_dict
-
-
-class Cnn6(nn.Module):
-    def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
-        fmax, classes_num, enable_fusion=False, fusion_type='None'):
-
-        super(Cnn6, self).__init__()
-
-        window = 'hann'
-        center = True
-        pad_mode = 'reflect'
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
-            win_length=window_size, window=window, center=center, pad_mode=pad_mode,
-            freeze_parameters=True)
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
-            n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
-            freeze_parameters=True)
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
-            freq_drop_width=8, freq_stripes_num=2)
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512)
-
-        self.fc1 = nn.Linear(512, 512, bias=True)
-        self.fc_audioset = nn.Linear(512, classes_num, bias=True)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        x = self.spectrogram_extractor(input)   # (batch_size, 1, time_steps, freq_bins)
-        x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)
-
-        x = x.transpose(1, 3)
-        x = self.bn0(x)
-        x = x.transpose(1, 3)
-
-        if self.training:
-            x = self.spec_augmenter(x)
-
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 16)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output}
-
-        return output_dict
-
-
-class Cnn10(nn.Module):
-    def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
-        fmax, classes_num, enable_fusion=False, fusion_type='None'):
-
-        super(Cnn10, self).__init__()
-
-        window = 'hann'
-        center = True
-        pad_mode = 'reflect'
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
-            win_length=window_size, window=window, center=center, pad_mode=pad_mode,
-            freeze_parameters=True)
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
-            n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
-            freeze_parameters=True)
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
-            freq_drop_width=8, freq_stripes_num=2)
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
-
-        self.fc1 = nn.Linear(1024, 1024, bias=True)
-        self.fc_audioset = nn.Linear(1024, classes_num, bias=True)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        x = self.spectrogram_extractor(input)   # (batch_size, 1, time_steps, freq_bins)
-        x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)
-
-        x = x.transpose(1, 3)
-        x = self.bn0(x)
-        x = x.transpose(1, 3)
-
-        if self.training:
-            x = self.spec_augmenter(x)
-
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 32)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output}
-
-        return output_dict
-
-
-def create_pann_model(audio_cfg, enable_fusion=False, fusion_type='None'):
-    try:
-        ModelProto = eval(audio_cfg.model_name)
-        model = ModelProto(
-            sample_rate = audio_cfg.sample_rate,
-            window_size = audio_cfg.window_size,
-            hop_size =audio_cfg.hop_size,
-            mel_bins = audio_cfg.mel_bins,
-            fmin = audio_cfg.fmin,
-            fmax = audio_cfg.fmax,
-            classes_num = audio_cfg.class_num,
-            enable_fusion = enable_fusion,
-            fusion_type = fusion_type
-        )
-        return model
-    except:
-        raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.')
-

spaces/AIGC-Audio/Make_An_Audio/ldm/models/diffusion/ddpm_audio_inpaint.py

@@ -1,1081 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-import os
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ddpm import DDPM, disabled_train
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-# add mel_dim and mel_length params to ensure correct shape
-class LatentDiffusion_audioinpaint(DDPM):
-    """main class"""
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 mel_dim=80,
-                 mel_length=848,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 test_repeat=1,
-                 test_numsteps = None,
-                 *args, **kwargs):
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__':
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.test_repeat = test_repeat
-        if test_numsteps == None:
-            self.test_numsteps = self.num_timesteps
-        self.concat_mode = concat_mode
-        self.mel_dim = mel_dim
-        self.mel_length = mel_length
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None  
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":# for no_text inpainting task
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":# for unconditional image generation such as human face、ImageNet
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                            force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):# encode_emb from autoencoder
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None:# 'crossattn' for txt2image, 'hybird' for txt_inpaint
-            if cond_key is None:
-                cond_key = self.cond_stage_key # 'caption' for txt_inpaint
-            if self.model.conditioning_key == 'hybrid':
-                xc = {}
-                assert cond_key == 'caption' # only txt_inpaint is implemented now
-                assert 'masked_image' in batch.keys() 
-                assert 'mask' in batch.keys()
-                masked_image = super().get_input(batch,'masked_image')
-                mask = super().get_input(batch,'mask')
-                if bs is not None:
-                    masked_image,mask = masked_image[:bs],mask[:bs]
-                masked_image,mask = masked_image.to(self.device),mask.to(self.device)
-                masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach()
-                resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:])
-                xc['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1)
-                xc[cond_key] = batch[cond_key]
-            else:
-                if cond_key != self.first_stage_key:
-                    if cond_key in ['caption', 'coordinates_bbox']:
-                        xc = batch[cond_key]
-                    elif cond_key == 'class_label':
-                        xc = batch
-                    else:
-                        xc = super().get_input(batch, cond_key).to(self.device)
-                else:# cond_key == 'image'
-                    xc = x
-            if not self.cond_stage_trainable or force_c_encode:# cond_stage_trainable is true for txt2img，force_c_encoder = True，when called in log_images
-                if isinstance(xc, list):
-                    # import pudb; pudb.set_trace()
-                    c = self.get_learned_conditioning(xc)# 因为log_images内接下来要调用sample_log,所以需要预先得到处理好的c
-                if isinstance(xc, dict):
-                    c = {}
-                    c['c_concat'] = xc['c_concat']
-                    c['c_crossattn'] = self.get_learned_conditioning(xc[cond_key])
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                if isinstance(c,dict):
-                    for k in c.keys():
-                        c[k] = c[k][:bs]
-                else:
-                    c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    # same as above but without decorator
-    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                df = self.split_input_params["vqf"]
-                self.split_input_params['original_image_size'] = x.shape[-2:]
-                bs, nc, h, w = x.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
-                z = unfold(x)  # (bn, nc * prod(**ks), L)
-                # Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
-                               for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)
-                o = o * weighting
-
-                # Reverse reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization
-                return decoded
-
-            else:
-                return self.first_stage_model.encode(x)
-        else:
-            return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)# get latent and condition
-        loss = self(x, c)
-        return loss
-
-    def test_step(self,batch,batch_idx):
-        # TODO make self.test_repeat work
-        cond = {}
-        cond[self.cond_stage_key] = batch[self.cond_stage_key]
-        cond[self.cond_stage_key] = self.get_learned_conditioning(cond[self.cond_stage_key]) # c: string -> [B, T, Context_dim]
-        cond['c_crossattn'] = cond.pop(self.cond_stage_key)
-        masked_image = super().get_input(batch,'masked_image')
-        mask = super().get_input(batch,'mask')
-        masked_image,mask = masked_image.to(self.device),mask.to(self.device)
-        masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach()
-        resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:])
-        cond['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1)
-        batch_size = len(batch[self.cond_stage_key])
-        # shape = [batch_size,self.channels,self.mel_dim,self.mel_length]
-        enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps)
-        xrec = self.decode_first_stage(enc_emb)
-        reconstructions = (xrec + 1)/2 # to mel scale
-        test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
-        savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
-        if not os.path.exists(savedir):
-            os.makedirs(savedir)
-
-        file_names = batch['f_name']
-        nfiles = len(file_names)
-        reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim
-        for k in range(reconstructions.shape[0]):
-            b,repeat = k % nfiles, k // nfiles
-            vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
-            v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
-            save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition
-            np.save(save_img_path,reconstructions[b])
-        
-        return None
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                if isinstance(c,dict):
-                    c[self.cond_stage_key] = self.get_learned_conditioning(c[self.cond_stage_key])
-                    c['c_crossattn'] = c.pop(self.cond_stage_key)
-                else:
-                    c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim]
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = torch.clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = torch.clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-        # make values to list to enable concat operation in 
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict. (txt2inpaint)
-            cond_tmp = {}# use cond_tmp to avoid inplace edit
-            for k,v in cond.items():
-                if not isinstance(v, list):
-                    cond_tmp[k] = [cond[k]]
-                else:
-                    cond_tmp[k] = cond[k]
-            cond = cond_tmp
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        if hasattr(self, "split_input_params"):
-            assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
-            ks = self.split_input_params["ks"]  # eg. (128, 128)
-            stride = self.split_input_params["stride"]  # eg. (64, 64)
-
-            h, w = x_noisy.shape[-2:]
-
-            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
-            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
-            # Reshape to img shape
-            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
-            if self.cond_stage_key in ["image", "LR_image", "segmentation",
-                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
-                c_key = next(iter(cond.keys()))  # get key
-                c = next(iter(cond.values()))  # get value
-                assert (len(c) == 1)  # todo extend to list with more than one elem
-                c = c[0]  # get element
-
-                c = unfold(c)
-                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
-            elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
-                # assuming padding of unfold is always 0 and its dilation is always 1
-                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
-                full_img_h, full_img_w = self.split_input_params['original_image_size']
-                # as we are operating on latents, we need the factor from the original image size to the
-                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
-                num_downs = self.first_stage_model.encoder.num_resolutions - 1
-                rescale_latent = 2 ** (num_downs)
-
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
-                # need to rescale the tl patch coordinates to be in between (0,1)
-                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
-                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
-                                        for patch_nr in range(z.shape[-1])]
-
-                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
-                patch_limits = [(x_tl, y_tl,
-                                 rescale_latent * ks[0] / full_img_w,
-                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
-                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
-                # tokenize crop coordinates for the bounding boxes of the respective patches
-                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
-                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
-                print(patch_limits_tknzd[0].shape)
-                # cut tknzd crop position from conditioning
-                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
-                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
-                print(cut_cond.shape)
-
-                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
-                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
-                print(adapted_cond.shape)
-                adapted_cond = self.get_learned_conditioning(adapted_cond)
-                print(adapted_cond.shape)
-                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
-                print(adapted_cond.shape)
-
-                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
-            else:
-                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
-
-            # apply model by loop over crops
-            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
-            assert not isinstance(output_list[0],
-                                  tuple)  # todo cant deal with multiple model outputs check this never happens
-
-            o = torch.stack(output_list, axis=-1)
-            o = o * weighting
-            # Reverse reshape to img shape
-            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-            # stitch crops together
-            x_recon = fold(o) / normalization
-
-        else:
-            # x_noisy is tensor with shape [b,c,mel_len,T]
-            # if condition is caption ,cond['c_crossattn'] is a list, each item shape is [1, 77, 1280]
-            x_recon = self.model(x_noisy, t, **cond)# tensor with shape [b,c,mel_len,T]
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None,**kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.mel_dim, self.mel_length)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.mel_dim, self.mel_length)
-            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
-                                                        shape,cond,verbose=False,**kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True,**kwargs)
-
-        return samples, intermediates
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, **kwargs):
-
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x # 原始输入图像
-        log["reconstruction"] = xrec # 重建得到的图像
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):# when cond_stage is first_stage. (bert embedder doesnot have decode)
-                xc = self.cond_stage_model.decode(c)# decoded masked image
-                log["conditioning"] = xc # 重建后的图像
-            elif self.cond_stage_key in ["caption"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
-                log["conditioning"] = xc # 含有文本的图像 
-                if self.model.conditioning_key == 'hybrid':
-                    log["decoded_maskedimg"] = self.first_stage_model.decode(c['c_concat'][:,:self.first_stage_model.embed_dim])# c_concat is the concat result of masked_img latent and resized mask. get latent here to decode
-            elif self.cond_stage_key == 'class_label':
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-                log['conditioning'] = xc # 文本为类标签的图像
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:# diffusion每一步的图像
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:# 
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                         ddim_steps=ddim_steps,eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with self.ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                             ddim_steps=ddim_steps,eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-            if inpaint:
-                # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
-                mask = torch.ones(N, h, w).to(self.device)
-                # zeros will be filled in
-                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-                mask = mask[:, None, ...]# N,1,H,W
-                with self.ema_scope("Plotting Inpaint"):
-                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_inpainting"] = x_samples
-                log["mask"] = mask
-
-                # outpaint
-                with self.ema_scope("Plotting Outpaint"):
-                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with self.ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.mel_dim, self.mel_length),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-

spaces/Aditya9790/yolo7-object-tracking/utils/autoanchor.py

@@ -1,160 +0,0 @@
-# Auto-anchor utils
-
-import numpy as np
-import torch
-import yaml
-from scipy.cluster.vq import kmeans
-from tqdm import tqdm
-
-from utils.general import colorstr
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary
-    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da.sign() != ds.sign():  # same order
-        print('Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-        m.anchor_grid[:] = m.anchor_grid.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    prefix = colorstr('autoanchor: ')
-    print(f'\n{prefix}Analyzing anchors... ', end='')
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1. / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
-    bpr, aat = metric(anchors)
-    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
-    if bpr < 0.98:  # threshold to recompute
-        print('. Attempting to improve anchors, please wait...')
-        na = m.anchor_grid.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            print(f'{prefix}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
-            check_anchor_order(m)
-            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
-            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
-        else:
-            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
-    print('')  # newline
-
-
-def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            path: path to dataset *.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    thr = 1. / thr
-    prefix = colorstr('autoanchor: ')
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
-        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
-              f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
-        for i, x in enumerate(k):
-            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
-        return k
-
-    if isinstance(path, str):  # *.yaml file
-        with open(path) as f:
-            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
-        from utils.datasets import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-    else:
-        dataset = path  # dataset
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans calculation
-    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
-    s = wh.std(0)  # sigmas for whitening
-    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
-    k *= s
-    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
-    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
-    k = print_results(k)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    npr = np.random
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k)
-
-    return print_results(k)

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/updater/base.py

@@ -1,27 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, List, Tuple
-
-from pydantic import BaseModel
-
-# from agentverse.agents import Agent
-from abc import abstractmethod
-
-from . import updater_registry as UpdaterRegistry
-
-if TYPE_CHECKING:
-    from agentverse.environments import BaseEnvironment
-
-
-@UpdaterRegistry.register("base")
-class BaseUpdater(BaseModel):
-    """
-    The base class of updater class.
-    """
-
-    @abstractmethod
-    def update_memory(self, environment: BaseEnvironment):
-        pass
-
-    def reset(self):
-        pass

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/GetChildrenHeight.js

@@ -1,6 +0,0 @@
-// Override
-var GetChildrenHeight = function () {
-    return 0;
-}
-
-export default GetChildrenHeight;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/ResolveWidth.js

@@ -1,16 +0,0 @@
-var ResolveWidth = function (width) {
-    if (width === undefined) {
-        width = Math.max(this.childrenWidth, this.minWidth);
-    } else {
-        /*
-        var minWidth = Math.max(this.childrenWidth, this.minWidth);
-        if (minWidth > width) {
-            // Warning
-        }
-        */
-    }
-
-    return width;
-}
-
-export default ResolveWidth;

spaces/Akmyradov/TurkmenTTSweSTT/vits/losses.py

@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/docs/install.md

@@ -1,51 +0,0 @@
-## v1.8.0 
-### Linux and Windows  
-```shell
-# CUDA 11.0
-pip --default-timeout=100 install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 10.2
-pip --default-timeout=100 install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0
-
-# CPU only
-pip --default-timeout=100 install torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-## v1.7.1  
-### Linux and Windows  
-```shell
-# CUDA 11.0
-pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 10.2
-pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
-
-# CUDA 10.1
-pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 9.2
-pip install torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CPU only
-pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-## v1.6.0  
-
-### Linux and Windows
-```shell
-# CUDA 10.2
-pip install torch==1.6.0 torchvision==0.7.0
-
-# CUDA 10.1
-pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 9.2
-pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CPU only
-pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
-```

spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/shanghainese.py

@@ -1,64 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('zaonhe')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ᴇ'),
-    ('B', 'bi'),
-    ('C', 'si'),
-    ('D', 'di'),
-    ('E', 'i'),
-    ('F', 'ᴇf'),
-    ('G', 'dʑi'),
-    ('H', 'ᴇtɕʰ'),
-    ('I', 'ᴀi'),
-    ('J', 'dʑᴇ'),
-    ('K', 'kʰᴇ'),
-    ('L', 'ᴇl'),
-    ('M', 'ᴇm'),
-    ('N', 'ᴇn'),
-    ('O', 'o'),
-    ('P', 'pʰi'),
-    ('Q', 'kʰiu'),
-    ('R', 'ᴀl'),
-    ('S', 'ᴇs'),
-    ('T', 'tʰi'),
-    ('U', 'ɦiu'),
-    ('V', 'vi'),
-    ('W', 'dᴀbɤliu'),
-    ('X', 'ᴇks'),
-    ('Y', 'uᴀi'),
-    ('Z', 'zᴇ')
-]]
-
-
-def _number_to_shanghainese(num):
-    num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
-    return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
-
-
-def number_to_shanghainese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def shanghainese_to_ipa(text):
-    text = number_to_shanghainese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text

spaces/Amrrs/portfolio-github/README.md

@@ -1,36 +0,0 @@
----
-title: Portfolio Github
-emoji: 🌖
-colorFrom: blue
-colorTo: green
-sdk: static
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './gcnet_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_512x512_80k_ade20k.py

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(decode_head=dict(num_classes=150))

spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-html/tippy.css

@@ -1 +0,0 @@
-.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py

@@ -1,412 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..utils import kaiming_init
-from .registry import PLUGIN_LAYERS
-
-
-@PLUGIN_LAYERS.register_module()
-class GeneralizedAttention(nn.Module):
-    """GeneralizedAttention module.
-
-    See 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks'
-    (https://arxiv.org/abs/1711.07971) for details.
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        spatial_range (int): The spatial range. -1 indicates no spatial range
-            constraint. Default: -1.
-        num_heads (int): The head number of empirical_attention module.
-            Default: 9.
-        position_embedding_dim (int): The position embedding dimension.
-            Default: -1.
-        position_magnitude (int): A multiplier acting on coord difference.
-            Default: 1.
-        kv_stride (int): The feature stride acting on key/value feature map.
-            Default: 2.
-        q_stride (int): The feature stride acting on query feature map.
-            Default: 1.
-        attention_type (str): A binary indicator string for indicating which
-            items in generalized empirical_attention module are used.
-            Default: '1111'.
-
-            - '1000' indicates 'query and key content' (appr - appr) item,
-            - '0100' indicates 'query content and relative position'
-              (appr - position) item,
-            - '0010' indicates 'key content only' (bias - appr) item,
-            - '0001' indicates 'relative position only' (bias - position) item.
-    """
-
-    _abbr_ = 'gen_attention_block'
-
-    def __init__(self,
-                 in_channels,
-                 spatial_range=-1,
-                 num_heads=9,
-                 position_embedding_dim=-1,
-                 position_magnitude=1,
-                 kv_stride=2,
-                 q_stride=1,
-                 attention_type='1111'):
-
-        super(GeneralizedAttention, self).__init__()
-
-        # hard range means local range for non-local operation
-        self.position_embedding_dim = (
-            position_embedding_dim
-            if position_embedding_dim > 0 else in_channels)
-
-        self.position_magnitude = position_magnitude
-        self.num_heads = num_heads
-        self.in_channels = in_channels
-        self.spatial_range = spatial_range
-        self.kv_stride = kv_stride
-        self.q_stride = q_stride
-        self.attention_type = [bool(int(_)) for _ in attention_type]
-        self.qk_embed_dim = in_channels // num_heads
-        out_c = self.qk_embed_dim * num_heads
-
-        if self.attention_type[0] or self.attention_type[1]:
-            self.query_conv = nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_c,
-                kernel_size=1,
-                bias=False)
-            self.query_conv.kaiming_init = True
-
-        if self.attention_type[0] or self.attention_type[2]:
-            self.key_conv = nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_c,
-                kernel_size=1,
-                bias=False)
-            self.key_conv.kaiming_init = True
-
-        self.v_dim = in_channels // num_heads
-        self.value_conv = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=self.v_dim * num_heads,
-            kernel_size=1,
-            bias=False)
-        self.value_conv.kaiming_init = True
-
-        if self.attention_type[1] or self.attention_type[3]:
-            self.appr_geom_fc_x = nn.Linear(
-                self.position_embedding_dim // 2, out_c, bias=False)
-            self.appr_geom_fc_x.kaiming_init = True
-
-            self.appr_geom_fc_y = nn.Linear(
-                self.position_embedding_dim // 2, out_c, bias=False)
-            self.appr_geom_fc_y.kaiming_init = True
-
-        if self.attention_type[2]:
-            stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
-            appr_bias_value = -2 * stdv * torch.rand(out_c) + stdv
-            self.appr_bias = nn.Parameter(appr_bias_value)
-
-        if self.attention_type[3]:
-            stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
-            geom_bias_value = -2 * stdv * torch.rand(out_c) + stdv
-            self.geom_bias = nn.Parameter(geom_bias_value)
-
-        self.proj_conv = nn.Conv2d(
-            in_channels=self.v_dim * num_heads,
-            out_channels=in_channels,
-            kernel_size=1,
-            bias=True)
-        self.proj_conv.kaiming_init = True
-        self.gamma = nn.Parameter(torch.zeros(1))
-
-        if self.spatial_range >= 0:
-            # only works when non local is after 3*3 conv
-            if in_channels == 256:
-                max_len = 84
-            elif in_channels == 512:
-                max_len = 42
-
-            max_len_kv = int((max_len - 1.0) / self.kv_stride + 1)
-            local_constraint_map = np.ones(
-                (max_len, max_len, max_len_kv, max_len_kv), dtype=np.int)
-            for iy in range(max_len):
-                for ix in range(max_len):
-                    local_constraint_map[
-                        iy, ix,
-                        max((iy - self.spatial_range) //
-                            self.kv_stride, 0):min((iy + self.spatial_range +
-                                                    1) // self.kv_stride +
-                                                   1, max_len),
-                        max((ix - self.spatial_range) //
-                            self.kv_stride, 0):min((ix + self.spatial_range +
-                                                    1) // self.kv_stride +
-                                                   1, max_len)] = 0
-
-            self.local_constraint_map = nn.Parameter(
-                torch.from_numpy(local_constraint_map).byte(),
-                requires_grad=False)
-
-        if self.q_stride > 1:
-            self.q_downsample = nn.AvgPool2d(
-                kernel_size=1, stride=self.q_stride)
-        else:
-            self.q_downsample = None
-
-        if self.kv_stride > 1:
-            self.kv_downsample = nn.AvgPool2d(
-                kernel_size=1, stride=self.kv_stride)
-        else:
-            self.kv_downsample = None
-
-        self.init_weights()
-
-    def get_position_embedding(self,
-                               h,
-                               w,
-                               h_kv,
-                               w_kv,
-                               q_stride,
-                               kv_stride,
-                               device,
-                               dtype,
-                               feat_dim,
-                               wave_length=1000):
-        # the default type of Tensor is float32, leading to type mismatch
-        # in fp16 mode. Cast it to support fp16 mode.
-        h_idxs = torch.linspace(0, h - 1, h).to(device=device, dtype=dtype)
-        h_idxs = h_idxs.view((h, 1)) * q_stride
-
-        w_idxs = torch.linspace(0, w - 1, w).to(device=device, dtype=dtype)
-        w_idxs = w_idxs.view((w, 1)) * q_stride
-
-        h_kv_idxs = torch.linspace(0, h_kv - 1, h_kv).to(
-            device=device, dtype=dtype)
-        h_kv_idxs = h_kv_idxs.view((h_kv, 1)) * kv_stride
-
-        w_kv_idxs = torch.linspace(0, w_kv - 1, w_kv).to(
-            device=device, dtype=dtype)
-        w_kv_idxs = w_kv_idxs.view((w_kv, 1)) * kv_stride
-
-        # (h, h_kv, 1)
-        h_diff = h_idxs.unsqueeze(1) - h_kv_idxs.unsqueeze(0)
-        h_diff *= self.position_magnitude
-
-        # (w, w_kv, 1)
-        w_diff = w_idxs.unsqueeze(1) - w_kv_idxs.unsqueeze(0)
-        w_diff *= self.position_magnitude
-
-        feat_range = torch.arange(0, feat_dim / 4).to(
-            device=device, dtype=dtype)
-
-        dim_mat = torch.Tensor([wave_length]).to(device=device, dtype=dtype)
-        dim_mat = dim_mat**((4. / feat_dim) * feat_range)
-        dim_mat = dim_mat.view((1, 1, -1))
-
-        embedding_x = torch.cat(
-            ((w_diff / dim_mat).sin(), (w_diff / dim_mat).cos()), dim=2)
-
-        embedding_y = torch.cat(
-            ((h_diff / dim_mat).sin(), (h_diff / dim_mat).cos()), dim=2)
-
-        return embedding_x, embedding_y
-
-    def forward(self, x_input):
-        num_heads = self.num_heads
-
-        # use empirical_attention
-        if self.q_downsample is not None:
-            x_q = self.q_downsample(x_input)
-        else:
-            x_q = x_input
-        n, _, h, w = x_q.shape
-
-        if self.kv_downsample is not None:
-            x_kv = self.kv_downsample(x_input)
-        else:
-            x_kv = x_input
-        _, _, h_kv, w_kv = x_kv.shape
-
-        if self.attention_type[0] or self.attention_type[1]:
-            proj_query = self.query_conv(x_q).view(
-                (n, num_heads, self.qk_embed_dim, h * w))
-            proj_query = proj_query.permute(0, 1, 3, 2)
-
-        if self.attention_type[0] or self.attention_type[2]:
-            proj_key = self.key_conv(x_kv).view(
-                (n, num_heads, self.qk_embed_dim, h_kv * w_kv))
-
-        if self.attention_type[1] or self.attention_type[3]:
-            position_embed_x, position_embed_y = self.get_position_embedding(
-                h, w, h_kv, w_kv, self.q_stride, self.kv_stride,
-                x_input.device, x_input.dtype, self.position_embedding_dim)
-            # (n, num_heads, w, w_kv, dim)
-            position_feat_x = self.appr_geom_fc_x(position_embed_x).\
-                view(1, w, w_kv, num_heads, self.qk_embed_dim).\
-                permute(0, 3, 1, 2, 4).\
-                repeat(n, 1, 1, 1, 1)
-
-            # (n, num_heads, h, h_kv, dim)
-            position_feat_y = self.appr_geom_fc_y(position_embed_y).\
-                view(1, h, h_kv, num_heads, self.qk_embed_dim).\
-                permute(0, 3, 1, 2, 4).\
-                repeat(n, 1, 1, 1, 1)
-
-            position_feat_x /= math.sqrt(2)
-            position_feat_y /= math.sqrt(2)
-
-        # accelerate for saliency only
-        if (np.sum(self.attention_type) == 1) and self.attention_type[2]:
-            appr_bias = self.appr_bias.\
-                view(1, num_heads, 1, self.qk_embed_dim).\
-                repeat(n, 1, 1, 1)
-
-            energy = torch.matmul(appr_bias, proj_key).\
-                view(n, num_heads, 1, h_kv * w_kv)
-
-            h = 1
-            w = 1
-        else:
-            # (n, num_heads, h*w, h_kv*w_kv), query before key, 540mb for
-            if not self.attention_type[0]:
-                energy = torch.zeros(
-                    n,
-                    num_heads,
-                    h,
-                    w,
-                    h_kv,
-                    w_kv,
-                    dtype=x_input.dtype,
-                    device=x_input.device)
-
-            # attention_type[0]: appr - appr
-            # attention_type[1]: appr - position
-            # attention_type[2]: bias - appr
-            # attention_type[3]: bias - position
-            if self.attention_type[0] or self.attention_type[2]:
-                if self.attention_type[0] and self.attention_type[2]:
-                    appr_bias = self.appr_bias.\
-                        view(1, num_heads, 1, self.qk_embed_dim)
-                    energy = torch.matmul(proj_query + appr_bias, proj_key).\
-                        view(n, num_heads, h, w, h_kv, w_kv)
-
-                elif self.attention_type[0]:
-                    energy = torch.matmul(proj_query, proj_key).\
-                        view(n, num_heads, h, w, h_kv, w_kv)
-
-                elif self.attention_type[2]:
-                    appr_bias = self.appr_bias.\
-                        view(1, num_heads, 1, self.qk_embed_dim).\
-                        repeat(n, 1, 1, 1)
-
-                    energy += torch.matmul(appr_bias, proj_key).\
-                        view(n, num_heads, 1, 1, h_kv, w_kv)
-
-            if self.attention_type[1] or self.attention_type[3]:
-                if self.attention_type[1] and self.attention_type[3]:
-                    geom_bias = self.geom_bias.\
-                        view(1, num_heads, 1, self.qk_embed_dim)
-
-                    proj_query_reshape = (proj_query + geom_bias).\
-                        view(n, num_heads, h, w, self.qk_embed_dim)
-
-                    energy_x = torch.matmul(
-                        proj_query_reshape.permute(0, 1, 3, 2, 4),
-                        position_feat_x.permute(0, 1, 2, 4, 3))
-                    energy_x = energy_x.\
-                        permute(0, 1, 3, 2, 4).unsqueeze(4)
-
-                    energy_y = torch.matmul(
-                        proj_query_reshape,
-                        position_feat_y.permute(0, 1, 2, 4, 3))
-                    energy_y = energy_y.unsqueeze(5)
-
-                    energy += energy_x + energy_y
-
-                elif self.attention_type[1]:
-                    proj_query_reshape = proj_query.\
-                        view(n, num_heads, h, w, self.qk_embed_dim)
-                    proj_query_reshape = proj_query_reshape.\
-                        permute(0, 1, 3, 2, 4)
-                    position_feat_x_reshape = position_feat_x.\
-                        permute(0, 1, 2, 4, 3)
-                    position_feat_y_reshape = position_feat_y.\
-                        permute(0, 1, 2, 4, 3)
-
-                    energy_x = torch.matmul(proj_query_reshape,
-                                            position_feat_x_reshape)
-                    energy_x = energy_x.permute(0, 1, 3, 2, 4).unsqueeze(4)
-
-                    energy_y = torch.matmul(proj_query_reshape,
-                                            position_feat_y_reshape)
-                    energy_y = energy_y.unsqueeze(5)
-
-                    energy += energy_x + energy_y
-
-                elif self.attention_type[3]:
-                    geom_bias = self.geom_bias.\
-                        view(1, num_heads, self.qk_embed_dim, 1).\
-                        repeat(n, 1, 1, 1)
-
-                    position_feat_x_reshape = position_feat_x.\
-                        view(n, num_heads, w*w_kv, self.qk_embed_dim)
-
-                    position_feat_y_reshape = position_feat_y.\
-                        view(n, num_heads, h * h_kv, self.qk_embed_dim)
-
-                    energy_x = torch.matmul(position_feat_x_reshape, geom_bias)
-                    energy_x = energy_x.view(n, num_heads, 1, w, 1, w_kv)
-
-                    energy_y = torch.matmul(position_feat_y_reshape, geom_bias)
-                    energy_y = energy_y.view(n, num_heads, h, 1, h_kv, 1)
-
-                    energy += energy_x + energy_y
-
-            energy = energy.view(n, num_heads, h * w, h_kv * w_kv)
-
-        if self.spatial_range >= 0:
-            cur_local_constraint_map = \
-                self.local_constraint_map[:h, :w, :h_kv, :w_kv].\
-                contiguous().\
-                view(1, 1, h*w, h_kv*w_kv)
-
-            energy = energy.masked_fill_(cur_local_constraint_map,
-                                         float('-inf'))
-
-        attention = F.softmax(energy, 3)
-
-        proj_value = self.value_conv(x_kv)
-        proj_value_reshape = proj_value.\
-            view((n, num_heads, self.v_dim, h_kv * w_kv)).\
-            permute(0, 1, 3, 2)
-
-        out = torch.matmul(attention, proj_value_reshape).\
-            permute(0, 1, 3, 2).\
-            contiguous().\
-            view(n, self.v_dim * self.num_heads, h, w)
-
-        out = self.proj_conv(out)
-
-        # output is downsampled, upsample back to input size
-        if self.q_downsample is not None:
-            out = F.interpolate(
-                out,
-                size=x_input.shape[2:],
-                mode='bilinear',
-                align_corners=False)
-
-        out = self.gamma * out + x_input
-        return out
-
-    def init_weights(self):
-        for m in self.modules():
-            if hasattr(m, 'kaiming_init') and m.kaiming_init:
-                kaiming_init(
-                    m,
-                    mode='fan_in',
-                    nonlinearity='leaky_relu',
-                    bias=0,
-                    distribution='uniform',
-                    a=1)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/logger/pavi.py

@@ -1,117 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import json
-import os
-import os.path as osp
-
-import torch
-import yaml
-
-import annotator.uniformer.mmcv as mmcv
-from ....parallel.utils import is_module_wrapper
-from ...dist_utils import master_only
-from ..hook import HOOKS
-from .base import LoggerHook
-
-
-@HOOKS.register_module()
-class PaviLoggerHook(LoggerHook):
-
-    def __init__(self,
-                 init_kwargs=None,
-                 add_graph=False,
-                 add_last_ckpt=False,
-                 interval=10,
-                 ignore_last=True,
-                 reset_flag=False,
-                 by_epoch=True,
-                 img_key='img_info'):
-        super(PaviLoggerHook, self).__init__(interval, ignore_last, reset_flag,
-                                             by_epoch)
-        self.init_kwargs = init_kwargs
-        self.add_graph = add_graph
-        self.add_last_ckpt = add_last_ckpt
-        self.img_key = img_key
-
-    @master_only
-    def before_run(self, runner):
-        super(PaviLoggerHook, self).before_run(runner)
-        try:
-            from pavi import SummaryWriter
-        except ImportError:
-            raise ImportError('Please run "pip install pavi" to install pavi.')
-
-        self.run_name = runner.work_dir.split('/')[-1]
-
-        if not self.init_kwargs:
-            self.init_kwargs = dict()
-        self.init_kwargs['name'] = self.run_name
-        self.init_kwargs['model'] = runner._model_name
-        if runner.meta is not None:
-            if 'config_dict' in runner.meta:
-                config_dict = runner.meta['config_dict']
-                assert isinstance(
-                    config_dict,
-                    dict), ('meta["config_dict"] has to be of a dict, '
-                            f'but got {type(config_dict)}')
-            elif 'config_file' in runner.meta:
-                config_file = runner.meta['config_file']
-                config_dict = dict(mmcv.Config.fromfile(config_file))
-            else:
-                config_dict = None
-            if config_dict is not None:
-                # 'max_.*iter' is parsed in pavi sdk as the maximum iterations
-                #  to properly set up the progress bar.
-                config_dict = config_dict.copy()
-                config_dict.setdefault('max_iter', runner.max_iters)
-                # non-serializable values are first converted in
-                # mmcv.dump to json
-                config_dict = json.loads(
-                    mmcv.dump(config_dict, file_format='json'))
-                session_text = yaml.dump(config_dict)
-                self.init_kwargs['session_text'] = session_text
-        self.writer = SummaryWriter(**self.init_kwargs)
-
-    def get_step(self, runner):
-        """Get the total training step/epoch."""
-        if self.get_mode(runner) == 'val' and self.by_epoch:
-            return self.get_epoch(runner)
-        else:
-            return self.get_iter(runner)
-
-    @master_only
-    def log(self, runner):
-        tags = self.get_loggable_tags(runner, add_mode=False)
-        if tags:
-            self.writer.add_scalars(
-                self.get_mode(runner), tags, self.get_step(runner))
-
-    @master_only
-    def after_run(self, runner):
-        if self.add_last_ckpt:
-            ckpt_path = osp.join(runner.work_dir, 'latest.pth')
-            if osp.islink(ckpt_path):
-                ckpt_path = osp.join(runner.work_dir, os.readlink(ckpt_path))
-
-            if osp.isfile(ckpt_path):
-                # runner.epoch += 1 has been done before `after_run`.
-                iteration = runner.epoch if self.by_epoch else runner.iter
-                return self.writer.add_snapshot_file(
-                    tag=self.run_name,
-                    snapshot_file_path=ckpt_path,
-                    iteration=iteration)
-
-        # flush the buffer and send a task ending signal to Pavi
-        self.writer.close()
-
-    @master_only
-    def before_epoch(self, runner):
-        if runner.epoch == 0 and self.add_graph:
-            if is_module_wrapper(runner.model):
-                _model = runner.model.module
-            else:
-                _model = runner.model
-            device = next(_model.parameters()).device
-            data = next(iter(runner.data_loader))
-            image = data[self.img_key][0:1].to(device)
-            with torch.no_grad():
-                self.writer.add_graph(_model, image)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/_distutils_hack/override.py

@@ -1 +0,0 @@
-__import__('_distutils_hack').do_override()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/py38compat.py

@@ -1,8 +0,0 @@
-def aix_platform(osname, version, release):
-    try:
-        import _aix_support
-
-        return _aix_support.aix_platform()
-    except ImportError:
-        pass
-    return "{}-{}.{}".format(osname, version, release)

spaces/AtomdffAI/wechatgpt4atom/channel/wechat/wechat_channel.py

@@ -1,176 +0,0 @@
-# encoding:utf-8
-
-"""
-wechat channel
-"""
-import itchat
-import json
-from itchat.content import *
-from channel.channel import Channel
-from concurrent.futures import ThreadPoolExecutor
-from common.log import logger
-from config import conf
-import requests
-import io
-
-thread_pool = ThreadPoolExecutor(max_workers=8)
-
-
-class WechatChannel(Channel):
-
-    qrcode = b''
-
-    newInstance=None
-
-    def __init__(self):
-        pass
-
-    def startup(self):
-        # login by scan QRCode
-        newInstance = itchat.load_sync_itchat()
-        self.newInstance = newInstance
-
-        @newInstance.msg_register(TEXT)
-        def handler_single_msg(msg):
-            self.handle(msg)
-            return None
-
-        @newInstance.msg_register(TEXT, isGroupChat=True)
-        def handler_group_msg(msg):
-            self.handle_group(msg)
-            return None
-
-        newInstance.auto_login(qrCallback=self.qrCallback)
-        # start message listener
-        newInstance.run()
-
-    def qrCallback(self, uuid, status, qrcode):
-        self.qrcode = qrcode
-    
-    def getQrCode(self):
-        return self.qrcode
-
-    def handle(self, msg):
-        logger.debug("[WX]receive msg: " + json.dumps(msg, ensure_ascii=False))
-        from_user_id = msg['FromUserName']
-        to_user_id = msg['ToUserName']              # 接收人id
-        other_user_id = msg['User']['UserName']     # 对手方id
-        content = msg['Text']
-        match_prefix = self.check_prefix(content, conf().get('single_chat_prefix'))
-        if from_user_id == other_user_id and match_prefix is not None:
-            # 好友向自己发送消息
-            if match_prefix != '':
-                str_list = content.split(match_prefix, 1)
-                if len(str_list) == 2:
-                    content = str_list[1].strip()
-
-            img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-            if img_match_prefix:
-                content = content.split(img_match_prefix, 1)[1].strip()
-                thread_pool.submit(self._do_send_img, content, from_user_id)
-            else:
-                thread_pool.submit(self._do_send, content, from_user_id)
-
-        elif to_user_id == other_user_id and match_prefix:
-            # 自己给好友发送消息
-            str_list = content.split(match_prefix, 1)
-            if len(str_list) == 2:
-                content = str_list[1].strip()
-            img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-            if img_match_prefix:
-                content = content.split(img_match_prefix, 1)[1].strip()
-                thread_pool.submit(self._do_send_img, content, to_user_id)
-            else:
-                thread_pool.submit(self._do_send, content, to_user_id)
-
-
-    def handle_group(self, msg):
-        logger.debug("[WX]receive group msg: " + json.dumps(msg, ensure_ascii=False))
-        group_name = msg['User'].get('NickName', None)
-        group_id = msg['User'].get('UserName', None)
-        if not group_name:
-            return ""
-        origin_content = msg['Content']
-        content = msg['Content']
-        content_list = content.split(' ', 1)
-        context_special_list = content.split('\u2005', 1)
-        if len(context_special_list) == 2:
-            content = context_special_list[1]
-        elif len(content_list) == 2:
-            content = content_list[1]
-
-        config = conf()
-        match_prefix = (msg['IsAt'] and not config.get("group_at_off", False)) or self.check_prefix(origin_content, config.get('group_chat_prefix')) \
-                       or self.check_contain(origin_content, config.get('group_chat_keyword'))
-        if ('ALL_GROUP' in config.get('group_name_white_list') or group_name in config.get('group_name_white_list') or self.check_contain(group_name, config.get('group_name_keyword_white_list'))) and match_prefix:
-            img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-            if img_match_prefix:
-                content = content.split(img_match_prefix, 1)[1].strip()
-                thread_pool.submit(self._do_send_img, content, group_id)
-            else:
-                thread_pool.submit(self._do_send_group, content, msg)
-
-    def send(self, msg, receiver):
-        logger.info('[WX] sendMsg={}, receiver={}'.format(msg, receiver))
-        self.newInstance.send(msg, toUserName=receiver)
-
-    def _do_send(self, query, reply_user_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['from_user_id'] = reply_user_id
-            reply_text = super().build_reply_content(query, context)
-            if reply_text:
-                self.send(conf().get("single_chat_reply_prefix") + reply_text, reply_user_id)
-        except Exception as e:
-            logger.exception(e)
-
-    def _do_send_img(self, query, reply_user_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['type'] = 'IMAGE_CREATE'
-            img_url = super().build_reply_content(query, context)
-            if not img_url:
-                return
-
-            # 图片下载
-            pic_res = requests.get(img_url, stream=True)
-            image_storage = io.BytesIO()
-            for block in pic_res.iter_content(1024):
-                image_storage.write(block)
-            image_storage.seek(0)
-
-            # 图片发送
-            logger.info('[WX] sendImage, receiver={}'.format(reply_user_id))
-            self.newInstance.send_image(image_storage, reply_user_id)
-        except Exception as e:
-            logger.exception(e)
-
-    def _do_send_group(self, query, msg):
-        if not query:
-            return
-        context = dict()
-        context['from_user_id'] = msg['ActualUserName']
-        reply_text = super().build_reply_content(query, context)
-        if reply_text:
-            reply_text = '@' + msg['ActualNickName'] + ' ' + reply_text.strip()
-            self.send(conf().get("group_chat_reply_prefix", "") + reply_text, msg['User']['UserName'])
-
-
-    def check_prefix(self, content, prefix_list):
-        for prefix in prefix_list:
-            if content.startswith(prefix):
-                return prefix
-        return None
-
-
-    def check_contain(self, content, keyword_list):
-        if not keyword_list:
-            return None
-        for ky in keyword_list:
-            if content.find(ky) != -1:
-                return True
-        return None

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/datasets/README.md

@@ -1,140 +0,0 @@
-# Use Builtin Datasets
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-Detectron2 has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
-  coco/
-  lvis/
-  cityscapes/
-  VOC20{07,12}/
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
-contains configs and models that use these builtin datasets.
-
-## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
-
-```
-coco/
-  annotations/
-    instances_{train,val}2017.json
-    person_keypoints_{train,val}2017.json
-  {train,val}2017/
-    # image files that are mentioned in the corresponding json
-```
-
-You can use the 2014 version of the dataset as well.
-
-Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
-which you can download with `./datasets/prepare_for_tests.sh`.
-
-## Expected dataset structure for PanopticFPN:
-
-Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
-into the following structure:
-```
-coco/
-  annotations/
-    panoptic_{train,val}2017.json
-  panoptic_{train,val}2017/  # png annotations
-  panoptic_stuff_{train,val}2017/  # generated by the script mentioned below
-```
-
-Install panopticapi by:
-```
-pip install git+https://github.com/cocodataset/panopticapi.git
-```
-Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
-
-## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
-```
-coco/
-  {train,val,test}2017/
-lvis/
-  lvis_v0.5_{train,val}.json
-  lvis_v0.5_image_info_test.json
-  lvis_v1_{train,val}.json
-  lvis_v1_image_info_test{,_challenge}.json
-```
-
-Install lvis-api by:
-```
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-```
-
-To evaluate models trained on the COCO dataset using LVIS annotations,
-run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
-
-## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
-```
-cityscapes/
-  gtFine/
-    train/
-      aachen/
-        color.png, instanceIds.png, labelIds.png, polygons.json,
-        labelTrainIds.png
-      ...
-    val/
-    test/
-    # below are generated Cityscapes panoptic annotation
-    cityscapes_panoptic_train.json
-    cityscapes_panoptic_train/
-    cityscapes_panoptic_val.json
-    cityscapes_panoptic_val/
-    cityscapes_panoptic_test.json
-    cityscapes_panoptic_test/
-  leftImg8bit/
-    train/
-    val/
-    test/
-```
-Install cityscapes scripts by:
-```
-pip install git+https://github.com/mcordts/cityscapesScripts.git
-```
-
-Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
-```
-These files are not needed for instance segmentation.
-
-Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
-```
-These files are not needed for semantic and instance segmentation.
-
-## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
-```
-VOC20{07,12}/
-  Annotations/
-  ImageSets/
-    Main/
-      trainval.txt
-      test.txt
-      # train.txt or val.txt, if you use these splits
-  JPEGImages/
-```
-
-## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
-```
-ADEChallengeData2016/
-  annotations/
-  annotations_detectron2/
-  images/
-  objectInfo150.txt
-```
-The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/__init__.py

@@ -1,5 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .build import PROPOSAL_GENERATOR_REGISTRY, build_proposal_generator
-from .rpn import RPN_HEAD_REGISTRY, build_rpn_head, RPN, StandardRPNHead
-
-__all__ = list(globals().keys())

spaces/Benson/text-generation/Examples/Ai Tipo De Teclado Ms Apk Completo Agrietado.md

@@ -1,87 +0,0 @@
-
-<h1>¿Qué es APK JustPlay y cómo descargarlo? </h1>
-<p>Si usted es un entusiasta de los juegos que le encanta jugar en su dispositivo móvil, es posible que haya oído hablar de APK JustPlay. Pero, ¿qué es exactamente y cómo se puede descargar? En este artículo, vamos a responder a estas preguntas y más. Vamos a explicar lo que es APK JustPlay, cuáles son sus beneficios, cómo descargarlo, y cómo usarlo. Al final de este artículo, usted tendrá una idea clara de lo que APK JustPlay puede ofrecer y cómo usted puede unirse al último programa de lealtad para los jugadores. </p>
-<h2>ai tipo de teclado más apk completo agrietado</h2><br /><p><b><b>Download</b> &#10040; <a href="https://bltlly.com/2v6M54">https://bltlly.com/2v6M54</a></b></p><br /><br />
- <h2>Introducción</h2>
-<h3>¿Qué es APK JustPlay? </h3>
-<p>APK JustPlay es una aplicación móvil que te recompensa con monedas de lealtad para jugar juegos en tu dispositivo. Es un programa de lealtad único que te ofrece una colección única de juegos que no encontrarás en ningún otro lugar. Puedes ganar monedas de lealtad por el tiempo que pasas jugando a estos juegos, y luego canjearlas por recompensas reales o donarlas a organizaciones benéficas fantásticas. APK JustPlay es desarrollado por JustPlay GmbH, un grupo de entretenimiento móvil con sede en Alemania que crea aplicaciones para teléfonos móviles y tabletas. </p>
- <h3>¿Cuáles son los beneficios de APK JustPlay? </h3>
-<p>APK JustPlay tiene muchos beneficios para los jugadores que quieren sacar provecho de su pasión por el juego. Estos son algunos de ellos:</p>
-<ul>
-<li> Usted puede ganar dinero diariamente mediante la recogida de puntos de lealtad para jugar juegos que te gustan. </li>
-<li>Puede elegir entre pagos de PayPal, tarjetas de regalo o donaciones para apoyar causas cercanas a su corazón. </li>
-<li>Puedes disfrutar de pagos diarios cada 3 horas, dándote la flexibilidad para canjear tus monedas cuando quieras. </li>
-<li>Usted puede descubrir una colección única de juegos que son exclusivos de APK JustPlay, y disfrutar de una variedad de géneros y temas. </li>
-<li>Usted puede hacer una diferencia mediante la donación de sus ganancias a su caridad preferida - y APK JustPlay coincidirá con cada dólar que da! </li>
-
-</ul>
- <h2>¿Cómo descargar APK JustPlay? </h2>
-<h3>Paso 1: Visite el sitio web oficial de APK JustPlay</h3>
-<p>El primer paso para descargar APK JustPlay es visitar su sitio web oficial en <a href="( 1 )">https://apkcombo.com/justplay/com.playjust.app/</a>. Aquí encontrará toda la información que necesita sobre la aplicación, como sus características, capturas de pantalla, comentarios y más. También verá un botón que dice "Descargar APK (33 MB)". Haga clic en él para proceder al siguiente paso. </p>
- <h3>Paso 2: Elige tu dispositivo y descarga el archivo APK</h3>
-<p>El siguiente paso es elegir el dispositivo y descargar el archivo APK. Un archivo APK es un archivo de paquete de Android que contiene todos los archivos y código necesarios para instalar una aplicación en su dispositivo. Verá una lista de dispositivos que son compatibles con APK JustPlay, como Android 7.0+, Android TV & Tablet, PC Windows, etc. Elija el que coincida con su dispositivo y haga clic en él. A continuación, verá un enlace que dice "Descargar ahora". Haga clic en él y esperar a que la descarga se complete. </p>
- <h3>Paso 3: Habilitar fuentes desconocidas en el dispositivo</h3>
-<p>El tercer paso es habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de la Google Play Store oficial. Para habilitar fuentes desconocidas, vaya a la configuración del dispositivo y busque la opción de seguridad o privacidad. Luego, encuentra la opción que dice "Permitir la instalación de aplicaciones desde fuentes desconocidas" o algo similar. Activa y confirma tu elección. Esto le permitirá instalar APK JustPlay en su dispositivo. </p>
- <h3>Paso 4: Instalar el archivo APK y lanzar la aplicación</h3>
-
- <h2>¿Cómo usar APK JustPlay? </h2>
-<h3>¿Cómo ganar monedas de lealtad jugando? </h3>
-<p>Ahora que ha descargado APK JustPlay, es posible que se pregunte cómo usarlo y ganar monedas de lealtad jugando. Es muy simple y divertido. Así es como:</p>
-<ol>
-<li>En la pantalla de inicio de la aplicación, verá una lista de juegos que están disponibles para jugar. Puedes desplazarte por ellos y elegir los que te interesan. </li>
-<li>Toca el icono del juego y verás una ventana emergente que te muestra cuántas monedas puedes ganar por minuto al jugar ese juego. También verá un botón que dice "Jugar ahora". Toque en él y el juego comenzará a cargar. </li>
-<li>Disfruta jugando el juego tanto como quieras. Cuanto más tiempo pases jugando, más monedas ganarás. Puedes ver tu balance de monedas en la esquina superior derecha de la pantalla. </li>
-<li>Cuando haya terminado de jugar, toque en el botón de atrás y volverá a la pantalla de inicio de la aplicación. Verá su saldo de monedas actualizado y un mensaje que lo felicita por ganar monedas. </li>
-</ol>
- <h3> ¿Cómo canjear sus monedas por recompensas reales o donar a organizaciones benéficas? </h3>
-<p>Una vez que hayas acumulado suficientes monedas jugando, puedes canjearlas por recompensas reales o donarlas a organizaciones benéficas. Así es como:</p>
-<ol>
-<li>Toque en el icono del menú en la esquina superior izquierda de la pantalla y seleccione "Recompensas". </li>
-<li>Verá una lista de recompensas que están disponibles para que usted elija. Puede filtrarlas por categoría, como efectivo, tarjetas de regalo o donaciones. </li>
-<li>Seleccione la recompensa que desea y toque en ella. Verás una ventana emergente que te muestra cuántas monedas necesitas para canjear esa recompensa y un botón que dice "Canjear ahora". Toque en él y confirme su elección. </li>
-<li>Usted recibirá un correo electrónico con instrucciones sobre cómo reclamar su recompensa o hacer su donación. Sigue las instrucciones y disfruta de tu recompensa o siéntete bien con tu donación. </li>
-</ol>
- <h3>¿Cómo descubrir nuevos juegos y temas? </h3>
-
-<ol>
-<li>Toque en el icono del menú en la esquina superior izquierda de la pantalla y seleccione "Descubrir". </li>
-<li>Verás una lista de temas que están disponibles para que explores, como acción, aventura, rompecabezas, deportes, etc.</li>
-<li> Seleccione el tema que desea y toque en él. Verá una lista de juegos que pertenecen a ese tema. </li>
-<li>Toque en cualquier icono del juego y verá una ventana emergente que le muestra una breve descripción del juego y un botón que dice "Jugar ahora". Toque en él y empezar a jugar. </li>
-</ol>
- <h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>En conclusión, APK JustPlay es una aplicación increíble que te recompensa con monedas de lealtad para jugar juegos en su dispositivo. Usted puede ganar dinero diariamente mediante la recogida de puntos de lealtad para jugar juegos que te gustan. Puede elegir entre retiros de PayPal, tarjetas de regalo o donaciones para apoyar causas cercanas a su corazón. Usted puede disfrutar de pagos diarios cada 3 horas, que le da la flexibilidad para canjear sus monedas cuando lo desee. Usted puede descubrir una colección única de juegos que son exclusivos de APK JustPlay, y disfrutar de una variedad de géneros y temas. Usted puede hacer una diferencia mediante la donación de sus ganancias a su caridad preferida - y APK JustPlay coincidirá con cada dólar que usted da! Puedes unirte a más de 10 millones de jugadores satisfechos que forman parte de la comunidad JustPlay y compartir tu experiencia de juego con ellos. </p>
-<p></p>
- <h3>Llamada a la acción e invitación a unirse a la comunidad JustPlay</h3>
-<p>Si usted está listo para unirse al último programa de lealtad para los jugadores, descargar APK JustPlay hoy y empezar a ganar monedas de lealtad jugando. Es gratis, fácil y divertido. No te arrepentirás. APK JustPlay es la mejor manera de disfrutar de los juegos y ser recompensado por ello. No se pierda esta oportunidad de unirse a la comunidad JustPlay y hacer una diferencia en el mundo. Descargar APK JustPlay ahora y empezar a jugar! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre APK JustPlay:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-
-<tr>
-<td>¿Es APK JustPlay seguro y legal? </td>
-<td>Sí, APK JustPlay es seguro y legal. Es desarrollado por una empresa de buena reputación que sigue todas las reglas y reglamentos de la Google Play Store. No contiene ningún virus, malware o spyware. No requiere ninguna información personal ni acceso a los datos de su dispositivo. No interfiere con el rendimiento de su dispositivo o la duración de la batería. Es una aplicación legítima que le paga por jugar. </td>
-</tr>
-<tr>
-<td>¿Cuántos juegos están disponibles en APK JustPlay? </td>
-<td>APK JustPlay tiene más de 100 juegos que están disponibles para que usted juegue. Estos juegos son exclusivos de APK JustPlay y no los encontrarás en ningún otro lugar. Cubren una amplia gama de géneros y temas, tales como acción, aventura, rompecabezas, deportes, etc. Nunca te aburrirás con APK JustPlay.</td>
-</tr>
-<tr>
-<td>¿Cuánto dinero puedo hacer con APK JustPlay? </td>
-<td>La cantidad de dinero que puede hacer con APK JustPlay depende de cuánto tiempo pasas jugando y cuántas monedas ganas. Cuanto más juegas, más ganas. Puedes ganar hasta $10 por día jugando juegos en APK JustPlay. También puedes ganar monedas extra invitando a tus amigos a unirse a la aplicación, completando encuestas, viendo videos y participando en concursos. </td>
-</tr>
-<tr>
-<td>¿Cuáles son los requisitos mínimos para usar APK JustPlay? </td>
-<td>Para utilizar APK JustPlay, es necesario tener un dispositivo que se ejecuta en Android 7.0 o superior. También necesita tener una conexión a Internet y suficiente espacio de almacenamiento en su dispositivo. No es necesario tener una cuenta de Google o una tarjeta de crédito para utilizar APK JustPlay.</td>
-</tr>
-<tr>
-<td>¿Cómo puedo contactar al equipo de soporte de APK JustPlay? </td>
-<td>Si tiene alguna pregunta, problema o comentario sobre APK JustPlay, puede ponerse en contacto con el equipo de soporte enviando un correo electrónico a <a href="">[email protected]</a>. También puede visitar la página <a href=">FAQ</a> o la página <a href="">Página de Facebook</a> de APK JustPlay para obtener más información. </td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Clash Mini Para PC.md

@@ -1,123 +0,0 @@
-<br />
-<h1>Clash Mini: Un juego de mesa divertido y lleno de estrategia</h1>
-<p>Si eres un fan del Clash Universe, te encantará Clash Mini, un nuevo juego de Supercell que te permite batirte en duelo en un divertido juego de mesa. Recoge, invoca y actualiza tu ejército de Minis, que son versiones adorables de tus personajes favoritos de Clash, y míralos chocar en emocionantes batallas en tiempo real. Predecir los movimientos de su oponente y montar su estrategia ganadora y la formación. Dirige tu ejército con héroes icónicos como el Rey Bárbaro, la Reina Arquera, la Doncella del Escudo y más. Cambia la marea de batalla intercambiando y actualizando tus Minis entre rondas. Juega casualmente por diversión o en partidos clasificados para aumentar tu posición en la liga. Clash Mini es fácil de aprender pero difícil de dominar. ¡Prepárate para tus Minis para lanzar el estruendo más grande! </p>
-<h2>descargar clash mini para PC</h2><br /><p><b><b>Download</b> &#10001; &#10001; &#10001; <a href="https://bltlly.com/2v6KXh">https://bltlly.com/2v6KXh</a></b></p><br /><br />
-<p>Pero ¿qué pasa si quieres jugar Clash Mini en una pantalla más grande, con mejores controles y más rendimiento? Bueno, puedes hacer eso jugando Clash Mini en tu PC. En este artículo, te mostraremos cómo descargar e instalar Clash Mini en tu PC, cómo jugarlo y algunos consejos y trucos para ayudarte a ganar más juegos. </p>
- <h2>Cómo descargar e instalar Clash Mini en su PC</h2>
-<p>Hay dos formas principales de jugar Clash Mini en su PC. Una es usar Windows 11 y emulación nativa de Android, que es la forma oficial de ejecutar aplicaciones Android en Windows. La otra es utilizar un emulador de Android como Bluestacks 5, que es un software de terceros que simula un dispositivo Android en su PC. Ambos métodos tienen sus pros y sus contras, así que puedes elegir el que más te convenga. </p>
- <h3>Opción 1: Usa Windows 11 y emulación nativa de Android</h3>
-
-<p>Para usar esta función, necesita tener un equipo con Windows 11 que cumpla con los requisitos mínimos para ejecutar aplicaciones Android. También necesita tener una cuenta de Microsoft y una cuenta de Amazon. Luego, debe seguir estos pasos:</p>
-<p></p>
-<ol>
-<li>Abra la aplicación Microsoft Store en su PC y busque "Subsistema de Windows para Android". Instálelo en su PC.</li>
-<li>Abra la aplicación de Microsoft Store de nuevo y busque "Amazon Appstore". Instálela en su PC.</li>
-<li>Abra la aplicación Amazon Appstore en su PC e inicie sesión con su cuenta de Amazon. </li>
-<li>Buscar "Clash Mini" en la aplicación Amazon Appstore e instalarlo en su PC.</li>
-<li>Abra el menú Inicio en su PC y busque "Clash Mini ". Haga clic en él para iniciar el juego. </li>
-</ol>
-<p>Felicidades, ha instalado y ejecuta Clash Mini en su PC con Windows 11 y emulación nativa de Android. Ahora puedes disfrutar del juego en una pantalla más grande, con mejores gráficos y un rendimiento más rápido. También puede utilizar el ratón y el teclado o un controlador para jugar el juego. </p>
- <h3>Opción 2: Utilice un emulador de Android como Bluestacks 5</h3>
-<p>Si no tienes una computadora con Windows 11, o si prefieres usar un método diferente, puedes usar un emulador de Android como Bluestacks 5 para jugar Clash Mini en tu PC. Un emulador de Android es un software que simula un dispositivo Android en su PC, lo que le permite ejecutar aplicaciones y juegos de Android en su PC. Bluestacks 5 es uno de los emuladores de Android más populares y fiables, con más de 500 millones de usuarios en todo el mundo. Ofrece alto rendimiento, compatibilidad, personalización y seguridad para jugar juegos de Android en PC.</p>
-<p>Para usar este método, necesita tener un PC que cumpla con los requisitos mínimos para ejecutar Bluestacks 5. También necesita tener una cuenta de Google. Luego, debes seguir estos pasos:</p>
-<ol>
-<li>Ir al sitio web oficial de Bluestacks 5 y descargar el instalador para su PC.</li>
-
-<li>Abre Bluestacks 5 e inicia sesión con tu cuenta de Google. </li>
-<li>Ir a la aplicación Google Play Store en Bluestacks 5 y buscar "Clash Mini". Instalarlo en Bluestacks 5.</li>
-<li>Ir a la pantalla de inicio de Bluestacks 5 y buscar "Clash Mini". Haga clic en él para iniciar el juego. </li>
-</ol>
-<p>Felicidades, has instalado y ejecutado con éxito Clash Mini en tu PC usando Bluestacks 5. Ahora puedes disfrutar del juego en una pantalla más grande, con mejores gráficos y un rendimiento más rápido. También puede utilizar el ratón y el teclado o un controlador para jugar el juego. </p>
- <h2>Cómo jugar Clash Mini en su PC</h2>
-<p>Ahora que ha instalado Clash Mini en su PC, es posible que se pregunte cómo jugarlo. Bueno, no se preocupe, tenemos todo cubierto. Estos son algunos pasos básicos y consejos sobre cómo jugar Clash Mini en su PC:</p>
- <h3>Elige tus Minis y Héroes</h3>
-<p>Lo primero que tienes que hacer es elegir tu ejército de Minis y Héroes. Minis son versiones lindas de personajes de choque que tienen diferentes habilidades y roles en la batalla. Los héroes son líderes poderosos que pueden aumentar tus Minis y liberar habilidades especiales. Puedes recoger minis y héroes abriendo cofres, completando misiones o comprándolos con gemas. También puedes mejorarlos con oro y cartas. </p>
-<p>Puedes tener hasta ocho Minis y un Héroe en tu ejército. Puedes personalizar tu ejército según tu preferencia y estrategia. También puede crear diferentes cubiertas para diferentes modos y situaciones. Para elegir tus Minis y Héroes, ve a la pestaña Ejército en el menú principal y arrástralos y suéltalos en las ranuras. También puede tocar en ellos para ver sus estadísticas y habilidades. </p>
- <h3>Organiza tu ejército en el tablero</h3>
-<p>Lo siguiente que tienes que hacer es organizar tu ejército en el tablero. El tablero es donde tienen lugar las batallas. Tiene nueve fichas para cada jugador, donde puedes colocar tus Minis. El tablero también tiene obstáculos que pueden bloquear o afectar los movimientos y ataques de tus Minis. </p>
-
- <h3>Actualiza tus Minis durante la batalla</h3>
-<p>La tercera cosa que necesitas hacer es actualizar tus Minis durante la batalla. Actualizar tus Minis puede hacerlos más fuertes, más rápidos o más duraderos. También puede desbloquear nuevas habilidades o efectos para ellos. Actualizar tus Minis puede darte una ventaja sobre tu oponente en la batalla. </p>
-<p>Puedes actualizar tus Minis durante la batalla usando el oro que ganas al derrotar a los Minis enemigos o desde cofres. Puede actualizar hasta tres veces por ronda, pero cada actualización cuesta más oro que la anterior. Para actualizar tus Minis durante la batalla, toca el botón de actualización en la parte inferior de la pantalla y selecciona el Mini que deseas actualizar. </p>
- <h3>Usa el ratón y el teclado o un controlador</h3>
-<p>Lo último que tienes que hacer es usar el ratón y el teclado o un mando para jugar. Jugar Clash Mini en tu PC te da la ventaja de tener mejores controles y precisión que jugar en un dispositivo móvil. Puedes usar el ratón y el teclado o un mando para interactuar con el juego y realizar varias acciones. </p>
-<p>Puede utilizar el ratón para arrastrar y soltar sus Minis en el tablero, para tocar los botones y menús, y para desplazarse y acercar y alejar. Puede utilizar el teclado para utilizar accesos directos y teclas de acceso rápido para un juego más rápido y fácil. También puede utilizar un controlador para jugar el juego, siempre y cuando sea compatible con su PC y el juego. Puedes personalizar tus controles y ajustes en el menú Opciones del juego. </p>
- <h2> Consejos y trucos para jugar Clash Mini en su PC</h2>
-<p>Ahora que sabes cómo jugar Clash Mini en tu PC, es posible que estés buscando algunos consejos y trucos para mejorar tus habilidades y ganar más juegos. Bueno, no te preocupes, te tenemos cubierto. Estos son algunos consejos y trucos para jugar a Clash Mini en tu PC:</p>
- <h3>Anticipa los movimientos de tu oponente</h3>
-
-<p>Por ejemplo, si ves que tu oponente tiene un montón de Minis a distancia, es posible que quieras colocar algunos Minis tanky delante de ellos para bloquear sus disparos. Si ves que tu oponente tiene un héroe que puede curar a sus Minis, es posible que desee centrarse en sacar a ese héroe primero. Si ves que tu oponente tiene un Mini que puede aturdir o congelar tus Minis, es posible que desees extender tus Minis o usar un Mini que pueda limpiarlos o inmunizarlos. </p>
- <h3>Ajusta tu estrategia según el modo</h3>
-<p>Otra habilidad importante en Clash Mini es ajustar tu estrategia de acuerdo al modo que estás jugando. Hay diferentes modos en Clash Mini, como eventos casuales, clasificados, amistosos y especiales. Cada modo tiene diferentes reglas, objetivos, recompensas y desafíos. Necesitas adaptar tu estrategia de acuerdo al modo en el que estás jugando y la situación que estás enfrentando. </p>
-<p>Por ejemplo, en el modo Casual, puedes jugar por diversión y experimentar con diferentes Minis y Héroes sin preocuparte por perder trofeos o rangos. En el modo Clasificado, necesitas jugar más serio y competitivo para subir las ligas y ganar recompensas. En el modo Amistoso, puedes jugar con o contra tus amigos o compañeros de clan por diversión o práctica. En el modo Eventos especiales, puedes jugar con reglas o modificadores únicos que cambian el modo de juego. </p>
- <h3>Experimenta con diferentes combinaciones y habilidades</h3>
-<p>Uno de los aspectos más divertidos de Clash Mini es experimentar con diferentes combinaciones y habilidades de Minis y Héroes. Hay muchos Minis y Héroes en Clash Mini, cada uno con sus propias habilidades y roles únicos. Puedes mezclarlos y combinarlos para crear diferentes sinergias y efectos. También puedes actualizarlos o intercambiarlos durante la batalla para cambiar sus habilidades o efectos. </p>
-
- <h3>Sincroniza tu progreso entre dispositivos</h3>
-<p>Una de las características más convenientes de Clash Mini es que puedes sincronizar tu progreso a través de dispositivos. Esto significa que puede jugar el juego en su PC o dispositivo móvil sin perder ningún dato o progreso. Puedes cambiar de dispositivo en cualquier momento sin problemas. </p>
-<p>Para sincronizar tu progreso entre dispositivos, necesitas vincular tu cuenta de juego con Google Play Games (para dispositivos Android) o Game Center (para dispositivos iOS). También necesita tener una conexión a Internet cuando cambie de dispositivo. Para vincular tu cuenta de juego con Google Play Games o Game Center, ve al menú Configuración del juego y toca el botón Enlace. </p>
- <h2>Conclusión</h2>
-<p>Clash Mini es un juego de mesa divertido y lleno de estrategia que te permite batirte en duelo en el Universo Clash. Puedes reunir, convocar y actualizar tu ejército de Minis y Héroes, verlos chocar en emocionantes batallas en tiempo real, predecir los movimientos de tu oponente y armar tu estrategia ganadora y formación. Puedes jugar casualmente por diversión o en partidos clasificados para aumentar tu posición en la liga. </p>
-<p>Pero ¿qué pasa si quieres jugar Clash Mini en una pantalla más grande, con mejores controles y más rendimiento? Bueno, puedes hacer eso jugando Clash Mini en tu PC. Puede usar Windows 11 y la emulación nativa de Android, que es la forma oficial de ejecutar aplicaciones Android en Windows. O puede utilizar un emulador de Android como Bluestacks 5, que es un software de terceros que simula un dispositivo Android en su PC. Ambos métodos tienen sus pros y sus contras, así que puedes elegir el que más te convenga. </p>
-<p>Jugar Clash Mini en tu PC te da la ventaja de tener mejores gráficos, un rendimiento más rápido y más precisión que jugar en un dispositivo móvil. También puede utilizar el ratón y el teclado o un controlador para jugar el juego. También puede sincronizar su progreso en todos los dispositivos, por lo que puede cambiar entre su PC y su dispositivo móvil en cualquier momento que desee. </p>
-
-<p>Entonces, ¿qué estás esperando? Descarga Clash Mini en tu PC hoy y disfruta del juego de mesa divertido y lleno de estrategia. ¡Te encantará! </p>
- <h2>Preguntas frecuentes</h2>
-<h4>¿Cuáles son los requisitos mínimos para ejecutar Clash Mini en el PC? </h4>
-<p>Para ejecutar Clash Mini en PC usando Windows 11 y emulación nativa de Android, necesita tener un equipo con Windows 11 que cumpla con estos requisitos mínimos:</p>
-<ul>
-<li>Procesador: 1 gigahertz (GHz) o más rápido con 2 o más núcleos en un procesador compatible de 64 bits o sistema en un chip (SoC)</li>
-<li>Memoria: 4 GB de RAM</li>
-<li>Almacenamiento: 64 GB o dispositivo de almacenamiento más grande</li>
-<li>Tarjeta gráfica: Compatible con DirectX 12 o posterior con controlador WDDM 2.0</li>
-<li>Pantalla: Pantalla de alta definición (720p) que es mayor que 9" en diagonal, 8 bits por canal de color</li>
-<li>Conexión a Internet: La conectividad a Internet es necesaria para realizar actualizaciones y para descargar y usar algunas características</li>
-</ul>
-<p>Para ejecutar Clash Mini en PC usando Bluestacks 5, necesita tener un PC que cumpla con estos requisitos mínimos:</p>
-<ul>
-<li>OS: Microsoft Windows 7 y superior</li>
-<li>Procesador: Procesador Intel o AMD</li>
-<li>RAM: Al menos 2GB de RAM</li>
-<li>HDD: 5GB de espacio libre en disco</li>
-<li>Usted debe ser un administrador en su PC</li>
-<li>Controladores gráficos actualizados de Microsoft o el proveedor de chipset</li>
-</ul>
- <h4>¿Cómo puedo obtener juegos de Google Play en Windows 11? </h4>
-<p>Si desea usar Google Play Games en Windows 11, debe instalarlo por separado desde la Appstore de Amazon. Para hacerlo, debes seguir estos pasos:</p>
-<ol>
-<li>Abra la aplicación Windows Subsistema para Android en su PC y vaya a la pestaña Opciones de desarrollador. </li>
-<li>Habilitar el modo de desarrollador y depuración ADB. </li>
-<li>Descargar el archivo APK de Google Play Games de una fuente de confianza. </li>
-<li>Conecte su PC a su dispositivo Android utilizando un cable USB. </li>
-<li> Abra una ventana del símbolo del sistema en su PC y escriba "dispositivos adb" para comprobar si se detecta su dispositivo. </li>
-
-<li>Abra la aplicación Google Play Games en su PC e inicie sesión con su cuenta de Google. </li>
-</ol>
- <h4>¿Cuáles son los mejores Minis y Héroes para usar en Clash Mini? </h4>
-<p>La respuesta a esta pregunta depende de su preferencia personal y estrategia. Sin embargo, algunos consejos generales son:</p>
-<ul>
-<li>Usa Minis y Héroes que tengan habilidades o efectos similares o complementarios, como daño por fuego, curación o blindaje. </li>
-<li>Usa Minis y Héroes que tengan habilidades o efectos opuestos o contrarios, como reducción de daños, inmunidad o limpieza. </li>
-<li>Utilice Minis y héroes que tienen interacciones especiales entre sí, como el príncipe azul y la princesa.</li>
-<li>Usa Minis y Héroes que se adapten al modo que estás jugando, como Casual, Clasificado, Amistoso o Eventos Especiales.</li>
-</ul>
- <h4>¿Cómo puedo ganar recompensas y puntos en Clash Mini? </h4>
-<p>Puedes ganar recompensas y puntos en Clash Mini haciendo varias actividades en el juego, como:</p>
-<ul>
-<li>Ganar batallas en los modos Casual, Clasificado, Amistoso o Eventos Especiales. </li>
-<li> Abrir cofres que contienen oro, tarjetas, gemas y otros artículos. </li>
-<li>Completar misiones que te dan oro, gemas y cofres. </li>
-<li>Participar en el Pase de Temporada que te da recompensas y beneficios exclusivos. </li>
-<li>Unirse a un clan y donar o solicitar tarjetas. </li>
-</ul>
- <h4>¿Cómo puedo encontrar amigos y chatear con otros jugadores en Clash Mini? </h4>
-<p>Puedes encontrar amigos y chatear con otros jugadores en Clash Mini usando las características sociales del juego, como:</p>
-<ul>
-<li>Agregar amigos usando sus etiquetas de jugador o códigos QR. </li>
-<li>Unirse o crear un clan e invitar a tus amigos a unirse. </li>
-<li>Chatear con tus compañeros de clan o amigos en el chat del clan o amigo. </li>
-<li>Enviar o recibir solicitudes de amigos, mensajes o emojis. </li>
-<li>Jugar con o contra tus amigos o compañeros de clan en modo amistoso. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,120 +0,0 @@
-"""
-pip._vendor is for vendoring dependencies of pip to prevent needing pip to
-depend on something external.
-
-Files inside of pip._vendor should be considered immutable and should only be
-updated to versions from upstream.
-"""
-from __future__ import absolute_import
-
-import glob
-import os.path
-import sys
-
-# Downstream redistributors which have debundled our dependencies should also
-# patch this value to be true. This will trigger the additional patching
-# to cause things like "six" to be available as pip.
-DEBUNDLED = False
-
-# By default, look in this directory for a bunch of .whl files which we will
-# add to the beginning of sys.path before attempting to import anything. This
-# is done to support downstream re-distributors like Debian and Fedora who
-# wish to create their own Wheels for our dependencies to aid in debundling.
-WHEEL_DIR = os.path.abspath(os.path.dirname(__file__))
-
-
-# Define a small helper function to alias our vendored modules to the real ones
-# if the vendored ones do not exist. This idea of this was taken from
-# https://github.com/kennethreitz/requests/pull/2567.
-def vendored(modulename):
-    vendored_name = "{0}.{1}".format(__name__, modulename)
-
-    try:
-        __import__(modulename, globals(), locals(), level=0)
-    except ImportError:
-        # We can just silently allow import failures to pass here. If we
-        # got to this point it means that ``import pip._vendor.whatever``
-        # failed and so did ``import whatever``. Since we're importing this
-        # upfront in an attempt to alias imports, not erroring here will
-        # just mean we get a regular import error whenever pip *actually*
-        # tries to import one of these modules to use it, which actually
-        # gives us a better error message than we would have otherwise
-        # gotten.
-        pass
-    else:
-        sys.modules[vendored_name] = sys.modules[modulename]
-        base, head = vendored_name.rsplit(".", 1)
-        setattr(sys.modules[base], head, sys.modules[modulename])
-
-
-# If we're operating in a debundled setup, then we want to go ahead and trigger
-# the aliasing of our vendored libraries as well as looking for wheels to add
-# to our sys.path. This will cause all of this code to be a no-op typically
-# however downstream redistributors can enable it in a consistent way across
-# all platforms.
-if DEBUNDLED:
-    # Actually look inside of WHEEL_DIR to find .whl files and add them to the
-    # front of our sys.path.
-    sys.path[:] = glob.glob(os.path.join(WHEEL_DIR, "*.whl")) + sys.path
-
-    # Actually alias all of our vendored dependencies.
-    vendored("cachecontrol")
-    vendored("certifi")
-    vendored("colorama")
-    vendored("distlib")
-    vendored("distro")
-    vendored("six")
-    vendored("six.moves")
-    vendored("six.moves.urllib")
-    vendored("six.moves.urllib.parse")
-    vendored("packaging")
-    vendored("packaging.version")
-    vendored("packaging.specifiers")
-    vendored("pep517")
-    vendored("pkg_resources")
-    vendored("platformdirs")
-    vendored("progress")
-    vendored("requests")
-    vendored("requests.exceptions")
-    vendored("requests.packages")
-    vendored("requests.packages.urllib3")
-    vendored("requests.packages.urllib3._collections")
-    vendored("requests.packages.urllib3.connection")
-    vendored("requests.packages.urllib3.connectionpool")
-    vendored("requests.packages.urllib3.contrib")
-    vendored("requests.packages.urllib3.contrib.ntlmpool")
-    vendored("requests.packages.urllib3.contrib.pyopenssl")
-    vendored("requests.packages.urllib3.exceptions")
-    vendored("requests.packages.urllib3.fields")
-    vendored("requests.packages.urllib3.filepost")
-    vendored("requests.packages.urllib3.packages")
-    vendored("requests.packages.urllib3.packages.ordered_dict")
-    vendored("requests.packages.urllib3.packages.six")
-    vendored("requests.packages.urllib3.packages.ssl_match_hostname")
-    vendored("requests.packages.urllib3.packages.ssl_match_hostname."
-             "_implementation")
-    vendored("requests.packages.urllib3.poolmanager")
-    vendored("requests.packages.urllib3.request")
-    vendored("requests.packages.urllib3.response")
-    vendored("requests.packages.urllib3.util")
-    vendored("requests.packages.urllib3.util.connection")
-    vendored("requests.packages.urllib3.util.request")
-    vendored("requests.packages.urllib3.util.response")
-    vendored("requests.packages.urllib3.util.retry")
-    vendored("requests.packages.urllib3.util.ssl_")
-    vendored("requests.packages.urllib3.util.timeout")
-    vendored("requests.packages.urllib3.util.url")
-    vendored("resolvelib")
-    vendored("rich")
-    vendored("rich.console")
-    vendored("rich.highlighter")
-    vendored("rich.logging")
-    vendored("rich.markup")
-    vendored("rich.progress")
-    vendored("rich.segment")
-    vendored("rich.style")
-    vendored("rich.text")
-    vendored("rich.traceback")
-    vendored("tenacity")
-    vendored("tomli")
-    vendored("urllib3")

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/_emoji_replace.py

@@ -1,32 +0,0 @@
-from typing import Callable, Match, Optional
-import re
-
-from ._emoji_codes import EMOJI
-
-
-_ReStringMatch = Match[str]  # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str]  # Callable invoked by re.sub
-_EmojiSubMethod = Callable[[_ReSubCallable, str], str]  # Sub method of a compiled re
-
-
-def _emoji_replace(
-    text: str,
-    default_variant: Optional[str] = None,
-    _emoji_sub: _EmojiSubMethod = re.compile(r"(:(\S*?)(?:(?:\-)(emoji|text))?:)").sub,
-) -> str:
-    """Replace emoji code in text."""
-    get_emoji = EMOJI.__getitem__
-    variants = {"text": "\uFE0E", "emoji": "\uFE0F"}
-    get_variant = variants.get
-    default_variant_code = variants.get(default_variant, "") if default_variant else ""
-
-    def do_replace(match: Match[str]) -> str:
-        emoji_code, emoji_name, variant = match.groups()
-        try:
-            return get_emoji(emoji_name.lower()) + get_variant(
-                variant, default_variant_code
-            )
-        except KeyError:
-            return emoji_code
-
-    return _emoji_sub(do_replace, text)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/before_sleep.py

@@ -1,71 +0,0 @@
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 typing
-
-from pip._vendor.tenacity import _utils
-
-if typing.TYPE_CHECKING:
-    import logging
-
-    from pip._vendor.tenacity import RetryCallState
-
-
-def before_sleep_nothing(retry_state: "RetryCallState") -> None:
-    """Before call strategy that does nothing."""
-
-
-def before_sleep_log(
-    logger: "logging.Logger",
-    log_level: int,
-    exc_info: bool = False,
-) -> typing.Callable[["RetryCallState"], None]:
-    """Before call strategy that logs to some logger the attempt."""
-
-    def log_it(retry_state: "RetryCallState") -> None:
-        local_exc_info: BaseException | bool | None
-
-        if retry_state.outcome is None:
-            raise RuntimeError("log_it() called before outcome was set")
-
-        if retry_state.next_action is None:
-            raise RuntimeError("log_it() called before next_action was set")
-
-        if retry_state.outcome.failed:
-            ex = retry_state.outcome.exception()
-            verb, value = "raised", f"{ex.__class__.__name__}: {ex}"
-
-            if exc_info:
-                local_exc_info = retry_state.outcome.exception()
-            else:
-                local_exc_info = False
-        else:
-            verb, value = "returned", retry_state.outcome.result()
-            local_exc_info = False  # exc_info does not apply when no exception
-
-        if retry_state.fn is None:
-            # NOTE(sileht): can't really happen, but we must please mypy
-            fn_name = "<unknown>"
-        else:
-            fn_name = _utils.get_callback_name(retry_state.fn)
-
-        logger.log(
-            log_level,
-            f"Retrying {fn_name} " f"in {retry_state.next_action.sleep} seconds as it {verb} {value}.",
-            exc_info=local_exc_info,
-        )
-
-    return log_it

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/errors.py

@@ -1,58 +0,0 @@
-"""setuptools.errors
-
-Provides exceptions used by setuptools modules.
-"""
-
-from distutils import errors as _distutils_errors
-
-
-# Re-export errors from distutils to facilitate the migration to PEP632
-
-ByteCompileError = _distutils_errors.DistutilsByteCompileError
-CCompilerError = _distutils_errors.CCompilerError
-ClassError = _distutils_errors.DistutilsClassError
-CompileError = _distutils_errors.CompileError
-ExecError = _distutils_errors.DistutilsExecError
-FileError = _distutils_errors.DistutilsFileError
-InternalError = _distutils_errors.DistutilsInternalError
-LibError = _distutils_errors.LibError
-LinkError = _distutils_errors.LinkError
-ModuleError = _distutils_errors.DistutilsModuleError
-OptionError = _distutils_errors.DistutilsOptionError
-PlatformError = _distutils_errors.DistutilsPlatformError
-PreprocessError = _distutils_errors.PreprocessError
-SetupError = _distutils_errors.DistutilsSetupError
-TemplateError = _distutils_errors.DistutilsTemplateError
-UnknownFileError = _distutils_errors.UnknownFileError
-
-# The root error class in the hierarchy
-BaseError = _distutils_errors.DistutilsError
-
-
-class RemovedCommandError(BaseError, RuntimeError):
-    """Error used for commands that have been removed in setuptools.
-
-    Since ``setuptools`` is built on ``distutils``, simply removing a command
-    from ``setuptools`` will make the behavior fall back to ``distutils``; this
-    error is raised if a command exists in ``distutils`` but has been actively
-    removed in ``setuptools``.
-    """
-
-
-class PackageDiscoveryError(BaseError, RuntimeError):
-    """Impossible to perform automatic discovery of packages and/or modules.
-
-    The current project layout or given discovery options can lead to problems when
-    scanning the project directory.
-
-    Setuptools might also refuse to complete auto-discovery if an error prone condition
-    is detected (e.g. when a project is organised as a flat-layout but contains
-    multiple directories that can be taken as top-level packages inside a single
-    distribution [*]_). In these situations the users are encouraged to be explicit
-    about which packages to include or to make the discovery parameters more specific.
-
-    .. [*] Since multi-package distributions are uncommon it is very likely that the
-       developers did not intend for all the directories to be packaged, and are just
-       leaving auxiliary code in the repository top-level, such as maintenance-related
-       scripts.
-    """

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/_appengine_environ.py

@@ -1,36 +0,0 @@
-"""
-This module provides means to detect the App Engine environment.
-"""
-
-import os
-
-
-def is_appengine():
-    return is_local_appengine() or is_prod_appengine()
-
-
-def is_appengine_sandbox():
-    """Reports if the app is running in the first generation sandbox.
-
-    The second generation runtimes are technically still in a sandbox, but it
-    is much less restrictive, so generally you shouldn't need to check for it.
-    see https://cloud.google.com/appengine/docs/standard/runtimes
-    """
-    return is_appengine() and os.environ["APPENGINE_RUNTIME"] == "python27"
-
-
-def is_local_appengine():
-    return "APPENGINE_RUNTIME" in os.environ and os.environ.get(
-        "SERVER_SOFTWARE", ""
-    ).startswith("Development/")
-
-
-def is_prod_appengine():
-    return "APPENGINE_RUNTIME" in os.environ and os.environ.get(
-        "SERVER_SOFTWARE", ""
-    ).startswith("Google App Engine/")
-
-
-def is_prod_appengine_mvms():
-    """Deprecated."""
-    return False

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/INSTALL.md

@@ -1,175 +0,0 @@
-## Installation
-
-Our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-has step-by-step instructions that install detectron2.
-The [Dockerfile](https://github.com/facebookresearch/detectron2/blob/master/docker/Dockerfile)
-also installs detectron2 with a few simple commands.
-
-### Requirements
-- Linux or macOS with Python ≥ 3.6
-- PyTorch ≥ 1.3
-- [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
-	You can install them together at [pytorch.org](https://pytorch.org) to make sure of this.
-- OpenCV, optional, needed by demo and visualization
-- pycocotools: `pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'`
-
-
-### Build Detectron2 from Source
-
-After having the above dependencies and gcc & g++ ≥ 5, run:
-```
-python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
-# (add --user if you don't have permission)
-
-# Or, to install it from a local clone:
-git clone https://github.com/facebookresearch/detectron2.git
-cd detectron2 && python -m pip install -e .
-
-# Or if you are on macOS
-# CC=clang CXX=clang++ python -m pip install -e .
-```
-
-To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the
-old build first. You often need to rebuild detectron2 after reinstalling PyTorch.
-
-### Install Pre-Built Detectron2
-```
-# for CUDA 10.1:
-python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/index.html
-```
-You can replace cu101 with "cu{100,92}" or "cpu".
-
-Note that:
-1. Such installation has to be used with the latest official PyTorch release (currently 1.4).
-   It will not work with your custom build of PyTorch.
-2. Such installation is out-of-date w.r.t. master branch of detectron2. It may not be
-	 compatible with the master branch of a research project that uses detectron2 (e.g. those in
-	 [projects](./projects) or [meshrcnn](https://github.com/facebookresearch/meshrcnn/)).
-
-### Common Installation Issues
-
-If you met issues using the pre-built detectron2, please uninstall it and try building it from source.
-
-Click each issue for its solutions:
-
-<details>
-<summary>
-Undefined torch/aten/caffe2 symbols, or segmentation fault immediately when running the library.
-</summary>
-<br/>
-
-This can happen if detectron2 or torchvision is not
-compiled with the version of PyTorch you're running.
-
-If you use a pre-built torchvision, uninstall torchvision & pytorch, and reinstall them
-following [pytorch.org](http://pytorch.org).
-If you manually build detectron2 or torchvision, remove the files you built (`build/`, `**/*.so`)
-and rebuild them.
-
-If you cannot resolve the problem, please include the output of `gdb -ex "r" -ex "bt" -ex "quit" --args python -m detectron2.utils.collect_env`
-in your issue.
-</details>
-
-<details>
-<summary>
-Undefined C++ symbols (e.g. `GLIBCXX`) or C++ symbols not found.
-</summary>
-<br/>
-Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
-
-This often happens with old anaconda.
-Try `conda update libgcc`. Then rebuild detectron2.
-
-The fundamental solution is to run the code with sufficiently new C++ runtime
-using `LD_PRELOAD=/path/to/libstdc++.so`
-
-</details>
-
-<details>
-<summary>
-"Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
-</summary>
-<br/>
-CUDA is not found when building detectron2.
-You should make sure
-
-```
-python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
-```
-
-print valid outputs at the time you build detectron2.
-
-Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config.
-</details>
-
-<details>
-<summary>
-"invalid device function" or "no kernel image is available for execution".
-</summary>
-<br/>
-Two possibilities:
-
-* You build detectron2 with one version of CUDA but run it with a different version.
-
-  To check whether it is the case,
-  use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
-	In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
-	to contain cuda libraries of the same version.
-
-	When they are inconsistent,
-	you need to either install a different build of PyTorch (or build by yourself)
-	to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-
-* Detectron2 or PyTorch/torchvision is not built for the correct GPU architecture (compute compatibility).
-
-	The GPU architecture for PyTorch/detectron2/torchvision is available in the "architecture flags" in
-	`python -m detectron2.utils.collect_env`.
-
-	The GPU architecture flags of detectron2/torchvision by default matches the GPU model detected
-	during building. This means the compiled code may not work on a different GPU model.
-	To overwrite the GPU architecture for detectron2/torchvision, use `TORCH_CUDA_ARCH_LIST` environment variable during building.
-
-	For example, `export TORCH_CUDA_ARCH_LIST=6.0,7.0` makes it work for both P100s and V100s.
-	Visit [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus) to find out
-	the correct compute compatibility number for your device.
-
-</details>
-
-<details>
-<summary>
-Undefined CUDA symbols or cannot open libcudart.so.
-</summary>
-<br/>
-The version of NVCC you use to build detectron2 or torchvision does
-not match the version of CUDA you are running with.
-This often happens when using anaconda's CUDA runtime.
-
-Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
-In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
-to contain cuda libraries of the same version.
-
-When they are inconsistent,
-you need to either install a different build of PyTorch (or build by yourself)
-to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-</details>
-
-
-<details>
-<summary>
-"ImportError: cannot import name '_C'".
-</summary>
-<br/>
-Please build and install detectron2 following the instructions above.
-
-If you are running code from detectron2's root directory, `cd` to a different one.
-Otherwise you may not import the code that you installed.
-</details>
-
-<details>
-<summary>
-ONNX conversion segfault after some "TraceWarning".
-</summary>
-<br/>
-Build and install ONNX from its source code using a compiler
-whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
-</details>

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/utils/optim.py

@@ -1,73 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# --------------------------------------------------------
-
-import torch.optim as Optim
-
-
-class WarmupOptimizer(object):
-    def __init__(self, lr_base, optimizer, data_size, batch_size, warmup_epoch):
-        self.optimizer = optimizer
-        self._step = 0
-        self.lr_base = lr_base
-        self._rate = 0
-        self.data_size = data_size
-        self.batch_size = batch_size
-        self.warmup_epoch = warmup_epoch
-
-
-    def step(self):
-        self._step += 1
-
-        rate = self.rate()
-        for p in self.optimizer.param_groups:
-            p['lr'] = rate
-        self._rate = rate
-
-        self.optimizer.step()
-
-
-    def zero_grad(self):
-        self.optimizer.zero_grad()
-
-
-    def rate(self, step=None):
-        if step is None:
-            step = self._step
-
-        if step <= int(self.data_size / self.batch_size * (self.warmup_epoch + 1) * 0.25):
-            r = self.lr_base * 1/(self.warmup_epoch + 1)
-        elif step <= int(self.data_size / self.batch_size * (self.warmup_epoch + 1) * 0.5):
-            r = self.lr_base * 2/(self.warmup_epoch + 1)
-        elif step <= int(self.data_size / self.batch_size * (self.warmup_epoch + 1) * 0.75):
-            r = self.lr_base * 3/(self.warmup_epoch + 1)
-        else:
-            r = self.lr_base
-
-        return r
-
-
-def get_optim(__C, model, data_size, lr_base=None):
-    if lr_base is None:
-        lr_base = __C.LR_BASE
-
-    std_optim = getattr(Optim, __C.OPT)
-    params = filter(lambda p: p.requires_grad, model.parameters())
-    eval_str = 'params, lr=0'
-    for key in __C.OPT_PARAMS:
-        eval_str += ' ,' + key + '=' + str(__C.OPT_PARAMS[key])
-
-    optim = WarmupOptimizer(
-        lr_base,
-        eval('std_optim' + '(' + eval_str + ')'),
-        data_size,
-        __C.BATCH_SIZE,
-        __C.WARMUP_EPOCH
-    )
-
-    return optim
-
-
-def adjust_lr(optim, decay_r):
-    optim.lr_base *= decay_r

spaces/CVPR/LIVE/atomic.cpp

@@ -1,27 +0,0 @@
-//A hacky solution to get around the Ellipse include
-
-#ifdef WIN32
-#include <windows.h>
-#include <cstdint>
-
-float win_atomic_add(float &target, float source) {
-	union { int i; float f; } old_val;
-	union { int i; float f; } new_val;
-	do {
-		old_val.f = target;
-		new_val.f = old_val.f + (float)source;
-	} while (InterlockedCompareExchange((LONG*)&target, (LONG)new_val.i, (LONG)old_val.i) != old_val.i);
-	return old_val.f;
-}
-
-double win_atomic_add(double &target, double source) {
-	union { int64_t i; double f; } old_val;
-	union { int64_t i; double f; } new_val;
-	do {
-		old_val.f = target;
-		new_val.f = old_val.f + (double)source;
-	} while (InterlockedCompareExchange64((LONG64*)&target, (LONG64)new_val.i, (LONG64)old_val.i) != old_val.i);
-	return old_val.f;
-}
-
-#endif

spaces/CVPR/LIVE/thrust/dependencies/cub/experimental/spmv_script.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-
-for i in 1 2 4 8 16 32 64 128 256 512 1024 2048 4096 8192 16384 32768 65536 131072 262144 524288 1048576 2097152 4194304 8388608 16777216
-do
-	echo `date`, `$1 --dense=$i $2 $3 $4 $5 $6 $7`
-done
-
-echo
-echo
-
-for i in `ls /home/dumerrill/graphs/spmv/*.mtx`
-do
-    if [[ ( "`head -n 50 $i | grep complex`" = "" ) && ( "`head -n 50 $i | grep array`" = "" ) ]] 
-    then
-    	echo `date`, `$1 --mtx=$i $2 $3 $4 $5 $6 $7 2>/dev/null`
-    fi
-done
-
-echo
-echo
-
-for i in `ls /scratch/dumerrill/graphs/mtx/*.mtx`
-#for i in `ls /cygdrive/w/Dev/UFget/mtx/*.mtx`
-do 
-    if [[ ( "`head -n 50 $i | grep complex`" = "" ) && ( "`head -n 50 $i | grep array`" = "" ) ]] 
-    then
-    	echo `date`, `$1 --mtx=$i $2 $3 $4 $5 $6 $7 2>/dev/null`
-    fi
-done 
-

spaces/CVPR/LIVE/thrust/thrust/addressof.h

@@ -1,33 +0,0 @@
-// Copyright (c) 2018 NVIDIA Corporation
-// Author: Bryce Adelstein Lelbach <[email protected]>
-//
-// Distributed under the Boost Software License v1.0 (boost.org/LICENSE_1_0.txt)
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-#if THRUST_CPP_DIALECT >= 2011
-#  include <thrust/detail/memory_wrapper.h>
-#endif
-
-namespace thrust
-{
-
-///////////////////////////////////////////////////////////////////////////////
-
-/*! Obtains the actual address of the object or function arg, even in presence of overloaded operator&.
- */
-template <typename T>
-__host__ __device__
-T* addressof(T& arg) 
-{
-  return reinterpret_cast<T*>(
-    &const_cast<char&>(reinterpret_cast<const volatile char&>(arg))
-  );
-}
-
-///////////////////////////////////////////////////////////////////////////////
-
-} // end namespace thrust
-

spaces/CVPR/Text2Human/Text2Human/data/pose_attr_dataset.py

@@ -1,109 +0,0 @@
-import os
-import os.path
-import random
-
-import numpy as np
-import torch
-import torch.utils.data as data
-from PIL import Image
-
-
-class DeepFashionAttrPoseDataset(data.Dataset):
-
-    def __init__(self,
-                 pose_dir,
-                 texture_ann_dir,
-                 shape_ann_path,
-                 downsample_factor=2,
-                 xflip=False):
-        self._densepose_path = pose_dir
-        self._image_fnames_target = []
-        self._image_fnames = []
-        self.upper_fused_attrs = []
-        self.lower_fused_attrs = []
-        self.outer_fused_attrs = []
-        self.shape_attrs = []
-
-        self.downsample_factor = downsample_factor
-        self.xflip = xflip
-
-        # load attributes
-        assert os.path.exists(f'{texture_ann_dir}/upper_fused.txt')
-        for idx, row in enumerate(
-                open(os.path.join(f'{texture_ann_dir}/upper_fused.txt'), 'r')):
-            annotations = row.split()
-            self._image_fnames_target.append(annotations[0])
-            self._image_fnames.append(f'{annotations[0].split(".")[0]}.png')
-            self.upper_fused_attrs.append(int(annotations[1]))
-
-        assert len(self._image_fnames_target) == len(self.upper_fused_attrs)
-
-        assert os.path.exists(f'{texture_ann_dir}/lower_fused.txt')
-        for idx, row in enumerate(
-                open(os.path.join(f'{texture_ann_dir}/lower_fused.txt'), 'r')):
-            annotations = row.split()
-            assert self._image_fnames_target[idx] == annotations[0]
-            self.lower_fused_attrs.append(int(annotations[1]))
-
-        assert len(self._image_fnames_target) == len(self.lower_fused_attrs)
-
-        assert os.path.exists(f'{texture_ann_dir}/outer_fused.txt')
-        for idx, row in enumerate(
-                open(os.path.join(f'{texture_ann_dir}/outer_fused.txt'), 'r')):
-            annotations = row.split()
-            assert self._image_fnames_target[idx] == annotations[0]
-            self.outer_fused_attrs.append(int(annotations[1]))
-
-        assert len(self._image_fnames_target) == len(self.outer_fused_attrs)
-
-        assert os.path.exists(shape_ann_path)
-        for idx, row in enumerate(open(os.path.join(shape_ann_path), 'r')):
-            annotations = row.split()
-            assert self._image_fnames_target[idx] == annotations[0]
-            self.shape_attrs.append([int(i) for i in annotations[1:]])
-
-    def _open_file(self, path_prefix, fname):
-        return open(os.path.join(path_prefix, fname), 'rb')
-
-    def _load_densepose(self, raw_idx):
-        fname = self._image_fnames[raw_idx]
-        fname = f'{fname[:-4]}_densepose.png'
-        with self._open_file(self._densepose_path, fname) as f:
-            densepose = Image.open(f)
-            if self.downsample_factor != 1:
-                width, height = densepose.size
-                width = width // self.downsample_factor
-                height = height // self.downsample_factor
-                densepose = densepose.resize(
-                    size=(width, height), resample=Image.NEAREST)
-            # channel-wise IUV order, [3, H, W]
-            densepose = np.array(densepose)[:, :, 2:].transpose(2, 0, 1)
-        return densepose.astype(np.float32)
-
-    def __getitem__(self, index):
-        pose = self._load_densepose(index)
-        shape_attr = self.shape_attrs[index]
-        shape_attr = torch.LongTensor(shape_attr)
-
-        if self.xflip and random.random() > 0.5:
-            pose = pose[:, :, ::-1].copy()
-
-        upper_fused_attr = self.upper_fused_attrs[index]
-        lower_fused_attr = self.lower_fused_attrs[index]
-        outer_fused_attr = self.outer_fused_attrs[index]
-
-        pose = pose / 12. - 1
-
-        return_dict = {
-            'densepose': pose,
-            'img_name': self._image_fnames_target[index],
-            'shape_attr': shape_attr,
-            'upper_fused_attr': upper_fused_attr,
-            'lower_fused_attr': lower_fused_attr,
-            'outer_fused_attr': outer_fused_attr,
-        }
-
-        return return_dict
-
-    def __len__(self):
-        return len(self._image_fnames)

spaces/CVPR/WALT/mmdet/apis/test.py

@@ -1,189 +0,0 @@
-import os.path as osp
-import pickle
-import shutil
-import tempfile
-import time
-
-import mmcv
-import torch
-import torch.distributed as dist
-from mmcv.image import tensor2imgs
-from mmcv.runner import get_dist_info
-
-from mmdet.core import encode_mask_results
-
-
-def single_gpu_test(model,
-                    data_loader,
-                    show=False,
-                    out_dir=None,
-                    show_score_thr=0.3):
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    prog_bar = mmcv.ProgressBar(len(dataset))
-    for i, data in enumerate(data_loader):
-        with torch.no_grad():
-            result = model(return_loss=False, rescale=True, **data)
-
-        batch_size = len(result)
-        if show or out_dir:
-            if batch_size == 1 and isinstance(data['img'][0], torch.Tensor):
-                img_tensor = data['img'][0]
-            else:
-                img_tensor = data['img'][0].data[0]
-            img_metas = data['img_metas'][0].data[0]
-            imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
-            assert len(imgs) == len(img_metas)
-
-            for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
-                h, w, _ = img_meta['img_shape']
-                img_show = img[:h, :w, :]
-
-                ori_h, ori_w = img_meta['ori_shape'][:-1]
-                img_show = mmcv.imresize(img_show, (ori_w, ori_h))
-
-                if out_dir:
-                    out_file = osp.join(out_dir, img_meta['ori_filename'])
-                else:
-                    out_file = None
-                model.module.show_result(
-                    img_show,
-                    result[i],
-                    show=show,
-                    out_file=out_file,
-                    score_thr=show_score_thr)
-
-        # encode mask results
-        if isinstance(result[0], tuple):
-            result = [(bbox_results, encode_mask_results(mask_results))
-                      for bbox_results, mask_results in result]
-        results.extend(result)
-
-        for _ in range(batch_size):
-            prog_bar.update()
-    return results
-
-
-def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
-    """Test model with multiple gpus.
-
-    This method tests model with multiple gpus and collects the results
-    under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
-    it encodes results to gpu tensors and use gpu communication for results
-    collection. On cpu mode it saves the results on different gpus to 'tmpdir'
-    and collects them by the rank 0 worker.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (nn.Dataloader): Pytorch data loader.
-        tmpdir (str): Path of directory to save the temporary results from
-            different gpus under cpu mode.
-        gpu_collect (bool): Option to use either gpu or cpu to collect results.
-
-    Returns:
-        list: The prediction results.
-    """
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    rank, world_size = get_dist_info()
-    if rank == 0:
-        prog_bar = mmcv.ProgressBar(len(dataset))
-    time.sleep(2)  # This line can prevent deadlock problem in some cases.
-    for i, data in enumerate(data_loader):
-        with torch.no_grad():
-            result = model(return_loss=False, rescale=True, **data)
-            # encode mask results
-            if isinstance(result[0], tuple):
-                result = [(bbox_results, encode_mask_results(mask_results))
-                          for bbox_results, mask_results in result]
-        results.extend(result)
-
-        if rank == 0:
-            batch_size = len(result)
-            for _ in range(batch_size * world_size):
-                prog_bar.update()
-
-    # collect results from all ranks
-    if gpu_collect:
-        results = collect_results_gpu(results, len(dataset))
-    else:
-        results = collect_results_cpu(results, len(dataset), tmpdir)
-    return results
-
-
-def collect_results_cpu(result_part, size, tmpdir=None):
-    rank, world_size = get_dist_info()
-    # create a tmp dir if it is not specified
-    if tmpdir is None:
-        MAX_LEN = 512
-        # 32 is whitespace
-        dir_tensor = torch.full((MAX_LEN, ),
-                                32,
-                                dtype=torch.uint8,
-                                device='cuda')
-        if rank == 0:
-            mmcv.mkdir_or_exist('.dist_test')
-            tmpdir = tempfile.mkdtemp(dir='.dist_test')
-            tmpdir = torch.tensor(
-                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
-            dir_tensor[:len(tmpdir)] = tmpdir
-        dist.broadcast(dir_tensor, 0)
-        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
-    else:
-        mmcv.mkdir_or_exist(tmpdir)
-    # dump the part result to the dir
-    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
-    dist.barrier()
-    # collect all parts
-    if rank != 0:
-        return None
-    else:
-        # load results of all parts from tmp dir
-        part_list = []
-        for i in range(world_size):
-            part_file = osp.join(tmpdir, f'part_{i}.pkl')
-            part_list.append(mmcv.load(part_file))
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        # remove tmp dir
-        shutil.rmtree(tmpdir)
-        return ordered_results
-
-
-def collect_results_gpu(result_part, size):
-    rank, world_size = get_dist_info()
-    # dump result part to tensor with pickle
-    part_tensor = torch.tensor(
-        bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
-    # gather all result part tensor shape
-    shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
-    shape_list = [shape_tensor.clone() for _ in range(world_size)]
-    dist.all_gather(shape_list, shape_tensor)
-    # padding result part tensor to max length
-    shape_max = torch.tensor(shape_list).max()
-    part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
-    part_send[:shape_tensor[0]] = part_tensor
-    part_recv_list = [
-        part_tensor.new_zeros(shape_max) for _ in range(world_size)
-    ]
-    # gather all result part
-    dist.all_gather(part_recv_list, part_send)
-
-    if rank == 0:
-        part_list = []
-        for recv, shape in zip(part_recv_list, shape_list):
-            part_list.append(
-                pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        return ordered_results

spaces/CVPR/ml-talking-face/docs/description.md

@@ -1,33 +0,0 @@
-This system generates a talking face video based on the input text.
-You can provide the input text in one of the four languages: Chinese (Mandarin), English, Japanese, and Korean.
-You may also select the target language, the language of the output speech.
-If the input text language and the target language are different, the input text will be translated to the target language using Google Translate API. 
-
-### Updates
-
-(2023.10.20.) It has been a year since the demonstration has suddenly shut down by MINDsLab (MAUM.AI for now).
-And today, I'm happy to share that ⭐I have restored the demonstration⭐ in my own lambdalabs instance!
-Over the past year, there have been numerous advancements in Gen AI, including multilingual TTS and talking face generation.
-This demo may become "old-fashioned" at this time 😅... but I hope that it would help other researchers taking a journey in the same field.
-
-⚠️By the way, I'm using A10G instance from lambdalabs with my own expense... I'm sorry, but I don't know when it will shut down again. 😵‍💫 I'll keep you posted on the status.
-
-<center><a href="https://www.buymeacoffee.com/deepkyu" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 35px !important;width: 160px !important;" ></a></center>
-
-
-(2022.06.17.) Thank you for visiting our demo!😊 This demo attracted a lot more attention than we anticipated. This, unfortunately, means that the computational burden is heavier than this demo was designed for. So, to maximize everyone's experience, we capped the length of the translated texts at:
-
-- 200 characters for English
-- 100 characters for Chinese, Japaense, and Korean.
-
-(2022.06.17.) We were originally planning to support any input text. However, when checking the logs recently, we found that there were a lot of inappropriate input texts. So, we decided to filter the inputs based on toxicity using [Perspective API @Google](https://developers.perspectiveapi.com/s/). Now, if you enter a possibily toxic text, the video generation will fail. We hope you understand.
-
-(2022.06.05.) Due to the latency from HuggingFace Spaces and video rendering, it takes 15 ~ 30 seconds to get a video result.
-
-<details>
-<summary><i>Outdated updates</i></summary>
-
-(2022.09.29.) ~~The core part of the demonstration has been working on the AWS instance of MINDsLab, and I found that it can't connect to the instance now. I want to fix this issue, but I'm sorry to say that I left the company last week. I've contacted the company, but it takes some time to restore the session. If you're in a hurry, please send the e-mail directly to MINDsLab ([email protected]).  
-Whatever the reason, I'm sorry again. Hope you understand.~~
-
-</details>

spaces/Caoyunkang/Segment-Any-Anomaly/SAM/README.md

@@ -1,107 +0,0 @@
-# Segment Anything
-
-**[Meta AI Research, FAIR](https://ai.facebook.com/research/)**
-
-[Alexander Kirillov](https://alexander-kirillov.github.io/), [Eric Mintun](https://ericmintun.github.io/), [Nikhila Ravi](https://nikhilaravi.com/), [Hanzi Mao](https://hanzimao.me/), Chloe Rolland, Laura Gustafson, [Tete Xiao](https://tetexiao.com), [Spencer Whitehead](https://www.spencerwhitehead.com/), Alex Berg, Wan-Yen Lo, [Piotr Dollar](https://pdollar.github.io/), [Ross Girshick](https://www.rossgirshick.info/)
-
-[[`Paper`](https://ai.facebook.com/research/publications/segment-anything/)] [[`Project`](https://segment-anything.com/)] [[`Demo`](https://segment-anything.com/demo)] [[`Dataset`](https://segment-anything.com/dataset/index.html)] [[`Blog`](https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/)]
-
-![SAM design](assets/model_diagram.png?raw=true)
-
-The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.
-
-<p float="left">
-  <img src="assets/masks1.png?raw=true" width="37.25%" />
-  <img src="assets/masks2.jpg?raw=true" width="61.5%" /> 
-</p>
-
-## Installation
-
-The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
-
-Install Segment Anything:
-
-```
-pip install git+https://github.com/facebookresearch/segment-anything.git
-```
-
-or clone the repository locally and install with
-
-```
-git clone [email protected]:facebookresearch/segment-anything.git
-cd segment-anything; pip install -e .
-```
-
-The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks.
-```
-pip install opencv-python pycocotools matplotlib onnxruntime onnx
-```
-
-
-## <a name="GettingStarted"></a>Getting Started
-
-First download a [model checkpoint](#model-checkpoints). Then the model can be used in just a few lines to get masks from a given prompt:
-
-```
-from segment_anything import build_sam, SamPredictor 
-predictor = SamPredictor(build_sam(checkpoint="</path/to/model.pth>"))
-predictor.set_image(<your_image>)
-masks, _, _ = predictor.predict(<input_prompts>)
-```
-
-or generate masks for an entire image:
-
-```
-from segment_anything import build_sam, SamAutomaticMaskGenerator
-mask_generator = SamAutomaticMaskGenerator(build_sam(checkpoint="</path/to/model.pth>"))
-masks = mask_generator_generate(<your_image>)
-```
-
-Additionally, masks can be generated for images from the command line:
-
-```
-python scripts/amg.py --checkpoint <path/to/sam/checkpoint> --input <image_or_folder> --output <output_directory>
-```
-
-See the examples notebooks on [using SAM with prompts](/notebooks/predictor_example.ipynb) and [automatically generating masks](/notebooks/automatic_mask_generator_example.ipynb) for more details.
-
-<p float="left">
-  <img src="assets/notebook1.png?raw=true" width="49.1%" />
-  <img src="assets/notebook2.png?raw=true" width="48.9%" />
-</p>
-
-## ONNX Export
-
-SAM's lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the [demo](https://segment-anything.com/demo). Export the model with
-
-```
-python scripts/export_onnx_model.py --checkpoint <path/to/checkpoint> --output <path/to/output>
-```
-
-See the [example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) for details on how to combine image preprocessing via SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export.
-
-## <a name="Models"></a>Model Checkpoints
-
-Three model versions of the model are available with different backbone sizes. These models can be instantiated by running 
-```
-from segment_anything import sam_model_registry
-sam = sam_model_registry["<name>"](checkpoint="<path/to/checkpoint>")
-```
-Click the links below to download the checkpoint for the corresponding model name. The default model in bold can also be instantiated with `build_sam`, as in the examples in [Getting Started](#getting-started).
-
-* **`default` or `vit_h`: [ViT-H SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)**
-* `vit_l`: [ViT-L SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)
-* `vit_b`: [ViT-B SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)
-
-## License
-The model is licensed under the [Apache 2.0 license](LICENSE).
-
-## Contributing
-
-See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
-
-## Contributors
-
-The Segment Anything project was made possible with the help of many contributors (alphabetical):
-
-Aaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom

spaces/Chris4K/llms_compare/Jumanji-Welcome-To-The-Jungle-English-Dual-Audio-Eng-Hindi-1080p.md

@@ -1,80 +0,0 @@
-## Jumanji: Welcome to The Jungle (English) dual audio eng hindi 1080p
-
-
-
- 
-
-  
-
- 
-
-**Jumanji: Welcome To The Jungle (English) Dual Audio Eng Hindi 1080p  [https://www.google.com/url?q=https%3A%2F%2Furllie.com%2F2txP3J&sa=D&sntz=1&usg=AOvVaw3aRa6XhDCOE--8taplWh7E](https://www.google.com/url?q=https%3A%2F%2Furllie.com%2F2txP3J&sa=D&sntz=1&usg=AOvVaw3aRa6XhDCOE--8taplWh7E)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- Here is a possible title and article for your keyword:  
-
-# Jumanji: Welcome to the Jungle - A Fun Adventure Movie with Dual Audio in English and Hindi
-
-  
-
-If you are looking for a fun and exciting movie to watch with your family or friends, you might want to check out Jumanji: Welcome to the Jungle. This is a 2017 adventure comedy film that is a sequel to the 1995 classic Jumanji. It stars Dwayne Johnson, Jack Black, Kevin Hart, Karen Gillan, Nick Jonas, and Bobby Cannavale as a group of teenagers who get sucked into a video game version of Jumanji and have to survive its dangers as different avatars.
-
-  
-
-One of the best features of this movie is that it has dual audio in English and Hindi. This means that you can enjoy the movie in your preferred language without missing any of the jokes or dialogues. You can also switch between the languages anytime you want with the help of subtitles. This way, you can experience the movie in a more immersive and engaging way.
-
-  
-
-Jumanji: Welcome to the Jungle is a movie that has something for everyone. It has action, comedy, romance, fantasy, and drama. It also has amazing visuals and special effects that will make you feel like you are in the game world. The movie has received positive reviews from critics and audiences alike, and has grossed over $962 million worldwide. It is one of the highest-grossing films of 2017 and one of the most successful films in the Jumanji franchise.
-
-  
-
-If you want to watch Jumanji: Welcome to the Jungle in dual audio with 1080p resolution, you can download it from various online sources. However, you should be careful about the quality and legality of the downloads. Some of them might be fake or contain viruses or malware that can harm your device. To avoid these risks, you should only download from trusted and verified sites that offer high-quality and safe downloads.
-
-  
-
-One of such sites is **opensubtitles.com**, which is a popular and reliable platform for downloading subtitles and movies in different languages. You can find Jumanji: Welcome to the Jungle subtitles in English[^1^] on this site, as well as other languages like Spanish, French, German, etc. You can also find Jumanji: Welcome to the Jungle movies in dual audio in English and Hindi[^3^] on this site, as well as other formats like BluRay, DVD, etc. You can download these files easily and quickly with just a few clicks.
-
-  
-
-Jumanji: Welcome to the Jungle is a movie that you don't want to miss. It is a fun and thrilling adventure that will keep you entertained from start to finish. With dual audio in English and Hindi, you can enjoy it even more in your preferred language. Download it today from opensubtitles.com and have a great time watching it!
-
-Here is a possible continuation of the article:  
-
-But what is Jumanji: Welcome to the Jungle about? And how is it different from the original Jumanji? Well, let's find out.
-
-  
-
-The movie starts with four high school students who are given detention for various reasons. They are Spencer, a nerdy gamer; Fridge, a football star; Bethany, a self-absorbed beauty; and Martha, a shy and smart girl. They are assigned to clean up an old storage room, where they find an old video game console with a cartridge of Jumanji. Curious, they decide to play the game and choose their avatars. However, they soon realize that they are not just playing the game, but are actually in the game.
-
-  
-
-They are transported to a jungle setting, where they discover that they have become their avatars. Spencer is now Dr. Smolder Bravestone, a muscular and charismatic explorer; Fridge is now Franklin "Mouse" Finbar, a short and weak zoologist; Bethany is now Professor Sheldon "Shelly" Oberon, an overweight and middle-aged cartographer; and Martha is now Ruby Roundhouse, a sexy and skilled martial artist. They also learn that they have three lives each, and if they lose them all, they die for real.
-
-  
-
-They meet Nigel, an NPC (non-player character) who gives them their mission: to return a magical jewel called the Jaguar's Eye to a giant statue and lift the curse that has fallen upon Jumanji. The jewel was stolen by Van Pelt, a corrupt explorer who has gained control over the animals of the jungle. Along the way, they encounter various obstacles and enemies, such as snakes, hippos, crocodiles, bikers, and Van Pelt's henchmen. They also meet Alex, another player who has been stuck in the game for 20 years as Jefferson "Seaplane" McDonough, a pilot and adventurer.
-
-  
-
-As they progress through the game, they learn to work together and use their strengths and weaknesses to their advantage. They also learn more about themselves and each other, and develop friendships and romances. They realize that Jumanji is not just a game, but a test of their courage and character. Will they be able to complete the game and return to their normal lives? Or will they be trapped in Jumanji forever?
-
-  
-
-To find out the answer, you have to watch Jumanji: Welcome to the Jungle in dual audio with 1080p resolution. It is a movie that will make you laugh, cry, cheer, and gasp. It is a movie that will make you feel like you are part of the adventure. It is a movie that you will love.
-
- dfd1c89656
-
- 
-
- 
-